Optimal Clustering of Time Periods for Electricity Demand-Side Management

Rogers, David F.; Polak, George G.

doi:10.1109/tpwrs.2013.2252373

Cited by 22 publications

(13 citation statements)

References 29 publications

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“…The optimization problem in (19) is equivalent to the optimization problem in (6). Unlike (6), the problem in (19) can be solved distributively.…”

Section: Max Xnbnmentioning

confidence: 99%

“…Unlike (6), the problem in (19) can be solved distributively. Next we propose a distributive algorithm to tackle this ESC optimization problem.…”

Section: Max Xnbnmentioning

confidence: 99%

“…Unlike the manual operation of equipment on a traditional power grid, the smart grid enables operators to manage and monitor several devices and sensors. Consequently, communication and metering technologies will enable smart devices in home to reduce peak load demand during high cost peak hours [1][2][3][4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

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Distributed Demand-side management with load uncertainty

Manasseh

Ohno

Mvuma

et al. 2014

Proceedings of the 2014 ITU Kaleidoscope Academic Conference: Living in a Converged World - Impossible Without Standards?

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In this paper, we consider load control in a multiple residence setup where energy consumption scheduling (ECS) devices in smart meters are employed for Demand-side management (DSM). Several residential end-users share the same energy source and each residential user has non adjustable loads, adjustable loads and a storage device. The ECS devices interact automatically by running an iterative distributed algorithm to find the optimal energy consumption schedule for each end-user, in order to reduce the total energy cost as well as the peak-to-average-ratio (PAR) in load demand in the system. Simulation results are provided to evaluate the performance of the proposed game theoretic based distributed DSM technique.

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“…The optimization problem in (19) is equivalent to the optimization problem in (6). Unlike (6), the problem in (19) can be solved distributively.…”

Section: Max Xnbnmentioning

confidence: 99%

“…Unlike (6), the problem in (19) can be solved distributively. Next we propose a distributive algorithm to tackle this ESC optimization problem.…”

Section: Max Xnbnmentioning

confidence: 99%

See 1 more Smart Citation

Distributed Demand-side management with load uncertainty

Manasseh

Ohno

Mvuma

et al. 2014

Proceedings of the 2014 ITU Kaleidoscope Academic Conference: Living in a Converged World - Impossible Without Standards?

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“…Li et al have employed a clustering technique in order to convert the RTP scheme to TOU pricing scheme, providing more flexibility and practicability for the customers. Similar research works are also presented by Granell et al and Wang et al Despite the advantages of the above‐mentioned studies (ie, Rogers and Polak, Biscarri et al, McLoughlin et al, Li et al, Granell et al, and Wang et al), they have not considered the elasticity of load in response to the obtained clusters, while the most important decisive factor in DR programs is the load elasticity and its responsiveness to the implemented DR schemes.…”

Section: Introductionmentioning

confidence: 99%

Optimal time period clustering of time‐of‐use schemes based on elastic loads' responsiveness

Samadi

Aghamohamadi

Mahmoudi

2019

Int Trans Electr Energ Syst

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Summary In time‐of‐use (TOU) schemes, most similar clusters to load demand can properly map the consumption pattern. However, the performance of such scheme is affected by the load responsiveness to the obtained clusters. This paper presents a time period clustering approach for TOU schemes to find the optimal clusters, using not only the load patterns but also its modified version obtained by responsiveness of elastic loads. A responsive load model is employed to simulate the load pattern modifications considering the hourly prices, load elasticities, and customers' benefit function. An imperialist competitive algorithm (ICA) has been designed to iteratively solve the problem. The proposed approach maximizes the load factors pertaining to the modified seasonal load patterns that are obtained on the basis of load responsiveness to the allocated clusters in each iteration of ICA. A comprehensive seasonal case study has been conducted to evaluate the performance of the proposed model. Results and comparisons show that the proposed clustering technique can significantly enhance the performance of the existing TOU schemes in terms of load factor improvement and peak reduction as it considers both load patterns and the actual load responsiveness to the obtained clusters.

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“…In the analysis of user electricity behaviors, medium-term and long-term analyses are made about user electricity behaviors in [17], and appropriate algorithms and the optimal clustering number of mining user electricity behavior similarity are compared in depth. User electricity loads are clustered according to the similarity between user electricity load curves in [18][19][20], through the analysis of the clustered load curves, a reasonable real-time electricity price strategy is put forward to achieve "peak load shifting" of the load curves. The loads are clustered according to user electricity behaviors to estimate the electricity consumption habits and capacities of different users, and an electricity price strategy based on user electricity behaviors is proposed accordingly [21][22][23].…”

Section: Introductionmentioning

confidence: 99%

Short-Term Load Forecasting Based on the Analysis of User Electricity Behavior

Guo

2016

Algorithms

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Abstract:The smart meter is an important part of the smart grid, and in order to take full advantage of smart meter data, this paper mines the electricity behaviors of smart meter users to improve the accuracy of load forecasting. First, the typical day loads of users are calculated separately according to different date types (ordinary workdays, day before holidays, holidays). Second, the similarity between user electricity behaviors is mined and the user electricity loads are clustered to classify the users with similar behaviors into the same cluster. Finally, the load forecasting model based on the Online Sequential Extreme Learning Machine (OS-ELM) is applied to different clusters to conduct load forecasting and the load forecast is summed to obtain the system load. In order to prove the validity of the proposed method, we performed simulation experiments on the MATLAB platform using smart meter data from the Ireland electric power cooperation. The experimental results show that the proposed method is able to mine the user electricity behaviors deeply, improve the accuracy of load forecasting by the reasonable clustering of users, and reveal the relationship between forecasting accuracy and cluster numbers.

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Optimal Clustering of Time Periods for Electricity Demand-Side Management

Cited by 22 publications

References 29 publications

Distributed Demand-side management with load uncertainty

Distributed Demand-side management with load uncertainty

Optimal time period clustering of time‐of‐use schemes based on elastic loads' responsiveness

Short-Term Load Forecasting Based on the Analysis of User Electricity Behavior

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