Multi Objective Optimization in Charge Management of Micro Grid Based Multistory Carpark

Distributed power supply with the use of renewable energy sources and intelligent energy flow management has undoubtedly become one of the pressing trends in modern power engineering, which also inspired researchers from other fields to contribute to the topic. There are several kinds of micro grid platforms, each facing its own challenges and thus making the problem purely multi objective. In this paper, an evolutionary driven algorithm is applied and evaluated on a real platform represented by a private multistory carpark equipped with photovoltaic solar panels and several battery packs. The algorithm works as a core of an adaptive charge management system based on predicted conditions represented by estimated electric load and production in the future hours. The outcome of the paper is a comparison of the optimized and unoptimized charge management on three different battery setups proving that optimization may often outperform a battery setup with larger capacity in several criteria.

[1]  Canbing Li,et al.  An Optimized EV Charging Model Considering TOU Price and SOC Curve , 2012, IEEE Transactions on Smart Grid.

[2]  Marina Gonzalez Vaya,et al.  Optimal Bidding Strategy of a Plug-In Electric Vehicle Aggregator in Day-Ahead Electricity Markets Under Uncertainty , 2015, IEEE Transactions on Power Systems.

[3]  Youxian Sun,et al.  Optimal Cooperative Charging Strategy for a Smart Charging Station of Electric Vehicles , 2016, IEEE Transactions on Power Systems.

[4]  Nima Amjady,et al.  Multi-objective electricity market clearing considering dynamic security by lexicographic optimization and augmented epsilon constraint method , 2011, Appl. Soft Comput..

[5]  Ling Guan,et al.  Optimal Scheduling for Charging and Discharging of Electric Vehicles , 2012, IEEE Transactions on Smart Grid.

[6]  Prasanta Ghosh,et al.  Optimizing Electric Vehicle Charging With Energy Storage in the Electricity Market , 2013, IEEE Transactions on Smart Grid.

[7]  Hewu Wang,et al.  Optimal decentralized valley-filling charging strategy for electric vehicles , 2014 .

[8]  Qingfu Zhang,et al.  MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition , 2007, IEEE Transactions on Evolutionary Computation.

[9]  S. S. Venkata,et al.  Coordinated Charging of Plug-In Hybrid Electric Vehicles to Minimize Distribution System Losses , 2011, IEEE Transactions on Smart Grid.

[10]  J. Driesen,et al.  The Impact of Charging Plug-In Hybrid Electric Vehicles on a Residential Distribution Grid , 2010, IEEE Transactions on Power Systems.

[11]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[12]  Chee Kheong Siew,et al.  Extreme learning machine: Theory and applications , 2006, Neurocomputing.

[13]  Ehab F. El-Saadany,et al.  PEVs modeling and impacts mitigation in distribution networks , 2013, IEEE Transactions on Power Systems.

[14]  Taha Selim Ustun,et al.  Implementing Vehicle-to-Grid (V2G) Technology With IEC 61850-7-420 , 2013, IEEE Transactions on Smart Grid.

[15]  Arobinda Gupta,et al.  A Review of Charge Scheduling of Electric Vehicles in Smart Grid , 2015, IEEE Systems Journal.

[16]  Tomáš Vantuch,et al.  The Power Quality Forecasting Model for Off-Grid System Supported by Multiobjective Optimization , 2017, IEEE Transactions on Industrial Electronics.

[17]  Ye Tian,et al.  PlatEMO: A MATLAB Platform for Evolutionary Multi-Objective Optimization [Educational Forum] , 2017, IEEE Computational Intelligence Magazine.

[18]  Zhile Yang,et al.  Computational scheduling methods for integrating plug-in electric vehicles with power systems: A review , 2015 .

[19]  Hans-Arno Jacobsen,et al.  Distributed Convex Optimization for Electric Vehicle Aggregators , 2017, IEEE Transactions on Smart Grid.

[20]  Qingfu Zhang,et al.  Multiobjective Optimization Problems With Complicated Pareto Sets, MOEA/D and NSGA-II , 2009, IEEE Transactions on Evolutionary Computation.

[21]  William G. Temple,et al.  Intelligent electric vehicle charging: Rethinking the valley-fill , 2011 .

[22]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[23]  P. T. Krein,et al.  Review of the Impact of Vehicle-to-Grid Technologies on Distribution Systems and Utility Interfaces , 2013, IEEE Transactions on Power Electronics.

[24]  A. Kraskov,et al.  Estimating mutual information. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[25]  Guang-Bin Huang,et al.  What are Extreme Learning Machines? Filling the Gap Between Frank Rosenblatt’s Dream and John von Neumann’s Puzzle , 2015, Cognitive Computation.

[26]  Yang Yang,et al.  Modelling, Analysis and Performance Evaluation of Power Conversion Unit in G2V/V2G Application—A Review , 2018 .

[27]  M. Ilic,et al.  Optimal Charge Control of Plug-In Hybrid Electric Vehicles in Deregulated Electricity Markets , 2011, IEEE Transactions on Power Systems.

[28]  Zafer Sahinoglu,et al.  Robust Optimization of EV Charging Schedules in Unregulated Electricity Markets , 2017, IEEE Transactions on Smart Grid.

[29]  Dionysios Aliprantis,et al.  Load Scheduling and Dispatch for Aggregators of Plug-In Electric Vehicles , 2012, IEEE Transactions on Smart Grid.

[30]  Leandro dos Santos Coelho,et al.  Optimal allocation, sizing of PHEV parking lots in distribution system , 2015 .

[31]  Qing-Shan Jia,et al.  Matching EV Charging Load With Uncertain Wind: A Simulation-Based Policy Improvement Approach , 2015, IEEE Transactions on Smart Grid.

[32]  David Seidl,et al.  A holistic approach to power quality parameter optimization in AC coupling Off-Grid systems , 2017 .