Probabilistic Modelling of Demand Diversity and its Relationship with Electricity Market Outcomes

Ziser, C. J.; Dong, Zhaoyang; Saha, Tapan Kumar

doi:10.1109/pes.2007.385774

Cited by 4 publications

(4 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Additionally, the degree to which inter-regional weather patterns and resultant demand patterns are correlated (or the level of demand diversity) changes historically from year to year, for different weather pattern scenarios (Ziser, Dong, and Saha 2007). Finally, an approach is required such that the underlying assumptions which the intra-year demand fluctuations and long-term forecasts are based on align, and that would allow for probabilistic modelling of demand forecasts as opposed to deterministic.…”

Section: Demand Forecasts For Market Studiesmentioning

confidence: 99%

See 1 more Smart Citation

Incorporating weather uncertainty in demand forecasts for electricity market planning

Ziser

Zhang

Wong

2012

International Journal of Systems Science

View full text Add to dashboard Cite

A major component of electricity network planning is to ensure supply capability into the future, through generation and transmission development. Accurate forecasts of maximum demand are a crucial component of this process, with future weather conditions having a large impact on forecast accuracy. This article presents an improved methodology for the consideration of weather uncertainty in electricity demand forecasts. Case studies based on the Australian national electricity market are used to validate the proposed methodology.

show abstract

Section: Demand Forecasts For Market Studiesmentioning

confidence: 99%

“…Demand models are developed using the ELM regression technique, and the SVM regression technique using RBF kernel (as detailed in references Ziser et al (2007) and ) for comparative purposes. Historical data for daily maximum demands from January 2000 to June 2007 were used for model training.…”

Section: Daily Maximum Demand Modellingmentioning

confidence: 99%

Incorporating weather uncertainty in demand forecasts for electricity market planning

Ziser

Zhang

Wong

2012

International Journal of Systems Science

View full text Add to dashboard Cite

show abstract

“…In addition, the diversity factor, defined as the ratio between the sum of individual peak demand in a group of customers and the coincident peak demand of this group, has been used for peak demand estimation in [7]. Other metrics such as the coincidence factor [8] and the conversion factor have also been applied for network planning. It is constructive to note that most of the above-mentioned metrics can only provide a single estimation value obtained based on heuristic formulation and engineering judgment, which is not suitable for the applications considering uncertainties.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic Peak Load Estimation in Smart Cities Using Smart Meter Data

Sun

Wang

Štrbac

et al. 2019

IEEE Trans. Ind. Electron.

View full text Add to dashboard Cite

Adequate capacity planning of substations and feeders primarily depends on an accurate estimation of the future peak electricity demand. Traditional coincident peak demand estimation is carried out based on the empirical metric, after diversity maximum demand, indicating individual peak consumption levels and demand diversification across multiple residents. With the privilege of smart meters in smart cities, this paper proposes a datadriven probabilistic peak demand estimation framework using fine-grained smart meter data and sociodemographic data of the consumers, which drive fundamental electricity consumptions across different categories. In particular, four main stages are integrated in the proposed approach: load modeling and sampling via the proposed variable truncated R-vine copulas method, correlation-based customer grouping, probabilistic normalized maximum diversified demand estimation, and probabilistic peak demand estimation for new customers. Numerical experiments have been conducted on real demand measurements across 2639 households in London, collected from Low Carbon London project's smart-metering trial. The mean absolute percentage error and the pinball loss function are used to quantitatively demonstrate the superiority of the proposed approach in terms of the point estimate value and the probabilistic result, respectively.

show abstract

“…Another metric of interest is the coincidence factor (CF), defined as the ratio of the coincident peak demand of a group of customers to the sum of individual peak demands in this group [7]. CF has also been widely used in the past for planning studies as in [8] and [9]. Other metrics such as the diversity factor and conversion factor have also been proposed in the past for planning purposes as showcased in [10]- [12].…”

Section: Introductionmentioning

confidence: 99%

Analysis of diversified residential demand in London using smart meter and demographic data

Sun

Konstantelos

Štrbac

2016

2016 IEEE Power and Energy Society General Meeting (PESGM)

View full text Add to dashboard Cite

In the interest of economic efficiency, design of distribution networks should be taillored to the demonstrated needs of its consumers. However, in the absence of detailed knowledge related to the characteristics of electricity consumption, planning has traditionally been carried out on the basis of empirical metrics; conservative estimates of individual peak consumption levels and of demand diversification across multiple consumers. Although such practices have served the industry well, the advent of smart metering opens up the possibility for gaining valuable insights on demand patterns, resulting in enhanced planning capabilities. This paper is motivated by the collection of demand measurements across 2,639 households in London, as part of Low Carbon London project's smart-metering trial. Demand diversity and other metrics of interest are quantified for the entire dataset as well as across different customer classes, investigating the degree to which occupancy level and wealth can be used to infer peak demand behavior.

show abstract

Probabilistic Modelling of Demand Diversity and its Relationship with Electricity Market Outcomes

Cited by 4 publications

References 12 publications

Incorporating weather uncertainty in demand forecasts for electricity market planning

Incorporating weather uncertainty in demand forecasts for electricity market planning

Probabilistic Peak Load Estimation in Smart Cities Using Smart Meter Data

Analysis of diversified residential demand in London using smart meter and demographic data

Contact Info

Product

Resources

About