Energy-related carbon emissions account for more than 85% of total carbon emissions in China, and the research on changes of energy-related carbon emissions is of great significance for achieving carbon peak and neutrality goals. Firstly, this paper uses the Logarithmic Mean Divisia Index (LMDI) to decompose the impacting factors of China's energy-related CO2 emissions changes from 1995 to 2017. From the aspects of economic scale, industry structure, energy intensity, energy structure, energy prices, per capita disposable income and population size, the model gives the contribution of related factors to energy-related CO2 changes in primary, secondary, tertiary and residential sectors. The results show that for the three industry sectors, economic growth is the primary drive of CO2 emission growth, while declining energy intensity, improved industrial structure and energy consumption structure show negative effects. For the residential sector, per capita disposable income and population size are the driving forces behind the CO2 emission growth, and energy prices show a significant negative effect. Secondly, in order to predict China's energy-related CO2 in 2030, three scenarios are designed and the Stochastic Impacts by Regression Population, Affluence and Technology (STIRPAT) model is implemented. In the low carbon scenario with the goal of achieving carbon peaks, China's energy-related CO2 emissions are expected to peak around 2025-2029 and the level is about 10.1 billion to 11.0 billion tons. Finally, in order to achieve CO2 emission peak before 2030 and carbon neutrality before 2060, it is recommended to build the China Energy Internet as the basic platform, to implement the clean replacement and electricity replacement, and accelerate the energy transition.
Electricity transaction of multi-microgrid system enables to improve the overall economy, alleviate the operating pressure of distribution network and promote the consumption of renewable energy. Nevertheless, the transactions of multi-microgrid system are obliged to process large-scale, complex structure, and highly real-time data of microgrid. Therefore, taking the uncertainty of wind power output into consideration, this paper puts forward a robust game bidding scheduling model for multi-microgrid system applying blockchain technology with data transparency and reliability. Firstly, the compatibility between the blockchain technology and the non-cooperative game model of multi-microgrid electricity transaction is analyzed. And then the paper establishes a multi-microgrid transaction architecture based on blockchain technology. Binary expansion method, duality theory, big M method, and C&CG algorithm are employed to achieve the optimal economic dispatch strategy and bidding strategy of each microgrid. Finally, the paper builds a transaction platform based on blockchain technology, and the corresponding case studies verify the effectiveness of the proposed method.
At present, the coupling of virtual power plant (VPP) and active distribution network (ADN) has been further deepened. Aiming at the optimal dispatching problem of ADN with VPP as a market participant, it is necessary to study the coordinated interaction between various flexible resources in ADN and uncertainty factors in VPP in different time stages. On this basis, with the goal of minimizing the comprehensive cost of ADN and maximizing the market revenue of VPP, a two-stage dispatching method for ADN considering VPP transaction is proposed. In the day-ahead stage, according to the flexibility boundary of the system, a bi-level optimization model is established considering the dispatching of flexible resources in ADN and day-ahead clearing of the VPP. On this basis, the bidding scheme of VPP and the operation strategy of ADN are adjusted in real time. Finally, an improved IEEE 33-node system is taken as an example for simulation analysis, and the results show that the proposed model can improve the market competitiveness of VPP and the operational flexibility of distribution networks both, which provides a reference for optimal dispatching under the future marketization development of power grid.
With the continuous increase of energy used by residents, integrated demand response (IDR) has become an important means to improve the safety and stability of the superior energy network and to improve the energy conversion efficiency and exploit the potential of the demand side. In order to solve the problem of optimal decision of all participants in an integrated energy system (IES) considering the IDR, this paper builds an operation optimization model based on the potential game theory with the objective function of maximizing revenue. In this model, multi-energy supplement is considered and the IES operator is introduced. The operator interacts with both the user and the superior energy network. The potential function corresponding to the model is constructed, and the problem of finding the optimal solution of the model is transformed into the problem of finding the maximum value of the potential function. In this paper, distributed optimization algorithm is used to solve the model. The simulation results show that the Nash equilibrium solution can be obtained quickly. The operator's income can be also increased, the peak-valley difference can be reduced, and the stability of the power grid can be also improved without reducing the user's benefit.
Under the background of high-proportion grid-connected renewable energy in the future, the planning of multi-type adjusting resources in the power system is needed to improve the consumption of centralized and distributed renewable energy. This paper proposes a generation planning framework that considers the interaction process between transmission and distribution, and coordinates the capacity of source-load-storage resources in power system. Then, according to the distributed optimization theory, the model is decomposed into two planning sub-problems for transmission and distribution network. For the non-convex and nonlinear terms in the decomposed sub-program model, convex relaxation and linearization techniques are used to transform the model into a mixed integer second-order cone program for solution. Finally, this paper simulates the global system constructed by the IEEE 14-node system and the IEEE 33-node system to verify the effectiveness of the proposed model and optimization method, and analyzes the economy of the system and renewable energy consumption under coordinate planning.
