Search

The construction of a virtual power plant (VPP) represents a powerful approach for harnessing the adjustable capacity of flexible resources. VPP actively participates in transmission network dispatching by aggregating these flexible resources. However, existing practical implementations and research cannot describe the influence of renewable energy's inherent unpredictability on the dispatching boundaries of VPPs. This shortfall leads to a lack of organic coordination between flexible resources and the power transmission network. Therefore, this paper suggests a way to characterize the dispatching boundaries of VPPs based on the concept of flexible operation region (FOR), aiming to better incorporate the flexible resources of decentralized access and lessen the issue of insufficient computing power caused by unified optimization. Initially, an analytical model for the VPP's FOR is presented, followed by dimensionality reduction of the FOR to the VPP's gateway. Subsequently, participation in transmission network scheduling is facilitated using an equivalent dimensionality reduction model. Based on standard example testing, the proposed method's effectiveness and accuracy have been verified in this paper. • This paper proposes a method to characterize the dispatching boundary of VPP based on the FOR. • Constructs the static voltage security region (SVSR) related to each node. • Thermal security region (THSR) related to each branch in the cluster in the power injection space. • This paper proposes a strategy to identify and eliminate the ineffective constraint • The boundary construction method of VPP is applied to the cooperative optimal dispatching. [ABSTRACT FROM AUTHOR]

Having no native energy resources of fossil fuels, with poor wind resource and scarcity of land, the Solar Photovoltaic (PV) roadmap identified solar electricity as the most feasible source of renewable energy for Singapore's energy mix and supply. Moving towards net-zero emissions and to combat climate change, the Singapore government is aiming to achieve 2-Gigawatt-peak (GWp) of solar electricity target by 2030. Accordingly, the share of solar energy in the national grid is targeted to be between ∼2–6% in 2030 and ∼ 3.5–8% in 2040, and carbon emission savings to be ∼0.5–1.4 and ∼ 0.8–2.1 million tonnes per annum in 2030 and 2040 respectively. Although these ambitious targets align with the government's plans for mitigating emissions, Singapore faces great challenge in terms of land availability to install ground-mounted solar PV panels. In this paper, a system dynamics model is developed to study- to what extent can Singapore achieve the targeted solar electricity goals by 2030 or even beyond based on Green Plan 2030, what policies can be identified to achieve these targets, and how much carbon savings can be achieved through Solar electricity deployment. Accordingly, this paper presents systems thinking and system dynamics (ST&SD) methodology to model the growth of Singapore's solar capacity, carbon emission savings and share of electricity demand met by solar electricity while focusing on key complex factors such as area utilisation, subsidies, PV panel efficiency etc. Results of our model simulations and projections, based on the key data and assumptions, and policy scenarios show that Singapore's solar capacity can be accelerated by the implementation of the proposed policies to reach 2GWp goal towards 2030 or even slightly ahead of this timeline. However, should the government revise its solar capacity targets higher for the years past 2030 i.e. to achieve 8% share of total electricity generation, perhaps by 2040, policies such as an increased area utilisation, subsidies and higher panel efficiency need to be introduced. Our model simulations incorporating and evaluating these policy scenarios yielded the results aligning with the projections mentioned above. The results and insights presented in this paper offer useful recommendations to the researchers and policy makers in the field of solar electricity system in Singapore, and to study further for better policy making. [Display omitted] • System dynamics modelling of the solar electricity targets of Singapore's Green Plan 2030 and beyond. • Evaluation of government's initiatives and policies towards 2-GWp Solar capacity goals. • Projection of carbon emissions savings by 2030 and beyond due to Solar deployment • Policy rrecommendations for the post-2030 solar electricity targets (8% share) • Estimating the % share of Solar energy mix to meet electricity demand. [ABSTRACT FROM AUTHOR]

This paper addresses the distributed state estimation-based centralized control design problem for Internet-of-Things (IoT)-enabled microgrid systems in the presence of deception attacks. Firstly, a microgrid with multiple synchronous generators is described in a state-space model. Furthermore, the states of the microgrid power system are monitored by using a set of sensors linked through IoT-enabled networks, where attacks are presumed to occur during measurements. In particular, the deception attacks are capable of compromising grid security through the malicious manipulation of measurement data. To tolerate this effect in this paper a resilient distributed state estimation design based control algorithm is developed. The microgrid system is then controlled by a centralized scheme that uses only estimates generated by a certain group of IoT-enabled networked nodes. Subsequently, an augmented system is developed that incorporates both the microgrid system and the estimator error dynamics. Using Lyapunov stability theory and matrix inequality approach, the proposed local and neighboring estimator based control gains are designed to ensure the accurate estimation of load and energy source output and continuous power supply. Specifically, the particle swarm optimization algorithm is used for optimizing the distributed state estimation-based controller to achieve the desired performance. Finally, simulation results demonstrate that the developed algorithm is capable of estimating the state of the microgrid and controlling its operations. The result reveals that microgrids can provide a constant flow of electricity while being resistant to deception attacks and disruptions caused by the networks. • Resilient control algorithm is developed for IoT-enabled microgrid system model. • The model considers noise, inaccurate measurement in sensors and deception attacks. • Estimated state is embedded in controller to obtain desired microgrid performance. • Algorithm illustrates the ability to resist deception attacks, noises and gains. [ABSTRACT FROM AUTHOR]

With the changes in energy structure and system configuration, the uncertainty on both the supply and demand sides of the power system will significantly increase. The approach of increasing reserves to cope with the uncertainty is becoming unsustainable both technically and economically. It is necessary to accurately characterize system flexibility and propose flexibility-oriented dispatching optimization strategies. Based on this, this paper conducts research on the evaluation system for flexibility supply-demand balance and the coordinated optimization dispatch strategy of generation-load-storage resources. Firstly, a flexibility demand-supply balance evaluation system is proposed, which covers the quantification of flexibility demand, the quantification of flexibility supply capability, and the design of flexibility metrics. Secondly, a two-stage optimization dispatching model for generation-load-storage resources is constructed. In stage 1, the optimization obtains the operating power of generation, load and storage resources to meet the load demand, while in stage 2, the flexibility supply potential is considered to optimize the redispatch power of the flexibility resources to meet the flexibility demand. Finally, simulations are conducted in a typical industrial park with generation, load, and storage resources, and the results show: 1) The quantification of flexibility demand based on the polytope form is more accurate, ensuring that the obtained flexibility supply-demand balance evaluation results can effectively support system flexibility dispatching; 2) Compared with the traditional dispatching strategy that consider reserve capacity, the operational cost of the dispatching strategy considering flexibility supply-demand characteristics in this paper decreased by 3.31%, environmental costs decreased by 4.56%, and the penalty cost for flexibility deficiency decreased by 17.37%. The research results can leverage the synergistic effect of generation -load-storage resources, achieving a scientific balance between the economy, low-carbon, and adequacy of the generation-load-storage system, and provide a scientific approach for considering flexible supply-demand balance in the power system dispatching, supporting the stable and sustainable operation of the high-penetration new energy system. • A comprehensive evaluation system for flexible supply-demand balance is designed; • The flexibility demand quantification model based on the polytope form is constructed; • A two-stage coordinated optimization dispatching model for generation-load-storage resources is constructed; • The two-stage dispatching power and costs of the generation-load-storage resources in 24 period are examined. [ABSTRACT FROM AUTHOR]

Due to the rise of distributed energy resources (DERs), virtual power plants (VPPs) have gained intensive academic and industrial attention. While current centralized VPPs benefit from their large aggregated capacity in the wholesale market, they lack effective incentivizing participants for asset investment and market participation. Conversely, the peer-to-peer (P2P) market offers better profits for prosumers through direct trading; however, transactions hinge on approval from associated authorities, such as electricity retailers, who read their power meters. Such an approval procedure becomes complex, even impossible, when more retailers are involved. Hosting the P2P market in a VPP environment can conveniently resolve this issue. This paper proposes a novel holistic P2P (H-P2P) market model hosted in a VPP environment to provide the VPP participants with financial incentives and optimize the network and market operations simultaneously. The proposed new market structure and trade principle allow the proposed H-P2P market to trade active and reactive energies for both financial benefits and network constraints. Using the novel transactive voltage/VAR control (TVVC) method proposed in this paper, the reactive energy trading through the P2P market can support effective voltage regulation and incentivize the VPP participants to provide such voltage control ancillary services actively. Finally, a novel multi-stage P2P negotiation process is introduced to divide the VPP regulatory, active and reactive energy convergence stages, which allows faster P2P decision-making with improved computing efficiency than the conventional multi-period alternating direction method of multipliers (ADMM). The case study validates that the proposed H-P2P market can increase the financial incentives of the prosumers who install PVs and batteries by a large extent and the pure load units by a relatively smaller percentage. [Display omitted] • A novel synergistic model integrating a holistic P2P market in an internal centralized VPP environment. • Simultaneous active and reactive energy trading in the holistic P2P market regulated by the VPP coordinator. • A novel transactive voltage/VAR control concept with P2P reactive energy trading for the internal VPP voltage control. • An innovative multi-stage P2P negotiation mechanism to converge the active and reactive energy transaction decisions. [ABSTRACT FROM AUTHOR]

The full utilization of solar energy resources along the road is an effective method to solve the energy shortage in transportation. The key to this is an accurate evaluation of solar energy resources, which provides the rationale for the optimal location of road photovoltaic (PV) projects. However, determining the availability of solar energy resources in road areas before route corridor planning remains difficult. To address the issue, this paper developed a standardized method. Firstly, this paper analyzed the critical factors affecting the availability of solar energy resources. They were meteorology, topography, land use type, geology, and location. On that basis, we developed a four-level evaluation indicator system. Then, it proposed a multi-indicator evaluation method based on the Analytic Hierarchy Process (AHP) and the Geographic Information System (GIS). It used AHP to calculate the weights of each indicator, with solar radiation being the most important. It used GIS to analyze and overlay the spatial distribution of multiple indicators to obtain the map of suitability grades, which could be used to route corridor selection for road PV projects. It also used a case study to demonstrate how the method is applied. The findings show that the method can accurately evaluate the availability of solar energy resources. Furthermore, it studied the appropriate division of evaluation units and it is advised that they shouldn't be larger than 30 m × 30 m. These results provide guidance as a reference for the application of road PV projects and site selection for route corridors worldwide. That will promote the integration of transportation and energy in the future. • The paper addresses the problem of solar energy resources evaluation in road areas. • The paper establishes a universal evaluation indicator system. • The paper proposes a standardized evaluation method based on AHP and GIS. • This method presents the obtained evaluation results as suitability grade map. • This method helps select route corridors with good power generation potential. [ABSTRACT FROM AUTHOR]

