Fushuan Wen

Bio: Fushuan Wen is an academic researcher from Zhejiang University. The author has contributed to research in topics: Electric power system & Electricity market. The author has an hindex of 49, co-authored 465 publications receiving 9189 citations. Previous affiliations of Fushuan Wen include Hong Kong Polytechnic University & University of Newcastle.

Optimal Planning of Electric-Vehicle Charging Stations in Distribution Systems

Abstract: With the progressive exhaustion of fossil energy and the enhanced awareness of environmental protection, more attention is being paid to electric vehicles (EVs). Inappropriate siting and sizing of EV charging stations could have negative effects on the development of EVs, the layout of the city traffic network, and the convenience of EVs' drivers, and lead to an increase in network losses and a degradation in voltage profiles at some nodes. Given this background, the optimal sites of EV charging stations are first identified by a two-step screening method with environmental factors and service radius of EV charging stations considered. Then, a mathematical model for the optimal sizing of EV charging stations is developed with the minimization of total cost associated with EV charging stations to be planned as the objective function and solved by a modified primal-dual interior point algorithm (MPDIPA). Finally, simulation results of the IEEE 123-node test feeder have demonstrated that the developed model and method cannot only attain the reasonable planning scheme of EV charging stations, but also reduce the network loss and improve the voltage profile.

Optimal Siting and Sizing of Distributed Generators in Distribution Systems Considering Uncertainties

Abstract: Some uncertainties, such as the uncertain output power of a plug-in electric vehicle (PEV) due to its stochastic charging and discharging schedule, that of a wind generation unit due to the stochastic wind speed, and that of a solar generating source due to the stochastic illumination intensity, volatile fuel prices, and future uncertain load growth could lead to some risks in determining the optimal siting and sizing of distributed generators (DGs) in distribution system planning. Given this background, under the chance constrained programming (CCP) framework, a new method is presented to handle these uncertainties in the optimal siting and sizing of DGs. First, a mathematical model of CCP is developed with the minimization of the DGs' investment cost, operating cost, maintenance cost, network loss cost, as well as the capacity adequacy cost as the objective, security limitations as constraints, and the siting and sizing of DGs as optimization variables. Then, a Monte Carlo simulation-embedded genetic-algorithm-based approach is employed to solve the developed CCP model. Finally, the IEEE 37-node test feeder is used to verify the feasibility and effectiveness of the developed model and method, and the test results have demonstrated that the voltage profile and power-supply reliability for customers can be significantly improved and the network loss substantially reduced.

Strategic bidding in competitive electricity markets: a literature survey

Abstract: Participants in a competitive electricity market develop bidding strategies in order to maximize their own profits. On the other hand, it is necessary for regulators to investigate strategic bidding behavior in order to identify possible market power abuse and to limit such abuse by introducing appropriate market management rules. An interesting body of work has been done on this subject, and this paper presents a literature survey based on more than 30 research publications.

Optimal bidding strategies and modeling of imperfect information among competitive generators

Abstract: The emerging electricity market behaves more like an oligopoly than a perfectly competitive market due to special features such as, a limited number of producers, large investment size (barrier to entry), transmission constraints, and transmission losses which discourage purchase from distant suppliers. This makes it practicable for only a few independent power suppliers to service a given geographic region and in this imperfect market each power supplier can increase its own profit through strategic bidding. The profit of each supplier is influenced to varying extents by differences in the degree of imperfection of knowledge of rival suppliers. A new framework to build bidding strategies for power suppliers in an electricity market is presented in this paper. It is assumed that each supplier bids a linear supply function, and that the system is dispatched to minimize customer payments. Each supplier chooses the coefficients in the linear supply function to maximize benefits, subject to expectations about how rival suppliers will bid. A stochastic optimization formulation is developed and two methods proposed for describing and solving this problem. A numerical example serves to illustrate the essential features of the approach and the results are used to investigate the potential market power.

Traffic-Constrained Multiobjective Planning of Electric-Vehicle Charging Stations

Abstract: Smart-grid development calls for effective solutions, such as electric vehicles (EVs), to meet the energy and environmental challenges. To facilitate large-scale EV applications, optimal locating and sizing of charging stations in smart grids have become essential. This paper proposes a multiobjective EV charging station planning method which can ensure charging service while reducing power losses and voltage deviations of distribution systems. A battery capacity-constrained EV flow capturing location model is proposed to maximize the EV traffic flow that can be charged given a candidate construction plan of EV charging stations. The data-envelopment analysis method is employed to obtain the final optimal solution. Subsequently, the well-established cross-entropy method is utilized to solve the planning problem. The simulation results have demonstrated the effectiveness of the proposed method based on a case study consisting of a 33-node distribution system and a 25-node traffic network system.

Energy function analysis for power system stability

Abstract: (1990). ENERGY FUNCTION ANALYSIS FOR POWER SYSTEM STABILITY. Electric Machines & Power Systems: Vol. 18, No. 2, pp. 209-210.

Introduction to Fuzzy Logic using MATLAB

Abstract: Classical Sets and Fuzzy Sets.- Classical and Fuzzy Relations.- Membership Functions.- Defuzzification.- Fuzzy Rule-Based System.- Fuzzy Decision Making.- Applications of Fuzzy Logic.- Fuzzy Logic Projects with Matlab.

Recent philosophies of automatic generation control strategies in power systems

Abstract: An attempt is made in This work to present critical literature review and an up-to-date and exhaustive bibliography on the AGC of power systems. Various control aspects concerning the AGC problem have been highlighted. AGC schemes based on parameters, such as linear and nonlinear power system models, classical and optimal control, and centralized, decentralized, and multilevel control, are discussed. AGC strategies based on digital, self-tuning control, adaptive, VSS systems, and intelligent/soft computing control have been included. Finally, the investigations on AGC systems incorporating BES/SMES, wind turbines, FACTS devices, and PV systems have also been discussed.