Carbon reduction in energy sectors is the key to achieve carbon neutrality. This paper firstly expounds the relationship between carbon neutrality and carbon budget. Secondly, a method of energy planning considering the carbon budget and carbon cycle is put forward under carbon neutrality targets. Then, this method is compared with other energy planning methods in terms of planning objectives, research paradigms, carbon constraints, and market factors. Finally, some proposals for future energy planning, such as coupled gas and power, carbon price setting, etc., are advanced. The methods proposed in this paper can provide references for subsequent energy planning considering carbon neutrality.
Promoting the electric energy substitution of integrated park is an important measure to improve environmental problems. The electric energy substitution of integrated park is affected by distribution network transformation. The program of electric energy substitution to configure wind-solar-battery system is proposed. Firstly, the load characteristic model under electric energy substitution is established which involves the load models of electricity replacing heat, cooling and fuel oil. Secondly, the bi-level optimal model for wind-solar-battery capacity allocation is proposed considering the reliability of distribution network and the demand response, which is concluded distribution network layer and wind-solar-battery system layer. Finally, Cplex is used to solve the problem, and it is obtained the optimal planning scheme of the wing-solar-battery system considering the reliability of distribution network and the demand response in the electric energy substitution of the integrated park. In the system of IEEE 33-node system, the electric energy substitution load data of an actual park is used to perform simulation. The results show that the use of wind-solar-battery system for power supply and the consideration of load demand response can solve the demand for electric energy substitution load growth in the park and realize the wind-solar-battery system's optimal economic configuration.
With the continuous advancement of the energy Internet, the degree of informatization of the power system has been constantly improved and the amount of electricity data on the end-user side has been growing rapidly. The change provides a data foundation for the detection of user energy consumption based on the big data analysis technology. To deal with the high-cost and low-efficiency problem of the traditional detection model for abnormal electricity consumption patterns, a full cycle detection model of abnormal electricity consumption is proposed, which includes data cleaning, feature screening and model training. Besides, for comprehensively considering the factors affecting the abnormal electricity consumption patterns, the evaluation index system including the power consumption slope index, the line-loss index, and the warning information index is built. The data cleaning and missed value preprocess are conducted on the initial data to improve the accuracy of abnormal electricity pattern detection, and XGBoost is used for abnormal detection. Finally, a numerical case is used to verify the availability of the proposed detection method. In terms of the detection accuracy and training time, the detection performance of XGBoost algorithm is the best by comparing it with decision tree, random forest and Adaboost.
The coordinated utilization of the resources of generation-grid-load-storage is an important means to promote the efficient operation of the regional integrated electric and heating system (RIEHS). This paper proposes an optimal dispatching method for RIEHS considering aggregation and transaction of generation-grid-load-storage. Firstly, the RIEHS dispatching framework is constructed, and the RIEHS dispatching organization process considering aggregation and transaction of generation-grid-load-storage is designed. Secondly, a two-stage optimization dispatching model of RIEHS is established. The first stage is the optimization of the aggregation and transaction of the resources of generation-grid-load-storage by the virtual power plant. The second stage is RIEHS dispatching optimization based on the transaction results of the resources of generation-grid-load-storage. Finally, based on the improved IEEE 33-node power distribution system and the 32-node Bali heating system, a RIEHS with three virtual power plants is constructed for simulation and analysis. The results show that the method proposed in the paper taps the response potential of user-side resources through transaction means, improves the operating income of virtual power plants and the economy of system operation. The effectiveness of the dispatching method has been verified.
As renewable energy generations represented by wind and photovoltaic power connect to the distribution network (DN) through multiple micro-grids (MMGs), the uncertainty will bring challenges to the reliability and economy of the operation of the DN and MMGs. In response to this, this paper proposes a data-driven robust dispatch method for DN and MMGs considering correlation between wind and solar output. Firstly, a distributed dispatch method is adopted to establish the dispatch model of the DN and a two-stage dispatch model of the MMGs, with the tie-line power as the coupling parameter of the two. Aiming at the uncertainty of renewable energy output as well as the temporal and spatial correlations, the wind-solar output ellipsoid uncertain set is constructed based on the data-driven algorithm, thereby establishing the two-stage data-driven robust dispatch model of the micro-grid. Finally, an improved column and constraint generation algorithm based on extreme scenarios is proposed to solve the robust dispatch problem of the micro-grid, and the analytical target cascading method is used to solve the overall dispatch problem of DN and MMGs. The simulation results show that the proposed method can capture the spatial-temporal correlation between wind and solar, improve the economy of dispatch while ensuring the robustness of the DN and MMGs dispatch, and has good convergence.
