Abstract:There are many classification-based methods for abnormal electricity consumption detection now,but most of them are based on short-term electricity consumption to judge long-term electricity consumption behavior. It is difficult to determine the threshold and ratio of these methods. In engineering application,the distribution of power consumption data in different regions and time periods is quite different,so the proportion and threshold value are quite different. It is difficult to apply the fixed proportion to all user data. To solve this problem,a method for judging abnormal electricity consumption based on reinforcement learning is proposed,which innovatively uses reinforcement learning model to dynamically generate threshold for different data sets. Firstly,the abnormal probability of the short-term behavior of several users output by the classifier is obtained. Then,the dynamic threshold is obtained by inputing the probability into the deep recurrent Q network (DRQN)of the enhanced learning model,where,the dynamic threshold can be Judgment threshold and judgment ratio as well. The experimental results show that,compared with the traditional voting method of manual parameter adjustment,this method has a significant improvement in the evaluation index,and also has a good performance in data sets with large differences in data distribution. It shows that this method has strong generalization ability in the real environment with complex data types.

Abstract:Load interval prediction conducting probabilistic analysis for load power quantifies the impact of uncertain factors accurately. Compared with traditional point prediction,interval prediction is beneficial to the safety and stability of the power system,and it reflects the trend of load changes more intuitively. It is proposed a rolling bus load interval prediction method based on variational mode decomposition (VMD) and long short-term memory neural network quantile regression (LSTMQR) in this paper. First of all,the bus load is decomposed into a series of subsequences with different frequency characteristics by VMD. After that,the optimal rolling steps of different subsequences are determined and LSTMQR is used to predict power intervals of different subsequences. Finally,the interval predictions of different subsequences are reconstructed to obtain the original load prediction results. It is verified by 220 kV and 10 kV bus load data to obtain that the proposed method above has a significant improvement in prediction accuracy and interval width by comparing with traditional interval prediction models.

Abstract:It is of great practical significance to study the fault diagnosis of transformers for safe and stable operation in power systems. The traditional transformer fault diagnosis method with dissolved gas characteristics in oil as input has a large limitation in dealing with sample imbalance data. To address this problem,a transformer fault diagnosis method based on Focal loss-stacked sparse denosiing auto-encoder (SSDAE) is proposed. The method is used to determine hyperparameters by category weights and adds Gaussian white noise as the original input,which facilitates the self-encoder to fully extract effective features,and thus obtaining an effective deep feature extraction model. The Focal loss function is used to optimize the model and a Softmax classifier is used to output the diagnosis results. The results of the case study show that compared with traditional transformer fault diagnosis methods such as three-ratio method,back propagation neural network (BPNN) and support vector machine (SVM) method,the method in this paper further improves the diagnosis accuracy.

Abstract:With the advancement of energy revolution and the proposal of two-carbon goal,the integrated energy system has been paid more and more attention by many researchers. Accurate multiple load forecasting is indispensable for efficient and correct scheduling and control of integrated energy system. Based on the above requirement,the transfer learning theory is introduced,and an improved domain adaptive neural network (DaNN) load forecasting model is proposed to unified model and forecast the cooling,heating and electrical load in the integrated energy system. Firstly,the feature pictures of multiple loads are constructed by historical data and be input into the parameter sharing layer of improved DaNN. Secondly,based on the characteristics of combined forecasting of multiple loads,the loss function of traditional neural network is improved. The maximum mean difference (MMD) index is added,and the training model is optimized. Finally,the forecast values of cooling,heating and electrical loads are output through three independent full connection layers. Through the actual case verification and comparison with the traditional model,it proves that improved DaNN model effectively improves the accuracy of multiple energy load forecasting of integrated energy system.

Abstract:To address the problem that traditional methods cannot accurately identify household loads containing high-order harmonics, a non-intrusive load identification method based on the fusion of multiple features containing V-I trajectory matrix, power and high-order harmonics is proposed. Firstly, the V-I trajectory matrix, power characteristics and harmonic characteristics of 11 kinds of family load are analyzed. Secondly a hybrid feature matrix construction method based on pixel image conversion is proposed. The power and high-order harmonic characteristics of the load are combined with the basic V-I pixel trajectory through binary coding conversion,which enriches the characteristic information of samples. Thirdly,the mixed feature matrix is used as the input of the convolutional neural network to realize the accurate recognition of the household load identification. In the calculation example,the algorithm proposed in this paper can accurately distinguish two loads of heaters and hair dryers with similar power characteristics but different high-order harmonic content. It achieves an identification accuracy rate of more than 93% for all types of household loads. This algorithm provides the technical support for accurately investigating potential safety risk of household electrical loads containing high-order harmonics in engineering application.

