Transaction-Oriented Dynamic Power Flow Tracing for Distribution Networks—Definition and Implementation in GIS Environment

Vega‐Fuentes, Eduardo; Yang, Jin; Lou, Chengwei; Meena, Nand K.

doi:10.1109/tsg.2020.3033625

Cited by 11 publications

(2 citation statements)

References 36 publications

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“…Load forecasting plays a pivotal role in distribution systems operation, such as congestion management [1] and energy transaction [2]. However, it is widely acknowledged that accurate short-term load forecast (STLF) in distribution systems is challenging due to the uncertain characteristics of loads.…”

Section: Introductionmentioning

confidence: 99%

Optimal Adaptive Prediction Intervals for Electricity Load Forecasting in Distribution Systems via Reinforcement Learning

Zhang

Wen

et al. 2023

IEEE Trans. Smart Grid

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Prediction intervals (PIs) offer an effective tool for quantifying uncertainty of loads in distribution systems. The traditional central PIs cannot adapt well to skewed distributions, and their offline training fashion is vulnerable to the unforeseen change in future load patterns. Therefore, we propose an optimal PI estimation approach, which is online and adaptive to different data distributions by adaptively determining symmetric or asymmetric probability proportion pairs for quantiles of PIs' bounds. It relies on the online learning ability of reinforcement learning (RL) to integrate the two online tasks, i.e., the adaptive selection of probability proportion pairs and quantile predictions, both of which are modeled by neural networks. As such, the quality of quantiles-formed PI can guide the selection process of optimal probability proportion pairs, which forms a closed loop to improve PIs' quality. Furthermore, to improve the learning efficiency of quantile forecasts, a prioritized experience replay (PER) strategy is proposed for online quantile regression processes. Case studies on both load and net load demonstrate that the proposed method can better adapt to data distribution compared with online central PIs method. Compared with offlinetrained methods, it obtains PIs with better quality and is more robust against concept drift.

show abstract

Section: Introductionmentioning

confidence: 99%

Optimal Adaptive Prediction Intervals for Electricity Load Forecasting in Distribution Systems via Reinforcement Learning

Zhang

Wen

et al. 2023

IEEE Trans. Smart Grid

View full text Add to dashboard Cite

show abstract

“…Load forecasting plays a pivotal role in distribution system operations, such as congestion management [1], energy transaction [2], etc. However, it is widely acknowledged that accurate short-term load forecast (STLF) in distribution systems is difficult due to the uncertain characteristics of loads.…”

Section: Introductionmentioning

confidence: 99%

Optimal Adaptive Prediction Intervals for Electricity Load Forecasting in Distribution Systems via Reinforcement Learning

Zhang¹,

Wen²,

Wu³

et al. 2022

Preprint

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Prediction intervals (PIs) offer an effective tool for quantifying uncertainty of loads in distribution systems. The traditional central PIs cannot adapt well to skewed distributions, and their offline training fashion is vulnerable to the unforeseen change in future load patterns. Therefore, we propose an optimal PI estimation approach, which is online and adaptive to different data distributions by adaptively determining symmetric or asymmetric probability proportion pairs for quantiles of PIs’ bounds. It relies on the online learning ability of reinforcement learning (RL) to integrate the two online tasks, i.e., the adaptive selection of probability proportion pairs and quantile predictions, both of which are modeled by neural networks. As such, the quality of quantiles-formed PI can guide the selection process of optimal probability proportion pairs, which forms a closed loop to improve PIs’ quality. Furthermore, to improve the learning efficiency of quantile forecasts, a prioritized experience replay (PER) strategy is proposed for online quantile regression processes. Case studies on both load and net load demonstrate that the proposed method can better adapt to data distribution compared with online central PIs method. Compared with offline-trained methods, it obtains PIs with better quality and is more robust against concept drift.

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P2P energy trading via public power networks: Practical challenges, emerging solutions, and the way forward

Zhou

Wang

2023

Front. Energy

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Peer-to-peer (P2P) energy trading is an emerging energy supply paradigm where customers with distributed energy resources (DERs) are allowed to directly trade and share electricity with each other. P2P energy trading can facilitate local power and energy balance, thus being a potential way to manage the rapidly increasing number of DERs in net zero transition. It is of great importance to explore P2P energy trading via public power networks, to which most DERs are connected. Despite the extensive research on P2P energy trading, there has been little large-scale commercial deployment in practice across the world. In this paper, the practical challenges of conducting P2P energy trading via public power networks are identified and presented, based on the analysis of a practical Local Virtual Private Networks (LVPNs) case in North Wales, UK. The ongoing efforts and emerging solutions to tackling the challenges are then summarized and critically reviewed. Finally, the way forward for facilitating P2P energy trading via public power networks is proposed.

show abstract

Transaction-Oriented Dynamic Power Flow Tracing for Distribution Networks—Definition and Implementation in GIS Environment

Cited by 11 publications

References 36 publications

Optimal Adaptive Prediction Intervals for Electricity Load Forecasting in Distribution Systems via Reinforcement Learning

Optimal Adaptive Prediction Intervals for Electricity Load Forecasting in Distribution Systems via Reinforcement Learning

Optimal Adaptive Prediction Intervals for Electricity Load Forecasting in Distribution Systems via Reinforcement Learning

P2P energy trading via public power networks: Practical challenges, emerging solutions, and the way forward

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