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| The Spot Market Improvement Mechanism and Continuous Regulation Empirical Research of Adaptive Adjustable Virtual Power Plants |
| Wang Hongbo1, Li Xiaoping1, Shao Lizheng1, Jin Tai2, Gao Hongchao3, Kang Chongqing3, Xu Tao4 |
1. State Grid Hubei Electric Power Co. Ltd Wuhan 430048 China;
2. Sichuan Energy Internet Research Institute Tsinghua University Chengdu 610213 China;
3. State Key Laboratory of Power System Operation and Control Tsinghua University Beijing 100084 China;
4. State Key Laboratory of Smart Power Distribution Equipment and System Tianjin University Tianjin 300072 China |
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Abstract The current spot market mechanisms have yet to incorporate the heterogeneity and flexibility of distributed energy resources (DER), and remain take centralized generation units and deterministic loads as the design paradigm, while the generation mechanism of quantity-price curve continues to follow the traditional bidding structure of conventional generating units, standardized participation mechanisms and bidding structures accommodating all types of virtual power plant have yet to be established. In scenarios with large-scale VPP engagement in spot markets, this mechanism causes exponential proliferation in both the dimensionality of nonlinear constraints and the scale of decision variables within market clearing models. This significantly increases computational complexity during the solution process, severely impeding widespread VPP integration and scaled development. Moreover, under prevailing spot market mechanisms, accurate electricity price forecasting constitutes a critical basis for VPPs to execute price arbitrage. When substantial prediction errors occur, VPPs incur heightened market operational risks while their provided flexibility deviates from system regulation requirements.
To address the aforementioned challenges,this paper focused on the adaptation challenges of VPPs participating in the electricity spot market mechanisms, and proposed an improved spot market mechanism that considers parameter equivalent mapping and linear clearing optimization. Firstly, based on the static decomposition method, a dynamic adjustment model of regulation reference curve that considers both price signal driving and environmental disturbance feedback was constructed, to overcome the limitations of static baseline decomposition in reflecting users' true response willingness. Secondly, considering that the current monotonic stepwise bidding mechanism is difficult to reflect the complex internal structure of VPPs, a multi-layer mapping mechanism integrating “resource aggregation-equivalent mapping-market interaction” was developed, on the basis,a parameterized representation method of “virtual energy storage” was proposed, which embedded resource configurations and operational dynamics into unified linear clearing constraints. Furthermore, an equivalent mapping model of multi-type DER response characteristics was constructed from the three dimensions of capacity, power, and efficiency constraints. Then, a bidding structure was further designed that integrates the marginal cost function of charging/discharging mileage and the final state-of-charge (SOC) value function, aiming to mitigate value distortion problems caused by conventional marginal cost-based bidding structure. Finally, an empirical analysis based on trial operation data from the Hubei Province electricity spot market was conducted, demonstrating the proposed mechanism's effectiveness in improving both the response execution rate and economic returns of VPP.
The following conclusions can be drawn from the analysis of the two-week trial operation results of VPP in Hubei Province: (1) During the bidding stage, the VPP operator has gradually established a relatively stable bidding strategy by adopting the spot market improvement mechanism proposed in this paper. (2) During the clearing stage, virtual power plants have their regulating capacity cleared every day, with the nodal clearing price ranging from 0 to 497 RMB/(MW·h). Except for Days 4 and 11, the spot prices during the midday period remained relatively low. (3) During the execution stage, the average daily execution deviation rate of VPP operators was 7.5%, and the maximum execution deviation rate did not exceed 15%.
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Received: 18 July 2025
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2026, Vol. 41 
