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Ultra-Short-Term Prediction of Wind power Based on Chaos Theory and ABC Optimized RBF Neural Network

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Ultra-Short-Term Prediction of Wind Power Based on Chaos The...

China International Electrical and energy Conference (CIEEC)

作者： Yang Cui Shi Yan Huiquan Zhang Siyu Huang Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education (Northeast Electric Power University) Ji Lin China

ISBN: (数字)9781728116754

ISBN: (纸本)9781728116761

Wind energy has strong randomness and volatility, which makes the prediction accuracy of wind power low. In an attempt to improve the accuracy and robustness of ultra-short term prediction of wind power, a wind power prediction method in ultra-short term combining chaos theory and artificial bee colony(ABC) algorithm to optimize RBF neural network is studied in this paper. Firstly, based on chaos theory, the historical wind power data with the highest correlation is found, which is used for RBF neural network training after phase space reconstruction. Then, the artificial bee colony algorithm is used to solve the optimal RBF neural network parameters through multiple iterations to construct the optimized neural network model. Finally, considering the uncertainty of the prediction model and data noise, the prediction results are interval, and the robustness of the prediction results is evaluated by calculating the confidence of the corresponding interval. The results of the example show that the method proposed in this paper has good accuracy and robustness.

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Dual Nwp Wind Speed Correction Based on Trend Fusion and Fluctuation Clustering and its Application in Short-Term Wind power Prediction

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SSRN 2024年

作者： Yang, Mao Che, Runqi Yu, Xinnan Su, Xin Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin132012 China

Accurate wind power prediction (WPP) can effectively ensure the safe and stable operation of power system. At present, the main input of short-term power prediction based on data-driven model is numerical weather prediction (NWP), and its prediction accuracy leads to the failure to effectively improve the accuracy of short-term power prediction. To solve this problem, this paper proposes a dual NWP wind speed (WS) correction method based on trend fusion and fluctuation clustering. First, the WS trend is used to construct a new input feature. Secondly, the error is decomposed by Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), and the trend and fluctuation components are defined by Pearson coefficient. The trend curve features in the short 24h period were extracted from two dimensions of time and value, and the trend and fluctuation curve databases under different scenarios were formed by K-Medoids clustering. Finally, the trend component is modified by the Attention-GRU model, and the corresponding trend component is searched and matched from the database to correct the fluctuation component. The NWP WS is modified by the superposition of the two components and the short-term WPP is made. And the prediction accuracy was improved by 10% and 13.1% respectively。 © 2024, The Authors. All rights reserved.

关键词： Wind speed

power grid frequency regulation participated by photovoltaic generation to adapt to different control modes of grid-side AGC

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Power grid frequency regulation participated by photovoltaic...

8th renewable power Generation Conference (RPG 2019)

作者： Gangui Yan Wenbo Hu Qi Jia Dan Wang Guoqing Zhou Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin 132012 China

ISBN: (数字)9781839531248

ISBN: (纸本)9781839531255

Large-scale grid-connection of photovoltaic (PV) generation causes the power system to face the problem of insufficient inertia and insufficient frequency modulation capability. One of the important means to solve this problem is to take part in grid frequency regulation actively. On the basis of spare certain active power, this paper puts forward the active power-frequency droop control strategy. By modifying the original control structure of the inverter, PV generation actively takes part in the grid frequency primary regulation. In order to achieve non-error adjustment of frequency, this paper further proposes a secondary frequency regulation control strategy that adapts to different grid-side AGC control modes (constant frequency control mode, constant tie line power control mode and tie line power frequency deviation control mode). The effectiveness and feasibility of the control strategies are verified based on the PSCAD/EMTDC simulation platform.

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LOW CARBON OPTIMAL SCHEDULING OF INTEGRATED energy system CONSIDERING WASTE HEAT UTILIZATION UNDER THE COORDINATED OPERATION OF INCINERATION power PLANT AND P2G

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arXiv

arXiv 2024年

作者： Limeng, Wang Shuo, Wang Na, Wang Yuze, Ma Yang, Li Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Northeast Electric Power University Ministry of Education Jilin132012 China

In order to improve energy utilization and reduce carbon emissions, this paper presents a comprehensive energy system economic operation strategy of Incineration power plant power-to-gas (P2G) with waste heat recovery. First, consider the coordinated operation of Incineration power plant - P2G, introduce the refined power-to-gas two-stage operation process, add Hydrogen fuel cells on the basis of traditional power-to-gas to reduce the energy ladder loss, and recycle the Methanation reaction heat;Secondly, in order to improve the energy utilization efficiency of Incineration, it is considered to install a waste heat recovery device containing a water source heat pump to recover the waste heat of flue gas and consume some electric energy, sourced from wind power, and add a CO2 separation device to combine the recovered CO2 with P2G to synthesize CH4 to achieve carbon recycling. Finally, within the framework of a tiered carbon trading mechanism an IES optimization model for electricity-heat with the goal of minimizing the system operating cost is constructed, and the GUROBI modeling optimization engine is used to solve this model. The results verify the effectiveness of the model. © 2024, CC BY-NC-ND.

