Peak-to-Average Ratio Constrained Demand-Side Management With Consumer's Preference in Residential Smart Grid

Liu, Yi; Yuen, Chau; Huang, Shisheng; Hassan, Naveed Ul; Wang, Xiumin; Xie, Shengli

doi:10.1109/jstsp.2014.2332301

Cited by 188 publications

(106 citation statements)

References 28 publications

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“…However, user comfort has not been modelled in an effective way. In [15,16], the energy consumption of different types of appliances is managed, such that the major focus is on appliance scheduling, Peak to Average Ratio (PAR). and electricity cost reduction, as well as comfort management.…”

Section: Related Workmentioning

confidence: 99%

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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Section: Related Workmentioning

confidence: 99%

Energy Optimization in Smart Homes Using Customer Preference and Dynamic Pricing

et al. 2016

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“…Authors in [15] presented demand-side energy consumption scheduling in the presence of PAR constraint and users preference in order to reduce the cost. Moreover, they introduce multi-objective optimization techniques which minimize the cost and inconvenience posed to users.…”

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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“…The closer PAPR gets to 1, the less power loss will occur on the grid side. In [27,28], distributed algorithm and game theory are proposed to reduce PAPR in DSM. One of the main reasons that electric prices vary in a day is that, in peak hours when total demand is high, more expensive generation sources are added to meet the increased demand [29].…”

Section: Win-win Situation For System Operator and Customermentioning

confidence: 99%

Optimal and Learning-Based Demand Response Mechanism for Electric Water Heater System

Lin

Xiao

2017

Energies

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This paper investigates how to develop a learning-based demand response approach for electric water heater in a smart home that can minimize the energy cost of the water heater while meeting the comfort requirements of energy consumers. First, a learning-based, data-driven model of an electric water heater is developed by using a nonlinear autoregressive network with external input (NARX) using neural network. The model is updated daily so that it can more accurately capture the actual thermal dynamic characteristics of the water heater especially in real-life conditions. Then, an optimization problem, based on the NARX water heater model, is formulated to optimize energy management of the water heater in a day-ahead, dynamic electricity price framework. A genetic algorithm is proposed in order to solve the optimization problem more efficiently. MATLAB (R2016a) is used to evaluate the proposed learning-based demand response approach through a computational experiment strategy. The proposed approach is compared with conventional method for operation of an electric water heater. Cost saving and benefits of the proposed water heater energy management strategy are explored.

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Peak-to-Average Ratio Constrained Demand-Side Management With Consumer's Preference in Residential Smart Grid

Cited by 188 publications

References 28 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

Optimal and Learning-Based Demand Response Mechanism for Electric Water Heater System

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