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Optimization of microgrids short term operation based on an enhanced genetic algorithm

: Nemati, M.; Bennimar, K.; Tenbohlen, S.; Tao, Liang; Mueller, H.; Braun, M.


Institute of Electrical and Electronics Engineers -IEEE-; Institute of Electrical and Electronics Engineers -IEEE-, Power & Energy Society -PES-:
IEEE PowerTech Eindhoven 2015 : June 29 - July 2, 2015, Eindhoven
Piscataway, NJ: IEEE, 2015
ISBN: 978-1-4799-7695-9 (Print)
ISBN: 978-1-4799-7692-8 (USB)
ISBN: 978-1-4799-7693-5 (Online)
PowerTech Conference <2015, Eindhoven>
Fraunhofer IWES ()

This paper outlines the optimization problem of unit commitment (UC) and economic dispatch (ED) in microgrids (MG). An improved real coded genetic algorithm (GA) has been developed to schedule the active and reactive powers of integrated controllable generators, battery storage systems (BSS) and shiftable loads in the system.
In the proposed GA method, both network restrictions (voltages and loadings) and unit constraints have been considered, and minimization of the operation costs and pollutant treatment costs have been formulated as objective functions. For network analysis, comprehensive load flow calculations based on Matpower program are conducted in the optimization process. The proposed GA method features a highly flexible set of sub-functions, intelligent convergence behavior, as well as diversified searching approaches and penalty methods for constraint violations. Moreover, a new Li-Ion BSS model with an event-driven ageing behavior has been introduced to the GA structure. In the end, the performance and effectiveness of the developed GA method is verified by a number of optimization study cases applied to a typical test microgrid. The simulation results have demonstrated the capabilities of the optimizer to detect feasible global optimal solution for microgrid UC and ED problem.