Time of Use pricing for residential customers case of Malaysia

Muzmar, M. A. R.; Abdullah, Md. Pauzi; Hassan, M. Y.; Hussin, Faridah

doi:10.1109/scored.2015.7449404

Cited by 17 publications

(10 citation statements)

References 7 publications

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“…The dotted line in the figure represents the desired flat demand, which is named here as average demand, PdAverage and is calculated as follows: Figure 1. Electricity Demand of a selected residential area in kWh [10] The main purpose of TOU price is to make the electricity demand pattern as flat as possible [11]. Hence, reducing the electricity consumption during high demand period known as peaks, and increasing consumption during low demand periods.…”

Section: Methodsmentioning

confidence: 99%

Impact of Different Time of Use Electricity Pricing Structure on Residential Consumer

Azman¹,

Abdullah²,

Hassan³

et al. 2018

IJEECS

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<span>Load profile for residential users is different from commercial users where peak load occurs outside of work hours compared to working hours. Consequently, the Time of Use-based electricity price must be different not only in terms of price, but also in terms of time block structure. This paper examines the impacts of different TOU structures on TOU prices and load profiles of residential consumer. Four TOU structures are tested on the real load profile for a selected residential consumer area in Malaysia. Two elasticity factors are used for each structure to represent two different groups of users, a group that responds highly to price changes and a group that does not. The TOU price set for each structure is determined optimally subject to the following constraints; the price difference between the TOU and a fixed price per hour should be minimized and the amount of difference between price increase and price drop should be equal. From the analysis, the TOU structure with 12-time blocks provides better price signals and peak load reduction.</span>

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Section: Methodsmentioning

confidence: 99%

Impact of Different Time of Use Electricity Pricing Structure on Residential Consumer

Azman¹,

Abdullah²,

Hassan³

et al. 2018

IJEECS

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“…Firstly, researchers have pointed out that the ratio of peak to off-peak TOU prices has to be significant. Otherwise, the profile of user power demand will not change effectively [56]. Nevertheless, large price differences in different time intervals can result in higher overall cost for some users, and, thus, may not be preferable.…”

Section: Electricity Price Policy For Energy Trading In Microgridsmentioning

confidence: 99%

A Novel Distributed Economic Model Predictive Control Approach for Building Air-Conditioning Systems in Microgrids

2018

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Abstract:With the penetration of grid-connected renewable energy generation, microgrids are facing stability and power quality problems caused by renewable intermittency. To alleviate such problems, demand side management (DSM) of responsive loads, such as building air-conditioning system (BACS), has been proposed and studied. In recent years, numerous control approaches have been published for proper management of single BACS. The majority of these approaches focus on either the control of BACS for attenuating power fluctuations in the grid or the operating cost minimization on behalf of the residents. These two control objectives are paramount for BACS control in microgrids and can be conflicting. As such, they should be considered together in control design. As individual buildings may have different owners/residents, it is natural to control different BACSs in an autonomous and self-interested manner to minimize the operational costs for the owners/residents. Unfortunately, such "selfish" operation can result in abrupt and large power fluctuations at the point of common coupling (PCC) of the microgrid due to lack of coordination. Consequently, the original objective of mitigating power fluctuations generated by renewable intermittency cannot be achieved. To minimize the operating costs of individual BACSs and simultaneously ensure desirable overall power flow at PCC, this paper proposes a novel distributed control framework based on the dissipativity theory. The proposed method achieves the objective of renewable intermittency mitigation through proper coordination of distributed BACS controllers and is scalable and computationally efficient. Simulation studies are carried out to illustrate the efficacy of the proposed control framework.

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“…As of 2018, 56.6% of households adopted the TOU rate plan [13]. Various pilot projects related to TOU rate plans have been attempted in several countries [1], [14] and extensive research is being conducted on effects of the TOU rate plan for households [15]- [20]. To ensure electricity consumers have a choice of electricity rate plans, the EU has recommended that electric power sellers should provide consumers with sufficient information regarding the dynamic rate plan [2].…”

Section: Introductionmentioning

confidence: 99%

Residential Electricity Rate Plans and Their Selections Based on Statistical Learning

Chung

Kang²,

Jung

et al. 2022

IEEE Access

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Demand response (DR) is one of the major benefits that utility companies can derive from the advanced metering infrastructure (AMI). In particular, the dynamic rate plan with DR is attracting attention as an electricity rate system suitable for the future power environment. In order for electricity consumers to select an appropriate electricity rate plan, it is necessary to provide information such as whether electricity bills are reduced by the plan and the estimated amount of electricity bill savings. In this paper, we first comparatively analyze the current progressive rate plan and a dynamic rate plan of the time-of-use (TOU). We next propose several prediction methods for households to provide information on whether the electricity bill amount can be reduced in advance when changing to the TOU rate plan from the progressive rate plan by using only the current monthly electricity usages and bills. In order to develop three different prediction methods based on statistical learning, we use the support vector machine, linear regression, and deep neural network techniques. As a ground truth training sequence, we use hourly electricity usages and bills obtained from ten apartment complexes through AMI, and an apartment complex is used for testing the designed methods. The decision accuracy for the test complex was more than 0.98 and the root mean square error of the saving prediction was 1.7%. INDEX TERMS advanced metering infrastructure (AMI), deep neural network (DNN), linear regression, support vector machine (SVM), progressive rate, time-of-use (TOU) rateNOMENCLATURE 10 DNN Deep neural network. DR Demand response. RMSE Root mean square error. SVM Support vector machine. TOU Time-of-use.

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Time of Use pricing for residential customers case of Malaysia

Cited by 17 publications

References 7 publications

Impact of Different Time of Use Electricity Pricing Structure on Residential Consumer

Impact of Different Time of Use Electricity Pricing Structure on Residential Consumer

A Novel Distributed Economic Model Predictive Control Approach for Building Air-Conditioning Systems in Microgrids

Residential Electricity Rate Plans and Their Selections Based on Statistical Learning

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