The power grid load shows a trend of diversified development. With the continuous increase of the power grid construction scale, it is very important to optimize the power supply of the distribution network and improve the utilization of assets and equipment by using the peak valley complementary characteristics of multiple loads. In this paper, the peak-valley fluctuation characteristics of load are classified and analyzed. Cluster analysis is used to study the load classification with stable peak-valley characteristics in the complex load composition. Based on the peak-peak superposition and peak-valley complementary characteristics of the classified load, and considering the load space dimension, the multi-load optimization combination method for the whole period and time segment is proposed, and verified by simulation.
The green energy transformation of big data centers with high energy consumption is important in promoting carbon neutrality. With zero carbon as the goal, this paper designs three scenarios of source-grid-load-storage collaboration in big data industrial park, namely, company-centric, user-centric, and market-centric. Then, the optimal allocation model of energy storage in zero-carbon big data industrial park with maximum profit is established, and its business model is analyzed with the financial evaluation method. Finally, this paper takes Zhangbemiaotan Big Data Industrial Park as an example, discusses the economy of the three scenarios combined with national policies, and puts forward suggestions on establishing a feasible business model of zero-carbon big data industrial Park.
Given that the aerial image to scan overhead transmission line contains complex backgrounds and small objects, it is difficult for traditional algorithms to accurately identify the details of power transmission lines, so this paper develops an object detection method based on improved YOLOv5s. To improve the detecting accuracy of small objects, the network structure is optimized by adding a larger scale detection layer and jump connections. Then, a self-attention mechanism is introduced to merge the feature relationships between spatial and channel dimensions. The self-attention mechanism could suppress the interference of complex backgrounds and boost the salience of objects. In addition, this paper proposes a small object enhanced complete intersection-over-union as the loss function of the bounding box regression. This loss function could adjust the derived loss for objects of different sizes automatically, therefore, improving the detection of small samples. The experimental results demonstrate that compared to classic YOLOv5s, the detection accuracy of our algorithm is improved by 4.2%.
As the batteries of Uninterruptible Power Supply (UPS) in the Internet Data Center (IDC) is only effective in the case of power failures, the large amounts of batteries are idle during normal operation. To meet the efficient, green and reliable power supply requirements of IDC, and activate the “sunk asset” of UPS batteries, the Energy storage type of UPS (EUPS) architecture with bidirectional power regulation and active grid support is proposed in this paper. The main target is to maximize the use of batteries in UPS through the function upgrading from backup to energy storage. The topology and control strategy of EUPS are analyzed first. And the cooperative mechanism of EUPS participating in the grid fast frequency and voltage regulation, AGC, AVC, peak-valley shaving and their combined is interpreted accordingly. Finally, the effectiveness of EUPS and its control strategy are verified through simulation cases.
The hot and cold power joint supply system (CCHP) has a high energy conversion rate, which plays a very important role in promoting the development of energy conservation and emission reduction work in China. However, due to the fixed electric heat ratio, the hot and cold power supply system needs to be closely coordinated with the power grid and ignition replenishment equipment to achieve the ideal effect. Through the flexible load parameter and user load variable value, the comprehensive objective function performs scheduling analysis. After a series of analysis results prove that the optimization strategy can effectively save cost and reduce greenhouse gas emissions.
In the power system integrated with offshore wind farm, energy storage is utilized for active power balance and voltage stability. This paper proposes a coordinated voltage control method for offshore wind farm with three types of reactive power sources. The detailed mathematical model of offshore wind farm with SVG and energy storage is established. By means of reactive power requirement calculation of point of interconnection (POI), the proposed method can compensate reactive power with comprehensive utilization of reactive power sources containing offshore wind farm, static var generator (SVG) system and energy storage. The compensation priority of each reactive power source is determined according to voltage variation conditions, and the reactive power support capability of the wind farm is improved by de-loading control. Based on real offshore wind farm in Jiangsu Province, the effectiveness of proposed voltage control method is verified in PSCAD.
With the promotion of the national carbon neutrality policy, GS's energy consumption structure and industrial structure have changed dramatically. Timely and reliable prediction of the changes of regional industrial structure plays an important role in regional development planning. Industrial electricity consumption characteristics are real-time and objective feedback of production and operation status. Therefore, analyzing the correlation law of industrial electricity can obtain the evolution trend of industrial structure. This paper excavates the socio-economic value of electricity data and establishes a set of analysis framework of regional industrial structure based on industrial electricity correlation topology. Firstly, based on centrality analysis, the regional industry center is obtained from the perspective of power consumption correlation. Secondly, the trends of development in different industries are obtained by curve clustering analysis. Finally, through the topological analysis of industrial electricity, it provides a power perspective to analyze the transformation and upgrading process of regional industrial structure in different periods. The analysis results of the electricity consumption of GS's industries from 2010 to 2020 show that with the promotion and implementation of the national policy and carbon neutrality policy, most industries in GS shows an overall upward trend, and the diversified and Clustered Development Characteristics of the manufacturing industry are obvious. At the same time, on one hand, the national economy is still highly dependent on traditional high energy consuming industries. On the other hand, the elimination of highly polluting industries has achieved remarkable results. The research conclusion can provide energy assistance for the government's precise governance.
