Litcius/Paper detail

Optimal scheduling of distribution network incorporating topology reconfiguration, BES and load response: A MILP model

Xuebo Qiao

2020CSEE Journal of Power and Energy Systems41 citationsDOIOpen Access PDF

Abstract

Distributed generation (DG) is becoming increasingly important due to the serious environmental pollution caused by conventional fossil-energy-based generation and the depletion of non-renewable energy. As the flexible resources in the active distribution network (ADN), battery energy system (BES) and responsive load (RL) are all able to assist renewable DG integration in day-ahead dispatch. In addition, the security and economic level can be well improved by adjusting network topology. Therefore, in this paper, a coordinated day-ahead scheduling method incorporating topology reconfiguration, BES optimization and load response is presented to minimize the total day-ahead operation cost in the ADN. Linearized current injection models are presented for renewable DG, RL and BES based on the linear power flow model, and an extensible linear switching operations calculation (ELSOC) method is proposed to address the network reconfiguration. Thus, a mixed integer linear programming (MILP) model is proposed to optimal coordinated operation of ADN. The correctness and effectiveness of the proposed method are demonstrated by the simulations on the modified test system. In addition, the combined scenario and Monte-Carlo method is used to handle the uncertainties of loads and DGs, and the results of different uncertainties can further verify the feasibility of the proposed model.

Topics & Concepts

Renewable energyDemand responseControl reconfigurationDistributed generationLinear programmingMathematical optimizationComputer scienceScheduling (production processes)CorrectnessInteger programmingTopology (electrical circuits)EngineeringElectricityElectrical engineeringEmbedded systemMathematicsAlgorithmOptimal Power Flow DistributionMicrogrid Control and OptimizationSmart Grid Energy Management