Abstract:In order to improve the efficiency of power system restoration after a power outage,it has become an inevitable trend that energy storage station which has advantages of fast response and frequency control is employed in system restoration. Currently,energy storage is mainly utilized as an auxiliary power source for renewable energy to smooth out fluctuations of renewable energy. Large-scale energy storage can also be utilized as black start power source to provide starting power for units to be restored. However,the above-mentioned studies have focused on the use of the output power of energy storage,while ignoring the role of energy storage and frequency modulation capabilities in improving system safety,thus failing to improve the recovery efficiency of the system to a greater extent. To this end a power system load restoration strategy that considers the power storage and frequency modulation capability is proposed. With the goal of restoring as many important loads as possible,a fuzzy chance constraint model for power system load restoration is constructed considering the power characteristics,frequency response characteristics and other safety constraints of energy storage. It is further transformed into a deterministic 0-1 programming problem through clear equivalence classes,and the artificial bee colony algorithm is employed to solve it. Taking the IEEE 39 system as an example for simulation analysis,the simulation results show that the strategy proposed in this paper improves the efficiency of load restoration.

Abstract:To solve the problems of current quality decreasing and poor grid stability of virtual synchronous generator (VSG) under the condition containing background harmonic,a power grid voltage feedforward control strategy based on current loop for VSG is proposed. In order to eliminate the influence of grid voltage background harmonics on grid connected current,the voltage feedforward control function is derived according to the input current transfer function. The impedance model of VSG with and without feedforward control are established based on the harmonic linearization method. The influence on both the impedance characteristics at different frequency range and stability of grid connection are compared and analyzed. The results show that the introduction of the feedforward control is equivalent to parallel virtual impedance at the output of VSG,so the amplitude frequency curve of output impedance in high frequency band moves up,which can improve the grid connected current quality under non ideal grid conditions. At the same time,the phase frequency characteristics of medium and high frequency band are corrected from capacitive to inductive,which can eliminate the risk of harmonic oscillation under grid connected conditions,and improve the stability of interactive system. Finally,a hardware in the loop experimental platform is built to verify the correctness of the control strategy model and related analysis.

Abstract:In response to the gap that the existing methods do not fully consider the random characteristics of the distributed photovoltaic generation (DPVG) outputs and the electric vehicle charging station (EVCS) charging loads,probabilistic power flow is analyzed based on the scenario probability method,and a chance constraint based DPVG-EVCS joint planning model is developed. The locations and capacities of EVCSs and DPVGs are optimized to minimize energy loss in the distribution systems under a premise of ensuring that the operating conditions of the distribution system meet the chance constraints. Then,co-evolutionary algorithm (CA) based on the genetic algorithm (GA) is used in the DPVG-EVCS joint planning model calculation. The optimization is decomposed into an EVCS planning sub-optimization and an DPVG planning sub-optimization. Two sub-optimizations are solved by GA in parallel. And cooperate with two GA populations to evolve through the ecosystem until the optimal solution to the optimization problem to be sought is obtained. Finally,the IEEE 33 bus distribution system is built for simulation. The results show that the proposed model can obtain a reasonable planning scheme. And the solution efficiency of CA is high,which can significantly improve the work efficiency of planners.

Abstract:In a hybrid cascade UHVDC transmission system which the receiving end consists of a line commutated converter(LCC) in series with multiple parallel voltage source converters(VSC),the VSC has sub-module overvoltage problems when the AC grid fault. A method to suppress the overvoltage of the sub-module by installing energy-consuming devices on the DC side of VSC is proposed. The fault ride-through principles and strategies based on energy-consuming devices as DC Chopper,thyristor and controllable metal oxide surge arrester (CMOA) are compared and analyzed. Based on the PSCAD/EMTDC simulation platform,a hybrid cascade UHVDC simulation model containing the actual control and protection host program is built,and the AC fault ride-through characteristics based on three energy-consuming devices are compared and analyzed. The results show that the method of installing energy-consuming devices on the DC side of the VSC can effectively suppress the overvoltage of the sub-module and achieve reliable AC fault ride-through. The CMOA scheme has the advantages of simple control principle and high reliability,and it is suitable to be the energy-consuming devices for the hybrid cascade UHVDC transmission system which the receiving end consists of LCC and multiple VSC.

