Abstract:With the rapid growth of the number of new energy vehicles, the impact of their energy supplement demands on the planning and operation of power grids cannot be ignored any more. At the same time, with the continuous increase in the installed capacity proportion of wind and photovoltaic power generation, the power grids also urgently need to utilize the energy supplement demand flexibilities of electric vehicles (EVs) to smooth the fluctuations of renewable energy generation output and load power. Promoting the coupling of power and transportation networks can bring social benefits and achieve a win-win situation for all parties. This paper firstly analyzes the key infrastructures and the major stakeholders for the coupling of power and transportation networks. Secondly, the basic mode and objectives of the coupling of the two networks under current and unmanned driving conditions are discussed. Thirdly, the research status of the facility planning, operation, dispatch and control considering the coupling of the two networks is reviewed and summarized. Finally, this paper analyzes and prospects several problems that need to be further studied in the planning and operation optimization for the coupling of the two networks.

Abstract:The deep electrification of the traffic system and the rapid development of novel power systems strengthen the coupling between the traffic system and the energy system, and the traffic network and the energy network show the trend of deep integration. The data integration between the two networks is the key technology to realize the efficient interaction and cooperative operation of the two networks. Based on the concept of traffic-energy Internet, the architecture of a traffic-energy integrated big data platform is proposed. The challenges and difficulties are analyzed facing the traffic-energy integrated big data platform in data fusion, analysis, application and security, and the key technologies required for the construction of the big data platform are summarized. Combined with the preliminary exploration of traffic-energy Internet in practical engineering projects, the typical application direction of the traffic-energy integrated big data platform is given. Finally, the application prospect of the traffic-energy integrated big data platform is prospected.

Abstract:In view of the problem of inaccurate prediction due to insufficient consideration of the interaction between vehicle-road-station-grid and other parties in the study of spatial-temporal distribution of charging loads for electric vehicles (EVs), a prediction model of spatial-temporal distribution of charging loads for EVs based on the universal gravity model is proposed. Firstly, the relationship between the external environment and the energy consumption of EVs is explored, taking into account the road network traffic flow and ambient temperature. Secondly, considering the influence of external environmental factors such as temperature, humidity and radiation on users' trips, a trip chain model modified by travel intention is obtained. Finally, considering the multi-information fusion, a charging station selection model for EVs based on the universal gravity model is proposed. The results show that the proposed model can take into account the mutual influence of EVs, road networks, charging stations and power grids, accurately calculate the spatial-temporal distribution of charging loads for EVs, and analyze the characteristics of charging loads for EVs in multiple scenarios and regions.

Abstract:Electric vehicle (EV) charging demand estimation is an important precondition for studying the vehicle-to-grid (V2G) interaction. Therefore, this paper proposes a charging demand prediction model of EVs driven by driving trajectory data, constructs a decision-making model of users to choose to participate in V2G response by further considering the multi-dimensional benefits of users, and analyzes the regulation potential of regional V2G response capabilities. Firstly, the big data set of driving trajectory is cleaned and mined, and a prediction model for the spatio-temporal distribution of EV charging demand is constructed based on the dynamic energy consumption theory. Secondly, based on the social behavior theory and considering the electricity demand utility, economic utility, environmental protection utility and social utility, the probabilistic selection model of EV users participating in V2G response is constructed. The model not only considers the heterogeneity of EV users, but also reflects the interactive influence of user decisions. Finally, a V2G responsive capacity regulation model is established to analyze the adjustment effect of V2G responsive resources on the regional load. The results show that the proposed model can not only effectively estimate the spatio-temporal distribution characteristics of EV charging demand in a certain urban area, but also obtain the number of potential EV users who choose to participate in V2G response in this area, which provides support for studying the regulation potential of V2G responsive resources on the regional load.

Abstract:The participation of electric vehicle (EV) users in local energy sharing of charging stations can not only meet the demand for energy sharing of charging stations, but also bring benefits to participants by optimizing the allocation of local energy resources. Considering that the high entry threshold and transaction cost of the traditional electricity market restrict local energy sharing, taking into account the response characteristics of EV users, a real-time energy sharing mechanism for charging stations based on price consistency is put forward. First, considering the influencing factors of driving behaviors for EV users, including road congestion, pre-sharing electricity price, distribution locational marginal price, and charging and discharging cost utility parameters of EVs, an EV user selection model is established. Then, based on the dual problem of the social welfare maximization model in the centralized dispatch mode, a real-time energy sharing mechanism based on price consistency is deduced in the local energy sharing network. The mechanism takes the sharing electricity price as a consistent variable, and the participants achieve the price identity through information exchange, thus maximizing the social welfare of energy sharing. Finally, the simulation is carried out in the coupling system including the road network, power grid and energy sharing network. The simulation results show that the road travel time and pre-sharing electricity price have the greatest impact on the choice behavior of EV users. The participation of EV users with high charging efficiency and low discharging cost in the energy sharing of charging stations can improve both the individual and overall economic benefits.

