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.

考虑到由于可再生能源（RER）和电动汽车（EV）的不确定性/间歇性而引起的风险因素，本文解决了混合动力系统的最优发电调度问题。在这项工作中考虑的混合动力系统包括火力发电单元，RER，例如风能和太阳能光伏（PV）单元，电池储能系统（BESS）和电动汽车（EV）。在此，制定两个目标函数，即最小化运营成本和系统风险，以开发混合动力系统的最优调度策略。提出的方法的目的是同时最小化运营成本和系统风险水平。运营成本最小化目标包括热力发电机，风电场，太阳能光伏发电装置，电动汽车，BESS，和调整成本是由于RER和EV的不确定性。在这项工作中，条件风险价值（CVaR）被视为风险指数，由于RER和EV的间歇性，它被用于量化风险。本文的主要贡献在于它能够通过优化运营成本和风险来确定最佳发电计划。这两个目标是通过使用基于多目标的非支配排序遗传算法-II（NSGA-II）算法解决的，并用于开发帕累托最优前沿。通过使用模糊最小-最大方法可获得最佳折衷的解决方案。提议的方法已在改进的IEEE 30总线和实用的Indian 75总线测试系统上实现。获得的结果显示了在运营成本和系统风险水平之间最佳折衷的解决方案，