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Risk-based optimal operation of hybrid power system using multiobjective optimization

机译：使用多目标优化的混合动力系统风险最优运行

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摘要

This paper solves an optimal generation scheduling problem of hybrid power system considering the risk factor due to uncertain/intermittent nature of renewable energy resources (RERs) and electric vehicles (EVs). The hybrid power system considered in this work includes thermal generating units, RERs such as wind and solar photovoltaic (PV) units, battery energy storage systems (BESSs) and electric vehicles (EVs). Here, the two objective functions are formulated, i.e., minimization of operating cost and system risk, to develop an optimum scheduling strategy of hybrid power system. The objective of proposed approach is to minimize operating cost and system risk levels simultaneously. The operating cost minimization objective consists of costs due to thermal generators, wind farms, solar PV units, EVs, BESSs, and adjustment cost due to uncertainties in RERs and EVs. In this work, Conditional Value at Risk (CVaR) is considered as the risk index, and it is used to quantify the risk due to intermittent nature of RERs and EVs. The main contribution of this paper lies in its ability to determine the optimal generation schedules by optimizing operating cost and risk. These two objectives are solved by using a multiobjective-based nondominated sorting genetic algorithm-II (NSGA-II) algorithm, and it is used to develop a Pareto optimal front. A best-compromised solution is obtained by using fuzzy min-max approach. The proposed approach has been implemented on modified IEEE 30 bus and practical Indian 75 bus test systems. The obtained results show the best-compromised solution between operating cost and system risk level, and the suitability of CVaR for the management of risk associated with the uncertainties due to RERs and EVs.
机译：本文解决了混合动力系统的最佳发电调度问题，考虑了由于可再生能源（RERS）和电动车辆（EVS）的不确定/间歇性质而导致的风险因素。在该工作中考虑的混合动力系统包括热发电单元，如风和太阳能光伏（PV）单元，电池储能系统（BESS）和电动车辆（EVS）。这里，制定了两个目标功能，即最小化运营成本和系统风险，以开发混合动力系统的最佳调度策略。建议方法的目的是同时最大限度地减少运营成本和系统风险水平。由于RERS和EVS中的不确定性，运营成本最小化目标由导致的热发电机，风电场，太阳能光伏单位，EVS，BESS和调整成本组成。在这项工作中，风险（CVAR）的条件价值被视为风险指数，它用于量化由于RERS和EVS的间歇性质而导致的风险。本文的主要贡献在于通过优化运营成本和风险来确定最佳发电时间表。通过使用基于多目标的NondoMinated分类遗传算法-II（NSGA-II）算法来解决这两个目标，用于开发Pareto最佳前部。通过使用模糊最小最大方法获得最佳损害的解决方案。已拟议的方法已在修改的IEEE 30总线和实用的印度75总线测试系统上实施。所获得的结果表明，运营成本和系统风险等级之间的最佳解决方案，以及CVAR对由于RES和EVS引起的不确定性相关的风险管理的适用性。

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