| 引用本文: | 芝昕雨,周 玮,李 洁,等.基于随机占优理论的虚拟电厂分布式资源聚合成本表征方法[J].电力系统保护与控制,2025,53(24):26-37.[点击复制] |
| ZHI Xinyu,ZHOU Wei,LI Jie,et al.A characterization method for distributed resources aggregation cost in virtual power plant based on stochastic dominance theory[J].Power System Protection and Control,2025,53(24):26-37[点击复制] |
|
| 摘要: |
| 随着新能源占比的不断攀升，电网灵活性不足问题凸显。虚拟电厂(virtual power plant, VPP)作为分布式资源整合管理的新途径，通过电源侧的多能互补及负荷侧的灵活互动，有效提升系统灵活调节能力。考虑分布式风光资源出力不确定性，提出了一种基于随机占优理论的虚拟电厂聚合成本风险决策方法。通过引入二阶随机占优约束管理聚合成本风险，有效解决了传统风险度量方法对分布假设依赖性强、灵活性不足的问题。基于条件风险价值(conditional value-at-risk, CVaR)风险管理模型明确了二阶随机占优约束的基准可行域。在此基础上，提出了结合夏普比率与CVaR的多指标单场景基准变量确定方法，为多元分布式资源聚合调节成本的风险决策提供参考。最后，通过11节点配电系统算例仿真分析，验证了所提风险决策方法在VPP资源调度中的有效性和优越性。 |
| 关键词: 虚拟电厂 随机占优理论 风险决策 成本函数 |
| DOI：10.19783/j.cnki.pspc.250296 |
| 投稿时间：2025-03-21修订日期：2025-04-23 |
| 基金项目:国家自然科学基金联合基金重点项目资助(U22A20223) |
|
| A characterization method for distributed resources aggregation cost in virtual power plant based on stochastic dominance theory |
| ZHI Xinyu1,ZHOU Wei1,LI Jie1,WU Siyu1,WANG Zhonghui2,ZOU Nan3 |
| (1. School of Electrical Engineering, Dalian University of Technology, Dalian 116024, China; 2. Electric Power
Dispatching and Control Center of State Grid Liaoning Electric Power Co., Ltd., Shenyang 110000, China; 3. Electric
Power Dispatching Center of State Grid Dalian Electric Power Supply Company, Dalian 116001, China) |
| Abstract: |
| With the increasing penetration of renewable energy, the issue of insufficient grid flexibility has become more prominent. As a new approach for integrating and managing distributed resources, virtual power plants (VPP) effectively enhance system flexibility by enabling multi-energy complementarity on the supply side and flexible interaction on the demand side. Considering the output uncertainty of distributed wind and solar resources, this paper proposes a VPP aggregation cost risk decision-making method based on stochastic dominance theory. By introducing second-order stochastic dominance constraints to manage aggregation cost risks, the method effectively addresses the limitations of traditional risk measurement models, which often rely heavily on distributional assumptions and lack flexibility. Based on the conditional value-at-risk (CVaR) risk management model, the feasible region for the second-order stochastic dominance constraints is clarified. On this basis, a multi-index, single-scenario benchmark variable determination method that integrates the Sharpe ratio and CVaR is proposed, providing guidance for risk-informed decision-making on the aggregation and regulation costs of heterogeneous distributed resources. Finally, simulation analysis on an 11-node distribution system verifies the effectiveness and superiority of the proposed risk decision-making method in VPP resource scheduling. |
| Key words: virtual power plant stochastic dominance theory risk decision-making cost function |
