Open-Source, Open-Architecture SoftwarePlatform for Plug-InElectric Vehicle SmartCharging in California

This interdisciplinary eXtensible Building Operating System–Vehicles project focuses on controlling plug-in electric vehicle charging at residential and small commercial settings using a novel and flexible open-source, open-architecture charge communication and control platform. The platform provides smart charging functionalities and benefits to the utility, homes, and businesses.This project investigates four important areas of vehicle-grid integration research, integrating technical as well as social and behavioral dimensions: smart charging user needs assessment, advanced load control platform development and testing, smart charging impacts, benefits to the power grid, and smart charging ratepayer benefits

Optimisation-based Approaches for Evaluating the Aggregation of EVs and PVs in Unbalanced Low-Voltage Networks

214 p.In the near future, it is expected that the distribution system operators face different technical challenges derived from the massification of electric mobility and renewable energy sources in the low voltage networks. The purpose of this thesis is to define different smart coordination strategies among different agents involved in the low voltage networks such as the distribution system operator, the aggregators and the end-users when significant penetration levels of these resources are adopted. New models for representing the uncertainty of the photovoltaic output power and the connection of the electric vehicles are introduced. A new energy boundary model for representing the flexibility of electric vehicles is also proposed. In combination with the above models, four optimisation models were proposed as coordination strategies into three different approaches: individual, population, and hybrid. The first model was defined at the aggregator level, whereas the other models were proposed at the distribution system operator level. Complementary experimental cases about the proposed optimisation model in the individual-based approach and the quadratic formulation in the hybrid approach for the PV power curtailment were carried out to test its response in real-time. Simulations results demonstrated that the proposed coordination strategies could effectively manage critical insertion levels of electric vehicles and photovoltaic units in unbalanced low voltage networks

Active and Reactive Power Control of Flexible Loads for Distribution-Level Grid Services

Electric vehicle (EV) charging/discharging can take place in any P-Q quadrants, which means EVs could provide reactive power at any state-of-charge (SOC). This dissertation shows four-quadrant operation of EVs and aggregation of EVs for support of grid operations. First, this work develops hierarchical coordination frameworks to optimally manage active and reactive power dispatch of number of spatially distributed EVs incorporating distribution grid level constraints. This work demonstrates benefits of coordinated dispatch of active and reactive power from EVs using a 33-node distribution feeder with large number of EVs (more than 5,000). Case studies demonstrate that, in constrained distribution grids, coordinated charging reduces the average cost of EV charging if the charging takes place at non-unity power factor mode compared to unity power factor. Similarly, the results also demonstrate that distribution grids can accommodate charging of increased number of EVs if EV charging takes place at non-unity power factor mode compared to the unity power factor. Next, this work utilizes detailed EV battery model that could be leveraged for its four-quadrant operations. Then, the developed work coordinates the operations of EVs and distribution feeder to support voltage profile on the grid in real time. The grid level problem is devised as a distribution optimal power flow model to compute voltage regulation signal to dispatch active/reactive power set points of individual EVs. The efficacy of the developed models are demonstrated by using a LV secondary feeder, where EVs\u27 operating in all four quadrants are shown to compensate the feeder voltage fluctuations caused by daily time varying residential loads, while honoring other operational constraints of the feeder. Furthermore, a novel grid application, called virtual power plant (VPP), is developed. Traditional nonlinear power flow problems are nonconvex, hence, time consuming to solve. In order to be used in real time simulation in VPP, an efficient linearized optimal power flow model is developed. This linearization method is used to solve a 534-bus power system with 3 VPPs in real-time. This work also implements VPP scheduling in real-time using OPAL-RT\u27s simulator in hardware-in-the-loop (HIL), where the loads are emulated using micro-controller devices

Optimal Charging Scheduling of Electric Vehicles in Smart Grids by Heuristic Algorithms

Transportation electrification has become an important issue in recent decades and the large scale deployment of electric vehicles (EVs) has yet to be achieved. The smart coordination of EV demand addresses an improvement in the flexibility of power systems and reduces the costs of power system investment. The uncertainty in EV drivers' behaviour is one of the main problems to solve to obtain an optimal integration of EVs into power systems. In this paper, an optimisation algorithm to coordinate the charging of EVs has been developed and implemented using a Genetic Algorithm (GA), where thermal line limits, the load on transformers, voltage limits and parking availability patterns are taken into account to establish an optimal load pattern for EV charging-based reliability. This methodology has been applied to an existing residential low-voltage system. The results indicate that a smart charging schedule for EVs leads to a flattening of the load profile, peak load shaving and the prevention of the aging of power system elements.This work has been partially funded by the Spanish Ministry of Industry, Energy and Tourism under contract DOMOCELL TSI-020100-2009-849

