The peak regulation capacity of gas‐fired power plants has always been an important flexibility resource of the power grid. Under the guidance of carbon emission reduction, the coal power units are gradually shut down, making the role of gas‐fired power plants more important. However, in practice, gas‐fired power plants often fail to show satisfactory flexibility. The main reasons are as follows: (1) Part of the capacity mechanism fails to effectively encourage gas‐fired power plants to provide reliable flexibility and (2) the unreliability of fuel supply for gas‐fired power plants. Aiming at these problems, the current capacity mechanism in different countries is first summarised and the applicability of the capacity mechanism for gas‐fired power plants under the government regulation and market‐oriented environment is analysed, respectively. Then, the characteristics of power dispatching and gas dispatching are analysed to explore the internal reasons for the unreliable fuel supply in gas‐fired power plants. Based on the above analysis, the gas‐electric coordination mechanism adapted to different development stages is proposed to solve the problem that the flexibility of gas‐fired power plants cannot be guaranteed. In summary, through the research of this study, it is found that the main reason for the limited flexibility of gas‐fired power plants is the lack of coordination among multiple entities belonging to different energy systems, such as electricity and gas. The cooperation mechanism proposed is an attempt to realise the cooperation between the electric system and the gas system, which provides the reference for closer collaboration among multiple energy systems in the future.
Dispatching efficiency of the grid can be improved by the coordination of grid and gas. The coordination scheme between gas and power grid.image
It is meaningful to study the real‐time state monitoring and identification of integrated energy system and grasp its state in time for stable operation. A state identification method based on multi‐class data equalisation and extreme gradient boost (XGBoost) is proposed for integrated energy systems. First, Latin hypercube sampling is used to simulate the load at different moments. Different system states are set up and combined with the simulative load at different moments to determine the system operation state at different moments. Then, the energy flow model is used to calculate the system power flow under different states, and the feature indexes are obtained to form the original data set. Aiming at the unbalanced data, the oversampling technology is used to preprocess data to achieve the balance of data sets. The pre‐processed data is utilised to train the XGBoost, and the optimal hyperparameters of the model are obtained based on the K‐fold cross‐validation and grid search. Finally, the pre‐processed data set is used to verify the proposed method. The calculation results show the accuracy of the identification model reaches 87.79%. Compared with traditional methods, the model can accurately identify the operating state of the electricity–heat energy system at any time section.
The massive perception data based on efficient analysis and intelligent decision have put forward higher requirements for high‐precision time synchronisation with the construction and development of smart power grid. However, multi‐reference source time‐frequency synchronisation of power system only selects the best method after comparison, which cannot make the most efficient use of the existing resources. It also cannot meet the need for high‐precision time synchronisation of future power system. The existing multi‐reference source synthesis algorithms cannot take into account both long‐term stability and high‐precision synchronous output. This article presents a multi‐reference source weighted improved noise model and the high‐precision output method. The multi‐reference source error after classification is eliminated by leading into classification vector and classification coefficient. The synthesised frequency offset or the time precision of output can be optimised as the objective function by weighted classification algorithm and genetic algorithm. A simulation example based on the synthesis of two satellite system clock sources and three local caesium reference sources shows that the peak value of long‐term output accuracy is controlled within 10 ns after classification weighted synthesis and optimisation, which is better than that of any single reference source.
A multi‐reference source weighted improved noise model is proposed in this paper, and the weighted classification algorithm and genetic algorithm are used to optimise the time‐frequency performance index of multi source synthetic output. The simulation results show that the peak to peak synchronisation accuracy after optimisation can be controlled within 10 ns, and the time error and frequency offset after synthesis is better than any single reference source.image
Spot pricing is often suggested as a method of increasing demand‐side flexibility in electrical power load. However, few works have considered the vulnerability of spot pricing to financial fraud via false data injection (FDI) style attacks. The authors consider attacks which aim to alter the consumer load profile to exploit intraday price dips. The authors examine an anomaly detection protocol for cyber‐attacks that seek to leverage spot prices for financial gain. In this way the authors outline a methodology for detecting attacks on industrial load smart meters. The authors first create a feature clustering model of the underlying business, segregated by business type. The authors then use these clusters to create an incentive‐weighted anomaly detection protocol for false data attacks against load profiles. This clustering‐based methodology incorporates both the load profile and spot pricing considerations for the detection of injected load profiles. To reduce false positives, the authors model incentive‐based detection, which includes knowledge of spot prices, into the anomaly tracking, enabling the methodology to account for changes in the load profile which are unlikely to be attacks.
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