The hybrid unified power flow controller (HUPFC) combines the advantages of unified power flow controller (UPFC) and ‘Sen’ transformer (ST), so HUPFC is widely applied in power flow control of the power system. However, little literature has studied the application of HUPFC to suppress sub-synchronous oscillation (SSO). To solve the problem of SSO in double-fed induction generator (DFIG) based wind farm connected to series compensated transmission system, a suppression strategy is proposed in the paper on the basis of the supplementary active resistance control (SARC) of HUPFC. Firstly, the principle of HUPFC and the suppression mechanism for SSO are introduced. Then, the SARC strategy is designed. By tracking sub-synchronous current in the transmission line, the SARC strategy makes HUPFC inject a sub-synchronous voltage which has the same phase as the sub-synchronous current and variable amplitude to the grid. Thereby, the system equivalent resistance is increased to a positive value to suppress SSO. Finally, the parameters design method of SARC is introduced and a detailed simulation model is carried out in PSCAD/EMTDC using the data of an actual wind farm in North China. The results of the frequency scanning and the time domain simulation both show that the proposed SARC strategy of HUPFC can effectively suppress SSO in DFIG-based wind farm connected to series compensated transmission system.
Aiming at the problem of power fluctuations of new energy sources, a regulation strategy is developed in this paper to suppress the power fluctuations of the tie-line power in an electricity-gas interconnected system with aggregated thermostatically controlled loads (ATCLs) and a micro-turbine. Firstly, the fluctuating power is decomposed by empirical mode decomposition, and then on the basis of the establishment of the ATCLs bilinear model and the mathematical model of the micro-turbine, an improved model predictive control method based on the Lyapunov function is proposed. The control law of the ATCLs and the micro-turbine is designed. The ATCLs and the micro-turbine are aggregated to suppress the high frequency and low frequency components of fluctuating power. The simulation results indicate that the proposed control method can make their output power effectively track the high frequency and low frequency components of fluctuating power under the condition that the temperature comfort constraint of the ATCLs is satisfied and the preset working state of the micro-turbine is maintained. It achieves a good smoothing effect of tie-line power under the condition that user comfort and adjustment range are considered.
With the rapid growth of solar energy integration, the difference between power system peak and valley load becomes larger due to the PV fluctuating output and reverse peak regulation. Due to the traditional peak-regulating capacity and motivation factor, relying on the existing single peak-regulating resource is difficult to meet the power balance requirements. Therefore, a new peak regulation strategy considering different PV penetration rate is proposed in this paper. Firstly, the multi-dimension space-time characteristics of PV are modeled, and then by establishing the deep interaction based peak-regulating model including source, network, load and storage, the optimal system peak-regulating strategy is obtained, and the dynamic match of different PV integration and system peak-regulating capacity is achieved. The simulation results show that the proposed peak-regulating strategy can meet the system peak-regulating requirements under different PV penetration rate, and can ensure the balance between supply and demand sides and effectively reduce the power loss at the peak load.
In the context of wind-solar grid integration, in order to accurately evaluate wind-solar correlation output and system transmission reliability margin (TRM), a TRM evaluation method taking into account the time-varying characteristics of wind-solar output is proposed. First, considering the time-varying correlation characteristics and seasonal characteristics of wind-solar output, a method based on the time-varying Copula function for wind-solar 24h joint output scene generation is proposed. The generated scene provides the basis for accurate TRM evaluation. Then, while considering the characteristics of the TRM time scale, the GlueVaR measurement system is introduced to measure the risk of transmission capacity deficiency, and a TRM expected net return model that distinguishes the different risk preferences of decision makers is constructed. Finally, applying wind-solar scene set, the sequential Monte Carlo simulation method is used to generate the system's time-series operation scene set, and the IEEE-RTS system is used to solve the model using optimization algorithms. Compared with previous methods, the simulation results indicated that the proposed method can not only improve the accuracy of wind-solar joint output scene generation, but also guarantee the reliable and economic operation of power system and realize differentiated evaluation of TRM.
Aiming at the problems of poor efficiency and weak anti-interference ability of energy-storage devices in the hybrid energy storage system of photovoltaic microgrid, this paper proposes an energy storage control strategy for hybrid photovoltaic microgrid applying the technology of feedforward linear active disturbance rejection control (FF-LADRC). Firstly, a bi-directional DC-DC mathematical model of the battery and super capacitor in the hybrid energy storage system is built. By introducing feedforward active disturbance control in the voltage loop control, the dynamic response speed and anti-jamming performance of the hybrid energy storage system are improved. A low-pass filter is set up to achieve power frequency division between different energy storage devices. At the same time, linear active disturbance rejection control is introduced into battery current loop and super capacitor current loop control to achieve coordinated control between different energy storage devices, thereby improving the power stability of the grid-connected side. The results of frequency domain analysis prove the effectiveness and stability of the proposed control strategy. The simulation results show that the proposed control strategy can quickly perform power frequency division distribution and coordinate the effective operation of the photovoltaic microgrid.