With the increasing penetration of large-scale renewable energy into the power grid, large-scale wind and solar power stations also begin to participate in the frequency regulation of the power grid. In order to solve the dynamic power allocation problem of different frequency-regulation resources, a multi-objective complementary control model with minimum total power deviation and regulation mileage payment is established. To solve the nonlinear optimization problem, the multi-objective manta ray foraging optimization algorithm (MMRFO) is adopted in this paper to quickly obtain high quality Pareto front to meet the real-time online frequency-regulation requirements of the power grid and improve the dynamic response capability of the regional power grid. Then, applying the entropy weight method, the grey target decision-making theory is designed to objectively select the compromise solution which takes into account both operation economy and power quality under different power perturbations. Finally, the validity of the proposed method is verified by an extended two-area load frequency control model.
In a DC distribution system with photovoltaic power, according to traditional droop control, when photovoltaic power output changes, there will be problems such as unbalanced power distribution and large deviation of bus voltage. Photovoltaic power output is seriously affected by environmental factors, which causes the effect of traditional droop control to deteriorate. In response to this problem, the droop characteristic curve can be adaptively adjusted according to the changes in the photovoltaic output, so that it can achieve accurate power distribution under heavy load conditions. At the same time, this solution can also achieve stable control of the bus voltage under light load conditions. Through the establishment of the output impedance model of droop control, the influence of the adaptive change of droop coefficient on the system's circulation suppression and current sharing is analyzed. In addition, a DC distribution network with photovoltaic power is built in MATLAB/Simulink for simulation verification. A combination of theoretical analysis and simulation verification shows that the proposed adaptive segmented droop control for flexible output of photovoltaic converter can dynamically adjust the power distribution task according to the photovoltaic power output. Specifically, the system power distribution accuracy can be improved when the load is heavy, and the DC bus voltage deviation can be reduced when the load is light.
In recent years, with the increasing demand of data automation and intelligent management in the field of power grid dispatching, knowledge graph has become an important technology to provide knowledge management, intelligent query, auxiliary decision-making and other functions. As the core element of knowledge graph, the accuracy of entity recognition will directly affect the quality of knowledge graph. Aiming at the field of power grid dispatching, this paper firstly analyzes the research status of entity recognition in power grid dispatching field, and defines the task objective of entity recognition. Then, according to the text data features of power grid dispatching, an algorithm structure is designed to meet the requirements of local and global feature extraction, and a named entity recognition model based on BiLSTM-CNN-CRF is constructed. Finally, the experimental results show that the recognition accuracy of this method reaches 93.1%, and the F1 value reaches 86.05%, which can effectively support the development of entity recognition in the field of power grid dispatching.
According to the theory of deep belief network (DBN) and optimal control, this paper proposes a DBN state optimal feedback algorithm, which is applied to the field of automatic generation control. Firstly, a full-state optimal feedback control strategy suitable for power generation control problem is designed, and the deep-belief network is introduced to learn the characteristics of full-state optimal feedback control. The nonlinear expression ability of deep-belief network is used to make up for the deficiency of traditional linear control, thus the approximate optimal power generation control with incomplete state information feedback is realized, which effectively solves the problem of state information measurement in optimal generation control, and improves the performance of automatic generation control. The simulation results show that the the DBN state optimal feedback algorithm is able to realize the approximate optimal generation control under the combined feedback of self-area frequency deviation and transmission power deviation. The effectiveness, feasibility and strong robustness of the proposed algorithm are verified.
In response to the fact that additional frequency control of doubly-fed induction generator (DFIG) in wind farms can have an impact on the frequency-regulation dynamic characteristics of power system. By developing the mathematical model of the coupling characteristics of multi-physical control links of DFIG, and the mathematical model of the dynamic characteristics of load-frequency control of interconnected power system considering DFIG access, taking the frequency-support control strategy with a combination of inertial support and droop control for wind power as the object of study, the dynamic characteristics of the DFIG involving the load-frequency control of the interconnected system using frequency support and the motion-mode coupling characteristics of the physical control link are investigated using small disturbance stability analysis. The analysis results show that the additional frequency control of DFIG does not significantly affect the small-disturbance stability of the system frequency-regulation dynamics. However, the inertia support control adds one more mode to the frequency-regulation dynamic characteristics of the power system, and results in the coupling of the frequency-regulation dynamic link of load-frequency control with the multiphysics control link of DFIG on partial oscillation and non-oscillation modes. The effects of additional frequency-control parameters on the dynamic characteristics of the oscillating modes are further investigated using damping ratio and oscillation frequency, and the rationality of the theoretical analysis is verified by time-domain simulation.
