Jun Tan | University of Wisconsin Milwaukee (original) (raw)

Papers by Jun Tan

Under the ambition of boosting Plug-in Electric Vehicle (PEV) charging speed to a level comparabl... more Under the ambition of boosting Plug-in Electric Vehicle (PEV) charging speed to a level comparable to the traditional refueling, Fast Charging Station (FCS) has been integrated into power distribution system. The location planning of FCS must allow for satisfactory charging service for PEV users as well as mitigate the detrimental effects on power grid caused by uncertainty and impulsiveness of charging demand. This paper proposed a location planning model for FCS, taking into account its impacts on the critical power grid assets. The multi-objective planning model simultaneously considered the role of FCS in the electricity and transportation sectors. This planning model is solved by the cross-entropy (CE) method. The validity and effectiveness of the CE approach have been demonstrated on a synthetic coupled network.

—This paper proposes a holistic framework for plug-in hybrid electric vehicles (PHEVs) to partici... more —This paper proposes a holistic framework for plug-in hybrid electric vehicles (PHEVs) to participate in frequency regulation in a competitive electricity market. It is challenging to use PHEVs as frequency regulation units since conflicts of interests exist among PHEVs, aggregators, and transmission system operator (TSO). PHEVs are also facing various uncertainties from power prices and the available regulation capacities. These challenges motivate us to model the system using a hierarchical game. At the upper level of the hierarchical game, the frequency regulation capacity bids of aggregators are formulated as a non-cooperative game. Based on the frequency regulation prices obtained from the non-cooperative game, we formulate a Markov game to coordinate the charging process of PHEVs at the lower level. The Markov game will optimize the regulation capacity of the aggregator and strengthen its ability in bidding a more favorable frequency regulation price in the upper level game. Thus, the benefits are well coordinated among PHEVs, aggregators, and TSO in the proposed game-theoretic framework. Furthermore, the uncertainties from power prices and available regulation capacities are elegantly handled by the proposed non-cooperative game and Markov game, respectively. Finally, various simulations are carried out to validate the effectiveness of the proposed hierarchical game approach.

This p a p er p ro p oses a holistic framework for p lu gin hybrid electric vehicles (PHEVs) to p... more This p a p er p ro p oses a holistic framework for p lu gin hybrid electric vehicles (PHEVs) to p artici p ate in frequency re g ulation in a dere g ulated electricity market. It is challen g in g to use PHEVs as frequency re g ulation units since conflicts of interests exist amon g PHEVs, a gg re g ators and Transmission System O p erator (TSO). PHEVs are also facin g various uncertainties from p ower p rices and the available re g ulation ca p acities. These challen g es motivate us to model the system usin g a hierarchical g ame. At the u pp er level of the hierarchical g ame, the frequency re g ulation ca p acity bids of a gg re g ators are formulated as a non-coo p erative g ame. Based on the frequency re g ulation p rices obtained from the non-coo p erative g ame, we formulate a Markov g ame to coordinate the char g in g p rocess of PHEVs at the lower level. The Markov g ame will o p timize the re g ulation ca p acity of the a gg re g ator and stren g then its ability in biddin g a more favorable frequency re g ulation p rice in the u pp er level g ame. Thus, the benefits are well accommodated amon g PHEVs, a gg re g ators and TSO in the p ro p osed g ame theoretic framework. Furthermore, the uncertainties from p ower p rices and available re g ulation ca p acities are handled very well by the p ro p osed non-coo p erative g ame and Markov g ame res p ectively. Finally, various simulations are carried out to validate the effectiveness of the p ro p osed hierarchical g ame a pp roach. Index Terms-Plu gin hybrid electric vehicle (PHEV), vehicle to-g rid (V2G), frequency re g ulation, decentralized control, game theory.

—This paper proposes an integrated electric vehicle (EV) charging navigation framework which take... more —This paper proposes an integrated electric vehicle (EV) charging navigation framework which takes into consideration the impacts from both the power system and transportation system. The proposed framework links the power system with transportation system through the charging navigation of EVs. It benefits the two systems by attracting EVs to charge at off-peak hours and saving the time of EV owners with real-time navigation. Based on the formulated framework, a non-cooperative game approach is proposed in this study to model the competition between electric vehicle charging stations (EVCSs). The simulation results show that the proposed integrated charging navigation approach is effective in improving both the reliability and economic profits of the power system.

