Simulation Platform for Coordinated Charging of Electric Vehicles

EMERALD is a project funded by the European Commission under the FP7 program focusing on energy use optimization on the integration of the FEVs into the transport and energy infrastructure. Between the objectives of EMERALD, enhanced power demand prediction and power flow support management system uses the power flow demand simulation platform considered in this paper. The power flow demand simulation platform is a software tool that defines the estimation of FEVs power demand according to different conditions as, arrival and departure curves, the estimation of power production based on renewable energy sources and the electricity cost. The tool coordinates scheduling for charging of FEVs in order to minimize the recharging cost, considering the energy balance between the generation and demand powerEuropean commission's FP

Charging Scheduling of Electric Vehicles with Local Renewable Energy under Uncertain Electric Vehicle Arrival and Grid Power Price

In the paper, we consider delay-optimal charging scheduling of the electric vehicles (EVs) at a charging station with multiple charge points. The charging station is equipped with renewable energy generation devices and can also buy energy from power grid. The uncertainty of the EV arrival, the intermittence of the renewable energy, and the variation of the grid power price are taken into account and described as independent Markov processes. Meanwhile, the charging energy for each EV is random. The goal is to minimize the mean waiting time of EVs under the long term constraint on the cost. We propose queue mapping to convert the EV queue to the charge demand queue and prove the equivalence between the minimization of the two queues' average length. Then we focus on the minimization for the average length of the charge demand queue under long term cost constraint. We propose a framework of Markov decision process (MDP) to investigate this scheduling problem. The system state includes the charge demand queue length, the charge demand arrival, the energy level in the storage battery of the renewable energy, the renewable energy arrival, and the grid power price. Additionally the number of charging demands and the allocated energy from the storage battery compose the two-dimensional policy. We derive two necessary conditions of the optimal policy. Moreover, we discuss the reduction of the two-dimensional policy to be the number of charging demands only. We give the sets of system states for which charging no demand and charging as many demands as possible are optimal, respectively. Finally we investigate the proposed radical policy and conservative policy numerically

Grid-able Plug-in Electric Vehicles in Smart Grids: Incorporation into Demand Response

Electric transportation has attracted a great deal of interest within the transport sector because of its notable potential to become a low-carbon substitute for conventional combustion engine vehicles. However, widespread use of this form of transportation, such as plug-in electric vehicles (PEVs), will constitute a significant draw on power grids, especially when associated with uncontrolled charging schemes. In fact, electric utilities are unable to control individual PEVs in order to manage their charging and avoid negative consequences for distribution lines. However, a control strategy could be directed at a single vehicle or group of vehicles. One effective approach could be to build on a supervisory control system, similar to a SCADA system that manages the aggregation of PEVs, a role that could be filled by aggregators that exchange data and information among individual PEVs and energy service providers. An additional consideration is that advances in intelligent technologies and expert systems have introduced a range of flexible control strategies, which make smart grid implementation more attractive and viable for the power industry. These developments have been accompanied by the initiation of a new paradigm for controllable PEV loads based on a number of advantages associated with a smart grid context. One of the established goals related to smart grids is to build on their ability to take advantage of all available energy resources through efficient, decentralized management. To this end, utilities worldwide are using IT, communication, and sensors to provide enhanced incorporation of operational tools and thus create a more robust and interactive environment able to handle generation-demand dynamics and uncertainties. One of these tools is demand response (DR), a feature that adjusts customers’ electricity usage through the offer of incentive payments. Motivated by this background, the goal of the work presented in this thesis was to introduce new operational algorithms that facilitate the charging of PEVs and the employment of their batteries for short-term grid support of active power. To allow both public parking lots and small residential garages to benefit from smart charging for end-user DR, a framework has been developed in which the aggregator handles decision-making through real-time interactions with PEV owners. Two interaction levels are implemented. First, for charging coordination with only one-round interaction, a fuzzy expert system prioritizes PEVs to determine the order in which they will be charged. Next, for smart charging, which includes battery discharging, a multi-stage decision-making approach with two-round interaction is proposed. Real-time interaction provides owners with an appropriate scheme for contributing to DR, while avoiding the inconvenience of pre-signed long-term contracts. A new stochastic model predicts future PEV arrivals and their energy demand through a combination of an artificial neural network (ANN) and a Markov chain. A new method is proposed for promoting collaboration of PEVs and photovoltaic (PV) panels. This technique is based on a determination of the ways in which smart charging can support simultaneous efficient energy delivery and phase-unbalance mitigation in a three-phase LV system. Simulation results derived from 38-bus and 123-bus distribution test systems have verified the efficacy of the proposed methods. Through case-study comparisons, the inefficiency of conventional charging regimes has been confirmed and the effectiveness of real-time interactions with vehicle owners through DR has been demonstrated. The most obvious finding to emerge from this study is that the use of a scoring-based (SCR) solution facilitates the ability of an aggregator to address urgent PEV energy demands, especially in large parking lots characterized by high levels of hourly vehicle transactions. The results of this study also indicate that significantly greater energy efficiency could be achieved through the discharging of PEV batteries when PEV grid penetration is high

