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Book ChapterDOI: 10.1007/978-81-322-2755-7_68

Demand Side Management Using Bacterial Foraging Optimization Algorithm

01 Jan 2016-pp 657-666
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.

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Topics: Smart grid (63%), Load profile (61%), Load shifting (56%) ...read more
Citations
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Proceedings ArticleDOI: 10.1109/AINA.2017.157
Samia Shah1, Rabiya Khalid1, Ayesha Zafar1, Sardar Mehboob Hussain1  +2 moreInstitutions (1)
27 Mar 2017-
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).

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Topics: Smart grid (64%), Energy management (55%), Energy consumption (55%) ...read more

6 Citations


Proceedings ArticleDOI: 10.1109/ICOMET.2018.8346369
03 Mar 2018-
Abstract: In this work, two meta-heuristic bio-inspired algorithms and our proposed hybrid technique (Bacterial foraging optimization algorithm (BFA) and BAT algorithm (BA) (HBB)) are proposed for optimizing and scheduling the appliances of residential consumers. BFA, BA and our proposed technique HBB are used for scheduling the appliances in order to find the optimal solution. Appliances have different power ratings and power consumption patterns. Three different operational time intervals of 5, 30 and 60 minutes are taken in this work and their comparison is carried out. Eighteen appliances are considered and they are classified into three categories: interruptible, non-interruptible and base load appliances. Single home scenario is considered in this work. Results show that proposed technique has significantly reduced electricity cost and peak-to-average ratio. Consumers have not only supposed to pay less electricity bill, however, utilities also have to bear less stress especially in on-peak hours.

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Topics: Bat algorithm (55%)

4 Citations


Proceedings ArticleDOI: 10.1109/IPACT.2017.8245143
01 Apr 2017-
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.

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Topics: Demand response (65%), Load profile (62%), Load management (61%)

4 Citations


Open accessJournal ArticleDOI: 10.4314/IJEST.V13I2.1
Abstract: The paradigm shifts in the electrical industry from demand-driven generation to supply-driven generation due to the incorporation of renewable generating sources is a growing research field. Implementing demand response in present-day distribution schemes is anattractive approach often adopted by microgrid (MiG) operator.This paper incorporates an incentivebased demand response (IBDR) method in a grid-connected microgrid (MiG) comprising of conventional generators (CGs), wind turbines (WTs), and solar PV units. The main aim is to collectively minimize the fossil fuel cost of CGs, lower the transaction cost of portable power from the grid, and maximize theMiG operator's profitafter implementing demand response. This multi-objective problem combining optimal economic load dispatch of MiG with an efficient demand-side response is solved using a proposed Quasi-opposed Grey Wolf Optimizer (QOGWO) algorithm. The effect of the proposed algorithm on demand-side management (DSM) is analyzed for two cases, (i) varying the value of power interruptibility (ii) varying the maximum limit of curtained power. Performance of QOGWO is compared with original GWO and a variant of GWO, Intelligent Grey Wolf Optimizer (IGWO). Results show the superior global search capability and complex constrained handling capability of QOGWO.

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Topics: Microgrid (57%), Demand response (57%), Wind power (52%)

1 Citations


References
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Open accessBook
01 Jan 1997-
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

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2,434 Citations


Journal ArticleDOI: 10.1109/TSG.2012.2195686
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.

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Topics: Smart grid (68%), Load balancing (electrical power) (62%), Load profile (61%) ...read more

929 Citations


Journal ArticleDOI: 10.1016/J.EPSR.2010.07.019
Abstract: This paper proposes a multi-agent system for energy resource scheduling of an islanded power system with distributed resources, which consists of integrated microgrids and lumped loads. Distributed intelligent multi-agent technology is applied to make the power system more reliable, efficient and capable of exploiting and integrating alternative sources of energy. The algorithm behind the proposed energy resource scheduling has three stages. The first stage is to schedule each microgrid individually to satisfy its internal demand. The next stage involves finding the best possible bids for exporting power to the network and compete in a whole sale energy market. The final stage is to reschedule each microgrid individually to satisfy the total demand, which is the addition of internal demand and the demand from the results of the whole sale energy market simulation. The simulation results of a power system with distributed resources comprising three microgrids and five lumped loads show that the proposed multi-agent system allows efficient management of micro-sources with minimum operational cost. The case studies demonstrate that the system is successfully monitored, controlled and operated by means of the developed multi-agent system.

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Topics: Microgrid (58%), Energy market (55%), Electric power system (55%) ...read more

296 Citations


Journal ArticleDOI: 10.1109/59.667401
Kah-Hoe Ng1, Gerald B. Sheblé1Institutions (1)
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.

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Topics: Load management (64%), Load regulation (58%), Linear programming (50%) ...read more

218 Citations


Journal ArticleDOI: 10.1109/59.119246
Yuan-Yih Hsu1, Chung-Ching Su1Institutions (1)
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. >

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Topics: Economic dispatch (64%), Power system simulation (52%), Schedule (51%) ...read more

198 Citations


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