Gene

Quantum mechanics

Algorithm

Engineering

Statistics

Pareto optimal

Robustness (evolution)

Theoretical computer science

Convergence (economics)

Distributed algorithm

Economics

Power (physics)

Biochemistry

Computer science

Mathematical optimization

Distributed generation

Distributed computing

Physics

Renewable energy

Upper and lower bounds

Graph

Mathematics

Electric power system

Mathematical analysis

Chemistry

Economic growth

Directed graph

Flexibility (engineering)

Economic dispatch

Multi-objective optimization

Electrical engineering

IEEE Transactions on Network Science and Engineering

This paper investigates the combined economic environmental dispatch problem of Microgrids in a distributed manner. In order to optimize the competing agminated operation cost and environmental impact objectives simultaneously, a distributed method which combines the distributed consensus based algorithm with dynamic weights is proposed to assign the energy among generation units, energy storage units, and load units. By this method, each unit only needs to share its local information among neighbours, thus it offers better flexibility, robustness and privacy. Besides, different from existing distributed optimization algorithms, the proposed algorithm can solve the multi-objective problem over time-varying directed communication graph with a fixed step-size. Furthermore, it is proved that the proposed algorithm can converge to the optimal solution if the step-size does not exceed some upper bound. The simulation results show that the proposed method can simultaneously coordinate the two conflicting goals of the economic and environmental aspects of Microgrids, and obtain the entire Pareto front, effectively reducing operating costs and pollutant emissions. The advantage of convergence speed is also shown compared with some distributed algorithms.

Distributed Optimal Economic Environmental Dispatch for Microgrids Over Time-Varying Directed Communication Graph