In order to solve the problem that traditional inertia estimation method under transient frequency disturbance is sensitive to the judgment of disturbance occurrence and inertial response duration, which leads to large estimation error, this paper proposes the idea of grid inertia estimation based on subspace method. By analyzing impulse response characteristics of the grid frequency response model at stages before transient frequency disturbance, inertial response and primary frequency-regulation response, the unified expression of grid inertia at each stage is obtained, and the grid frequency response model at all stages are taken as a system to be estimated. Subspace method is used to identify the parameters of the system model, and finally grid inertia is estimated. The effectiveness and accuracy of this method are verified by simulation.
The on-load tap changer is important equipment to realize voltage and power regulation in HVDC system. In recent years, several serious faults arose during on-load tap changer adjustment process. To reduce the risk of tap changer failure, it is necessary to study the measures to avoid unnecessary adjustment of the tap changer. The control strategies of on-load tap changer of different types of HVDC converter stations are analyzed in this paper, and the reasons for the unnecessary continual adjustment of tap changer in some stations are revealed. On that basis, research on the optimizing of the operation mode, control parameters and control strategy is carried out, and three kinds of measures to reduce the adjustment times of the on-load tap changers are presented. Simulation models are set up to compare the effect of these measures. Finally, the feasibility of these measures is further verified by both simulation and site operation experience, the relative merits and application conditions for the suggested measures are also discussed.
The structure of multi-infeed DC (MIDC) system is complex, and there is coupling between DC systems. The commutation failure (CF) of one DC system may leads to the successive CF of the adjacent DC system, resulting in a serious regional power loss. In order to effectively suppress the successive CF of MIDC system and realize the orderly recovery, an incremental recovery strategy based on additional current order is proposed in this paper. In this strategy, the voltage of the converter bus is used as a criterion of reactive power margin, and the corresponding additional current order is calculated according to an appropriate scale factor. Considering the different intensity of the DC systems, the multi-infeed short circuit ratio (MISCR) is utilized to calculate the scale factor. Finally, by the superposition of voltage dependent current order limiter (VDCOL) and the additional current order, the current setting value at rectifier side is obtained to realize the incremental recovery. The test system is built in PSCAD/EMTDC, and the simulation results verify the validation of the proposed strategy.
Traditional one-step monthly electricity consumption forecasting methods for urban complex cannot provide accurate forecasting results due to the serious challenge of model overfitting. In this paper, a monthly electricity forecasting method based on multi-layer decomposition-accumulation principle is proposed. The loads of urban complex are firstly subdivided into three categories according to their characteristics. Subsequently, for each category of load, the historical hourly electricity consumption data is collected and decomposed according to its week label so as to improve the forecasting performance of the multi-step forecasting model. Then, improved empirical mode decomposition (IEMD) algorithm is used to separate the fluctuations and trends of different scales in the electricity consumption series, and extreme gradient boosting algorithm (XGBoost) is utilized to establish multi-step forecasting model for each component. Finally, all of the forecasting results are accumulated to obtain monthly electricity consumption forecasting results. Results show that the proposed method can effectively capture the changes in the electricity consumption series. Its forecasting accuracy is improved by 18.2%-34.9% compared with the traditional method.
Considering the importance of evaluation of power prediction for power system operation and dispatching, this paper takes the actual demand of gird dispatching into consideration and analyzes the irrationality of existing prediction evaluation indices from two aspects: the output of units in different periods and the demand of grid dispatching for forecasting accuracy in different periods. And some new evaluation indices are put forward. Firstly, the flexibility matrix of the system in different periods is calculated and pretreated. Secondly, the processed flexibility matrix is input into the technique for order preference by similarity to an ideal solution (TOPSIS) model. According to the closeness of the evaluation object and the idealized target, the flexibility in different periods is sorted and normalized to obtain the weight coefficient matrix, and then a new evaluation index is formed. Finally, the evaluation index proposed in this paper is compared with other indices. The simulation results show that the evaluation indices proposed in this paper can better reflect the influence of system flexibility on power prediction evaluation and meet the requirements of grid dispatching.
