Incentive-based demand response in grid-connected microgrid using quasi-opposed grey wolf optimizer

Abstract

The paradigm shifts in the electrical industry from demand-driven generation to supply-driven generation due to the incorporation of renewable generating sources is a growing research field. Implementing demand response in present-day distribution schemes is anattractive approach often adopted by microgrid (MiG) operator.This paper incorporates an incentivebased demand response (IBDR) method in a grid-connected microgrid (MiG) comprising of conventional generators (CGs), wind turbines (WTs), and solar PV units. The main aim is to collectively minimize the fossil fuel cost of CGs, lower the transaction cost of portable power from the grid, and maximize theMiG operator's profitafter implementing demand response. This multi-objective problem combining optimal economic load dispatch of MiG with an efficient demand-side response is solved using a proposed Quasi-opposed Grey Wolf Optimizer (QOGWO) algorithm. The effect of the proposed algorithm on demand-side management (DSM) is analyzed for two cases, (i) varying the value of power interruptibility (ii) varying the maximum limit of curtained power. Performance of QOGWO is compared with original GWO and a variant of GWO, Intelligent Grey Wolf Optimizer (IGWO). Results show the superior global search capability and complex constrained handling capability of QOGWO.

Demand Response Management of a Residential Microgrid Using Chaotic Aquila Optimization

“…It helps to make proper tariff arrangements under the above two scenarios of DER arrangements. Optimum generation scheduling of a grid-connected microgrid system equipped with solar PV system, wind, and diesel generator is carried out in [8,9]. Here, incentive-based DR is incorporated to maximize operator benefit.…”

“…Among the analytical approaches, game theory [8], linear programming (LP) [11,13,34], Monte-Carlo simulation [15], and mixed integer nonlinear programming (MINLP) [31,33,34] were used to solve this problem.. The NI algorithms include particle swarm optimization (PSO) [7,17], grey wolf optimizer [9], Genetic Algorithm (GA) [10,26], Jaya algorithm [22], Artificial Bee Colony Algorithm (ABC) [26], and NSGA-III [28]. The NI technique is found to be very effective for the solution of nonlinear, discontinuous, and multi-modal functions, and it mostly provides a reasonable solution efficiently [38,39].…”

“…The battery storage system is rated its minimum (SOC min ) and maximum (SOC max ) range, to increase the lifespan; it should be operated in its specified range. The battery constraints are represented as [9],…”

“…An air conditioner is used as a thermostatically controlled appliance in this study. The building's temperature at each hour is determined as in (9) given below [7]:…”