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The problem of power abandonment and fluctuation with a high proportion of renewable energy connected to the grid can be solved by configuring energy storage. However, the cost of energy storage currently is high and it is difficult to promote and apply on a large scale. In addition, existing research focuses on planning for energy storage. The optimal capacity of energy storage in a single season ignores the impact of seasonal fluctuation in wind power and photovoltaic output on the scale of energy storage. In order to solve the above problems, an optimal allocation method for energy storage considering seasonal fluctuation of renewable energy output and load demand is proposed. The investment and operation cost of energy storage is calculated by the equivalent number of cycles. And the expected continuous discharge time is used to deal with the correlation of capacity and power. The capacity and power of energy storage are configured from the perspective of the full life cycle benefits of energy storage and the comprehensive utilization rate of wind and photovoltaic. The simulation results show that, compared with the pursuit of maximizing energy storage revenue or using a single typical day for energy storage configuration, the method proposed can not only ensure the economy of energy storage, but also further improve the utilization rate of renewable energy.

Summary: This paper considers the co‐optimization operation problem of the multiple micro energy grids (MEGs), and proposes a novel concept of the flexible boundary. Then, a three‐level co‐optimization model of reconfiguration, dispatching, and reserve is designed considering different time scales. Furthermore, an improved ant colony algorithm based on the information entropy and the chaotic search is proposed. The Shenzhen international low‐carbon park in China is selected as an example. The results show that: (a) The capacity configuration scheme considering the flexible boundary conditions could decrease the reserve capacity of power, heating, and cooling from the upper energy grid, namely, 75%, 50%, and 75%, and delay 537 × 104¥ the investment cost of the new capacity by utilizing the complementary characteristics between different MEGs. (b) The proposed three‐level co‐optimization model could establish the multi‐level optimal cooperative operation schemes of multi‐MEGs and decrease the reserve power from the upper energy grid and incentive‐based demand response. Among them, UPG provides MEG 1, MEG 2, and MEG 3 with cooling load reserve capacity reduction of 53.95, 85.44, and 38.3 kw h. The positive reserve provided by IBDR for MEG 1, MEG 2, and MEG 3 decreased by 30.53, 20.21, and 52 kW h. (c) The proposed improved ant colony algorithm can consider the operational characteristics of each energy entity in different stages of multi‐MEGs and obtain the global optimal operation scheme. Compared the traditional ant colony algorithm, the average convergence times decrease by 18% and the optimization degree of the objective value increase by 12.38%. (d) The sensitivity analysis shows the conditional value at risk method can formulate the optimal dispatching schemes of multi‐MEGs according to the risk attitudes of decision makers. When confidence degree is between 0.3 and 0.8, the decision‐makers have a high‐risk sensitivity and are more willing to avoid uncertain risks of wind power plant and photovoltaic power. Overall, the proposed model and solution algorithm could achieve the optimal operation strategy of the micro energy grids. [ABSTRACT FROM AUTHOR]