A binary symmetric based hybrid meta-heuristic method for solving mixed integer unit commitment problem integrating with significant plug-in electric vehicles

Conventional unit commitment is a mixed integer optimization problem and has long been a key issue for power system operators. The complexity of this problem has increased in recent years given the emergence of new participants such as large penetration of plug-in electric vehicles. In this paper, a new model is established for simultaneously considering the day-ahead hourly based power system scheduling and a significant number of plug-in electric vehicles charging and discharging behaviours. For solving the problem, a novel hybrid mixed coding meta-heuristic algorithm is proposed, where V-shape symmetric transfer functions based binary particle swarm optimization are employed. The impact of transfer functions utilised in binary optimization on solving unit commitment and plug-in electric vehicle integration are investigated in a 10 unit power system with 50,000 plug-in electric vehicles. In addition, two unidirectional modes including grid to vehicle and vehicle to grid, as well as a bi-directional mode combining plug-in electric vehicle charging and discharging are comparatively examined. The numerical results show that the novel symmetric transfer function based optimization algorithm demonstrates competitive performance in reducing the fossil fuel cost and increasing the scheduling flexibility of plug-in electric vehicles in three intelligent scheduling modes

Demand Side Management of Electric Vehicles in Smart Grids: A survey on strategies, challenges, modeling, and optimization

The shift of transportation technology from internal combustion engine (ICE) based vehicles to electricvehicles (EVs) in recent times due to their lower emissions, fuel costs, and greater efficiency hasbrought EV technology to the forefront of the electric power distribution systems due to theirability to interact with the grid through vehicle-to-grid (V2G) infrastructure. The greater adoptionof EVs presents an ideal use-case scenario of EVs acting as power dispatch, storage, and ancillaryservice-providing units. This EV aspect can be utilized more in the current smart grid (SG) scenarioby incorporating demand-side management (DSM) through EV integration. The integration of EVswith DSM techniques is hurdled with various issues and challenges addressed throughout thisliterature review. The various research conducted on EV-DSM programs has been surveyed. This reviewarticle focuses on the issues, solutions, and challenges, with suggestions on modeling the charginginfrastructure to suit DSM applications, and optimization aspects of EV-DSM are addressed separatelyto enhance the EV-DSM operation. Gaps in current research and possible research directions have beendiscussed extensively to present a comprehensive insight into the current status of DSM programsemployed with EV integration. This extensive review of EV-DSM will facilitate all the researchersto initiate research for superior and efficient energy management and EV scheduling strategies andmitigate the issues faced by system uncertainty modeling, variations, and constraints

Power System Steady-State Analysis with Large-Scale Electric Vehicle Integration

It is projected that the electric vehicle will become a dominant method of transportation within future road infrastructure. Moreover, the electric vehicle is expected to form an additional role in power systems in terms of electrical storage and load balancing. This paper considers the latter role of the electric vehicle and its impact on the steady-state stability of power systems, particularly in the context of large-scale electric vehicle integration. The paper establishes a model framework which examines four major issues: electric vehicle capacity forecasting; optimization of an object function; electric vehicle station siting and sizing; and steady-state stability. A numerical study has been included which uses projected United Kingdom 2020 power system data with results which indicate that the electric vehicle capacity forecasting model proposed in this paper is effective to describe electric vehicle charging and discharging profiles. The proposed model is used to establish criteria for electric vehicle station siting and sizing and to determine steady-state stability using a real model of a small-scale city power system

Multi-objective energy storage power dispatching using plug-in vehicles in a smart-microgrid

This paper describes a multi-objective power dispatching problem that uses Plug-in Electric Vehicle (PEV) as storage units. We formulate the energy storage planning as a Mixed-Integer Linear Programming (MILP) problem, respecting PEV requirements, minimizing three different objectives and analyzing three different criteria. Two novel cost-to-variability indicators, based on Sharpe Ratio, are introduced for analyzing the volatility of the energy storage schedules. By adding these additional criteria, energy storage planning is optimized seeking to minimize the following: total Microgrid (MG) costs; PEVs batteries usage; maximum peak load; difference between extreme scenarios and two Sharpe Ratio indices. Different scenarios are considered, which are generated with the use of probabilistic forecasting, since prediction involves inherent uncertainty. Energy storage planning scenarios are scheduled according to information provided by lower and upper bounds extracted from probabilistic forecasts. A MicroGrid (MG) scenario composed of two renewable energy resources, a wind energy turbine and photovoltaic cells, a residential MG user and different PEVs is analyzed. Candidate non-dominated solutions are searched from the pool of feasible solutions obtained during different Branch and Bound optimizations. Pareto fronts are discussed and analyzed for different energy storage scenarios. Perhaps the most important conclusion from this study is that schedules that minimize the total system cost may increase maximum peak load and its volatility over different possible scenarios, therefore may be less robust

A Novel Reinforcement Learning-Optimization Approach for Integrating Wind Energy to Power System with Vehicle-to-Grid Technology

High integration of intermittent renewable energy sources (RES), specifically wind power, has created complexities in power system operations due to their limited controllability and predictability. In addition, large fleets of Electric Vehicles (EVs) are expected to have a large impact on electricity consumption, contributing to the volatility. In this dissertation, a well-coordinated smart charging approach is developed that utilizes the flexibility of EV owners in a way where EVs are used as distributed energy storage units and flexible loads to absorb the fluctuations in the wind power output in a vehicle-to-grid (V2G) setup. Challenges for people participation in V2G, such as battery degradation and insecurity about unexpected trips, are also addressed by using an interactive mechanism in smart grid. First, a static deterministic model is formulated using multi-objective mixed-integer quadratic programming (MIQP) assuming known parameters day ahead of time. Subsequently, a formulation for real-time dynamic schedule is provided using a rolling-horizon with expected value approximation. Simulation experiments demonstrate a significant increase in wind utilization and reduction in charging cost and battery degradation compared to an uncontrolled charging scenario. Formulating the scheduling problem of the EV-wind integrated power system using conventional stochastic programming (SP) approaches is challenging due to the presence of many uncertain parameters with unknown underlying distributions, such as wind, price, and different commuting patterns of EV owners. To alleviate the problem, a model-free Reinforcement Learning (RL) algorithm integrated with deterministic optimization is proposed that can be applied on many multi-stage stochastic problems while mitigating some of the challenges of conventional SP methods (e.g., large scenario tree, computational complexity) as well as the challenges in model-free RL (e.g., slow convergence, unstable learning in dynamic environment). The simulation results of applying the combined approach on the EV scheduling problem demonstrate the effectiveness of the RL-Optimization method in solving the multi-stage EV charge/discharge scheduling problem. The proposed methods perform better than standard RL approaches (e.g., DDQN) in terms of convergence speed and finding the global optima. Moreover, to address the curse of dimensionality issue in RL with large action-state space, a heuristic EV fleet charging/discharging scheme is used combined with RL-optimization approach to solve the EV scheduling problem for a large number of EVs