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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]

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]

Climate change is driving the adoption of sustainable energy, with low-cost solar photovoltaics and wind power at the forefront. However, land-constrained regions and islands have a limited onshore renewable energy potential. Wave power may prove useful for such regions, supported by growing literature in the field. This study delves into wave power's techno-economic potential, addressing a gap in previous assessments focused solely on theoretical or technical prospects. Utilising hourly wave data and a wave energy converter manufacturer's power matrix, global wave electricity yield is estimated. Considering projected costs, levelised cost of electricity is used to gauge economic viability. Although wave power is currently expensive, the results suggest that it could become cost-competitive with offshore wind power in the 2030s, with levelised cost of electricity below 70 €/MWh by 2035 in areas with good wave energy resources. Finally, the paper contributes openly accessible, hourly capacity factor data of global wave power generation, empowering further energy system modelling research. This study paves the way for informed decision-making on wave power's role in a diversified, sustainable energy future. • Global-local wave power potential is estimated. • Economic potential estimation is presented with projection until 2050. • Point absorbers have a wide operating range leading to high yields and low costs. • Wave power is a feasible alternative for power supply in coastal regions and islands. • Energy systems can benefit from the complementarity of wave power and solar PV. [ABSTRACT FROM AUTHOR]

Power pooling has emerged as a regional strategy for accelerating generation capacity expansion in West Africa with the aim of leveraging vast domestic energy resources and promoting investment in regional power infrastructure. As part of their climate action pledges, most West African countries have committed to increasing the shares of variable renewable energy (VRE), particularly solar photovoltaic and wind power, in their generation mix. It, however, appears that approaches to utility-scale capacity expansion planning tend to overlook the inherent temporal intermittency and spatial variability of VRE-based generation. Moreover, despite influencing the techno-economic rationale for grid expansion and off-grid electrification, as well as the trade-offs between high renewable supply areas and grid expansion, the spatial distribution of demand has been overlooked in planning approaches, leading to conservative and weak prospects for the contribution of utility-scale VRE. Such inconsistencies with the region-wide potentials and policy ambitions highlight that it is paramount for West Africa to design its power pooling such that spatial and temporal fluctuations of VRE supply are duly considered in capacity expansion planning while taking advantage of complementarities between VRE supply and national electricity demand profiles. To address this, the present paper applies a long-term generation capacity expansion model, OptGen, soft-linked to a least-cost operation module, the SDDP tool, which is enhanced by geospatial electrification analysis using the OpeN Source Spatial Electrification Tool (OnSSET), to a subset of four member countries of the West African Power Pool – Burkina Faso, Côte d'Ivoire, Ghana, and Mali – for the 2023–2040 time horizon. The results highlight that current frameworks lead to missed opportunities for bridging the supply-demand gap in all countries, not only in terms of VRE generation capacity, but also of cross-border power trade. • Long-term capacity expansion planning and dispatch models are soft-linked. • The spatial and hourly distributions of demand and renewables supply are considered. • The 10% limit on renewables share used in the regional master plan is not optimal. • The current system operation is not conducive to optimal cross-border trade. • The relaxed transmission scenario yields higher dispatch factors for renewables. [ABSTRACT FROM AUTHOR]

Enhancing the reliability of energy networks and minimizing downtime is crucial, making self-healing smart grids indispensable for ensuring a continuous power supply and fortifying resilience. As smart grids increasingly incorporate decentralized prosumers, innovative coordination strategies are essential to fully exploit their potential and improve system self-healing capabilities. To address this need, this paper presents a novel bi-level strategy for managing the self-healing process within a smart grid influenced by Hydrogen Refueling Stations (HRSs), Electric Vehicle Charging Stations (EVCSs), and energy hubs. This approach taps into the combined potential of these prosumers to boost system self-healing speed and reliability. In the initial stage, the Smart Grid Operator (SGO) conducts self-healing planning during emergencies, communicating required nodal capacities to prevent forced load shedding and outlining incentives for smart prosumers. Subsequently, prosumers schedule their activities and contribute flexible capacities to the SGO. Bridging the first and second stages, an adaptive Alternating Direction Method of Multipliers (ADMM) algorithm ensures convergence between the SGO and prosumer schedules within a decentralized framework. This strategy underwent implementation on a 118-node distribution system using GAMS. Results demonstrate that the proposed concept reduces Forced Load Shedding (FLS) by 32.04% and self-healing costs by 17.48% through effective utilization of smart prosumers' flexible capacities. Furthermore, outcomes indicate that the SGO reduces FLS by 6.69% by deploying Mobile Electrical Energy Storages (MEESs) and Mobile Fuel Cell Trucks (MFCTs) to critical nodes. • Introducing a bi-level self-healing strategy for smart grids with smart prosumers • Developing an adaptive ADMM for decentralized coordination among SGO and prosumers • Utilizing the flexible capacities of energy hubs, EVCSs and HRSs to reduce FLS • Embedding a robust strategy into the bi-level methodology to address uncertainties [ABSTRACT FROM AUTHOR]

The concept of the virtual power plant (VPP) has attracted extensive attention due to its distinguished capability of integrating various types of distributed energy resources (DERs). Defining the feasible operation region (FOR) is a crucial prerequisite for VPP's participation in the provision of power system services. However, considering the heterogeneity and uncertainty of DERs, it is always a challenging task to calculate the accurate FOR of VPP. In this paper, we propose a novel internal resource aggregation approach to estimate the FOR of VPP. The design of the proposed aggregation model consists of two stages. In the first stage, all the operational constraints of various DERs at the same node are expressed with a unified polytope form, and then these constraints are aggregated into one single polytope operation constraint by the computational geometry method. In the second stage, based on the aggregated polytope operation constraint at each node, combined with the dynamic line rating (DLR) and network constraints, a data-driven distributionally robust FOR estimation problem is formulated to determine the boundaries of the FOR. Numerical experiments have been conducted to validate the feasibility and superiority of the proposed approach and its applicability in handling multi-period FOR estimation. • A data-driven distributionally robust feasible operation region estimation framework is established. • The proposed approach balances computational efficiency and accuracy. • Dynamic line rating contributes to the expansion of the feasible operation region. • Time-varying feasible operation region estimation is conducted. [ABSTRACT FROM AUTHOR]

