A review on peak load shaving strategies

Uddin, Moslem; Romlie, Mohd Fakhizan; Abdullah, M. F.; Halim, Syahirah Abd; Bakar, Ab Halim Abu; Kwang, Tan Chia

doi:10.1016/j.rser.2017.10.056

Cited by 374 publications

(186 citation statements)

References 101 publications

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“…Moreover, electrical energy supply cost is largely depended on load factor. Higher the value of load factor lower is the overall cost per unit generated . Increasing the load factor could diminish the average unit cost of the kWh for both demand and supply and therefore lead to substantial savings for the power utility and subsequently the consumers.…”

Section: Proposed Clustering Model and The Solution Methodsologymentioning

confidence: 99%

“…Higher the value of load factor lower is the overall cost per unit generated. 28 Increasing the load factor could diminish the average unit cost of the kWh for both demand and supply and therefore lead to substantial savings for the power utility and subsequently the consumers. Therefore, load factor improvement is the main goal of DR schemes such as TOU that is met by shifting/curtailing load pattern during time by which the utilities can level their energy production and costs.…”

Section: Proposed Modelmentioning

confidence: 99%

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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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Section: Proposed Clustering Model and The Solution Methodsologymentioning

confidence: 99%

Section: Proposed Modelmentioning

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

View full text Add to dashboard Cite

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“…6(b). The difference is that peak shaving is focused on flatting the load peak, while load leveling attempts to flatten the entire load [13]. These functions can reduce fuel consumptions and carbon dioxide (CO 2 ) emissions, decrease maintenance costs, and increase system reliability.…”

Section: A Peak Shaving and Load Levelingmentioning

confidence: 99%

Review on Distributed Energy Storage Systems for Utility Applications

Chang

Zhang

et al. 2017

CPSS TPEA

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“…Compared to the clustering approaches, these methods directly quantify customerlevel features from SM data and use them to determine the segmentation strategies. Nevertheless, the previously-proposed metrics fall short of considering customers' impact on system peak demand, which is a major problem considering that continuous growth in system peak load raises the possibility of power failure and increases the marginal cost of supply [11]. Furthermore, previous works have only focused on observable customers.…”

Section: Introductionmentioning

confidence: 99%

A Data-Driven Customer Segmentation Strategy Based on Contribution to System Peak Demand

Yuan

Dehghanpour

et al. 2020

IEEE Trans. Power Syst.

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Advanced metering infrastructure (AMI) enables utilities to obtain granular energy consumption data, which offers a unique opportunity to design customer segmentation strategies based on their impact on various operational metrics in distribution grids. However, performing utility-scale segmentation for unobservable customers with only monthly billing information, remains a challenging problem. To address this challenge, we propose a new metric, the coincident monthly peak contribution (CMPC), that quantifies the contribution of individual customers to system peak demand. Furthermore, a novel multi-state machine learning-based segmentation method is developed that estimates CMPC for customers without smart meters (SMs): first, a clustering technique is used to build a databank containing typical daily load patterns in different seasons using the SM data of observable customers. Next, to associate unobservable customers with the discovered typical load profiles, a classification approach is leveraged to compute the likelihood of daily consumption patterns for different unobservable households. In the third stage, a weighted clusterwise regression (WCR) model is utilized to estimate the CMPC of unobservable customers using their monthly billing data and the outcomes of the classification module. The proposed segmentation methodology has been tested and verified using real utility data.

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A review on peak load shaving strategies

Cited by 374 publications

References 101 publications

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

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

Review on Distributed Energy Storage Systems for Utility Applications

A Data-Driven Customer Segmentation Strategy Based on Contribution to System Peak Demand

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