Optimal energy management in a microgrid incorporating demand response using modified glowworm swarm algorithm

Abstract

Renewable energy systems (RES) are becoming more attractive owing to their credits in sustainability, low environmental degradation levels, and energy security. Consequently, microgrids (MG) are experiencing an increased penetration of RES. Effective coordination between generation and consumption becomes an essential assignment in MG energy management that should be done at a minimum operating cost and optimal scheduling. Even though RES is advantageous, their outputs are unpredictable and fluctuate. Simultaneously, load demand and market electricity prices vary randomly over time. Under such a situation, renewable energy output, load demand, and market electricity prices exhibit uncertainty and it is a real challenge to optimize the economics of MG. These uncertainties expose the complexity of MG energy management problems in estimating optimal energy schedules and operating costs. newlinePreviously, many researchers solved the MG energy management problem only through deterministic approaches. The deterministic energy management generally fails to address uncertainties in MG leading to computational inaccuracies. However, probabilistic MG energy management is essential to track the best operating points. Utilizing the appropriate uncertainty quantification method is important while dealing with uncertain variables in MG. Further, researches on demand response (DR) is motivated to utilize the flexibility of demand side resources for maximizing utility profits and it is vital to balance both supply and demand at a minimum cost under such instances. Furthermore, assessing MG energy management problem considering uncertainty requires a powerful optimization tool that should exhibit more robustness and avoid local optimal traps. newline