Techno-economic performance assessment of large-scale distribution network supported by renewable sources and compressed air energy storages considering uncertainties and demand response

IF 4.9 3区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Computers & Electrical Engineering Pub Date : 2025-08-04 DOI:10.1016/j.compeleceng.2025.110600

Ahmed T. Hachemi , Mohamed Hashem , Abdelhakim Saim , Imen Ben Hamida , Medhat E.M. Ali , Francisco Jurado , Mohamed Ebeed

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引用次数: 0

Abstract

The increasing integration of renewable distributed generations (RDGs) has made energy management (EM) in distribution networks (DNs), along with energy storage systems (ESSs), critically important. This study proposes a probabilistic EM framework for the techno-economic assessment of large-scale DNs, focusing on voltage stability, voltage profile, and total annual cost. The framework optimally allocates and operates wind turbines (WTs), photovoltaic units (PVs), and compressed air energy storage systems (CAESs), while incorporating real-time pricing (RTP)-based demand side response (DSR) and uncertainties. These uncertainties related to solar irradiance, temperature, wind speed, load demand, and energy price are modeled using Monte Carlo Simulation. A novel Adaptive Chernobyl Disaster Optimizer (ACDO) is developed and employed to solve the multi-objective optimization problem. The robustness of ACDO is evaluated against other metaheuristic algorithms using ten benchmark functions and the Friedman test. The proposed EM strategy is validated on the IEEE 118-bus DN through three case studies: (i) RDGs only, (ii) RDGs with CAESs, and (iii) RDGs with CAESs and DSR. Results from case (iii) show an 87.1 % cost reduction, a 6.95 % improvement in voltage stability, and a 54.9 % reduction in voltage deviations. These outcomes confirm the effectiveness of the proposed ACDO-based EM in enhancing DN performance.

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考虑不确定性和需求响应的可再生能源和压缩空气储能支持的大型配电网技术经济绩效评价

可再生分布式发电（rdg）的日益整合使得配电网（DNs）中的能源管理（EM）以及储能系统（ess）变得至关重要。本研究提出了一个概率EM框架，用于大规模DNs的技术经济评估，重点是电压稳定性，电压分布和年度总成本。该框架优化分配和运行风力涡轮机（WTs）、光伏机组（pv）和压缩空气储能系统（CAESs），同时纳入基于实时定价（RTP）的需求侧响应（DSR）和不确定性。这些与太阳辐照度、温度、风速、负荷需求和能源价格相关的不确定性使用蒙特卡罗模拟进行建模。提出了一种新的自适应切尔诺贝利灾难优化器（ACDO），并将其应用于多目标优化问题。ACDO的鲁棒性通过使用10个基准函数和Friedman测试对其他元启发式算法进行评估。通过三个案例研究在IEEE 118总线DN上验证了所提出的EM策略：(i)仅RDGs，（ii）带CAESs的RDGs，以及（iii）带CAESs和DSR的RDGs。案例（iii）的结果表明，成本降低了87.1%，电压稳定性提高了6.95%，电压偏差降低了54.9%。这些结果证实了所提出的基于acdo的EM在提高DN性能方面的有效性。

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来源期刊

Computers & Electrical Engineering 工程技术-工程：电子与电气

CiteScore

9.20

自引率

7.00%

发文量

661

审稿时长

47 days

期刊介绍： The impact of computers has nowhere been more revolutionary than in electrical engineering. The design, analysis, and operation of electrical and electronic systems are now dominated by computers, a transformation that has been motivated by the natural ease of interface between computers and electrical systems, and the promise of spectacular improvements in speed and efficiency. Published since 1973, Computers & Electrical Engineering provides rapid publication of topical research into the integration of computer technology and computational techniques with electrical and electronic systems. The journal publishes papers featuring novel implementations of computers and computational techniques in areas like signal and image processing, high-performance computing, parallel processing, and communications. Special attention will be paid to papers describing innovative architectures, algorithms, and software tools.