Electrical power systems are the lifeblood of modern civilization, providing the essential energy infrastructure that powers our homes, industries, and technologies. As our world increasingly relies on electricity, and modern power systems incorporate renewable energy sources, the challenges have become more complex, necessitating advanced forecasting and optimization to ensure effective operation and sustainability. This review paper provides a comprehensive overview of electrical power systems and delves into the crucial roles that forecasting and optimization play in ensuring future sustainability. The paper examines various forecasting methodologies from traditional statistical approaches to advanced machine learning techniques, and it explores the challenges and importance of renewable energy forecasting. Additionally, the paper offers an in-depth look at various optimization problems in power systems including economic dispatch, unit commitment, optimal power flow, and network reconfiguration. Classical optimization methods and newer approaches such as meta-heuristic algorithms and artificial intelligence-based techniques are discussed. Furthermore, the review paper examines the integration of forecasting and optimization, demonstrating how accurate forecasts can enhance the effectiveness of optimization algorithms. This review serves as a reference for electrical engineers developing sophisticated forecasting and optimization techniques, leading to changing consumer behaviors, addressing environmental concerns, and ensuring a reliable, efficient, and sustainable energy future.

Nowadays, owing to the advancement of technology, plug-in electric vehicles (PEVs) enable microgrids (MGs) to balance local generation and demand through vehicle-to-grid technology and increase MG reliability and flexibility. In this article, PEVs are considered as an energy storage system for MGs with the aim of improving the overall network operation and MGs’ individual utility rather than the efficiency and reliability of each MG. We propose a hybrid optimization approach that combines enhanced crow search algorithm (ECSA) with coalitional game theory to solve two key challenges: optimal PEV fleet scheduling and efficient power exchange between sets (MG+PEV). The algorithm operates in three stages: 1) ECSA optimizes PEV charging/discharging schedules using a novel dual-guidance position update mechanism that simultaneously moves toward promising solutions and away from poor ones, significantly improving convergence speed and solution quality compared to standard CSA through more balanced exploration-exploitation dynamics, 2) a cooperative game theory-based framework forms optimal trading coalitions among sets through auction-based power exchange, working synergistically with ECSA’s solutions to maximize system-wide benefits, and 3) the Shapley value method ensures fair profit distribution within coalitions. Comparative analysis demonstrates the algorithm’s superior performance against existing methods in reducing peak demand and operational costs while maintaining battery longevity and system stability.

In recent years, the growth of utilizing rural microgrids (RMGs) has been accompanied by various challenges. These necessitate the development of appropriate models for optimal generation in RMGs and RMGs’ coordination. In this paper, two distinct models for RMGs are presented. The first model includes an islanded rural microgrid (IRMG) and the second model consists of three RMGs that are interconnected with one another and linked to the distribution network. The proposed models take into account the uncertainty in load, photovoltaics (PVs), and wind turbines (WTs) with consideration of their correlation by using a scenario-based technique. Three objective functions are defined for optimization: minimizing operational costs including maintenance and fuel expenses, reducing voltage deviation to maintain power quality, and decreasing pollution emissions from fuel cells and microturbines. A new optimization method, namely the Improved Multi-Objective Crow Search Algorithm (IMOCSA), is proposed to solve the problem models. IMOCSA enhances the standard Crow Search Algorithm through three key improvements: an adaptive chaotic awareness probability to better balance exploration and exploitation, a mutation mechanism applied to the solution repository to prevent premature convergence, and a K-means clustering method to control repository size and increase algorithmic efficiency. Since the proposed problem is a multi-objective non-linear optimization problem with conflicting objectives, the idea of the Pareto front is used to find a group of optimal solutions. To assess the effectiveness and efficiency of the proposed models, they are implemented in two different case studies and the analysis and results are illustrated.