A Communication-Based Appliance Scheduling Scheme for Consumer-Premise Energy Management Systems

Chen, Chen; Nagananda, K. G.; Xiong, Gang; Kishore, Shalinee; Snyder, Lawrence

doi:10.1109/tsg.2012.2224388

Cited by 69 publications

(33 citation statements)

References 8 publications

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“…However, the authors reduced the peaks and cost while user comfort was comprised. The authors in [8] proposed joint access and load scheduling under DR schemes in order to reduce cost. Authors formulated an optimization problem for the EMC, enabling it to compute the target power level for the home while incorporating the effects of price variations and local wind power uncertainty.…”

Section: Related Workmentioning

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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Section: Related Workmentioning

confidence: 99%

Optimal Residential Load Scheduling Under Utility and Rooftop Photovoltaic Units

et al. 2018

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“…Our solution is completely decentralized and does not involve additional nodes besides the local HEM systems, thus, in our scenario, the adversarial entities are represented by the game players themselves. Papers [17], [13] assume that exchanging aggregated power consumption data at household level (e.g., on hourly basis) is sufficient to hide the usage patterns of single electric appliances to untrustworthy neighbours. However, several studies on Non-Intrusive Load Monitoring (see, e.g., [18], [19]) prove that the power consumption patterns of individual appliances can easily be inferred from house-aggregated measurements.…”

Section: Related Workmentioning

confidence: 99%

A privacy-friendly game-theoretic distributed scheduling system for domestic appliances

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2014

2014 IEEE International Conference on Smart Grid Communications (SmartGridComm)

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Abstract-Game-theoretic Demand Side Management (DSM) systems have been investigated as a decentralized approach for the collaborative scheduling of the usage of domestic electrical appliances within a set of households. Such systems allow for the shifting of the starting time of deferrable devices according to the current energy price or power grid condition, in order to reduce the individual monthly bill or to adjust the power load experienced by the grid while meeting the users' preferences about the time of use. The drawback of DSM distributed protocols is that they require each user to communicate his/her own energy consumption patterns to the other users, which may leak sensitive information regarding private habits.This paper proposes a distributed Privacy-Friendly DSM system which preserves users' privacy by integrating data aggregation and perturbation techniques: users decide their schedule according to aggregated consumption measurements perturbed by means of Additive White Gaussian Noise (AWGN). We evaluate the noise power and the size of the set of users required to achieve a given privacy level, quantified by means of the Kullback-Leibler divergence. The performance of our proposed DSM system are compared to the ones obtained by a benchmark system which does not support privacy preservation in terms of social cost, peak demand and convergence time. Results show that privacy can be preserved at the cost of increasing the peak demand and the number of game iterations, whereas social cost is only marginally incremented.

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“…The first is to equip the grid with high capacity storage banks, capable of storing energy surpluses and to release them in case of energy production deficits [2]. However, today's state-of-the-art tech- 15 nology is still immature to allow a widespread introduction of storage plants, which would require tremendous installation and maintenance costs. A second possibility is to induce some modifications in the user's energy utilization behavior by designing time-variable tariffs or introducing incentives to shift the use of some appliances to off-peak hours [3].…”

Section: Introductionmentioning

confidence: 99%

“…Papers [15,16] assume that exchanging aggregated power consumption data at household level (e.g., on hourly basis) is sufficient to hide the usage patterns of single electric appliances to untrustworthy neighbours. However, several studies on Non-Intrusive Load Monitoring (see, e.g., [17,18]) prove that 120 the power consumption patterns of individual appliances can easily be inferred from house-aggregated measurements.…”

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confidence: 99%

Privacy-friendly load scheduling of deferrable and interruptible domestic appliances in Smart Grids

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2015

Computer Communications

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The massive integration of renewable energy sources in the power grid ecosystem with the aim of reducing carbon emissions must cope with their intrinsically intermittent and unpredictable nature. Therefore, the grid must improve its capability of controlling the energy demand by adapting the power consumption curve to match the trend of green energy generation. This could be done by scheduling the activities of deferrable and/or interruptible electrical appliances.However, communicating the users' needs about the usage of their appliances also leaks sensitive information about their habits and lifestyles, thus arising privacy concerns. This paper proposes a framework to allow the coordination of energy consumption without compromising the privacy of the users: the service requests generated by the domestic appliances are divided into crypto-shares using Shamir Secret Sharing scheme and collected through an anonymous routing protocol by a set of schedulers, which schedule the requests by directly operating on the shares. We discuss the security guarantees provided by our proposed infrastructure and evaluate its performance, comparing it with the optimal scheduling obtained by means of an Integer Linear Programming formulation.

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A Communication-Based Appliance Scheduling Scheme for Consumer-Premise Energy Management Systems

Cited by 69 publications

References 8 publications

Optimal Residential Load Scheduling Under Utility and Rooftop Photovoltaic Units

Optimal Residential Load Scheduling Under Utility and Rooftop Photovoltaic Units

A privacy-friendly game-theoretic distributed scheduling system for domestic appliances

Privacy-friendly load scheduling of deferrable and interruptible domestic appliances in Smart Grids

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