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

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

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

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

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

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

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

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

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]

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]

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]

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]

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]

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]

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]

The case study presented in this paper is an innovative urban roof-mounted energy system constituted by a hybrid solar system for domestic use. Utilizing this untapped energy is the key value for home renewable energy supply. It allows the improvement of the energy yield per area unit of roof or façade. The Photovoltaic/Thermal (PVT) panel considered presents the particularity of the addition of a transparent insulating cover to reduce the heat losses on its front side. It has been developed by the manufacturer Endef Engineering in collaboration with the University of Zaragoza. In this paper, the design of a PVT system to feed the domestic heat water requirements of multi-housing building is explained. The electricity production is also considered, in accordance with the Spanish regulation for self-consumption. The work developed began with the redesign of the hybrid solar plant, which individually supplies hot water to each dwelling and power for common consumption of the building, including a charging system for electric vehicles. Then, the PVT panel developed and manufactured by Endef Engineering and the complete thermal and electrical system are simulated in Trnsys. The monitoring of a real working installation is used in this paper to validate the proposed model. The presented case study is located in Zaragoza (Spain), in a residential apartment block, with an electrical installed power of 4.14 kWp and 20.5 kW of thermal capacity. The proposed model allows the calculation of the heat and electricity production and efficiency of the whole system with error lower than 6.5%. [ABSTRACT FROM AUTHOR]

• Proposed a novel dynamic power system scheduling to reduce costs of generation. • Evaluation of PV system used for partial supply of the load demand of the grid. • Applying a novel price-based demand response program to reduce RES uncertainty. • Presenting a multi-vector energy model based on power-heat and water-power nexus. Increasing technology developments and economic considerations have also made the use of renewable energy sources (RESs) and energy storage systems (ESSs) inevitable. To achieve the holistic planning of a microgrid consisting of several energy resources, this paper proposes a novel multi-vector energy system based on electricity, heating, and water generation sources. To supply electrical energy from heat generation units, a water-power nexus (WPN), an ESS, photovoltaic (PV) sources, and a combined heat and power (CHP) system are used. Considering the importance of Hydro generation in planning, combined energy-water and water-only systems are used to supply water demand, while heat-power and heating generation systems are used to supply heating. To make the model more realistic, the effects of the valve point, maximum and minimum generation constraints, increasing/decreasing rate, energy, water, and heating demand balance were taken into account. The main objective function of this study was to minimize multi-vector energy system costs in 24 h. To promote demand-side performance, the price-based demand response program was used to reduce the final costs in the entire study period. The proposed models have been formulated as a mixed-integer linear problem solved by the CPLEX solver in GAMS. Simulation results show that the use of RES and their proper management can reduce the generation of a thermal generator, which reduces the costs considerably. With the demand response program, the costs were reduced by 1.03%. This paper presents a systematic method for optimal system control that can provide a regulatory basis for the use of integrated generation sources. [ABSTRACT FROM AUTHOR]

This paper analyzes the optimal planning and operation of aggregated distributed energy resources (DER) with participation in the electricity market. Aggregators manage their portfolio of resources in order to obtain the maximum benefit from the grid, while participating in the day-ahead wholesale electricity market. The goal of this paper is to propose a model for aggregated DER systems planning, considering its participation in the electricity market and its impact on the market price. The results are the optimal planning and management of DER systems, and the appropriate energy transactions for the aggregator in the wholesale day-ahead market according to the size of its aggregated resources. A price-maker approach based on representing the market competitors with residual demand curves is followed, and the impact on the price is assessed to help in the decision of using price-maker or price-taker approaches depending on the size of the aggregated resources. A deterministic programming problem with two case studies (the average scenario and the most likely scenario from the stochastic ones), and a stochastic one with a case study to account for the market uncertainty are described. For both models, market scenarios have been built from historical data of the Spanish system. The results suggest that when the aggregated resources have enough size to follow a price-maker approach and the uncertainty of the markets is considered in the planning process, the DER systems can achieve up to 50% extra economic benefits, depending on the market share, compared with a non-aggregated business-as-usual approach (not implementing DER systems). [ABSTRACT FROM AUTHOR]

Highlights • An optimisation strategy that controls both the energy supply and the energy demand. • Artificial neural networks predict the energy demand and renewable energy supply. • An error management stage adjusts the solution to react to prediction errors. • Resulted in a 52.92% increase in profit while reducing CO 2 emissions by 3.75%. Abstract Decentralisation of energy generation and distribution to local districts or microgrids is viewed as an important strategy to increase energy efficiency, incorporate more small-scale renewable sources and reduce greenhouse gas emissions. To achieve these goals, an intelligent, context-aware, adaptive energy management platform is required. This paper will demonstrate two district energy management optimisation strategies; one that optimises district heat generation from a multi-vector energy centre and a second that directly controls building demand via the heating set point temperature in addition to the heat generation. Several Artificial Neural Networks are used to predict variables such as building demand, solar photovoltaic generation, and indoor temperature. These predictions are utilised within a Genetic Algorithm to determine the optimal operating schedules of the heat generation equipment, thermal storage, and the heating set point temperature. Optimising the generation of heat for the district led to a 44.88% increase in profit compared to a rule-based, priority order baseline strategy. An additional 8.04% increase in profit was achieved when the optimisation could also directly control a proportion of building demand. These results demonstrates the potential gain when energy can be managed in a more holistic manner considering multiple energy vectors as well as both supply and demand. [ABSTRACT FROM AUTHOR]

