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Proceedings ArticleDOI

Particle swarm optimization for demand side management in smart grid

TL;DR: In this paper, a new load shifting approach for demand side management in smart grid energy management is discussed, which minimizes the cost incurred by users while taking into account users' individual preferences for the loads by setting priorities and preferred time intervals for load scheduling.
Abstract: Demand side management is a useful and necessary tool in smart grid energy management system to reduce total power demand during peak demand periods and hence, enhancing grid sustainability and reducing overall cost. This paper discusses a new load shifting approach for demand side management in smart grid energy management. This approach optimizes the consumption curves of household, commercial and industrial consumers. The proposed algorithm in this approach minimizes the cost incurred by users while taking into account users' individual preferences for the loads by setting priorities and preferred time intervals for load scheduling.
Citations
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Journal ArticleDOI

100 citations


Additional excerpts

  • ...Logenthiran et al modeled a day-ahead load scheduling technique that incorporates the PSO algorithm based on the customers' inputs and forecasted hourly electricity rates.(150) In this study, the authors considered the shiftable and non-shiftable loads controlled by a central controller of the SG....

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Journal ArticleDOI
01 Apr 2019-Energy
TL;DR: Simulation results show that the proposed algorithm is able to optimize energy usage by balancing load scheduling and contribution of renewable sources, while maximizing user comfort and minimizing peak-to-average ratio.

61 citations

Journal ArticleDOI
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.

39 citations

Journal ArticleDOI
TL;DR: In this paper, an optimization tool for a general hybrid renewable energy system (HRES) is developed: it generates an operating plan over a specified time horizon of the setpoints of each device to meet all electrical and thermal load requirements with possibly minimum operating costs.

33 citations

Journal ArticleDOI
TL;DR: An objective function for the optimization problem is defined, its search space is presented, and it is proved that the search space has a magnification of at least 50 times the maximum depths of charge and discharge in an hour of the ESS.
Abstract: We present a day-ahead scheduling strategy for an Energy Storage System (ESS) in a microgrid using two algorithms - Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The scheduling strategy aims to minimize the cost paid by consumers in a microgrid subject to dynamic pricing. We define an objective function for the optimization problem, present its search space, and study its structural properties. We prove that the search space has a magnification of at least $50\times (B_{c} - B_{d} + 1)$ , where $B_{c}$ and $B_{d}$ are the maximum depths of charge and discharge in an hour (in percentage) of the ESS respectively. In a simulation involving load, energy generation, and grid price forecasts for three microgrids of different sizes, we obtain ESS schedules that provide average cost reductions of 11.31% (using GA) and 14.31% (using PSO) over the ESS schedule obtained using Net Power Based Algorithm.

31 citations


Cites background from "Particle swarm optimization for dem..."

  • ...This demand response strategy is called load shifting and multiple papers have dealt this with this topic [8]–[10]....

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References
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Proceedings ArticleDOI
06 Aug 2002
TL;DR: A concept for the optimization of nonlinear functions using particle swarm methodology is introduced, and the evolution of several paradigms is outlined, and an implementation of one of the paradigm is discussed.
Abstract: A concept for the optimization of nonlinear functions using particle swarm methodology is introduced. The evolution of several paradigms is outlined, and an implementation of one of the paradigms is discussed. Benchmark testing of the paradigm is described, and applications, including nonlinear function optimization and neural network training, are proposed. The relationships between particle swarm optimization and both artificial life and genetic algorithms are described.

35,104 citations


Additional excerpts

  • ...Particle Swarm Optimization (PSO) was first introduced by Kennedy and Eberhart in 1995 [11] as an optimization technique where a solution of a problem or a ‘particle’ is searched in swarm of a fixed number of particles....

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Journal Article
TL;DR: Depending on the type and depth of penetration of distributed energy resource units, load characteristics and power quality constraints, and market participation strategies, the required control and operational strategies of a microgrid can be significantly, and even conceptually, different than those of the conventional power systems.
Abstract: The environmental and economical benefits of the microgrid and consequently its acceptability and degree of proliferation in the utility power industry, are primarily determined by the envisioned controller capabilities and the operational features. Depending on the type and depth of penetration of distributed energy resource (DER) units, load characteristics and power quality constraints, and market participation strategies, the required control and operational strategies of a microgrid can be significantly, and even conceptually, different than those of the conventional power systems.

