Bidirectional coordinating dispatch of large-scale V2G in a future smart grid using complementarity optimization

An empirically-validated methodology to simulate electricity demand for electric vehicle charging

Plug-in electric vehicles (PEVs) represent economic and eco-friendly means of transportation. High PEV penetration levels are expected during 2020 – 2050. Consequently, a considerable portion of the total power consumption in the future smart grid will be due to the PEV charging demands. Therefore, PEV charging has drawn significant attention from both academia and industry. In this paper, we shed some light on key research issues related to PEV charging in the future smart grid. Specifically, we discuss the challenges related to charging facility planning and PEV charging coordination for both mobile and parked vehicles. In addition, we summarize our main research results related to the aforementioned topics. Finally, future research directions are provided.

Stochastic two-stage reliability-based Security Constrained Unit Commitment in smart grid environment

This dissertation contributes to our understanding of how plug-in hybrid electric vehicles (PHEVs) and plug-in battery-only electric vehicles (EVs)—collectively termed plug-in electric vehicles (PEVs)—could be successfully integrated with the electric power system. The research addresses issues at a diverse range of levels pertaining to light-duty vehicles, which account for the majority of highway vehicle miles traveled, energy consumed by highway travel modes, and carbon dioxide emissions from on-road sources. Specifically, the following topics are investigated: (i) On-board power electronics topologies for bidirectional vehicle-to-grid and grid-to-vehicle power transfer; (ii) The estimation of the electric energy and power consumption by fleets of light-duty PEVs; (iii) An operating framework for the scheduling and dispatch of electric power by PEV aggregators; (iv) The pricing of electricity by PHEV aggregators and how it affects the decision-making process of a cost-conscious PHEV owner; (v) The impacts on distribution systems from PEVs under aggregator control; (vi) The modeling of light-duty PEVs for long-term energy and transportation planning at a national scale.

This paper proposes a vehicle trip chain information-based hierarchical stochastic energy management system (Hi-SEMS) for a microgrid integrated with highly uncertain plug-in electric vehicles (PEVs) fleet, renewable energy sources, and loads. The two-layer Hi-SEMS scheme aims at optimal scheduling of PEV fleet to counteract the operational uncertainties encountered in real-time operation and minimizing the microgrid daily operating cost comprising the fuel cost and the emission cost. In the proposed work, the realistic behavior of PEVs fleet is modeled using a stochastic trip chain information to accurately forecast the charge/discharge demand taking into account the driver’s behavior as foundation. Realistic statistical trip data, multi-location charge/discharge of PEVs fleet to include coupled spatial-temporal dynamics of heterogeneous PEVs fleet is considered for intelligent charge/discharge scheduling. The effectiveness of the proposed strategy is verified on modified LV CIGRE and modified IEEE 33 bus radial distribution test networks and is compared with four different cases on each of the two test systems. The simulation results exhibit that the proposed Hi-SEMS outperforms the other four cases in terms of daily operating cost, the incentive to the PEVs owner, and robustness to multiple uncertainties.

Plug-in Electric Vehicles (PEVs) are a clean form of transportation that will require a new control paradigm between the energy supply and the transportation sectors as PEV adoption continues to grow. Meeting the projected PEV energy demands will require implementing PEV / grid integration methods beyond charging PEVs at incentivized times. MATLAB was used to simulate the daily vehicle trip demands and charging processes of hundreds of PEVs. The National Household Travel Survey (NHTS) data was used as the source of vehicle travel data, and EV Project provided measured PEV charging data to calibrate the PEV population models. GridLAB-D analyses used a prototypical feeder and PEV population models to evaluate residential PEV V1G (unidirectional) charging in multiple locations, V1G PEV distribution feeder impacts, Time-Of-Use (TOU) rate effects on PEV charging, and charging control methods. The key findings were: optimized charging using California TOU rates could result in savings of over $20 per month for non-TOU program participants; non-TOU participants consume 30-40% of PEV charging energy; about 50% of PEV drivers have their transportation fuel bill reduced by 50 dollars per month; 6.6kW charging will cause distribution transformer power limits to be exceeded on prototypical feeders if the household PEV adoption rate reaches 0.75 PEVs per home; distribution feeder loading is proportional to PEV charging rate; a method was devised to remotely identify when residential transformer overloads occur using changes in PEV charging rate; and the low-cost TOU period is composed of a distribution of PEVs needing small, medium and larger energy needs that can be more effectively shifted as groups to mitigate the TOU peak power and enable a higher PEV adoption rate per household without overloading distribution transformers.