Under the goal of global carbon reduction, hydropower-wind-photovoltaic complementary operation (HWPCO) in the clean energy base (CEB) has become the key to achieving a high-quality clean energy supply. However, stochastic wind power and photovoltaic need the help of adjustable hydropower to get onto the grid in the HWPCO process, which makes the conflict between different power generation entities more and more complex. This paper takes Yalong River CEB as the research object and sets up the separate operation scenario and complementary operation scenario of hydropower stations and wind-photovoltaic plants. Meanwhile, the corresponding day-ahead schedule model considering the stability of the power system is established. On this basis, this paper analyzes changes in profit and loss indexes of each power generation entity and clarifies the profit and loss relationship of different power generation entities in the CEB. Further, based on the model group for quantifying contributions and the compensation electricity contribution value, this paper proposes the benefit compensation mechanism between the cascade hydropower station and wind-photovoltaic plants and the benefit compensation mechanism between each hydropower station within the cascade hydropower station. The main results of the research are as follows: (1) after HWPCO, the total revenue of wind-photovoltaic plants increases by 61.48 million RMB (8.61 million dollars), and the total revenue of the cascade hydropower station decreases by 10.77 million RMB (1.51 million dollars), which means wind-photovoltaic plants are the beneficiary and the cascade hydropower station is the loss. (2) after the benefit compensation, the total revenue of wind-photovoltaic plants increases by 20.97 million RMB (2.94 million dollars), and the total revenue of the cascade hydropower station increases by 29.74 million RMB (4.16 million dollars) compared with the hydropower-wind-photovoltaic separate operation (HWPSO). By the benefit compensation mechanism proposed in this paper, the benefit of each power generation entity can reach a relative balance in the Yalong River clean energy base. • Quantify the profit and loss relationship between different power generation entities in the HWPCO process. • Establish the model group for quantifying contributions between different power sources. • The incremental benefit of HWPHS is reasonably distributed. [ABSTRACT FROM AUTHOR]

Forecasting energy demand has become increasingly important due to technological advances, especially new power systems and population growth. Accurate predictions of energy demand and supply are crucial for academics and policymakers. Also, energy consumption is non-stationary and dynamic in time, requiring an adaptive forecasting algorithm. This paper presents a new adaptive hybrid approach for energy time series forecasting using statistical methods. Some of the most popular energy demand time series applications are used to demonstrate the superiority of the proposed algorithm, which covers a wide range of critical energy demand forecasting situations at various scales. The paper's objectives are presented in three phases. In Phase I, ARFIMA is used to forecast the entire original time series as a statistical inspector and later for comparison. Although ARFIMA's fractionality feature helps to capture long and short-term memory behavior in time series, to be more adaptive and become close to a nonlinear algorithm that can handle non-stationarity, and Gaussian and non-Gaussian time series closely, a novel dynamic statistical structural break detection framework is developed, and used in phase II to identify the time series' change points and associated time indexes. Since time series are partitioned based on structural change detections, they approach one in terms of the Hurst exponent and exhibit pure long memory, which is ideal for ARFIMA modeling. Phase III captures non-stationary statistical properties and memory characteristics by applying adaptive ARFIMA modeling to segmented time series. Finally, the predicted partitions are concatenated. Various performance assessment metrics, e.g., Mean Absolute Percentage Error (MAPE), tested on the final achieved results show that the proposed adaptive algorithm outperforms the forecasting capabilities of the existing algorithms on the same energy demand time series used in the current paper. MAPE of the proposed approach for the four utilized real-life cases are 1.72%, 1.35%, 0.22%, and 4.4%, respectively. Therefore, the proposed model is a satisfactory method for energy demand forecasting due to its high accuracy. • A novel adaptive hybrid algorithm is developed for energy demand forecasting. • The prediction system uses ARFIMA and a novel dynamic structural break detection framework. • The adaptive prediction method can forecast energy demand with high accuracy. • The structural break detection works dynamically, allowing ARFIMA to be adaptive. • The prediction method captures the energy time series' nonstationary statistical features. [ABSTRACT FROM AUTHOR]

Nowadays, the use of renewable generations, energy storage systems (ESSs) and microgrids (MGs) has been developed due to better controllability of distributed energy resources (DERs) as well as their cost-effective and emission-aware operation. The development of MGs as well as the use of hierarchical control has led to data transmission in the communication platform. As a result, the expansion of communication infrastructure has made MGs as cyber-physical systems (CPSs) vulnerable to cyber-attacks (CAs). Accordingly, prevention, detection and isolation of CAs during proper control of MGs is essential. In this paper, a comprehensive review on the control strategies of microgrids against CAs and its defense mechanisms has been done. The general structure of the paper is as follows: firstly, MGs operational conditions, i.e., the secure or insecure mode of the physical and cyber layers are investigated and the appropriate control to return to a safer mode are presented. Then, the common MGs communication system is described which is generally used for multi-agent systems (MASs). Also, classification of CAs in MGs has been reviewed. Afterwards, a comprehensive survey of available researches in the field of prevention, detection and isolation of CA and MG control against CA are summarized. Finally, future trends in this context are clarified. • Microgrid (MG) control as a cyber-physical system (CPS) against cyber-attacks (CAs) is one of the concerns of researchers. • CAs include deception attacks, denial of service (DoS) attacks and replay attacks. • Defense mechanisms against CAs consist of prevention, detection and isolation of CAs and resilience control of MG. • Future trends in the context include new methods of CAs prevention, detection and isolation, new resilient control methods. • Retrofitting existing equipment and/or accommodating advanced technologies into networks enhance resilience of MG. [ABSTRACT FROM AUTHOR]

Defossilization of the current energy system is a major requirement to decelerate anthropogenic climate change. However, a defossilized energy system is vastly more complex than current fossil-based energy systems: The integration of distributed energy resources and sector-coupling increases connectivity, demands interdisciplinary workflows, and creates a need for more sophisticated design processes. Inspired by the semiconductor and automotive industries, digitalization of the design process using platform-based design (PBD), coupled with the energy hub concept, can improve cost-effective energy systems design and accelerate the industry's contributions to achieving net-zero emissions. PBD is an efficient and effective methodology to manage and de-risk the complexity of integrated energy system design, leading to affordable and reliable solutions due to the inherent techno-economic analysis underlying the decision-making process. Combining the PBD framework with the energy hub concepts establishes a powerful design workflow for developing holistic energy systems from a single building up to the district and city scales. The fundamental tenets of this workflow, as discussed in this paper, are (1) the separation of functions from architectures, (2) the identification of abstraction levels at which systems can be analyzed and optimized, and (3) the ability to repurpose components at all levels of abstraction to aid design reuse and allow performance feedback at every stage of the process. We argue that PBD can become the next frontier in energy system design. PBD, as presented in this paper, is not limited to the energy sector, and it can also be a sub-process of an even more holistic infrastructure design. Spatial planning, architecture, and civil engineering can all be further integrated with the PBD concept, allowing societies to reach ambitious sustainability goals faster, at lower cost, and with greater resilience. • Innovative design methodologies to effectively navigate energy system transformation. • Novel framework combining platform-based design (PBD) and the Energy Hub concept. • Inspiration provided by the digitalization process of the semiconductor and automotive industries. • Proposal of a new "design-build-operate" workflow for the energy sector. • Augmentation of scalability, risk mitigation, and time-to-market reduction for energy systems. [Display omitted] [ABSTRACT FROM AUTHOR]

The trend towards reducing greenhouse gas emissions by dipping the use of traditional sources of energy in marine power grids, as well as the rapid expansion of renewable energy sources (RESs), were the driving forces behind the incorporation of RESs in maritime microgrid systems and enquiry of the ensuing prevalent control mechanism. The frequency stability of a Marine Microgrid System (MMGS) is a critical aspect that directly affects its reliable and efficient operation. This study describes a method for frequency stabilization in independent maritime microgrids consists of diverse renewable energy resources including wind turbine generators, sea wave energy/tidal power generation, solar generation, bio-diesel generator and energy storage systems. This paper aims to develop a new optimal cascaded order proportional integral based proportional derivative for marine load frequency control. Due to the fact that the performance of the controller is highly dependent on the parameters of the controller, optimizing these coefficients can have a significant impact on the output performance of the LFC control. In light of this, this paper presents the sewing training-based optimization (STBO), a new human-based metaheuristic algorithm to optimize the coefficient of the suggested controller. The essential stimulation of STBO is the teaching procedure of sewing to beginner tailors. By utilizing a population of candidate solutions, the algorithm iteratively refines the control parameters to find the optimal set that minimizes frequency deviations and enhances system stability. The responses of the cascaded PI-PD controller are linked to those of the PID and PI controllers in order to demonstrate its superiority. To evaluate the efficiency of STBO, it is compared to well-established recent techniques such as fitness dependent optimization, grey wolf optimization and Jellyfish search optimization. From the results, it is observed that our proposed approach improved the settling time by 25.35%, 45.89%, and 29.35%, reduced peak overshoot by 78.34%, 67.71%, and 78.23%, and similarly reduced undershoot by 81.56%, 56.22% and 76.56% as compared to FDO, GWO and JSO techniques respectively. Finally, the sensitivity analysis is performed under ±50% load variation and ± 40% power system parameters to prove the robustness of the proposed controller. • A diverse renewable energy resources including renewable energy resources and energy storage systems (ESS,s) incorporating with battery energy storage, capacitive energy storage, and flywheel energy storage system has been considered for marine microgrid. • A novel cascaded order PI-PD controller has been designed and implemented for the suggested distinct renewable and energy storage based maritime microgrid. • The coefficient of the proposed controller is optimized with a new human-based meta-heuristic algorithm known as sewing training-based optimization (STBO). • Multi objective functions including integral time absolute error (ITAE), integral time square error (ITSE) and integral square error (ISE) are formulated for the proposed power system. • Finally, sensitivity testing of the STBO adjusted PI-PD controller under uncertain parameters such as turbine gain variation (K CE), droop factor (R), and inertia constant (M) is performed to prove the robustness of the proposed frequency control technique. [ABSTRACT FROM AUTHOR]

