Quantum mechanics

Load profile

Engineering

Scheduling (production processes)

Power (physics)

Particle swarm optimization

Computer science

Mathematical optimization

Distributed generation

Pareto principle

Demand response

Physics

Renewable energy

Mathematics

Fuzzy logic

Energy storage

Artificial intelligence

Microgrid

Cluster analysis

Electricity

Machine learning

Electrical engineering

Electric Power Systems Research

In this paper, a new model for day-ahead optimal scheduling of microgrid in the presence of energy storage system, distributed generation resources and demand side management is proposed. The proposed model consists of two layers: the clustering layer and the planning layer. In the clustering layer, the customers of microgrid are clustered based on their consumption by C-Means algorithm. In the planning layer, the objective functions minimize the operation cost and emission pollution using column and constraint generation (C&CG) optimization algorithm. Also, the electrical demand profile is flattened by optimal shifting of electrical loads. Then, the best solution available among the non-dominated Pareto solutions is selected by the fuzzy decision-making method. Moreover, the uncertain behavior of renewable energy sources output, the stochastic modelling electrical loads and electrical energy price are modelled by Monte Carlo technique. Finally, a case study is analyzed to confirm the proposed approach. Numerical results and comparison with ɛ-constraint and particle swarm optimization approaches show the proposed model capability in scheduling a uniform microgrid operation.

Day-ahead optimal scheduling of microgrid with considering demand side management under uncertainty