Abstract
The smart grid plays a vital role in decreasing electricity cost through Demand Side Management (DSM). Smart homes, a part of the smart grid, contribute greatly to minimizing electricity consumption cost via scheduling home appliances. However, user waiting time increases due to the scheduling of home appliances. This scheduling problem is the motivation to find an optimal solution that could minimize the electricity cost and Peak to Average Ratio (PAR) with minimum user waiting time. There are many studies on Home Energy Management (HEM) for cost minimization and peak load reduction. However, none of the systems gave sufficient attention to tackle multiple parameters (i.e., electricity cost and peak load reduction) at the same time as user waiting time was minimum for residential consumers with multiple homes. Hence, in this work, we propose an efficient HEM scheme using the well-known meta-heuristic Genetic Algorithm (GA), the recently developed Cuckoo Search Optimization Algorithm (CSOA) and the Crow Search Algorithm (CSA), which can be used for electricity cost and peak load alleviation with minimum user waiting time. The integration of a smart Electricity Storage System (ESS) is also taken into account for more efficient operation of the Home Energy Management System (HEMS). Furthermore, we took the real-time electricity consumption pattern for every residence, i.e., every home has its own living pattern. The proposed scheme is implemented in a smart building; comprised of thirty smart homes (apartments), Real-Time Pricing (RTP) and Critical Peak Pricing (CPP) signals are examined in terms of electricity cost estimation for both a single smart home and a smart building. In addition, feasible regions are presented for single and multiple smart homes, which show the relationship among the electricity cost, electricity consumption and user waiting time. Experimental results demonstrate the effectiveness of our proposed scheme for single and multiple smart homes in terms of electricity cost and PAR minimization. Moreover, there exists a tradeoff between electricity cost and user waiting.
Highlights
Growing electricity cost and environmental pollution minimization are two increasing worldwide problems [1]
We propose a novel Demand Side Management (DSM) approach for residential consumers to tackle the home appliances’ scheduling problem, which is based on meta-heuristic techniques, which are implemented in an energy management controller; an embedded system
With the invention of the smart grid, DSM and Demand Response (DR) are the key enabling technologies for the equilibrium of electricity demand and electricity supply, which both enhance the stable operation of the electric grid and minimize the electricity cost for end consumers
Summary
Growing electricity cost and environmental pollution minimization are two increasing worldwide problems [1]. There are two types of DR programs; (i) the first one is the incentive-based DR programs [5], where the home appliances are wirelessly shifted to the OFF state by the utility with a short notice when a peak is created in any time interval; (ii) price-based DR programs comprise another type of DR program where the electricity provider motivates the consumers to manage their home appliances in an efficient way for cost reduction, minimization of electricity consumption and PAR [6]. Consumers minimize the electricity cost and PAR, which is beneficial for the utility company. We propose a novel DSM approach for residential consumers to tackle the home appliances’ scheduling problem, which is based on meta-heuristic techniques, which are implemented in an energy management controller; an embedded system.
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