Considering the abundant wind and solar energy resources on waters, this paper integrates wind assistant propulsion and photovoltaic technology with ship electric power system as sail-photovoltaic-hybrid power system (sail-PV-HPS). When properly designed, sail-PV-HPS can present environmental and economic benefits compared with conventional sail-diesel power system. However, the optimal design of sail-PV-HPS is a complex task due to environmental uncertainties. In order to fill such gap, this paper proposes a probabilistic optimization method to determine the size parameters of sail-PV-HPS, pursing minimum GHG emission and lifecycle cost. Two pairs of joint distributions of wind speed and wind direction as well as solar irradiance and ambient temperature are established based on coupla function considering the interconnection and seasonal characteristics of the meteorology variables. The performance of the proposed approach is assessed by the retrofit of the power system of an unmanned ocean surveillance trimaran sailing in the Yellow Sea area. A deterministic optimization and a quasi-probabilistic optimization are performed to highlight the effect and importance of taking the uncertainties and their correlation into consideration. Results show that the sail-PV-HPS designed by the proposed probabilistic method outperforms the sail-PV-HPS given by the deterministic optimization and a quasi-probabilistic optimization, as well as, the original power system of the trimaran in terms of GHG emission and lifecycle cost. • Probabilistic optimization is proposed perusing minimum GHG emissions and life cycle cost for hybrid power systems. • Uncertainties in wind direction, wind speed, solar irradiance and ambient temperature are considered simultaneously. • Joint distributions are established with copula function to describe interconnection between the meteorology variables. [ABSTRACT FROM AUTHOR]

In this paper, a scheme of pulse combustion mode with pulse input of gas is proposed to solve the problems of low output electrical performance and energy conversion efficiency of combustion-based thermoelectric systems under limited heat dissipation conditions, which is based on experiments. The characteristics of the thermoelectric system show that pulse combustion mode can increase the instantaneous temperature difference, so as to achieve the excitation effect on the output power. The relationships among output power, system efficiency, excitation intensity and pulse input parameters, and the geometry of thermoelectric modules are obtained. The influence of pulse input parameters on the excitation intensity is interactive, and top dead center of pulse inlet power is the parameter that has the greatest influence on the performance. Compared to the constant combustion mode, the pulse combustion mode can increase the output power by up to 28.3%. A maximum output power of 7.15 W with air-cooled heat dissipation and a maximum system efficiency of 3.26% were experimentally obtained, which is a 30% increase in efficiency over current air-cooled heat dissipation thermoelectric systems. Therefore, this paper can provide solutions for more practical power supply, provide effective and feasible guidance for the study of thermoelectric systems, and provide useful insights into the electrical properties of thermoelectric systems. [Display omitted] • An air-cooled combustion based thermoelectric system was built. • A scheme of pulse combustion mode with pulse input of gas is proposed. • The pulse combustion mode can increase the output power by up to 28.3%. • High output power and efficiency results were obtained by experiment. • The power shortage is alleviated under limited heat dissipation conditions. [ABSTRACT FROM AUTHOR]

In response to Japan's ambitious pursuit of carbon neutrality by 2050, this paper investigates the potential for tidal power generation in the Seto Inland Sea (SIS), Japan, utilising high-resolution ocean modelling and an analytic hierarchy process (AHP) combined with GIS-based spatial analysis. The analysis incorporates a 100 m resolution tidal current and water depth layers from the ocean model. The criteria used for evaluation were divided into main criteria, namely, proximity to existing infrastructure, wave height, and shipping density, and subcriteria, namely, distance from ports, power stations, highly populated areas, and coastlines. The identified optimal locations include the Naruto Strait, Akashi Strait, Matsushima Island, Hanaguri Strait, Funaori Strait, Taizaki Island, Kurushima Strait, Obatake Strait, and Tsuwaji Strait. With a focused analysis of the Obatake Strait in Yamaguchi Prefecture, analytical assessments of potential tidal power were conducted by considering two tidal turbines, Openhydro and Voith. The results indicate a power supply for approximately 100,000 households and a reduction of approximately 198,000 t-CO 2 per year in the most optimistic scenario. In a less optimistic scenario, with the installation of only 10% of the maximum capacity, a power supply for approximately 10,000 households and a reduction of approximately 19,800 t-CO 2 per year are observed. As Japan targets carbon neutrality by 2050, this research offers vital and practical insights for policymakers, energy planners, and environmentalists, identifying the Seto Inland Sea as a strategic area for tidal power development, aligning with Japan's commitment to a sustainable and resilient energy landscape. • High-resolution ocean model used with GIS for optimal site selection of tidal power • The Seto Inland Sea is vital for tidal power with optimal sites equals to 1.18 km2. • Tidal power quantification is based on the dominant tidal velocity • 10% of tidal power in Obatake Strait supplies 60% of Yanai City households • 10% of tidal power in Obatake Strait resulted in 19,800 t-CO 2 reduction per year [ABSTRACT FROM AUTHOR]

With a rising number of buildings being equipped with private distributed energy resources (DERs) such as rooftop PV panels and energy storage devices, an effective energy management method for a building cluster becomes increasingly imperative. This paper proposes a novel decentralized transactive energy management (TEM) method for a hybrid cluster of residential and commercial buildings, which enables peer-to-peer (P2P) energy trading among the DER owners and consumers. The strategic interactions among the DER owners and consumers are modeled as a multi‑leader-multi-follower (MLMF) Stackelberg game and formulated as a bi-level model. The DER owners, the commercial and residential consumers are all autonomous entities, optimizing their individual welfare functions and sharing necessary trading-related information, which are expressed as the upper-level leaders' models and the lower-level followers' models, respectively. To preserve the privacy and autonomy of each entity within the building cluster, a distributed algorithm incorporated with an efficient P2P pricing mechanism is designed for the formulated MLMF Stackelberg game model. Simulation results demonstrate the effectiveness of the proposed method on mitigating the reliance of the building cluster on the power grid, motivating the DERs to actively participate in P2P trading, and reducing the consumers' energy consumption costs • A decentralized transactive energy management framework is designed for a real-world hybrid building cluster. • The strategic interactions among the owners of distributed energy resources (DERs) and the consumers within the building cluster are captured by a multi‑leader-multi-follower Stackelberg game. • A novel distributed algorithm is developed to protect the privacy and autonomy of participants in a large-scale building cluster. [ABSTRACT FROM AUTHOR]

A current challenge in energy system modelling is integrating high temporal and spatial resolution with a detailed representation of technologies and sector coupling within a single model. This paper presents a pioneering sectoral optimisation approach for transformation pathways in the German federal states. Spatial resolution in the energy system model REMod is increased and the model is extended from a single-node to a multi-node model for Germany by integrating energy exchanges between regions into a simulation-based optimisation. The approach highlights the challenge of adjusting energy distribution to address the mismatch between high final energy demand in some states and the potential for renewable energy resources in others. The extended model was compared with the results of the single-node model with the same boundary conditions. The comparison showed that increasing the spatial resolution did not significantly affect the overall cost of energy system transformation. However, the composition of the energy system changes, leading to approximately 6% more renewable capacity being installed in a scenario with ten regions. The results for the German federal states indicate a concentration of onshore wind development in the northern federal states with higher full load hours, especially in Lower Saxony/Bremen with 36 GW and Schleswig-Holstein/Hamburg/Mecklenburg-Vorpommern with 38 GW in 2045. The expansion of photovoltaic capacity in densely populated states such as North Rhine-Westphalia (73 GW), Bavaria (71 GW), and Baden-Württemberg (54 GW) reflects the need to meet growing electricity demand. The solar photovoltaic to wind power ratio varies between the federal states and influences the choice of flexibility options, including power-to-x technologies or gas and hydrogen turbines. By 2045, Germany's primary energy supply will shift to states with abundant renewable resources. Spatial flexibility is essential to balance electricity demand and solar and wind potential, mainly through state interconnections. • Sector-coupled energy system model with high temporal and spatial resolution developed. • Simulation-based optimisation as alternative to linear optimisation techniques. • Transformation path on federal states level for Germany modelled. • Transmission grid expansion is needed to transmit renewable energy from Northern to Southern Germany. [ABSTRACT FROM AUTHOR]

Under the trend of energy saving and carbon reduction, reducing household energy consumption, which is an important part of social energy consumption, has become a hot topic. In order to solve the problem of mismatch between supply and demand of household environment control systems as well as to better meet the individual needs of users, we innovatively designed a solar-driven domestic regional environment mobile robot as auxiliary heating and cooling method. In this paper, a simplified thermal network analysis of the thermal conditions of a dwelling is utilized to calculate the energy saving and carbon reduction potential on summer and winter reference days in Beijing and Guangzhou, China. The simulation results show that the system has good energy saving potential and carbon reduction effect. In particular, the single-day energy saving potential of the system on winter days in Beijing reaches to 583.6 kWh compared to the traditional method. Under the power supply mode, the single-day carbon reduction can reach 462 kg CO 2 on winter days in Guangzhou, and it can reach 333.2 kg CO 2 on summer days in Beijing. We also discuss the effect of regional space size and clothing thermal resistance. As the local space size and the clothing thermal resistance increases, the energy saving potential and carbon reduction benefits of the system decrease, but the overall impact is small. The proposed mobile robot following in the vicinity of home inhabitants to help maintain their thermal comfort, can improve the efficiency of dwellings heating and cooling systems and utilize the limited solar energy resources in family area. • We have proposed the regional household auxiliary heating and cooling method, which is more energy efficient; • Solar-driven domestic environment mobile robot (DREMR) is low-carbon and environmentally friendly; • TEC/THP components combined with human-machine interface can effectively meet the user's individual needs; • The energy demand and carbon emission analyses models can also be used in other household environment control systems. [ABSTRACT FROM AUTHOR]

