Integrated Intelligent Energy

Review on situational awareness technology in a low-carbon oriented new power system

GE Leijiao, CUI Qingxue, LI Mingwei, LIU Zifa, XIA Mingchao

2023, 45(1): 1-13. doi:10.3969/j.issn.2097-0706.2023.01.001

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Constructing a new energy oriented power system is not only an important direction for the power system transformation and upgrading in China，but also a key means to achieve the goals of carbon peaking and carbon neutrality. The way of adapting situational awareness technology to diverse and differentiated scenarios has become the breakthrough point of the reliable，safe，high-quality，low-carbon and economic operation of the new power system. Typical features and main problems of the new power system are analysis from four aspects， source， network， load and energy storage. Then， to achieve the low-carbon and economic operation of the new power grid， key points in three application phases of situational awareness technology in the power grid which are situational perception， situational understanding and situational forecast are expounded. In the end， application and prospects of the situational awareness technology in the low-carbon oriented new power system are elaborated by taking the characteristics of low-carbon and economic operation of the new power system into consideration， which provides reference for the construction and operation of subsequent new power systems.

Prediction model of stochastic power in industrial parks based on Markov chain

WEI Yanping, WANG Jun, LI Nanfan, SHI Changli

2023, 45(1): 14-22. doi:10.3969/j.issn.2097-0706.2023.01.002

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To promote the consumption of distributed energy in industrial parks， a multi-scenario prediction model based on Markov chain is proposed for stochastic power prediction. Firstly， ARIMA model and Markov chain are respectively used to build the load model of a park according to the random characteristics of renewable energy and the load. Then， posterior information is used to adjust the Markov probability matrix adaptively to improve its prediction accuracy which is affected by the periodical fluctuation of load varying with production，season or other factors. To improve the accuracy of multi-step prediction in a prediction horizon，a multi-scenario prediction model based on scenario tree is proposed， considering the multi-step prediction scenarios and their probabilities. The model can make more effective use of Markov probability matrix. Finally，a case study is implemented based on historical power data of an industrial park. The results show that compared with the unadjusted Markov model， the adaptive model is of a lower load prediction error，and the prediction error of the one with 7-day adjustment time is as low as 0.034 5 p.u. Compared with the commonly used Maximum Likelihood Estimation，the proposed multi-scenario prediction model is of a lower weighted average error.

Causality analysis of climate sensitive loads in integrated energy system based on convergence cross mapping

JIN Li, ZHANG Li, REN Juguang, TANG Yang, TANG Qiao, LIU Xiaobing

2023, 45(1): 23-30. doi:10.3969/j.issn.2097-0706.2023.01.003

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Climate sensitive loads in integrated energy system （CSLs-IES） refer to the electric， cold， heat and other multiple loads that are significantly affected by climate conditions. The loads are of strong randomness， uncertainty and coupling transformation relationships，and their characteristic impacted by climate factors are difficult to be quantified accurately by existing analysis methods. Taking an integrated energy system as the study object， its load characteristics are analysed. Starting from establishing an index system considering a single or multiple meteorological elements， the strong correlation periods are extracted by sliding window method， then the causality analysis model and calculation process are constructed based on the causality strength and sensitivity calculated by convergent cross mapping （CCM）.The proposed model is verified by a case and its results show that the proposed method can effectively identify the impact level of meteorological elements on multiple loads， which is important for the further research on energy system planning and load forecasting.

Ultra-short-term load forecasting based on BiLSTM network and error correction

GAO Ming, HAO Yan

2023, 45(1): 31-40. doi:10.3969/j.issn.2097-0706.2023.01.004

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Accurate power load forecasting is important to maintain the balance of power supply and demand and the economy and stability of power system. Since power load are random and volatile under the influence of meteorological factors，accurate forecasting on power load is a technical challenge. An ultra-short-term load forecasting model is built based on bi-directional long short-term memory （BiLSTM） neural network and error correction. Maximum information coefficient （MIC） is used to describe the nonlinear relationships between the various influencing factors and load data， so as to filter the input characteristics. Considering the time-series characteristics of the load sequence， the initial load forecasting model is established based on the BiLSTM network. To lessen the prediction error， complete ensemble empirical mode decomposition with adaptive noise（CEEMDAN）algorithm is used to decompose the error sequence into several error components， whose BiLSTM prediction models are built respectively to modify the initial predicted results. The simulation experiment on a power distribution network in Tianjin， a northern municipality in China， is carried out. The experimental results show that the prediction values made by the BiLSTM-based model is more accurate that made by the models based on other neural networks.

Short-term new energy power prediction based on TL-LSTM

ZHENG Zhen, ZHU Feng, MA Xiaoli, TIAN Shuxin, JIANG Haozhe

2023, 45(1): 41-48. doi:10.3969/j.issn.2097-0706.2023.01.005

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New energy power prediction is the key to realize accurate situation awareness of active distribution network.To deal with the uncertainty and volatility brought by new energy power generation，a transfer learning and long short-term memory（TL-LSTM）-based power prediction method is proposed.First，the k-shape clustering algorithm is used to cluster the time-series data provided by new energy sources in different regions，while each cluster can generate several training models.Then，the clusters closest to the target sequence are selected as auxiliary data clusters using shape-based distance （SBD）metrics for migration learning.The training of TL-LSTM models is completed with the pre-trained models corresponding to the auxiliary data clusters，and the difference treatment is used in all the model training processes to avoid the prediction lags.Finally，the effectiveness of the proposed prediction method is verified by a wind farm and a photovoltaic power plant in China as typical examples.The results show that the method improves the accuracy of short-term prediction on new energy units'outputs，and is widely applicable to new energy power prediction under small sample size.

