A Real-Time Information Based Demand-Side Management System in Smart Grid

Ye, Feng; Qian, Yi; Hu, Rose Qingyang

doi:10.1109/tpds.2015.2403833

Cited by 65 publications

(42 citation statements)

References 20 publications

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“…• Price-based DR programs: Most of the published articles focus on DSM and direct load control (DLC) strategies to manage the energy demand during critical peak hours [2][3][4][5].…”

Section: Related Workmentioning

confidence: 99%

“…On the other hand, end users participate in DR programs to minimize their electricity bills. Cost-sensitive customers can take monetary benefits by taking into consideration time varying prices and efficient scheduling techniques [2,3]. DR-based load shifting is helpful in reducing the electricity cost of end users, however, at the cost of user comfort.…”

Section: Introductionmentioning

confidence: 99%

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Energy Optimization in Smart Homes Using Customer Preference and Dynamic Pricing

et al. 2016

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Abstract:In this paper, we present an energy optimization technique to schedule three types of household appliances (user dependent, interactive schedulable and unschedulable) in response to the dynamic behaviours of customers, electricity prices and weather conditions. Our optimization technique schedules household appliances in real time to optimally control their energy consumption, such that the electricity bills of end users are reduced while not compromising on user comfort. More specifically, we use the binary multiple knapsack problem formulation technique to design an objective function, which is solved via the constraint optimization technique. Simulation results show that average aggregated energy savings with and without considering the human presence control system are 11.77% and 5.91%, respectively.

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“…• Price-based DR programs: Most of the published articles focus on DSM and direct load control (DLC) strategies to manage the energy demand during critical peak hours [2][3][4][5].…”

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Energy Optimization in Smart Homes Using Customer Preference and Dynamic Pricing

et al. 2016

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“…The authors in [11] proposed two market models in order to cope with the gap between demand and supply. In [12], authors proposed a novel appliances commitment algorithm to optimally schedule appliances under operational constraints and economical considerations in order to maximize comfort and reduce cost. However, peaks may emerge while reducing cost because most users start operation during off-peak timeslots.…”

Section: Related Workmentioning

confidence: 99%

“…Optimal scheduling of appliances under operational constraints and economical consideration [12] Novel appliances commitment algorithm Comfort maximization and cost reduction Peaks may emerge while reducing cost Demand curve smoothing by controlling the charging strategies of electric vehicles [13] Decentralized algorithm Minimization of the peak demand loads User comfort is compromised while reducing peaks in demand…”

Section: Minimization Of Cost Higher Communication Overhead and Compumentioning

confidence: 99%

Optimal Residential Load Scheduling Under Utility and Rooftop Photovoltaic Units

et al. 2018

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Abstract:With the rapid advancement in technology, electrical energy consumption is increasing rapidly. Especially, in the residential sector, more than 80% of electrical energy is being consumed because of consumer negligence. This brings the challenging task of maintaining the balance between the demand and supply of electric power. In this paper, we focus on the problem of load balancing via load scheduling under utility and rooftop photovoltaic (PV) units to reduce electricity cost and peak to average ratio (PAR) in demand-side management. For this purpose, we adopted genetic algorithm (GA), binary particle swarm optimization (BPSO), wind-driven optimization (WDO), and our proposed genetic WDO (GWDO) algorithm, which is a hybrid of GA and WDO, to schedule the household load. For energy cost estimation, combined real-time pricing (RTP) and inclined block rate (IBR) were used. The proposed algorithm shifts load from peak consumption hours to off-peak hours based on combined pricing scheme and generation from rooftop PV units. Simulation results validate our proposed GWDO algorithm in terms of electricity cost and PAR reduction while considering all three scenarios which we have considered in this work: (1) load scheduling without renewable energy sources (RESs) and energy storage system (ESS), (2) load scheduling with RESs, and (3) load scheduling with RESs and ESS. Furthermore, our proposed scheme reduced electricity cost and PAR by 22.5% and 29.1% in scenario 1, 47.7% and 30% in scenario 2, and 49.2% and 35.4% in scenario 3, respectively, as compared to unscheduled electricity consumption.

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“…Raj, Aravind, Sundaram and Vasudevan [13] have discussed about real-time based DSM systems where the real time information regarding the consumption pattern of the users is sent to the grid through smart meter based on which the grid can predict the next demand which helps it to prevent overload and supply electricity more reliably and efficiently. Ye, Qian and Hu have defined a real-time information based DSM system to minimize the PAR of the system using game-theoretic approach [14].…”

Section: Related Workmentioning

confidence: 99%

Intelligent Scheduling of Demand Side Energy Usage in Smart Grid Using a Metaheuristic Approach

Das¹,

Satyananda²,

Nayak³

2018

IJISA

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Abstract-As the global demand for electricity is growing continuously, the sources use more fossil fuels to generate electricity which in turn increases the level of carbon dioxide in the atmosphere. Moreover the electrical system becomes unreliable during the peak hours if the demand for electricity is very high. So there is a need to have a grid system which can handle these cases in a smarter way. A Smart Grid is such an electrical grid system which can control and manage electricity demand in a more reliable and economic manner using various energy efficient resources and a variety of operational measures like smart meters, smart appliances and smart communication system. The smart grid uses a technique called energy demand management at consumer side which motivates the consumers to control and reduce their demand for energy during peak hours. This makes the whole system more reliable and efficient. The demand side management (DSM) includes various methods such as increasing awareness among the consumers and giving them some financial incentives which can encourage them to be a part of the DSM program. In this paper a novel Demand Side Management technique has been proposed for a typical smart grid scenario which comprises users with energy storage devices using a metaheuristic approach to have an optimal load scheduling that results in reduced peak hour demands.

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A Real-Time Information Based Demand-Side Management System in Smart Grid

Cited by 65 publications

References 20 publications

Energy Optimization in Smart Homes Using Customer Preference and Dynamic Pricing

Energy Optimization in Smart Homes Using Customer Preference and Dynamic Pricing

Optimal Residential Load Scheduling Under Utility and Rooftop Photovoltaic Units

Intelligent Scheduling of Demand Side Energy Usage in Smart Grid Using a Metaheuristic Approach

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