Alamir Nehmedo, Kamel Salah, Megahed Tamer F, Hori Maiya, Abdelkader Sobhy M
Electrical Power Engineering, Egypt-Japan University of Science and Technology, 21934, New Borg El-Arab City, Egypt.
Department of Electrical Engineering, Faculty of Engineering, Aswan University, 81542, Aswan, Egypt.
Sci Rep. 2024 Oct 8;14(1):23418. doi: 10.1038/s41598-024-72706-3.
This paper presents a multi-layer, multi-objective (MLMO) optimization model for techno-economic-environmental energy management in cooperative multi-Microgrids (MMGs) that incorporates a Demand Response Program (DRP). The proposed MLMO approach simultaneously optimizes operating costs, MMG operator benefits, environmental emissions, and MMG dependency. This paper proposed a new hybrid ε-lexicography-weighted-sum that eliminates the need to normalize or scalarize objectives. The first layer of the model schedules MMG resources with DRP to minimize operating costs (local generation and power transactions with the utility grid) and maximize MMG profit. The second layer achieves the environmental operation of the MMG, while the third layer maximizes MMG reliability. This paper also proposed a new application of a recently developed enhanced equilibrium optimizer (EEO) for solving the three-layer EM problem. In addition, the uncertainties of solar power generation, wind power generation, load demand, and energy prices are considered based on the probabilistic 2m + 1 Point estimation method (PEM) approach. Three case studies are presented to verify the proposed MLMO approach on an MMG test system. In Case I, a deterministic EM is solved to simulate the MMG as a single layer to minimize costs and maximize benefits through DRP, while Case II solves the MLMO optimization problem. Simulation results show that the proposed MLMO technique reduces environmental emissions by 2.45% and 3.5% in its optimization layer and at the final layer, respectively. The independence index is also enhanced by 2.49% and 4.8% in its layer only and as a total increase, respectively. Case III is for the probabilistic EM simulation; due to the uncertain variables effect, the mean value in this case is increased by about 2.6% over Case I.
本文提出了一种用于合作多微电网（MMG）中技术经济环境能源管理的多层多目标（MLMO）优化模型，该模型纳入了需求响应计划（DRP）。所提出的MLMO方法同时优化运营成本、MMG运营商收益、环境排放和MMG依赖性。本文提出了一种新的混合ε-字典序加权和方法，该方法无需对目标进行归一化或标量化。模型的第一层通过DRP调度MMG资源，以最小化运营成本（本地发电和与公用电网的电力交易）并最大化MMG利润。第二层实现MMG的环境运行，而第三层最大化MMG的可靠性。本文还提出了一种新的应用，即使用最近开发的增强均衡优化器（EEO）来解决三层能源管理（EM）问题。此外，基于概率2m + 1点估计方法（PEM）考虑了太阳能发电、风力发电、负荷需求和能源价格的不确定性。通过三个案例研究，在一个MMG测试系统上验证了所提出的MLMO方法。在案例I中，求解确定性EM以将MMG模拟为单层，通过DRP最小化成本并最大化收益，而案例II求解MLMO优化问题。仿真结果表明，所提出的MLMO技术在其优化层和最终层分别将环境排放降低了2.45%和3.5%。独立性指标在其层内和总体上分别提高了2.49%和4.8%。案例III用于概率EM仿真；由于不确定变量的影响，该案例中的平均值比案例I增加了约2.6%。
