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FRONTIERS IN energy RESEARCH 2022年 10卷

作者： Sun, Yong Huang, Yutong Yang, Mao Key Laboratory of Modern Power System Simulation and Control and Renewable Energy Technology Ministry of Education (Northeast Electric Power University) Jilin China State Grid Jilin Electric Power Company Ltd. Changchun China

As errors in point forecasts of wind power are unavoidable, interval forecasts can adequately describe the uncertainty in wind power and thus provide further guidance to dispatchers in their decision making. Current interval prediction methods are still incomplete in terms of tapping into the physical variability of wind power, especially for the specific time scale of the ultra-short term. This paper therefore proposes a new framework for interval forecasting of ultra-short-term wind power that incorporates the power fluctuation process. Firstly, a fluctuating process of wind power series is defined and a Kalman-SOM method for clustering the fluctuating processes of wind power is constructed. Secondly, a quantile regression forest interval prediction model is constructed for multiple fluctuation processes for ultra-short-term time scales. Finally, the effectiveness of the overall framework is validated at a wind farm in Jilin Province, China. Compared with the traditional interval prediction method. The interval bandwidth is reduced by 0.86% on average, and the interval coverage is increased by 1.4% on average. The results demonstrate the effectiveness and feasibility of the method in this paper.

关键词： wind power ultra-short-term prediction self-organizing map fluctuation quantile regression forest

Coordination of Electricity and Heat Dispatch Strategy Combining Carbon Trading and Peak Shaving Compensation Mechanism

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Coordination of Electricity and Heat Dispatch Strategy Combi...

2020 International Conference on Electronic, Electrical and Computer Applications, ICEECA 2020

作者： Ge, Weichun Li, Zhao Zhao, Dong Li, Zhenyu Ye, Qing Fu, Yu Yu, Na State Grid Liaoning Province Electric Power Company Limited Shenyang110006 China Key Laboratory of Modern Power System Simulation and Control and Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin132012 China Fuxin Power Supply Company of Liaoning Electric Power Co. Ltd Liaoning Fuxin123000 China

Integrated power generation group's participation in the carbon trading market and peaking auxiliary service market is one of the effective ways to promote low carbon power development and wind power consumption. Aiming at the serious problem of wind abandonment during the low temperature period of winter heating period in Northeast China, a multi-market mechanism is proposed. The carbon trading is analysed. Firstly, and the regional peak demand optimization model based on multi-scenario stochastic programming and the maximum wind power acceptance power calculation model of regional power grid is constructed to calculate the peak shaving demand and existing peak shaving in each period of the regional power grid. Maximum wind power acceptance under resources;Based on the coordination of carbon quotas among power generation groups and the subsection compensation mechanism of compensated peak shaving costs, and aiming at the minimum sum of total coal consumption costs, wind abandonment penalties and compensation costs, integrated dispatching model considering carbon trading and peak shaving compensation is established. Finally, an example is given to verify the rationality and effectiveness of the proposed strategy. © 2020 Published under licence by IOP Publishing Ltd.

关键词： Wind power

Optimal Scheduling of Multiple energy Storage-Thermal power Units under Electricity Market 4

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Optimal Scheduling of Multiple Energy Storage-Thermal Power ...

4th IEEE New energy and energy Storage system control Summit Forum, NEESSC 2024

作者： Li, Baoju Xu, Shuguang Fu, Jiyue Fu, Xiaobiao Wang, Yao Zhu, Xingxu Li, Cuiping Li, Junhui Dispatching and Control Center State Grid Jilin Electric Power Co. Ltd Chang Chun Jilin130021 China Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin132012 China The Power Dispatching and Control Center State Grid Jilin Electric Power Co. Ltd Chang Chun Jilin130021 China

ISBN: (纸本)9798350389012

Aiming at the increasing burden of power system peaking caused by the integration of large-scale new energy generation into the grid and the imperfect participation mechanisms for peaking entities in the power market, this paper proposes an optimal dispatch method for multiple storage and thermal power units. Firstly, to reduce the impact of the volatility of new energy generation on system peaking, the planned market trading power of new energy is optimally allocated with the goal of minimizing the net load on the grid. Secondly, a multi-storage lease transaction mechanism and an energy storage lease declaration model based on the whole life cycle cost are designed, and the charging and discharging power of individual storage units is optimized with the objective of maximizing revenue from multi-storage leasing. Finally, typical daily data are selected and simulated in the improved IEEE 30-bus system. The results show that the proposed method reduces the curtailment rate of new energy and effectively improves the economic efficiency of peaking for power market entities. © 2024 IEEE.

