集成可再生能源和pev的安全约束机组承诺问题的最优规模

Pravin G. Dhawale, Vikram Kumar Kamboj, S. K. Bath, Chaman Verma, M. Răboacă, C. Filote, Deepak Kumar
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引用次数: 0

摘要

太阳能和pev联合发电的SCUCP的优化规模对电力系统的运行和规划至关重要。提出了一种混沌算法,在优化太阳能和电动汽车系统规模的同时,使成本最小化。该框架集成了基于出行模式和基础设施特征的太阳能发电概率预测模型和现实电动汽车充电曲线。它的目标是在考虑系统需求、成本、排放和可靠性的情况下,找到太阳能发电厂和电动汽车充电基础设施的最佳规模。混沌算法通过考虑SCUCP的复杂性,有效地解决了优化问题。该研究使用不同尺寸的基准测试系统和电力系统对所提出的方法进行了评估。结果通过确定太阳能发电厂和充电基础设施的最佳容量,从而提高系统可靠性,减少排放,并最大限度地降低成本,从而实现明智的决策。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Optimal Sizing of Security Constrained Unit Commitment Problem Integrated with Renewable Energy Sources and PEVs
Optimal sizing of SCUCP with solar energy and PEVs is crucial for electric power system operation and planning. This paper presents a Chaotic Arithmetic Optimization Algorithm to minimize cost while optimizing the sizing of solar energy and PEV systems. The proposed framework integrates probabilistic forecasting models for solar power generation and realistic EV charging profiles based on travel patterns and infrastructure characteristics. It aims to find optimal sizes for Solar power plants and EV charging infrastructure, considering system demand, cost, emissions, and reliability. The Chaotic Arithmetic Optimization Algorithm efficiently solves the optimization problem by accounting for the complexities of SCUCP. The research evaluates the proposed approach using benchmark test systems of varying dimensions and power systems. The results enable informed decision-making by identifying optimal capacities for solar power plants and charging infrastructure, leading to improved system reliability, reduced emissions, and minimized costs.
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