Optimized configuration of distributed photovoltaic and energy storage system based on improved particle swarm algorithm

HU Zuyuan, JIN Xianlin, TAN Yazhi, FAN Jingyi

2023, 45(1): 49-57. doi:10.3969/j.issn.2097-0706.2023.01.006

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The proposal of the goals of carbon peaking and carbon neutrality makes a further request on the grid connection of distributed energy systems. To address the uncertainty triggered by the grid-connected distributed photovoltaic （PV） systems， the optimal configuration of distributed PV and energy storage systems is studied. Based on the typical PV output scenarios selected by clustering process， a hybrid integral non-linear programming model for the optimal configuration of a photovoltaic and energy storage system was established while taking economic benefits， load fluctuation and peak-shaving rates into consideration. The model was solved by adaptive particle swarm optimization algorithm， and the influence of different loads and electricity prices on energy storage capacity and on the operation of the system was analyzed. The results show that the energy storage system can effectively stabilize the photovoltaic output， optimize the load curve and improve the overall operating performance of the system， which verifies the feasibility of the model and the effectiveness of the solution.

Review of researches on grid security and stability control with the participation of electrochemical energy storage

ZHAO Xin, QIAN Benhua, WANG Rui, LIU Hu, ZHAI Shuo, ZHAO Ziyi

2023, 45(1): 58-66. doi:10.3969/j.issn.2097-0706.2023.01.007

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In order to promote energy transformation and optimize energy mix of China，the proportion of renewable energy in the energy mix has been increasing. However， the randomness and volatility of renewable energy generation brings negative impact on the safe and stable operation of power grid. Since electrochemical energy storage technology has the characteristics of rapid response and flexible power adjustment， it facilitates the safe and stable operation of power grid. Firstly， the types of energy storage technologies and completed electrochemical energy storage projects are summarized. After that， the role of energy storage in power system stability control is explained from three aspects： frequency stability， static voltage stability and transient voltage stability. Finally， the expectation on the participation of electric vehicle charging stations and electric vehicles in power system regulation is made. Energy storage technologies will be the key component of power systems.

Impact of wind and solar power grid connection on microgrid reliability

WENG Zhipeng, ZHOU Jinghua, LI Jin, ZHAN Zhengdong

2023, 45(1): 67-74. doi:10.3969/j.issn.2097-0706.2023.01.008

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Increasing the proportion of grid-connected wind and solar power is conducive to achieving the carbon peaking and carbon neutrality. However， the randomness of wind and solar power will affect the reliable operation of microgrid. The reliability analysis can provide an evaluation basis for the smooth access of wind and solar power to microgrid. Firstly， the model of the wind and solar power output considering the randomness is established. Then， probability， frequency，duration and other indicators are introduced to analyze the reliability of microgrid. Finally， the effects of Beta distribution parameters（α and β）， wind speed， wind and solar permeability on system reliability are studied. The results show that the Beta distribution parameters can affect the photovoltaic power output， and then affect the system reliability level. Wind speed is also one of the factors affecting reliability. On the premise of avoiding damage to wind turbines cause by excessive wind speed， getting wind power accessed into power grid from areas with rich wind resources is conducive to the system reliability. When α=2.00， β=0.80， increasing the proportion of photovoltaic power is beneficial to restraining the probability indexes and customer average interruption duration index（CAIDI）， but will continue to increase the system average interruption frequency index（SAIFI）.

Application and practice of the situation awareness system for the community level virtual power plant

SONG Zhenhui, MA Conggan, WANG Zhao

2023, 45(1): 75-81. doi:10.3969/j.issn.2097-0706.2023.01.009

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Situation awareness technology is an important guarantee for the improvement of state observability and system stable operation. In order to further participate in electricity market regulation，a community level virtual power plant system should be of excellent external characteristics. Therefore，it is particularly necessary for power plants to response to emergencies intelligently and rapidly by efficient and timely means. For a community level virtual power plant system，the most important thing is the establishment of a real-time awareness system based on situation awareness technology. The construction of a typical community level integrated energy system's situation awareness system is expounded，and the analysis results can provide references for relevant researches.

Study on orderly charging strategy of electric vehicles in residential areas

DONG Weijie, CUI Quansheng, HAO Lanxin, WANG Yilong, LIU Guolin

2023, 45(1): 82-87. doi:10.3969/j.issn.2097-0706.2023.01.010

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In view of the serious hidden danger to the power grid and long charging time caused by the random parking behaviors and disordered charging process of electric vehicles （EVs） in residential areas，an EV orderly charging control strategy with delayed charging is proposed.After analyzing the researches on EV orderly charging at home and abroad，the overall framework of an orderly charging strategy for EVs in urban communities is designed.The strategy can coordinate EV charging behaviors by delayed charging.It can ensure the state of charge （SoC） of the EV at departure time by setting its charging start time which is varies with the calculated charging priority，so as to meet users' demand for SoC to the greatest extent.The simulation analysis on a case show that the proposed strategy with delayed charging can achieve the effect of peak-load shifting on the premise of meeting users' expectations for EV charging results.

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