The design of a building's energy supply is a difficult task, where uncertainty in the requirements has to be taken into account and different conflicting objectives have to be optimized. To date, there exist various studies that consider different objective functions, different concepts of dealing with uncertainty, and different approximation techniques to find efficient solutions. However, there is no approach that gives the decision maker a complete overview of all trade-off solutions with their immediate consequences for the design configuration. This paper addresses the question of how much money and carbon emissions can be saved by allowing a certain amount of undersupply in the heating and cooling of an office building. To explore the trade-offs between the three objectives of minimizing cost, carbon emissions and inconvenience, the Pareto front of the multicriteria linear program is approximated with a Sandwiching algorithm. Then, it is explored using Pareto navigation. This allows to see all (approximate) Pareto points and also to visually observe the behavior of the decision variables by navigating across the front by pulling sliders in a specific software. In a case study, the navigation is demonstrated and ways to overcome modeling limitations that arise from using a fully linear programming approach are shown. [Display omitted] • Introduce novel workflow for optimal co-design for building energy supply. • First-time application of Sandwiching and Pareto navigation to energy system design. • Focus on finding trade-offs between costs, carbon emissions and inconvenience. • Demonstrate approach in case study for an office building at an early planning stage. [ABSTRACT FROM AUTHOR]

Flexible microgrids with dynamic boundaries have recently been introduced in the literature. With the ability to reconfigure the topology of the microgrids dynamically through remotely controlled switches, flexible microgrids with dynamic boundaries can further improve the resiliency and energy efficiency of microgrids with distributed energy resources (DERs). This paper focuses on the optimal operation considering one of the predominant characteristics of microgrids and distribution systems – unbalanced networks and loads. In existing literature, balanced modeling of microgrids is more common due to its attractive simplicity. The three-phase power unbalance has not been considered as a constraint on the generation units in a microgrid. Negative sequence constraints have also been neglected. In this article, we propose a set of constraints that is specifically related to the capabilities of inverter interfaced resources to supply unbalanced current/power when the microgrid is islanded from the main distribution grid. We incorporate the new set of constraints into two optimization formulations leveraging two convex relaxations of the three-phase power flow equations: mixed-integer linear programming (MILP) and mixed-integer semidefinite programming (MISDP) that optimize the dispatch of controllable switches and DERs in the microgrid. The algorithms are then extended to networked microgrids with grid-forming sources. We test the algorithms on a realistic community microgrid model in Puerto Rico as well as standardized IEEE distribution test feeders. The testing results demonstrate the performance of the proposed algorithms. The MILP is fast and scalable, and the MISDP enforces the negative sequence voltage constraints. • Optimal operation of dynamic boundary MGs considering network and load unbalance. • Integration of unbalanced power and voltage constraints to ensure stable operation. • Fast and scalable mixed-integer linear programming model. • Mixed-integer semidefinite programming captures unbalanced voltage. • Better renewable utilization and resiliency in dynamic boundary microgrids. [ABSTRACT FROM AUTHOR]

Complementary operation has been proven an effective way to handle the increasing levels of renewable energy sources (RESs) integration into the grid. However, due to the relative higher levels of forecast uncertainty associated with RESs outputs, when the hybrid power system operates according to the day-ahead plan in real-time operation, the performance of the system may deviate significantly from the initial expectation in the day-ahead plan. Few research works gauge the effectiveness of the day-ahead plan from the perspective of real-time operation, which inadvertently makes this problem under explored. To handle this problem, in this study, using the wind-hydro hybrid power system (WHHPS) as an example, a novel tighten-constraint method is proposed to guarantee the effectiveness of the day-ahead plan in real-time operation. First, the conventional day-ahead planning model and the real-time operation model are proposed to guide the operation of the WHHPS. Second, considering the lack of connection between day-ahead planning and real-time operation in current research, a novel metric, i.e. Reliability , denoted by R , is proposed to evaluate the performance of the day-ahead plan from the perspective of real-time operation given inherent prediction errors of wind power. Third, a data-driven tighten-constraint method is proposed by introducing an adjustment parameter, denoted by λ , to improve the reliability of the day-ahead plan and eventually guarantee the effectiveness of the day-ahead plan from the perspective of real-time operation. Finally, a bilevel Stackelberg model is proposed and reformulated to calculate the adjustment parameter and the whole procedure of using the proposed method is clarified. The effectiveness of the proposed method is tested and verified through a series of case studies at the end of this paper. The results show that (1) the proposed method can improve the reliability of the day-ahead plan under different reservoir status; (2) the proposed method can guarantee a high reliability level of the day-ahead plan without adding any additional computation burden; (3) improving the prediction precision of the adjustment parameter can enhance the efficiency of resource utilization for power generation; (4) over time, the increase in available historical data can enhance prediction accuracy of adjustment parameter and improve the effectiveness of the proposed method even further. • Method to improve the performance of the day-ahead plan in real-time operation for WHHPS. • A metric to evaluate the day-ahead plan from the perspective of real-time operation. • A bilevel game theory based model to provide the data-driven base. [ABSTRACT FROM AUTHOR]

This paper proposes a novel magnetically excited variable cross section bending beam piezoelectric energy harvester (ME-VCB-PEH) to overcome the limitations of current piezoelectric energy harvesters, such as narrow bandwidth and single-directional collection. Firstly, a nonlinear distributed parameter model of the ME-VCB-PEH is established based on Hamilton's principle, and the optimal distance between magnets is derived, providing a theoretical basis for subsequent research. Secondly, through simulation and experimentation, the optimal structural parameters of the ME-VCB-PEH are determined. Finally, an energy harvesting test platform is constructed to test its output performance and application effectiveness. The results show that compared to rectangular and trapezoidal beams, the VCB structure exhibits lower frequency, wider bandwidth, and higher output voltage. The designed ME-VCB-PEH effectively harvests vibration energy in three axial directions below an external frequency of 80 Hz, effectively meeting the auxiliary power supply needs of temperature sensors and wireless transmission modules for the roadheader under normal operating conditions. This technology provides an important technical reference for self-powered wireless sensing technology and plays a significant role in IoT and sensing applications. • A magnetically excited variable cross section bending beam piezoelectric energy harvester is first proposed. • A nonlinear distribution parameter model for energy harvesting was developed based on Hamilton's principle. • Satisfying the vibration spectrum of the roadheader and capable of providing auxiliary power supply. [ABSTRACT FROM AUTHOR]

To sustain energy supply capability and mitigate vulnerability to operational failures, enhancing resilience in multi-energy systems (MES) becomes imperative in the face of deliberate attacks and natural disruptions. In this paper, a resilience enhancement optimization model (REOM) is proposed, incorporating various enhancement measures such as adding backup, strengthening physical protection, and constructing new lines. The objective is to minimize the betweenness centrality loss of the MES after being attacked or disrupted, where the betweenness centrality is improved by integrating the power output of energy supply equipment as weighted factors. To accelerate the calculation of shortest paths in large-scale MES, an approximate algorithm for shortest paths is proposed to extract necessary information of shortest paths, through selecting landmarks and converting the target path into the summation of different segments with the landmarks. Then, the REOM is transformed into a bi-level problem while the lower-level one is linearized into a mixed-integer linear programming (MILP) problem, and a genetic algorithm-MILP (GA-MILP) is employed to solve it. Assessing the optimal resilience enhancement plan, a comparison is made with the results obtained under two different attack strategies in the remaining two scenarios through three validation indicators: network fragmentation, decline in energy transfer efficiency, and load loss rate. Through simulations on both a 67-bus power-gas system and a virtual European power-gas system, it was observed that the weighted betweenness centrality loss decreased by 45.37% and 14.95%, respectively, compared to scenarios without the incorporation of the optimal resilience enhancement plan, effectively reinforcing system robustness. [Display omitted] • A complex network theory-based resilience enhancement optimization model (REOM) is established for multi-energy system. • An approximate algorithm for shortest paths (AA-SPC) is proposed to extract shortest-path information efficiently. • A self-built virtual 1310-bus European power-gas system is applied for REOM. • A genetic algorithm-mixed-integer linear programming (GA-MILP) is proposed to solve the REOM. [ABSTRACT FROM AUTHOR]

The situation in an urban distribution network becomes intricate under high-impact, low-probability events. Furthermore, the growing quantity of components within the urban distribution network renders their analysis more challenging. Thus, the desired recovery strategy should not only meet the requirements of restoration rates but also guarantee a stable power supply. To address this issue, this paper proposes a multi-objective decision analysis framework, which consists of EV user requirements, MPS routing, and emergency repair crew schedule, to generate a more precise recovery strategy. These objectives' dynamic behaviors in the transportation network are transformed into spatiotemporal characteristic variables and introduced into inequality constraints. Also, the Wasserstein generative adversarial network with gradient penalty is employed in this framework to characterize solar and wind realistic output. By minimizing the weighted sum composed of MPS operational costs, EV user expenses, load loss, and voltage fluctuation, an optimized recovery strategy is provided to the grid operator. Case studies investigate the effectiveness of recovery strategy in IEEE 33-bus and 123-bus benchmark systems. The results indicate the proposed approach can effectively handle complex scenarios achieving rapid and reliable restoration, while also preserving regular charging behavior and maintaining a stable power voltage supply. • Enhance resilience for urban distribution networks under disaster events. • Coordinate multiple recovery components and renewable energy integration. • Consider public requirements for fast restoration, power quality, and EV charging. • Involve renewable energy uncertainty generation. • Identify the strategy selection in response to energy uncertainty. [ABSTRACT FROM AUTHOR]

The transition to renewable energy sources, particularly sources like wind and solar induces a dependency on weather in the supply side of electrical grids. At the same time, the move to electric mobility with uncontrolled charging induces extra peak loads on these grids. These developments can cause grid congestion or an imbalance between the renewable power supply and the demand. Locally balancing the power supply and demand in grid-connected microgrids can alleviate such issues on the main grid. This paper presents a model based control strategy to address the challenge of locally balancing the power supply and demand in a grid-connected microgrid to avoid reaching the threshold rated power output set for large buildings. The microgrid under consideration consists of photovoltaic power sources and a large fleet of electric vehicle chargers (> 150). A model predictive controller is developed that treats the daily vehicle charging as a batch process. Given vehicle charge objectives, the controller utilizes vehicle charger occupancy and photovoltaic power generation forecasting services to distribute power optimally over a fixed period of time. The optimization problem is formulated as a quadratic programming problem and is implemented utilizing open-source Python libraries. The controller was integrated into the control system of a microgrid situated at a corporate office in the Netherlands. The control system oversaw the operation of 174 vehicle chargers. The effectiveness of the model predictive control technology was demonstrated over a three-week period and led to an average daily grid peak power reduction of 59%. • A model-based control strategy is presented in a grid-connected microgrid. • The microgrid under consideration consists of PV and 174 vehicle chargers. • The controller's strategy minimizes power grid peaks and maximizes EV charge energy provided by the PV cells. • The controller was integrated and implemented in the microgrid system. • An average daily grid peak power reduction of 59% was achieved over a three week period. [ABSTRACT FROM AUTHOR]

