Demand Side Management Using Bacterial Foraging Optimization Algorithm
01 Jan 2016-pp 657-666
TL;DR: The proposed demand side management strategy uses load shifting technique to handle the large number of loads and bacterial foraging optimization algorithm (BFOA) is implemented to solve the minimization problem.
Abstract: Demand side management (DSM) is one of the most significant functions involved in the smart grid that provides an opportunity to the customers to carryout suitable decisions related to energy consumption, which assists the energy suppliers to decrease the peak load demand and to change the load profile. The existing demand side management strategies not only uses specific techniques and algorithms but it is restricted to small range of controllable loads. The proposed demand side management strategy uses load shifting technique to handle the large number of loads. Bacterial foraging optimization algorithm (BFOA) is implemented to solve the minimization problem. Simulations were performed on smart grid which consists of different type of loads in residential, commercial and industrial areas respectively. The simulation results evaluates that proposed strategy attaining substantial savings as well as it reduces the peak load demand of the smart grid.
TL;DR: In this paper , a review of demand-side management (DSM) for the complex residential sector considering various operational constraints, objectives, identifying various factors that affect better planning, scheduling, and management, to project the key features of various approaches and possible future research directions.
TL;DR: In this paper , the authors present a survey of residential appliance scheduling in smart homes and propose new viewpoints on appliance scheduling for smart homes based on the DSM techniques used in the literature.
Abstract: The residential sector is a major contributor to the global energy demand. The energy demand for the residential sector is expected to increase substantially in the next few decades. As the residential sector is responsible for almost 40% of overall electricity consumption, the demand response solution is considered the most effective and reliable solution to meet the growing energy demands. Home energy management systems (HEMSs) help manage the electricity demand to optimize energy consumption without compromising consumer comfort. HEMSs operate according to multiple criteria, including electricity cost, peak load reduction, consumer comfort, social welfare, environmental factors, etc. The residential appliance scheduling problem (RASP) is defined as the problem of scheduling household appliances in an efficient manner at appropriate periods with respect to dynamic pricing schemes and incentives provided by utilities. The objectives of RASP are to minimize electricity cost and peak load, maximize local energy generation and improve consumer comfort. To increase the effectiveness of demand response programs for smart homes, various demand-side management strategies are used to enable consumers to optimally manage their loads. This study lists out DSM techniques used in the literature for appliance scheduling. Most of these techniques aim at energy management in residential sectors to encourage users to schedule their power consumption in an effective manner. However, the performance of these techniques is rarely analyzed. Additionally, various factors, such as consumer comfort and dynamic pricing constraints, need to be incorporated. This work surveys most recent literature on residential household energy management, especially holistic solutions, and proposes new viewpoints on residential appliance scheduling in smart homes. The paper concludes with key observations and future research directions.
01 Apr 2017
TL;DR: Genetic Algorithm is involved to the average load profile of the devices used by the residential consumers on the hourly basis and the result achieves substantial savings in cost along with a peak load reduction.
Abstract: Load control on the consumer side is termed as Demand Response and there is a purpose and urgency to develop new technologies for achieving energy conservation and demand response. There are several DR techniques and algorithms. However, this paper features the study of residential consumer behavior and its response towards Demand Side Management using load-shifting concept. The primary objective of DR is to reduce the overall peak load demand and the operational cost. Genetic Algorithm is involved to the average load profile of the devices used by the residential consumers on the hourly basis. MatLab is used for the simulation purpose and the result achieves substantial savings in cost along with a peak load reduction.
••27 Mar 2017
TL;DR: A bio-inspired technique, binary particle swarm optimization (BPSO), is used for the optimal scheduling of appliances in a smart home and results illustrate the effectiveness of the HEMS in terms of electricity cost, demand, user comfort and peak to average ratio (PAR).
Abstract: With the advent of smart grid (SG) and the emergence of information and communication technology, smart meters, bidirectional communication, smart homes and storage systems the energy consumption patterns at the consumer premises have been revolutionized. Moreover, with the rise of renewable energy sources (RESs), storage systems and electric vehicles (EVs) a profound amelioration in the energy management systems has been observed. Home energy management systems (HEMSs) help to control, manage and optimize the energy in smart homes. In this paper, we present a HEMS using multi-agent system (MAS) for smart homes. The HEMS uses priority techniques with the integration of electrical supply system (ESS). Furthermore, a bio-inspired technique, binary particle swarm optimization (BPSO), is used for the optimal scheduling of appliances in a smart home. Simulation results illustrate the effectiveness of the HEMS in terms of electricity cost, demand, user comfort and peak to average ratio (PAR).
