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Hybrid Game Optimization Allocation of Shared Energy Storage in Smart Buildings Considering Source-Load Uncertainty
ZHANG Chen, WU Dongliang, WANG Kaisheng, LEI Xia, YANG Ning, SUN Xiaoke
Distributed Energy ›› 2025, Vol. 10 ›› Issue (5) : 30-40.
PDF(4729 KB)
PDF(4729 KB)
Hybrid Game Optimization Allocation of Shared Energy Storage in Smart Buildings Considering Source-Load Uncertainty
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.
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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