Large-scale power-to‑hydrogen (P2H) stations with multi-stack configurations, are emerging as valuable flexible resources for the power grid. The energy management strategy (EMS) determines multi-stack operation statuses. Nonetheless, existing EMS focus on production without adequately addressing the implications for grid-side power factor (PF) and potential security concerns. This paper addresses this limitation by presenting a model that characterizes the PF of a multi-stack P2H system across varying operation statuses defined by current and temperature. Through this model, we highlight a clear trade-off between the PF constraint and production targets in multi-stack scheduling. Subsequently, we introduce an improved EMS framework for multi-stack P2H that seeks a balance between PF, production, and security. This EMS is organized as a two-layer execution structure to guarantee control accuracy and tractability, which includes a model-based robust multi-stack scheduling programming and a rule-based real-time increment correction algorithm in series. Case studies compare multi-stack scheduling strategies under the proposed EMS with the traditional production-oriented strategy. The effectiveness of the extended PF and security dimensions is verified to comprehensively improve the responsiveness to power instructions. Furthermore, we outline five representative cluster-level scheduling strategies aligned with different load scenarios, offering insights for practical industrial implementations. • A general framework of energy management strategy for large-scale power-to-hydrogen (P2H) stations. • The tradeoff between the power factor constraint and the production target in determining multi-stack scheduling strategies. • A two-layer EMS is executed by a model-based hour-ahead robust scheduling and a rule-based real-time increment correction. • Outlining five cluster-level scheduling strategies aligned with various load scenarios of multi-stack P2H stations. [ABSTRACT FROM AUTHOR]

The uncertainty of wind power poses a serious challenge in peak regulation for multi-area interconnected power system, and this paper proposes a method for quantitatively assessing the peaking resource demand of a multi-area interconnected power system, which takes into account the complementary characteristics of inter-area regulating resources and the wind power forecast error. First, wind power historical data are analysed with a data-driven model-based wind power forecast error, to reduce the impact of renewable energy uncertainty on power system peaking demand. Subsequently, a multi-area peaking demand analysis method considering power flow constraints, which is proposed for the effective utilization of peaking resources in areas with complementary characteristics. The method can adequately quantify the area power margin, adjustable power, and area grid peaking demand. On this basis, a two-layer assessment model of multi-area peaking resource demand is constructed, the areas are divided into different categories of peaking aggregation based on the area peaking characteristics. The upper layer takes the area operating cost as the optimisation objective, to derive the optimal output of each area in the system, and the lower layer ensures the optimal matching of power among clusters, which is solved by using a Nash equilibrium. Finally, the effectiveness and superiority of the method is verified by case analysis, and the numerical results show that compared with the traditional global optimal method, the cost of operation and transmission has been decreased by 21.5% and the security of power flows has been significantly enhanced. • proposes a methodology for assessing peaking resources that incorporates a two-tier optimization structure. • propose a methodology for assessing the peaking demand of a multi-regional interconnected system, on the basis of analysing the characteristics of regional peaking. • propose a method to consider the uncertainty of new energy and the constraint of power flowt simultaneously. • propose an analysis method for the power system's demand for peak shaving resources under different power flow constraint margins. [ABSTRACT FROM AUTHOR]

Peer-to-Peer (P2P) electricity trading is an emerging means for allowing communities to share local energy production and demand response through direct trading. It is projected to play an important role in the future energy market by increasing the incentive to own and operate distributed energy resources (DERs) and participate in demand response (DR). A common approach to implementing P2P trading is with blockchain, however, existing solutions feature limitations. Typically, concerns involve either scalability and high transaction costs or projects implementing a permissioned blockchain that does not provide the trust and decentralization desired. In addition, the majority of market designs are simplified and do not capture the distinct complexity of electricity markets. They do not consider the inter-temporal constraints of appliances and DERs that need to run for multiple periods. Complex market designs that address these issues have not been implemented in blockchain based systems due to the computational limitations of such systems. To improve these two aspects, this paper proposes a market formulation that supports products running multiple periods with flexible scheduling, modeled as a mixed integer linear programming problem that optimizes social welfare. The trading platform is then implemented as a side chain of Polkadot, a network of blockchains designed to be secure, scalable, and economically feasible to foster an ecosystem of application-specific sidechains. This design is demonstrated to improve social welfare and achieve scalability by benchmarking the number of transactions that can be included per second, based upon typical assumptions of the market size. The result is a decentralized and performant system that allows for complex market designs to be implemented in a blockchain environment. This approach simplifies the process for participants, allowing them to bid DER or DR devices, directly and easily, even when this requires constraints across periods. • MILP optimization considering multi-period energy products and flexible scheduling. • Parachain of Polkadot blockchain for peer-to-peer energy trading. • Trust of a public blockchain but still has control over cost and throughput. • Demonstration of how off-chain market computation can be validated on blockchain. [ABSTRACT FROM AUTHOR]

• Graphical approach to assess matching between photovoltaic electricity and loads. • Combines the conventional measures self-sufficiency and self-consumption. • Possible to visualize improved matching with energy storage and load shifting. • Contribution of photovoltaic electricity for Swedish nearly zero energy buildings. For on-site renewable energy supply, such as photovoltaic (PV) electricity generation, an important issue is the daily and seasonal matching between on-site supply and demand. The matching potential is frequently expressed using the load matching indicators such as self-sufficiency and self-consumption. This paper presents the Energy matching chart, which is a novel graphical approach to visualize the PV-load matching. The chart uses self-sufficiency and self-consumption to provide information regarding the matching in both size and time. Using the Energy matching chart, the matching between PV production and load presented in previous studies is graphically analyzed and compared. Furthermore, the potentials for the two most common measures for improving the matching, namely energy storage and load shifting, are investigated. The results show that energy storage has, on average, a significantly higher potential for increasing the PV-load matching than load shifting. The second part of the paper, illustrates the benefits of the Energy matching chart by evaluating the Swedish implementation of the EU legislation on nearly Zero Energy Buildings (nZEBs). The assessment is performed for detached houses and evaluates the feasibility to use PV and battery systems for Swedish nZEBs. The results show that on-site PV production can help buildings to meet the nZEB regulations, but there are limitations due to the poor time-dependent matching. With a combined PV-battery system, the potential to meet the regulation is significantly larger. To summarize, the Energy matching chart has the potential to become a useful tool in the assessment of PV system and the evaluation of load matching measures. [ABSTRACT FROM AUTHOR]

Highlights • Literature on energy production from roadways is surveyed. • e-Roads, which produce energy by energy harvesting modes, are proposed. • In addition to power production, e-Roads can support smart infrastructure. • Scaling-up of e-Road concept could potentially supply most of China's power. Abstract Increasing and projected shortages in non-renewable energy sources have directed attention toward the potential for using regenerated energy from road traffic. There are currently three primary techniques for harvesting energy from roadways—piezoelectricity, thermoelectricity and photoelectricity—although, to date very few studies have focussed on the potential for integrating two or more of these, with most examining the application and optimisation of single-mode harvesting. To address this gap, this paper reviews and analyses the body of past research on road energy harvesting with the goal of developing a 'dynamic-heat-light division recommendation' based on the literature. From this research, a new concept of 'e-Roads' is proposed and defined in detail. If full use can be made of roadways as e-Roads under the division recommendation made in this paper, these structures have the potential to serve as one of the largest sources of energy in the near future. In this manner, this study serves as a reference for road construction and road network planning as well as a source of ideas for the development of intelligent traffic systems. [ABSTRACT FROM AUTHOR]

Highlights • Three major uncertainties of VPP are classified and reviewed comprehensively. • Detailed mathematical descriptions are summarized to quantize the uncertainties. • Components of objective functions and constraints with uncertainties are refined. • Tools and platforms for the optimization of VPP with uncertainties are presented. • Three demonstration projects of VPP considering uncertainties are introduced. Abstract A virtual power plant (VPP) is a system that integrates several types of power sources, so as to give a reliable and friendly overall power supply. The sources are often a cluster of distributed generation systems with intermittent renewable energies. Uncertainties are the important issues in researches and applications of VPP. In this paper, renewable power, market price and load demand are classified as major factors of uncertainties, and a comprehensive review of these three factors are given. Based on the classification, the detailed mathematical descriptions are summarized. And then, optimization objectives and constraints, which are adopted to improve the running performance of VPP with uncertainties, are summed up systematically. Solution approaches and tools for the optimization are also presented. At last, demonstration projects are introduced to show how uncertainties are handled in practice. This review paper can provide a rational assistance for researchers who focus on VPP. [ABSTRACT FROM AUTHOR]

Highlights • Review of the application of reinforcement learning (RL) for demand response (DR) • DR is relevant for integrating renewable energy source into the smart grid. • Considering RL/DR from energy generation and storage, to supply and user satisfaction. • Typical algorithms and open research directions are discussed. • Most articles focus on single-agent systems in stationary environments. Abstract Buildings account for about 40% of the global energy consumption. Renewable energy resources are one possibility to mitigate the dependence of residential buildings on the electrical grid. However, their integration into the existing grid infrastructure must be done carefully to avoid instability, and guarantee availability and security of supply. Demand response, or demand-side management, improves grid stability by increasing demand flexibility, and shifts peak demand towards periods of peak renewable energy generation by providing consumers with economic incentives. This paper reviews the use of reinforcement learning, a machine learning algorithm, for demand response applications in the smart grid. Reinforcement learning has been utilized to control diverse energy systems such as electric vehicles, heating ventilation and air conditioning (HVAC) systems, smart appliances, or batteries. The future of demand response greatly depends on its ability to prevent consumer discomfort and integrate human feedback into the control loop. Reinforcement learning is a potentially model-free algorithm that can adapt to its environment, as well as to human preferences by directly integrating user feedback into its control logic. Our review shows that, although many papers consider human comfort and satisfaction, most of them focus on single-agent systems with demand-independent electricity prices and a stationary environment. However, when electricity prices are modelled as demand-dependent variables, there is a risk of shifting the peak demand rather than shaving it. We identify a need to further explore reinforcement learning to coordinate multi-agent systems that can participate in demand response programs under demand-dependent electricity prices. Finally, we discuss directions for future research, e.g., quantifying how RL could adapt to changing urban conditions such as building refurbishment and urban or population growth. [ABSTRACT FROM AUTHOR]

