The consumption of renewable energy promotes the development of energy storage technology. Shared energy storage has attracted extensive attention because of its decentralized characteristics. In the optimal scheduling of shared energy storage, there is a problem of benefit distribution among multiple subjects. Therefore, this paper proposes an operation optimization method for shared energy-storage power station applying Nash negotiation theory. In this paper, the joint model of shared energy-storage power station and industrial users is constructed, and the cooperative operation model of various operators is established according to Nash negotiation theory. According to the mean inequality, the non-convex nonlinear problem is equivalent to two sub-problems: system revenue maximization and power transaction payment negotiation, which are solved by alternating direction method of multipliers (ADMM). Through the comparative analysis before and after the cooperative negotiation, it is concluded that the optimization method used in this paper can effectively improve the benefits of each subject and promote the consumption of new energy.
With the deepening practice of demand response (DR), we need to rely on highly reliable information exchange to support fine DR control in the future. However, in the DR wireless communication scenario, the complex channel environment greatly affects the efficient information exchange of DR, and the existing technology is difficult to match and meet the characteristics of DR services. Aiming at the problem of channel quality in DR wireless transmission, this paper designs a flexible transmission strategy for DR service, which is realized by adaptive coding. First of all, the types of DR services and the differential requirements of communication services during the execution period are analyzed at the service level. Then, the precoding technology is introduced to improve the channel gain. Finally, a double-layer adaptive coding strategy is used to optimize the communication transmission under different DR services and different DR periods, while taking into account the processing delay of the algorithm. Simulation results show that the proposed strategy can not only reduce the system error rate, but also reasonably plan the computing resources.
Aiming at the problem of high energy consumption when the mass information processing tasks of IoT devices are unloaded to the cloud in ubiquitous electric internet of things (EIoT), this paper proposes an energy-saving and unloading mechanism based on network virtualization in ubiquitous electric internet of things. Firstly, the task-request status and network-state information are collected by the virtual network controller in the virtual network management layer, and a multi-objective integer programming model is constructed. Then the collaboration terminal nodes and resources allocation priority are determined by the routing and priority decision algorithm. On the basis of above results, abstract virtual resources are allocated according to the remaining power of the network unit, and then the virtual link is embedded into the unused resources of the physical link to realize the reuse of the resources and achieve the goal of energy saving. Finally, a validity assurance strategy is proposed to ensure the validity of the reused link. According to the simulation analysis, it is proved that the energy-saving and unloading mechanism proposed in this paper has significant effects in reducing network energy consumption and optimizing network performance.
There are concentrated energy demands and a variety of sources and loads in agricultural parks. When the multiple agricultural parks are dispersedly accessed to rural distribution network and lack of reasonable operation methods, it is bound to have a negative impact on the safety of rural distribution network and the benefit of agricultural parks. In view of the above problems, an optimal operation method based on central decoupling and evolutionary game for multiple agricultural integrated energy system (AIES) is proposed. Firstly, the AIES architecture with electricity-gas-heat and model of the demand response are constructed. Secondly, the rural distribution network is decoupled from the AIES by central decoupling. A two-layer game model including rural network layer and multi-park layer is established. The voltage safety margin and the energy cost are considered in the rural distribution network layer. The satisfaction of crop energy supply and economic benefit are considered in the agricultural park layer. Thirdly, the evolutionary game based on multi-objective particle swarm optimization is proposed. The problem of strong rationality and difficulty in achieving Nash equilibrium in a complex game with multiple parks and goals are solved. Finally, the feasibility and effectiveness of the method are verified by simulation examples. The optimal operation of multiple AIES is achieved.
With the increase in the penetration rate of new energy based distributed power generation, its characteristics such as strong intermittent and high volatility will have a greater impact on grid voltage fluctuations. How to calculate the steady-state voltage of the power grid with complex distributed power access more quickly is of great significance. In this paper, by sampling a great deal of real output data of photovoltaic and wind power, a comprehensive probability model is improved and generated on the basis of the traditional probability model, and the probability distribution deviation caused by the temporal and spatial characteristics is corrected through the Markov transition probability matrix. Then, taking the actual output data of adjacent photovoltaic and wind power stations in a certain area of southern China as a sample, the steady-state voltage at each node of the distribution network under different scenarios is calculated in the distribution network topology. Finally, the results of the calculation example show that the power grid model generated by the improved method simulation in this paper has high authenticity and applicability, and the calculated voltages have high accuracy rate. Moreover, compared with the traditional power system flow calculation, the calculation time is greatly reduced, so that it has better follow-up in the control effect, and is suitable for the calculation of the steady-state voltage of the complex power system with new energy.