The natural frequency characteristic coefficient (β) of the power system is a significant basis for setting the frequency bias coefficient (B) in the automatic generation control (AGC) strategy. Setting B equal to β is the ideal principle of the B coefficient setting, because the AGC power adjustment is able to exactly reflect the power mismatch under this circumstance. However, the β coefficient is nonlinear and time-varying. The existing B coefficient setting methods cannot effectively track the changes of the β coefficient. In this regard, the interval prediction method for the β coefficient based on deep neural network (DNN) and Bootstrap is proposed. With the powerful capability of nonlinear feature extracting, DNN is utilized to establish the mapping relationship among power disturbance, reserve capacity, unit commitment, and the β coefficient. Thus, the prediction of the β coefficient can be achieved. In addition, combined with the Bootstrap method, the confidence interval of the predicted β coefficient is further obtained, which provides great supports for setting the B coefficient. Finally, simulation results verify the effectiveness and robustness of the proposed method.
In recent years, the proportion of cable lines in the transmission grid has increased. It may have impact on the resonance characteristics between the converter station and the transmission grid. In order to analyze the effect of the AC outlet mode of converter station on energizing the no-load converter transformer, the phenomenon of inrush current and ferromagnetic resonance is introduced, and different AC outlet modes with overhead lines and cables are established. Taking typical ± 500 kV DC project in China as an example, by means of the PSCAD/EMTDC, the influence of inrush current and ferromagnetic resonance caused by different outlet modes, the ratio of the cable in total lines and the closing resistance of the breaker are analyzed. Results show that different outlet modes have no significant effect on the inrush current, but the cable outlet mode is more likely to cause ferromagnetic resonance, and the higher the cable ratio is, the easier the ferromagnetic resonance occurs. Meanwhile, the suppression effect of closing resistance on inrush current and ferromagnetic resonance is verified.
Aiming at the problem of the non-linear characteristics of the distribution network load changing with time and space, the short-term load forecasting accuracy is insufficient and the model training time cost is high. In this paper, a short-term load forecasting model based on phase space reconstruction (PSR) and stochastic configuration network (SCN) is designed. Firstly, the meteorological data related to the load in the distribution network data is reduced by principal component analysis (PCA), and is combined with the load sequence to form a multivariable time series. Using chaotic time series theory, the paper reconstructs the phase space through mutual information method and false nearest neighbor method, and finally uses stochastic configuration network to predict power load. The proposed method is verified with historical load and meteorological data of public data sets of European power grid. The results show that, compared with the support vector machine (SVM) optimized by the grid search method, backpropagation neural network (BP), long and short-term memory network (LSTM), and autoregressive integrated moving average (ARIMA), the proposed method can complete load forecasting relatively, accurately and efficiently. The analysis of the calculation example verifies that the proposed method has the characteristics of high level of intelligence and efficient operation, and has certain practical value.
To solve the coordinated dispatch and spatiotemporal coupling of various adjustable resources after high-proportion renewable distributed generation (RDG) is connected to the distribution network, a two-stage reactive power optimization for distribution network considering reactive power margin is proposed. The Copula theory is used to establish a prediction model which considers the uncertainty and correlation of wind power and photovoltaic output. In the first stage, a day-ahead multi-objective optimization model is established by considering voltage deviation, operation cost and dynamic reactive power margin. In the second stage, the continuous regulating device is used to respond the fluctuations of photovoltaic and wind power, which realizes the differentiated management of reactive power regulating devices with different fast and slow time constants while fully coordinating the adjustable resources in the distribution network in a short time scale. Finally, the simulation example carried out on the improved IEEE 33-node system shows that the collaborative optimization strategy can improve the safety and economic benefits of the system, and verify the influence of the wind-photovoltaic correlativity on the system.
Icing of transmission line seriously threatens the safe operation of power system. Therefore, it is necessary to investigate icing prediction of transmission lines. With the development of its performance, artificial intelligence technology gradually shows advantages in power grid icing monitoring. To a certain extent, the existing statistical regression model of ice thickness prediction for transmission line can partly predict the ice accretion growth. However, these traditional models are only suitable for short icing periods and difficult to realize in actual engineering because of their requirement of high data acquisition frequency. This research collected the transmission line's ice observation data from 2015 to 2019 got by Chongqing Transmission and Transformation Engineering Co, Ltd.. By analyzing the data, the characteristics and rules of transmission line icing under high humidity environment in southwestern China are obtained. Then, according to the ice growth's physical process on transmission line, the research selects measurable parameters in practical work as the impact factor of ice accretion growth. On this basis, an artificial intelligence ice-thickness prediction model based on adaptive mutation particle swarm optimization (AMPSO) is proposed. According to the training results, the AMPSO-BP neural network is more accurate and reliable on ice thickness prediction, compared to the traditional BP neural network.