Accurate prediction of electric vehicle charging load has practical significance for power grid dispatching, power market trading, charging station planning and construction. Because the characteristics of electric vehicle charging load are different from the traditional electric load, it is necessary to carry out targeted research on the influencing factors and prediction model of electric vehicle charging load. Considering the differences of time series characteristics and influencing factors of different types of electric vehicle charging load, a prediction model of electric vehicle charging load considering daily type, maximum and minimum temperature is established. Fuzzy C-means (FCM) is used to cluster the charging load, data feature attributes are mined, and similar daily load is extracted. The least square support vector machine (LS-SVM) is used to predict the similar daily load after clustering. The prediction results are compared with the test set, and the results show that the prediction accuracy of the proposed model is higher than that of the LS-SVM method, which verifies the effectiveness of the prediction model.
The electric vehicle (EV) group can charge and discharge orderly under the control of the electric vehicle aggregator and use the remaining capacity to participate in the auxiliary service market. Firstly, the trading framework of electric energy market and frequency-regulation market under the participation of EV aggregators is established, and the market mode of EV aggregators and the cost of each market subject are analyzed. Then, according to the game equilibrium theory of oligarch competition, a multi-period equilibrium model of energy and frequency-regulation market is established. Finally, the nonlinear complementary method is applied to solve the equilibrium model. The example analysis verifies the rationality and effectiveness of the model, and shows that the electric vehicle aggregator has the effect of ‘peak load cutting and valley filling’ on the load and active-power price after participating in the main and auxiliary market competition; the up and down frequency-regulation capacity price of the system has been reduced, and the reduced of down frequency-regulation capacity price is more obvious; the purchase cost of the system’s frequency-regulation service has been reduced, and the user’s profit and social welfare will be increased throughout the day. It can provide reference for the future market planning and the operation of electric vehicle aggregator.
For the shortage of only electricity being considered in the resident energy consumption optimization, a new multi-objective optimization method for residential energy consumption coupling electricity-heat energy conversion appliances and district energy center (DEC) is proposed in this paper. To achieve generality, a general modeling method for resident consumption with DEC coupled is proposed according to the concept of energy hub at first time. Further, by constructing a mixed integer quadratic programming model which takes the minimum energy cost and least user discomfort as the object, the method takes the input power of residential energy conversion appliances and the energy conversion and storage equipment in DEC as the decision variables, and takes the feasibility and safety constraints into account. The multi-objective optimization of residential electricity-thermal energy consumption is realized. At the same time, to obtain the multi-objective optimal compromise solution, the satisfaction index of Pareto optimal solution is introduced according to fuzzy membership function. Finally, the verification and comparative analysis of the proposed algorithm are carried on energy consumption optimization for residents in a certain area in winter. The results show that the coupling and coordinated operation of household energy conversion appliances and DEC can optimize residential electricity and heat energy consumption at the same time and reduce the cost of residential energy consumption efficiently. Besides, the proposed multi-objective optimization scheduling model can provide users with multi-factor optimization scheme considering the economy and comfort of users.
At present, the coverage of measuring equipment in distribution network is low, so only part of the nodes’ load data can be collected in real time. This situation makes it impossible to use the optimization based on power flow calculation in the real-time reactive power optimization of distribution network. Considering the above situation, this paper proposes a data-driven reactive power optimization method based on partial real-time visible distribution network. According to the historical operation data, the optimal power flow is used to generate the reactive power optimization strategy offline, and the mapping between the real-time measured node load data and the reactive power optimization strategy is established by training the neural network to realize the real-time reactive power optimization of the partial real-time visible distribution network. Finally, in the modified IEEE 33-bus system, the proposed method is compared with the 9-zone diagram method to verify the effectiveness of the proposed method.
This study designs a robust fractional-order PID control (RFOPID) strategy of supercapacitor energy storage systems (SCES). At first, the nonlinearities, unmodelled dynamics and parameter uncertainties of SCES are estimated effectively by a high-gain perturbation observer (HGPO). Furthermore, a fractional-order PID (FOPID) controller is utilized to completely compensated the perturbation estimation. No accurate SCES model is required while only the dq-axis currents need to be measured, which is easy to implement in hardware. Finally, three case studies are used to evaluate the performance of RFOPID control. Simulation results show that RFOPID has the best control performance and the strongest robustness under various operation conditions compared with other controller.