Abstract:With the rapid popularization of electric vehicles, the coupling between the traffic network and power grid is further deepened, and the travel mode of the traffic network will have a significant impact on the charging load of electric vehicles. The traditional charging load simulation method relies on the individual modeling of the traffic network and electric vehicles, and has strong assumptions. A method of electric vehicle charging load scenario generation based on data-driven convolutional autoencoder and conditional generative adversarial network (CGAN) is proposed. Firstly, the convolutional autoencoder based on unsupervised learning is used to reduce the dimension of travel prediction data in traffic network and adaptively extract the feature information. Secondly, a CGAN suitable for the generation of the day-ahead current traffic network charging load scenario is designed, and the feature information extracted from the convolutional autoencoder is used to implicitly learn the conditional probability distribution of electric vehicle charging load corresponding to different travel modes of traffic network. Thus, the generation of the day-ahead electric vehicle charging load scenario is realized, which provides the support for the operation of the power grid and charging station. Finally, taking an actual city traffic network as an example, the necessity of the proposed convolutional autoencoder and the effectiveness of the CGAN are verified.

Abstract:With the large-scale popularization and application of electric vehicles, the coupled operation characteristics of the transportation network and distribution network are becoming increasingly significant. In this context, a large number of uncertainties will be propagated along with the interaction process of the coupled system, and affect the safe operation of the transportation network and distribution network. For the quantification of the influence of uncertain factors, an analysis method of probabilistic joint flow for transportation network and distribution network considering multiple uncertainties of road-vehicle-source-load. First, a probabilistic traffic assignment model of the transportation network and a probabilistic optimal power flow model of the distribution network are established, respectively. A decentralized iterative algorithm based on Monte Carlo simulation in an equilibrium state is proposed for probabilistic joint flow computation. Then, the global sensitivity analysis method based on Sobol’ method is introduced to quantify the influence of multiple uncertainties of road-vehicle-source-load on the coupled operation state variables of the transportation network and distribution network, and identify the uncertainties that significantly affect the probabilistic joint flow distribution of the transportation network and distribution network. Finally, the proposed method is applied to a coupled case system of the transportation network and distribution network and its effectiveness is verified by simulation results.

Abstract:Electric vehicle (EV) is a cross-domain subject with dual properties of traffic tool and mobile load. Large-scale centralized charging of EV will impact the power grid and aggravate the traffic congestion. Thus, the spatio-temporal optimal dispatch strategy for the EV charging load based on the interaction of vehicle-road-grid is proposed to regulate the charging behavior of EVs. Firstly, the dynamic traffic network model and the modified Floyd algorithm are used to simulate the EV driving path for predicting the spatio-temporal distribution characteristics of EV charging load. Secondly, based on the prediction results, the optimal dispatch strategy based on the Stackelberg game is proposed combining with the electricity price response degree model for the multi-objective optimization of the benefits of power grid, traffic networks and EV owners. Finally, a regional traffic network in Beijing, China and the IEEE 33-bus distribution system are simulated to verify the effectiveness of the proposed model. The results demonstrate that the proposed optimal dispatch strategy can not only realize the balanced distribution of load in time and space among charging stations, but also can improve the traffic flow of the traffic network around the charging stations and reduce the charging cost of EV users.

Abstract:The increasing penetration of electric vehicles has significantly strengthened the coupling between urban transportation systems and power systems. Comprehensively considering the multi-stakeholder characteristics of the coupled power-transportation system and the optimal dispatch requirements on multiple objectives such as economic costs and carbon emissions, a coordinated operation model of the power-transportation system based on multi-objective optimization is proposed. Firstly, the mixed user equilibrium model and the second-order cone DistFlow model are adopted to describe the distribution of traffic flow and power flow. Secondly, the function models describing economic costs and carbon emissions are established, and three groups of multi-objective optimal dispatch models are established according to different stakeholders and dispatch objectives. Thirdly, the enhanced epsilon-constrained method combined with several linearization methods is adopted to efficiently derive the Pareto frontier of each multi-objective optimization. A method for capturing a fair compromise solution is proposed to balance the interest of each stakeholder. Finally, based on a 12-bus ring transportation network and a modified IEEE 33-bus distribution network, the dispatch results of each group of multi-objective problems are thoroughly analyzed.

