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Bi-level Optimal Scheduling of Wind-Solar-Hydro-Thermal Multi-Energy Complementary System Considering Pumped Storage
YAN Xin-jun, WANG Hong-xu
Journal of Changjiang River Scientific Research Institute ›› 2025, Vol. 42 ›› Issue (10) : 38-45.
PDF(6168 KB)
PDF(6168 KB)
Bi-level Optimal Scheduling of Wind-Solar-Hydro-Thermal Multi-Energy Complementary System Considering Pumped Storage
[Objective] In response to the operational challenge caused by high penetration of wind and solar power in modern power systems, this study aims to propose a bi-level optimized scheduling model for a multi-energy complementary power generation system incorporating pumped storage. The model seeks to enhance renewable energy utilization, optimize system economic performance, and improve system stability. The novelty lies in the integration of a bi-level optimization framework with a deep peak shaving strategy, while introducing CO2 emission intensity and thermal power output coefficient as evaluation indicators for multi-objective coordination of economy, environmental performance, and stability. [Methods] The upper-level model optimized the joint dispatch of wind, solar, hydro, and pumped storage with objectives of maximizing wind and solar output, minimizing net load fluctuation, and minimizing curtailed electricity. The lower-level model optimized the economic performance of the system, aiming at minimizing thermal power operational costs, pumped storage costs, and curtailed electricity penalties. Constraints included wind and solar output limits, hydro and pumped storage capacity limits, thermal unit ramping capabilities, and power balance requirements. The CPLEX solver combined with the YALMIP toolbox was employed to solve the high-dimensional nonlinear mixed-integer programming problem. CO2 emission intensity and thermal power output fluctuation coefficient were adopted as additional evaluation metrics to quantify environmental performance and system stability. [Results] Simulation results indicated that integrating pumped storage reduced total cost by 46 000 CNY (1.02%) and CO2 emission intensity by 6.4% in the summer scenario, while the thermal power output fluctuation coefficient decreased from 33.34% to 7.88%. In winter, thermal output stability improved to 7.67%. Increasing wind-solar penetration from 31.25% to 47.62% lowered system costs by 39.5% and reduced CO2 emission intensity by 58.3%. Enhancing deep peak shaving from 50% to 70% reduced total cost by 19.2% and decreased thermal power output fluctuation coefficient by 44.2%. [Conclusions] The introduction of pumped storage power station significantly enhances system flexibility, increasing renewable energy utilization by over 12% and reducing thermal unit peak regulation pressure by 50%. The bi-level optimization model ensures low-cost operation while reducing CO2 emission intensity by more than 0.1 kg/kWh and maintaining thermal power output fluctuation coefficient below 8%. A combination of high wind-solar penetration (>40%) and deep peak shaving (70%) achieves optimal comprehensive benefits, providing theoretical support for scheduling of high renewable energy penetration power systems. This study provides an innovative methodology for the design and optimization of wind-solar-hydro-thermal-pumped storage multi-energy systems, and the findings can be generalized to other clean energy bases.
optimal scheduling / multi-energy complementarity / hierarchical optimization / pumped storage / multi-objective / deep peak shaving
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Aiming at the grid connection of a high-proportion renewable energy, this paper proposes a multi-objective day-ahead optimal dispatch model for a high-proportion new energy power system including wind power, photovoltaic, thermal power and pumped storage. This model considers the operating costs of thermal power units, pollutants penalties and renewable energy curtailment under new operating conditions such as deep peak shaving and frequent ramps of thermal power units. This model takes the lowest system operating costs, the largest wind power and photovoltaic output and the smallest net load fluctuations as the optimization goals, and uses the NSGA-Ⅱ algorithm to optimize the solution. Through the simulation calculation of different scheduling scenarios on a typical day, the results show that the calculation model of the total operating costs of the system established in this paper can take into account the economy, environmental protection and consumption of the system. The multi-objective optimization scheduling strategy proposed in this paper can promote the consumption of high proportion of renewable energy, alleviate the peak shaving pressure of thermal power units, reduce the total costs of system operation, guide the thermal power flexibility transformation of power system, and ensure the safe, stable and economic operation of power system.
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Aiming at the capacity allocation of pumped-storage power stations in the association system of wind power, photovoltaic, thermal power and pumped storage, a bi-level programming of association system model is constructed and a solve loop is proposed. The upper-level model takes the minimum total curtailment of photovoltaic and wind power as the goal to determine the capacity allocation of the pumped-storage power station. The lower-level model aims to maximize the economic and environmental benefits of the association system, while improving the operating conditions of the system and solving the operation scheduling problem of pumped-storage power stations. The targets of the upper and lower-level are solved by the improved grey wolf optimizer (GWO) based on chaos optimization of Tent map. The effectiveness of the model and algorithm is verified by simulation analysis of two typical daily scenes in a certain area in winter and summer. The results show that the constructed bi-level programming model is effective for scientifically determining the capacity of pumped-storage power stations in the association system. And under the condition of satisfying the optimization of operation scheduling, it can improve the operating conditions of the system and realize the expected goal of maximizing economic and environmental benefits. |
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