Day-ahead optimal bidding strategy of microgrid with demand response program considering uncertainties and outages of renewable energy resources
Day-ahead optimal bidding strategy of microgrid with demand response program considering uncertainties and outages of renewable energy resources
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DOI：
10.1016/j.energy.2019.116441
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发表时间：
2020-01-01
期刊：
影响因子：
9
通讯作者：
Basu, Mousumi
中科院分区：
文献类型：
--
作者：
Das, Saborni;Basu, Mousumi
在重构的电力市场中，由于整合了先进的智能电网技术、分布式能源、高效的能源存储系统和需求响应计划（DRPs），微电网正在成为更智能、更可靠和更经济的电力供应商。此外，MG运营商制定的更好的竞标策略，提高了MG市场参与者的利润。但是，可再生能源的高间断性及其较高的停电率使得竞价策略效率低下。为了解决这些问题，本文提出了考虑可再生能源和DRP不确定性的最优竞价策略。利用帐篷混沌映射在置信区间内以非重复和自适应的方式生成负荷情景和所有可能的可再生能源输出情景。引入了可再生能源不正确估计的储备成本和惩罚成本，以设计更稳健的投标。此外，利用CVaR标准对参与竞争性能源市场的风险进行了评估。采用混合整数非线性规划方法对投标模型进行优化。利用“随机解的值”来研究随机规划在不确定性整合投标问题中的有效性。(C) 2019 Elsevier Ltd.版权所有。
In restructured electricity markets, microgrids are becoming smarter, more reliable and more economic electricity providers with respect to the incorporation of advanced smart grid technologies, distributed energy resources, efficient energy storage systems, and demand response programs (DRPs). Moreover, better bidding strategies, prepared by MG operators, boost the profits of MG market players. But, highly intermittent nature of renewable energy resources and their higher rate of outages make bidding strategies inefficient. To solve these issues, this study suggests an optimal bidding strategy considering uncertainty of renewable energy resources and DRP based on their outage probabilities. Tent chaos mapping is used to generate load scenarios and all possible renewable power output scenarios within the confidence intervals in non-repetitive and adaptive manner. Reserve and penalty costs for incorrect estimation of renewable energies are invoked to design more robust bidding. Moreover, the risk of participation in the competitive energy market is assessed using CVaR criteria. The proposed bidding model is optimized using mixed integer nonlinear programming. 'Value of stochastic solution' is used to investigate the efficiency of the stochastic programming in uncertainty integration into the bidding problem. (C) 2019 Elsevier Ltd. All rights reserved.