关键词： Waste heat

Dual Nwp Wind Speed Correction Based on Trend Fusion and Fluctuation Clustering and its Application in Short-Term Wind power Prediction

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SSRN 2024年

关键词： Wind speed

Short-Term Interval Prediction of Photovoltaic power Considering Correlation Information Matching of Meteorological Resources and Trend Mutation Perception

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SSRN 2023年

作者： Yang, Mao Li, Yi Zhang, Wei Su, Xin Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin 132012 China

Since errors in deterministic forecasting of photovoltaic power cannot be avoided, interval prediction can adequately characterize the uncertainty in photovoltaic power, which in turn provides further guidance for decision making in the dispatch department. The current interval prediction method is still incomplete in mining the physical change process of photovoltaic power, and there is a problem of low matching degree of gridded meteorological forecast data in the provincial area. Aiming at the above problems, a short-term interval prediction framework of photovoltaic power considering meteorological correlation information matching and trend mutation perception is proposed in this paper. Firstly, the NWP information is reconstructed for low matching degree power stations, and the weather type is predicted by photovoltaic output level. Next, the prediction interval is calculated based on the state transition probability matrices for trend-change factor, time-correlation factor, and numerical-mutation factor. Finally, the framework of this paper is applied to a photovoltaic power plant in Jilin Province, China, and the results of the arithmetic example show that, compared with other methods, the average RMSE of this paper's method is reduced by 5.3%, and the CWC is improved by at least 2.1%, which verifies the effectiveness of this paper's method. © 2023, The Authors. All rights reserved.

关键词： Decision making

A novel frequency regulation control strategy based on the external characteristics of PV generation

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A novel frequency regulation control strategy based on the e...

8th renewable power Generation Conference (RPG 2019)

作者： Gangui Yan Dan Wang Qi Jia Wenbo Hu Guoqing Zhou Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin 132012 China

ISBN: (数字)9781839531248

ISBN: (纸本)9781839531255

Large-scale grid-connected PV generation results in the power system facing with the problem of reduced inertia and inadequate frequency regulation capability, which is urgent for PV generation to take participate in frequency regulation. In this paper, the research object is the double-stage PV generation, a novel frequency regulation control strategy based on the external characteristics of PV generation is proposed. f-U PV compensation is introduced into DC low-voltage control loop, which makes the DC low-voltage vary with the grid frequency, thus controlling the output power of PV generation. The influence mechanism of the control strategy on the frequency dynamic process of power system is also analysed based on the frequency response model (SFR). The results show that: the larger the ΔU PV generated by the f-U PV compensation unit, the more power of PV generation can be generated; the control strategy essentially affects the damping coefficient of the system, which not only affects the transient process of the system frequency, but also the steady-state frequency deviation.

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Resilience assessment and improvement for electric power transmission systems against typhoon disasters: A data-model hybrid driven approach

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arXiv

arXiv 2022年

作者： Yang, Rui Li, Yang Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education [Northeast Electric Power University Jilin132012 China

In response to the damage to electric power transmission systems caused by typhoon disasters in coastal areas, a planning-targeted resilience assessment framework that considers the impact of multiple factors is established to accurately find the weak links of the transmission system and improve the system resilience. Firstly, this paper constructs the attenuation model of the wind field and the comprehensive failure model of the system, in which the model drive to establish the cumulative failure model of the transmission system is adopted, and multiple data-driven schemes to correct the cumulative failure rate by relying on the feature factor information of the transmission system is adopted. At the same time, an analytic hierarchy process-weighted arithmetic averaging (AHP-WAA) method is introduced to select the optimal data-driven evaluation scheme. Secondly, this paper adopts the impact-increment-based state enumeration (IISE) method to establish resilience indicators for systems and corridors separately. On this basis, the optimal promotion strategy is selected according to the construction difficulty, resilience improvement ability, and cost analysis. Finally, simulations on the IEEE RTS-79 system have been carried out considering the influence of the real typhoon scenarios, micro-topographic and transmission corridor information factors. The results demonstrate that the hybrid-driven system resilience assessment and improvement method can assist planners in accurately judging the resilience level of the system against typhoon disasters and selecting the best resilience improvement strategy based on the cost-effectiveness ratio. Copyright © 2022, The Authors. All rights reserved.

关键词： Analytic hierarchy process

Wind power Ultra-Short-Term Prediction Method Based on Nwp Wind Speed Correction and Double Clustering Division of Transitional Weather Process

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SSRN 2023年

作者： Yang, Mao Guo, Yunfeng Huang, Yutong Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education [Northeast Electric Power University Jilin132012 China

Wind power prediction technology is important for building novel power systems with a high proportion of renewable energy. Numerical weather prediction (NWP) data quality has a significant impact on the accuracy of ultra-short-term wind power prediction (USTWPP). However, existing NWP do not reflect the adaptability of different weather processes, because of it’ s forecasting errors. In view of this, this paper proposes an USTWPP method based on NWP wind speed correction and transitional weather process division. The BILSTM-INFO-HKELM was first used to correct the NWP wind speed, and then the multiple clustering method was used to divide the transitional weather processes to establish a model for USTWPP based on different scenarios, the overall method was finally applied to a wind farm in west inner Mongolia, China. Compared to the pre-correction, the wind speed forecasted RMSE was reduced by 1.702 and the MAE by 1.366. Based on the wind power ultra-short-term prediction method proposed in this paper, the average reduction in RMSR is 4.96% and in MAE is 4.36% compared to the various comparison methods in this paper. The wind power ultra-short-term prediction method combining wind speed correction and double clustering division of weather scenarios can significantly improve prediction accuracy. © 2023, The Authors. All rights reserved.

关键词： Wind power