Microgrids (MGs) are promising solutions to improve power distribution system (PDS) resilience against natural disasters. However, the existing MG formation approaches based on the linearized Distflow (LinDistflow) model always demand MG roots and their corresponding topologies. This can result in an increased number of variables and constraints in the optimization problem, and deteriorate their computational performance. To this end, an adaptive LinDistflow model is proposed based on the single commodity flow model in graph theory in this paper. Specifically, we show that active and reactive powers can be represented as commodities, which are sent from one node to each of its adjacent nodes in the graph. Then, the power flow and nodal voltage calculation based on the commodity flow only requires adjacent node information of the original topology rather than various MG topologies caused by the dynamic deployment of mobile energy resources (MERs). Furthermore, by incorporating the adaptive LinDistflow model as constraints, a dynamic MG formation approach is proposed for resilient load restoration considering large-scale MER deployment. The problem is formulated as a mixed-integer nonlinear programming problem (MINLP). A linearization technique is proposed based on the propositional logic constraints. It employs the propositional logic that partitions the solution space into two separated regions. Accordingly, the region that the solution lies in can be selected linearly. The effectiveness of the proposed approach is demonstrated based on the IEEE 37-Node, 123-Node and 8500-Node Test Feeders. • An adaptive Linearized Distflow model is proposed based on single commodity flow. • Dynamic microgrid formation for large-scale deployment of mobile energy resources. • The propositional logic constraint is employed to address the problem nonlinearity. • Computational complexity is analyzed to show the efficiency improvement. [ABSTRACT FROM AUTHOR]

Aiming at a large number of decentralized resources such as rural biomass, rooftop photovoltaic, and decentralized wind power, a novel rural virtual power plant (VPP) has been conceptualized. The VPP is integrated by biomass waste conversion system (BWC), carbon to energy (C2P), demand response aggregator (DRA), and distributed renewable energy (DRE), namely, BDCD-VPP. Then, this paper proposes a data-driven two-stage robust optimization dispatching model for BDCD-VPP considering carbon-green certificates equivalent conversion mechanism. This model aims to address the uncertainty of wind power plants (WPP) and photovoltaic (PV) power generation. Strong duality theorem (SDT) and C&CG algorithm are applied to construct this model. Thirdly, the Aumann-Shapley method (A-S) was enhanced by incorporating risk factor, cost factor, and carbon reduction factor. This refinement results in the development of a synergistic scheduling revenue allocation method for electricity‑carbon-green certificates. Finally, the Lankao rural energy revolution pilot program in China is selected as the case study, the results showed: (1) The BDCD-VPP aggregates distributed energy sources such as the rural WPP and the PV to realize electricity‑carbon-electricity cycle effect. The BDCD-VPP generates a green certificate revenue of 47.17¥/MWh and exhibited carbon emissions of 0.37 t/MWh. The proposed Carbon-Green Certificates Equivalent Conversion Mechanism increases BDCD-VPP benefit ratio by 3.52%. (2) The proposed two-stage robust optimization dispatching model enhances the ability of BDCD-VPP to adapt to uncertainty. Compared to the day-ahead stage, biomass power generation (BPG) and waste power generation (WPG) increase upward peaking power by 24.05% and 9.99% in intra-day stage. Flue gas treatment system (FG-TS), gas-power plant carbon capture (GPPCC) and power to gas (P2G) increase downward peaking power by 65.15%, 70.99% and 25.94%. (3) Utilizing the proposed benefit allocation methodology, BPG and WPG need to concede 38.25¥/MWh and 65.02¥/MWh for carbon emissions associated with electricity. WPP and PV need to concede 64.25¥/MWh and 33.71¥/MWh for power generation uncertainty. GPPCC, P2G, DRA, and small hydropower station (SHS) obtain revenues of 24.48 ¥/MWh, 55.07 ¥/MWh, 70.38 ¥/MWh, and 51.30 ¥/MWh. Compared to the A-S, the proposed benefit allocation methodology exhibits 6.92% improvement in satisfaction. Overall, the proposed data-driven two-stage robust optimization dispatching model and benefit allocation strategy facilitates the optimal aggregation and utilization of rural distributed energy resources, considering the interests of various stakeholders. This is conducive to achieving a clean and low-carbon transformation of the overall energy structure. • Proposed a novel rural virtual power plant considering biomass waste conversion and carbon to power. • Designed carbon-green certificate equivalence conversion mechanism. • Proposed a data-driven scenarios generation and reduction strategy for uncertainty factors. • Construct a two-stage robust optimization dispatching model and strong duality C&CG algorithm. • Construct an improved Aumann-Shapley-based benefit allocation strategy [ABSTRACT FROM AUTHOR]

The Danish government plans two energy islands to collect offshore wind power for power distribution and green fuel production. Wind power is often criticized for lacking stability, which challenges downstream fuel synthesis processes. Solid oxide electrolysis cells (SOEC) are promising for green hydrogen production on a commercial scale, but the impact of fluctuating power on SOEC remains uncertain. This paper explores the feasibility of a Wind-SOEC coupled system by conducting a 2104-h durability test with the state-of-the-art Topsoe TSP-1 stack. Three periods of steady operation and two periods of dynamic operation were conducted. Wind power fluctuation was simulated during the dynamic period, and two control strategies were used to handle it. The constant flow (CF) and constant conversion (CC) strategies maintain the feedstock flow rate and conversion ratio of steam-to‑hydrogen, respectively. Compared to steady operation, the stack shows no signs of additional degradation in dynamic operation. Thus, the TSP-1 stack has been proven robust and flexible enough to handle fluctuating wind power supplies under both operation strategies. Further, stack performance during dynamic periods was compared and analyzed by removing degradation effects. Accordingly, SOEC stacks with CC control will consume less external heat than CF to maintain a heat balance. Nevertheless, SOEC systems with CF and CC control strategies may have different efficiency or hydrogen production costs. Tech-economic analyses will be needed to investigate control strategies at the system level. • A 2104 h SOEC stack test with simulated fluctuating wind power input was conducted. • Deployed constant flow and constant conversion strategies in the dynamic period. • The Topsoe stack is robust and flexible for harsh and varying working environments. • Fluctuations didn't introduce extra degradation in the current test. [ABSTRACT FROM AUTHOR]

