Genetic-Algorithm-Based Optimization Approach for Energy Management

Arabali, A.; Ghofrani, M.; Etezadi-Amoli, M.; Fadali, M. Sami; Baghzouz, Y.

doi:10.1109/tpwrd.2012.2219598

Cited by 316 publications

(137 citation statements)

References 30 publications

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“…In short, the existing optimization techniques in [8,10,[12][13][14] are unable to handle the complexity of cost minimization and UC maximization problems due to their non-flexible nature. In fact, the solution of these non-linear problems lead to high computational complexity.…”

Section: Related Workmentioning

confidence: 99%

“…In this way, appliances' waiting time is minimized, and UC is achieved maximally. This is the multi-objective problem; several authors handle it using different approaches, as mentioned in the literature [12][13][14][15][16][17]. Here, it is handled by the metaheuristics for scheduling the residential area loads in order to reduce the electricity cost and maximize the UC.…”

Section: Uc Maximizationmentioning

confidence: 99%

“…In the literature, many DSM techniques are proposed [8][9][10][11] to achieve the aforementioned objectives. However, UC is not considered in most of these techniques, like [8,10,[12][13][14][15][16][17]. In these works, [11,18,19] aim to reduce the electricity cost, and [20,21] focus on minimizing the aggregated power consumption using integer linear programming and mixed integer linear programming.…”

Section: Introductionmentioning

confidence: 99%

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A Hybrid Genetic Wind Driven Heuristic Optimization Algorithm for Demand Side Management in Smart Grid

et al. 2017

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Abstract:In recent years, demand side management (DSM) techniques have been designed for residential, industrial and commercial sectors. These techniques are very effective in flattening the load profile of customers in grid area networks. In this paper, a heuristic algorithms-based energy management controller is designed for a residential area in a smart grid. In essence, five heuristic algorithms (the genetic algorithm (GA), the binary particle swarm optimization (BPSO) algorithm, the bacterial foraging optimization algorithm (BFOA), the wind-driven optimization (WDO) algorithm and our proposed hybrid genetic wind-driven (GWD) algorithm) are evaluated. These algorithms are used for scheduling residential loads between peak hours (PHs) and off-peak hours (OPHs) in a real-time pricing (RTP) environment while maximizing user comfort (UC) and minimizing both electricity cost and the peak to average ratio (PAR). Moreover, these algorithms are tested in two scenarios: (i) scheduling the load of a single home and (ii) scheduling the load of multiple homes. Simulation results show that our proposed hybrid GWD algorithm performs better than the other heuristic algorithms in terms of the selected performance metrics.

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Section: Related Workmentioning

confidence: 99%

Section: Uc Maximizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Hybrid Genetic Wind Driven Heuristic Optimization Algorithm for Demand Side Management in Smart Grid

et al. 2017

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“…In this system, temperature is adjusted in a way to minimise electricity cost while keeping temperature in a comfortable range. Arabali et al (2013) used stochastic models renewable energy generation like solar and wind for matching it with the HVAC load. Purpose of exercise is to increase efficiency and reduce cost.…”

Section: Related Workmentioning

confidence: 99%

Multi objective optimisation framework for smart metering in smart grids using multi-objective particle swarm optimisation

Bansal¹,

Singh²

2017

IJDSSS

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Abstract:Demand of electricity is increasing day by day. One major consumer of electricity is residential sector. Smart grid offers demand response management to handle such increasing demand. In home energy management system (HEMS), demand response is implemented using smart meters. In this paper, an algorithm is proposed to optimise the demand in home environment and unease level of user is also cared about at the same time. The proposed algorithm is implemented using multi-objective optimisation framework. It is evident from results that cost of electricity is reduced because of optimal demand and on the other hand, unease level of user is within the acceptable limit.Keywords: smart grid; smart meter; energy management; multi objective optimisation.Reference to this paper should be made as follows: Bansal, P. and Singh, A. (2017) 'Multi objective optimisation framework for smart metering in smart grids using multi-objective particle swarm optimisation', Int.

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“…In recent years, several optimal load control schedules which are based on linear programming or dynamic algorithms are used mainly for peak shaving, load shifting, or contingency reserves to minimize production cost or meet reliability requirements over a limited time horizon [38][39][40].…”

Section: Control Effectivenessmentioning

confidence: 99%

Controllable Load Management Approaches in Smart Grids

Shen

Jiang

2015

Energies

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Abstract:With rapid smart grid technology development, the customer can actively participate in demand-side management (DSM) with the mutual information communication between the distributor operation company and the smart devices in real-time. Controllable load management not only has the advantage of peak shaving, load balance, frequency regulation, and voltage stability, but is also effective at providing fast balancing services to the renewable energy grid in the distributed power system. The load management faces an enormous challenge as the customer has a large number of both small residential loads and dispersed renewable sources. In this paper, various controllable load management approaches are discussed. The traditional controllable load approaches such as the end users' controllable appliances, storage battery, Vehicle-to-Grid (V2G), and heat storage are reviewed. The "broad controllable loads" management, such as the microgrid, Virtual Power Plant (VPP), and the load aggregator are also presented. Furthermore, the load characteristics, control strategies, and control effectiveness are analyzed.

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Genetic-Algorithm-Based Optimization Approach for Energy Management

Cited by 316 publications

References 30 publications

A Hybrid Genetic Wind Driven Heuristic Optimization Algorithm for Demand Side Management in Smart Grid

A Hybrid Genetic Wind Driven Heuristic Optimization Algorithm for Demand Side Management in Smart Grid

Multi objective optimisation framework for smart metering in smart grids using multi-objective particle swarm optimisation

Controllable Load Management Approaches in Smart Grids

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