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To improve the consumption rate of renewable energy and ensure the stability of power and heat supply within the industrial park, a park-integrated energy system (PIES), which incorporates electric heating equipment, energy storage devices, and multiple micro sources, is established. Based on the established PIES, a price-based demand response (PDR) mechanism is introduced to establish a multi-objective optimization and scheduling model with two time scales: day-ahead scheduling and intra-day scheduling, aiming to minimize the impact on the main grid and ensure the flexibility of system operation and real-time scheduling. With the objectives of minimizing operating costs and maximizing environmental benefits for PIES, a hybrid algorithm named Non-Dominated Sorting Beluga Whale Optimization (NSBWO), which combines the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) and the Beluga Whale Optimization (BWO) algorithm is proposed to solve the optimization problem of the model. Simultaneously, an adaptive penalty function is introduced in the algorithm to handle constraint problems in optimization and additional penalty term for wind and solar curtailment is included within the constraints to improve the consumption rate of sustainable energy. The proposed NSBWO algorithm exhibits more robust search capabilities and faster convergence speed than the NSGA-II and MOPSO algorithm. Simulation results show that the proposed NSBWO algorithm can achieve low-carbon economic scheduling of the system in both long-term and short-term time scales and improve renewable energy consumption. Due to its flexibility, intra-day scheduling has lower operating costs than day-ahead scheduling.
Sun et al. (Sat,) studied this question.