Bi-directional coordination of plug-in electric vehicles with economic model predictive control

© 2017 by the authors. Licensee MDPI, Basel, Switzerland. The emergence of plug-in electric vehicles (PEVs) is unveiling new opportunities to de-carbonise the vehicle parcs and promote sustainability in different parts of the globe. As battery technologies and PEV efficiency continue to improve, the use of electric cars as distributed energy resources is fast becoming a reality. While the distribution network operators (DNOs) strive to ensure grid balancing and reliability, the PEV owners primarily aim at maximising their economic benefits. However, given that the PEV batteries have limited capacities and the distribution network is constrained, smart techniques are required to coordinate the charging/discharging of the PEVs. Using the economic model predictive control (EMPC) technique, this paper proposes a decentralised optimisation algorithm for PEVs during the grid-To-vehicle (G2V) and vehicle-To-grid (V2G) operations. To capture the operational dynamics of the batteries, it considers the state-of-charge (SoC) at a given time as a discrete state space and investigates PEVs performance in V2G and G2V operations. In particular, this study exploits the variability in the energy tariff across different periods of the day to schedule V2G/G2V cycles using real data from the university's PEV infrastructure. The results show that by charging/discharging the vehicles during optimal time partitions, prosumers can take advantage of the price elasticity of supply to achieve net savings of about 63%

A Bayesian real-time electric vehicle charging strategy for mitigating renewable energy fluctuations

A novel pricing and scheduling mechanism is proposed here for Plug-in electric vehicles (PEVs) charging/discharging to track and synchronize with a renewable power generation pattern. Moreover, the proposed mechanism can be used in the demand-side management and ancillary service applications, respectively for the peak shaving and frequency regulation responding. We design a fully distributed stochastic optimization mechanism using Bayesian pure strategic repeated game by which the PEVs optimally schedule their demands. We also use a mixed Bayesian-diffusion Kalman filtering strategy for the customers to collaboratively estimate and track the stochastic price and regulation signals for the upcoming scheduling window. In the proposed paper all the characteristics of the PEVs, as well as the uncertainty about their deriving patterns are considered. As our framework converges to an equilibrium even with incomplete information, is agent-based, and the agents share the information only with their optional neighbors, it is scale-free, robust, and secure

Demand side management studies on distributed energy resources: A survey

The number of distributed environmentally friendly energy sources and generators necessitates new operating methods and a power network board to preserve or even increase the efficiency and quality of the power supply. Similarly, the growth of matriculates promotes the formation of new institutional systems, in which power and power exchanges become increasingly essential. Because of how an inactive entity traditionally organizes distribution systems, the DG’s connection inevitably changes the system’s qualifications to which it is connected. As a consequence of the Distributed Generation, this presumption is currently legal and non-existent. This article glides on demand side management and analysis on distributed energy resources. Investigation of DSM along with zonal wise classification has been carried out in this survey. Its merits and applications are also presented.Universidad Tecnológica de Bolíva

Smart charging strategies for electric vehicle charging stations

Although the concept of transportation electrification holds enormous prospects in addressing the global environmental pollution problem, consumer concerns over the limited availability of charging stations and long charging/waiting times are major contributors to the slow uptake of plug-in electric vehicles (PEVs) in many countries. To address the consumer concerns, many countries have undertaken projects to deploy a network of both fast and slow charging stations, commonly known as electric vehicle charging networks. While a large electric vehicle charging network will certainly be helpful in addressing PEV owners\u27 concerns, the full potential of this network cannot be realised without the implementation of smart charging strategies. For example, the charging load distribution in an EV charging network would be expected to be skewed towards stations located in hotspot areas, instigating longer queues and waiting times in these areas, particularly during afternoon peak traffic hours. This can also lead to a major challenge for the utilities in the form of an extended PEV charging load period, which could overlap with residential evening peak load hours, increasing peak demand and causing serious issues including network instability and power outages. This thesis presents a smart charging strategy for EV charging networks. The proposed smart charging strategy finds the optimum charging station for a PEV owner to ensure minimum charging time, travel time and charging cost. The problem is modelled as a multi-objective optimisation problem. A metaheuristic solution in the form of ant colony optimisation (ACO) is applied to solve the problem. Considering the influence of pricing on PEV owners\u27 behaviour, the smart charging strategy is then extended to address the charging load imbalance problem in the EV network. A coordinated dynamic pricing model is presented to reduce the load imbalance, which contributes to a reduction in overlaps between residential and charging loads. A constraint optimization problem is formulated and a heuristic solution is introduced to minimize the overlap between the PEV and residential peak load periods. In the last part of this thesis, a smart management strategy for portable charging stations (PCSs) is introduced. It is shown that when smartly managed, PCSs can play an important role in the reduction of waiting times in an EV charging network. A new strategy is proposed for dispatching/allocating PCSs during various hours of the day to reduce waiting times at public charging stations. This also helps to decrease the overlap between the total PEV demand and peak residential load