Abstract:With the improvement of the electric vehicle penetration, the spatio-temporal coupling between the electrified transportation network and the active distribution network is becoming stronger. With the technology of dynamic wireless charging, when the jam or congestion occurs in the transportation network or the distribution network, the interaction between the two networks may lead to cascading jam-congestion, which has an adverse impact on the security and economy of the system. Combining the dynamic traffic assignment model of the transportation network based on the differential variational inequality with the multi-period AC optimal power flow model of the the distribution network based on the mixed-integer second-order cone programming, the dynamic spatio-temporal coupling model of transportation-distribution networks is established to simulate the spatio-temporal distribution of electric vehicle charging load and congestion cost. Through the joint operation simulation of the transportation-distribution networks, the propagation mechanism of jam-congestion in the transportation-distribution networks is analyzed in depth. Then, the active-reactive power co-optimization scheme for the active distribution network is proposed, and the impact of the rational allocation of active/reactive power resources on the optimization effect is studied.

Abstract:Aiming at the influence of the dynamic change characteristics of traffic flow on the fault recovery process of distribution network under extreme disasters, in the background of the coupling of the power grid and transportation network, considering the coordination and scheduling between mobile and fixed emergency recovery resources and fault repair, a multi-time-section optimization strategy for the post-disaster fault recovery of the distribution network is proposed. Firstly, the cell transmission model is used to establish the transportation network model, which can predict the traveling time of the road repair crews and mobile energy storage vehicles. Secondly, a mixed-integer linear programming model is established, which aims at minimizing the load reduction amount and the scheduling cost of restoration resources, and takes the emergency repair sequence of faulty equipment and the scheduling plan of restoration resources as decision variables. The multi-time-section optimization method is used to solve the model. In addition, a method for dynamic reconfiguration of grids is proposed. By adding a virtual root node to form a virtual island with each power loss node, and dynamically adding and deleting the number of nodes in the island with the change of the grid, it is ensured that the entire distribution network can always meet the radial constraints, and the adaptability of the algorithm to fault scenarios is improved. Finally, examples are used to verify the effectiveness of the proposed method in improving the resilience of the distribution network.

Abstract:In recent years, extreme natural disasters represented by typhoons have led to frequent power outages in urban distribution networks, which has brought challenges to the sustainable power supply of urban power grids. Under the background of the rapid growth of the number of electric vehicle (EV) user groups, aiming at the above problems, a vehicle-to-grid (V2G) based resilience promotion strategy for the urban distribution network is proposed to enhance the ability of urban power grid to deal with typhoon disasters. Firstly, the failure rate model of the distribution network component is established, and the distribution network failure scenarios under typhoon disasters are generated by random sampling. Secondly, considering the impact of the upcoming typhoon disaster on the road congestion and users’ travel intention, an EV scheduling model in the early stage of the disaster is established. Then, based on the theory of consumer psychology, the post-disaster incentive response mechanism for the reverse power transmission is formulated to guide EVs in the V2G station to participate in the power supply restoration. Finally, considering the reconfiguration of the distribution network and the reverse charging of V2G stations, the post-disaster power supply restoration strategy with the participation of V2G is formulated. The example analysis shows that, compared with the mobile emergency generator (MEG), the scheme of V2G participating in the power supply restoration has a better economy and restoration effect, which verifies the feasibility and effectiveness of the proposed resilience promotion strategy.

Abstract:In order to solve the load restoration problem after an extreme disaster, a spatio-temporal hierarchical scheduling strategy for electric vehicle (EV) energy is proposed to assist the critical load restoration by urban road repair. In the first stage, the optimal spatial allocation of EV energy is determined by building and integrating the route planning model of EVs and the multi-period coordinated critical load restoration model. In the second stage, the route planning model of road repair crews is established, and then the integrated optimization with the route planning model of EVs is conducted. The repairing orders and routes of road repair crews are determined, and the time and remaining energy when EVs reach the charging stations are solved. In the third stage, the power supply sources and critical loads of the distribution network are considered to determine the optimal allocation of the energy on the time scale with the objective of maximizing the weighted supply time. Finally, considering the post-disaster fault scenarios in the coupled transportation and distribution network, the scheduling results of different strategies are compared and analyzed, and the assistance effect of the road repair on load restoration is explored. The results show that the proposed strategy can give full play to the spatio-temporal flexibility of EV energy, reasonably coordinate the action decisions between EVs and road repair crews, and then improve the restoration effect of critical loads to a certain extent.