Energy storage is nowadays recognised as a key element in modern energy supply chain. This is mainly because it can enhance grid stability, increase penetration of renewable energy resources, improve the efficiency of energy systems, conserve fossil energy resources and reduce environmental impact of energy generation. Although there are many energy storage technologies already reviewed in the literature, these technologies are currently at different levels of technological maturity with a few already proven for commercial scale application. Most of the review papers in energy storage highlight these technologies in details, however; there remains limited information on the real life application of these technologies for energy storage purpose. This review paper aims to address this gap by providing a detailed analysis of real life application and performance of the different energy storage technologies. The paper discusses the concept of energy storage, the different technologies for the storage of energy with more emphasis on the storage of secondary forms of energy (electricity and heat) as well as a detailed analysis of various energy storage projects all over the world. In the final part of this paper, some of the challenges hindering the commercial deployment of energy storage technologies are also highlighted. [ABSTRACT FROM AUTHOR]

• Real case studies of Tazouta and Fez rural area in Morocco. • Hybrid biomass, wind, and solar electricity generation in areas those are rich in renewable energies. • Potential of bioenergy for producing power in rural areas of Fez-Meknes region in Morocco. High dependence of Morocco's energy sector on imported fossil fuels and subsequent associated expensive import bills, as well as global agreements with greenhouse gas emission reduction, has motivated Morocco to utilize renewable energy sources such as hydro, wind, and solar for energy generation. However, in recent years, the use of wind and solar energies in Morocco has accelerated more than hydropower usage because of the reduction in water levels as a result of rainfall drop, global warming, and natural droughts. Morocco has a great potential for electricity generation from solar and wind energies because of many sunny days, vast desert areas, long coastlines, and appropriate wind blow. However, accommodations of wind and solar generating units in electric power systems are difficult because of wind speed variations, solar irradiation uncertainty, and costly energy storage systems. Regarding the huge amounts of biowaste in Morocco, power generation from biomass can resolve this problem and compensate for the decrease in wind and solar energy generation during cloudy, rainy, and/or non-windy moments. Therefore, in the present paper, the capability of hybrid electric energy generation, using biomass, wind, and solar in a Moroccan village of Tazouta and the rural area of Fez city is evaluated. The proposed mixed-integer linear programming model was implemented in A Mathematical Programming Language (AMPL) using a linear solver of CPLEX. The biomass, solar, and wind power plants are biogas units fed by cow manure, 1-kW photovoltaic (PV) modules, and 5.1-kW wind turbines, respectively. The evaluation of the results indicates that hybrid utilization of wind, PV, and biomass energy resources is a more appropriate way for electricity generation in Tazouta and Fez rural areas compared to the individual operation of wind turbines and other combinations of mentioned renewable energy sources. [ABSTRACT FROM AUTHOR]

This paper proposes a game-theoretic framework for economic operations of future residential distribution systems with high participation of distributed electricity prosumers. Recently, the large-scale grid integration of distributed renewable energy is becoming a promising solution to restructuring the current power grid infrastructure and ensuring the reliability of energy supply. The wide adoption of distributed renewable energy mainly depends on the participation level of residential consumers and prosumers. Accordingly, there is an urgent need to investigate the economic operations of this new, highly complex retail electricity market that potentially has millions of interactive endpoints. This paper identifies the new roles of utilities and distributed electricity prosumers in the future retail electricity market. The game-theoretic algorithms used to clear the retail electricity market price consider the group coalition scenarios of multiple electricity prosumers. Several numerical case studies are conducted to illustrate and validate the proposed framework. [ABSTRACT FROM AUTHOR]

Highlights • A new framework of energy and water systems on island has been proposed. • Water demand and renewable energy potential of the Maldives are estimated. • Feasibility analysis of renewable energy-driven island was done in the Maldives. • It is possible for the Maldives to be a “zero input” system as to energy & water. Abstract Water and energy supply systems are essential parts of the infrastructure on islands. For small islands that are far from continents, water shortage is usually the main constraint on economic and social development. In order to maintain island water security, desalination plants are built to supply fresh water. The plants need a great deal of energy, which increases demand for energy and the cost of transportation. Thus, it is necessary to design a new island system driven by renewable energy. This study investigated the existing type of water and energy supply systems in some typical islands of the world, and analyzed their advantages and disadvantages. The energy supply systems can be classified into three categories: imported conventional energy supply system (ICESS); imported conventional energy & renewable energy supply system (ICE&RESS); and integrated energy supply system (IESS). Water supply systems can also be classified into three categories: imported water supply system (ImWSS); imported water and unconventional water supply system (IW&UWSS); and integrated water supply system (InWSS). The nexus of energy and water is very complicated on islands. This paper presents a framework for an interconnected energy and water system on an island. The new framework reveals a roadmap from “full input of energy & water (FIEW)” through “semi-input of energy & water (SIEW)” to “zero input of energy & water (ZIEW)”, which leads an island's energy and water resources to become gradually independent from the mainland. The new framework also reduces transportation costs and carbon emissions. The proposed framework is applied to the Maldives to aid design of a renewable energy-driven water supply system. The characteristics and mutual adaptability of three types of renewable energy (solar, wind, and biomass energy) and water supply systems is discussed. The results show that a ZIEW system can be realized in the Maldives with a reduction in the cost of renewable energy. ZIEW system has great potential for application in island regions in the future. [ABSTRACT FROM AUTHOR]