Abstract:The popularization and application of distributed renewable energy,the demand of energy saving and emission reduction,and the change of user terminal load characteristics have brought great changes to the traditional AC power supply,DC power supply is widely concerned because of its powerful energy-saving advantage. As DC/DC converter is a key part of DC power supply system,the stability of the converter is required. At present,small signal modeling is widely used in converters,and the modeling accuracy is not high,and the system may become unstable in the face of large disturbance. Based on the switching system theory,a switching control method of energy storage staggered parallel bi-directional DC/DC converter is proposed in this paper,and the large signal modeling of the system is directly carried out,and the modeling accuracy is high. Firstly,the energy storage function of the system is selected as the common Lyapunov function and the optimal switching rate is designed. Then,the stability of the system at the switching equilibrium point under the switching rate is analyzed. Finally,the simulation is carried out in Matlab and a bidirectional DC/DC converter prototype based on SiC MOSFET is built for verification. Experiments verify the effectiveness of this switching control strategy.

Abstract:Under the background of the large-scale increase in wind power utilization,the application of typical scenarios to deal with the uncertainty of wind output is of great significance. Aiming at the spatial-temporal correlation among the output of multiple wind farms,an improved scenario generation and reduction method is proposed,and an evaluation method is introduced to test the quality of the generated scenarios. The exponential function is used to construct a multivariate covariance matrix that reflects the temporal correlation of wind power,and the Copula function is used to build a multi-wind farm spatial correlation model. A large number of initial scenes are generated by performing spatio-temporal correlation non-linear transformation and equal probability inverse transformation on the cumulative probability distribution function of random numbers and historical data. The K-means clustering method is improved,and the optimal number of clusters is determined by the elbow method and the clustering effectiveness index,and then the representative spatial-temporal correlation wind scenarios are reduced. Finally,four evaluation indicators are proposed to test the quality of the scenarios. The calculation results show that the volatility,climbing situation and spatial-temporal correlation of the scenarios generated by the proposed method are more consistent with historical data. The proposed method has a higher coverage of actual measured wind power values than another method does.

Abstract:As one of the hot topics in the field of new energy forecasting,it is necessary for the research of short-term wind power forecasting to pay attention to the engineering application of the model while improving forecasting accuracy. Hence,a combined XGBoost forecasting model based on partial maximum information coefficient is proposed. To begin with,a feature selection algorithm based on partial maximum information coefficient is designed. By introducing partial mutual information,while mining meteorological features that have a greater impact on wind power,it can also eliminate the adverse effects of coupled information. On this basis,in order to take the accuracy and computational efficiency of the model into account and reduce the forecasting risk of a single model,a combined forecasting model with XGBoost as the underlying algorithm is constructed to further realize wind power forecasting. Two wind farms with large differences are used as examples for verification analysis. The results show that the combined XGBoost forecasting model based on partial maximum information coefficient feature selection algorithm can not only improve the forecasting accuracy of short-term wind power,but also has higher calculation efficiency compared with similar combined forecasting models,which is beneficial to engineering application.

Abstract:The traditional voltage sag frequency estimation considering protection action characteristics needs to obtain detailed protection configuration information. However,the protection configuration of distribution network is diverse. Under the influence of different factors such as transition resistance,generator scheduling and fault type,the protection areas of each stage of stage protection may change greatly,and the traditional method may produce large error in the evaluation of voltage sag duration. In this paper,a method of voltage sag frequency estimation based on improved K-means clustering algorithm is proposed. In the case of unknown line protection configuration,based on the historical monitoring data of voltage sag and protection action information,the improved K-means clustering algorithm is used to cluster the voltage sag amplitude duration,infer the line protection configuration,and calculate the protection action time and protection action sag threshold. According to the calculation results,the distribution network voltage sag frequency is estimated considering different fault types,different operation modes and transition impedance. The simulation of the bus 5 distribution network in IEEE RBTS-6 bus test system verifies the effectiveness and superiority of the proposed method.