Plug-in Electric Vehicles (PEVs) play a pivotal role in transportation electrification. The flexible nature of PEVs' charging demand can be utilized for reducing charging cost as well as optimizing the operating cost of power and transportation networks. Utilizing charging flexibility of geographically spread PEVs requires design and implementation of efficient optimization algorithms. To this end, we propose a fully distributed algorithm to solve the PEVs' Cooperative Charging with Power constraints (PEV-CCP). Our solution considers the electric power limits that originate from physical characteristics of charging station, such as on-site transformer capacity limit, and allows for containing charging burden of PEVs on the electric distribution network. Our approach also enables plug-and-play and valley-filling capabilities of PEV charge scheduling. It distributes computation among agents (PEVs) to solve the PEV-CCP problem in a distributed fashion through an iterative interaction between neighboring agents. The structure of each agent's update functions ensures an agreement on a price signal while enforcing individual PEV constraints. In addition to converging towards the globally-optimum solution, our algorithm ensures the feasibility of each PEV's decision at each iteration. We have tested performance of the proposed approach using a fleet of PEVs. Simulation results verify the plug-and-play capability of the proposed approach by efficiently tracking the changes in terms of arrival/depart of PEVs.

This paper presents a model predictive control (MPC) for off-board plug-in electric vehicle (PEV) chargers with photovoltaic (PV) integration using two-level four-leg inverter topology. The PEV charger is controlled by a unified controller that incorporates direct power and current MPC to dynamically control decoupled active-reactive power flow in a smart grid environment as well as to control PEV battery charging and discharging reliably. PV power generation with maximum power tracking is seamlessly integrated with the power flow control to provide additional power generation. Fast dynamic response and good steady-state performance under all power flow modes and various environmental conditions are evaluated and analyzed. From the results obtained, the charger demonstrates less than 1.5% total harmonic distortion as well as low active and reactive power ripple of less than 7% and 8% respectively on the grid for all power flow modes. The PEV battery also experiences a low charging and discharging current ripple of less than 2.5%. Therefore, the results indicate the successful implementation of the proposed charger and its control for PV integrated off-board PEV chargers.

Real-time charging coordination of plug-in electric vehicles based on hybrid fuzzy discrete particle swarm optimization

This paper presents a trustworthy unit commitment study to schedule both Renewable Energy Resources (RERs) with conventional power plants to potentially decarbonize the electrical network. The study has employed a system with three IEEE thermal (coal-fired) power plants as dispatchable distributed generators, one wind plant, one solar plant as stochastic distributed generators, and Plug-in Electric Vehicles (PEVs) which can work either loads or generators based on their charging schedule. This paper investigates the unit commitment scheduling objective to minimize the Combined Economic Emission Dispatch (CEED). To reduce combined emission costs, integrating more renewable energy resources (RER) and PEVs, there is an essential need to decarbonize the existing system. Decarbonizing the system means reducing the percentage of CO2 emissions. The uncertain behavior of wind and solar energies causes imbalance penalty costs. PEVs are proposed to overcome the intermittent nature of wind and solar energies. It is important to optimally integrate and schedule stochastic resources including the wind and solar energies, and PEVs charge and discharge processes with dispatched resources; the three IEEE thermal (coal-fired) power plants. The Water Cycle Optimization Algorithm (WCOA) is an efficient and intelligent meta-heuristic technique employed to solve the economically emission dispatch problem for both scheduling dispatchable and stochastic resources. The goal of this study is to obtain the solution for unit commitment to minimize the combined cost function including CO2 emission costs applying the Water Cycle Optimization Algorithm (WCOA). To validate the WCOA technique, the results are compared with the results obtained from applying the Dynamic Programming (DP) algorithm, which is considered as a conventional numerical technique, and with the Genetic Algorithm (GA) as a meta-heuristic technique.

Coordinated charging of Plug-In Electric Vehicles (PEVs) in residential distribution systems is a new concept currently being explored in the wake of smart grids. Utilities are exploring these options as there are concerns about potential stresses and network congestions that may occur with random and uncoordinated multiple domestic PEV charging activities. Such operations may lead to degraded power quality, poor voltage profiles, overloads in transformer and cables, increased power losses and overall a reduction in the reliability and economy of smart grids. Future smart grids communication network will play an important role in PEV operation because the battery chargers can be remotely coordinated by the utility and harnessed for storing surplus grid energy and reused to support the grid during peak times. Based on a recently proposed PEV charging algorithm, this paper focuses on the impact of coordinated charging on distribution transformer loading and performance. Simulation results are presented to explore the ability of the PEV coordination algorithm in reducing the stress on distribution transformers at different PEV penetration levels. The performance of various distribution transformers within the simulated smart grid is examined for a modified IEEE 23 kV distribution system connected to several low voltage residential networks populated with PEVs.