关键词： Charge storage

Multi-Timescale Risk Scheduling for Transmission and Distribution Networks for Highly Proportional Distributed energy Access

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SSRN 2023年

作者： Wang, He Liu, Ziyue Bian, Jing Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin132012 China

With the incorporation of more renewable energy sources, distribution networks are shifting from passive to active. The enhanced coupling of transmission and distribution networks is enhanced and bidirectional tides affect the reliable and secure operation of such networks. This paper proposes a multi-timescale risk dispatch study for transmission and distribution networks that have a high proportion of distributed energy access to meet the rising demand for coupling between the two types of networks. First, a day-ahead scheduling model that takes into account various operational risks is developed with the goal of minimizing total operating costs, and it optimizes the source network load and storage of multiple adjustable resources of the transmission network and distribution network jointly under the risk limit constraint. Second, an intraday scheduling model is built with the goal of minimizing the overall risk value of the transmission network and distribution network, and the day-ahead scheduling plan is modified to realize this, considering the magnitude of different operational risk values of the transmission network and distribution network. Finally, the effectiveness of the proposed multi-time-scale risk dispatch model is verified through the analysis of arithmetic cases, and it is proved that the model can enable the interaction and coordination between transmission and distribution networks as well as the synergistic optimization of operating costs and operating risks. © 2023, The Authors. All rights reserved.

关键词： Electric power transmission networks

Fault Location Method of Dc Distribution Network Based on Multivariate Information Fusion

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SSRN 2023年

作者： Wang, He Zhang, Zekun Yu, Huanan Li, Shiqiang Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin132012 China

The accuracy of Direct Current (DC) distribution network fault location for a single electrical or switching quantity fault information is difficult to meet the requirements. This paper proposes a fault location method of DC distribution network based on multivariate information fusion. Firstly, the compressed sensing algorithm is used to reconstruct the fault transient electrical data to obtain the sparse characteristics of the high-frequency fault current. Secondly, the Bayesian network is established to obtain the fault probability of the faulty component based on the protection action inf0ormation generated by the faulty component, and then the sparse fault characteristics and fault probability of the fault current are normalized to obtain the fault degree of the electrical quantity and switching quantity. Finally, the traditional Dempster Shafer (DS) evidence theory fusion algorithm is improved to solve the problem of conflict of uncertain subsets by weight allocation method, and then the fault degree of electrical quantity and switching quantity is fused by combining Bayesian estimation method and weighting method, so as to achieve accurate fault location. simulation experiments conducted using MATLAB software demonstrate that the proposed method can effectively reduce the evidence conflict at the fault point, and the fault location accuracy is improved by 2% to 4% compared with the traditional DS evidence theory, which has a good engineering application prospect for the fault location of DC distribution network. © 2023, The Authors. All rights reserved.

关键词： Bayesian networks

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.

关键词：

Strategic Optimization and Demand Response for Thermal Load Management in Multi-Regional Integrated energy systems: A Stackelberg Game Approach

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arXiv

arXiv 2024年

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

In the context of high fossil fuel consumption and inefficiency within China's energy systems, effective demand-side management is essential. This study examines the thermal characteristics of various building types across different functional areas, utilizing the concept of body coefficient to integrate their unique structural and energy use traits into a demand response framework supported by real-time pricing. We developed a Stackelberg game-based bi-level optimization model that captures the dynamic interplay of costs and benefits between integrated energy providers and users. This model is formulated into a Mixed Integer Linear Programming (MILP) problem using Karush-Kuhn-Tucker (KKT) conditions and linearized with the Big M method, subsequently solved using MATLAB and CPLEX. This approach enables distinctive management of heating loads in public and residential areas, optimizing energy efficiency while balancing the interests of both providers and users. Furthermore, the study explores how the proportion of different area types affects the potential for reducing heat loads, providing insights into the scalability and effectiveness of demand response strategies in integrated energy systems. This analysis not only highlights the economic benefits of such strategies but also their potential in reducing dependency on traditional energy sources, thus contributing to more sustainable energy system practices. © 2024, CC BY-NC-ND.

关键词： Demand response

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

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

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