This paper evaluates the trade-offs between economic and environmental performance of an autonomous energy system utilising an existing Micro hydro power plant while improving its future reliability. The analysis primarily focuses on developing sustainable alternative to excessive reliance on Diesel Gensets in fulfilling the increasing seasonal shortfall in electricity supply from standalone Micro hydros. First, a preliminary assessment is conducted using hypothetical future shortfall in electricity supply from a Micro hydro of 10%, 20% and 30%, compared to a baseline of 2% shortfall, which shows drastic increase in the environmental costs (combined human health and ecological) by as much as 400%, 900%, 1400% respectively from continued use of conventional Diesel Genset. In the next step, a ‘Micro hydro sustainability indicator’ is formulated as the ratio of environmental costs to net present costs of different hybrid options. This is estimated through a mixed assessment framework, which combines consumer engagement for understanding the current and the projected future diurnal and the seasonal electrical loads along with quantitative evaluation of the corresponding costs. Finally, a demonstration case study implements this framework at the Khun Pang micro hydropower project in Si Lanna National Park within Chiang Mai province, northern Thailand for two scenarios – Scenario 1 (circa 2016–17, annual shortfall of 4% i.e. 571 kW h); Scenario 2 (circa 2025, projected future annual shortfall of 12.5% i.e. 3904 kW h). For smaller unmet load of up to 4% in Scenario 1, Diesel Genset turns out to be the most preferred hybrid option, irrespective of whether the environmental costs were included alongside the net present costs or not. However, for an increased future load of 12.5% in Scenario 2, including the environmental costs makes the hybrid Micro hydro-PV-Diesel-Battery system cost-competitive to the Diesel only option. Considering a 25-year project lifespan, it becomes the most sustainable solution for retrofitting micro hydro facilities in ecologically sensitive locations in order to meet future shortfall in electricity supply, with improved renewable penetration of up to 97.5%. [ABSTRACT FROM AUTHOR]

• Nonlinear characteristics of lithium-ion battery are modeled with EECM and Peukert's law. • Load profiles are analyzed by energy and power demands and fluctuation characteristics. • Ageing effects of operating condition is considered by the fluctuation feature of load profile. • Adaptive deterministic approach is used to obtain optimal size of the battery system. • Proposed battery system size optimization is applied and verified in EVs and PV generators. For achieving decarbonization globally, most electric applications, such as electric vehicles (EVs) and photovoltaic (PV) generators, are moving toward the use of eco-friendly and renewable energy sources. Currently, one of the most utilized energy storage devices is the lithium-ion battery, which has various advantages than other systems. In addition, a lithium-ion battery presents nonlinear characteristics during operation. Therefore, these features should be considered in the design of battery systems with load requirements. In this paper, energy and power characteristics were analyzed in terms of battery system size in relation to EVs and PV generators. If a battery system is designed to supply power less than the energy demand, it will be utilized beyond the designed and safety range, and there will be generation of waste residual energy. However, if a battery system is designed to supply power more than the demand, additional investment and maintenance cost will be incurred to setup the battery system. Thus, this paper proposes an optimal sizing method for a lithium-ion battery system considering its nonlinear features using Peukert's law and the analyzed load conditions. To obtain the ideal size of the battery system, an adaptive deterministic approach is implemented. After determining the optimal battery system size, a simulation of the battery system with a load profile is conducted to verify that it satisfies all constraints of the battery system. [ABSTRACT FROM AUTHOR]

[Display omitted] • Various TES-biomass trade-offs were analysed from a baseload operation approach. • A new evaluation metric is proposed to assess the market profitability of power systems. • Uncertainty assessment was performed through Monte-Carlo simulations. • Most risk-averse investors would prefer hybrid solar-biomass plants without TES. • Hybrid baseload plants can achieve grid parity without add-on tariff subsidies. CSP-biomass hybrid power plants are promising options for displacing electricity from flexible fossil systems and increasing the share of dispatchable renewable energy in power grids. However, they are investment-intensive technologies whose competitiveness in electricity markets is still uncertain. This paper explores for the first time the profitability of CSP-biomass hybrid systems, assuming their participation as price-taking technologies in the Iberian day-ahead electricity market. For this purpose, a new economic evaluation metric called the Profitability Factor is proposed to extend the scope of the levelized cost of electricity (LCOE) by including the variability of market electricity prices. Besides, a stochastic approach integrating uncertainty related to investment costs, electricity prices and biomass costs through Monte-Carlo sampling methods has been applied. Results show that CSP-biomass schemes have up to 33% probability of being profitable in the electricity market before considering external financial support. High biomass share (approx. 76%) leads to a profitability increase of up to 0.11 USD/kWh (median value) compared to an equivalent CSP stand-alone plant. In addition, best-performing hybrid configurations would require a maximum add-in tariff between 0.040 and 0.071 USD/kWh (95% probability) to achieve grid parity. The resulting external financial support is significantly lower than those set through Power Purchase Agreements for the first generation CSP stand-alone plants (still in operation). Thus, CSP-biomass hybrid plants could provide flexibility and other services to power grids, which, if valued in the electricity market, would further increase the technology's competitiveness. [ABSTRACT FROM AUTHOR]

• Linearized multi-objective robust optimization. • Mathematical modeling for decision-making in newly established electricity markets. • Improving the upper approximation method for quadratic functions. • In non-robust and robust planning, retailer costs are reduced by 5.57 % and 5.32 %, respectively. • After robust optimization, the power consumed by the electrolyzer decreased by 14.19 %. Using renewable energy sources (RES) and green hydrogen has increased dramatically as one of the best solutions to global environmental issues. Applying demand response programs (DRPs) in this context could enhance the system's efficiency. Evaluating different DRPs' performances and assessing economic impacts on different parts of the electricity market is essential. The inherent uncertainty of RES and prices is inevitable in electricity markets. As a result of the lack of information, it is crucial to mitigate the risks as much as possible, such as risks related to changes in demand, unit outages, or other traders' bid strategies. This research introduces a robust multi-objective optimization method to reach the most confident plan for the retailer based on uncertainty in RES and price. The integration of different DRPs is assessed according to the cost to retailers and benefits for consumers using a multi-objective model to survey the impacts of different parts' decisions on each other. The trade-off among DRPs is considered in this model, and they are traded using a new model to illustrate the daily effect of these programs in monthly operations. This paper uses hydrogen storage (HS) integrated with PV as a distributed energy resource. As the Iranian electricity market has just been established, this research proposes a framework for decision-making in new electricity markets to join future smart energy systems. The mid-term pricing evaluates the system's performance for more accurate monthly results. Also, the operation cost of the hydrogen storage is modeled to assess its performance in non-robust and robust scheduling. Mixed-integer linear programming (MILP) has been used to model this problem in GAMS. A developed linearizing method is considered with a controllable amount of errors to reduce the volume and time of the computation. Finally, the cost of consumers in non-robust and robust market planning in the presence of DRPs is reduced by 8.77 % and 9.66 %, respectively, and HS has a compelling performance in peak-shaving and load-shifting. [ABSTRACT FROM AUTHOR]

