Impact of Divergence in BBO on Efficient Energy Strategy of Demand Side Management

Abstract

Demand Response (DR) keeps up an important character in the growth of electricity generation with transmission and distribution in the phase of advanced grid automation. For future smart grid, basically two types of Demand Side Management strategies have been used with a number of shiftable or controllable devices. In this research article, two new variants of Biogeography Based Optimization (BBO) algorithm named as Iteration Control BBO and Pandemic BBO are proposed for solving the minimization problem and definitions of these variants have been made by change in migration control � ⃗ a vector. These two variants are indirectly support the principle of No Free Lunch theorem. The beauty of these variants is that they are reported the impact of divergence in BBO on efficient energy strategy of DSM. The simulation results reported in this paper claims that the used DSM techniques acquire financial savings, while lowering and shifting peak load.

Industrial Demand-Side Management by Means of Differential Evolution Considering Energy Price and Labour Cost

“…Genetic algorithm and particle swarm optimisation are traditional ones that have been widely applied for managing electric vehicles loads [20], optimising micro-market strategies [21], and operating smart grids [22]. More advanced algorithms have been applied to DSM, such as biogeography-based optimisation and whale optimisation algorithm [23,24].…”