A Review of Energy Storage Economics, Load Forecasting, and Hybrid Control Strategies for AC Microgrids in Modern Power Systems

Alshdaifat, Yaser Ibrahim Rashed; Prasad, Krishnamachar; Kilby, Jeff

doi:10.3390/electronics15122549

A Review of Energy Storage Economics, Load Forecasting, and Hybrid Control Strategies for AC Microgrids in Modern Power Systems

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Journal article

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Date

2026-06-09

Authors

Alshdaifat, Yaser Ibrahim Rashed

Prasad, Krishnamachar

Kilby, Jeff

Item type

Journal Article

Publisher

MDPI AG

Abstract

As power grids transition towards highly renewable generation on a global scale, maintaining dynamic stability is becoming a major challenge. Replacing traditional synchronous generators with inverter-based renewables strips the grid of rotational inertia, leaving active distribution networks highly vulnerable to frequency deviations and voltage spikes. To avoid expensive poles and wires upgrades, Battery Energy Storage Systems (BESS) are increasingly being deployed as Non-Network Solutions (NNS). However, the current literature reveals a distinct gap between the macro-scale economic planning of these storage assets and the micro-scale dynamic control actually required to keep the grid resilient. To address this gap, this review proposes a multi-layer deterministic synthesis framework that links physical renewable modelling, degradation-aware techno-economic planning, deterministic forecasting, and EMS dispatch through offline time-domain control validation for AC-microgrid energy storage integration. The research examines how advanced central control units within battery management systems can rigorously and jointly estimate State of Charge (SoC) and State of Energy (SoE) to ensure accurate grid-aware dispatch. Furthermore, the study explores the integration of degradation-aware economic modelling in HOMER Pro with dynamic transient control in MATLAB/Simulink R2025b, driven by hybrid metaheuristic optimization algorithms like Grey Wolf Optimizer (GWO) and Particle Swarm Optimization (PSO). This analysis demonstrates that integrating energy storage must be treated as a tightly coupled multidimensional optimization problem to successfully deliver the secure and sustainable infrastructure needed to solve the modern energy trilemma.

Keywords

0906 Electrical and Electronic Engineering, 4009 Electronics, sensors and digital hardware, energy storage systems, non-network solutions (NNS), techno-economic optimization, active distribution networks, hybrid metaheuristics, battery management systems (BMS), grid resilience