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In this paper, the role of a Battery Energy Storage System (BESS) in the United Kingdom (UK) electricity market is investigated. Such device is selected since research works related to the topic demonstrate that BESSs help facing challenges caused by renewable energy sources increasing penetration in the power systems' field. Indeed, in the next future, BESSs will assume a relevant role thanks to their fast response time. In the present paper, an optimization model is implemented to encode the UK market rules and output the optimal set of offers on day-ahead and intraday markets, dynamic frequency response services and imbalance settlement. The market rules are formalised in a mathematical model considering market temporal sequencing and BESS technical limitations. The tool also models the state of energy management, necessary since BESSs are energy limited assets. Objective function and constraints are linearized to define a Mixed Integer Linear Programming problem to reduce the computational burden and to integrate the model in the Energy Management System previously developed at University of Genoa. Specific tests are performed including and excluding ancillary services to evaluate the related advantages. The obtained outcomes reveal that ancillary services are the most convenient options within all the implemented scenarios. [ABSTRACT FROM AUTHOR]

This paper proposes an energy management system (EMS) for nanogrids to balance the power supply and forecasted demand in consideration of forecasting errors arising from high instantaneous demand. The proposed EMS employs a power-balancing optimization process for forecasted demand and a reference power modulation strategy for forecasting errors. This power-balancing optimization utilizes nanogrid sources, such as photovoltaics, fuel cells, and batteries, to meet forecasted demand and a supercapacitor charging process to overcome issues with a low energy density. The proposed reference power modulation strategy is utilized to allocate power from a hybrid energy storage system consisting of a battery and supercapacitor in order to compensate for forecasting errors. In addition, this proposed strategy considers battery and supercapacitor constraints such as the power changing rate and total power limitations. The power-balancing optimization process also operates at faster sampling rate than the reference power modulation process in order to improve the computational efficiency. The performance of the proposed EMS is evaluated using real data obtained from the Korea Electric Power Exchange. • This paper proposes a cascaded structure energy management system (EMS) for nanogrid to balance power for both forecasted demand and forecast errors including high instantaneous demand. • The proposed EMS comprises a power balancing optimization process for forecasted demand and reference power modulation strategy for the forecast errors. The power balancing optimization utilizes nanogrid sources, such as PV, fuel cell, and battery, to meet forecasted demand as well as a supercapacitor charging process for addressing low energy density characteristics. • The proposed reference power modulation strategy is utilized to allocate power from the HESS comprising a battery and supercapacitor, in order to compensate forecast errors. In addition, this proposed strategy is performed considering battery and supercapacitor constraints such as their power changing rate, total power limitation. [ABSTRACT FROM AUTHOR]

Due to the inherent uncertainty and the mismatch between renewable energy output and load demand, renewable energy-based system optimization is increasingly important in China. Therefore, electricity demand response (EDR) is critical to the stability and efficiency of an integrated renewable energy system (IRES). The customers' demand response under the time-of-use (TOU) mechanism is the key to the entire process. However, how to optimize integrated renewable energy system based on accurately identifying the electricity demand response participation of customers is one of the main issues at this stage. To solve the problem, this paper presents a novel approach for integrated renewable energy system optimization considering electricity demand response management and multistage energy storage systems from the perspective of Fujian Province, China. To enable large-scale renewable energy integration, the original output is processed using the Hampel identifier (HI)-Savitzky-Golay filter (SGF) technology. A machine learning (ML)-based identification model is developed for EDR to determine whether the customers are participating or not. Driven by the time-of-use mechanism, customers can make dynamic adjustments based on the Logistics function. After that, an integrated renewable energy system model optimization strategy is proposed based on satisfying customers' electricity demand response. Throughout the optimization process, the multistage energy storage system plays a vital role in the residual fluctuation absorption for renewable energy filtering, the dynamic adjustment process of the demand response, and the integrated renewable energy system optimization. Through extensive case studies and discussions, it is demonstrated that random forest (RF) and support vector machine (SVM) reach 80 % identification accuracy with a 20 % testing ratio. According to the optimization approach proposed in this paper, the minimum operation cost can be calculated to be 9.336*106 CNY. The methodology presented in this paper establishes a special relationship between electricity demand response identification and dynamic adjustments, and provides an optimization model for Fujian Province, China, which is a reliable reference for future work. [ABSTRACT FROM AUTHOR]

