Optimization in load scheduling of a residential community using dynamic pricing

Roy, Tamal; Das, Avijit; Ni, Zhen

doi:10.1109/isgt.2017.8086087

Cited by 21 publications

(8 citation statements)

References 13 publications

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“…The continuous pattern shows the importance of peak avoidance, since it is a continuous behavior can be avoided by demand side management. It is also important to take care of the customer needs and comfort [Roy et al 2017].…”

Section: House Modelmentioning

confidence: 99%

Demand Side Management and its Possibilities in Jordan

2020

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The increase in energy consumption and energy bills in Jordan have been escalating rapidly, which requires a special concern as a large percent of the energy is imported. The need for the reducing peak demand of the distribution network is essential to decrease the overall electricity generation cost. This study was aimed at presenting a model for a home that manages its energy consumption, and the maximum savings possible if the load shifting to off-peak times was applied. It also introduced a tariff that is more concerned in time of use rather than consumption only. The power consumption profile is collected for a sample house. The profile for a week was registered and graphed. The pricing suggested was calculated per day. Moreover, some samples applied worldwide were discussed to find a suitable model. It was found that a saving rate of 16% is achievable if the time of use charge is applied. Additionally, a peak load reduction of 3.5 kWH average per day (in the peak hours) is possible.

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Section: House Modelmentioning

confidence: 99%

Demand Side Management and its Possibilities in Jordan

2020

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“…The objective of the utility provider is to minimize the cost of energy production and purchasing [1]. In this scenario, a prior knowledge about the energy demand can help utility providers to make proper planning of generation units scheduling and amount of energy to be purchased [2]. The accurate electricity load forecasting has a significant role in power system, but any…”

Section: Introductionmentioning

confidence: 99%

A New Hybrid Model for Short-Term Electricity Load Forecasting

Haq

2019

IEEE Access

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Nowadays electricity load forecasting is important to further minimize the cost of day-ahead energy market. Load forecasting can help utility operators for the efficient management of a demand response program. Forecasting of electricity load demand with higher accuracy and efficiency can help utility operators to design reasonable operational planning of generation units. But solving the problem of load forecasting is a challenging task since electricity load is affected by previous history load, several exogenous external factors (i.e., weather variables, social variables, working day or holiday), time of day, and season of the year. To solve the problem of short-term load forecasting (STLF) and further improve the forecasting accuracy, in this paper we have proposed a novel hybrid STLF model with a new signal decomposition and correlation analysis technique. To this end, load demand time series is decomposed into some regular low frequency components using improved empirical mode decomposition (IEMD). To compensate for the information loss during signal decomposition, we have incorporated the effect of exogenous variables by performing correlation analysis using T-Copula. From the T-Copula analysis, peak load indicative binary variable is derived from value at risk (VaR) to improve the load forecasting accuracy during peak time. The data obtained from IEMD and T-Copula is applied to deep belief network for predicting the future load demand of specific time. The proposed data driven method is validated on real time data from the Australia and the United States of America. The performance of proposed load forecasting model is evaluated in terms of mean absolute percentage error (MAPE) & root mean square error (RMSE). Simulation results verify that, the proposed model provides a significant decrease in MAPE and RMSE values compared to traditional empirical mode decomposition based electricity load forecasting. INDEX TERMS Short-term load forecasting, demand response, smart grid, improved empirical mode decomposition, T-Copula, peak load indicative variable, VaR, and deep belief network.

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“…Latest energy scheduling methods having different pricing schemes and communication techniques are reviewed in [27] by presenting DR algorithms for the residential area. In [28] an energy management model is proposed for the scheduling of home appliances under time-of-use pricing. The authors optimized the model by using genetic algorithm.…”

Section: Introductionmentioning

confidence: 99%

“…They used clonal selection algorithm for the optimization of the model. In [28,29], authors did not consider automatic appliances, consumer comfort and advance real time pricing scheme, which could definitely improve the results in real time. A robust optimization model is proposed in [30] for the household load scheduling considering the intermittency in the household PV system.…”

Section: Introductionmentioning

confidence: 99%

Energy Scheduling of Residential Appliances by a Pigeon-Inspired Algorithm under a Load Shaping Demand Response Program

Kakran¹,

Chanana²

2019

IJEEI

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In this paper, an energy management system (EMS) is modelled in a novel way, for the scheduling of interruptible and uninterruptible appliances of a residential consumer. It is considered that the local renewable energy is generated by the rooftop PV panels installed at the home, to convert a consumer into a prosumer. An energy controlling unit (ECU) schedules the home appliances according to the price signals received from the utility company within the user preferred durations so that the cost of electricity consumption could be minimized. A novel delay factor is also modelled to maintain the comfort level of the consumer. It is assumed that the consumer participates in a demand response (DR) program, based on real time price combined with inclined block rate (RTP-IBR) pricing scheme. Due to this, the peak to average ratio (PAR) of power is decreased and maintained within the satisfactory limits. Finally, for the optimization of the formulated objective function, a pigeon-inspired optimization (PIO) algorithm is used, due to its effectiveness and fast convergence rate over the other similar algorithms. At the end, the results of energy scheduling have been compared and verified against the results achieved by the particle swarm optimization (PSO) algorithm.

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Optimization in load scheduling of a residential community using dynamic pricing

Cited by 21 publications

References 13 publications

Demand Side Management and its Possibilities in Jordan

Demand Side Management and its Possibilities in Jordan

A New Hybrid Model for Short-Term Electricity Load Forecasting

Energy Scheduling of Residential Appliances by a Pigeon-Inspired Algorithm under a Load Shaping Demand Response Program

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