Abstract:Based on the parameters of the ±10 kV DC distribution system in Tongli,Suzhou,the transient characteristics of DC unipolar grounding and inter-electrode short-circuit faults are studied. Using the system structure and control strategy of the demonstration project,the electromagnetic transient model is established in the environment of PSCAD. Many fault cases are considered in our work,including the AC side,the converter side,the DC side,as well as the load side. The overvoltage and overcurrent of the system are discussed. Furthermore,the impact of grounding fault resistance on the transients on the DC side of system is quantitatively studied. The simulation results indicate that there is a continuous DC component on the AC side in the case of single pole to ground fault on DC distribution system. The converter is not blocked,and the DC-DC converter high-voltage side capacitor is discharged. The grounding resistance of fault has a significant effect on the voltage and current transients of DC side. The pole-to-pole ground fault produces a severe overcurrent,which triggers the converter overcurrent protection,which triggers the converter overcurrent protection. The fault current is an important cause of the overvoltage. The overvoltage at both ends of the inductive element is large,which has little impact on the AC side of the system.

Abstract:As a large number of distributed power sources are connected to the distribution network,the structure of the distribution network is changed from a feed-supply radiation network to a multi-terminal power network. The pilot protection scheme is the most effective way to solve the protection problem of distribution network with DGs. Limited to the level of distribution network hardware and communication,data on both ends of lines can't be sampled synchronously by pilot protection. To solve the problem above,a fault information self-synchronizes technology is put forward. As the moment of fault is the same time for both ends of lines,the technology uses the moment of fault as a time reference. The measurement about the interval between two adjacent peak points of the same trend before and after the fault time is carried out,and then intervals is used to calculate the current phase changes direction,which can effectively overcome the difficulty of acquisition of the synchronous data in distribution network. Based on fault information self-synchronizes technology,the novel pilot protection determines the fault location by comparing the direction of phase change on both sides of the protected circuit. Simulation results verify the accuracy of fault information self-synchronization technology,the novel pilot protection has good operation characteristics.

Abstract:The super-hydrophobic coating has excellent hydrophobicity and self-cleaning property. Therefore,it has a broad application prospect in the field of anti-pollution flashover. In order to study the application effects of superhydrophobic coating in special industrial dust area,the super-hydrophobic coating provided by a domestic enterprise is selected,and the super-hydrophobic coating insulator and the same type of uncoated composite insulator are used in Xiangshui,Jiangsu Province. The contamination accumulation characteristics of the super-hydrophobic coating,the aging properties of coating and the protective performance of the coating to silicone rubber are analyzed through the contamination level test,hydrophobicity test and microscopic test. The results show that the surface contamination amount of super-hydrophobic coating insulator is reduced by 37% compared with ordinary silicone rubber insulator. Although the super-hydrophobic coating ages after running for 10 months,the coating plays a good protective role on the internal silicone rubber material and effectively prevents the internal material from aging.

Abstract:Very fast transient overvoltage (VFTO) is a special electromagnetic transient phenomenon when the isolation switch is switched in a gas insulated substation (GIS). VFTO affects the normal behavior of secondary equipments through conduction and radiation. In order to explore the interference characteristics of VFTO on secondary equipments,a self-made interference measuring device is used in this paper to conduct field measurement of the common mode interference on the secondary side of the potential transformer (PT) in 1 000 kV GIS. Secondly,the performances of five time-frequency analysis methods are compared,and the time-frequency analysis of the measured waveforms is carried out using the synchronous compression wavelet transform with the better performance. The measured results in this paper show that the peak-to-peak voltage of common-mode interference voltage on the secondary side of PT reaches up to 9.65 kV. The time-frequency analysis results show that the 7.8 MHz frequency component has the highest amplitude,so it is the dominant frequency component of PT secondary side interference throughout the waveform. The results in this paper provide references for the electromagnetic immunity test and electromagnetic protection design of secondary equipment in GIS.

