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A hybrid technique for residential load scheduling in smart grids demand side management
Abstract
Demand side management (DSM) and demand response (DR) are the key functions in smart grids (SGs). DR provides an opportunity to a consumer in making decisions and shifting load from on-peak hours to off-peak hours. The number of incentive base pricing tariffs are established by a utility for the consumers to reduce electricity consumption and manage consumers load in order to minimize the peak to average ratio (PAR). Throughout the world, these different pricing approaches are in use. Time of use tariff (ToU) is considered in this paper, to comparatively evaluate the performance of the heuristic algorithms; bacterial foraging algorithm (BFA), and harmony search algorithm (HSA). A hy-bridization of BFA and HSA (HBH) is also proposed to evaluate the performance parameters; such as electricity consumption cost and PAR. Furthermore, consumer satisfaction level in terms of waiting time is also evaluated in this research work. Simulation results validate that proposed scheme effectively accomplish desired objectives while considering the user comfort.
This paper proposes a new demand response scheduling framework for an array of households, which are grouped into different categories based on socio-economic factors, such as the number of occupants, family decomposition and employment status. Each of the households is equipped with a variety of appliances. The model takes the preferences of participating households into account and aims to minimize the overall production cost and, in parallel, to lower the individual electricity bills. In the existing literature, customers submit binary values for each time period to indicate their operational preferences. However, turning the appliances "on" or "off" does not capture the associated discomfort levels, as each appliance provides a different service and leads to a different level of satisfaction. The proposed model employs integer values to indicate household preferences and models the scheduling problem as a multi-objective mixed integer programming. The main thrust of the framework is that the multi-level preference modeling of appliances increases their "flexibility"; hence, the job scheduling can be done at a lower cost. The model is evaluated by using the real data provided by the Department of Energy & Climate Change, UK. In the computational experiments, we examine the relation between the satisfaction of consumers based on the appliance usage preferences and the electricity costs by exploring the Pareto front of the related objective functions. The results show that the proposed model leads to significant savings in electricity cost, while maintaining a good level of customer satisfaction.
This brief provides a detailed introduction, discussion and bibliographic review of the nature1-inspired optimization algorithm called Harmony Search. It uses a large number of simulation results to demonstrate the advantages of Harmony Search and its variants and also their drawbacks. The authors show how weaknesses can be amended by hybridization with other optimization methods. The Harmony Search Method with Applications will be of value to researchers in computational intelligence in demonstrating the state of the art of research on an algorithm of current interest. It also helps researchers and practitioners of electrical and computer engineering more generally in acquainting themselves with this method of vector-based optimization.
Smart grid is an emerging technology which is considered as an ultimate solution to meet the increasing power demand challenges. Modern communication technologies has enabled the successful implementation of smart grid, which aims at provision of demand side management mechanisms, such as demand response. In this paper, we propose residential load scheduling model for demand side management. It is assumed that electric prices are announced on day-ahead basis. The major focus of this work is to minimize consumer electricity bill at minimum user discomfort. Load scheduling is formulated as an optimization problem, and an optimal schedule is achieved by solving the minimization problem. Simulation results validate that teacher learning based optimization performs better as compared to genetic algorithm, showing comparable results with linear programming with less computational efforts. TLBO is able to obtain the desired trade-off between consumer electric bill and user discomfort.
In this paper, we comparatively evaluate the performance of home energy management controller which is designed on the basis of heuristic algorithms; genetic algorithm (GA), binary particle swarm optimization (BPSO) and ant colony optimization (ACO). In this regard, we introduce a generic architecture for demand side management (DSM) which integrates residential area domain with smart area domain via wide area network. In addition, problem formulation is carried via multiple knapsack problem. For energy pricing, combined model of time of use tariff and inclined block rates is used. Simulation results show that all designed models for energy management act significantly to achieve our objections and proven as a cost-effective solution to increase sustainability of smart grid. GA based energy management controller performs more efficiently than BPSO based energy management controller and ACO based energy management controller in terms of electricity bill reduction, peak to average ratio minimization and user comfort level maximization.
Home energy management system technology can provide a smart and efficient way of optimizing energy usage in residential buildings. This paper presents a home energy management system algorithm that monitors and controls household appliances based on time-of-use (TOU) energy pricing models while accounting for multiple inhabitants sharing a home and its appliances. This algorithm helps to manage and schedule usage by prioritizing multiple users with preferred usage patterns. Two different scenarios will be implemented to develop and test the influence of a multiple-users and load priority (MULP) algorithm on reducing energy consumption, energy cost and carbon footprint. In the first scenario, TOU pricing and different demand limits are used, while the second scenario focuses on the TOU pricing with different demand limits combined with the MULP model. Simulation results show that the combination of the MULP model and the TOU pricing leads to significant reductions in user payments and total energy consumption. Copyright © 2015 John Wiley & Sons, Ltd.
We explain the biology and physics underlying the chemotactic
(foraging) behavior of E. coli bacteria. We explain a variety of
bacterial swarming and social foraging behaviors and discuss the control
system on the E. coli that dictates how foraging should proceed. Next, a
computer program that emulates the distributed optimization process
represented by the activity of social bacterial foraging is presented.
To illustrate its operation, we apply it to a simple multiple-extremum
function minimization problem and briefly discuss its relationship to
some existing optimization algorithms. The article closes with a brief
discussion on the potential uses of biomimicry of social foraging to
develop adaptive controllers and cooperative control strategies for
autonomous vehicles. For this, we provide some basic ideas and invite
the reader to explore the concepts further
Bacterial foraging optimization algorithm: theoretical foundations, analysis, and applications
- S Das
- A Biswas
- S Dasgupta
- A Abraham
Das, S., Biswas, A., Dasgupta, S. and Abraham, A., 2009. "Bacterial foraging optimization algorithm: theoretical foundations, analysis, and applications." In Foundations of Computational Intelligence Volume 3 (pp. 23-55). Springer Berlin Heidelberg.