Although electric power networks and district heating networks are physically coupled, they are not operated in a coordinated manner. With increasing penetration of renewable energy sources, a coordinated market-based operation of the two networks can yield significant advantages, as reduced need for grid reinforcements, by optimizing the power flows in the coupled systems. Transactive control has been developed as a promising approach based on market and control mechanisms to coordinate supply and demand in energy systems, which when applied to power systems is being referred to as transactive energy. However, this approach has not been fully investigated in the context of market-based operation of coupled electric power and district heating networks. Therefore, this paper proposes a transactive control approach to coordinate flexible producers and consumers while taking into account the operational aspects of both networks, for the benefit of all participants and considering their privacy. A nonlinear model predictive control approach is applied in this work to maximize the social welfare of both networks, taking into account system operational limits, while reducing losses and considering system dynamics and forecasted power supply and demand of inflexible producers and consumers. A subtle approximation of the operational optimization problem is used to enable the practical application of the proposed approach in real time. The presented technique is implemented, tested, and demonstrated in a realistic test system, illustrating its benefits. • Transactive control design for coupled electric power and district heating networks. • Development and application of an accurate and computationally efficient system model. • Technical and economic optimal system operation considering participant privacy. [ABSTRACT FROM AUTHOR]

In the transition to renewable energy systems, national plans are being created for several countries around the world. Concurrently, regions, municipalities and cities are planning for CO 2 neutral and renewable energy systems. Both developments are necessary, which raises the question whether these two types of energy planning are coordinated. How should national plans specify local actions, and how should local plans take into account the surrounding development of the energy system? Most local plans rely on the surrounding energy systems as they need to integrate with the energy system to export excess production or import during demands with insufficient capacity. This paper suggests a methodology to analyse how well these local plans integrate with the surrounding national energy system. The methodology is applied to the two Danish examples of Copenhagen and Sønderborg. Both examples connect to the Danish 2030 scenario defined in the Coherent Energy and Environmental System Analysis study. Based on the results the study concludes that the suggested methodology is applicable for evaluating how well local and national energy systems integrate, and can potentially be used in bettering energy planning to include the benefits of local action and national coordination. [ABSTRACT FROM AUTHOR]

Abstract: The paper analyses the issues for consideration listed in the stock taking document “New Energy Strategy for Europe 2011–2020” and explores the impact of their implementation on the use of renewable energy sources. The main objective of the paper is to introduce and provide a summary of the theoretical and methodological achievements required for an analysis of the implications of such an EU level strategy. It also provides a summary of the results of the analysis of the impacts of the Strategy on a specific field; namely, on the use of renewable energy sources. Contradictions inherent in the Strategy are discussed as well, and a proposal is put forward to aid the resolution of these contradictions. The paper also points out that the further development of the theoretical and methodological achievements may result in the creation of a simulation model, the application of which could effectively aid the process of strategic planning and the testing of strategies prior to approval. Finally, the paper discusses how an EU level and/or a global network of energy security centres could promote the development and implementation of an energy policy ensuring the appropriate utilization of renewable energy sources. [Copyright &y& Elsevier]

Abstract: This paper discusses methodology of energy system modelling when reduction of local externalities, such as damage to the human health from energy production-related air pollution, is in focus. Ideally, the local energy externalities should be analysed by adopting the impact pathway approach of ExternE study, and following the pollutants from their release to the personal uptake and resulting health effects. This would require inclusion of air pollution modelling and monetary valuation of the impacts into an energy system optimisation process. However, this approach involves a complex study and generalisations are needed. The way local externalities are included in the existing energy system models is identified and discussed in the paper. Only a few studies include localisation aspects when internalising local externalities in an energy system optimisation. The performed analysis of the Danish heat and power sector verifies that it is cheaper for the society to include externalities in the planning of an energy system than to pay for the resulting damages later. Total health costs decrease by around 18% and total system costs decrease by nearly 4% when health externalities are included in the optimisation. Furthermore, including localisation aspects can reduce health costs of the heat and power sector in Denmark by additional 7%. [ABSTRACT FROM AUTHOR]

Abstract: This paper includes a review of the different computer tools that can be used to analyse the integration of renewable energy. Initially 68 tools were considered, but 37 were included in the final analysis which was carried out in collaboration with the tool developers or recommended points of contact. The results in this paper provide the information necessary to identify a suitable energy tool for analysing the integration of renewable energy into various energy-systems under different objectives. It is evident from this paper that there is no energy tool that addresses all issues related to integrating renewable energy, but instead the ‘ideal’ energy tool is highly dependent on the specific objectives that must be fulfilled. The typical applications for the 37 tools reviewed (from analysing single-building systems to national energy-systems), combined with numerous other factors such as the energy-sectors considered, technologies accounted for, time parameters used, tool availability, and previous studies, will alter the perception of the ‘ideal’ energy tool. In conclusion, this paper provides the information necessary to direct the decision-maker towards a suitable energy tool for an analysis that must be completed. [Copyright &y& Elsevier]

