Stochastic optimization for retailers with distributed wind generation considering demand response
Stochastic optimization for retailers with distributed wind generation considering demand response
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DOI：
10.1007/s40565-017-0368-y
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发表时间：
2018-01
影响因子：
6.3
通讯作者：
Hessam Golmohamadi;Reza Keypour
中科院分区：
文献类型：
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作者：
Hessam Golmohamadi;Reza Keypour
建立了可再生分布式发电(RDG)零售商的多阶段随机模型，以确定在竞争电力市场中存在的交易策略。利用自回归滑动平均(ARIMA)方法考虑了风电市场批发电价、用户用电量和风电发电量的不确定性。在所提出的方法中，为零售商提供了三个交易大厅来对冲不确定性。在第一阶段，零售商参与前一天的市场向客户供货，第二阶段，针对日内市场，允许零售商修改其客户的消费/RDG生产计划。由于存在不利的不确定性，特别是在可再生电力生产中，在第三阶段考虑了实时市场，以减少送电时间的不确定性。在目标函数中加入了考虑资本、运维(O&M)成本的风能资源成本函数，增加了该机制的适用性。该方法是通过条件风险价值(CVaR)方法对风险厌恶和风险承担的零售商提出的。为了研究零售策略对消费模式和消费者电费的影响，本文讨论了分时电价需求响应方案。结合分解约束和析取约束，将混合整数非线性规划问题转化为混合整数线性规划问题。最后，通过一个包含风电资源、储能系统和零售商的案例分析了该方法的有效性。
In this paper, a multi-stage stochastic model is presented for a renewable distributed generation (RDG)-owning retailer to determine the trading strategies existing in a competitive electricity market. Uncertainties associated with wholesale electricity market price, clients' consumption and power output of wind resources are considered through auto regressive integrated moving average (ARIMA) approach. In the proposed method, three trading floors are addressed for the retailer to hedge against the uncertainties. In the first stage, the retailer participates in day-ahead market to supply the clients and in the second stage, intraday market is addressed to allow the retailer to modify the schedule of its clients' consumption/RDG production. Due to unfavorable uncertainties, especially in renewable power production, real-time market is considered in the third stage to diminish the uncertainty at power delivery time. Cost function of wind resources considering capital, operation and maintenance (O&M) cost is incorporated in the objective function to increase the applicability of the mechanism. The proposed approach is formulated for risk-averse and risk-taker retailer through conditional value at risk (CVaR) approach. In order to study the impact of retail strategies on consumption pattern and consumers' electricity bills, time-of-use (TOU) demand response programs are discussed in this paper. Formulating the problem, the mixed integer non-linear programming (MILNP) problem is transformed into mixed integer linear programming (MILP) by jointly using decomposition and disjunctive constraints. Finally, a case study containing wind power resources, energy storage system and retailer is considered to analyze the proficiency of the proposed approach.