Multi-objective And Multi-constraint Optimization And Control Model For Joint Scheduling Of Clean Energy

China has an imbalanced energy distribution and a scarcity of per capita resources.The only way to assure national energy security and stability is to actively promote renewable energy.Processes for generating clean energy power,such as wind and solar electricity,are pollution-free and will never run out of resources,which provide benefits that thermal power cannot compare,however,it is has a great impact that randomness and fluctuation of output to load requires a continuous and stable power supply.Because of these drawbacks,the use of scenarios and modes of wind and solar power is limited.Therefore,optimizing,coordinating,and controlling the output ratio between diverse energy sources is a huge technological challenge.This paper aims at the problems of low solution accuracy,multi-objective function conflict,the low economic benefit of battery charging and discharging,and difficulty in controlling renewable energy output in multi-energy dispatching algorithms,the main achievements are as follows:(1)A combined dispatching model of wind-water-thermal based on mixed-integer linear programming is proposed to address the problems of high power generation costs,large fluctuations in thermal power output,the poor regulation ability of hydropower stations,and low accuracy of the solution algorithm in the combined dispatch of windwater-thermal.When the water storage capacity is different,the simulation reveals that the hydropower station has a significant impact on the dispatching system,and the solution efficiency of different algorithms on the same model will also be highly different.A multi-objective gray wolf algorithm based on Pareto optimality is proposed to address the conflict between objective function economic optimality and environmental optimality in multi-energy scheduling.The method of updating the position in the particle swarm is introduced into the gray wolf algorithm,and the convergence and distribution of the algorithm are tested in the standard function library,which provides more choices for decision-makers without expert experience.(2)A translational load scheduling model based on the exterior point method is suggested to address the low utilization rate of renewable energy and poor battery economics in the day-ahead optimum dispatching of wind-solar-storage-power systems.The influence of battery charge and discharge on optimal dispatching is investigated,as well as the cost of power consumption,income,renewable energy utilization rate in various periods,pricing,and dispatching modalities.The findings of the experiments reveal that the translational load model provides a flexible power supply approach that may successfully cut power costs and boost renewable energy usage rates.(3)A dual optimum scheduling model of wind-solar-thermal based on model predictive control is suggested to address the problem of poor prediction model accuracy in day-ahead optimization.Mixed-integer linear programming is used to optimize the wind-solar-thermal model for the day-ahead,and model predictive control is used to manage the power output in a short period.The findings of the experiments reveal that model predictive control can change the output of the unit in the finite time domain and dynamically follow the orders issued before dispatch,resulting in smooth output control of renewable energy units.