The management of power grid operation and maintenance generates a large amount of unstructured text data, of which the text records contain knowledge related to grid topology, assets and equipment. Its data value is difficult to be fully explored. This paper uses natural language processing techniques to analyze power grid operation and maintenance text records. Firstly, the keyword frequency of the preprocessed text is calculated, and the keyword weight is analyzed by TF-IDF. Then the Word2Vec algorithm is adopted to extract features from the text and generate word vectors. Finally, the topic clustering analysis is performed based on the K-Means algorithm. In the example analysis part, 570 pieces of power grid operation and maintenance text records were adopted as sample data. The test results show that TF-IDF algorithm can reflect the importance of high-frequency keywords in power grid operation, and the topic classification model can effectively summarize the topics of text records. This proves that the proposed key information intelligent extraction model can improve the management efficiency of power grid operation and maintenance work.
In this paper, a BP neural network (BPN) algorithm model is utilized to forecast the electric energy data of distributed photovoltaic (PV) users. One month's forward active power and voltage data of PV users are collected. The data was collected every hour. So, 24 data were collected every day. Then a BPN algorithm training model are established, First 20 of the days were considered for training data and final 10 days were considered for testing data. Through simulation experiment, the graph of predicted value and actual value of the forward active power and voltage of distributed PV users are obtained. It is concluded that the BPN algorithm model is an accurate model in predicting PV users' data, and the model is more accurate in predicting voltage than in predicting forward active power. The BPN algorithm model could be an effective model for a short-term forecasting of local small distributed PV station output, and has certain significance for the power management department to formulate energy management and dispatching schemes for stability and safekeeping on large grid after PV grid connection.
Electric Vehicles have been increasingly integrated into the power system and would take a lager proportion of load in the future. This research examines the influences of the large-scale EV on multi-energy system in terms of cost, renewable energy accommodation and carbon emission reduction. The multi-energy system optimal operation dispatch model is proposed to dispatch the different energy conversion devices and energy storage devices considering EV integration. We establish the EV models considering the controlled charging scheme and V2G scheme into the dispatch model. The multi-energy system carbon emission flow theory is used to track carbon emissions and allocate the carbon emission on end-use energy including EV. The results show that the large-scale EV integration helps enhance system economic profits and effectively reduce carbon emissions.
The uncertainty of wind power output and real-time electricity price poses challenges for the online operation of wind-storage integrated systems (WSIS). This paper proposes an advanced online dispatch algorithm for WSIS that combines Lyapunov Optimization (LO) and the Deep Deterministic Policy Gradient algorithm (DDPG). LO policy is regarded as the base policy, while DDPG provides an improved policy that is trained with LO operation data. A policy switching criterion is proposed to determine when to replace the LO policy with the DDPG policy for real-time operation. Case studies verify the effectiveness of the proposed method.
Hydrogen production from electrolytic water can realize the absorption of new energy, and hydrogen production from new energy electricity has no direct carbon emissions, but the electricity production process will produce different degrees of indirect carbon emissions to hydrogen production from electrolytic water according to different power generation sources. Therefore, this paper takes the carbon price into account in the cost of hydrogen production from electric water, and analyzes the levelized cost of hydrogen production from electrolytic water under different power sources, It is concluded that the total cost of hydrogen production in the low valley electricity electrolysis water hydrogen production process is most sensitive to the carbon price, and the cost of hydrogen production under each power source increases with the carbon price, and gradually slows down.
As the third line of defense equipment to prevent the frequency collapse of the power system, with the wide access of renewable energy, the under frequency and under voltage automatic load shedding device has the risk of miss and mal operation due to the change of power grid characteristics. Firstly, this paper aims at the risk points of incorrect tripping of the power reverse transmission line, the failure of the device due to excessive RoCoF and voltage suspension under fault, On the basis of combing the existing criteria, the causes of incorrect action of the device are analyzed, A comprehensive improved criteria is proposed for judging allowable cut state by multi-path acquisition line load, the RoCoF unlocking condition and the under voltage special wheel unlocking condition. Finally, the validity of the proposed criterion is verified by simulation test.