Abstract:As a key element of the new infrastructure strategy of China, the charging facilities of electric vehicles have developed rapidly in recent years. However, the development of charging facilities faces the problem of imbalance between supply and demand. Therefore, it is necessary to analyze the user demands and behavior characteristics through real data and build an effective public charging facility planning system to serve the development of electric vehicles. To provide a feasible framework for the planning of real-world charging stations, by taking Shanghai as a practical case, this paper carries out practical big data mining analysis, which integrates multi-source real-world data such as service vehicle trajectory data, charging station data, passenger vehicle travel statistical data, traffic road condition data, and interest point retrieval data. From the macro-perspective, the correlation between the traveling-charging behavior of vehicle users and the city temporal-spatial characteristics is analyzed. From the micro-perspective, the behavioral preferences of individual vehicle users are modeled, where the user charging demand is predicted by the decision tree model, and the user's charging station selection behavior is described by the Huff attraction model. On this basis, the massive user behavior is reconstructed and simulated, and a simulation framework of the urban integrated energy-traffic network is established. The construction of existing charging facilities is evaluated from multiple dimensions such as the overall supply and demand and the individual service quality. Finally, a future charging facility expansion planning framework considering multiple objectives is proposed, which provides data support and decision-making basis for the charging facility planning of electric vehicles.

Abstract:An integrated energy system (IES) involves many different forms of energy, and exergy can be used as a measure of energy quality. Based on the idea of “flow”, a modeling method for the exergy flow mechanism in the IES network is proposed. First, the exergy flow analysis scope is defined based on the energy flow. The exergy flow mechanism of the heat system is analyzed. The concepts including exergy-potential and exergy-potential difference of heat system are defined. And the exergy flow mechanism model of the heat system is established. Then, the relevant laws are extended to other energy networks, and the concept of exergy-potential and exergy flow mechanism model of power systems and natural gas systems are proposed. The energy station is equivalent to an exergy loss node, and the advantage of the exergy flow mechanism model is verified through the exergy balance analysis of each link. Finally, a test system is given to intuitively show the exergy flow distribution of IES, and the exergy loss of each link is analyzed. By analyzing the overall and local exergy balance relationship, it is verified that the exergy flow mechanism model has more advantages than the traditional black-box model. The advantages of applying exergy flow theory in the high-quality energy system are discussed.

Abstract:With the increase of the proportion of renewable energy generation in the local power grid and the reduction of the frequency regulation capacity, low-frequency impedance changes of the system and the weakening of the frequency maintenance characteristic lead to the potential low-frequency instability of the local power grid. The current weak grid assumption and the analysis method of fixed fundamental frequency do not correspond to the frequency regulation characteristics of the power grid, resulting in limitation on analysis of the instability mechanism for the low-frequency oscillation coupled with the frequency fluctuation. Aiming at the local power grid with diesel synchronous generator sets and grid-connected inverters, a framework of power generation model dominated by renewable energy is constructed with voltage, current, fundamental frequency disturbance, which covers the external characteristics of impedances and disturbance characteristics of the fundamental frequency. Based on the established coupling relationship between power grid strength, penetration rate, and grid-side equivalent impedance, the low-frequency impedance changes at the source grid side and the induced low-frequency instability inducements are analyzed after the strong grid is weakened. The return ratio matrix containing the fundamental frequency disturbance term is constructed, the potential impact of the frequency characteristic term on the low-frequency instability of the system is analyzed, and the system stability with different penetration rates is predicted. Finally, combined with the results of the hardware-in-the-loop experiment, compared with the traditional fixed fundamental frequency analysis method under the critical instability condition, the proposed model can accurately predict the low-frequency instability problem of the local power grid with low-frequency regulation capacity dominated by a high proportion of renewable energy. The correctness of the above theoretical analysis is verified.

Abstract:When multiple wind farms and energy storage stations participate in frequency regulation, due to different operation conditions, there are certain differences in the cost of providing frequency regulation output. In addition, when the wind farm participates in frequency regulation, the fatigue load on the wind turbines in the wind farm will also increase, which will affect the operation safety of the wind turbines. In order to solve the above problems, this paper proposes a bi-level frequency response control strategy based on wind power and energy storage. It can reduce the total cost of frequency regulation and the total load in each wind farm by coordinating the output of the wind farms and energy storage stations and the wind turbines in the field while meeting the frequency regulation requirements of the system. First, the loss, degradation and risk cost of wind farms and energy storage stations during frequency regulation are analyzed and quantified, and the active power distribution control strategy of the station on the station coordination layer is established, while the lowest cost output plan of each station is determined. Then, based on the linear relationship between wind turbine output, wake fluctuation and fatigue load, an additive increase multiplicative decrease algorithm is adopted to build a decentralized active power control strategy for the wind farm on the unit coordination layer, which coordinates the output of the wind turbines. While maintaining the frequency regulation performance of the wind farm, the strategy can reduce the total load in the field. Finally, an IEEE RTS-79 bus system is built in MATLAB/Simulink, which contains two 100×5 MW doubly-fed wind farms and two 100 MW/100 MW·h lithium battery energy storage stations. The simulation verifies the effectiveness of the proposed strategy.