• A new energy-as-a-service business model for aggregators of prosumers. • A new techno-economic simulation framework to compute business model benefits. • The new business model increases the average profitability of the aggregator by 438%. • The new business model provides free electricity to prosumers. Traditional retail business models price electricity using volumetric tariffs, which charge customers for the unit of energy consumed. These tariffs were designed for passive consumers with low flexibility. In this paper, we argue that these volumetric tariffs are unsuitable for prosumers with high flexibility since they are unable to adequately value the flexibility of their distributed energy resources in multiple electricity markets. This reduces the interest of prosumers participating in aggregators' business models. To address this issue, we propose a new business model for aggregators of prosumers, based on the concept of energy-as-a-service. In this business model, prosumers pay a monthly fee for aggregators to represent and optimize them in multiple wholesale electricity markets, including in energy and ancillary service markets. The monthly fee is computed by a new techno-economic simulation framework proposed in this paper, which can also be used to estimate the profitability of the new business model from the perspectives of both the aggregator and prosumers. Our experiments on a portfolio of real prosumers from Australia show that the new business model maximizes the economic benefits of both the aggregator and prosumers by increasing the average profit of the aggregator by 438% and reducing the average electricity cost of prosumers from $583/year to $0 when compared to two of the most common retail business models available in the Australian market. In other words, the economic benefit for prosumers is free electricity. In addition to this benefit, the new business model also provides simplicity and predictability to prosumers, as they are offered a guaranteed outcome before providing the services. [ABSTRACT FROM AUTHOR]

This paper investigates the cyber resiliency of future power systems with high penetration of distributed energy resources using advanced distributed and (or) hierarchical control architectures. Specifically, we simulate cyberattacks on three prototypical use cases, and we identify attack scenarios that are the most damaging to the overall system performance. We show that these attacks can have a significant impact on grid operation. Results provide additional insights into the robustness of the system to the most common cyberattacks. • This paper delves into the realm of cyber-resiliency, examining its implications across three distinct use cases encompassing distributed, distributed-hierarchical, and hierarchical control architectures. • Through exhaustive numerical simulations, we discern and highlight the precise signals within the system that exhibit heightened susceptibility to cybersecurity attacks. • The results obtained from our analysis offer valuable insights into the robustness of these systems, shedding light on their ability to withstand potential cybersecurity threats. [ABSTRACT FROM AUTHOR]

• A synergistic trading mode of electric energy and FRP markets is designed. • A declaration-dispatching strategy for VPP participating in the electric energy and FRP markets is constructed. • The distributionally robust optimization (DRO) technique is used to deal with the uncertainty in VPP. • VPP's declaration strategy and cost-benefits in 96 period are examined. • The characteristics of DRO model in data-driven feature, non-conservative result and stable run time have been proved. The volatility and randomness of renewable energy output make the flexible ramping ability demand of power system more urgent, and flexible ramping product (FRP) can effectively improve the flexibility of the power system. Virtual power plant (VPP) can regulate distributed generation, which is conducive to the regulation potential of flexible resources. To solve the problems of FRP and electric energy markets synergy and the wind power output uncertainty faced by the VPP in the declaration process, this paper proposes a comprehensive declaration strategy for the VPP to participate in the power market considering FRP and uncertainties. Firstly, this paper designs a synergistic trading mode covering flexibility resource demand determination and the virtual bidding curve formation for electric energy and FRP markets, in which the virtual bidding curve can reasonably compensate for the unit opportunity cost. Secondly, a comprehensive declaration-dispatching strategy decision-making model for VPP is constructed, and a two-stage distributed robust optimization (DRO) technology is used to deal with the wind power output uncertainty in the model, and flexible resources such as energy storage are used to mitigate energy deviation in the VPP. Finally, simulations implemented on a typical VPP are delivered to show that: 1) Virtual bidding curve realizes the accurate compensation for the units providing FRP. 2) Compared with the single market, VPP can increase the expected profit by 20.44% and reduce wind curtailment cost by 59.68% in the joint declaration of multi-markets.3) VPP can effectively suppress output uncertainty through energy storage system. 4) DRO model has significant advantages in data-driven, less conservative results and stable running time. [ABSTRACT FROM AUTHOR]

This paper proposes a computation offloading method for performance optimization in the fast frequency control ancillary service (FFCAS) provision of a virtual power plant (VPP). VPP aggregates massive demand-side resources to provide power systems with the FFCAS. It faces excessive communication and computation tasks. Edge computing addresses these issues through computation offloading. Heavy tasks can be partially offloaded to the edge to relieve the burden of the central server. Existing offloading methods, however, are not specific for the VPP. To fill the gap, first, an age of information (AoI) model is introduced to characterize the data flow of an edge-enabled VPP. Next, an AoI-based FFCAS performance evaluation model is proposed considering the impacts of communication delay, communication failures, and computational delay. Then an FFCAS performance optimization model is formulated. It aims to maximize VPP's profit through communication offloading and the DSR portfolio determination. Simulation results show that the proposed method can efficiently improve VPP's profit. • Develop an age of information (AoI) model to characterize the data flow of an edge-enabled VPP. AoI has been widely applied in the communication field recently, but few works have applied AoI to VPP. • Propose an AoI-based FFCAS performance evaluation model considering the impacts of communication delay, communication failures, and computational delay. • Formulate an FFCAS performance optimization model. It aims to maximize VPP's profit through communication offloading and the DSR portfolio determination. • Diverse scenarios are considered in case studies, including variations in offloading ratios, cost coefficients of edge computing resources, computing power, etc. Results demonstrated that edge computing could improve VPP's profit efficiently. • The benefits of edge computing become small when the related operating cost is too high or the computing power of the VPP cloud control center is strong enough. The proposed method can serve as a tool for the VPP to determine the implementation of edge computing whether or not. [ABSTRACT FROM AUTHOR]

As the energy market evolves into a dynamic and interactive landscape, the distributed nature of virtual power plant (VPP) becomes increasingly significant. This shift highlights the limitations of traditional centralized optimization approaches in capturing the complex interplay among various stakeholders. In response, this article introduces a novel multi-entity distributed collaborative optimization strategy for an integrated energy system (IES) that incorporates a VPP, electric vehicles, and carbon capture technologies, under the Stackelberg master-slave game framework. Within this framework, the VPP operator (VPPO) assumes the role of the leader, while the energy supply aggregator (ESA), customer side residential load aggregator (CSRLA), electric vehicles aggregator (EVA), and carbon treatment system aggregator (CTSA) are positioned as the followers. The paper delves into the development of interaction strategies for each entity, aiming at achieving optimal objectives. The study undergoes by presenting the mathematical model of the VPP-integrated energy system, which is seamlessly integrated into the master-slave game structure. This integration facilitates the creation of a distributed multi-entity cooperative optimization model featuring a single leader and multiple followers, with the uniqueness of the Stackelberg equilibrium being rigorously established. Therefore, the research employs a hybrid nutcracker optimization algorithm and quadratic programming (NOA-QP) method to address the engineering challenge of optimizing operator energy pricing. The case study conducted demonstrates that the proposed scheduling methodology improves the VPP system profit by 10.80%, increases the aggregation profit by up to 1317.22%, optimizes the carbon emissions by 15.59%, and significantly improves the solution efficiency of the VPP by 99.29%. • The virtual power plant (VPP) aggregated by multiple entities is studied. • Stackelberg is involved for adaptability and stability in different market environments. • NOA-QP is proposed to avoid disclosure of participant information. • 10.80% of profits are improved and 15.59% of carbon emissions are reduced. • 99.29% model solving speed can be improved through proper modelling and scheduling. [ABSTRACT FROM AUTHOR]

This paper proposes a computation offloading method for performance optimization in the fast frequency control ancillary service (FFCAS) provision of a virtual power plant (VPP). VPP aggregates massive demand-side resources to provide power systems with the FFCAS. It faces excessive communication and computation tasks. Edge computing addresses these issues through computation offloading. Heavy tasks can be partially offloaded to the edge to relieve the burden of the central server. Existing offloading methods, however, are not specific for the VPP. To fill the gap, first, an age of information (AoI) model is introduced to characterize the data flow of an edge-enabled VPP. Next, an AoI-based FFCAS performance evaluation model is proposed considering the impacts of communication delay, communication failures, and computational delay. Then an FFCAS performance optimization model is formulated. It aims to maximize VPP's profit through communication offloading and the DSR portfolio determination. Simulation results show that the proposed method can efficiently improve VPP's profit. • Develop an age of information (AoI) model to characterize the data flow of an edge-enabled VPP. AoI has been widely applied in the communication field recently, but few works have applied AoI to VPP. • Propose an AoI-based FFCAS performance evaluation model considering the impacts of communication delay, communication failures, and computational delay. • Formulate an FFCAS performance optimization model. It aims to maximize VPP's profit through communication offloading and the DSR portfolio determination. • Diverse scenarios are considered in case studies, including variations in offloading ratios, cost coefficients of edge computing resources, computing power, etc. Results demonstrated that edge computing could improve VPP's profit efficiently. • The benefits of edge computing become small when the related operating cost is too high or the computing power of the VPP cloud control center is strong enough. The proposed method can serve as a tool for the VPP to determine the implementation of edge computing whether or not. [ABSTRACT FROM AUTHOR]

