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考虑源荷不确定性的智能楼宇共享储能混合博弈优化配置
Hybrid Game Optimization Allocation of Shared Energy Storage in Smart Buildings Considering Source-Load Uncertainty
为解决智能楼宇在源侧光伏出力与荷侧用电需求双重不确定性下存在的供需失衡问题，并在降低楼宇储能投资与用电成本的同时，提升共享储能系统的经济性与鲁棒性，构建了一种基于混合博弈的共享储能双层优化配置模型，其中：储能运营商与楼宇用户构成主从博弈关系，运营商作为领导者制定内部交易电价，用户作为跟随者进行需求响应；楼宇间则采用合作博弈，通过双边Shapley值法实现成本公平分摊。源荷不确定性分别采用Wasserstein距离构建光伏出力模糊集，并结合条件风险价值（conditional value at risk， CVaR）刻画负荷波动带来的投资风险。利用KKT（Karush-Kuhn-Tucker）条件将双层模型转化为单层混合整数线性规划问题进行求解。基于江苏某园区智能楼宇群的仿真表明：所提策略可有效削减储能冗余容量12.3%，降低各楼宇平均用电成本8.7%，同时提升运营商收益并缩短投资回收期；相较于传统鲁棒与确定性优化方法，在保障系统鲁棒性的同时显著提升经济性。所提出的混合博弈优化配置策略能够协同处理源荷双重不确定性，实现共享储能资源的高效利用与多方共赢，可为智能楼宇群的低碳经济运行提供有效路径。
In response to the supply-demand imbalance faced by intelligent buildings under the dual uncertainties of photovoltaic output on the source side and electricity demand on the load side， this study aims to reduce energy storage investment and electricity costs while enhancing the economic viability and robustness of shared energy storage systems. To achieve this goal， we develop a bi-level optimization model for shared energy storage based on hybrid game theory. In this model， energy storage operators and building users form a leader-follower game relationship； operators act as leaders setting internal transaction prices while users respond as followers through demand response strategies. Additionally， cooperative game theory is employed among buildings to fairly allocate costs using bilateral Shapley value methods.The uncertainty in source-load dynamics is characterized by constructing fuzzy sets for photovoltaic output using Wasserstein distance and incorporating conditional value at risk （CVaR） to depict investment risks arising from load fluctuations. The Karush-Kuhn-Tucker （KKT） conditions are utilized to transform the bi-level model into a single-layer mixed-integer linear programming problem for solution. Simulation results based on an intelligent building cluster in Jiangsu demonstrate that the proposed strategy effectively reduces redundant energy storage capacity by 12.3% and lowers average electricity costs across buildings by 8.7%， while simultaneously increasing operator profits and shortening payback periods for investments. Compared with traditional robust optimization methods and deterministic approaches， our method significantly enhances economic performance without compromising system robustness. The proposed hybrid game optimization strategy can collaboratively address dual uncertainties in sources and loads， facilitating efficient utilization of shared energy storage resources while achieving mutual benefits for all parties involved. This approach provides an effective pathway toward low-carbon operational efficiency for clusters of intelligent buildings.
智能楼宇 / 混合博弈 / 共享储能 / 需求响应 / 双边Shapley
intelligent buildings / hybrid game / shared energy storage / demand response / bilateral Shapley
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Under the guidance of the double carbon goal, the total installed capacity of new energy in China has been increasing year by year, and the problem of new energy consumption has become prominent. With the increasing reform of the electricity market, small and medium loads such as electric heating can participate in electricity market transactions through aggregation, promote source-load interaction, and promote new energy consumption. In this paper, in the day-ahead market, wind farms are used as power generators, and electric heating load aggregators are used as demand side. According to the length of thermal inertia time, the segmented bidding curves of are provided. The sorting method is used to conduct bilateral transactions of electricity prices in the trading center. The improved Shapley value method is used to comprehensively consider the user’s economy and comfort, and reasonably allocate the transaction electricity on the demand side according to the personal satisfaction. Finally, the effectiveness of the method proposed in this paper is verified by an example. The results show that the method proposed in this paper can coordinate the benefits of source and load to maximize social welfare; at the same time, it can improve the overall satisfaction of the society by taking into account the user’s comfort and economy. |
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To further promote the efficient use of energy storage and the local consumption of renewable energy in a multi-integrated energy system (MIES), a MIES model is developed based on the operational characteristics and profitability mechanism of a shared energy storage station (SESS), considering concentrating solar power (CSP), integrated demand response, and renewable energy output uncertainty. We propose a corresponding MIES model based on co-operative game theory and the CSP and an optimal allocation method for MIES shared energy storage. The model considers the maximum operating benefit of the SESS as the upper objective function and the minimum operating cost of the MIES as the lower objective function. First, the Karush–Kuhn–Tucker conditions of the lower-layer model are transformed into constraints of the upper-layer model, and the Big-M method is used to linearize the nonlinear problem and convert the two-layer nonlinear model into a single-layer linear model. Second, based on the Nash negotiation theory, the benefits of each IES in the MIES are allocated. Finally, the fuzzy chance constraints are used to relax the power balance constraints, and the trapezoidal fuzzy numbers are transformed into a deterministic equivalence class to assess the impact of renewable energy output uncertainty on system operation. The validity and rationality of the proposed two-layer model are verified through simulation, and the results demonstrate that the proposed shared storage capacity leasing model can effectively reduce the total operation cost, increase the profitability of the shared storage operator, and increase the utilization rate of the SESS.
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