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An energy management system for stand-alone microgrid consisting of a wind turbine (WT) generator, a diesel generator, an energy storage system (ESS), and a sea water desalination system is proposed in this paper. The coordinated control of the distributed generations and ESS is researched with two operation modes. Then, a real-time rolling horizon energy management method is presented based on hour-ahead wind speed forecast. The operation mode of the microgrid system and the reference output power of WT generator are determined according to the forecasted wind speed and state of charge of the ESS, which can achieve the goal of maximizing utilization of wind energy and minimizing utilization of diesel generator on the basis of system stable operation. The proposed energy management method has been tested on the real-time digital simulator system. The results clearly verify the effectiveness of the proposed method.Index Terms-Coordinated control, desalination system, energy management system (EMS), stand-alone microgrid.

Hybrid energy storage systems (HESSs) have become an effective solution for smoothing the active power variations of photovoltaic (PV). In order to reduce the required capacities and costs of the HESS, a coordinated control scheme is developed to mitigate the power variations of a PV plant by using the HESS and the active power curtailment (APC) of PV. Furthermore, a multi-objective optimization model is established to dispatch the output power of batteries and supercapacitors, considering the overall losses and the state-of-charge (SOC) deviation of the supercapacitor. Based on the proposed smoothing strategy, an allocation model is developed to optimize the energy and power capacities of the HESS with the aim of maximizing the annual net income of the PV and HESS plant. The numerous simulations are carried out to verify the effectiveness of the proposed smoothing and allocation methods by using the real data of a PV plant. In addition, we also discuss the impacts of the different dispatching strategies of the HESS, grid requirements of power variations, and solution methods on the HESS allocation results. INDEX TERMS Hybrid energy storage system (HESS), photovoltaic (PV) power fluctuation, capacity allocation, active power curtailment.

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Large fluctuations may occur on the energy supply and the load sides when large-scale renewable energies are integrated, leading to great challenges in power systems. The renewable power curtailment is especially numerous in the integrated electricity-heat energy system (IEHES) on account of electricityheat coupling. The flexible resources (FRs) on both the energy supply and load sides are introduced into the optimal dispatch of the IEHES and further modeled to alleviate the renewable fluctuations in this paper. On the energy supply side, three kinds of FRs based on electricity-heat coordination are modeled and discussed. On the load side, the shiftable electricity demand resource is characterized. On this basis, the solution for FRs participating in IEHES dispatch is given, with goals of maximizing the renewable penetration ratio and lowering operation costs. Two scenarios are performed, and the results indicate that the proposed optimal dispatch strategy can effectively reduce the renewable energy curtailment and improve the flexibility of the IEHES. The contribution degrees of different FRs for renewable integration are also explored.

Combining an energy storage system (ESS) with a wind farm is an effective way to increase the penetration rate of wind power. ESS sizing is an important part in wind farm planning nowadays. In this paper, a basic method for determining the optimal capacity of an ESS integrated with a wind power generator to meet the requirements of grid integration is presented. With the proposed method, the necessary ESS capacity which can provide the best benefits between the regulation effects and energy storage size was calculated. The segmentation method and automatic segmentation method are proposed to improve the performance of the basic method. Further work on expanding the method to determine the necessary capacity of ESS for real-time control is studied. The time window method is used to enable the proposed method available under all working conditions. The simulation results verify the effectiveness of the proposed method.

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