Since distributed power supply is connected to distribution networks on a large scale, its topology and components are becoming more and more complex. As the direction of fault current at feeder switches is difficult to determine, a new matrix algorithm for equivalent decoupling fault location in complex distribution networks is proposed in this paper. In order to simplify the topology of complex distribution network effectively, the distribution network is decoupled according to the principle of depth priority to make it a network composed of tree trunks. A new fault-location matrix algorithm for complex distribution network is presented to solve the problem of large computation and complex steps in fault-location matrix algorithm for complex distribution network. Compared with traditional matrix algorithm, this method is more accurate for T-section and terminal section of power network fault location. Finally, through the typical park distribution network model and the improved IEEE 33-node system case, the method proposed in this paper can not only locate the faults in the distribution network, but also simplify the complex distribution network structure. Compared with the traveling wave method, the proposed method shortens the locating time and has better application value.
Aiming at the problems that the existing reliability evaluation methods of distribution network cannot fully evaluate low voltage system and the types of distribution terminals are not comprehensively considered, a collaborative evaluation method for medium and low voltage reliability considering multiple terminal configurations is proposed. Firstly, the basic idea of reliability collaborative evaluation of medium and low voltage distribution network is explained from three aspects: evaluation object, evaluation index and evaluation framework. Secondly, combining with the concept of feeder partition and considering the influence of various terminals on power supply reliability, the fault finding and impact analysis logic based on multi-module intelligent terminal device configuration is established. Thirdly, combined with the proposed fault analysis logic, a low- and medium-voltage Monte Carlo reliability assessment method based on collaborative analysis of different voltage levels is proposed. Finally, taking the IEEE RBTS BUS-2 system as an example, a comparative analysis of the system reliability levels in different scenarios is carried out to verify the effectiveness of the method.
In the related research of transient power-angle stability assessment and transient voltage stability assessment, independent assessment models are usually constructed, respectively, which hinders the information sharing among different tasks and wastes computing and storage resources. Considering the similarities and differences among different assessment tasks, in order to better realize the two assessments at the same time, a multi-task transient stability assessment model based on sub-layer of hybrid gated recurrent unit (GRU) is proposed in this paper. Because the power system transient process has obvious time-series characteristics, the GRU sub-layer is used to extract the time-series characteristics of the measured data efficiently. The gating mechanism is introduced into the model structure to automatically adjust the proportion of each sub-layer in constructing the feature representation of different evaluation tasks, which not only promotes the information sharing among different tasks, but also weakens the negative impact of the differences among different tasks on model training. The experimental results in IEEE 39-bus test system show that the model proposed in this paper has better evaluation performance and computing speed.
Fine power consumption behavior portrait and classification has been one of the key factors for power enterprises to accurately grasp the electricity consumption law of power consumers, improve service level and market competitiveness. To solve the issues of fragmentary portraits of electricity consumption behavior in current power user classification research, base classifier redundancy and class imbalance in ensemble learning load classification, a two-stage power consumer classification method based on digital feature portraits of power consumption behavior is proposed. In the first stage, a classification method for power user daily load curves is proposed combining spectral clustering and integrated strong base classifier. Firstly, a strong base classifier is developed on the basis of LSTM network to improve the weak learning capability of base classifier in ensemble learning. Secondly, an Optimal Selection Ensemble (OSE) strategy based on minimum regularized surrogate empirical risk is proposed to solve the problem of base classifier redundancy. Thirdly, a Density Based Gaussian Synthetic (DBGS) minority over-sampling technique is proposed for class imbalance. In the second stage, the power consumption behavior portraits with daily load-pattern occurrence probability as the digital features are constructed, and the portraits are classified by spectral clustering. Finally, the effectiveness of the proposed method is verified by the measured user load data.
The traditional decomposition method of power consumption sequence is difficult to effectively combine with the analysis of regional industry development trend. Therefore, this paper proposes a medium-term load forecasting model based on the decomposition of the clustering industry electricity curve which combines with the industry development trend. Firstly, the dynamic time warping algorithm is used to calculate the periodicity of the industrial power consumption, to classify the industry which has changed development trend. Secondly, the k-means algorithm is used to cluster pre-classified industries according to similar electricity consumption characteristics, and the seasonal decomposition algorithm is used to decompose the power consumption sequence of the clustering industries. Finally, the support vector regression model is established for each power consumption sub-sequence, and the electricity data of a city in Jiangxi province is taken as an example. The results show that the proposed method can separate the industry power consumption with different electricity consumption characteristics, help to analyze the local industry economic development, and improve the accuracy of regional medium-term load forecasting.
