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| Accurate Regulation Method for Virtual Power Plants Considering Static Voltage Load Characteristics |
| Tian Jiyuan1, Fan Shuai1, Yang Dongyang2, Huang Renke3, He Guangyu1 |
1. Key Laboratory of Control of Power Transmission and Conversion Ministry of Education Shanghai Jiao Tong University Shanghai 200240 China;
2. Electric Power Science Research Institute of Hainan Power Grid Corporation Haikou 570105 China;
3. Global Institute of Future Technology Shanghai Jiao Tong University Shanghai 200240 China |
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Abstract Virtual power plant (VPP) serves as an effective mechanism for exploiting flexibility from demand-side resources in modern power systems. Current research typically categorizes demand-side resources into controllable and uncontrollable types, treating the latter as parameters independent of decision variables in optimization models. However, loads inherently possess static voltage characteristics, meaning voltage variations induced by VPP regulation inevitably affect the actual power consumption of uncontrollable resources. This phenomenon causes difficulties in achieving accurate VPP regulation and limits its application scope to simple scenarios such as peak shaving.
This study proposes an accurate regulation method for VPPs that accounts for the static voltage load characteristics. The objective is to overcome the limitations of conventional approaches where uncontrollable resources are inaccurately modeled, leading to suboptimal regulation performance. The research addresses a critical gap in VPP operation strategies by recognizing that uncontrollable loads should not be considered as fixed parameters but as functions related to decision variables through network voltage profiles.
The methodology involves three main components. First, a comprehensive VPP operation model is developed that incorporates the topology of the low-voltage distribution network within the park area. This model explicitly represents how uncontrollable loads passively respond to voltage changes caused by control actions on other resources. Second, to address the computational challenges introduced by load static voltage characteristics, a model linearization method and a measurement feedback-based model calibration mechanism are proposed. These approaches enable efficient online solution of the optimization problem while maintaining modeling accuracy. Third, an integrated control framework is established that minimizes adjustment utility losses while meeting regulation targets. This framework optimizes the dispatch of various controllable resources including distributed generators, energy storage systems, and flexible loads, while accounting for their impact on uncontrollable loads through voltage variations.
Results from case studies demonstrate that the proposed method significantly improves regulation accuracy compared to conventional approaches. Specifically, compared to traditional methods that treat loads as rigid in control strategies, the proposed method reduces the average tracking relative error from 4.82% to 0.78%. Additionally, the adjustment utility loss is reduced by 35% compared to the energy storage droop control correction strategy based on measurement feedback. The advantages of the proposed method become more pronounced as the load static voltage characteristics in the network become more significant. Under high equivalent conservation voltage reduction (CVR) coefficients, the proposed method maintains an average tracking relative error of only 0.09%, while traditional methods reach 5.74%. Even under low equivalent CVR coefficients, the proposed method still maintains a tracking error of 2.22%. The method demonstrates good adaptability by maintaining stable tracking performance under various regulation depths and tracking accuracy requirements.
The proposed methodology resolves limitations of current VPP implementations that typically offer only single-scenario applications such as peak shaving. By enabling more precise control, this approach establishes a foundation for VPPs to provide multiple types of regulation services including load following, automatic generation control, and operating reserves. The method enhances the overall efficiency and reliability of VPP operations in distribution networks with high penetration of demand-side resources.
This research primarily focuses on the impact mechanism of loads with static voltage characteristics on VPP regulation accuracy, particularly their passive voltage response characteristics. The current study has several limitations that warrant future research. The control period is relatively short, thus neglecting the constraints from energy storage state of charge (SOC). In longer control periods or normalized control scenarios, the temporal and spatial coupling constraints from energy storage SOC cannot be ignored. More complex distributed energy resources (DERs) models would introduce new complexities to the control method. For instance, considering the actual curtailment costs of photovoltaic (PV) systems would require addressing uncertainties in PV forecasting through methods such as Monte Carlo simulation. Additionally, implementing continuous data collection and coordinated control of various dispersed resources throughout the network poses new challenges to resource measurement systems and control systems. Future research should focus on VPP precise control methods that consider longer time periods, more DER types, more accurate DER models, finer granularity, and collaborative optimization of flexible loads with static voltage characteristics.
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Received: 04 July 2024
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2025, Vol. 40 