The increasing frequency of the occurrence of high impact low probability (HILP) disruptive events has posed huge threats to the power system. Therefore, power system resilience improvement has drawn world-wide attention. Electric buses (EB) are equipped with a large capacity of batteries which could provide sufficient energy and capacity values to run islanded micro-grids (MG) for hours, with a particular focus on V2G, which will deliver system resilience during extreme operational conditions. In this paper, an innovative reciprocal transport-power system coordination scheme is proposed to simultaneously mitigate the adverse impacts on both systems. By using this economic incentive-free approach, EBs are endogenously motivated to provide resilience-oriented V2G service to the power system, while increasing the transport service survivability during HILP disruptive events. Additionally, a novel EB dispatch rescheduling method considering the trade-off of both the requirement of energy supply security and transport service fulfilment is proposed. By using this method, the essential load curtailment of local MGs can be further alleviated, while the impacts of reduced charging service on transport service can also be further mitigated. Through a series of case studies, we illustrate that EBs have significant potential to provide resilience enhancement to the urban energy system based on appropriate dispatch strategies. Meanwhile, the interests of the power system and the transport system can be well reconciled by using the proposed approach during HILP disruptive events. • Present an algorithm for resilient operation of power system and transport system. • Propose a reciprocal coordination scheme reconciling the interests of both systems. • Investigate the values of strategic EB dispatch rescheduling in improving resilience. • Quantify energy values and capacity values of EBs in enhancing system resilience. [ABSTRACT FROM AUTHOR]

The utilization of modeling techniques for Proton Exchange Membrane Fuel Cells (PEMFC) presents a viable and proficient approach for comprehending, enhancing, and increasing the efficiency of these sustainable energy sources. This is of paramount importance in tackling worldwide anxieties regarding ecological contamination and the lessening of non-renewable resources of energy. The authors of this research paper introduce a novel approach for the efficient detection of output voltage in PEMFC. This approach involves the utilization of an optimized Ridgelet Neural Network (RNN) in conjunction with a Hybrid Northern Goshawk Optimization (HNGO) algorithm. The primary aim of the proposed approach is to reduce the discrepancy among the determined and anticipated resulted voltages of the PEMFC. This is a vital aspect in increasing the fuel cells' efficacy. The authors conducted simulations to assess the efficacy of the proposed approach. The outcomes designated that the RNN/HNGO method outperforms existing methods when it comes to accuracy in tracking voltage signal predictions. Moreover, the proposed approach yields a comparatively lower level of forecast error compared to previously published studies. The aforementioned results underscored the possibility of utilizing the suggested approach in enhancing the performance of PEMFC through practical means. Additionally, the authors propose that the methodology they have presented has the potential to be expanded to encompass other performance metrics of PEMFCs, including power and efficiency. The authors propose the integration of the RNN/HNGO method with additional optimization techniques or its utilization in hybrid systems as a means of augmenting its performance. The authors concluded by acknowledging the need for additional research to experimentally validate the proposed methodology and optimizing its parameters in order to ensure its reliability and applicability in real-world scenarios. • Optimal identification of PEMFC output voltage using an optimized RNN. • Optimizing RNN using a Hybrid Version of Northern Goshawk Optimization Algorithm. • Minimization of error between observed and predicted output voltages in PEMFCs. • Improved accuracy in tracking voltage signal predictions compared to existing methods. • Comparison with other published works to validate its effectiveness. [ABSTRACT FROM AUTHOR]

This paper proposes an aggregated flexibility estimation method considering the distributed electricity-hydrogen (H 2) interactions for virtual power plants (VPPs) to enhance the economic benefits from the peak-regulation market (PRM) while facilitating the accommodation of renewable generation. Firstly, various distributed energy resources (DERs) such as electric vehicles (EVs), air conditioning systems (ACs), and electricity-H 2 coupled hydrogen refueling stations (HRSs) are modeled as virtual storage (VS) with characterized charging/discharging response parameters. Besides, the temporal coupling power profile of VS can be abstracted as a polytope from the geometric perspective, and the aggregated high-dimensional flexibility region of VPP is formulated by the Minkowski sum for these polytopes. Furthermore, a day-ahead peak-regulation optimal scheduling model considering the conditional value at risk (CVaR) is developed for the electricity-H 2 integrated VPP participating the PRM, and the dimensionality of the aggregated polytope is reduced by the inscribed hyperbox approximation to provide a compact and concise scheduling region. Comparative studies have validated the aggregated power range and net income can be improved by 13.05% and 15.11% with the mechanism of electricity and hydrogen integrations. • The aggregated flexibility of distributed electricity and H 2 resources is unlocked. • Different types of virtual storage models are designed for EVs, ACs, and HRSs. • An inscribed hyperbox based aggregated flexibility projection method is proposed. • A concise day-ahead peak-regulation optimal scheduling model is developed for VPPs. [ABSTRACT FROM AUTHOR]

The demand response, as an effective means of carbon reduction in the demand side, generally uses carbon emission factor (CEF) to evaluate the carbon emissions. However, in the existing research, the output of demand-side renewable energy units is neglected in CEF, which will lead to an inaccurate assessment of carbon emissions and is not helpful for the carbon reduction by the demand response. In this paper, a bi-level incentive-based integrated demand response (IBIDR) approach is proposed to reduce the carbon emission cost with multi-energy time-varying CEFs. In particular, the multi-energy time-varying CEFs model is proposed considering the demand-side proportion of solar output in the total energy consumption. To explore the role of CEF in IBIDR, the coupling effect of the multi-energy time-varying CEFs on the response willingness model is constructed to promote the interaction among different energy resources to reduce the carbon emission. Finally, the proposed bi-level IBIDR optimization model is transformed into a single-level nonlinear problem based on the backward induction method, which can obtain the optimal incentive strategy of IBIDR. Simulation results show the effectiveness of the proposed approach in reducing the incentive cost and carbon emission cost of multi-energy aggregators. • A low carbon bi-level IBIDR model is proposed to reduce the carbon emission. • Multi-energy time-varying CEF model is established to calculate the carbon emission. • Coupling impact of the multi-energy CEFs on the response willingness model is constructed to promote the interaction among different energy resources. • The proposed bi-level IBIDR model is transformed into a single-level model through the backward induction method. [ABSTRACT FROM AUTHOR]