Under the background of carbon neutrality, the traditional coal chemical industry needs to upgrade the industrial park to reduce the carbon emission in the production process. Therefore, this paper studies a method to transform the coal chemical industry park by using renewable energy to produce hydrogen, and proposes an energy management scheme coupled with renewable energy to produce hydrogen in the coal chemical industry park. Firstly, based on time of use electricity price, renewable energy generation and load, an energy management strategy aiming at maximizing daily income is proposed to obtain the optimal energy management scheme with the highest daily income. Then, based on this strategy, the annual revenue maximization optimization model considering the investment and operating costs of photovoltaic, hydrogen production, hydrogen storage and electricity storage is established, and the optimal capacity configuration of different equipment in the system is obtained. The simulation model is established on the MATLAB-YALMIP platform, and GUROBI is used to solve the optimization model. The proposed scheme can save about 76,800 tons of standard coal and reduce carbon emissions by 181,247.95 tons per year. In addition, a system that only considers photovoltaic and electricity storage coupling is also established as a comparison, and the net profit of the system under this scheme is reduced by 36.47 % compared with the proposed PV-storage-hydrogen system, which highlights the economic advantages of the proposed photovoltaic, electricity storage and green hydrogen coupling system. [ABSTRACT FROM AUTHOR]

This paper presents a tool that allows to perform an ex-ante estimation of the profitability to participate to the electric energy and service markets for a microgrid or a polygeneration power plant. The tool is the evolution of an Energy Management System (EMS) previously developed by the University of Genoa and Renantis Solutions Srl. Constraints are added to the original EMS to model the Ancillary Service Market (ASM) and Day Ahead Market (DAM) mechanisms in the framework of a planning tool that allows to get estimates of the microgrid/plant costs and revenues over a long-time horizon. As the market outcome strongly depends on the Transmission System Operator (TSO) acceptance/rejection of bids and offers, a statistical approach is proposed. The optimization process is split in two steps: the first one optimally dispatches the energy units and proposes DAM offers and ASM bid and offers over a specified time horizon. Then, a Monte Carlo method is launched; at each extraction the TSO acceptance/rejection of each ASM bid and offer is simulated and there is a second step of optimization in which the energy sources are optimally re-dispatched to meet the bidding program and so to avoid imbalance power exchange that would increase the energy cost. The cost/revenue is thus computed at each extraction. The result is the probability density function of the costs/revenues that allows to estimate the profitability of the participation to the electricity markets. One test performed on a real microgrid shows that the computational time needed to complete one iteration related to a time horizon of one month is approximately 20 s. Moreover, the comparison of the outcomes with two independent launches to define the set of bids and offers to be accepted in ASM demonstrates that the statistical analysis allows to avoid misleading conclusions on the costs/revenues. Consequently, the fact that the developed EMS allows to perform a statistical analysis in a reasonable time enables the possibility of comparing multiple scenarios and gives an indication related to the range of cost/revenues in which the microgrid/plant may incur. Finally, the computational efficiency of the proposed approach allows to conclude that the developed EMS is appropriate to perform a long-time horizon (of the order of magnitude of years) analysis and, at the same time, allows to model all the involved components with a high level of details. [ABSTRACT FROM AUTHOR]

The uncertainty of the renewable energy output and load demand poses significant challenges to the efficiency and stability of energy community operation. To reduce the adverse effects of uncertainty factors, this paper proposes a dual time-scale energy management method for a distributed energy community. In the day-ahead scheduling, an adaptive robust optimization model is proposed. This model uses the Dirichlet process mixture model (DPMM) to establish the fuzzy set of uncertainty for solar power generation and load demand. The column-and-constraint generation algorithm (C&CG) to solve the optimization model. In the intra-day scheduling, a rolling optimization method based on model predictive control (MPC) is combined with short-term source-load forecasting, which ensures consistency between the intra-day and day-ahead scheduling plans and real-time supply-demand balance. Experiments show that the robustness of energy management in the proposed method is enhanced significantly in various scenarios. At the same time, the operating costs of energy community are also reduced with different degrees of conservatism. [ABSTRACT FROM AUTHOR]

