An approach of DSM techniques for domestic load management using fuzzy logic

Ravibabu, P.; Praveen, Anagha; Chandra, C; Reddy, P. Rashmi; Teja, M. Karthik Ravi

doi:10.1109/fuzzy.2009.5277401

Cited by 23 publications

(12 citation statements)

References 3 publications

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“…Only air conditioner system and water heater participated in demand response program and other house hold loads have not been considered for this model. A fuzzy logic controller is designed by Ravibabu [5] to reduce the gap between the demand and the supply of electrical energy loads in both peaks hours and off peak hours aiming to properly utilize the available power for the vital loads and power wastage can be restricted. However, by limiting the demand, the impact on the customer comfort level has not been considered.…”

Section: Related Workmentioning

confidence: 99%

An intelligent approach of achieving demand response by fuzzy logic based domestic load management

Rahman

Hettiwatte

Gyamfi

2014

2014 Australasian Universities Power Engineering Conference (AUPEC)

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Abstract-Demand response is an important demand-side resource that allows consumers to consume less electricity when the system is under stress. Existing demand response mechanism reduces power consumption by forcefully shutting down the consumers' loads or punishing the consumers with high consumption prices during high peak hours without considering their comfort level. This paper presents a methodology to design a model for domestic load management based on fuzzy logic techniques where three optimization parameters -comfort, cost and demand response are taken into account. Furthermore a comparative analysis for the power consumption and cost saving performance is carried out to show the benefit of using renewable energy sources along with a fuzzy logic based load controller. Simulation results show that the proposed controller successfully limits the power consumption during the peak hours and concurrently maximizes the savings of energy consumption cost without violating consumers' comfort level.

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Section: Related Workmentioning

confidence: 99%

An intelligent approach of achieving demand response by fuzzy logic based domestic load management

Rahman

Hettiwatte

Gyamfi

2014

2014 Australasian Universities Power Engineering Conference (AUPEC)

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“…This is how the demand side management problem is currently solved in the framework of the concept for the creation of intelligent power systems (smart grids [1], [2], [3], [4]). Projects for automated demand side management that aim to decrease peak loads in power systems have been developed and are being implementation [5], [6], [7].…”

Section: Introductionmentioning

confidence: 99%

Demand response for different power supply systems

Степанов

Suslov

Stashkevich

et al. 2014

IEEE PES Innovative Smart Grid Technologies, Europe

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Daily electric load schedules of electric power systems (EPS) are derived from the graphs of individual consumers, who are generally unbalanced. Changing the schedules of electrical loads (power consumption) gives consumers the opportunity to significantly adjust the total electrical load schedule of the EPS by reducing the need for generation capacity and current costs for the production and transmission of electricity. The authors consider the possibilities of including electricity consumption from different electricity intensive enterprises and residential consumers in the projects for demand side management in a market environment. The example of the copper smelting plant is used to describe the procedure for determining the maximum parameters of electricity demand limitation in terms of the technical capabilities caused by the necessity for an enterprise to perform its main production functions. The economic estimation of conditions under which the consumers would be interested in participating in the load management of the electric power system is presented. This paper presents a methodology for optimizing the daily schedules of active consumers and the optimization results for the three cases of consumers.

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“…C) Differential tariffs: By using different electricity tariffs, for instance during peak and off-peak hours, residential consumers can be encouraged to adapt their behavior [5]- [8]. A practical example of this is the UK's Economy 7, where during 7 hours per day (at night) the load of the residential consumer is automatically switched to another energy meter where it is charged at a lower price.…”

Section: Introductionmentioning

confidence: 99%

“…Due to this potential, DSM at the residential level (i.e., low-voltage (LV) networks) has received increased attention over the last decade [1]- [4]. Indeed, a number of DSM approaches have been proposed in the literature [5]- [10]. Some of these are summarized below:…”

Section: Introductionmentioning

confidence: 99%

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Investigating the impact of demand side management on residential customers

Zhang

Ochoa

Kirschen

2011

2011 2nd IEEE PES International Conference and Exhibition on Innovative Smart Grid Technologies

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Abstract--With the advent of smart meters and smart appliances, residential demand side management (DSM) schemes could potentially happen on a considerably larger scale. From the Distribution Network Operator perspective, intelligent control of appliances could help reduce peaks in demand and thus defer reinforcements of the low-voltage (LV) network. For the consumer, in a scenario where dynamic pricing is in place, peak reduction might also provide another strategy to reduce energy costs. However, the successful implementation of these techniques is contingent on the willingness of residential consumers to accept them. This, in turn, depends on the negative effects that this control might have on the consumers' comfort and convenience. This paper investigates the impact of a DSM scheme that shifts residential high-power appliances (loads) to reduce the overall peak of households connected to the same LV feeder. Simulations are run using high-resolution historical data for Central-East England. Results are encouraging; showing not only that the proposed DSM scheme has great potential for peak reduction, but, more importantly, that the negative impact on the consumers is minimal. These findings could be used to promote such schemes and increase public acceptance.Index Terms--Customer inconvenience, demand side management, energy efficiency, high-resolution domestic load data, residential electricity demand. I. INTRODUCTIONEMAND SIDE MANAGEMENT (DSM) is a mechanism through which the load of some customers is managed (i.e., reduced or shifted to a different time period) in response to certain conditions (e.g., price, network constraints, emergencies, etc.). As a concept, DSM is not new and has been applied by transmission and distribution network operators mainly to reduce costs or relieve dangerous system operating conditions. These activities usually involved mostly industrial and commercial customers and took place within an agreed contractual framework.With the advent of smart meters and smart appliances, residential DSM schemes open up the possibility of applying such schemes on a much larger scale. Indeed, in the UK, residential customers account for approximately 36% of the A) Load priority techniques: Domestic appliances of an individual consumer (or a group of consumers) are classified by their importance. When a given condition is reached, single loads (i.e., appliances) can be disconnected by a central management system according to the specified priority [5]- [6].B) Control of appliances: Rather than disconnecting appliances, their power requirements are reduced or their operation is delayed [7]. It has been reported that peak load reductions of up to 60% could be achieved in UK households by developing 'load-conscious' control algorithms for cooking and washing appliances [1].C) Differential tariffs: By using different electricity tariffs, for instance during peak and off-peak hours, residential consumers can be encouraged to adapt their behavior [5]-[8]. A practical example of this is the UK's Economy...

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An approach of DSM techniques for domestic load management using fuzzy logic

Cited by 23 publications

References 3 publications

An intelligent approach of achieving demand response by fuzzy logic based domestic load management

An intelligent approach of achieving demand response by fuzzy logic based domestic load management

Demand response for different power supply systems

Investigating the impact of demand side management on residential customers

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