Open AccessRetracted : Optimal energy dispatch in microgrids with renewable energy sources and demand response
Author(s) -
Vallem V. V. S. N. Murty,
Kumar Ashwani
Publication year - 2020
Publication title -
international transactions on electrical energy systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.428
H-Index - 42
ISSN - 2050-7038
DOI - 10.1002/2050-7038.12328
Subject(s) - microgrid , diesel generator , renewable energy , demand response , energy storage , energy management , automotive engineering , state of charge , computer science , engineering , diesel fuel , electricity , power (physics) , energy (signal processing) , electrical engineering , battery (electricity) , statistics , physics , mathematics , quantum mechanics
Summary 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 (MGEM) is a challenging task for microgrid operator (MGO) for optimal energy utilization 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 stand‐alone microgrids integrated with photovoltaic, wind turbine, fuel cell, microturbine, diesel generator, and battery storage system. Techno‐economic benefits are demonstrated for the hybrid power system considering uncertainty of load and generation. So far, MGEM problem has been addressed with the aim of minimizing operating cost only. However, the issues of power losses and emission‐related objectives need to be addressed for effective MGEM. In this paper, MGEM is formulated as mixed‐integer linear programming, and a new multi‐objective solution is proposed for MGEM. Demand response is also included in the optimization problem to demonstrate its impact on optimal energy dispatch and techno‐commercial benefits. Simulation results are obtained for the optimal capacity of each component, charging/discharging scheduling, state of charge, power import from main grid, net present cost, cost of energy, initial cost, operational cost, fuel cost, and cost of emissions taking care of seasonal load variation throughout the year. The results show that CO 2 emission in stand‐alone microgrid is reduced by 41.74% compared with traditional system with diesel generator. Simulation results obtained with the proposed method has been compared with various evolutionary algorithms to verify its effectiveness.