• Quantitative techno-economic comparison of hybrid energy storage systems. • Proposing a novel probabilistic reliability index to handle long-term uncertainty. • Rule-based strategy with charging/discharging priority and operating thresholds. • Tailored multi-objective sizing and operation strategy co-optimization models. • Sensitivity analysis of load profile and energy storage cost reduction scenarios. The application of energy storage technologies is crucial to the extensive exploitation of renewable energy for power generation in off-grid areas because energy storage can mitigate the intermittency of renewables and balance the supply–demand mismatch. A hybrid energy storage system (HESS) with two or more heterogenous and supplementary energy storages outperforms single energy storage from the perspective of reliability and cost-effectiveness, but how to select the optimal HESS configuration is still unknown. To this end, this paper investigates the techno-economic comparison of ten HESSs in off-grid renewable energy system applications, including all pairwise combinations of thermal energy storage, pumped hydro storage, hydrogen storage, battery, and supercapacitor. Moreover, a novel probabilistic reliability index called loss of power supply probability with probability of exceedance (LPSP-PoE) is proposed to quantitatively handle the long-term uncertainties of renewable energy resources. The rule-based coordinated energy management strategies considering the charging/discharging priority and optimized operating thresholds are established for different HESSs, and then the multi-objective optimization models aiming to minimize net present cost and LPSP-PoE are developed to search the optimal components sizing and operating thresholds simultaneously. Finally, the results of case studies show that: (1) the optimal net present cost of thermal energy storage-battery at the highest reliability level is 3.3472 billion USD, which is 6.98 %∼69.85 % lower than the figure for other HESSs, indicating that thermal energy storage-battery is the most cost-effective HESS configuration; (2) The optimal charging/discharging priority can lead to 29.6 %∼75.2 % cost reduction, and the optimal operating thresholds can always achieve the lowest LPSP, illustrating the superiority of the proposed operation strategies; (3) the techno-economic performance of all HESSs varies significantly with the PoE level and load profile, while their rankings remain basically unchanged, showing the robustness of the comparison results. [ABSTRACT FROM AUTHOR]

• Discusses the broad classification of energy management system based on supervisory control, operating time platform, and decision-making strategy. • Provides a 360° critical analysis and comparison of energy management (EM) strategies developed for microgrids (MGs) along with their demand response programs. • Presents the various uncertainty handling and modeling approaches used to manage the intermittent behavior of RERs, and load demand. • Major recommendations and future research directions in the field of EM in MGs are also explored. Energy management (EM) can be defined as the process of monitoring, planning, optimizing, and saving energy to obtain an energy-efficient system. A microgrid (MG) is considered a sustainable energy system but due to the uncertain nature of renewable energy resources (RERs) and electrical load, it requires an optimal and efficient energy management system (EMS). It is vital for optimal usage of distributed energy resources (DERs) in a secure and reliable way. This function of EMS is performed by an advanced decision-making approach which is present in it. In recent years, many researchers have focussed on the development of advanced EM strategies for MG to establish a self-sustained MG. Therefore, a comparative study is required to have a 360° view of the domain EM in MGs. In this regard, this article presents a critical and comparative analysis of the EM strategies developed for the MG's optimal decision-making along with their demand response strategies. The broader classification of EMS based on supervisory control, operating time platform, and an approach for making a decision is discussed along with their limitations and solutions. To manage the dynamic and intermittent nature of RERs and load, different uncertainty handling and modeling approaches are summarized. A systematic review strategy consists of some tasks is adopted by the authors, for example (i) Extraction of research articles related to EM in MG; (ii) Filtering out the important articles to prepare a relevant research article database (iii) Critically analyzing the EM strategy developed in each article. In a broader perspective, this paper presents an up-to-date systematic analysis of EM strategies for MGs developed by various researchers. Finally, some recommendations and future research directions are also suggested. We hope that this article will help the readers to map the conceptual structure of this research field. [ABSTRACT FROM AUTHOR]

e-harbours is a unique European project that was set out to identify viable energy business cases on the exploitation of energy flexibility, which optimise their operations to match energy demand and supply while taking account of the additional volatility in supply caused by renewable energy sources, improve energy efficiency, and reduce dependence on fossil fuels. In this paper, we propose an integrated multi-criteria decision analysis based framework to assess the relative performance of 21 energy business cases, which implemented different demand-side management strategies. Our proposed methodology has the ability to address complex problems involving multiple conflicting interests from various stakeholders, different forms of data, and different fuzzy and crisp relations. We find that business cases based on contract optimisation and offering reserve capacity were ranked relatively high, while those based on trading on the wholesale market or hybrid approaches fared less well. Despite finding viable pilot business cases, e-harbours found that there was little enthusiasm among industrial partners to scale up the pilots. Consequently, EU governments should consider offering attractive incentive programmes for industry engagement in achieving their objectives in reducing greenhouse gas emissions, improving energy supply security, diversifying energy supplies, and improving Europe’s industrial competitiveness. [ABSTRACT FROM AUTHOR]

This paper explores the conditions under which renewable energy sources (RES) penetration could jeopardize power system reliability, as well as which flexibility options could help integrate high levels of RES. For this purpose, we used an energy-planning model from the TIMES family, which provides a realistic representation of power systems and plausible options for their long-term development, completed by a thermodynamic description of power systems to assess their reliability. We applied this model to the case of France and built contrasted scenarios, from 0% to 100% renewable energy penetration by 2050. We also tested different assumptions on Variable Renewable Energy (VRE) production, imports, demand flexibility and biomass potential. We show that high renewable energy penetration would need significant investments in new capacities, new flexibility options along with imports and demand-response, and that it is likely to deteriorate power system reliability if no technologies dedicated to this issue are installed. [ABSTRACT FROM AUTHOR]

