Microgrid with hybrid renewable energy sources is a promising solution where the distribution network expansion is unfeasible or not economical. Integration of renewable energy sources provides energy security

substantial cost savings and reduction in greenhouse gas emissions

enabling nation to meet emission targets. Microgrid energy management is a challenging task for microgrid operator (MGO) for optimal energy utilization in microgrid with penetration of renewable energy sources

energy storage devices and demand response. In this paper

optimal energy dispatch strategy is established for grid connected and standalone microgrids integrated with photovoltaic (PV)

wind turbine (WT)

fuel cell (FC)

micro turbine (MT)

diesel generator (DG) and battery energy storage system (ESS). Techno-economic benefits are demonstrated for the hybrid power system. So far

microgrid energy management problem has been addressed with the aim of minimizing operating cost only. However

the issues of power losses and environment i.e.

emission-related objectives need to be addressed for effective energy management of microgrid system. In this paper

microgrid energy management (MGEM) is formulated as mixedinteger linear programming and a new multi-objective solution is proposed for MGEM along with demand response program. Demand response is included in the optimization problem to demonstrate it’s impact on optimal energy dispatch and techno-commercial benefits. Fuzzy interface has been developed for optimal scheduling of ESS. Simulation results are obtained for the optimal capacity of PV

WT

DG

MT

FC

converter

BES

charging/discharging scheduling

state of charge of battery

power exchange with grid

annual net present cost

cost of energy

initial cost

operational cost

fuel cost and penalty of greenhouse gases emissions. The results show that CO2 emissions in standalone hybrid microgrid system is reduced by 51.60% compared to traditional system with grid only. Simulation results obtained with the proposed method is compared with various evolutionary algorithms to verify it’s effectiveness.