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Summary: As the uncertainty of renewable energy output brings more and more risks to the day‐ahead dispatch of the power grid, an optimization scheduling strategy of a smart energy system based on improved master‐slave game model is proposed. Risk factors related to the uncertainties of renewable energy are introduced into the master‐slave game model. Taking the smart energy system as the leader and the end users as the follower, an optimized operation model of the smart energy system based on the improved master‐slave game model is established, which is transformed into a single‐layer linear programming model according to the Karush‐Kuhn‐Tucher conditions and the duality theorem. The benefits of the system and electric vehicle users in four application scenarios are obtained by the YALMIP algorithm and the sensitivity affecting the economics of the smart energy system is analyzed. The validity of the model is verified by a simulation analysis of actual operation data from the smart energy system in China. The simulation results show that the method proposed in this paper can increase the revenue of the smart energy system by 7%, reduce the risk cost and charging cost of electric vehicle users by 63.92% and 48.34%. [ABSTRACT FROM AUTHOR]

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]

Summary: Power‐to‐gas storage (P2GS) provides a new approach for clean energy accommodation, which could be used to support the development of the microenergy grid (MEG). This paper designs a novel structure of MEG including power sources, heating sources, and P2GS. The risk condition values (CVaR) is applied to analyze the uncertainties from wind power plant, photovoltaic power generation, and solar collectors, and a multiobjective optimal model is proposed for balancing the operation economic benefit and the risk level. Then different membership functions are selected for converting the multiobjective model into a mixed integer programming model. Finally, an industrial park in northern China was selected to verify the proposed model. The results show that (a) complementary effect exists in different clean energy sources and P2GS can convert excess energy into CH4 for power generation, which are better for achieving the power‐to‐gas‐to‐power cycle optimization effect. (b) The proposed optimal model could balance revenue and risk, rationally control its operation risk while pursuing the maximum operation benefits, which is better to achieve MEG's optimal equilibrium operation. (c) Maximum emission trade allowance could enhance MEG's enthusiasm for clean energy accommodation, and P2GS could be a feasible way for near‐zero carbon emissions. Therefore, the proposed risk aversion optimal model could increase the economic benefits and control the risks and provide decision support for decision makers. [ABSTRACT FROM AUTHOR]