This paper is a microgrid study of the University of California, San Diego (UCSD), a large campus with diverse distributed energy resources (DER). It highlights a microgrid’s “missing money”, which sharply differs from a natural-gas-fired generation plant’s “missing money” due to large-scale wind generation development. In response to UCSD’s expressed financial interest, we assess three strategies for integrating renewable generation: peak load shifting, onsite photovoltaic firming and grid support. While all three strategies are technically feasible and can be cost-effective under certain conditions, California’s current tariff structures and market prices do not offer sufficient incentives to motivate UCSD to offer these services. Alternative incentive mechanisms, which may resemble to those used to encourage renewable generation development, are necessary to induce UCSD’s DER offer for renewables integration. Such mechanisms are also relevant to commercial and industrial loads across California, including the vast combined heat and power resources. [ABSTRACT FROM AUTHOR]

[Display omitted] • A model is proposed to maximize market performance including tradable certificates. • The long-term impacts of regulations on market are discussed by fine and subsidy. • Three important objectives for government are investigated under four scenarios. • The obtained results are validated by Turkey's energy data as a real case study. • Fine policy is more effective than a subsidy to empower renewable electricity. In the last decades, due to the increasing environmental concerns such as energy-resource depletion and global warming, energy efficiency instruments and renewable energy production has been promoted more than ever. This paper investigates a comprehensive comparison between two energy markets, i.e., the tradable white certificate (TWC) market and the tradable green certificate (TGC) market. How to coordinate the two energy markets and what the coordination impacts become the main issues under a reward-penalty mechanism. This multi-agent problem is discussed in an electricity supply chain consisting of two populations of traditional energy suppliers who want to decide about using one of them under government intervention. In this regard, the behavior of the suppliers is analyzed under three different Scenarios, including traditional power generation, energy efficiency program (EEP), and renewable portfolio standards (RPSs). An evolutionary game is applied to formulate the problem. The results show that a mandatory fine policy is more useful than an energy-saving subsidy to motivate the traditional suppliers to move toward sustainable energy generation. This policy also helps governments to meet their sustainable-oriented goals, as we discussed in this work. The findings can be applied to extend sustainable-based energy projects in developing countries. [ABSTRACT FROM AUTHOR]

• A realistic and integrated PSH and CH model is proposed for optimum energy management. • PSH balances energy supply and demand for intermittent renewable energy. • PSH sites with added water streams feeding upper reservoir incur net positive generation. • PSH reduced pumping energy consumption and enhanced off-peak annual energy production. The energy sector contributes to around 60% of total greenhouse gas emissions. To limit climate change, the world is going through an energy transition to clean and affordable energy, therefore the share of variable renewable energy is increasing day by day. Power systems require significant flexibility to operate reliably. Pumped storage hydropower allows load balancing and stable integration of intermittent renewable energy in the electrical grid. All energy storage technologies, including pumped storage hydropower, are considered a net negative contributor to the grid since they draw more energy than they deliver. This paper uniquely investigates the true potential of pumped storage hydropower and its optimum operation along with existing conventional hydropower. It considers power, energy production, and utilization for both installations to determine an equilibrium condition for a net positive energy balance. The case study of the 300 MW Balakot conventional hydropower plant in Khyber Pakhtunkhwa, Pakistan indicates that the pumped storage hydropower sites, where additional water streams reach the upper storage reservoir, can reduce pumping energy consumption by up to 166 GWh/year. Accordingly, a pumped storage hydropower facility is proposed at Paras, Pakistan, modeled, and optimized in an integrated manner with the Balakot conventional hydropower plant for a net positive annual energy generation and profitability. The integrated power and energy modeling and capacity optimization of the hydropower complex highlight the importance of suitable site selection for pumped storage hydropower near existing conventional hydropower reservoirs. Value-added contribution of this configuration is that it can enhance annual energy generation from integrated conventional hydropower, reduce the upfront capital cost of the project by 10–15%, squeeze the project implementation schedule and result in a smaller carbon footprint to meet the ultimate objective of limiting climate change. Additionally, it unlocks huge untapped pumped storage hydropower potential besides existing conventional hydropower. Furthermore, the study guides the scientific and engineering organizations on optimum capacity utilization without impeding energy generation from conventional hydropower facilities. [ABSTRACT FROM AUTHOR]

• Decentralized electricity market using blockchain has been designed in the community microgrid connected with utility DSO. • Smart contract functions to interact between the peers is especially programmed to support P2P energy intra-trading system. • A software simulation setup is described for the establishment of the blockchain network with the microgrid EMS system. • Pricing strategy under different cases of SDR with the battery storage flexibility is developed using Cryptocurrency "Atom". • Pricing strategy incorporates the effect of price and incentive responsiveness of loads and shows the effect on P2P price. The growing integration of distributed generations and battery storage equipped with smart meters paves a way to smartly manage the Distributed Energy Resources (DER) using a digital platform to improve the overall performance of the microgrid system. The deployment of distributed energy resources, particularly solar photovoltaic and wind generation, has transformed conventional power consumers into active prosumers. As a result of the expansion and digitalization of power distribution infrastructures, peer-to-peer (P2P) energy intra-trading has evolved as a new paradigm in electricity trade in the community microgrid system. Blockchain is being used to encourage P2P energy intra-trading amongst prosumers, consumers, and Renewable Energy Sources (RES) owners because of its transparency, security, and speed in completing transactions. This P2P concept is further extended in this work to establish a self-sustained community microgrid system in energy trading with a case study to showcase the merits of blockchain technology in providing a secure and effective trading platform for mass users. As a result, the proposed work presents a solution for a secured energy management system that uses blockchain technology to create a decentralized microgrid energy market model that depicts P2P energy transactions with the incorporation of a battery storage system. Again, the microgrid P2P market settles the clearing price considering the probable response of customers when the price varies and incentive is paid to customers and prosumers to change the pattern of using of loads. According to the findings, end-users benefit from energy savings and self-sufficiency due to the combination of automated P2P trade and storage flexibility. Furthermore, a sort of crypto-currency "Cosmos (Atom)" is simulated in the P2P market model and published via the blockchain's mining mechanism. The proposed design framework presented in this paper has addressed the implementation of an effective energy management platform for developing a self-sustained microgrid system using distributed energy resources. [ABSTRACT FROM AUTHOR]

