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<p>This research introduces a comprehensive decentralized household energy management framework that integrates dynamic user behavior, renewable energy generation, advanced storage technologies, and a DSO-coordinated peer-to-peer (P2P) trading system. Each home independently computes its energy profile, including grid imports, exports, and P2P trades, based on individual needs without influence from other homes. These forecasts are submitted to the DSO, which balances energy requests by fairly distributing available energy. The model employs mixed-integer linear programming (MILP) to minimize energy costs while maximizing user comfort and renewable energy utilization, capturing variability in photovoltaic (PV) generation and efficiency losses in energy storage systems (ESS) and electric vehicle (EV) operations. It also adapts to time-of-use (TOU) and real time pricing (RTP) schemes, optimizing schedules in response to price fluctuations. Simulation results on a 12-household community demonstrate a 29.1% reduction in total energy costs compared to the baseline, achieving €111.75 versus €157.56, while maintaining supply–demand balance under ± 10–12% uncertainties in load and PV output. The framework improves local energy efficiency and user comfort, offering a scalable, privacy-preserving solution for modern household energy management.</p>

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Robust optimization based decentralized household energy management with dynamic renewable integration and peer-to-peer trading

  • 期刊论文

  • 发表日期：

    2026年6月1日

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  • Ubaid ur Rehman

摘要

This research introduces a comprehensive decentralized household energy management framework that integrates dynamic user behavior, renewable energy generation, advanced storage technologies, and a DSO-coordinated peer-to-peer (P2P) trading system. Each home independently computes its energy profile, including grid imports, exports, and P2P trades, based on individual needs without influence from other homes. These forecasts are submitted to the DSO, which balances energy requests by fairly distributing available energy. The model employs mixed-integer linear programming (MILP) to minimize energy costs while maximizing user comfort and renewable energy utilization, capturing variability in photovoltaic (PV) generation and efficiency losses in energy storage systems (ESS) and electric vehicle (EV) operations. It also adapts to time-of-use (TOU) and real time pricing (RTP) schemes, optimizing schedules in response to price fluctuations. Simulation results on a 12-household community demonstrate a 29.1% reduction in total energy costs compared to the baseline, achieving €111.75 versus €157.56, while maintaining supply–demand balance under ± 10–12% uncertainties in load and PV output. The framework improves local energy efficiency and user comfort, offering a scalable, privacy-preserving solution for modern household energy management.