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Virtual power plants (VPP) emerge as a new participant that, in order to maximise their visibility and income, represents a group of distributed energy resources (DER) in the electricity market. However, this DER aggregation brings challenges, such as fluctuating renewable sources dependent on weather variables and guaranteeing power set points. One way to deal with these intermittencies is to incorporate the energy storage system (ESS) into the VPPs. Therefore, this paper presents a novel bidding strategy of VPP that includes modelling the uncertainty associated with solar generation using information gap decision theory (IGDT) and the optimal sizing of ESS systems so as to deal with solar generation fluctuations. Additionally, a study is carried out to determine the economic viability of this methodology in the short, medium and long terms using the return on investment (ROI).

Current power grid and market development, characterized by large growth of distributed energy sources in recent years, especially in Europa, are according energy storage systems an increasingly larger field of implementation. Existing storage technologies, e.g. pumped-storage power plants, have to be upgraded and extended by new but not yet commercially viable technologies (e.g. batteries or adiabatic compressed air energy storage) that meet expected demands. Optimal sizing of storage systems and technically and economically optimal operating strategies are the major challenges to the integration of such systems in the future smart grid. This paper surveys firstly the literature on the latest niche applications. Then, potential new use case and operating scenarios for energy storage systems in smart grids, which have been field tested, are presented and discussed and subsequently assessed technically and economically.

This paper presents two economic criteria for guiding the energy storage system (ESS) sizing in grid-connected microgrids. The internal power output model and the economic operation model of ESS are firstly established. Then, the combination of heuristic adjustment strategy and hybrid particle swarm optimization algorithm are introduced to solve the optimal operation model of ESS. Then according to the ESS life model and cost-benefit analysis, a static investment economic criterion which is easy and simple to be calculated is proposed to demonstrate the economic feasibility of ESS investment programs in the short term. Considering the time value of currency, a dynamic investment economic criterion is proposed later for long-term investment projects. Furthermore, the ESS sizing boundary of achieving profits could be also obtained according to the criteria which can indicate the economic attractiveness or resistance to ESS investors in the microgrid. A case study has verified its effectiveness. At the same time, sensitivity analysis is given to show the impact on key parameters, such as investment unit price and electricity purchase price on ESS investment.

The fuel cell system (FCS) is commonly combined with an energy storage system (ESS) for enhancing the performance of the ship. Consequently, the battery ESS size and power allocation strategy are critical for the hybrid energy system. This paper focuses on designing a method to solve these two problems. First, a battery degradation model is employed to assess the ESS lifetime. Subsequently, the sizing problem and the optimal power allocation are integrated into a cost‐minimization problem, which is solved by a double‐loop optimization approach. The inside loop utilizes the battery degradation model to calculate ESS lifetime. In the outside loop, a power allocation strategy based on the hybrid Particle Swarm Optimization algorithm and Gray Wolf Optimization algorithm is presented. Finally, the power allocation strategy is extended to real‐time implementation by the equivalent consumption minimization strategy (ECMS) and an improved ECMS is proposed to make the FCS operates near the maximal efficiency point. Compared with ECMS, the operating cost reduces by 0.26%. The result indicates that the proposed method can optimize the ESS size efficiently, and the power allocation strategy can assure the stable operation of the fuel cell ship. This paper focuses on designing a method to optimize the size of the battery energy storage system.

Transmission system operators impose several grid-code constraints on large-scale wind farms to ensure power system stability. These constraints may reduce the net profit of the wind farm operators due to their inability to sell all the power. The violation of these constraints also results in an imposition of penalties on the wind farm operators. Therefore, an operation strategy is developed in this study for optimizing the operation of wind farms using an energy storage system. This facilitates wind farms in fulfilling all the grid-code constraints imposed by the transmission system operators. Specifically, the limited power constraint and the reserve power constraint are considered in this study. In addition, an optimization algorithm is developed for optimal sizing of the energy storage system, which reduces the total operation and investment costs of wind farms. All parameters affecting the size of the energy storage systems are also analyzed in detail. This analysis allows the wind farm operators to find out the optimal size of the energy storage systems considering grid-code constraints and the local information of wind farms.

Heat energy storage systems were fabricated with the impregnation method using MgO and Mg(OH)2 as supporting materials and polyethylene glycol (PEG-6000) as the functional phase. MgO and Mg(OH)2 were synthesized from the salt Mg(NO3)·6H2O by performing hydrothermal reactions with various precipitating agents. The precipitating agents were NaOH, KOH, NH3, NH3 with pamoic acid (PA), or (NH4)2CO3. The result shows that the selection of the precipitating agent has a significant impact on the crystallite structure, size, and shape of the final products. Of the precipitating agents tested, only NaOH and NH3 with PA produce single-phase Mg(OH)2 as the as-synthesized product. Pore size distribution analyses revealed that the surfaces of the as-synthesized MgO have a slit-like pore structure with a broad-type pore size distribution, whereas the as-synthesized Mg(OH)2 has a mesoporous structure with a narrow pore size distribution. This structure enhances the latent heat of the phase change material (PCM) as well as super cooling mitigation. The PEG/Mg(OH)2 PCM also exhibits reproducible behavior over a large number of thermal cycles. Both MgO and Mg(OH)2 matrices prevent the leakage of liquid PEG during the phase transition in phase change materials (PCMs). However, MgO/PEG has a low impregnation ratio and efficiency, with a low thermal storage capability. This is due to the large pore diameter, which does not allow MgO to retain a larger amount of PEG. The latent heat values of PEG-1000/PEG-6000 blends with MgO and Mg(OH)2 were also determined with a view to extending the application of the PCMs to energy storage over wider temperature ranges.