—This paper proposes an integrated electric vehicle (EV) charging navigation framework, which tak... more —This paper proposes an integrated electric vehicle (EV) charging navigation framework, which takes into consideration the impacts from both the power system and transportation system. The proposed framework links the power system with transportation system through the charging navigation of massive EVs. It benefits the two systems by attracting EVs to charge at off-peak hours and saving the time of EV owners with real-time navigation. Based on the formulated framework, a hierarchical game approach is proposed in this paper to effectively navigate EVs to electric vehicle charging stations (EVCSs). At the upper level of the hierarchical game, a non-cooperative game is proposed to model the competition between EVCSs. Based on the pricing strategies obtained from the non-cooperative game, multiple evolutionary games are formulated at the lower level to evolve EVs' strategies in choosing EVCSs. The simulation results show that the proposed integrated charging navigation approach is effective in improving both the reliability of the power distribution grid and economic profits of the charging stations.

—This paper proposes a framework for offering reliability-differentiated services in a residentia... more —This paper proposes a framework for offering reliability-differentiated services in a residential distribution network with plug-in hybrid electric vehicles (PHEVs). A reliability-differentiated pricing mechanism is developed to satisfy the different reliability requirements of the customers while encouraging the customers to consume electricity in such a way that the reliability of the overall distribution system can be enhanced. Based on the formulated reliability-differentiated system, a hierarchical game approach is proposed in this study to coordinate the charging process of PHEVs in a decentralized fashion. The simulation results show that the hierarchical game approach is effective in enhancing both the reliability and economic profits of the system. Index Terms—Plug-in hybrid electric vehicle (PHEV), vehicle-to-grid (V2G), reliability-differentiated pricing, decentralized control, game theory.

—This paper proposes a framework for enabling the reliability-differentiated services in a reside... more —This paper proposes a framework for enabling the reliability-differentiated services in a residential distribution network with plug-in hybrid electric vehicles (PHEVs). A reliability-differentiated pricing mechanism is developed to satisfy the different reliability requirements of the customers while encouraging the customers to consume electricity in such a way that the reliability of the overall distribution system can be enhanced. A dynamic spinning reserve pricing scheme is developed to stimulate PHEVs to participate in spinning reserve by providing economic benefits when the state of the distribution system becomes risky. Based on the formulated reliability-differentiated system, a hierarchical game approach is proposed in this paper to coordinate the charging process of PHEVs in a decentralized fashion. At the upper level of the hierarchical game, an evolutionary game is formulated to optimize the management of vehicle-to-grid (V2G) capacity of each PHEV. Under the V2G strategies obtained from the evolutionary game, a noncooperative game is formulated at the lower level to coordinate the charging sequences of PHEVs. Various simulation studies are carried out to verify the effectiveness of the proposed hierarchical game approach. The simulation results show that the hierarchical game approach is effective in enhancing both reliability of the distribution system and economic profits of the PHEVs.

—This paper proposes a novel Markov decision process (MDP) with dynamic transition probabilities ... more —This paper proposes a novel Markov decision process (MDP) with dynamic transition probabilities for the stochastic modeling of the charging process of plug-in hybrid electric vehicles (PHEVs). In the proposed dynamic MDP, PHEVs can be controlled in such a way that the effectiveness of the control strategy is maintained in the presence of uncertainties such as early departure events. Then a game theory based decentralized system is formulated to coordinate the PHEVs fleet in a residential network. The authors also proposed a decentralized coordinated optimization (DCO) algorithm to solve the formulated Nash game. Various simulations are carried out to verify the effectiveness of the proposed DCO approach. The results show that the DCO approach is robust in the face of uncertainties and is effective in enhancing both power quality and economic profits. Index Terms—Plug-in hybrid electric vehicle (PHEV), vehicle-to-grid (V2G), decentralized control, real-time pricing, Markov decision process (MDP), game theory.

