Adaptive data decomposition based quantile-long-short-term memory probabilistic forecasting framework for power demand side management of energy system

Yang, Wei; Li, Jia; Xu, Yan

doi:10.1016/j.compeleceng.2023.108584

Cited by 7 publications

(4 citation statements)

References 19 publications

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“…According to Table 10 and Figure 14, the capacity configuration of energy storage in different application scenarios and application functions has been widely studied from the application distribution of energy storage in the power grid. 1) Renewable energy power generation mainly includes smoothing power fluctuation, [ 76–80 ] reducing reserve capacity, new energy output plan tracking, [ 81,82 ] new energy output climbing control, [ 42,82 ] improving power prediction error, et al 2) Transmission and distribution network side functions mainly include: optimizing new energy grid connection, delaying transmission and distribution line congestion, [ 83 ] peak shaving and valley filling, [ 84–87 ] improving power supply quality, [ 88–91 ] et al Considering the application function of energy storage to stabilize wind power fluctuation, the articler in ref. [92] proposes supercapacitor energy storage and battery energy storage based on wavelet packet decomposition.…”

Section: Discussion On Applicability Profit Model Challenges and Pros...mentioning

confidence: 99%

“…2) Transmission and distribution network side functions mainly include: optimizing new energy grid connection, delaying transmission and distribution line congestion, [83] peak shaving and valley filling, [84][85][86][87] improving power supply quality, [88][89][90][91] et al Considering the application function of energy storage to stabilize wind power fluctuation, the articler in ref. [92] proposes supercapacitor energy storage and battery energy storage based on wavelet packet decomposition.…”

Section: Applicabilitymentioning

confidence: 99%

See 1 more Smart Citation

Life Cycle Assessment of Energy Storage Technologies for New Power Systems under Dual‐Carbon Target: A Review

Yi,

Chang,

et al. 2024

Energy Tech

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Aiming at the grid security problem such as grid frequency, voltage, and power quality fluctuation caused by the large‐scale grid‐connected intermittent new energy, this article investigates the life cycle assessment of energy storage technologies based on the technical characteristics and performance indicators. First, the new power system under dual‐carbon target is reviewed, which is compared with the traditional power system from the generation side, grid side, and user side. Based on the power characteristics of the new power system, the energy storage mechanism and energy storage characteristics of mechanical energy storage, electrochemical energy storage, chemical energy storage, electromagnetic energy storage, and thermal energy storage are described. Then, compared with the existing research strategies, a comprehensive life cycle assessment of energy storage technologies is carried out from four dimensions: technical performance, economic cost, safety assessment, and environmental impact. Moreover, the suitable scenarios and application functions of various energy storage technologies on the power generation side, grid side, and user side are compared and analyzed from the working principle, key technologies, advantages and disadvantages, and technical difficulties of energy storage technologies. Finally, combined with the energy storage technologies in the electricity market, four profit models are analyzed.

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Section: Discussion On Applicability Profit Model Challenges and Pros...mentioning

confidence: 99%

Section: Applicabilitymentioning

confidence: 99%

Life Cycle Assessment of Energy Storage Technologies for New Power Systems under Dual‐Carbon Target: A Review

Yi,

Chang,

et al. 2024

Energy Tech

View full text Add to dashboard Cite

show abstract

“…Furthermore, considerable emphasis has been placed on exploring artificial intelligence-driven probabilistic forecasting methods, such as neural networks and deep learning, which have emerged as prominent techniques in this field [6][7][8]. In the field of microgrids, there exist cases where probabilistic methods have been used for load forecasting [9][10]. In Reference [9], a probabilistic normal load forecasting model was built using the artificial neural network (ANN).…”

Section: Introductionmentioning

confidence: 99%

“…In Reference [9], a probabilistic normal load forecasting model was built using the artificial neural network (ANN). Reference [10] proposed an adaptive data decomposition based quantile-long-short-term memory(QLSTM) probabilistic forecasting framework to reflect the future load information more comprehensively. However, forecasting the peak load of residential users within a month is more challenging compared to forecasting load of a microgrid.…”

Section: Introductionmentioning

confidence: 99%

Forecasting-based electricity tariff selection for resident users with photovoltaic and energy storage considering forecast uncertainties

Liu,

Sun,

Wang

et al. 2023

Preprint

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With the diversification of electricity price structures, an increasing number of power utilities have incorporated demand charges into their tariff structures. Individual households equipped with photovoltaic (PV) and energy storage systems can adapt to this trend by adopting appropriate energy management strategies. Due to the high uncertainty in photovoltaic and load generation for household users, it is often challenging for them to make the most advantageous choice among the diverse electricity tariffs. This paper proposes a rolling prediction method based on Long Short-Term Memory (LSTM) networks for monthly peak power demand, taking into account historical peak power and applying corrective measures within the same month. Additionally, a profit evaluation method for electricity tariff schemes considering forecast uncertainties is presented. The predictive capabilities of load and PV power are characterized using kernel density estimation, and a large number of scenarios are generated using Monte Carlo simulation. A probabilistic economic evaluation is conducted for different tariff schemes, enabling the optimal selection of electricity tariffs. To validate the effectiveness of the proposed methods, analysis is performed using household data from Arizona, and the results demonstrate that the proposed methods can reduce electricity expenses and help households choose the correct electricity tariff scheme.

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Shipping market time series forecasting via an Ensemble Deep Dual-Projection Echo State Network

Song,

Chen

2024

Computers and Electrical Engineering

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Adaptive data decomposition based quantile-long-short-term memory probabilistic forecasting framework for power demand side management of energy system

Cited by 7 publications

References 19 publications

Life Cycle Assessment of Energy Storage Technologies for New Power Systems under Dual‐Carbon Target: A Review

Life Cycle Assessment of Energy Storage Technologies for New Power Systems under Dual‐Carbon Target: A Review

Forecasting-based electricity tariff selection for resident users with photovoltaic and energy storage considering forecast uncertainties

Shipping market time series forecasting via an Ensemble Deep Dual-Projection Echo State Network

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