The accurate and comprehensive equivalent circuit model (ECM) of Proton Exchange Membrane electrolyzer (PEME) is needed urgently in the design process of power supply for electrolytic hydrogen production such as rectifier or dc/dc converter. However, the existing ECM can only simulate the steady-state and dynamic electrical characteristics of electrolyzer in the electrolysis process under current controlled mode. In this paper, in-situ electrochemical impedance spectroscopy (EIS) is employed to measure the electrode impedance (R) and double-layer capacitance (C) of PEME. It's worth noting that for gaining the value of R and C, only the electrochemical circle fit method is used without further proceeding. Based on this method to identify parameters, a novel and generic ECM of PEME is proposed to simulate stable electrical characteristics of three different PEMEs under current controlled mode and voltage controlled mode, respectively, in the start-up, electrolysis, and turn-off stages. Specially in this model, diodes' switching characteristics are innovatively employed to simulate the start-up stage; on the other hand, an RC circuit connects with the diode parallelly to model the turn-off stage. Moreover, it can precisely characterize the dynamic and transient electrical behaviors of PEME. Finally, the validity of proposed ECM of PEME is verified based on small scale experiments. • The model simulates PEM electrolyzer's electrical characteristics under voltage control. • The model applies to simulating PEM electrolyzer' electrical feature at various power levels and aging stages. • The model can simulate the multi-time scale electrical characteristics of PEM electrolyzers. • The model is based on circuit principles for its modeling approach and no longer employs empirical formulas. [ABSTRACT FROM AUTHOR]

Low-carbon buildings (LCBs) are normally equipped with distributed energy resources (DERs) such as photovoltaic (PV) panels and batteries, thereby creating LCB prosumers that both produce and consume energy. Peer-to-peer (P2P) energy sharing among LCB prosumers could bring higher economic benefits for themselves, and facilitate better local power balance for the power grid. To fully harness the benefits of P2P energy sharing for both LCB prosumers and the power grid, a bi-level optimization method for LCB prosumers and the power grid operator is proposed in this paper. The grid operator at the upper level imposes the optimal time-varying network charge to LCB prosumers at the lower level to maximize its profit. And LCB prosumers with the objective of minimizing their costs adjust the schedules including P2P energy sharing and their heating loads to respond to the grid operator's optimal network charge prices. Additionally, to further exploit the energy resources including heating loads in LCBs, the model predictive control (MPC) approach is integrated with the bi-level optimization in the presence of uncertainties. The bi-level optimization belongs to the NP-hard category, making it challenging to solve. To reduce the computational complexity, the bi-level optimization problem is converted to the single-level programming using Karush-Kuhn-Tucker (KKT) conditions. Numerical results illustrate that the bi-level optimization method can obtain a balanced scheduling scheme between the grid operator and LCB prosumers. In addition, incorporating the thermal inertia of building heating loads can provide more operational flexibility for both the grid operator and LCB prosumers. Furthermore, the costs of LCB prosumers based on MPC approach can be reduced with the decrease of forecast error level. • A bi-level optimization for grid operator and LCB prosumers with P2P is developed. • The time-varying pricing strategy for network charge of grid operator is proposed. • The bi-level solution method integrated with MPC approach is investigated. • The utilities of bi-level optimization through comparative scenarios are analyzed. [ABSTRACT FROM AUTHOR]

This paper presents a two-stage framework to facilitate the integration of distributed energy resources (DERs) within emerging distribution-level markets. Market prices significantly influence the decision-making process for prosumers and electric vehicle (EV) owners participating in the market, necessitating a robust optimization approach to formulate resilient offering and bidding strategies under uncertain market conditions. The proposed model is tailored for a distribution network equipped with a neighborhood battery, enabling prosumers to charge or discharge the battery profitably. Robust half-hourly-based offer and bid curves are submitted by prosumers and EV owners, reflecting their market price predictions and accommodating different levels of uncertainty, treated as scenarios. The social welfare maximization problem is formulated using a scenario-based approach, considering the received offer/bid curves to clear the market. Results show that considering the discharge capability of EVs leads to an increase in the overall social welfare of distribution networks. Additionally, when both the capabilities of discharge and export power are considered, it can be observed that the presence of a neighborhood battery can lead to an improvement of more than 35.83% in overall social welfare. • Employing robust optimization to drive prosumers and EV owners' strategies in markets • Ensuring robust solutions by incorporating offer/bid curves and AC OPF constraints • Assessing neighborhood battery impact on prosumers' bill and network constraints [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]

• 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]

• 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]

Achieving carbon neutrality is probably one of the most important challenges of the 21st century for our societies. Part of the solution to this challenge is to leverage renewable energies. However, these energy sources are often located far away from places that need the energy, and their availability is intermittent, which makes them challenging to work with. In this paper, we build upon the concept of Remote Renewable Energy Hubs (RREHs), which are hubs located at remote places with abundant renewable energy sources whose purpose is to produce carbon-neutral synthetic fuels. More precisely, we model and study the Energy Supply Chain (ESC) that would be required to provide a constant source of carbon-neutral synthetic methane, also called e-NG (electric Natural Gas) or e-methane (electric methane), in Belgium from an RREH located in Morocco. To be carbon neutral, a synthetic fuel has to be produced from existing carbon dioxide (CO 2) that needs to be captured using either Direct Air Capture (DAC) or Post Combustion Carbon Capture (PCCC). In this work, we detail the impact of three different carbon sourcing configurations on the price of the e-methane delivered in Belgium. Our results show that sourcing CO 2 through a combination of DAC and PCCC is more cost-effective, resulting in a cost of 146 €/MWh for e-methane delivered in Belgium, as opposed to relying solely on DAC, which leads to a cost of 158 €/MWh. Moreover, these scenarios are compared to a scenario where CO 2 is captured in Morocco from a CO 2 emitting asset that allow to deliver e-methane for a cost of 136 €/MWh. • Model energy supply chain from Morocco to Belgium for electric methane; • Explore three CO2 sources for electric methane synthesis; • Analyse the pros and cons of each CO2 source on price and energy efficiency. [ABSTRACT FROM AUTHOR]