Deeply mining power consumer load law and perceiving electricity consumption behavior are of great significance for improving the service quality of power grid and the experience of power consumer. To deal with the issues of missing data, category imbalance and performance defects of classification model, this paper proposes a power user load curve classification method based on data enhancement and improved temporal convolution network (TCN). Firstly, a two-phase data enhancement method is proposed considering the global distribution characteristics of load data. In the first phase, the low rank tensor completion (LRTC) method based on tensor singular value threshold algorithm is introduced to complete the missing data. In the second phase, the generation adversarial network based on Wasserstein distance (WGAN) is used to enhance the minority samples to solve the problem of class imbalance. Secondly, a modified deep TCN classification model integrating bi-directional time-series features is constructed to realize accurate identification of large-scale power consumption curves. Finally, through the open-source criteria temporal dataset for classification and the actual load dataset, the proposed classification model shows better performance in convergence speed and classification accuracy, and the proposed data enhancement method can effectively improve the classification effect of models.
Short-term power load forecasting plays an important role in the safe operation of power grid and the formulation of reasonable dispatching plan. In order to improve the accuracy of power load time-series forecasting, a short-term power load forecasting model based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and short-term memory neural network based on attention mechanism (LSTM-Attention) is proposed in this paper. The complete ensemble empirical mode decomposition with adaptive noise effectively decomposes the load time series into multiple levels of regular and stable eigenmode components, and suppresses the boundary effect through the neural network model prediction maximum combined with the image continuation method to improve the decomposition accuracy. At the same time, the long short-term memory neural network based on attention mechanism adaptively extracts the input characteristics of power load data and assigns weights for prediction. Finally, the final prediction results are obtained after superposition and reconstruction of each prediction modal component. Experiments are carried out on different seasonal data of actual power load, and the results of other power load forecasting models are analyzed and compared to verify that the forecasting method has better performance in power load forecasting accuracy.
The difference in frequency characteristics between inverter-based distributed generators (DGs) and traditional synchronous generator-based DGs involved in frequency regulation in the microgrid (MG) may cause deterioration of the transition process of parallel operation and may cause system collapse. Aiming at this problem, this paper firstly conducts theoretical analysis and simulation on the frequency characteristics of the two types of DGs, and clarifies the differences in frequency characteristics of them. On this basis, the frequency characteristics of inverter-based DGs are reshaped applying virtual synchronous generator (VSG) technology and classic control theory, and the influence of reshaping parameters is analyzed. Finally, the parallel operation characteristics after reshaping are verified by MATLAB simulation, which proves the feasibility and effectiveness of the proposed method.
It is necessary for wind power to have reserve capacity and participate in system frequency regulation to build a new generation of power system with renewable energy as the main body. However, the existing reserve-capacity allocation methods rarely consider the active participation of wind power in frequency regulation, and most methods cannot optimize the economy and stability at the same time. A rolling optimization method of reserve capacity considering wind power frequency control is proposed, which is suitable for wind farms to participate in system frequency regulation by using hierarchical centralized frequency-control method. In this method, economy and frequency stability are taken into account by multi-objective chance-constrained programming. The parameters of wind power and load prediction error are corrected in rolling calculation. The optimal allocation schemes of reserve capacity of wind farm, thermal power plants and hydropower plants with different confidence are obtained by using hybrid intelligent algorithm. The simulation results of IEEE 39-node test system show that the method solves the limitation of traditional reserve-capacity allocation method, improves the frequency stability and operation economy of the system.
Power system long-term sequential simulation and production simulation are important basis for renewable energy and power grid planning, and also effective tools to verify the scientificity of planning and design scheme. First of all, in order to improve the efficiency of long-term sequential simulation of large-scale power systems, this paper proposes a method of linearization of power flow of transmission grid according to the characteristics of transmission grids, and verifies the accuracy and calculation efficiency of linear power flow applying IEEE standard calculation examples. The results show that this method not only has higher calculation accuracy, but also can effectively reduce the calculation time for the power flow of large-scale power systems. Then, according to different site-selection and capacity-determination schemes of renewable energy, the linear power flow model is used to carry out the long-term fast sequential simulation of the power system. The simulation has obtained key power flow information such as node-voltage amplitude and branch power, which is of great significance to the grid structure of the power system and the planning and design of the location and capacity of renewable energy units.
