摘要
市场中的电价波动给发电厂商机组检修规划带来较大风险,该文提出以天为单位的随机规划模型,用以规避电价波动影响。针对以周为单位的确定性模型的不足,构建了以规划期内发电厂商的总经济效益最优为目标函数的随机规划模型,主要包括各时段的售电效益和检修费用,考虑相关约束条件后,确定机组检修时段,将该模型由日前竞争市场模式扩展至包含双边交易的复杂市场模式。以拉丁超立方抽样–蒙特卡罗(Latin hypercube sampling MonteCarlo,LHS-MC)方法为工具,评估电价波动风险,并针对该随机模型的特点,将其分为外层和内层2个子问题,分别利用遗传算法和线性规划方法求解。该模型充分考虑电价预测的不确定性,规避了电价波动风险,降低了相应的风险损失。通过算例对比验证了本模型的有效性和可行性。
This paper proposed a day-based stochastic unit maintenance scheduling （UMS） model for power producers in power market. Its objective is to maximize the benefits over the entire scheduling periods, considering the fluctuating electricity prices. The current deterministic UMS model was discussed and the stochastic model was introduced to optimize outage scheduling. In the proposed scheme, the potential benefit of power producers mainly includes expected energy-selling profits in spot markets as well as maintenance cost at each period. It is extended from the pool-based market to a hybrid market containing bilateral contract and energy market. In order to evaluate the related risk from the uncertain energy prices, a framework for the Latin Hypercube Sampling Monte Carlo （LHS-MC） simulation was adopted, which outperformed the ordinary Monte Carlo method. According to its characteristics, the stochastic model can be decomposed into external and internal parts, solved by Genetic Algorithm （GA） and linear programming method respectively. Compared with the current models, the model takes into consideration of the influences of the uncertain electricity prices and lowers the associated risk. Numerical examples on a four-unit producer were utilized to demonstrate its usefulness and effectiveness.
出处
《中国电机工程学报》
EI
CSCD
北大核心
2009年第28期95-101,共7页
Proceedings of the CSEE
基金
国家重点基础研究专项经费项目(2004CB217905)~~