Deep decarbonization of power system operations requires the maximal utilization of available renewable resources. At distribution-level operations, however, grid operators can face numerous challenges in integrating renewables at scale owing to the inherent intermittence of renewable energy resources. These include phenomena such as voltage fluctuations, which are typically mitigated through control actuators such as on-load tap changers (OLTC) as well as energy storage devices, such as battery energy storage systems (BESS). On the one hand, high intermittence of the available renewable portfolio may require increasingly aggressive control of actuators, thereby accelerating the probability of equipment failure. On the other hand, integrating BESS operations and having a diverse renewable generation portfolio can typically help stagger power/energy flow to mitigate the aforementioned adverse impacts. In this paper, we employ a Bayesian framework for equipment lifetime estimation to understand the impact of including tidal energy resources and BESS in distribution system operations for feeders having substantial distribution photovoltaic generation. Our results indicate that while tidal energy alone may slightly decrease equipment reliability, the adverse impact on reliability is significantly magnified by a generation portfolio consisting of tidal generation and photovoltaic generation. We also study the tidal and photovoltaic hosting capacity problem with and without energy storage systems using equipment reliability as an added constraint. We conclude that energy storage increases the reliability-constrained hosting capacity of the distribution system. • OpenDSS-based simulation-oriented investigation of renewable energy integration. • Tidal power variability negatively impacts the reliability of on-load tap changers (OLTC). • Equipment reliability concerns can limit the renewable hosting capacity of a distribution system. • Energy storage can lessen the operational stress on OLTCS thus increasing equipment lifetime. • Energy storage can facilitate the integration of high-capacity distributed energy resources. [ABSTRACT FROM AUTHOR]

Frequency security is one of the most critical factors for ensuring the safe and stable operation of power systems. In wind-photovoltaic-hydro-thermal multi-power systems (WPHTMPS), the increase in new energy share and the water hammer effect weaken the frequency modulation (FM) capability, threatening the frequency security of the power system. To address this issue and ensure the frequency security of WPHTMPS under the trend of large-scale development of wind and photovoltaic (PV) power, this paper proposes a novel expansion planning method for wind and PV power in WPHTMPS that considers FM capability in the planning stage. The method realizes the coordinated optimization of long-term planning and short-term operation while adhering to the frequency security constraints. The constraints are derived from the multi-power system frequency response model (MPSFR) which integrates virtual inertia, synchronous inertia, and the water hammer effect. However, due to the high-order characteristic of the MPSFR model, the frequency security constraints cannot be expressed explicitly to be embedded in the planning model by current study. Therefore, the boundary equivalence method is proposed to deal with this problem, which is effective to convert the frequency security constraints into inertia constraints and embed them in the planning model according to their relationship with installed capacity and unit commitment. Finally, simulation for a power system in Southwest China validates the proposed method, with results indicating that around 13% more capacity of new energy is needed to provide adequate FM reserve and power supply. Furthermore, it is demonstrated that the FM capability of new energy can improve the installed share of new energy from 46.5% to 70% while effectively ensuring frequency security. • The frequency modulation capability is necessary in generation expansion planning. • An integrated system frequency response model with multiple power sources is built. • A two-layer model optimizing long-term planning and short-term operation is built. • The frequency security constraint is linearized by the boundary equivalence method. [ABSTRACT FROM AUTHOR]

The utilization of Vehicle-to-Grid (V2G) technology, which facilitates the integration of electric vehicles (EVs) into the power grid, is promising for energy management systems. Among them, electric taxi (ET) is an important part of EVs. The relatively stable and predictable operating patterns of the ET fleets could have a scale effect, effectively managing load demand and supply with V2G technology. This paper investigates the potential and flexibility of V2G technology for urban power system based on real-world ET trajectory data. To evaluate the flexibility of V2G more precisely, a general framework is proposed to infer vehicle charging, energy consumption, and reconstruction of the State of Charge (SoC) for ETs. The V2G potential estimation model is established for each ET, followed by a bottom-up agent-based model to simulate flexibility changes in the system during various V2G events. The proposed methodology is tested on 19,900 ETs in Shenzhen over a month to explore the potential and flexibility of V2G technology. The results show that the V2G system could supply at least 50 MW for 1 h, 30 MW for 2 h, and 20 MW for 3 h during peak periods, while simultaneously recovering within 2–3 h, without disrupting the regular operations of ETs. The great potential of ETs to supply additional energy during peak periods and recover energy during valley periods is highlighted, which could lead to a more efficient and sustainable management of energy resources. • Real trajectory data with 19,900 ETs is used for V2G potential and resilience analysis. • A method infers charging patterns, energy use, and reconstructs State of Charge is proposed. • A general method framework for modeling ET-V2G system on supply side is proposed. • An agent-based model is introduced for ET-V2G system modeling and simulation. • ET-V2G system can supply up to 60 MW and recover within 2–3 h during peak periods. [ABSTRACT FROM AUTHOR]

In recent years, the use of distributed energy resources has accelerated to deal with global warming. As these resources are decentralized, the current centralized energy market must be modified to accommodate their characteristics. Advances in information technology have made it possible to implement peer-to-peer (P2P) markets. However, completely removing the distribution system operator from P2P exchanges can risk the network's security. Conversely, establishing network security only by the operator can decline the independence of agents. This paper proposes a decentralized framework for implementing the P2P market based on the alternating direction method of multipliers, which maintains the distribution system's constraints, considering the agents' independent nature. The agents reach convergence with minimal exchange of information while maintaining the network constraints, including voltage and current limits. The network constraints are included using a proposed sensitivity approach in the sub-problem of each agent. In this approach, by calculating the sensitivity coefficients of voltage and current, the network security can be evaluated with the changes of active and reactive powers by agents. The simulation results demonstrate that the proposed framework can efficiently maintain network constraints. The short running time of the P2P trading with the proposed framework makes it feasible for practical applications. • Developing a new P2P trading framework under distribution network constraints. • Preserving the independent nature of P2P participants. • The alternating direction method of multipliers is applied in the framework. • Applying the network constraints using a sensitivity approach in the sub-problems. • The studies prove the efficacy of the framework in maintaining network constraints. [ABSTRACT FROM AUTHOR]