Cites methods from "Demand Side Management Using Bacter..."
...It has been mentioned in  that the existing DSM strategies use specific algorithms and techniques....
•01 Jan 1997
TL;DR: The Oxford University Press and the Institute of Physics have joined forces to create a major reference publication devoted to EC fundamentals, models, algorithms and applications, intended to become the standard reference resource for the evolutionary computation community.
Abstract: From the Publisher: Many scientists and engineers now use the paradigms of evolutionary computation (genetic agorithms, evolution strategies, evolutionary programming, genetic programming, classifier systems, and combinations or hybrids thereof) to tackle problems that are either intractable or unrealistically time consuming to solve through traditional computational strategies Recently there have been vigorous initiatives to promote cross-fertilization between the EC paradigms, and also to combine these paradigms with other approaches such as neural networks to create hybrid systems with enhanced capabilities To address the need for speedy dissemination of new ideas in these fields, and also to assist in cross-disciplinary communications and understanding, Oxford University Press and the Institute of Physics have joined forces to create a major reference publication devoted to EC fundamentals, models, algorithms and applications This work is intended to become the standard reference resource for the evolutionary computation community The Handbook of Evolutionary Computation will be available in loose-leaf print form, as well as in an electronic version that combines both CD-ROM and on-line (World Wide Web) acess to its contents Regularly published supplements will be available on a subscription basis
TL;DR: A heuristic-based Evolutionary Algorithm that easily adapts heuristics in the problem was developed for solving this minimization problem and results show that the proposed demand side management strategy achieves substantial savings, while reducing the peak load demand of the smart grid.
Abstract: Demand side management (DSM) is one of the important functions in a smart grid that allows customers to make informed decisions regarding their energy consumption, and helps the energy providers reduce the peak load demand and reshape the load profile. This results in increased sustainability of the smart grid, as well as reduced overall operational cost and carbon emission levels. Most of the existing demand side management strategies used in traditional energy management systems employ system specific techniques and algorithms. In addition, the existing strategies handle only a limited number of controllable loads of limited types. This paper presents a demand side management strategy based on load shifting technique for demand side management of future smart grids with a large number of devices of several types. The day-ahead load shifting technique proposed in this paper is mathematically formulated as a minimization problem. A heuristic-based Evolutionary Algorithm (EA) that easily adapts heuristics in the problem was developed for solving this minimization problem. Simulations were carried out on a smart grid which contains a variety of loads in three service areas, one with residential customers, another with commercial customers, and the third one with industrial customers. The simulation results show that the proposed demand side management strategy achieves substantial savings, while reducing the peak load demand of the smart grid.
TL;DR: In this article, a multi-agent system for energy resource scheduling of an islanded power system with distributed resources, which consists of integrated microgrids and lumped loads, is proposed.
TL;DR: In this paper, profit-based load management is introduced to examine generic direct load control scheduling, based upon the cost/market price function, the approach aims to increase the profit of utilities.
Abstract: Conventional cost-based load management ignores the rate structure offered to customers. The resulting cost savings may cause revenue loss. In a deregulated power industry where utilities absorb the ultimate consequence of their decision making, reexamination of load management must be conducted. In this paper, profit-based load management is introduced to examine generic direct load control scheduling. Based upon the cost/market price function, the approach aims to increase the profit of utilities. Instead of determining the amount of energy to be deferred or to be paid back, the algorithm controls the number of groups power customer/load type to maximize the profit. In addition to the advantage of better physical feel on how the control devices should operate, the linear programming algorithm provides a relatively inexpensive and powerful approach to the scheduling problem.
TL;DR: In this paper, an approach based on dynamic programming is presented for the dispatch of direct load control (DLC) for the Taiwan power system, where the objective is to coordinate DLC strategies with system unit commitment such that the system production cost is minimized.
Abstract: An approach based on dynamic programming is presented for the dispatch of direct load control (DLC). The objective is to coordinate DLC strategies with system unit commitment such that the system production cost is minimized. To achieve this goal, the DLC dispatch is first integrated into the unit commitment problem. An optimization technique based on dynamic programming is then developed to reach the optimal DLC dispatch strategy and system generation schedule. To demonstrate the effectiveness of the approach, results from a sample study performed on the Taiwan power system are described. >