The paper attempts to analyse the environmental and economic impacts of the Multi-fuel Solar Hybrid Power Plant concept called SolComBio. An environmental and economic assessment is performed to investigate whether the concept could be suitable for energy source in remote areas compared with the conventional local energy supply systems. The technological concept relies on the combination of a solar thermal receiver and a multi-fuel pulverized biomass burner system to overcome the impediments of local or regional power supply under environmental friendly and economic sound conditions. Through the hybridisation with multi-fuel pulverized burner system, SolComBio concept overcomes the drawbacks of solar systems and offers flexibility of operation time and also possibility of fuel changes, which makes it suitable for areas with seasonal biomass production. Two cases of constructing and operating the plant in a Brazilian and Indonesian remote region are addressed. According to the calculations, SolComBio seems to be promising from the environmental side; nonetheless, depending on the conditions at the local land and feedstock markets the SolComBio concept could be competitive or not. This study offers a reflection how a technological concept can arouse interest of remote regions from the perspective of systems analysis. [ABSTRACT FROM AUTHOR]

• Integrating machine learning and vine-copula to catch the complex spatial correlations among the renewable resources. • Utilization of stochastic model predictive control to optimize the long-term hydro-wind-solar operation. • Potential assessment of hydropower to offset the effects of seasonal uncertainties and mismatch between renewable resources and requirements. It is popular to combine the hydropower plant with the wind and solar power plants to supply electricity, and the joint is termed as the hydro-wind-solar renewable energy supply system (RESS). The long-term optimal operation of the RESS is a challenging task, due to the nature of different spatial and temporal variabilities associated with renewable energy resources, and the significant operation uncertainties due to the changing natural environment. To address the task, the stochastic model predictive control (MPC), based on probabilistic forecasting and rolling stochastic optimization, is designed and implemented for the long-term operations of the RESSs in Yunnan province, China. The paper tests out the system efficiencies for different penetration levels of wind and solar power, and finds out that (1) the Long Short-Term Memory performs best among candidate point prediction algorithms; (2) the stochastic MPC considering the correlations among renewable resources helps the RESS operate with a higher efficiency; (3) the large-scale hydropower plant has great potential to offset the effects of seasonal uncertainties and demand-generation mismatch, but cannot completely avoid the electricity shortage enabled by unexpected scenarios of renewable resources. [ABSTRACT FROM AUTHOR]

• Seeks synergy between onshore power supply and microgrid for port electrification. • Expand the conventional scheduling paradigm of port operation. • Develops a two-stage method to co-optimize seaside operation and port energy management. • Reveals the environmental and economic advantages of the joint scheduling approach. The high environmental impacts of maritime transportation have led to an increasing interest in adopting electricity as the ideal energy source within the sector. In this paper, we propose a novel integrated day-ahead scheduling algorithm to jointly optimize the seaside/yard operation and the port energy system management within one unified framework by harnessing the synergy between two of the most prominent maritime electrification techniques: onshore power supply and microgrid. We formulate the joint scheduling problem as a two-stage model. In the first stage, the port authority determines the optimal berth allocation for the incoming vessels considering their cargo volumes, energy demands, and the availability of OPS facility and cargo handling equipment (i.e., quay/yard cranes). In the second stage, acting as the port microgrid operator, the port authority determines the optimal day-ahead scheduling of the container handling activities and operation of port microgrid assets for each time slot. Uncertainty from renewable energy generation and port load forecast is also incorporated in the problem formulation. The simulation-based case study shows that the proposed joint scheduling algorithm is capable of enhancing energy independence, system-wide efficiency, operational reliability, and economy of the port microgrid in comparison with the conventional berth allocation strategy. We hope our work provides insights into how electrification can help the maritime sector reinforce its commitment to sustainability while remaining competitive. [ABSTRACT FROM AUTHOR]

• Gas grid working with hydrogen/methane can make dispatchable renewable electricity. • No retrofitting of the grid is required until 50% of RES share. • Till 50% of RES share, it is convenient to use hydrogen only to shave the RES peaks. • Hydrogen injected in the grid allows converting renewable electricity into heat. • Gas grid can be used as unique energy storage system till 65% of RES share. The temporal and geographical availability of renewable energy sources is highly variable, which imposes the importance of correct choices for energy storage and energy transport systems. This paper presents a smart strategy to utilize the natural gas distribution grid to transport and store the hydrogen. The goal is twofold: evaluating the capacity limits of the grid to accommodate "green hydrogen" for preset increasing shares of renewable energy sources (RESs) and determining at the same time the optimal mix of wind, photovoltaic (PV), biomethane and power-to-gas systems that minimizes the investment and operation costs. To this end, the energy supply system of an entire country is modelled and optimized considering the real characteristics and pressure levels of the gas grid, which is assumed to be the only storage mechanism of green hydrogen. The operational concept is to fill up the gas grid with hydrogen during the day and with natural gas during the night while always consuming the natural gas-hydrogen blend. Green hydrogen is generated by electrolysers powered by PVs, wind turbines and biomethane power systems. Results of the optimizations showed that: i) as long as the share of RES does not exceed 20%, there is no need to use the gas grid as RES storage system, ii) from 20 to 50% of RES share the gas grid receives the surplus of electricity in the peaks that would be necessary to "complete" the dispatchability of RES electricity, iii) above 50%, the excess of electricity in the peaks has to be used to generate the thermal energy required by the consumers. The gas grid can be used as unique renewable energy carrier and storage system up to 65% of RES share. [ABSTRACT FROM AUTHOR]

