Real-Time Opportunistic Scheduling for Residential Demand Response

Yi, Peizhong; Dong, Xihua; Iwayemi, Abiodun; Zhou, Chi; Li, Shufang

doi:10.1109/tsg.2012.2225155

Cited by 182 publications

(106 citation statements)

References 11 publications

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“…Authors in [27] proposed a new heuristic harmony model for load shifting in smart grid, however authors in [28] emphasized on cost minimization. Game theory model was proposed by [29] and [30] with different objective functions as PAR minimization and load shifting.…”

Section: Related Workmentioning

confidence: 99%

Efficient Power Scheduling in Smart Homes Using Hybrid Grey Wolf Differential Evolution Optimization Technique with Real Time and Critical Peak Pricing Schemes

et al. 2018

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With the emergence of automated environments, energy demand by consumers is increasing rapidly. More than 80% of total electricity is being consumed in the residential sector. This brings a challenging task of maintaining the balance between demand and generation of electric power. In order to meet such challenges, a traditional grid is renovated by integrating two-way communication between the consumer and generation unit. To reduce electricity cost and peak load demand, demand side management (DSM) is modeled as an optimization problem, and the solution is obtained by applying meta-heuristic techniques with different pricing schemes. In this paper, an optimization technique, the hybrid gray wolf differential evolution (HGWDE), is proposed by merging enhanced differential evolution (EDE) and gray wolf optimization (GWO) scheme using real-time pricing (RTP) and critical peak pricing (CPP). Load shifting is performed from on-peak hours to off-peak hours depending on the electricity cost defined by the utility. However, there is a trade-off between user comfort and cost. To validate the performance of the proposed algorithm, simulations have been carried out in MATLAB. Results illustrate that using RTP, the peak to average ratio (PAR) is reduced to 53.02%, 29.02% and 26.55%, while the electricity bill is reduced to 12.81%, 12.012% and 12.95%, respectively, for the 15-, 30-and 60-min operational time interval (OTI). On the other hand, the PAR and electricity bill are reduced to 47.27%, 22.91%, 22% and 13.04%, 12%, 11.11% using the CPP tariff.

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Section: Related Workmentioning

confidence: 99%

Efficient Power Scheduling in Smart Homes Using Hybrid Grey Wolf Differential Evolution Optimization Technique with Real Time and Critical Peak Pricing Schemes

et al. 2018

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“…How-ever, a number of solutions have been proposed to maximize the integration of EVs with existing power system. The development of demand response (DR) enabled appliances, smart home energy management (HEM) systems, smart energy meters, and advanced information technologies ensure transparency of EVs in the existing power system up to a remarkable extent [6,7]. Therefore, electric utilities have more options to occupy residential consumers in DR programs [8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Water-filling algorithm based approach for management of responsive residential loads

Haider

Mehmood

Rafique

et al. 2017

J. Mod. Power Syst. Clean Energy

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Integration of large number of electric vehicles (EVs) with distribution networks is devastating for conventional power system devices such as transformers and power lines etc. This paper proposes a methodology for management of responsive household appliances management and EVs with water-filling algorithm. With the proposed scheme, the load profile of a transformer is retained below its rated capacity while minimally affecting the associated consumers. When the instantaneous demand at transformer increases beyond its capacity, the proposed methodology dynamically allocates demand curtailment limit (DCL) to each home served by transformer. The DCL allocation takes convenience factors, load profile and information of flexible appliances into account to assure the comfort of all the consumers. The proposed scheme is verified by modeling and simulating five houses and a distribution transformer. The smart appliances such as an HVAC, a water heater, a cloth dryer and an EV are also modeled for the study. Results show that the proposed scheme performs to reduce overloading effects of the transformer efficiently and assures comfort of the consumers at the same time.

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“…Research efforts using additional optimization variables such as user convenience [11,15,16] have been recently presented as opposed to using only the electricity bill and peak consumption. These studies argue that users are trying to reduce the electricity bill without sacrificing the user's convenience.…”

Section: Introductionmentioning

confidence: 99%

“…However, this scheduling scheme does not consider various types of appliances, instead only focusing on appliances that can be reserved. Yi et al [15] proposed an energy scheduling scheme that uses a stopping method to achieve the minimum electricity bill and waiting time in a smart grid. They make a fake schedule based on the optimal stopping approach at the first stage as if there are no power constraints.…”

Section: Introductionmentioning

confidence: 99%

“…However, previous studies [11,15,16,20] are limited in that they assume that the user sets the preferred operation time for each appliance (the preferred time is referred to as user convenience in this paper). In this paper, a demand response scheme is proposed that automatically estimates a user's convenience without configuring the convenience for fully-automated energy scheduling.…”

Section: Introductionmentioning

confidence: 99%

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Automated Energy Scheduling Algorithms for Residential Demand Response Systems

et al. 2017

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Abstract:Demand response technology is a key technology for distributing electricity tasks in response to electricity prices in a smart grid system. In the current demand response research, there has been much demand for an automated energy scheduling scheme that uses smart devices for residential customers in the smart grid. In this paper, two automated energy scheduling schemes are proposed for residential smart grid demand response systems: semi-automated scheduling and fully-automated scheduling. If it is possible to set the appliance preference, semi-automated scheduling will be conducted, and if it is impossible, fully-automated scheduling will be operated. The formulated optimization problems consider the electricity bill along with the user convenience. For the fully-automated scheduling, the appliance preference can automatically be found according to appliance type from the electricity consumption statistics. A performance evaluation validates that the proposed scheme shifts operation to avoid peak load, that the electricity bill is significantly reduced, and that user convenience is satisfied.

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Real-Time Opportunistic Scheduling for Residential Demand Response

Cited by 182 publications

References 11 publications

Efficient Power Scheduling in Smart Homes Using Hybrid Grey Wolf Differential Evolution Optimization Technique with Real Time and Critical Peak Pricing Schemes

Efficient Power Scheduling in Smart Homes Using Hybrid Grey Wolf Differential Evolution Optimization Technique with Real Time and Critical Peak Pricing Schemes

Water-filling algorithm based approach for management of responsive residential loads

Automated Energy Scheduling Algorithms for Residential Demand Response Systems

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