Abstract:The problem of wide-band oscillations caused by the high proportion of renewable energy and power electronic equipment is becoming increasingly prominent. However, the existing oscillation monitoring methods based on synchrophasor data are limited by the current communication bandwidth, and it is difficult to monitor the wide-band oscillations from several hertz to hundreds of hertz globally. Therefore, a wide-area location method of wide-band oscillations based on the signal compression of the autoencoder and long short-term memory (LSTM) network is proposed, which uses the data compression and decoding capability of the autoencoder to realize the wide-area monitoring analysis of wide-band oscillation signals. Firstly, the power system measurement signals are encoded and compressed at sub-stations to realize the transmission of wide-band oscillation signals under the existing bandwidth, and effectively reduce the redundancy of oscillation data. Secondly, a feature matrix can be directly generated based on the compressed data, and the LSTM network can be adopted to locate the source of oscillations at the master station. In addition, the master station can decode the compressed data uploaded by the sub-stations, so the compressed data or decoded data can be used for the analysis and control of wide-band oscillations according to the requirements. Finally, the subsynchronous, supersynchronous, as well as medium and high-frequency oscillations are fully considered, and the load changes and random noise are taken into account for simulation. The results show that this method has a high reproduction, location accuracy, and good anti-noise performance.

Abstract:With the increasing penetration rate of renewable energy generation, the problem of voltage violation in the distribution system becomes more frequent, and efficient voltage management strategies are urgently needed to ensure the secure and economic operation of the distribution system. First, this paper establishes a dual-timescale voltage management model for the distribution system to realize the coordinated control of voltage regulators with different time response characteristics. Then, the voltage management models of the two time scales are modeled as Markov decision process (MDP). Effectively considering the temporal coupling relationship between the two time scales and the physical characteristics of controllable devices, the dual-timescale real-time voltage management is realized by using the multi-agent deep deterministic policy gradient algorithm and the double deep Q network algorithm to solve the model, respectively. Finally, the effectiveness of the proposed model and method is demonstrated by case studies on the IEEE 33-bus standard distribution system.

Abstract:Non-intrusive load monitoring is an effective way to obtain load data and realize load perception. A combined support vector machine identification method for making the process of the non-intrusive load monitoring universal and practical based on the structured characteristic spectrum, is studied to automatically execute the process without disturbing users and achieve high identification accuracy. By constructing the characteristic spectrum of typical loads, the disordered waveform data are transformed into structured signature data, which makes the signature graph universal and distinguishable. Based on the structured characteristic spectrum, the support vector machine classifier model of typical loads is established, and the combined support vector machine classifier of each type of load is formed based on the base classifier model. The idea of “gathering weak into strong” is used to ensure that each combined classifier has high classification accuracy, so as to realize the accurate load identification. Based on the universal graph and the classifier model, the real-time non-intrusive load identification can be realized through the processing flow of event waveform extraction, waveform data structuration and classifier decision. By the acquired actual load data for verification, the characteristic spectrum of the typical load is developed, the collected data of multiple households based on the combined support vector machine model are classified, and the high accuracy identification for the load data of different users is achieved, which verifies that the proposed method has good universality and effectiveness.

Abstract:With the transformation of energy structure, electric vehicles (EVs) will become important tools of intercity passenger transportation. The intercity passenger transportation company (IPTC) can maximize its operation profits by participating in the scheduling strategy of optimizing EVs for electricity markets. Therefore, a price-maker decision-making tool based on a bi-level operation model is provided for IPTC to participate in the day-ahead electricity market. At the upper level of the model, IPTC as the leader of the bi-level model adjusts the vehicle scheduling and market bidding strategy according to the market clearing situations to maximize the profits of participating in the day-ahead electricity market. Meanwhile, a vehicle transportation spatio-temporal distribution model is built at the upper level of the model to simulate the transfer situation of intercity passenger vehicles between the passenger stations in different cities, which can help IPTC combine the operation of vehicle allocation, intercity shift scheduling with its bidding behavior in the day-ahead market. At the lower level of the model, the power trading center as the follower of the bi-level model clears the joint electric energy and reserve market according to the quotations of various market entities. Finally, the IEEE 39-bus system is used for simulation calculation to verify the validity of the proposed bi-level model.