Abstract: This paper describes a method to characterize the fluctuating electricity generation of renewable energy sources (RESs) in a power system and compares the different parameters for California and Germany. Based on this method describing the fluctuation and residual load, the potential contribution of grid-connected vehicles to balancing generation from renewable energy sources is analyzed for a 2030 scenario using the agent-based simulation model PowerACE. The analysis reveals that integrating fluctuating RES is possible with less effort in California because of a higher correlation between RES generation and the load curve here. In addition, RES capacity factors are higher for California and therefore the ratio of installed capacity to peak load is lower. Germany, on the other hand, faces extreme residual load changes between periods with and without supply from RES. In both power system scenarios, grid-connected vehicles play an important role in reducing residual load fluctuation if smart charging is used. Uncontrolled charging or static time-of-use tariffs do not significantly improve the grid integration of RES. [Copyright &y& Elsevier]

Abstract: Climate change, added to security of supply concerns, has been leading many countries to strongly support the development of electric vehicles (EVs) not only as a cleaner and more energy efficient source of transportation but also as a flexible electricity storage option. However, the massive deployment of EVs will impose important challenges to power systems operation. In this context, this paper investigates the impact of EVs with V2G capability to power system operation. Since this impact depends on the power system considered and on the number (and type) of EVs, different scenarios for renewable energy sources (RES) generation and EV penetration are studied. A detailed analysis of the impact of EVs the Spanish power system – demand, generation, use of flexible units (pumped storage) and power reserve, and energy curtailment – is performed. For this purpose, a mid-term operation model that computes generation dispatch and simulates hourly power system operation is used. The advantage of this model is that it includes EVs in the unit commitment and represents in detail mobility and connection patterns, which allows the estimation of more realistic results. [Copyright &y& Elsevier]

Abstract: The lifespan and stability of power supply are the most critical issues for implantable biomedical devices (IMDs). Extracting energy from the ambient sources or human body therefore attracts a lot of attentions for in vivo therapies. Micro-electromechanical systems (MEMSs) based energy harvesters are expected to be one of the potential solutions to supply electrical power to IMDs owing to its tiny size, light weight and recharge-free attributes. However, the performance of the micro-energy harvester for implantable biomedical applications is limited by many inherent congenital factors. In this paper, three main topics are comprehensively studied and discussed. At first, the energy sources to be scavenged from human body are particularly investigated and characterized. Secondly, the operation principle and key bottlenecks of the currently available MEMS-based energy harvesters are reviewed and presented. Finally, the performance, frequency tuning methods and biocompatibility of micro-energy harvester are evaluated and summarized. [Copyright &y& Elsevier]

Abstract: If considering the increased utilisation of renewable electricity during the last decade, it is realistic to assume that a significant part of future power production will originate from renewable sources. These are normally intermittent and would cause a fluctuating electricity production. A common suggestion for stabilising intermittent power in the grid is to produce hydrogen through water electrolysis thus storing the energy for later. It could work as an excellent load management tool to control the intermittency, due to its flexibility. In turn, hydrogen could be used as a fuel in transport if compressed or liquefied. However, since hydrogen is highly energy demanding to compress, and moreover, has relatively low energy content per volume it would be more beneficial to store the hydrogen chemically attached to carbon forming synthetic methane (i.e. biogas). This paper presents how biogas production from a given amount of biomass could be increased by addition of renewable electricity. Commonly biogas is produced through digestion of organic material. Recently also biomass gasification is gaining more attention and is under development. However, in both cases, a significant amount of carbon dioxide is produced as by-product which is subject for separation and disposal. To increase the biogas yield, the separated carbon dioxide (which is considered as climate neutral) could, instead of being seen as waste, be used as a component to produce additional methane through the well-known Sabatier reaction. In such process the carbon could act as hydrogen carrier of hydrogen originating from water electrolysis driven by renewable sources. In this study a base case scenario, describing biogas plants of typical sizes and efficiencies, is presented for both digestion and gasification. It is assessed that, if implementing the Sabatier process on gasification, the methane production would be increased by about 110%. For the digestion, the increase, including process improvements, would be about 74%. Hence, this method results in greatly increased biogas potential without the addition of new raw material to the process. Additionally, such model would present a great way to meet the transport sector’s increasing demand for renewable fuels, while simultaneously reducing net emissions of carbon dioxide. [Copyright &y& Elsevier]

Abstract: This paper aims to test the ETSAP [2] ETSAP (Energy Technology Systems Analysis Programme) is an IEA Implementation Agreement since 1976, developing the MARKAL-TIMES family of models. 2 -TIAM global energy system model and to try out how far it can go towards a global 100% renewable energy system with the existing model database. This will show where limits in global resources are met and where limits in the data fed to the model until now are met. Results from the modelling are global and regional energy consumption, emission of greenhouse gasses and thereby potential increase in global mean temperature. Furthermore, total system costs are compared to a reference scenario. Existing analysis with TIAM shows different ways of reaching the 2°C target (increase in global mean temperature) and also how uncertainty on different factors influences the optimal solution. The main conclusion from existing analysis with TIAM is that the 2°C target is possible to reach, but it will be expensive. Another very important point is that if uncertainty on the value of the climate sensitivity (Cs ) is taken into account then the optimal strategy calls for early action compared to a full foresight optimisation using the most probable value of Cs . [ABSTRACT FROM AUTHOR]