Model Predictive Control for Smart Grids with Multiple Electric-Vehicle Charging Stations

Next-generation power grids will likely enable concurrent service for residences and plug-in electric vehicles (PEVs). While the residence power demand profile is known and thus can be considered inelastic, the PEVs' power demand is only known after random PEVs' arrivals. PEV charging scheduling aims at minimizing the potential impact of the massive integration of PEVs into power grids to save service costs to customers while power control aims at minimizing the cost of power generation subject to operating constraints and meeting demand. The present paper develops a model predictive control (MPC)- based approach to address the joint PEV charging scheduling and power control to minimize both PEV charging cost and energy generation cost in meeting both residence and PEV power demands. Unlike in related works, no assumptions are made about the probability distribution of PEVs' arrivals, the known PEVs' future demand, or the unlimited charging capacity of PEVs. The proposed approach is shown to achieve a globally optimal solution. Numerical results for IEEE benchmark power grids serving Tesla Model S PEVs show the merit of this approach

Enhancement of Charging Resource Utilization of Electric Vehicle Fast Charging Station with Heterogeneous EV Users

This thesis presents innovative charging resource allocation and coordination strategies that maximize the limited charging resources at FCS with heterogeneous EV users. It allows opportunistic EV users (OEVs) to exploit available charging resources with dynamic event-driven charging resource allocation and coordination strategies apart from primary EV users (PEVs) (registered or scheduled EV users). Moreover, developed strategies focus on the limited charging resources that are allocated for primary/ registered EV users (PEVs) of the FCS who access the FCS with specific privileges according to prior agreements. But the available resources are not optimally utilized due to various uncertainties associated with the EV charging process such as EV mobility-related uncertainties, EVSE failures, energy price uncertainties, etc. Developed strategies consider that idle chargers and vacant space for EVs at the FCS is an opportunity for further utilizing them with OEVs using innovative charging resource coordination strategies. This thesis develops an FCS-centric performance assessment framework that evaluates the performance of developed strategies in terms of charging resource utilization, charging completion and the quality of service (QoS) aspects of EV users. To evaluate QoS of EV charging process, various parameters such as EV blockage, charging process preemptage, mean waiting time, mean charging time, availability of FCS, charging reliability, etc are derived and analyzed. In addition, the developed innovative charging resource allocation and coordination strategies with resource aggregation and demand elasticity further enhance the charging resource utilization while providing a high QoS in EV charging for both PEVs and OEVs.publishedVersio

