A hybrid GA–LP scenario-based framework for day-ahead scheduling of PV–BES systems with profit–robustness trade-off

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Chinese explanation / 中文解读

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Original abstract

This paper proposes a scenario-based scheduling framework for a photovoltaic-battery energy storage (PV-BES) system participating in day-ahead energy arbitrage markets. The proposed framework accounts for uncertainties in both PV generation and electricity prices. A hybrid optimization approach is developed, combining a genetic algorithm (GA) with a linear programming (LP) model. The optimization problem is decomposed into two layers. In the first layer, the GA determines the BES commitment schedule, while in the second layer, a coupled LP is solved to obtain the shared BES dispatch schedule, held fixed across all scenarios, with the grid exchange power varying per scenario. A robustness weight is incorporated to control the trade-off between profitability and robustness. Solving the problem across multiple values of robustness weight yields a Pareto frontier, enabling operators to select a scheduling strategy aligned with their risk preference. A case study based on real PV generation and electricity price data is conducted to validate the effectiveness of the proposed framework. The results are further compared with those obtained from three benchmark approaches: deterministic MILP, a naive rule-based method, and classical robust optimization.

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