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Distributed Dual Horizon Energy Management Framework for Battery Sizing Forecasting and Market Aware Scheduling
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DOI：10.1002/ente.70537.png)
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Abstract
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The increasing integration of renewable energy, electric vehicles (EVs), and distributed energy resources in modern microgrids creates challenges for efficient, reliable, and cost-effective energy management. Existing methods often suffer from inaccurate forecasting, suboptimal battery sizing, limited market awareness, and poor distributed coordination. To address these challenges, this research proposes a distributed dual-horizon stochastic energy management framework that jointly optimizes long-term battery planning and short-term operational scheduling. The long-horizon module employs a Symbiotic mechanism-based Supernova Explosion Optimization Algorithm (SSEOA) to determine optimal battery size, considering lifecycle costs, degradation, and operational expenses. The short-horizon module uses a Quaternion-Enhanced Attention Network (QEAN) for high-accuracy forecasting of renewable generation, load demand, pricing, and battery states, enabling multi-objective adaptive optimal power flow. A distributed coordination layer leverages Interpretable Multi-Agent Reinforcement Learning integrated with Hills Ecology Optimization (IMRL-HEOA) to manage EV charging, storage, demand response, and peer-to-peer energy trading securely and scalably. Simulation results demonstrate daily cost savings up to ₹625, voltage stability within ±2%, renewable utilization above 80%, and smoother load profiles, outperforming conventional machine learning, deep learning, and reinforcement learning methods. The framework offers a holistic, resilient, and market-aware energy management solution suitable for modern microgrids.
Keywords:
battery sizing optimization
distributed dual-horizon energy management
internet of things
market-aware energy management
predictive load management
renewable energy forecasting
voltage stability
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