Exploiting Mobile Energy Storages for Overload Mitigation in Smart Grid

The advancement of battery and electronic technologies pushes forward transportation electrification, accelerating the commercialization and prevalence of plug-in electric vehicles (PEVs). The development of PEVs is closely related to the smart grid as PEVs are considered as high power rating electric appliances that require frequent charging. As PEVs become regular transportation options, charging stations (CSs) are also extensively deployed in the smart grid to meet the PEV charging demand. During peak traffic hours, the increasing PEV charging demand could exceed the loading capacities of CS-connected transformers, causing heavy charging overload in-station. Without proper overload mitigation, the energy imbalance issues will result in severe feeder degradation and power quality issue. Therefore, solutions for CS overload mitigation are in urgent demand. Considering the rechargeable nature of PEV batteries, PEVs can serve as potential mobile energy storages (MESs) to carry energy from power nodes with excess energy to overloaded CSs to compensate the overloads. Compared to infrastructure upgrade and installing stationary energy storages at CSs, the utilization of PEVs not only minimizes the additional upgrade/installation expenditure, but also maximizes the energy utilization in the smart grid with high flexibility. However, the PEV utilization for overload mitigation is confronted with a variety of challenges due to vehicular mobility and the fear of battery degradation. Because of vehicular mobility, the CS operation dynamics become stochastic processes, increasing the difficulty of the CS demand estimation. Without accurate demand estimation, the overload condition cannot be timely predicted and controlled. Moreover, the stochastic on-road traffic could impair the time-efficiency of the PEV overload mitigation service. Further, as the overload mitigation service demands frequent charging and discharging, the fear of battery degradation could impede PEV owners from providing the service, making the overload mitigation tasks harder to fulfill. In this thesis, we address the above challenges to effectively utilize PEVs for overload mitigation in the smart grid. In specific, different approaches are designed according to the PEV properties at different commercialization stages. First, at the early PEV commercialization stage, power utility company purchases large battery capacity PEVs as utility-owned MESs (UMESs) whose only responsibility is fulfilling the energy compensation task. The fleet of UMESs is rather small due to the company's limited budget, and therefore UMESs priorly serve the CSs with large energy imbalance (e.g., 500-1000kWh). Thus, the stochastic CS charging demand needs to be accurately estimated and then UMESs can be scheduled to these CSs for overload mitigation. To achieve this objective, we develop a two-dimensional Markov Chain model to characterize the stochastic process in-station so that the CS charging demand can be precisely estimated. Based on the estimated CS demand status, a two-tier energy compensation framework is designed to schedule UMESs to the heavily overloaded CSs in a timely and cost-efficient manner. Second, at the medium stage of PEV commercialization, vehicle-fleet based companies are motivated by legislation to purchase a large fleet of PEVs which can be served as potential MESs, referred to as legislation-motivated MESs (LMESs). To deliver energy to overloaded CSs using LMESs would introduce a large amount of additional traffics to the transportation network. When injecting these LMES traffics into an already busy transportation network, unexpected traffic delay could occur, delaying the overload mitigation service. To avoid the potential traffic delay incurred by LMES service, we develop an energy-capacitated transportation network model to measure the road capacity of accommodating additional LMES traffics. Based on the developed model, a loading-optimized navigation scheme is proposed to calculate the optimal navigation routes for LMES overload mitigation. To stimulate LMESs following the optimal navigation, we propose a dynamic pricing scheme that adjusts the service price to align the LMES service routes with the optimal routes to achieve a time-efficient service result. Third, when PEVs are prevalent in the automobile market and become regular transportation options for every household, on-road private-owned PEVs can be efficiently used as energy porters to deliver energy to overloaded CSs, named as private MESs (PMESs). As the primary objective of PMESs is to reach their planned destinations, the monetary incentive is demanded to stimulate them actively participating in the overload mitigation tasks. Therefore, a hierarchical decision-making process between the utility operator (UO) and PMESs is in demand. Moreover, considering PMESs have different service preferences (e.g., the fear of battery degradation, the unwillingness of long service time, etc.), individual PMES decision making process on the task should be carefully modelled. Thus, we propose to characterize the price-service interaction between the operator and PMESs as a Stackelberg game. The operator acts as the leader to post service price to PMESs while PMESs act as followers, responding to the posted price to maximize their utility functions. In summary, the analysis and schemes proposed in this thesis can be adopted by the local power utility company to utilize PEVs for overload mitigation at overloaded power nodes. The proposed schemes are applicable during different PEV commercialization stage and present PEVs as a flexible solution to the smart grid overload issue

Online Coordinated Charging of Plug-In Electric Vehicles in Smart Grid to Minimize Cost of Generating Energy and Improve Voltage Profile

This Ph.D. research highlights the negative impacts of random vehicle charging on power grid and proposes four practical PEV coordinated charging strategies that reduce network and generation costs by integrating renewable energy resources and real-time pricing while considering utility constraints and consumer concerns