Smart grids have been introduced to replace conventional power distribution systems without real time monitoring for accommodating the future market penetration of plug-in electric vehicles (PEVs). When a large number of PEVs require simultaneous battery charging, charging coordination techniques have become one of the most critical factors to optimize the PEV charging performance and the conventional distribution system. In this case, considerable computational complexity of a central controller and exchange of real time information among PEVs may occur. To alleviate these problems, a novel threshold-based random charging (TBRC) operation for a decentralized charging system is proposed. Using PEV charging thresholds and random access rates, the PEVs themselves can participate in the charging requests. As PEVs with a high battery state do not transmit the charging requests to the central controller, the complexity of the central controller decreases due to the reduction of the charging requests. In addition, both the charging threshold and the random access rate are statistically calculated based on the average of supply power of the PEV charging system that do not require a real time update. By using the proposed TBRC with a tolerable PEV charging degradation, a 51% reduction of the PEV charging requests is achieved.

Plug-in electric vehicles (PEVs) appear to offer a promising option for mitigating greenhouse emission. However, uncoordinated PEV charging can weaken the reliability of power systems. The proper accommodation of PEVs in a power grid imposes many challenges on system planning and operations. This work aims to investigate optimal PEV coordination strategies with cost-benefit analysis. In Part I, we first present a new method to calculate the charging load of PEVs with a modified Latin hypercube sampling (LHS) method for handling the stochastic property of PEVs. We then propose a new two-stage optimization model to discover the optimal charging states of PEVs in a given day. Using this model, the peak load with charging load of PEVs is minimized in the first stage and the load fluctuation is minimized in the second-stage with peak load being fixed as the value obtained in the first stage. An algorithm based on linear mixed-integer programming is provided as a suitable solution method with fast computation. Finally, we present a new method to calculate the benefit and cost for a PEV charging and discharging coordination strategy from a social welfare approach. These methods are useful for developing PEV coordination strategies in power system planning and supporting PEV-related policy making.

The massive and random charging activities by increasing penetration of the plug-in electric vehicles (PEVs) shall bring significant peak load in the near future. And the massive Greenhouse Gas (GHG) emission caused by electricity generation also brings challenges to the environment. And the charging management mechanism therefore is the key to charging PEVs with low GHG emission and help utility operators optimize generation strategy. The paper firstly develops an hourly dispatch model to simulate GHG emissions caused by electricity generations, and an access network structure composing submeters and smart meter mesh network is proposed. Then a novel coordinated charging management mechanism using queuing theory is implemented to minimize the PEV charging load impact to the electricity grid. To assess the mechanism performance, the paper constructs an hourly PEV charging model to simulate the grid impact and GHG emissions. And the two detailed and comprehensive scenarios with incoordinated and coordinated charging mechanism are simulated while key parameters are generated, analyzed, and approved. Simulation results show that the coordinated charging mechanism works efficiently on reducing the grid peak load, improving the utility utilization, and reducing GHG emission. Study findings in this paper will help support informed decision-making regarding PEV development and deployment in support of national energy and environmental policy.

Plug-in electric vehicles (PEVs) could provide valuable ancillary service for power systems with intermittent renewables. This paper proposes a charging model of a large amount of PEVs to mitigate the high-level wind power fluctuations in a distribution network so as to regulate system frequency. With the prerequisite that all PEV daily driving patterns are completely satisfied, the PEV charging power is economically allocated to counterbalance the wind generation intermittency. Afterward, a center-free control scheme based on the consensus algorithm is designed for PEVs to share the fluctuating wind generation in a fully distributed manner. The scheme is robust and flexible to practical PEV charging behaviors including arrival and departure time, initial and desired SOC, as well as the frequently changed departure time. Comprehensive simulations on a distribution system with coal and diesel generators, several wind farms and 2000 PEVs demonstrate that the proposed PEV charging scheme could effectively regulate the system frequency in the real time while the PEV users’ charging requirements could be flexibly satisfied.