As the energy market evolves into a dynamic and interactive landscape, the distributed nature of virtual power plant (VPP) becomes increasingly significant. This shift highlights the limitations of traditional centralized optimization approaches in capturing the complex interplay among various stakeholders. In response, this article introduces a novel multi-entity distributed collaborative optimization strategy for an integrated energy system (IES) that incorporates a VPP, electric vehicles, and carbon capture technologies, under the Stackelberg master-slave game framework. Within this framework, the VPP operator (VPPO) assumes the role of the leader, while the energy supply aggregator (ESA), customer side residential load aggregator (CSRLA), electric vehicles aggregator (EVA), and carbon treatment system aggregator (CTSA) are positioned as the followers. The paper delves into the development of interaction strategies for each entity, aiming at achieving optimal objectives. The study undergoes by presenting the mathematical model of the VPP-integrated energy system, which is seamlessly integrated into the master-slave game structure. This integration facilitates the creation of a distributed multi-entity cooperative optimization model featuring a single leader and multiple followers, with the uniqueness of the Stackelberg equilibrium being rigorously established. Therefore, the research employs a hybrid nutcracker optimization algorithm and quadratic programming (NOA-QP) method to address the engineering challenge of optimizing operator energy pricing. The case study conducted demonstrates that the proposed scheduling methodology improves the VPP system profit by 10.80%, increases the aggregation profit by up to 1317.22%, optimizes the carbon emissions by 15.59%, and significantly improves the solution efficiency of the VPP by 99.29%. • The virtual power plant (VPP) aggregated by multiple entities is studied. • Stackelberg is involved for adaptability and stability in different market environments. • NOA-QP is proposed to avoid disclosure of participant information. • 10.80% of profits are improved and 15.59% of carbon emissions are reduced. • 99.29% model solving speed can be improved through proper modelling and scheduling. [ABSTRACT FROM AUTHOR]

Natural gas plays an important role in the energy peak-shaving process of Integrated Energy Systems (IES). Accurate prediction of natural gas supply over longer time scales is one of the most important conditions to ensure the stability and security of IES energy supply. However, as the sequence length of the predicted time series increases, it becomes more difficult to extract valid dependencies from the data. At the same time, there are complex spatio-temporal coupling characteristics of various energy resources in the IES, which makes the construction of long sequence prediction models for natural gas supply very difficult. To solve this problem, we propose a systematic method for long-sequence prediction with natural gas supply in IES based on spatio-temporal causal network of multi-energy. First, the proposed approach uses a causal analysis algorithm to represent the complex coupled spatio-temporal relationships of multiple energy resources in an IES. Then, the natural gas time-series are decomposed through the Variational Modal Decomposition (VMD), to reveal its inherent trend and cyclical characteristics. Finally, the long-sequence prediction model is proposed by fusing the attention mechanism into Graph Convolutional Neural Network (GCN). Attentional mechanisms are introduced to obtain important long-term dependencies in the long sequence prediction process. We use a integrated energy dataset from Spain to test the validity and superiority of the method. The results show that the proposed model in this paper improves the Root Mean Square Error (RMSE) by at least 9.5% and reduces the Mean Absolute Error (MAE) by at least 14.5% compared to several baseline models. • A long sequence prediction method for natural gas supply is presented. • Obtaining long-term dependences Using the Attention Mechanism. • Using VMD to get trends and cycles in natural gas supply. • Characterizing energy couplings using causal network. [ABSTRACT FROM AUTHOR]

The optimization of multi-community integrated energy systems (MCIES), as a proficient means of integrating distributed energy resources and diverse loads, poses a compelling challenge. This challenge revolves around strategically harnessing the energy-carbon synergy advantages within these systems, while factoring in the heterogeneity among different communities and the constraints imposed by electrical connectivity devices. Facing the challenge, this paper proposes an individualized adaptive distributed approach for energy-carbon coordination in MCIES with power transformers (PT-MCIES). Firstly, a feasible region is constructed based on current and temperature limits to assess the loading capacity of power transformers. Based on this, a transactive energy (TE) based co-optimization model for PT-MCIES considering energy sharing and carbon trading is developed, where rolling horizon optimization is adopted to cope with the randomness from renewable energy and loads. For the TE problem, the alternating direction method of multipliers (ADMM) algorithm is employed to achieve distributed optimization. Uncertain renewable energy and loads, along with variable rolling horizons, make it difficult for the traditional ADMM to ensure convergence speed in the co-optimization of PT-MCIES. Accordingly, an individualized adaptive ADMM (IAADMM) embedded a spectral penalty parameter selection rule is adopted. Simulation results show that the proposed scheduling model can effectively guarantee the safe operation of power transformers with the suggestion of a feasible region, and achieve higher economic and environmental benefits than the reference one that ignores energy-carbon coordination. Furthermore, the proposed IAADMM exhibits superior performance in terms of convergence speed and robustness to the initial penalty parameter when compared to ADMM and its various variants. • Feasible region is constructed to assess the loading capacity of transformers. • Energy sharing and carbon trading are considered in scheduling. • A co-optimization method is proposed for congestion mitigation of transformers. • IAADMM is proposed to handle the influence of variable rolling horizon. [ABSTRACT FROM AUTHOR]

Constructing a new power system with renewable energy as the main component is an important measure for coping with extreme weather and maintaining the stability and efficiency of the power system; in particular, pumped storage is an effective means of smoothing fluctuations in the wind and photovoltaic power output. Therefore, considering the reutilization of abandoned mines, this paper constructs an integrated abandoned mine pumped storage/wind power/photovoltaic system. By establishing the mathematical model and capacity configuration model of the system, an analysis of wind/photovoltaic output characteristics is carried out. A nondominated sorting genetic algorithm is used to solve the optimisation problem, with the goals of maximizing the power supply guarantee rate and new energy absorption rate and minimizing the levelized cost of energy. On this basis, a grid power transmission channel optimization strategy is established to ensure that power transmission to the grid is maximized. The monthly power supply guarantee rate and monthly new energy absorption rate in the configuration results are compared and analysed, and the sensitivity of wind power, optical power, initial investment cost, and effective on-grid power to the levelized cost of electricity is considered. Then, by combining the abandoned mine data, eight different sets of parameters of pumped storage are selected for the optimal configuration study, and the factors influencing the pumped storage regulation capacity are studied. Finally, based on the original three objective functions, the storage equivalent utilization rate, system complementarity, power generation revenue, new energy access rate and new energy utilization rate are added to the analysis, and the hierarchical analysis method combined with fuzzy comprehensive evaluation is used to evaluate the comprehensive benefits of pumped storage with nine different parameters. The optimized capacity configuration of the standard pumped storage of 1200 MW results in a levelized cost of energy of 0.2344 CYN/kWh under the condition that the guaranteed power supply rate and the new energy absorption rate are both >90%, and the study on the factors influencing the regulating capacity of pumped storage concludes that the rated head, the adjustable reservoir capacity and the installed capacity of the pumped storage are positively correlated with its regulating capacity. Through comprehensive benefit evaluation, it is concludes that pumped storage type 5 provides the greatest comprehensive benefit. This study provides valuable reference information for reducing the impact of renewable energy on the power grid and realizing the reuse of abandoned mines. • An abandoned mine pumped storage/wind/photovoltaic integrated system is proposed. • Study the effect of capacity and head on mine pumped storage regulating capacity. • Study the comprehensive benefit of different scale abandoned mine integrated system. • Optimal configuration of the integrated system achieves the stability and economy. [ABSTRACT FROM AUTHOR]

Electricity management systems are experiencing significant challenges due to the increased penetration of distributed energy resources. Electricity flows in distribution networks are transforming from unidirectional to bi-directional form. Consumers are transitioning to prosumers with different characteristics, where they take more active roles in electricity generation and consumption. Aggregators are vital financial intermediary agents in the power system transitions, as they could aggregate energy profiles of prosumers. The market competition between aggregators and interactions between prosumers and aggregators are complex and dynamic, which requires a holistic framework to model the market competition. This paper proposes an intelligent aggregation framework with edge computing, enabling decentralized competition for multiple aggregators and prosumers, which can be solved with a graph-based consensus algorithm. This study mathematically proves the proposed algorithm's convergence guarantee and convergence rate. In addition, the proposed framework is applied to an open-source dataset to demonstrate its applicability. Lastly, a benchmark analysis is conducted to show that the proposed algorithm has better communication complexity than the benchmark algorithms. • A dynamic market competition model for prosumer aggregation. • The solution state for the market to reach equilibrium is unique. • A graph-based algorithm for the market model with edge computing. • A proposed algorithm with a linear convergence rate. [ABSTRACT FROM AUTHOR]

Local Energy Markets (LEMs) have been proposed as an effective solution to coordinate distributed energy resources within emerging smart local energy systems. However, LEMs are still at an early stage of development and are not widely implemented yet. Extensible open-source software tools are needed to simulate different market designs and evaluate their feasibility and effectiveness. This paper presents OPLEM, an open-source Python package for modelling and testing LEM designs. It offers a modular and flexible framework to create and test market designs adapted for distribution networks. OPLEM integrates two distinct models—one for designing and simulating local energy markets, and another for network operation. Unlike existing tools, OPLEM platform offers a comprehensive solution that bridges the gap between market design and network operation assessment, enabling stakeholders to gain deeper insights into the interactions between market dynamics and grid performance. Additionally, the inclusion of participant and asset models further enhances the tool's versatility, allowing for a holistic analysis. Thanks to its modular structure, OPLEM allows the development of customised LEM designs and initially incorporates four popular LEM designs: Time-of-Use pricing market, centralised dispatch with distribution locational marginal pricing, bilateral peer-to-peer market and auction market. By combining market design and network modelling functionalities, the tool empowers users to explore various scenarios and optimise market clearings to enhance both economic efficiency and grid reliability. • An open-source Python platform for local energy markets coined OPLEM was developed. • Designed in a modular fashion to ensure the seamless integration of new models and functionalities. • OPLEM bridges the gap between market design and network operation assessment. • It offers 4 common local market designs: ToU, Centralised, P2P and auction markets. [ABSTRACT FROM AUTHOR]

This paper proposes a nanogrid for home applications. Compactness, rapid installation and minor changes to the existing equipment are among the basic concepts behind the proposed nanogrid. With this in mind, the authors designed the proposed nanogrid as a compact object. To this aim, the power electronic converters of a typical nanogrid are no longer distributed and placed close to peripherals; they now are grouped together. The electric wires of the local power distribution system connect the grouped converters to the distributed peripherals (e.g. photovoltaic panels, batteries storage systems, loads). As a benefit, when the proposed nanogrid is installed between the meter and the switchboard of an existing dwelling, no significant changes to local equipment, devices and electrical system are required. A central controller governs the proposed nanogrid. Such a controller ensures the power balancing, implementing the continuous controllers of all the power converters belonging to the proposed nanogrid. In addition, such a controller optimizes generation and demand, implementing a decisional process based on behavioural rules. In this paper, a behaviour tree models the behavioural rules. The behaviour tree serves the discrete controller to decide the operation point of the nanogrid. The behaviour tree drives the central controller to pursue strategic targets so as to ensure power supply continuity to critical loads, maximize the exploitation of renewable energy sources and minimize power flow at the point of delivery. This paper also illustrates a single-phase 1 kW prototype of the proposed nanogrid; the prototype is subject to four tests and conditions: black start, utility failure, utility restore and over generation. Besides the capability to pursue the strategic targets mentioned above, the experimental results also demonstrate the proposed nanogrid’s capability to adjust the current imported from the utility grid, to change from grid-connected mode to stand-alone mode and vice versa, to compensate for a surge of the DC bus voltage. [ABSTRACT FROM AUTHOR]