Data analysis is one of the most important tools that can contribute to unveiling critical relationships. Econometric approaches are evolving continuously, while older methods although are not completely abandoned, they may be used vis-à-vis the new ones for comparison purposed. This special issue has called for papers that have used novel research methods in a panel data framework for the studying and modelling renewable energy use and management. This special issue is useful for readers because it provides them an overview of the those novel methodologies employed in the field of renewable energy economics and econometrics. [ABSTRACT FROM AUTHOR]

PHPS is a mature energy storage technology, leading to more efficient energy management and further integration of Renewable Energy Sources (RES). This paper investigates the implementation of such a system at Kastraki, Greece, combined with a second upstream reservoir with a total capacity of 10 hm3 and evaluates its efficiency. The position and the geometric characteristics of the second reservoir are determined with ArcGIS. When excess of energy exists, water is pumped from Kastraki reservoir and stored in the upper one. Otherwise, the stored water is used for energy production. The simulation is performed for 40 years with a monthly time step. Furthermore, another simulation for a typical day in July is conducted, when water and energy needs are increased. The system is evaluated in terms of covering the water supply and energy needs, as well as in terms of efficient energy management. Several results can be obtained, as follows: i) the reliability of the PHPS system for the coverage of monthly water supply and irrigation needs is 82.84 %. ii) The coverage of water supply needs for a typical day in July is 99.99 %. iii) The average monthly coverage of energy needs amounts to 83.86 %. [ABSTRACT FROM AUTHOR]

In this paper, a new design and flexible energy management strategy are presented for microgrids. The proposed intelligent energy management system (IEMS) achieves effective integration between the resilient microcontroller, chosen for its rapid response speed and its capability to perform multiple operations simultaneously, and the optimization techniques to enhance the power quality. The IEMS is designed using the FPGA board, chosen for its flexibility and capability to handle multiple and complex operations simultaneously. The experimental testing of the IEMS demonstrates a significant level of effectiveness in managing energy. To enhance system performance and ensure cost-effective reliability, advanced optimization techniques are employed. This study deals with a complex multi-objective optimization problem involving the limitations of energy generation, load demand, and a hydrogen-battery hybrid energy storage system. The moth-flame optimization (MFO) algorithm is chosen to solve this optimization problem due to its rapid convergence rate and accuracy. The effectiveness of the MFO algorithm is assessed by comparing it with several new algorithms. The obtained results show the robust performance of the IEMS and its high responsiveness to dynamic operational scenarios. It can observe, gather, and analyze data in real-time. It achieves a remarkable 1.287 % reduction in operating costs within a short timeframe. [ABSTRACT FROM AUTHOR]

PHPS is a mature energy storage technology, leading to more efficient energy management and further integration of Renewable Energy Sources (RES). This paper investigates the implementation of such a system at Kastraki, Greece, combined with a second upstream reservoir with a total capacity of 10 hm3 and evaluates its efficiency. The position and the geometric characteristics of the second reservoir are determined with ArcGIS. When excess of energy exists, water is pumped from Kastraki reservoir and stored in the upper one. Otherwise, the stored water is used for energy production. The simulation is performed for 40 years with a monthly time step. Furthermore, another simulation for a typical day in July is conducted, when water and energy needs are increased. The system is evaluated in terms of covering the water supply and energy needs, as well as in terms of efficient energy management. Several results can be obtained, as follows: i) the reliability of the PHPS system for the coverage of monthly water supply and irrigation needs is 82.84 %. ii) The coverage of water supply needs for a typical day in July is 99.99 %. iii) The average monthly coverage of energy needs amounts to 83.86 %. [ABSTRACT FROM AUTHOR]

