PSO-BP-Based Optimal Allocation Model for Complementary Generation Capacity of the Photovoltaic Power Station

To improve the operation efficiency of the photovoltaic power station complementary power generation system,an optimal allocation model of the photovoltaic power station complementary power generation capacity based on PSO-BP is proposed.Particle Swarm Optimization and BP neural network are used to establish the forecasting model,the Markov chain model is used to correct the forecasting error of the model,and the weighted fitting method is used to forecast the annual load curve,to complete the optimal allocation of complementary generating capacity of photovoltaic power stations.The experimental results show that thismethod reduces the average loss of photovoltaic output prediction,improves the prediction accuracy and recall rate of photovoltaic output prediction,and ensures the effective operation of the power system.

Non-dominated sorting culture differential evolution algorithm for multi-objective optimal operation of Wind-Solar-Hydro complementary power generation system

Due to the intermittency and instability of Wind-Solar energy and easy compensation of hydropower, this study proposes a Wind-Solar-Hydro power optimal scheduling model. This model is aimed at maximizing the total system power generation and the minimum ten-day joint output. To effectively optimize the multi-objective model, a new algorithm named non-dominated sorting culture differential evolution algorithm(NSCDE) is proposed. The feasibility of NSCDE was verified through several well-known benchmark problems. It was then applied to the Jinping Wind-Solar-Hydro complementary power generation system. The results demonstrate that NSCDE can provide decision makers a series of optimized scheduling schemes.

Coordinative Optimization of Hydro-photovoltaic-wind-battery Complementary Power Stations

The depletion of fossil energy and the deterioration of the ecological environment have severely restricted the development of the power industry.Therefore,it is extremely urgent to transform energy production methods and vigorously develop renewable energy sources.It is therefore important to ensure the stability and operation of a large multi-energy complementary system,and provide theoretical support for the world’s largest single complementary demonstration project with hydro-wind-PV power-battery storage in Qinghai Province.Considering all the multiple power supply constraints,an optimization scheduling model is established with the objective of minimizing the volatility of output power.As particle swarm optimization(PSO)has a problem of premature convergence and slow convergence in the latter half,combined with niche technology in evolution,a niche particle swarm optimization(NPSO)is proposed to determine the optimal solution of the model.Finally,the multiple stations’coordinated operation is analyzed taking the example of 10 million kilowatt complementary power stations with hydropower,wind power,PV power,and battery storage in the Yellow River Company Hainan prefecture.The case verifies the rationality and feasibility of the model.It shows that complementary operations can improve the utilization rate of renewable energy and reduce the impact of wind and PV power’s volatility on the power grid.