Assessing climate sensitivity of peak electricity load for resilient power systems planning and operation: A study applied to the Texas region

Alipour, Panteha; Mukherjee, Sayanti; Nateghi, Roshanak

doi:10.1016/j.energy.2019.07.074

Cited by 46 publications

(31 citation statements)

References 27 publications

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“…Climate, technological, and socioeconomic factors are commonly used in predictive models of electricity demand 1,2 to ensure reliable planning and operation in the electricity sector by adequately balancing supply and demand. However, more frequent and intense climate extremes such as sustained heat waves 3,4 cause unanticipated changes in load 5 , challenging the reliability of electricity demand predictions. This poses a significant risk to the resilient operation of power systems 6 .…”

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confidence: 99%

The critical role of humidity in modeling summer electricity demand across the United States

2020

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Cooling demand is projected to increase under climate change. However, most of the existing projections are based on rising air temperatures alone, ignoring that rising temperatures are associated with increased humidity; a lethal combination that could significantly increase morbidity and mortality rates during extreme heat events. We bridge this gap by identifying the key measures of heat stress, considering both air temperature and near-surface humidity, in characterizing the climate sensitivity of electricity demand at a national scale. Here we show that in many of the high energy consuming states, such as California and Texas, projections based on air temperature alone underestimates cooling demand by as much as 10-15% under both present and future climate scenarios. Our results establish that air temperature is a necessary but not sufficient variable for adequately characterizing the climate sensitivity of cooling load, and that near-surface humidity plays an equally important role.

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confidence: 99%

The critical role of humidity in modeling summer electricity demand across the United States

2020

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“…In this case, we compare the proposed CMSDM with the method proposed by [19], [34], and [35], which can be referred to as the HEFM, BART, and Bi-LSTM method based on AM and RU. The datasets used in this case are the daily HEFM is an online second learning method based on multimodels, and it uses LSSVM as the decision model in the decision stage.…”

Section: Case Iv: Comparison Between Cmsdm and Other State-of-the-mentioning

confidence: 99%

“…Bayesian additive regression trees (BART) is a Bayesian sum-of-tree model [36]. In STLF, BART is considered to be an accurate model that could effectively capture the nexus between electricity consumption and climate variability [34]. In this case, we compare the CMSDM with HEFM and BART based on the dataset of the daily total demand of New England.…”

Section: Case Iv: Comparison Between Cmsdm and Other State-of-the-mentioning

confidence: 99%

Short-Term Power Load Forecasting Based on Cross Multi-Model and Second Decision Mechanism

2020

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Short-term load forecasting (STLF) plays a vital role in the reliable, secure, and efficient operation of power systems. Since electric load variation results from diverse factors, accurate and stable load forecasting remains a challenging task. To increase the forecasting accuracy and stability, in this paper, we newly propose a short-term load forecasting method based on the cross multi-model and second decision mechanism. First, we combine horizontal and longitudinal training set selection method to construct the cross training sets, which acquire both the horizontal and longitudinal characteristics of the load variation. Second, to improve the generalization ability and extend the application scope, we construct forecasting multi-models by training multiple forecasting algorithms with cross training sets. Finally, to aggregate the forecasting outputs obtained by the forecasting multi-models, we propose a second decision mechanism based on a decision multi-model and adaptive weight allocation strategy, which overcomes the limited learning ability shortcoming of single decision models and further improves the forecasting accuracy. Case studies based on electrical load data from the state of Maine, the region of New England, Singapore, and New South Wales of Australia show that both the accuracy and the stability of the proposed method are superior to the compared models. INDEX TERMS Short-term load forecasting, multi-model, cross training set, second decision mechanism, model aggregation

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“…In this section, a comparative study is conducted on AP-SLET, state of the art forecasting method [30], ANN-FTL [9], and BART [39]. Reference [30] proposed a training set construction method based on the day-to-day topological network.…”

Section: E Comparative Experiments On Ap-slet and State-of-the-art Fomentioning

confidence: 99%

“…To compare the result with BART, both BART and AP-SLET are applied to ERCOT [40]. Similar to [39], detrending is performed prior to applying AP-SLET. As shown in Table 7, compared to BART, RMSE, and MAE of AP-SLET are reduced by 34.05% and 35.38% respectively.…”

Section: E Comparative Experiments On Ap-slet and State-of-the-art Fomentioning

confidence: 99%

An Adaptive and Parallel Forecasting Strategy for Short-Term Power Load Based on Second Learning of Error Trend

2020

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Modeling an accurate forecasting model for short-term load is still challenging due to the diverse causes of load changing and lack of information on many of these causes. In this paper, error trend is used to reveal the trend effect caused by unknown load affecting factors and proposed adaptive second learning of error trend (A-SLET) to self-adapt the trend effect. Furthermore, the training set is classified based on balance point temperature and then parallelly trained and tested adaptive forecaster for hot days and adaptive forecaster for cold days with proper data. Combining A-SLET with parallel forecasting and training set classification, Adaptive and Parallel forecasting strategy based on Second Learning of Error Trend (AP-SLET) is proposed. The work studied two distinct load patterns, one in the USA and the other in Australia. Considering the yearly forecasting horizon, MAPE of the adaptive and parallel forecasting strategy is 1.87%-4.04% for ME-Maine of New England and 2.81%-4.41% for New South Wales. Compared to the state-of-art forecasting methods, MAPE of the adaptive and parallel forecasting strategy is reduced by 17.03%-33.33%, RMSE and MAE are reduced by 34.05% and 35.38% respectively. The experimental results demonstrate the proposed strategy can transform unknown and unavailable load affecting factors into known forecasting features and then adapt it to improve forecasting performance. The proposed strategy is also forecaster independent and equally applicable to almost all load scenarios regardless of geographical and seasonal differences. INDEX TERMS Adaptive and parallel forecasting, short-term load forecasting, smart grid, second learning of error trend, training set classification.

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Assessing climate sensitivity of peak electricity load for resilient power systems planning and operation: A study applied to the Texas region

Cited by 46 publications

References 27 publications

The critical role of humidity in modeling summer electricity demand across the United States

The critical role of humidity in modeling summer electricity demand across the United States

Short-Term Power Load Forecasting Based on Cross Multi-Model and Second Decision Mechanism

An Adaptive and Parallel Forecasting Strategy for Short-Term Power Load Based on Second Learning of Error Trend

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