Large scale distributed photovoltaic(DPV) integration will bring new challenges to dispatching due to its uncertainty, and further increase the regulating capacity requirement of power system. As a result, a robust evaluating approach of DPV hosting capacity in uncertain environment is proposed. Based on the relationship between DPV hosting capacity and its conditional value at risk(CVaR), a robust optimization model is constructed, which considering the probability distribution characteristics of DPV. The base point and regulating capacity of automatic generation control(AGC) units are coordinated optimized to accurately evaluate the DPV hosting capacity The original model is transformed into a deterministic linear programming problem using piecewise linear approximation and uncertain variables eliminating. The effectiveness of the proposed approach is verified through a 6-bus system.
The access of a high proportion of distributed photovoltaics (PV) increases the uncertainty factors of the distribution network. In this paper, the correlation between distributed PV power and load power is considered, and the method of combining genetic algorithm (GA) and Latin hypercubic sampling (LHS) is used to obtain distributed PV power and load power sampling data that are relatively similar to the rank correlation coefficient of historical data. Then, combined with the data obtained from sampling and the related distribution network topology and line parameters, Newton Raphson's method is used to calculate the power flow of the distribution network, and then the probability power flow calculation method is evaluated. On this basis, the changes of various electrical indicators such as power flow in the distribution network with the access of distributed photovoltaic units and the probability exceeding the specified range are further analyzed.
Frequency control of power grids has become a relevant research topic due to the massive integration of renewable generation in power systems. Frequency control of traditional thermal generating units with relatively slow ramp rate cannot meet the frequency regulation requirements of power grid. Thus, the inclusion of energy storage system (ESS) at the thermal generation frequency control output can be used to improve the speed of load following and increase the profiles of ancillary service. In this paper, the economic assessment of energy storage system investments in thermal generation station is studied. A methodology has been presented here for the financial calculations of the ESS providing frequency regulation. A numerical case study based on frequency profiles of Yunnan power grid is simulated.
The large-scale offshore wind farms (OWFs) penetrating into the power system has significantly changed the inertia distribution. The spatial-temporal characteristic of inertia leads to spatial frequency dynamics. Although the OWFs could provide inertial support to compensate the weak inertial area though virtual inertial control technology. While the typhoon attack happens, the OWFs should operate under typhoon mode and even shut down. However, the spatial-temporal inertia cannot be described from the perspective of the center of inertia, which will cause frequency security problem in the weak inertial area. Therefore, this paper focuses on the inertia distribution and analysis the frequency response results with different unit schedules. Then, a novel spatial-temporal inertia oriented operation scheduling strategy is proposed to guarantee the frequency security of the total power system. Various cases are simulated to verified the effectiveness of the proposed scheduling strategy.
The electricity emissions factor is the link between electricity sector and carbon emissions. At present, China uses electricity emissions factors in five application scenarios: greenhouse gas (GHG) inventory compilation, national and local statistical accounting, sector and enterprise accounting, product carbon footprint accounting, and voluntary emissions reduction accounting. This paper introduces the development history and current situation of five application scenarios, and introduces the current use of electricity emissions factors. Based on the global and China's emissions reduction trends, this paper raises four principles, and puts forward short-, medium- and long-term policy suggestions for the development of electricity emissions factors.
Accurate residential load forecasting plays an important role to improve the economy and security of power system operation. However, as the unbalanced distribution of residential load and the intertwined effects of multiple factors, it is difficult for a single neural network to make accurate predictions and its ability to generalize is limited. In this regard, this paper proposes a CNN-LSTM and non-uniform quantization based method for one-hour ahead residential load forecasting. First, we solve the unbalanced distribution of residential load by non-uniform quantization, which converts the load to an approximately normal distribution and fits the learning of neural networks. Then, the equivalent load after non-uniform quantization and its influencing factors are interwoven to form intertwining diagrams to facilitate the extraction of nonlinear relationships. Next, considering the intertwined effects of multiple factors, we use CNN-LSTM to extract temporal and spatial characteristics between multiple factors and cope with complex load patterns. We train and validate the proposed method using a real-world dataset, and the experiment results show that the proposed method outperforms the existing load forecasting methods.
With the increasing number of wind turbines in the power grid, the problem of frequency stability has been widely concerned. It has become a consensus in the renewable energy industry that wind turbines should have frequency regulation capability. The frequency regulation process of wind power calls active power, and the change of active power may affect the load of wind turbine. Furthermore, it will harm the reliability of wind turbines and the safety of renewable energy power systems. In this paper, a wind turbine model was built through MATLAB-Bladed co-simulation, and the influence of frequency regulation process of wind turbine on mechanical load was studied. The results showed that the effect on load of hub Mx of wind turbine by frequency regulation control is severe and should be paid more attention to.
