Optimal Scheduling of Grid Transactive Home Demand Responsive Appliances Using Polar Bear Optimization Algorithm

Abstract

The conventional power system has been evolving towards a smart grid system for the past few decades. An integral step in successful realization of smart grid is to deploy renewable energy resources, particularly rooftop photovoltaic systems, at smart homes. With demand response opportunities in smart grid, residential customers can manage the utilization of their demand responsive appliances for getting economic benefits and incentives in return. In this regard, this paper proposes an effective home energy management system for residential customer to optimally schedule the demand responsive appliance in the presence of local photovoltaic and energy storage systems. For efficient home-to-grid energy transactions in home energy management system, the stochastic nature of photovoltaic power generation is modeled with the beta probability distribution function for solar irradiance. The main contribution of this paper is the application of polar bear optimization (PBO) method for optimally solving the scheduling problem of demand responsive appliances in home energy management system to minimize electricity consumption cost as well as peak-to-average ratio. The effectiveness of the proposed metaheuristic optimization technique is proven by performing different case studies for a residential consumer with different base load, uninterruptible deferrable, and interruptible deferrable appliances under a real-time energy price program. Comparative results with different metaheuristic techniques available in the literature show that the electricity consumption cost and peak-to-average ratio are effectively optimized using the proposed PBO algorithm.INDEX TERMS Demand response, home energy management system, metaheuristic optimization, photovoltaic generation, polar bear optimization, smart appliances.

A Novel Robust Smart Energy Management and Demand Reduction for Smart Homes Based on Internet of Energy

“…The constraints given in Equations ( 9) and ( 10) are considered for limiting the maximum charging and discharging states of energy storage system. δ b (t) is a binary variable at time t [37].…”

“…T c (t) is photovoltaic cell temperature at time t. G stc and T stc are solar irradiance and temperature at standard temperature condition (stc). NOCT is nominal operating cell temperature [37].…”

A Compressive Receding Horizon Approach for Smart Home Energy Management

“…Conversely, DR focuses on real-time grid price adjustments and incentive payments, in which demand-side load changes are promoted to reduce demand at specific periods [7]. In [8], the problem of scheduling household electrical loads was defined as an instance of a demand responsive appliance (DRA) optimal scheduling problem. In DRA problems, the operation of electrical appliances is deferred to periods of cheaper electricity or higher generation to improve certain parameters, such as electricity consumption, electricity cost, peakto-average ratio (PAR) or resident comfort.…”

“…In DRA problems, the operation of electrical appliances is deferred to periods of cheaper electricity or higher generation to improve certain parameters, such as electricity consumption, electricity cost, peakto-average ratio (PAR) or resident comfort. As noted in [8], the DRA optimal scheduling problem is often framed as a multidimensional constrained nonlinear problem. In recent literature surveys and publications [3], [8], common approaches employed within DSM and DR programs are categorised into the following four groups: (i) mathematical optimisation, (ii) heuristic-based, (iii) model-based predictive control, and (iv) machine learning approaches.…”

“…DA allocation remains the norm, with genetic algorithms [18], [19] and swarm intelligence-based techniques representing common choices. Particle swarm optimization is the most popular swarm-based heuristic [5], [20]- [22], and other include artificial bee colonies [23], polar bears [8], grey wolf optimization [24] and differential evolution [25]. Explicit multiobjective optimisation problem instances have also been addressed by evolutionary approaches; see, e.g., [26].…”