Abstract:The insulation aging status of high-voltage cross linked polyethylene (XLPE) power cables affects the reliability of power supply. Therefore,it is of great significance to study the detection and evaluation methods of cable insulation aging status. The domestic and international researchers have obtained certain research results in the detection and evaluation of the insulation aging state of high-voltage cables. It is summarized the commonly used offline and online detection methods and insulation state evaluation methods for high-voltage cables in this paper. The offline detection method has high accuracy,but it is not suitable for large-scale sampling detection for the service cables. The online detection method is subject to many environmental interference factors,but the existing interference always affects the detect results. The online detection method has certain detection limitations and lacks a large amount of test data support. There is no widely recognized evaluation standard and system as the evaluation method for the aging state of cable insulation. On the basis of summarizing the existing methods,it is ponied out the difficulties of cable insulation aging state evaluation in this paper,thus putting forward the research direction that can be improved in the future.

Abstract:Non-intrusive load monitoring and disaggregation (NILMD) technology is an important data acquisition method for deep improvement of residents' energy service and the interaction between power supply and demand. However,it is unable to disaggregate accurately the electrothermal load by the NILMD algorithm of edge detection which is widely used in current engineering. In order to solve this problem,a NILMD algorithm for typical electrothermal load based on three dimensional characteristics vector is proposed in this paper. Firstly,the edge detection algorithm is used to extract the electrothermal events through active power,reactive power and current harmonic,and the three dimensional characteristics vector model is constructed together with the non-electric characteristics such as running duration,start and stop times based on active power. Then,the learning rules and algorithm for typical electrothermal load detailed disaggregation are designed by sequential covering. Finally,the disaggregation accuracy of the electrothermal load is more than 85% based on experimental verification. Experimental results show that the NILMD algorithm for typical electrothermal load proposed in this paper effectively improve the disaggregation accuracy of four typical electrothermal load.

Abstract:The storage battery in rail transit uninterruptible power supply system directly affects the safety and reliability of the load,but its capacity is often configured far beyond normal use,leading to waste of resources. The main reasons for this problem are the overestimation of load and using constant power discharge model in the setting stage. Besides,there is a lack of research on the relationship between load type and backup power supply time. Therefore,a new method for battery capacity reduction is proposed in this paper. In order to get the maximum operating load more accurately,the loads are clustered first. Secondly,different prediction methods are used for different types of loads. The two parameter Weibull simulated load curve is adopted to predict the maximum load for fluctuating loads. The load coefficient method is directly used for the steady load with weak time series correlation. Then,the step load method is used for further reduction of battery capacity,considering the different backup time requirements of different loads. Finally,the battery capacity is reduced based on the actual data of the uninterruptible power supply system of Suzhou rail transit line 3,verifying the accuracy and effectiveness of this method.

Abstract:Energy storage power station plays an important role in transferring power demand,stabilizing renewable energy and load fluctuation. The optimal allocation of energy storage power station is the key problem to be solved in the planning and construction process. In this paper,a two-layer robust optimal configuration model based on information gap decision theory (IGDT) is proposed. In order to reduce the negative impact brought by uncertain parameters in the operation process of the system,a day-ahead robust optimal operation model of energy storage power station is built at the bottom of the model to impove elonomic efficiency. IGDT is introduced in the upper layer of the model to combine economy and robustness,and thus maximizing the volatility for the system under certain expected objectives. Benders decomposition method is used in the model solving. Finally,the effectiveness and feasibility of the proposed model are verified through the analysis of numerical examples.

Abstract:On-line monitoring of transformer vibration signals is an important method to detect the operating status of transformers. The power supply mode of the vibration sensors restricts the application of vibration detection methods. To access the power supply for the vibration sensor,a transformer vibration energy harvesting device based on piezoelectric material is designed,which uses the collected vibration energy to supply the vibration sensor. Firstly,according to the vibration characteristics of transformer,the output voltage and output power models of the three-cantilever piezoelectric energy extraction structure are established using the multi-modal energy harvesting method. Then,the relationship between the output power of the energy harvesting device and the external excitation frequency is analyzed by Comsol Multiphysics simulation. Finally,the transformer vibration energy harvesting experiment platform is built,and the actual output power of the piezoelectric energy collecting device is measured to be 11.547 μW. Using the vibration energy harvesting device could supply power to the oscillation sensor,which can ensure the power supply of the online monitoring equipment and reduce the influence of the external power supply on the safe and stable equipment operation.