Non-intrusive power load monitoring and identification is the key technology to realize customer-side intelligent sensing in the ubiquitous power Internet of things. Aiming at the problems of high feature redundancy, low identification accuracy and low calculation efficiency in the existing identification model, a non-intrusive power load identification model based on Fisher principal component analysis and kernel extreme learning machine is proposed. Firstly, the steady-state characteristics such as current, power and harmonic contents are selected as the original input variables, and the Fisher principal component analysis (FPCA), which combines Fisher score and principal component analysis, is used to eliminate the invalid features with poor separability and to eliminate the correlation among the effective features at the same time. Then, radial basis function is introduced to build the network structure, and genetic algorithm (GA) is used to optimize the model parameters such as penalty coefficient, so as to build the kernel extreme learning machine(KELM) classification model for load identification. Finally, the open TIPDM load data set is used for example analysis. The simulation results show that the proposed model has better identification accuracy and calculation efficiency than the traditional load identification models, and it can effectively identify common household loads.
Load portrait modeling of power user is an important user-oriented method to create differentiated labels by mining the load characteristics in power consumption data. Most of the existing research focuses on the study of portrait methods, but lacks comprehensive load characteristic label system. This paper proposes a general method of load characteristic analysis based on data-driven. The load characteristic label system is constructed from power consumption regularity, smoothness, load control capability and epidemic impact, which are most concerned by dispatching department. Firstly, the typical load curve is extracted from massive actual load data by using fuzzy C-means clustering algorithm. Considering the power consumption characteristics of each industry from above four aspects, a comprehensive load characteristic label system and the load characteristic portrait models of different power users are established. Secondly, the load characteristic label is refined and every definition and calculation method of corresponding index is given. Furthermore, the index boundary is determined by fuzzy clustering algorithm, and the smoothness label is scored by entropy weight method. Finally, the data of typical users in different industries are analyzed from an example, and universal index boundaries are given, which provide a new idea for load modeling of users in various industries.
Large industrial user is a multi-energy supply system. Traditional integrated energy planning of large industrial users ignores the participation of various energy sources. This paper presents a new approach for solving the integrated energy system(IES) planning problem for large industrial user particularly considering the combined cooling, heating and power (CCHP), as well as the transfer of cooling, heat and electric loads with the production tasks. The proposed optimization model is to minimize the comprehensive cost of the system which includes the interaction cost with the selling company, the investment cost, operation and maintenance cost, and fuel cost of CCHP, and the transfer cost of production tasks. Prevailing constraints include multi-energy coupling balance constraint, technical constraint of power generation equipment and load scheduling limits of transferrable production tasks. The model optimizes the capacity configuration of CCHP. The model is a large-scale mixed integer linear programming (MILP) model, whose simulated results of the case studies validate that consideration of CCHP can increase the flexibility of energy consume and effectively reduce the comprehensive cost of large industrial users.
Edge computing and consensus are two important research topics in the field of distributed computing. Edge computing delegates some resources to the edge near terminal devices to improve the efficiency and quality of computing, while consensus provides security for distributed systems by achieving the consistency among the states of distributed individuals. After a brief review of related literature, this paper summarizes and analyzes the reciprocity between edge computing and consensus: the edge computing architecture provides innovation for the theoretical model of consensus study, and consensus provides desirable security for edge computing systems. In this paper, edge computing and consensus algorithm are combined organically, and the concept of "consensus edge computing" is established. The application of this combination is prospectively analyzed under the background of energy internet, and then possible development directions are enumerated.
Vulnerability of embedded terminals used for measurement and control has been one crucial cyber-security threat for intelligent substation. According to fragility analysis, a security evaluation index system for embedded terminals in intelligent substation is proposed in this paper. Research on security testing technologies for embedded terminals in intelligent substation is also carried on to cover all indices. Supported by an intelligent substation laboratory, a security assessment for five types of embedded terminal is conducted, which gives the assessment process, results and rectification suggestions. The work in this paper can be used to evaluate the availability, to discover and patch the vulnerability, and finally to improve the level of security for intelligent substation. Meanwhile, this study provides a reference for online security protection for secondary system of intelligent substation.
