A bio-inspired approach for probabilistic energy management of micro-grid incorporating uncertainty in statistical cost estimation

Abstract

This article presents a stochastic framework in probabilistic optimal energy management (OEM) of renewable-based microgrid (MG) incorporating uncertainty. OEM of MG is an optimization problem to optimize functional cost of energy associated with distributed generation and responsive loads. Effective coordination between power generation and consumption becomes an essential assignment in MG operation that should be done at a minimum cost and proper scheduling. Renewable energy, load demand and market bids are random which requires a computationally amenable robust uncertainty model to deal with its uncertainty effects. Hence, OEM problem has been formulated using k-point estimation method (k-PEM) based on moments of input random variable (IRV). Inherent randomness associated with renewable energy parameters ascends the MG problem to be stochastic which requires a powerful heuristic tool to obtain optimal operating schedules and cost estimation. Hence, modified glowworm swarm optimization (MOGSO), a bio-inspired algorithm based on swarm intelligence is proposed to solve optimization functions globally in both deterministic and probabilistic approaches. In order to visualize the effectiveness of proposed hybrid PEM-MOGSO method, the MG OEM problem is solved considering three different scenarios to scrutinize its robustness in manipulating optimal operating schedules and statistical cost estimates.