Novel Flexibility Indices of Controllable Loads in Relation to EV and Rooftop PV

In order to analyze the flexibility of controllable loads in smart homes, two flexibility indices are proposed in this study. The first index, the electric vehicle (EV) flexibility index, determines the ability of any particular controllable load to avoid the EV charging intervals during its operation. The second index, the photovoltaic (PV) flexibility index, evaluates the ability of any controllable load to absorb PV power during its operation. Both these indices can be utilized by homeowners or policymakers in installing/updating PVs and controllable home appliances. Higher index values imply more flexibility and thus those devices are more beneficial for the homeowners. In order to capture different consumption patterns in different homes, five home clusters are considered in this study. In each cluster, the controllable loads are grouped into three groups based on their flexibility level and utilization purpose. The performance of the proposed method is analyzed for the two commonly used charging levels in the residential sector, i.e. level 1 and level 2. In addition, sensitivity analysis of different uncertain factors such as PV power, the arrival time of EVs, and daily mileage of EVs is also carried out. Simulation results have shown the effectiveness of the proposed method in determining the flexibility of different controllable loads with respect to EVs and PV.

[1]  Sergio Grammatico,et al.  Distributed Demand Side Management With Stochastic Wind Power Forecasting , 2020, IEEE Transactions on Control Systems Technology.

[2]  Morteza Nazari-Heris,et al.  Intelligent approach for residential load scheduling , 2020, IET Generation, Transmission & Distribution.

[3]  Fabrizio Granelli,et al.  Development of Home Energy Management Scheme for a Smart Grid Community , 2020, Energies.

[4]  Mehran Salehi Shahrabi,et al.  Long-term planning of supplying energy for greenhouses using renewable resources under uncertainty , 2020 .

[5]  Bong Jun Choi,et al.  State-of-the-Art Artificial Intelligence Techniques for Distributed Smart Grids: A Review , 2020 .

[6]  Daniel Zimmerle,et al.  A method to estimate residential PV generation from net-metered load data and system install date , 2020, Applied Energy.

[7]  Heiner Stuckenschmidt,et al.  Improving Smart Charging Prioritization by Predicting Electric Vehicle Departure Time , 2020, IEEE Transactions on Intelligent Transportation Systems.

[8]  Ali Ehsan,et al.  Active Distribution System Reinforcement Planning With EV Charging Stations—Part I: Uncertainty Modeling and Problem Formulation , 2020, IEEE Transactions on Sustainable Energy.

[9]  Hak-Man Kim,et al.  An Effort-Based Reward Approach for Allocating Load Shedding Amount in Networked Microgrids Using Multiagent System , 2020, IEEE Transactions on Industrial Informatics.

[10]  Goran Strbac,et al.  Dynamic modelling of consumers’ inconvenience associated with demand flexibility potentials , 2020 .

[11]  Yongmin Zhang,et al.  Optimal Charging Scheduling by Pricing for EV Charging Station With Dual Charging Modes , 2019, IEEE Transactions on Intelligent Transportation Systems.

[12]  Seung Ho Hong,et al.  Demand Response for Home Energy Management Using Reinforcement Learning and Artificial Neural Network , 2019, IEEE Transactions on Smart Grid.

[13]  Ram Rajagopal,et al.  Data-Driven Load Modeling and Forecasting of Residential Appliances , 2018, IEEE Transactions on Smart Grid.

[14]  Magdy M. A. Salama,et al.  Assessment and Enhancement Frameworks for System Reliability Performance Using Different PEV Charging Models , 2018, IEEE Transactions on Sustainable Energy.

[15]  Marco Levorato,et al.  Residential Consumer-Centric Demand Side Management , 2018, IEEE Transactions on Smart Grid.

[16]  Chunyan Miao,et al.  Optimal Electric Vehicle Fast Charging Station Placement Based on Game Theoretical Framework , 2018, IEEE Transactions on Intelligent Transportation Systems.

[17]  Hak-Man Kim,et al.  A Multi-Agent System-Based Approach for Optimal Operation of Building Microgrids with Rooftop Greenhouse , 2018, Energies.

[18]  Boonruang Marungsri,et al.  Intelligent Algorithm for Optimal Load Management in Smart Home Appliance Scheduling in Distribution System , 2018, 2018 International Electrical Engineering Congress (iEECON).

[19]  Dale Hall,et al.  Emerging best practices for electric vehicle charging infrastructure , 2017 .

[20]  MengChu Zhou,et al.  Optimal Load Scheduling of Plug-In Hybrid Electric Vehicles via Weight-Aggregation Multi-Objective Evolutionary Algorithms , 2017, IEEE Transactions on Intelligent Transportation Systems.

[21]  Vincent W. S. Wong,et al.  Residential Demand Side Management Under High Penetration of Rooftop Photovoltaic Units , 2016, IEEE Transactions on Smart Grid.

[22]  Adam J. Collin,et al.  Assessment of the Cost and Environmental Impact of Residential Demand-Side Management , 2016, IEEE Transactions on Industry Applications.

[23]  Caisheng Wang,et al.  Development of autonomous schedules of controllable loads for cost reduction and PV accommodation in residential distribution networks , 2015, 2015 IEEE Electrical Power and Energy Conference (EPEC).

[24]  Yang Wang,et al.  Adaptive Real Power Capping Method for Fair Overvoltage Regulation of Distribution Networks With High Penetration of PV Systems , 2014, IEEE Transactions on Smart Grid.

[25]  Mike Barnes,et al.  The Impact of Transport Electrification on Electrical Networks , 2010, IEEE Transactions on Industrial Electronics.

[26]  Sayyad Nojavan,et al.  Robust optimization of renewable-based multi-energy micro-grid integrated with flexible energy conversion and storage devices , 2021 .

[27]  Chadi Assi,et al.  Demand-Side Management by Regulating Charging and Discharging of the EV, ESS, and Utilizing Renewable Energy , 2018, IEEE Transactions on Industrial Informatics.

[28]  Global EV Outlook , 2022 .