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An efficient bidding strategy for selecting most economic horizon in restructured electricity market with hybrid generation and energy storage

机译:在混合发电和储能的重组电力市场中选择最经济视野的有效投标策略

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In the era of deregulation, constant efforts are being targeted towards the better power services by improving reliability, competitive power price, reduction of market power, and many more. The task of incorporating renewable energy sources while profit maximization becomes quite challenging. The uncertainty associated with them brings about a great deal of investment risk. This paper proposes a method for strategic bidding that not only helps in profit maximization but also keeps the risk factor under check. Despite using non-conventional energy; profit can still be maximized. The proposed approach is based on the Discrete-Time Markov Process (DTMDP) which helps to identify the most profitable economic zone for bidding in the power market while using hybrid generation. The system comprises of conventional and wind generation along with energy storage system. The DTMDP algorithm helps to mitigate uncertainty by learning from past scenarios. The past scenarios are hybrid generations without ESS. The proposed approach compares the present horizon with the past and decides whether to store or to dispatch energy from ESS. Any changes in the past scenario will directly impact the decision taken in the present horizon. Depending on the action taken reward is calculated. To determine the probabilistic wind generation Weibull probability distribution function has been used. The risk of each horizon has been calculated by using CVaR as risk assessment tool. The overall profit along with CVaR rating helps to decide the most profitable horizon for biding. Modified IEEE 14 bus system has been used for the case study to prove the efficacy of the proposed method.
机译:在放松管制的时代,通过提高可靠性,有竞争力的电价,减少市场支配力等等,人们一直致力于开发更好的电力服务。在利润最大化的同时整合可再生能源的任务变得非常具有挑战性。与它们相关的不确定性带来了大量的投资风险。本文提出了一种战略投标方法,该方法不仅有助于实现利润最大化,而且可以控制风险因素。尽管使用非常规能源;利润仍然可以最大化。所提出的方法基于离散马尔可夫过程(DTMDP),该过程有助于确定使用混合动力发电的电力市场中最有利可图的投标区域。该系统包括常规发电,风力发电以及能量存储系统。 DTMDP算法通过从过去的场景中学习来帮助减轻不确定性。过去的场景是没有ESS的混合世代。所提出的方法将当前范围与过去进行了比较,并确定是存储还是从ESS分配能量。过去情景中的任何更改都将直接影响当前阶段的决策。根据所采取的行动来计算奖励。为了确定概率风的产生,已经使用了威布尔概率分布函数。通过使用CVaR作为风险评估工具来计算每个范围的风险。总体利润以及CVaR评级有助于确定最有利可图的出价范围。案例研究中使用了改进的IEEE 14总线系统,以证明该方法的有效性。

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