Integration of Distributed Energy Resources (DERs) can introduce challenges such as Over-Voltage (OV) and line congestion in distribution networks. Recently, the concept of dynamic export limits as well as Community Energy Storage (CES) have gained attention as potential solutions for these challenges. Within these concepts, this paper presents a novel framework where the Distribution Network Service Provider (DNSP) provides prosumers with dynamic export limits for networks with a CES. Moreover, this framework enables prosumers (through leasing) and the DNSP to collaboratively manage the CES. In each time step, the DNSP calculates the charging/discharging power for the unleased portion of the CES and collects data on the expected export and charging/discharging power from prosumers leasing the CES. Subsequently, while considering network voltage and current limits and a fairness objective, the DNSP computes and communicates dynamic export limits to prosumers, allowing them to manage their assets accordingly. The performance of the proposed framework is evaluated via various case studies conducted on a real-world LV test feeder with 49 prosumers. The results emphasize the critical role of dynamic export limits in resolving OV and congestion issues in DER-rich networks. Furthermore, the CES proves to be effective in reducing active power curtailment and increasing total generation, with a notable increase of 4.6%. • A Novel deployment of CES, which DNSP and prosumers can simultaneously utilize it. • Incorporation of adjustable fairness objective in the curtailment of prosumers. • Maintaining prosumers' privacy by providing them with dynamic export limits. [ABSTRACT FROM AUTHOR]

Self-sustained wireless sensing nodes (WSN) with energy harvesting, low power consumption, and intelligent sensing are the key devices in artificial intelligence of things (AIoT) area. This paper designs a tri-hybrid generator with electromagnetic-piezoelectric-triboelectric units integrated into one vibration energy harvesting structure. The tri-hybrid generator has three functions with electromagnetic unit for power supply with 13.1 mW, two piezoelectric units as the accelerometer for amplitude and frequency sensing with broadband decoupling function, triboelectric unit for triggering the self-wakeup WSN. The tri-hybrid generator structure is simulated and optimized by theory and finite element modeling, and the performance is tested in a vibration platform. To achieve self-sustained WSN, this study designs the power management circuit with maximum power point tracking and undervoltage lock function, combining periodic wake-up with overload self-wakeup to decrease 94.2% WSN power consumption. Self-sustained WSN is realized in the laboratory and verified on the vehicle engine. Finally, the application of wireless vibration sensing is verified on the vehicle engine. This work has a wide range of applications in vibration scenarios and hopefully promotes the development of AIoT. • An excellent performance EMG-PEG-TENG hybrid vibration energy harvester. • EMG as power supply, PEG as amplitude-frequency sensor, TENG as trigger. • Low voltage initiated management circuit design with MPPT and UVLO. • Low power WSN design with periodic wake up and interrupt self-wake up. [ABSTRACT FROM AUTHOR]

In the realm of economic scheduling and optimal operation within integrated energy systems, integrated energy load aggregation plays a pivotal role. Accurate prediction of integrated energy load aggregation enables the refined management of energy resources, thereby enhancing energy utilization efficiency. This paper merges the advantages of clustering algorithms, prediction algorithms and ensemble learning to propose a short-term prediction approach for integrated energy load aggregation. Firstly, the intrinsic distribution of energy consumption data is analyzed through Affinity Propagation (AP) clustering with multi-dimensional similarity. This analysis yields the division of all samples into clusters based on data characteristics. Subsequently, the Transfer entropy is harnessed to conduct correlation analysis within each cluster. For constructing predictive models for each individual cluster, a prediction algorithm based on the Feature-Temporal Attention-enhanced Bi-directional Simple Recurrent Unit (FTA-Bi-SRU) is employed. The Dynamic Weight Average (DWA)-based multi-task loss equilibrium and Multi-Term Adam (MTAdam) loss function are combined to enhance the efficiency of the forecasting model. Finally, a Light Gradient Boosting Machine (LightGBM) model with Bayesian optimization is employed to integrate the predictive outcomes of each individual cluster to generate the overall predictive outcomes for integrated energy load aggregation. The proposed methodology is experimented on real-world energy consumption data to ascertain the efficacy of the prediction method. The experimental results substantiate the superiority of the suggested forecasting approach. • This marks the inaugural discussion of load forecasting for integrated energy load aggregation is discussed. • A multi-dimensional similarity Affinity Propagation (AP) clustering algorithm is introduced to group multi-energy users. • A method based on transfer entropy for correlation analysis is presented. • An Feature-Temporal Attention based Bi-directional Simple Recurrent Unit (FTA-Bi-SRU) prediction model is introduced. • A Bayes-optimized Light Gradient Boosting Machine (BO-LightGBM) ensemble framework is proposed to enhance overall prediction. [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 analyzed the temporary overvoltage (TOV) problem in the network distribution system. The TOV shows different patterns depending on the type of distributed energy resources (DERs) and the grounding method. In particular, the inverter-based distributed generations (IBDGs) also show different patterns as inverter's dynamic characteristics depending on the classification of grid forming (GFM) and grid following (GFL). The paper reviewed existing factors affecting TOV according to the inverter type. In addition, the TOV was simulated in various topologies of distribution systems, indicating that distribution system topology can solve the TOV problem regardless of the inverter type. This paper compared the TOV in the radial distribution system and the networked distribution system (NDS), and as a result, the operation through NDS can be a solution to the TOV problem. In addition, TOV in the NDS is analyzed through simulation, considering various elements of the distribution system to prove a more robust solution. [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]

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]

This paper offers a comprehensive overview of advanced water desalination systems powered by renewable energy sources, specifically focusing on developments in the past decade. Solar energy is particularly emphasized as a pivotal resource for powering diverse desalination methods, including membrane distillation, reverse osmosis, humidification-dehumidification, forward osmosis, and hybrid systems. The primary objectives of this study encompass evaluating current advancements in renewable energy systems, especially solar energy, for driving water desalination processes. Furthermore, the study aims to identify gaps in research, challenges, and recommendations within these processes, and to assess the performance and refinement potential of hybrid desalination systems. Through a comprehensive analysis of various investigations, integrated effectiveness and cost-efficiency are examined, focusing on practical energy utilization. Notably, the analysis reveals the potential of hybrid desalination systems to reduce energy consumption. For instance, the hybridization of forward osmosis with membrane distillation showcases a significant energy ratio decrease from 0.89 to 0.64. Moreover, the combination of humidification-dehumidification with reverse osmosis is shown to reduce energy usage while enhancing freshwater output by up to 38% compared to standalone reverse osmosis. However, challenges persist, such as the need for improved integration of hybrid systems with concentrated solar collectors like photovoltaic thermal and concentrated photovoltaic thermal technologies. • A comprehensive review of water desalination systems powered by renewable energy. • The importance of combination of solar energy with hybrid desalination systems. • Renewable energy integration with different desalination techniques. • A comparative analysis of integration of renewable energy with hybrid desalination. • Challenges and future recommendations for successful integration of renewable energy with desalination. [ABSTRACT FROM AUTHOR]