Electrification of the propulsion system has opened the door to a new paradigm of propulsion system configurations and novel aircraft designs, which was never envisioned before. Despite lofty promises, the concept must overcome the design and sizing challenges to make it realizable. A suitable modeling framework is desired in order to explore the design space at the conceptual level. A greater investment in enabling technologies, and infrastructural developments, is expected to facilitate its successful application in the market. In this review paper, several scholarly articles were surveyed to get an insight into the current landscape of research endeavors and the formulated derivations related to electric aircraft developments. The barriers and the needed future technological development paths are discussed. The paper also includes detailed assessments of the implications and other needs pertaining to future technology, regulation, certification, and infrastructure developments, in order to make the next generation electric aircraft operation commercially worthy.

Providing access to clean, reliable, and affordable energy by adopting hybrid power systems is important for countries looking to achieve their sustainable development goals. This paper presents an optimization method for sizing a hybrid system including photovoltaic (PV), wind turbines with a hydroelectric pumped storage system. In this paper, the implementation of different optimization techniques has been investigated to achieve optimal sizing of grid-connected hybrid renewable energy systems. A comprehensive study has been carried out between Whale Optimization Algorithm (WOA), Water Cycle Algorithm (WCA), Salp Swarm Algorithm (SSA), and Grey Wolf optimizer (GWO) to validate each one. Moreover, the optimal sizing of the system’s components has been studied using real-time information and meteorological data of Ataka region located in Egypt. The purpose of the optimization process is to minimize the cost of energy from this hybrid system while satisfying the operation constraints including high reliability of the hybrid power supply, small fluctuation in the energy injected to the grid, and high utilization of the photovoltaic and wind complementary properties. MATLAB software package has been used to evaluate each optimization algorithm for solving the considered optimization problem. Simulation results proved that WOA has the most promising performance over other techniques.

A hydrogen energy storage system (HESS) is one of the many rising modern green innovations, using excess energy to generate hydrogen and storing it for various purposes. With that, there have been many discussions about commercializing HESS and improving it further. However, the design and sizing process can be overwhelming to comprehend with various sources to examine, and understanding optimal design methodologies is crucial to optimize a HESS design. With that, this review aims to collect and analyse a wide range of HESS studies to summarise recent studies. Two different collections of studies are studied, one was sourced by the main author for preliminary readings, and another was obtained via VOSViewer. The findings from the Web of Science platform were also examined for a more comprehensive understanding. Major findings include the People’s Republic of China has been active in HESS research, as most works and active organizations originate from this country. HESS has been mainly researched to support power generation and balance load demands, with financial analysis being the common scope of analysis. MATLAB is a common tool used for HESS design, modelling, and optimization as it can handle complex calculations. Artificial neural network (ANN) has the potential to be used to model the HESS, but additional review is required as a form of future work. From a commercialization perspective, pressurized hydrogen tanks are ideal for hydrogen storage in a HESS, but other methods can be considered after additional research and development. From this review, it can be implied that modelling works will be the way forward for HESS research, but extensive collaborations and additional review are needed. Overall, this review summarized various takeaways that future research works on HESS can use.

This study presents an optimization approach for sizing photovoltaic (PV) and battery energy storage systems (BESSs) within a DC microgrid, aiming to enhance cost-effectiveness, energy reliability, and environmental sustainability. PV generation is modeled based on environmental parameters such as solar irradiance and ambient temperature, while battery charging and discharging operations are managed according to real-time demand. A simulation framework is developed in MATLAB 2021b to analyze PV output, battery state of charge (SOC), and grid energy exchange. For demand-side management, the Long Short-Term Memory (LSTM) deep learning model is employed to forecast future load profiles using historical consumption data. Moreover, a Multi-Layer Perceptron (MLP) neural network is designed for comparison purposes. The dynamic load prediction, provided by LSTM in particular, improves system responsiveness and efficiency compared to MLP. Simulation results indicate that optimal sizing of PV and storage units significantly reduces energy costs and dependency on the main grid for both forecasting methods; however, the LSTM-based approach consistently achieves higher annual savings, self-sufficiency, and Net Present Value (NPV) than the MLP-based approach. The proposed method supports the design of more resilient and sustainable DC microgrids through data-driven forecasting and system-level optimization, with LSTM-based forecasting offering the greatest benefits.