—This paper proposes a two-layer evolution strategy particle swarm optimization (ESPSO) algorithm... more —This paper proposes a two-layer evolution strategy particle swarm optimization (ESPSO) algorithm to integrate PHEVs into a residential distribution grid. A novel business model is developed for PHEVs to provide ancillary service and participate in peak load shaving. A virtual time-of-use rate is proposed to reflect the load deviation of the system. Then, an objective function is designed to aggregate the peak load shaving, power quality improving, charging cost, battery degradation cost and frequency regulation earnings into one cost function. The ESPSO approach can benefit the system in four aspects by: (1) improving the power quality; (2) reducing the peak load; (3) providing frequency regulation service; and (4) minimizing the total virtual cost. Finally, various simulations are carried out based on different control strategies, and the results have demonstrated the effectiveness of the proposed algorithm. Index Terms—Plug-in hybrid electric vehicle (PHEV), vehicle-to-grid (V2G), frequency regulation, virtual time-of-use (vTOU) rate, battery degradation, stochastic modeling, evolution strategy particle swarm optimization (ESPSO).

— With the increasing awareness on environmental protection and energy resources reservation, plu... more — With the increasing awareness on environmental protection and energy resources reservation, plug-in hybrid electric vehicles (PHEVs) are being developed and deployed around the world. However, the large-scale deployment of PHEVs will impose a great burden on the residential power grid, so effective control strategies, especially those for vehicle-to-grid (V2G), are much needed. In this paper, the stochastic behaviors of PHEVs are examined in real scenarios, and the V2G process is modeled considering various limits. Possible exchange power trajectories are enumerated and pruned based on the proposed load distance for each PHEV in order to generate the optimized solution space. Particle swarm optimization (PSO) is applied for several time-steps with the updating of load profile and PHEV groups for global optimization. Some simulation studies are carried out with different fitness functions considering the load variance, cost and comfort of PHEV users. Simulation results show that the proposed optimization method is able to shave the residential peak load effectively, and its performance with different fitness functions are analyzed and compared. Index Terms—Plug-in hybrid electric vehicle, stochastic modeling, vehicle-to-grid, load shaving, particle swarm optimization.

—Plug-in Hybrid Electric Vehicles (PHEVs) are emerging as a new form of distributed energy storag... more —Plug-in Hybrid Electric Vehicles (PHEVs) are emerging as a new form of distributed energy storage which could benefit the power system in many ways. The vehicle to grid (V2G) technology makes it possible for PHEVs to participate in ancillary services. Much research has been conducted on the possibility for PHEVs to provide frequency regulation and many algorithms have been developed to improve either economic benefits or ancillary service quality. But few studies have considered the negative effects of PHEVs on power systems while using them as ancillary services. This study proposed a new load frequency control system with PHEVs. The authors also developed a particle swarm optimization (PSO) based intelligent optimization algorithm to reduce the peak load of the system and provide frequency regulation at the same time. The PHEVs' impacts on load frequency control (LFC) are studied in detail in this paper. Finally, the proposed LFC algorithm is tested in a distribution grid, and the results are compared with other control strategies. Index Terms—plug-in hybrid electric vehicle (PHEV), load frequency control (LFC), stochastic modeling, particle swarm optimization (PSO).

— This paper presents a methodology for modeling and controlling the load demand in a residential... more — This paper presents a methodology for modeling and controlling the load demand in a residential distribution grid due to plug-in hybrid electric vehicle (PHEV) battery charging and discharging. To take the stochastic nature of start charging time, charging during and initial state of charge (SOC) into consideration, this paper built a stochastic model for PHEV in a residential distribution grid close to real-world scenarios. The authors proposed a smart charging and vehicle-to-grid (V2G) strategy based on particle swarm optimization algorithm. The objective of this control strategy is to improve the power quality and flatten the load demand in the studied system. Then simulations are carried out at different PHEV penetration levels for three different charging scenarios: the uncoordinated charging, the proposed smart charging without V2G and the proposed smart charging with V2G. The results show that uncoordinated charging will seriously increase the peak load and cause large voltage deviation, while the proposed smart charging method can effectively reduce the voltage deviation and flatten the load demand curve. It is found that when V2G is considered in the proposed smart charging method, the peak load will decrease and the voltage deviation will be smaller too at a low PHEV penetration level, but with the increase of PHEV penetration level, the advantages of V2G will decrease. Index Terms—Plug-in hybrid electric vehicle, stochastic modeling, smart charging, vehicle-to-grid, power quality, particle swarm optimization.