1,335 citations


"Particle swarm optimization for dem..." refers background in this paper

  • ...Some focuses on reducing the peak load, some on smoothening the load profile and bringing it close to the optimal load curve, and some on scheduling of the loads according to the time intervals where the power is most available and preferably the cheapest as well [3]....

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Journal ArticleDOI
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.

1,070 citations

Proceedings ArticleDOI
04 Nov 2010
TL;DR: This paper analytically model the subscribers' preferences and their energy consumption patterns in form of carefully selected utility functions based on concepts from microeconomics and proposes a distributed algorithm which automatically manages the interactions among the ECC units at the smart meters and the energy provider.
Abstract: In this paper, we consider a smart power infrastructure, where several subscribers share a common energy source. Each subscriber is equipped with an energy consumption controller (ECC) unit as part of its smart meter. Each smart meter is connected to not only the power grid but also a communication infrastructure such as a local area network. This allows two-way communication among smart meters. Considering the importance of energy pricing as an essential tool to develop efficient demand side management strategies, we propose a novel real-time pricing algorithm for the future smart grid. We focus on the interactions between the smart meters and the energy provider through the exchange of control messages which contain subscribers' energy consumption and the real-time price information. First, we analytically model the subscribers' preferences and their energy consumption patterns in form of carefully selected utility functions based on concepts from microeconomics. Second, we propose a distributed algorithm which automatically manages the interactions among the ECC units at the smart meters and the energy provider. The algorithm finds the optimal energy consumption levels for each subscriber to maximize the aggregate utility of all subscribers in the system in a fair and efficient fashion. Finally, we show that the energy provider can encourage some desirable consumption patterns among the subscribers by means of the proposed real-time pricing interactions. Simulation results confirm that the proposed distributed algorithm can potentially benefit both subscribers and the energy provider.

995 citations


"Particle swarm optimization for dem..." refers background or methods in this paper

  • ...Some of the methodologies are based on load shifting and load curtailment as seen in [4]-[8] or smart pricing methods as seen in [9]-[10]....

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  • ...In smart pricing methods [9],[10], consumers and energy providers work together to achieve real-time prices while maximizing overall utility for consumers and minimizing total cost for energy providers....

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Journal ArticleDOI
TL;DR: Simulation results confirm that the proposed pricing method can benefit both users and utility companies and verify some important properties of the proposed VCG mechanism for demand side management such as efficiency, user truthfulness, and nonnegative transfer.
Abstract: In the future smart grid, both users and power companies can potentially benefit from the economical and environmental advantages of smart pricing methods to more effectively reflect the fluctuations of the wholesale price into the customer side. In addition, smart pricing can be used to seek social benefits and to implement social objectives. To achieve social objectives, the utility company may need to collect various information about users and their energy consumption behavior, which can be challenging. In this paper, we propose an efficient pricing method to tackle this problem. We assume that each user is equipped with an energy consumption controller (ECC) as part of its smart meter. All smart meters are connected to not only the power grid but also a communication infrastructure. This allows two-way communication among smart meters and the utility company. We analytically model each user's preferences and energy consumption patterns in form of a utility function. Based on this model, we propose a Vickrey-Clarke-Groves (VCG) mechanism which aims to maximize the social welfare, i.e., the aggregate utility functions of all users minus the total energy cost. Our design requires that each user provides some information about its energy demand. In return, the energy provider will determine each user's electricity bill payment. Finally, we verify some important properties of our proposed VCG mechanism for demand side management such as efficiency, user truthfulness, and nonnegative transfer. Simulation results confirm that the proposed pricing method can benefit both users and utility companies.

764 citations


"Particle swarm optimization for dem..." refers background or methods in this paper

  • ...Some of the methodologies are based on load shifting and load curtailment as seen in [4]-[8] or smart pricing methods as seen in [9]-[10]....

    [...]

  • ...In smart pricing methods [9],[10], consumers and energy providers work together to achieve real-time prices while maximizing overall utility for consumers and minimizing total cost for energy providers....

    [...]