Wave energy resources have high, yet largely untapped potential as candidate generation technology. In this paper, we perform a data-driven analysis to characterize the impact of wave energy integration on bulk-scale power systems and market operations. Through data-driven sensitivity studies centered on an optimization-based production cost modeling formulation, our work characterizes the inflection point beyond which wave integration starts impacting power system operations, considering present day transmission infrastructure. Furthermore, our analysis also considers the joint effects of wave energy integration and system-wide transmission expansion. Finally, potential resilience scenarios such as wildfire-driven transmission contingencies and heat wave events are investigated, whereby the contributions of grid-integrated wave energy in alleviating the effects of the resilience events are analyzed. As our demonstration test bed, we consider a reduced-order network topology for the U.S. Western Interconnection with wave energy generation integrated at carefully selected sites across the coastal areas of Washington, Oregon, and northern California. Our results indicate that over a representative year of operations, wave energy integration systematically reduces locational marginal prices (LMPs) of energy and price volatility, especially during periods of high wave resource availability (winter months for the U.S. west coast). Average, maximum, and minimum of hourly LMPs over a typical year of operation was reduced by 2.95, 51.28, and 1.13 $/MWh respectively (over a baseline scenario with no wave energy integration), when the selected network model had a total of 5000 MW wave power installed capacity during the representative year of study. The effects of wave energy integration can remain localized with existing transmission infrastructure (identified to be most pronounced in the Pacific Northwest region in the example we studied). However, with concurrent transmission expansion, the impacts of wave energy integration are likely to have a higher geographical spread. Our results also indicate that wave energy may be able to assist power system operations during resilience events such as major transmission contingencies and heat wave events, although such benefits might be dependent on factors such as proximity of affected area to wave resources, availability of adequate resource potential and adequate transmission capacity. • Production cost model of a bulk power system with wave power is developed. • Sensitivity studies to infer impact of wave power on grid operations at varying capacities. • Wave power reduces electricity prices during periods of higher resource availability. • Wave power benefits can percolate inland with system-wide transmission expansion. • Wave power can have added benefits in resilience events like transmission outages. [ABSTRACT FROM AUTHOR]

In the 21st century, urbanization has caused rapid growth in the global construction area, resulting in high building resource consumption and long-term environmental impacts. Analyzing the resource consumption of public buildings and identifying the key factors that affect the consumption of building resources is important for developing of targeted policies. In this paper, we analyzed each branch of the People's Bank of China based on energy, water, and land resource consumption as well as the structure of energy consumption. Based on resource-consumption indicators, we divided 31 provincial-level administrative regions into four resource consumption levels; 65% were in the low-to-medium resource consumption levels. Medium-sized buildings had the highest energy consumption level and a more balanced energy consumption structure. The energy consumption per capita, water consumption, and utilization rate of small-sized buildings were low. To reduce resource consumption by public buildings, the construction of large-sized buildings should be limited. • The comprehensive evaluation method is used to calculate the resource consumption of the People's Bank of China. • Regression equations were established for energy consumption and resource consumption. • Medium-sized institutions had the highest energy consumption level and a more balanced energy consumption structure. [ABSTRACT FROM AUTHOR]

This paper proposes a reconfigurable power module system for hybrid micro-grid to reduce the designing cost with standardized software and hardware components. The developed system is composed of three layers including application function layer, interconnection layer and physical layer with the capability to be interfaced with different types of energy resources for the hybrid micro-grid. By implementing the multi-layer power module system, the energy conversion designing procedures for different types of resources can be generalized and automated with less cost on the specified application scenarios. The control functions for different energy resources can be extracted from the application function layer. The desired number of standardized power modules can be integrated from the physical layer to form different power converter topologies. The coordination of the power converter, micro-controller, sampling, high level control is managed by the interconnection layer to construct the complete energy conversion system. The major advantageous results cover three aspects. Firstly, the proposed reconfigurable system generalizes the energy conversion of the hybrid micro-grid with different AC and DC resources. Secondly, the designing procedures of the hybrid micro-grid energy conversion system are simplified with less cost for various interfaced applications. Thirdly, the energy conversion performance and reliability of the micro-grid is improved with standardized power modules and self-restoration capability. The experimental test verified the proposed system. • Reconfigurable hybrid micro-grid based on standardized power electronic module system. • Multi-layer reconfigurable system is designed to be interfaced with distributed energy resources. • Standardized power electronic modules form a range of power converter topologies. • Repetitive design costs of hybrid micro-grid energy conversion systems are reduced while improving performance. [ABSTRACT FROM AUTHOR]

Modern power grids are fast evolving with the increasing volatile renewable generation, distributed energy resources (DERs) and time-varying operating conditions. The DERs include rooftop photovoltaic (PV), small wind turbines, energy storages, flexible loads, electric vehicles (EVs), etc. The grid control is confronted with low inertia, uncertainty and nonlinearity that challenge the operation security, efficacy and efficiency. The ongoing digitization of power grids provides opportunities to address the challenges with data-driven and control. This paper provides a comprehensive review of emerging data-driven dynamical modeling and control methods and their various applications in power grid. Future trends are also discussed based on advances in data-driven control. • Broad range of linear/nonlinear data-driven modeling and control are compared. • Advancement of Koopman-based methods in power grids is reviewed for the first time. • Selective grid applications and suitable data-driven methods are summarized. • Historical path and future trends of data-driven methods in power grids are provided. [ABSTRACT FROM AUTHOR]