Some scholars have addressed the relationship between the Human Development Index (HDI) and energy consumption in terms of the semi-logarithmic or hyperbolic function. One of the results from this relationship is to divide the world countries into two categories, developed and developing countries. This classification considers pre-developing countries and developing countries into one category (developing) while the proportion of people with fundamental energy needs in the former is far higher than in the latter. The objective of this paper is to allocate a separate class for pre-developing which their fundamental energy needs have not completely satisfied. The methodology of the study is divided into two parts. Initially, based on the longitudinal data (112 country’s data during the period of 2005–2013), a linear Quality of Life (QoL) indicator is proposed in terms of six variables. Then an S-shape (sigmoid) curve is fitted to the QoL indicator data against energy consumption (total primary energy supply as a proxy) per capita (ECpc) and electricity consumption per capita (Elcpc) data. Three types of countries, developed, developing, and pre-developing are identified based on the sigmoid function. The results of the proposed model demonstrate that the “pre-developing” category has different QoL and ECpc as compared to developing and developed classes. Another result of the paper shows the entry of new technologies has influenced the QoL and Elcpc to a greater extent in developing countries than in developed countries. One-way analysis of variance is a method to shed light on the latter consequence. In the case of pre-developing countries, the pertinent analysis shows an insignificant impact. This paper concludes the new classification of countries appropriately addresses variation of QoL against ECpc in each class. [ABSTRACT FROM AUTHOR]

The transformation of electricity network users from passive to active agents, as a result of decreasing costs of distributed energy resources, requires several adaptions, one of which is revising the distribution network charges. Often current network charge designs do not ensure network cost recovery and lack to incentivize efficient network investments and usage. New network charge methodologies are required to guide and incentivize customers in an efficient way while maximizing system economic efficiency. This paper proposes an efficient methodology that ensures network cost recovery while promoting efficient usage of the network as well as efficient network investments. The proposed network charge design consisting of two components: a peak coincidence network charge (PCNC) and fixed charge. The PCNC is a forward-looking charge as it considers the cost of future network reinforcements required and assigned to customers during peak hours of the network utilization. Fixed charges allocate the residual of the network costs following Ramsey-pricing principles. This paper compares the outcome from economic optimum customers’ response to four different network charges: (i) volumetric charges (ii) fixed charges (iii) peak demand charge (iv) PCNC plus fixed charges. Two case studies for two different load profiles are simulated using linear programming to minimize their total costs within each charges design, considering the possibility of buying electricity from the grid and investing on onsite generation or curtail load. Finally, the paper highlights through the case studies how customer’s response is highly influenced by different network charge designs, and compares the consequences of these responses in terms of network cost recovery and total system costs. The paper concludes with practical issues that need to be considered for the implementation of the proposed network charges design. [ABSTRACT FROM AUTHOR]

Microgrids are extensively integrated into electrical systems due to their many technical, economic, and environmental advantages. However, they encounter a challenge as they experience high-frequency fluctuations caused by the stochastic nature of renewable energy generation, electric loads, and the presence of Electric Vehicles (EVs). Therefore, various techniques, algorithms, and controllers have been introduced to ensure effective Load Frequency Control (LFC) and maintain a stable power system in microgrids. These methods aim to ensure that the system's frequency remains stable and within an acceptable range, especially when faced with changing load demands and other factors. This paper presents a novel enhanced control approach, Particle Swarm Optimization-Trained Artificial Neural Network (PSO-TANN), to optimize the load frequency model of a microgrid with vehicle-to-grid integration. The results are then compared under various scenarios, including renewable energy integration, EV charging and discharging dynamics, and varying load demands. The comparative analysis involves assessing the performance of the conventional Proportional–Integral–Derivative (PID) controller, the PSO-PID controller, and the newly proposed controlling technique. The suggested controller attains 99.904% efficiency with a negligible mean squared error of 1.1112 × 10−7, decreasing the integrated time absolute error to 1.0 × 10−4. It shows rapid response, precise targeting, and quick peak output ability, with marginal overshoot and undershoot, and a transient time of 28.5626 s, efficiently controlling microgrid frequency. Stability analysis validates the effectiveness of the proposed PSO-TANN controller in ensuring stability within the microgrid's LFC system during uncertainties and disturbances. This establishes resilience, diminishes settling time, and maintains reliable performance while controlling frequency. • An investigation into an islanded microgrid's Load Frequency Control (LFC) model framework has been conducted, comprising diesel generator, solar PV system, wind turbine, and EVs operating in vehicle-to-grid mode to supply power to electric loads, consequently impacting the frequency dynamics of the microgrid system. • A novel enhanced control approach, Particle Swarm Optimization-Trained Artificial Neural Network (PSO-TANN) has been implemented to optimize the load frequency model of a microgrid. • An extensive comparative analysis with other controllers, including PID and PSO-PID, is conducted across diverse scenarios involving renewable energy integration and EV charging and discharging dynamics under various load disturbances to reduce settling time, control system frequency, and ensure reliable performance. • The stability analysis validates the efficiency of the proposed PSO-TANN controller in ensuring reliability during uncertainties and disturbances, maintaining overall robustness and stability of the LFC system within the microgrid. [ABSTRACT FROM AUTHOR]

Buildings are typically integrated with multiple distributed energy resources (DERs), enabling them to act as building prosumers engaged in both energy production and consumption. Peer-to-peer (P2P) energy trading among building prosumers is crucial to improve their benefits. However, further exploration is required to balance the benefits between building prosumers and the system operator (e.g., the integrated community energy system (ICES) operator) since they are different entities. In this context, this paper proposes a comprehensive network charge and energy sale pricing scheme for the ICES operator on heterogeneous building prosumers with P2P multi-energy trading. The interaction between the ICES operator and building prosumers is modelled as a bi-level optimization problem that belongs to the hierarchical structure, while considering the heterogeneity of thermal insulation performance of buildings. At the upper level, the ICES operator optimizes the electricity/heat network charge prices and electricity/heat sale prices to maximize its revenue. At the lower level, building prosumers with a parallel structure optimize the schedules including P2P multi-energy trading and buildings' heating loads to minimize their costs. Furthermore, to address the bi-level optimization problem with parallel and hierarchical coupling structures, an accelerated asynchronous distributed algorithm based on alternating direction method of multipliers (ADMM) is developed, integrating a warm start strategy and a dual update accelerated iteration strategy for further improving computational efficiency. Finally, case studies demonstrate that the proposed scheme can effectively benefit both the ICES operator and building prosumers in P2P multi-energy trading at the same time. Meanwhile, the feasibility and effectiveness of the proposed algorithm are validated. • An optimal pricing scheme for ICES operator on building prosumers is proposed. • A bi-level optimization is proposed for ICES operator and prosumers with P2P. • An accelerated asynchronous distributed algorithm based on ADMM is developed. • Benefits of ICES operator and prosumers through several scenarios are analyzed. [ABSTRACT FROM AUTHOR]

The integration of distributed energy resources (DERs) such as wind, solar, batteries, etc. into the grid presents opportunities as well as challenges with regard to efficient coordination and trading. Virtual power plants (VPPs) have emerged as an important paradigm for aggregating and managing numerous DERs. This paper proposes a decentralized blockchain-based peer-to-peer (P2P) multilayer energy trading framework to facilitate optimized coordination of DERs within a VPP context. A control center, prosumers, and service operators constitute the model's multi-layered architecture. Profits from feed-in tariffs, ancillary services, and P2P sales are optimized for revenue while expenses are minimized using a mixed-integer linear programming formulation. Smart contracts enable prosumers to trade energy units in the Ethereum blockchain with ease via a participatory web platform. The effectiveness of the framework in synchronizing power supply and demand within the VPP by coordinating the charging and discharging of storage systems in accordance with consumption patterns is demonstrated through performance evaluation. In contrast to conventional centralized architectures, the proposed framework utilized decentralized architecture efficiently consolidates and optimizes distributed energy resources (DERs) to improve grid stability and renewable energy utilization. [Display omitted] • A novel multi-layer framework for peer-to-peer (P2P) energy trading, within VPP. • An interactive web-based trading platform using the Ethereum blockchain to facilitate exchange of energy units. • Implementation of MILP-based DER management model for optimal trading. [ABSTRACT FROM AUTHOR]

China's energy transformation has been accompanied by potential energy security issues. However, the influence degree and driving mechanism behind this phenomenon remain unclear. In this paper, we developed a complex network-based technical resilience assessment framework and quantified both the historical trajectory and the prospective pathway of energy technical resilience in China. We proposed to reveal the relationship between cleaner energy supply and technical resilience by defining three energy technology resilience indicators: energy robustness, energy recovery rate, and energy performance loss. The results showed a consistent decline in energy technical resilience within China, predominantly characterized by an average diminution of 6.83% in energy robustness from 2010 to 2018. Taking carbon neutrality as a target, China's energy system transformation, characterized by increasing clean energy supply, leads to a significant improvement of 7.73% in energy robustness from 2020 to 2060. Conversely, the nationally determined contribution scenario exhibited negligible changes (±0.57%) in energy robustness, while a decline of 2.21% was noted under the business as usual scenario. Furthermore, energy robustness exhibited increments of 12.07%, 2.3%, and 4.17% for every 10% rise in the share of clean energy supply during 2020–2060, demonstrating greater resilience for cleaner energy systems against disruptions, including energy import interruptions, malicious attacks, and random failures. It has been concluded that enhancing the supply of clean energy can significantly contribute to bolstering China's energy technical resilience, while simultaneously addressing climate change concerns. • Network analysis is used to assess China's energy technical resilience. • Energy technical resilience is evaluated regarding robustness, recovery rate, and performance loss. • Energy system robustness significantly increases under carbon neutrality scenario. • Increasing clean energy supply strengthens resilience against shocks. [ABSTRACT FROM AUTHOR]