On-Line Optimal Charging Coordination of Plug-In Electric Vehicles in Smart Grid Environment

This PhD research proposes a new objective function for optimal on-line PEV coordination. A new enhanced on-line coordinated charging using coordinated aggregated particle swarm particle optimization (OLCC-CAPSO) has been used to solve the PEV coordination objective objection and associated constraints. The objective function provides a chance for all PEVs to start charging as quickly as possible, while customer satisfaction function is being optimized subject to network criteria including voltage profiles, generator and distribution transformer ratings

Optimal Online Charging Coordination of Plug in Electric Vehicles in Unbalanced Grids for Ancillary Voltage Support

This PhD thesis will propose an optimal online charge control through genetic algorithm for G2V coordination of PEVs (OL-C-TP) in unbalanced systems. Moreover the algorithm will be extended to also include V2G coordination and offer ancillary voltage support (OL-CD-TPQ) by considering two different methods based on the utility time-of-day prices for exporting reactive power and droop controller for decentralized exporting of reactive power. Then the performance of OL-CD-TPQ by switching PEVs in three phase unbalanced networks is improved

Electric Vehicle (EV)-Assisted Demand-Side Management in Smart Grid

While relieving the dependency on diminishing fossil fuels, Electric Vehicles (EVs) provide a promising opportunity to realise an eco-friendly and cost-effective means of transportation. However, the enormous electricity demand imposed by the wide-scale deployment of EVs can put power infrastructure under critical strain, potentially impacting the efficiency, resilience, and safety of the electric power supply. Interestingly, EVs are deferrable loads with flexible charging requirements, making them an ideal prospect for the optimisation of consumer demand for energy, referred to as demand-side management. Furthermore, with the recent introduction of Vehicle-to-Grid (V2G) technology, EVs are now able to act as residential battery systems, enabling EV customers to store energy and use them as backup power for homes or deliver back to the grid when required. Hence, this thesis studies Electric Vehicle (EV)-assisted demand-side management strategies to manage peak electricity demand, with the long-term objective of transforming to a fully EV-based transportation system without requiring major upgrades in existing grid infrastructure. Specifically, we look at ways to optimise residential EV charging and discharging for smart grid, while addressing numerous requirements from EV customer's perspective and power system's perspective. First, we develop an EV charge scheduling algorithm with the objective of tracking an arbitrary power profile. The design of the algorithm is inspired by water-filling theory in communication systems design, and the algorithm is applied to schedule EV charging following a day-ahead renewable power generation profile. Then we extend that algorithm by incorporating V2G operation to shape the load curve in residential communities via valley-filling and peak-shaving. In the proposed EV charge-discharge algorithm, EVs are distributedly coordinated by implementing a non-cooperative game. Our numerical simulation results demonstrate that the proposed algorithm is effective in flattening the load curve while satisfying all heterogeneous charge requirements across EVs. Next, we propose an algorithm for network-aware EV charging and discharging, with an emphasis on both EV customer economics and distribution network aspects. The core of the algorithm is a Quadratic Program (QP) that is formulated to minimise the operational costs accrued to EV customers while maintaining distribution feeder nodal voltage magnitudes within prescribed thresholds. By means of a receding horizon control approach, the algorithm implements the respective QP-based EV charge-discharge control sequences in near-real-time. Our simulation results demonstrate that the proposed algorithm offers significant reductions in operational costs associated with EV charging and discharging, while also mitigating under-voltage and over-voltage conditions arising from peak power flows and reverse power flows in the distribution network. Moreover, the proposed algorithm is shown to be robust to non-deterministic EV arrivals and departures. While the previous algorithm ensures a stable voltage profile across the entire distribution feeder, it is limited to balanced power distribution networks. Therefore, we next extend that algorithm to facilitate EV charging and discharging in unbalanced distribution networks. The proposed algorithm also supports distributed EV charging and discharging coordination, where EVs determine their charge-discharge profiles in parallel, using an Alternating Direction Method of Multipliers (ADMM)-based approach driven by peer-to-peer EV communication. Our simulation results confirm that the proposed distributed algorithm is computationally efficient when compared to its centralised counterpart. Moreover, the proposed algorithm is shown to be successful in terms of correcting any voltage violations stemming from non-EV load, as well as, satisfying all EV charge requirements without causing any voltage violations