Accommodating a High Penetration of Plug-in Electric Vehicles in Distribution Networks

The last few decades have seen growing concern about climate change caused by global warming, and it now seems that the very future of humanity depends on saving the environment. With recognition of CO2 emissions as the primary cause of global warming, their reduction has become critically important. An effective method of achieving this goal is to focus on the sectors that represent the greatest contribution to these emissions: electricity generation and transportation. For these reasons, the goal of the work presented in this thesis was to address the challenges associated with the accommodation of a high penetration of plug-in electric vehicles (PEVs) in combination with renewable energy sources. Every utility must consider how to manage the challenges created by PEVs. The current structure of distribution systems is capable of accommodating low PEV penetration; however, high penetration (20 % to 60 %) is expected over the next decades due to the accelerated growth in both the PEV market and emission reduction plans. The energy consumed by such a high penetration of PEVs is expected to add considerable loading on distribution networks, with consequences such as thermal overloading, higher losses, and equipment degradation. A further consideration is that renewable energy resources, which are neither exhaustible nor polluting, currently offer the only clean-energy option and should thus be utilized in place of conventional sources in order to supply the additional transportation-related demand. Otherwise, PEV technology would merely transfer emissions from the transportation sector to the electricity generation sector. As a means of facilitating the accommodation of high PEV penetration, this thesis proposes methodologies focused on two main themes: uncontrolled and coordinated charging. For uncontrolled charging, which represents current grid conditions, the proposal is to utilize dispatchable and renewable distributed generation (DG) units to address the high PEV penetration in a way that would not be counterproductive. This objective is achieved through three main steps. First, the benefits of allocating renewable DG in distribution systems are investigated, with different methodologies developed for their evaluation. The benefits are defined as the deferral of system upgrade investments, the reduction in the energy losses, and the reliability improvement. The research also includes a proposal for applying the developed methodologies for an assessment of the benefits of renewable DG in a planning approach for the optimal allocation of the DG units. The second step involves the development of a novel probabilistic energy consumption model for uncontrolled PEV charging, which includes consideration of the drivers’ behaviors and ambient temperature effect associated with vehicle usage. The final step integrates the approaches and models developed in the previous two steps, where a long-term dynamic planning approach is developed for the optimal allocation of renewable and dispatchable DG units in order to accommodate the rising penetration of PEV uncontrolled charging. The proposed planning approach is multi-objective and includes consideration of system emissions and costs. The second theme addressed in this thesis is coordinated PEV charging, which is dependent on the ongoing development of a smart grid communication infrastructure, in which vehicle-grid communication is feasible via appropriate communication pathways. This part of the work led to the development of a proposed coordinated charging architecture that can efficiently improve the performance of the real-time coordinating PEV charging in the smart grid. The architecture is comprised of two novel units: a prediction unit and an optimization unit. The prediction unit provides an accurate forecast of future PEV power demand, and the optimization unit generates optimal coordinated charging/discharging decisions that maximize service reliability, minimize operating costs, and satisfy system constraints