Multi-objective Optimal Scheduling Of Microgrid Considering Wind And Solar Power Uncertainty And Demand Side Management

Renewable energy sources（RES）such as wind energy and solor energy has been developed rapidly because of energy transition.However,there are increasing challenges to the security and stability of energy system with the large-scale grid integration of RES.Microgrid（MG）represents a promising solution for increasing renewables.By determining the optimal generation dispatch and controlling load profile using demand side management（DSM）strategy,microgrid optimal scheduling can improve the penetration of RES and increase the profit of MG while ensuring the efficiency and reliability of the MG.Due to the uncertainty of wind and solar irradiance affected by the weather,which will reduce the reliability of microgrid scheduling,considering the uncertain factors of wind and solar,combined with DSM strategy,this paper propose a day-ahead economic/environmental stochastic optimal scheduling model,and an improved multi-objective optimization algorithm is proposed to solve the optimal model according to the characteristics of the model.First,a grid-connected microgrid including wind turbine（WT）,photovoltaic（PV）,diesel generator（DE）,micro turbine（MT）,batteries（BAT）and controllable load is constructed.The probability distribution of WT and PV is fitted according to the historical wind speed and illumination data.Monte Carlo method is used to generate scenarios for uncertain parameters including wind speed and illumination at each time of the day.And the uncertainty model of WT and PV is established based on the combination of the K-means clustering and the Simultaneous Backward Reduction（SBR）.Then,considering a DSM strategy based on load shifting technique in future smart grids,taking the access quantity of controllable devices at each time of the day as the decision variables,a multi-objective optimization model of DSM is established with maximizing renewable energy utilization,minimizing utility bills and the difference between load demand in the off-peak and peak period as the objective function and controllable devices’ demand as the constraint.The user side participates in microgrid scheduling through the DSM strategy,improved load structure.Then,taking the distributed generators’ output as the decision variables,taking economic and environmental benifits as the optimization objectives,and taking power balance constraint,generation and ramp rate constraints,battery limits,interaction constraints with the main grid and system spinning reserve as the constraints,a multi-objective optimal scheduling model of MG is established.The established uncertainty model and the optimal load are used as the input of MG dispatching.Finally,the adaptive orthogonal learning multi-objective particle swarm optimization algorithm（AOLMOPSO）proposed in this paper is used to solve the above microgrid optimal scheduling model aiming at the comprehensive optimization of economics and environmental protection.The effectiveness of the proposed approach has been analyzed for a typical microgrid test system and three case studies are developed to evalute the proposed framework: case study 1 is the basic scheduling model of MG without considering the uncertainty and DSM,case study 2 is the the scheduling model of microgrid without considering the uncertainty but considering the DSM,and case study 3 is the scheduling model of microgrid proposed by this paper considering the uncertainty and DSM.The scheduling models of microgrid and the proposed AOLMOPSO algorithm are implemented in MATLAB R2018 b.The simulation results show that compared with the model without considing the uncertainty of WT and PV,the scheduling result of the proposed scheduling model is more reliable and compared with the model without considering demand side management,the scheduling result of the proposed scheduling model has lower economic and environmental cost,higher utilization rate of renewables and more utility bills.