摘要
为了给大用户构建合适的购电方案,首先分析大用户在不同购电途径中的购电成本,建立大用户的购电模型。接着引入风险价值方法(VaR方法)对建立的购电模型进行优化,同时考虑风险偏好,用风险系数表示大用户对风险的喜好程度。最后用改进的粒子群算法进行求解,得到大用户的最优购电策略。以制订未来某个月购电方案为例,在MATLAB上进行仿真分析,证明上述方法的可行性,得出最优购电策略和购电量随风险系数变化曲线。结果表明,随着风险系数的增大,大用户的主要购电途径逐渐从现货市场向中长期合约市场和期权市场转变。
In order to build a suitable power purchase scheme for large users,the power purchase cost of large users in different ways should be firstly analyzed,and then the power purchase model of large users can be established.After that,the value at risk(VaR)method was introduced to optimize the power purchase model,so as to consider the risk preference.At the same time,the risk coefficient was used to express the risk preference of large users.Finally,the improved particle swarm optimization algorithm was used to solve the problem,and the optimal power purchase strategy of large users was obtained.Taking the power purchase plan for a certain month in the future as an example,the feasibility of the above method was proved by simulation on MATLAB.The results show that the optimal power purchase strategy and the change curve of power purchase with risk factor are obtained.The final conclusion is that with the increase of risk coefficient,the main power purchase channels of large consumers will gradually turnabout from spot market to medium and long-term contract market and option market.
作者
张杰
薛太林
闫祥东
解张超
Zhang Jie;Xue Tailin;Yan Xiangdong;Xie Zhangchao(School of Electric Power,Civil Engineering and Architecture,Shanxi University,Taiyuan Shanxi 030013,China)
出处
《电气自动化》
2022年第3期38-40,43,共4页
Electrical Automation
关键词
大用户购电模型
风险价值
风险系数
改进粒子群算法
购电策略
large user power purchase model
value at risk
risk coefficient
improved particle swarm optimization algorithm
power purchase strategy
