| Abstract: |
| 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. |
| Key words: Microgrid energy management, Renewable energy sources, Storage system, Demand response |
| DOI：10.1186/s41601-019-0147-z |
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| Fund: |
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