EMS-Data-Based Load Modeling to Evaluate the Effect of Conservation Voltage Reduction at a National Level

Nam, Soon-Ryul; Kang, Sang-Hee; Lee, Joo‐Ho; Choi, Eun-Jae; Ahn, Seon-Ju; Choi, Joon-Ho

doi:10.3390/en6083692

Cited by 10 publications

(10 citation statements)

References 26 publications

(31 reference statements)

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“…Dynamic-load modeling can accurately reflect the load characteristics; however, it requires high time-resolution data. On the other hand, the parameters of static-load modeling can be estimated using SOMAS data because relatively low time-resolution data are sufficient for static-load modeling [32][33][34]. In particular, ZIP (constant impedance, current, and power) load modeling has a simple structure, and its parameters can be derived from just a few data samples.…”

Section: Linearized Load Modeling Based On Somas Data Resulting From mentioning

confidence: 99%

“…Given that nationwide CVR is usually in the range 2.0%-5.0%, it is difficult to accurately determine ZIP parameters using SOMAS data obtained from nationwide CVR [34]. Therefore, instead of the ZIP load model, this paper uses a linearized load model, which was proposed in [34] and is described briefly below.…”

Section: Linearized Load Modeling Based On Somas Data Resulting From mentioning

confidence: 99%

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Evaluation of the Effects of Nationwide Conservation Voltage Reduction on Peak-Load Shaving Using SOMAS Data

Nam

Kang

Lee³

et al. 2013

Energies

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Abstract:In this paper we propose a new method to evaluate the effects of nationwide conservation voltage reduction (CVR) on peak-load shaving, using substation operating results management system (SOMAS) data. Its evaluation is based on a national CVR factor, which is defined as the weighted average of CVR factors associated with all transformer banks and weighting coefficients are determined by the reconstructed loads corresponding to each transformer bank. To make use of the data resulting from nationwide CVR without installing additional measuring devices, we adopt a linearized static-load model with a linearizing parameter. SOMAS data are used to evaluate the effects of nationwide CVR on peak-load shaving in the Korean power system. Evaluation results show that the national CVR factor of the Korean power system has small values in the summer season and large values in the winter season. This means that the effect of nationwide CVR on peak-load shaving in the Korean power system presents stronger benefits during winter months.

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Section: Linearized Load Modeling Based On Somas Data Resulting From mentioning

confidence: 99%

Section: Linearized Load Modeling Based On Somas Data Resulting From mentioning

confidence: 99%

Evaluation of the Effects of Nationwide Conservation Voltage Reduction on Peak-Load Shaving Using SOMAS Data

Nam

Kang

Lee³

et al. 2013

Energies

Self Cite

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“…The effect of load reduction on the generation reduction appears insignificant for this small test case, which is different from the case for bulk power systems. A judgment matrix for the PTDF can be built as shown in Equation (20). …”

Section: Ptdf For the Test Systemmentioning

confidence: 99%

“…It is noteworthy that many utilities have recently evaluated their CVR performance through numerous commissions and strategies as well as the technical and economic benefits of peak demand reduction and energy savings [1][2][3][4][5][6][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22]. The Pacific Northwest National Laboratory (PNNL) reported that the CVR on a national level in the U.S. can be extrapolated to be 3.04% reduction in annual energy consumption [17].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Conservation Voltage Reduction with Analytic Hierarchy Process: A Decision Support Framework in Grid Operations Planning

Liu

Zhu

et al. 2016

Energies

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This paper presents a systematic framework to evaluate the performance of conservation voltage reduction (CVR) by determining suitable substations for CVR in operations planning. Existing CVR planning practice generally only focuses on the energy saving aspect without taking other underlying attributes into account, i.e., network topology and reduced voltage effects on other substations. To secure the desired operating reserve and avoid any adverse impacts, these attributes should be considered for implementing CVR more effectively. This research develops a practical decision-making framework based on the analytic hierarchy process (AHP) to quantify several of the aforementioned attributes. Candidate substations for CVR deployment are prioritized such that performances are compared in terms of power transfer distribution factor (PTDF), voltage sensitivity factor (VSF), and CVR factor. In addition, to meet a specified reserve requirement, an integer programming approach is adopted to select potential substations for CVR implementations. Case studies for a Korean electric power system under diverse operating conditions are performed to demonstrate the effectiveness of the proposed method.

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“…The aggregation concept may be extended further so that CVR factors determined at the appliance, customer and feeder levels are used to form a CVR factor for the entire network. Other research [4]- [5] has sought to overcome the complexity of extrapolating a deluge of individual load traits to the network level by introducing a linearized approximation to the established definition for CVRp outlined in (1). This paper explores new ways of interpreting load behaviors in the presence of voltage fluctuations, beyond that of established modelling practice, with the intention of assisting the analysis and predictability of network voltage optimization tools, such as CVR.…”

Section: Introductionmentioning

confidence: 99%

Small-Signal Refinement of Power System Static Load Modelling Techniques

McLorn

McLoone

2017

Communications in Computer and Information Science

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Abstract. Loads are often represented as a weighted combination of constant impedance (Z), current (I) and power (P) components, so called ZIP models, by various power systems network simulation tools. However, with the growing need to model nonlinear load types, such as LED lighting, ZIP models are increasingly rendered inadequate in fully representing the voltage dependency of power consumption traits. In this paper we propose the use of small-signal ZIP models, derived from a neural network model of appliance level consumption profiles, to enable better characterizations of voltage dependent load behavior. Direct and indirect approaches to small-signal ZIP model parameter estimation are presented, with the latter method shown to be the most robust to neural network approximation errors. The proposed methodology is demonstrated using both simulation and experimentally collected load data.

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EMS-Data-Based Load Modeling to Evaluate the Effect of Conservation Voltage Reduction at a National Level

Cited by 10 publications

References 26 publications

Evaluation of the Effects of Nationwide Conservation Voltage Reduction on Peak-Load Shaving Using SOMAS Data

Evaluation of the Effects of Nationwide Conservation Voltage Reduction on Peak-Load Shaving Using SOMAS Data

Evaluation of Conservation Voltage Reduction with Analytic Hierarchy Process: A Decision Support Framework in Grid Operations Planning

Small-Signal Refinement of Power System Static Load Modelling Techniques

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