The increasing effects of global warming and energy depletion have raised concerns about the pollution caused by traditional oil and fossil energy usage. Distributed energy resources (DERs) have emerged as promising techniques to address these issues. However, the growing proportion of power injection from DERs presents technical challenges such as power quality decline and power flow reversal, which may destabilize the grid. Non-intrusive load monitoring (NILM) is a promising and cost-effective approach that can provide residential power information to improve grid scheduling, dispatching, and optimize residential power consumption behavior. This paper proposes a novel NILM method that can decompose the power of both household appliances and DERs integrated with residential houses. The proposed approach employs a data segment method that utilizes adaptive window lengths to identify the behavior of DERs based on the operating periods and characteristics of different equipment. A non-intrusive load monitoring method is proposed based on the ResNet-seq2seq framework to address the problem of model degradation and gradient vanishing. Post-processing techniques are applied to the output results using a feature list of the target equipment. Experiments are conducted on a dataset organized based on the REDD and Pecan Street datasets to demonstrate the accuracy and effectiveness of the proposed method. The results show that compared to the start-of-the-art CNN-seq2seq model, the proposed method achieved 54.95%, 69.18%, 56.95%, and 84.98% reduction in MAE for PV, EV, refrigerators, and microwaves, respectively. • A novel ResNet-seq2seq-based NILM approach is proposed for load disaggregation of residential houses integrating DERs. • Wavelet denoising and adaptive window length data segments are used for data pre-processing and accurate disaggregation. • Residual connections are employed to solve model degradation and vanishing gradient issues as network depth increases. • A post-processing technique is applied to modify the output of the network and improve disaggregation accuracy. [ABSTRACT FROM AUTHOR]

With the progress of decarbonization, renewable-powered microgrids are attracting wide attention. To cope with the fluctuation of renewable power at different timescales, both long-term and short-term energy storage devices are required. This paper studies the operation of renewable-dominated isolated microgrids integrated with hybrid seasonal-battery storage. A data-driven scheduling-correction framework is proposed. By leveraging the historical data of renewable power and load, the scheduling module generates ex-post optimal state-of-charge (SoC) sequences of the seasonal energy storage ahead of the operating year. In each period of real-time operation, the correction module performs two steps: the first step is to update the reference SoC for the seasonal storage based on the ex-post optimal SoC sequences and the newly observed data; the second step is to solve a bi-objective rolling-horizon optimization problem which minimizes the instant operating cost while steering the SoC of seasonal storage to its reference value. An appealing feature of the proposed method is that the long-term renewable power forecasts are not required. A microgrid system is devised to verify the proposed framework. Numerical tests show that the scheduling-correction framework outperforms existing rolling horizon approaches in compromising economy, power supply reliability, and renewable energy utilization. • A data-driven scheduling-correction framework is proposed to provide real-time actions with a global vision, making better use of the seasonal hydrogen storage. In the scheduling module, ex-post optimal SoC sequences of the seasonal storage are generated using historical data. In the correction module, the real-time operation relies on a bi-objective rolling-horizon optimization problem which minimizes the operating cost while following the SoC reference of the seasonal storage, which is sequentially updated along with time. • The kernel regression technique is established to sequentially update the SoC reference of the seasonal hydrogen storage. This technique calibrates a conditional probability distribution based on the newly observed data; the resulting distribution (weight coefficients) interprets how likely the current year is similar to each of the past years. The reference is updated in each period according to the weighted sum of the ex-post optimal SoC sequences. • Systematic tests are conducted to compare the cost, load shedding, and renewable energy curtailment achieved by different methods. Numerical results show that the scheduling-correction framework can fully exploit the seasonal storage. The proposed method outperforms existing rolling horizon approaches in compromising economy, power supply reliability, and renewable energy utilization. [ABSTRACT FROM AUTHOR]

Collaboration between transmission and distribution system operators (TSOs and DSOs) is crucial to improve power supply reliability during a massive blackout. However, these two systems usually operate separately in reality. To coordinate them, a new fully parallel decentralized TSO + DSO service restoration (D-TDSR) scheme is proposed in this paper. Based on analytical target cascading (ATC) method, the proposed D-TDSR is able to formulate the local restoration model for each TSO and DSO by decomposing shared boundary information in an iterative process. By introducing the diagonal quadratic approximation in ATC, all local restoration models are solved in a fully parallel manner with no need for a central coordinator. This parallel implementation increases the computation efficiency of decentralized restoration procedure. Furthermore, to enhance the ability of TSO and DSOs to support each other, the potential benefits of flexible regulation in DSO, including soft open points and network reconfiguration, are fully exploited, thus improving critical load restoration speed, and reducing voltage deviations. The effectiveness of the proposed D-TDSR is validated using an IEEE standard test system. • A decentralized load restoration for transmission and distribution system is proposed. • SOP flexibility is fully exploited to improve load restoration level and speed. • The fully parallel restoration procedure is proposed to reduce the computation time of each iteration. • The warm start mode is developed, which further accelerates the convergence speed of the restoration model. [ABSTRACT FROM AUTHOR]

In Industry 4.0, the production data obtained from the Internet of Things has reached the magnitude of big data with the emergence of advanced information and communication technologies. The massive and low-value density of big data challenges traditional clustering and correlation analysis. To solve this problem, a big data-driven correlation analysis based on clustering is proposed to improve energy and resource utilisation efficiency in this paper. In detail, the production units with abnormal and energy-intensive consumption can be classified by using clustering analysis. Additionally, feature extraction is carried out based on clustering analysis and the same cluster data is migrated to the training data set to improve correlation analysis accuracy. Then, correlation analysis can balance the relationship between energy supply and demand, which can reduce carbon emission and enhance sustainable competitiveness. The sensitivity analysis results show that the feature extraction method can improve the correlation analysis accuracy compared to the original analysis model. In conclusion, the big data-driven correlation analysis based on clustering can uncover the potential relationship between energy consumption and product yield, thus improving the efficiency of energy and resources. • An architecture of big data clustering and correlation analysis is proposed. • A PCA-guided ISODATA is proposed for data pre-processing and processing. • The feature extraction method has been proposed based on clustering. • A correlation analysis model is established for data mining. • The models can improve the scheduling efficiency of energy and resources. [ABSTRACT FROM AUTHOR]