Abstract:With the application of new energy in grid-connected system and the development of ultra-high voltage direct current transmission,the grid's requirements for reactive power regulation have gradually increased. Considering that,large-scale synchronous condensers have been put into use again. However,it is difficult to extract the characteristic signal of the inter-turn short-circuit fault in rotor windings of synchronous motors. In order to improve the stability of condensers,a certain relationship between the field current and the number of turns is derived using the Parker equation in the dq coordinate system,and the differential equation simulates the excitation current. Then the characteristic energy value of the fault signal is extracted through wavelet packet decomposition and reconstruction,and it is input to the radial basis function neural network for fault diagnosis. It is proved by Matlab simulation that the diagnostic method proposed in this paper can effectively detect the degree of short-circuit faults among the turns of the rotor in condensers.

Abstract:Failure data without exact observed failure time of equipment is referred to as censored data. Aiming at the characteristics of censored failure data of protective equipment under the background of big data,a method for evaluating the operating life of protective equipment considering censored data under the background of big data is proposed. Firstly,based on analyzing failure-data features of protective equipment,the failure data were preprocessed. By combining the expectation-maximization (EM) algorithm with the exponential distribution model and the weibull distribution model,failure model parameters of protective equipment were estimated. Secondly,the estimated parameters are substituted into the failure model to obtain reliability indices,such as the reliability,failure probability density,failure rate,and meantime between failures. Subsequently,through simulation study,the estimation accuracy of model parameters in accidental-failure and aging-failure periods obtained by different methods was comparatively analyzed,and the effectiveness of the proposed method for processing censored data was verified. Finally,taking a certain type of protective equipment as an example to investigate reliability indices,the feasibility of applying this method to plan equipment maintenance cycle is verified.

Abstract:With the frequent occurrence of severe weather such as low temperature,rain,snow and ice in the world,the damage caused by ice coating on electric power lines is becoming more and more serious. In view of the existing composite cross arm design,it is mainly based on statics and does not consider the dynamic analysis under complex operational loads. In this paper,the mechanical properties of 10 kV composite insulated cross arm under icing conductor-breaking condition are studied. Firstly,the condition of breaking one or more conductorsare numerically calculated based on the location of the disconnection point. Then,the dynamic stress response characteristics of the composite crossarm under icing conductor-breaking conditionare discussed and compared with the stresscharacteristics under ice covered static load. Finally,the dynamic amplification coefficient is obtained to measure the impact of conductor broken. The result shows that the stress of key section on the cross arm increases to different extents and the root of the composite cross arm directly related to the position of the breakpoint is greatly affected when the conductor is broken,which should be considered in the design. The stress amplification coefficient of the critical section dangerous point on the cross arm after wire breaking is 1.1~1.3. The mutual influence between them is small when multiple conductors are broken at the same time.

Abstract:The safety margin of high voltage direct current (HVDC) cable system is the precondition to ensure the long-term safe operation of cable lines. To obtain the highest service voltage of the HVDC cable system,the conductivity characteristics of insulation of cables and accessories are tested in this paper,and the dependence of conductivity on temperature and electric field intensity and the expression of conductivity are obtained. A safety margin test method is proposed based on ±80 kV HVDC cable system,the breakdown voltage of the HVDC cable system at the maximum operating temperature of 90℃ is tested by applying voltage step by step. The electric field distribution of cable system breakdown is calculated according to the conductivity of cable insulation and accessory insulation,and the safety margin of the cable system is obtained by comparing the electric field intensity during the breakdown of the cable system with the electric field intensity required for long-term operation. The research shows that the safety margin test method proposed in this paper can obtain the safety margin of HVDC cable system,and the research results provide theoretical and experimental basis for the safe operation of HVDC cable engineering.

Abstract:In order to improve the real-time performance of power grid control decision-making,a scheme is proposed in the paper. This scheme decomposes the economic optimization problem that takes into account the expected fail-safe constraints into the main problem of the ground state optimal power flow with the goal of minimizing the power generation cost and the sub-problem of the expected fail-safe check. At the same time,a decomposition and coordination algorithm based on tunable space slice parallel is proposed to solve the problem without alternate iterations. First of all,the adjustable space is sliced to form multiple slicing schemes according to different proportions. Based on the parallel computing platform and the slicing schemes,the primal dual interior point method is used to solve the base-case optimal power flow master problem. Secondly,security check of contingencies is carried out by parallel computing for different optimized cases. Then,the solution with the minimum cost is chosen from the safe solutions. Finally,the effectiveness of the proposed method is verified by cases in the regional comprehensive energy system of a city in Guizhou province.