—This paper presents a methodology for modeling the load demand of Plug-in hybrid electric vehicl... more —This paper presents a methodology for modeling the load demand of Plug-in hybrid electric vehicles (PHEVs). The accurate prediction of PHEVs-induced loads needs a comprehensive study of PHEV characteristics. The authors divide the PHEV characteristics into two categories: driving pattern and vehicle parameters. Due to the stochastic nature of vehicle arrival time, departure time and daily mileage, probabilistic methods are used to model the driving pattern by many researchers. But the three elements of driving pattern are correlated which each other, making the probability density functions (PDFs) based probabilistic methods inaccurate. Based on the National Household Travel Survey (NHTS) database, the authors proposed a fuzzy logic based stochastic model to study the relationship between the three elements of driving pattern. Moreover, the authors proposed a load profile modeling framework (LPMF) for PHEVs to synthesize both the characteristics of driving pattern and vehicle parameters into a load profile prediction system. Finally, the proposed LPMF of PHEVs is tested in a residential distribution grid, and the results are compared with deterministic and probabilistic models of PHEVs. Index Terms—Plug-in hybrid electric vehicle (PHEV), load profile, stochastic modeling, fuzzy logic, National Household Travel Survey (NHTS), particle swarm optimization (PSO).

—Plug-in hybrid electric vehicles (PHEVs) are an increasingly attractive response to the future t... more —Plug-in hybrid electric vehicles (PHEVs) are an increasingly attractive response to the future transportation challenges because of their potential economic and environmental benefits. When integrating PHEVs into the distribution system, it is necessary to evaluate the impact of PHEVs on distribution system reliability from the perspective of system adequacy. This paper develops a probabilistic reliability model for integrated distribution and PHEV systems. A comprehensive and time sequential Monte Carlo simulation method is applied to generate the artificial operation history for each component of a residential distribution system, and a complete simulation procedure considering the PHEVs integration is proposed. The IEEE-34 feeder system is utilized as the residential distribution network in case studies, and simulation results are presented and discussed.

— As a solution to relieving the environmental pollution and energy depletion, plug-in hybrid ele... more — As a solution to relieving the environmental pollution and energy depletion, plug-in hybrid electric vehicles (PHEVs) are expected to sweep across the market in the upcoming years. However, high penetration of PHEVs may pose a great challenge to the current power grid. A large number of PHEVs charging simultaneously in a small distribution grid can easily increase the peak load, and induce power quality issues such as voltage deviation and frequency change. In this paper, particle swarm optimization (PSO) algorithm is used to control the charging sequence of PHEVs in order to improve the power quality. A new objective function is proposed and used in the PSO algorithm to minimize voltage deviations. Its relationships with other objective functions are also studied. Further, the performances of these different objective functions on reducing voltage deviation and reducing the peak load are studied and compared. This study is carried out on a small residential distribution grid with different PHEV penetrations considering the real-world scenarios. Index Terms—plug-in hybrid electric vehicle (PHEV), smart charging, power quality, particle swarm optimization (PSO).

This paper presents a methodology for modeling the load demand of plug-in hybrid electric vehicle... more This paper presents a methodology for modeling the load demand of plug-in hybrid electric vehicles (PHEVs). Due to the stochastic nature of vehicle arrival time, departure time and daily mileage, probabilistic methods are chosen to model the driving pattern. However, these three elements of driving pattern are correlated with each other, which makes the probability density functions (PDFs) based probabilistic methods inaccurate. Here a fuzzy logic based stochastic model is built to study the relationship between the three elements of driving pattern. Moreover, a load profile modeling framework (LPMF) for PHEVs is proposed to synthesize both the characteristics of driving pattern and vehicle parameters into a load profile prediction system. Based on this stochastic model of PHEV, a two-layer evolution strategy particle swarm optimization (ESPSO) algorithm is proposed to integrate PHEVs into a residential distribution grid. A novel business model is developed for PHEVs to provide ancillary service and participate in peak load shaving. A virtual time-of-use rate is used to reflect the load deviation of the system. Then, an objective function is developed to aggregate the peak load shaving, power quality improvement, charging cost, battery degradation cost and frequency regulation earnings into one cost function. The ESPSO approach can benefit the system in four major aspects by: (1) improving the power quality; (2) reducing the peak load; (3) providing frequency regulation service; and (4) minimizing the total virtual cost. Finally, simulations are carried out based on different control strategies and the results have demonstrated the effectiveness of the proposed algorithm.