计及风光资源不确定性的独立型微电网容量优化配置

Capacity Optimization Configuration of Stand-Alone Microgrid Considering Uncertaintyof Wind and Solar Resources

围绕风光柴储独立型微网的容量优化配置问题展开研究,为考虑风光资源不确定性给微网带来的运行风险,提出了一种基于条件风险价值(CVaR)的微网容量随机优化配置模型。首先,利用拉丁超立方抽样方法模拟出大量风光出力场景,并用K-medoids聚类算法进行场景消减,得到特征明显、出现概率大的风光场景数据;其次,以微网系统供电可靠性为约束条件,综合考虑微网的经济性和可再生能源利用率指标,结合CVaR建立以年均综合费用最小为目标的随机优化模型;最后,采用二进制粒子群优化算法对这一整数优化模型进行求解,利用电源损失风险指标和负荷风险损失指标分别对确定性优化模型和随机优化模型得到的配置结果进行评价。仿真算例结果表明,相比确定性优化模型,所提出的随机优化模型得到的配置方案具有更强的鲁棒性,能够适应未来多种可能的运行场景。

This study focuses on the capacity optimization configuration of a wind-solar-diesel-battery standalone microgrid. To address the operational risks brought by the uncertainty of wind and solar resources, we propose a stochastic optimization configuration model for microgrid capacity based on Conditional Value at Risk(CVaR). Firstly, a large number of wind and solar output scenarios are simulated using Latin hypercube sampling, and then scenario reduction is conducted using the K-medoids clustering algorithm to obtain data on windand solar scenarios with distinct features and high occurrence probabilities. Secondly, considering the reliability ofmicrogrid system supply as a constraint, we comprehensively consider the economic indicators and renewable energy utilization rates of the microgrid, combined with CVaR, to establish a stochastic optimization model with theobjective of minimizing the average annual comprehensive cost. Finally, a binary particle swarm optimization algorithm is employed to solve this integer optimization model, and the deterministic and stochastic optimization results of the configuration are evaluated using power loss risk indicators and load loss risk indicators, respectively.Simulation results demonstrate that the proposed stochastic optimization model for configuration yields more robust solutions, capable of adapting to various potential operational scenarios in the future.