In this paper, a new design and flexible energy management strategy are presented for microgrids. The proposed intelligent energy management system (IEMS) achieves effective integration between the resilient microcontroller, chosen for its rapid response speed and its capability to perform multiple operations simultaneously, and the optimization techniques to enhance the power quality. The IEMS is designed using the FPGA board, chosen for its flexibility and capability to handle multiple and complex operations simultaneously. The experimental testing of the IEMS demonstrates a significant level of effectiveness in managing energy. To enhance system performance and ensure cost-effective reliability, advanced optimization techniques are employed. This study deals with a complex multi-objective optimization problem involving the limitations of energy generation, load demand, and a hydrogen-battery hybrid energy storage system. The moth-flame optimization (MFO) algorithm is chosen to solve this optimization problem due to its rapid convergence rate and accuracy. The effectiveness of the MFO algorithm is assessed by comparing it with several new algorithms. The obtained results show the robust performance of the IEMS and its high responsiveness to dynamic operational scenarios. It can observe, gather, and analyze data in real-time. It achieves a remarkable 1.287 % reduction in operating costs within a short timeframe. [ABSTRACT FROM AUTHOR]

Home energy management systems are essential for the optimization of resources in complex demand scheduling problems that require energy efficiency in homes. This can be achieved through the use of Renewable Electricity Sources (RES), for cleaner and more sustainable energy generation, as well as participation in Demand Response (DR) programs, for lower energy prices. The problem becomes more complex when it is considered a community of households. This paper aims to address individual and community household participation in DR programs and RES sharing while considering constraints imposed on the operation schedule of appliances, through load shifting optimization. For that, a Genetic Algorithm is proposed, implemented, and validated, which focuses on minimizing energy costs. It takes into account dynamic pricing, distributed generation, and household community energy sharing. Using real household workload data, two case studies are presented, one for the cost optimization of an individual household and another for a photovoltaic energy-sharing household community containing twenty houses. Both case studies represent five days of scheduling, where each house can have up to five appliances able to shift. The business as usual costs are 16.74 EUR and 269.99 EUR for the individual and community case studies, respectively. Results show improvements of up to 24.3% (12.67 EUR in the optimized schedule, 4.07 EUR in savings) for the individual case study and 11.8% (238.21 EUR in the optimized schedule, 31.78 EUR in savings) for the household community. In a community, households can expect cost reductions of 1.5%–26.8% when compared to individual scheduling. [ABSTRACT FROM AUTHOR]

—As a result of the increasing demand for different energies with technological development, and with the rapid development of energy hubs (EHs), issues such as computation speed, big data and information privacy, robustness, etc. are becoming increasingly vital. Also, the problem of communication packet loss leads to an incompatibility between the generated and consumed energy. In response to the above problems, this paper proposes an Internet of things-Fog-Cloud distributed consensus-based energy management algorithm to optimally solve the energy mismatch problem in an EH environment considering communication information loss and uncertainties of some EH elements. A MATLAB/Simulink-based model of a hypothetical EH system is modified to support real-time communication. Two case studies are applied to explain the performance and effectiveness of the proposed algorithm under different packet loss scenarios. The system's sensitivity was studied depending on the number of iterations and the uncertainties of the renewable energy sources, energy prices, and load demands. Also, a comparison was made between the results obtained by the proposed method and the optimization results using the genetic algorithm and particle swarm optimization techniques. The results prove the applicability and feasibility of the proposed method for the demand management system in EHs. The results show that the proposed algorithm decreases the daily system operating cost from 372.421 to 369.941 $ and the total CO 2 emissions cost from 4848.5 to 4103.1 $. In addition, the daily energy mismatch between the generated and consumed values decreases from 579.85 to 0 kWh in the electrical system and from 435.14 to 0.073 kWh th in the heating system. Based on the previous results, the proposed algorithm of EHs contributes significantly to the development of sustainable cities. [ABSTRACT FROM AUTHOR]