| Abstract: |
| This paper presents a day-ahead optimal energy management strategy for economic operation of industrial microgrids
with high-penetration renewables under both isolated and grid-connected operation modes. The approach is based on a
regrouping particle swarm optimization (RegPSO) formulated over a day-ahead scheduling horizon with one hour time
step, taking into account forecasted renewable energy generations and electrical load demands. Besides satisfying its local
energy demands, the microgrid considered in this paper (a real industrial microgrid, “Goldwind Smart Microgrid System”
in Beijing, China), participates in energy trading with the main grid; it can either sell power to the main grid or buy from
the main grid. Performance objectives include minimization of fuel cost, operation and maintenance costs and energy
purchasing expenses from the main grid, and maximization of financial profit from energy selling revenues to the main
grid. Simulation results demonstrate the effectiveness of various aspects of the proposed strategy in different scenarios. To
validate the performance of the proposed strategy, obtained results are compared to a genetic algorithm (GA) based
reference energy management approach and confirmed that the RegPSO based strategy was able to find a global
optimal solution in considerably less computation time than the GA based reference approach. |
| Key words: Energy management, Genetic algorithm, Microgrid, Regrouping particle swarm optimization, Renewableenergy |
| DOI：10.1186/s41601-017-0040-6 |
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