In view of the strong randomness and volatility of wind power generation, in the wind power dispatching system, the method of applying the traditional forecast interval to describe its uncertainty has defects. In response to this problem, this paper optimizes the output forecast range of the wind farm by introducing abandonment restrictions, and obtains a safe wind power output range that can ensure the safe operation of the dispatching system. On this basis, a two-layer robust optimal range dispatch model is established to make the operating cost of conventional units and the cost of abandoning power in wind farms are the smallest, and the impact of system climbing reserve on the safe output range of wind power is analyzed. Since the valve point effect of the conventional unit is considered, the model presents nonlinear characteristics. This paper uses an improved teaching and learning optimization algorithm and a linear programming method to solve the model. Finally, an improved 10-machine system verifies the effectiveness and superiority of the proposed model and solution method.
Secondary frequency drop that may be caused by rotor speed recovery process is a key issue that restricts the ability of wind turbines to provide upward adjustment after wind turbines provide frequency response. In this paper, a study on kinetic energy control of wind turbines in Yunnan Power Grid is carried out, and a method for setting kinetic energy control parameters of wind turbines is proposed. In the early stage of disturbance, integrated inertia control is used to quickly suppress the rate of frequency change and reduce frequency deviation; in the middle and later stages of disturbance, it is coordinated with conventional synchronous units such as thermal and hydropower or frequency limit controller to avoid the problem of secondary frequency drop and realize the optimization of the overall frequency dynamic process. Simulation research shows that increasing the virtual inertia control coefficient Kdf is not conducive to improving the maximum frequency deviation, and will cause serious overshoot and reverse modulation after the frequency enters the dead zone of FLC; the droop conrol coeffircent Kpf is the key to improve the maximum frequency deviation and reduce the amount of action about FLC; the wind turbines operating under the maximum power point tracking almost have the same ability to improve the maximum deviation of the frequency when their Kdf and Kpf are same.
With the ever-increasing penetration of wind power in an interconnected power system, the power flow distribution is becoming more and more complicated and uncertain. To analyze the stable operating characteristics of an interconnected power system with wind power integrated and enhance the accommodation capability for intermittent renewable generation, this paper applies probabilistic eigenvalue sensitivity indices to determine optimal access points of wind farms. Under multiple operation conditions of an interconnected power system, the relationship between the system state matrix and the residual index is studied, the probability sensitivity indices are developed, and the correlation factors and strongly related units that cause the low-frequency oscillation of the system are identified. The eigenvalue analysis method and dynamic time-domain simulation are used to analyze and compare the influences of “adding” and “replacing” wind power configuration schemes on the system oscillation characteristics, and to determine the optimal access points of wind farms. Finally, two numerical examples are employed to demonstrate the feasibility and efficiency of the proposed approach.
The increasing number of household photovoltaic power generation systems has changed the power flow distribution of the distribution network, and at the same time has a significant impact on the line loss of the distribution network. In order to quantify the operating economy of distributed photovoltaic generation (DPG) connected to the three-phase four-wire low-voltage distribution network, this paper takes the three-phase unbalanced line loss as the research objective, and firstly establishes a balanced and unbalanced line loss calculation models suitable for low-voltage distribution network. By introducing three influencing factors of DPG access mode, location and capacity, the line loss variation model under different DPG access modes is established. A model for estimating the three-phase unbalanced line loss of the active low-voltage distribution network considering the DPG access mode is proposed. This method can be calculated by using both real-time current data and easily collected active power data. The accuracy and feasibility of this method is verified by taking typical parameters of low-voltage distribution network as an example, and the method can provide reference basis for line loss calculation of DPG single-phase and three-phase access to low-voltage distribution network.
Incremental electricity distribution and retail companies are imposed the responsibility in the guaranteed mechanism for accommodating renewable energy generation, and it is hence of great significance to study their optimal investment strategies for business development as well as the design and improvement of the guaranteed mechanism. Given this background, the optimal portfolio investment decision-making method and prospect theory are presented for incremental electricity distribution and retail companies. Firstly, the cost-benefit analysis of the investment decision-making behavior of incremental electricity distribution and retail companies in response to accommodation responsibility is performed for attaining the return rate and risk of each investment behavior. Secondly, a portfolio investment decision-making model of incremental electricity distribution and retail companies is developed. Then, an improved portfolio investment decision-making model based on the prospect theory is built for incremental electricity distribution and retail companies so as to more accurately attain their optimal investment behaviors. Finally, case studies on an incremental electricity distribution and retail company are performed to demonstrate the proposed decision-making model. Simulation results show that the proposed strategies for incremental electricity distribution and retail companies could maximize the utility associated with investment decision-making.
