192 research outputs found

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

    Get PDF
    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

    Assessing and Mitigating Impacts of Electric Vehicles on Active Distribution Systems

    Get PDF
    The transportation sector is expected to undergo a worldwide shift to zero-carbon emission automobiles. Major research advancements and government policies have been addressing the financial and technical barriers to electric vehicle (EV) use. Battery packs constitute an important component of EV technology. Improvements in battery pack technology are leading to lower battery cost, higher battery density, and increased driving range, making EVs more appealing to the consumers. On the other hand, EV charging loads can cause power quality issues such as harmonic distortion, voltage drop, power unbalance, power losses and transformer aging. EV increased charging load is urging the need of assessing its negative impacts on the grid to protect power system components. A comparison of the impacts of different levels of EV charging on the grid can allow EV users and utilities to understand the risks associated with their choices. Harmonic distortion due to nonlinear devices can be evaluated using harmonic power flow methods. Decoupled harmonic power flow technique is widely used in power systems analysis due to its simplicity and computational efficiency. Mitigation techniques to reduce harmonic impacts on the grid are crucial for power system reliability and maintenance. Incorporating distributed generation (DG) units into the network can achieve harmonic compensation of EV charging. A genetic algorithm is proposed to determine the current harmonic spectrum of each DG unit, accomplishing an optimal harmonic compensation of EV charging. DG integration improves grid power quality and voltage profile. It also helps in reducing voltage and current disturbances produced by EV loads

    Risk-Constrained Stochastic Scheduling of a Grid-Connected Hybrid Microgrid with Variable Wind Power Generation

    Get PDF
    This paper presents a risk-constrained scheduling optimization model for a grid-connected hybrid microgrid including demand response (DR), electric vehicles (EVs), variable wind power generation and dispatchable generation units. The proposed model determines optimal scheduling of dispatchable units, interactions with the main grid as well as adjustable responsive loads and EVs demand to maximize the expected microgrid operator’s profit under different scenarios. The uncertainties of day-ahead (DA) market prices, wind power production and demands of customers and EVs are considered in this study. To address these uncertainties, conditional value-at-risk (CVaR) as a risk measurement tool is added to the optimization model to evaluate the risk of profit loss and to indicate decision attitudes in different conditions. The proposed method is finally applied to a typical hybrid microgrid with flexible demand-side resources and its applicability and effectives are verified over different working conditions with uncertainties

    Enhancing System Reliability Utilizing Private Electric Vehicle Parking Lots Accounting for the Uncertainties of Renewables

    Get PDF
    Integration of renewable energy sources into electric grids comes with significant challenges. The produced energy from renewable sources such as wind and solar is intermittent, non-dispatchable and uncertain. The uncertainty in the forecasted renewable energy will consequently impact the accuracy of the forecasted generation. That, in turn, will increase the difficulties for the grid operators to meet the demand-supply balance in the grids. Moreover, the shift to electric vehicles (EV) also adds complications to grid operators planning since their demand profiles are unlike anything that is currently connected to the grid. With the advent of the smart grid, many new interesting and practical technologies will become a reality. Unfortunately, most of these elements will not be physically realized in systems for the immediate future. This thesis will maintain an overarching constraint of only using aspects of the smart grid that can be implemented, given today’s infrastructure, within the next three to five years. For that reason, all loads connected to the system are considered to be uncontrollable except EVs when they are connected to a “commercial parking lot”. The goal of this body of work is to investigate the benefits of the utility providing incentives, in terms of reducing the price of electricity for charging EVs through a commercial parking lot, for the sole goal of enhancing reliability through optimized scheduling of charging time periods. Moreover, since the penetration of renewable energy is only predicted to increase over time, their impact will also be investigated. Since both EVs and renewables add uncertainty and randomness whenever connected, these elements need to be accurately and adequately modelled. There will be five electrical components that will need stochastic models built. The first two, base electric load and dispatchable/distributed generators, will be modelled based on the IEEE - Reliability Test System (IEEE-RTS), with the later using a Monte-Carlo (MC) simulation. The next two, solar and wind energy, will be extrapolated from historical weather patterns through a Markov-Chain Monte-Carlo (MCMC) simulation. Finally, the EV will be virtually generated from historical commercial parking lot data also using a MCMC. Three scheduling algorithms were implemented in this work. The first is a base case, in which the EVs charged in a first-come first-serve basis, this situation that would arise if no information at all is shared with the parking lot owner. The results of the simulation had the value of Expected Energy Not Served (EENS) came out to be 38.05 MWh/year (the lower the better). With the second algorithm, a basic Demand Side Management (DSM) algorithm was implemented, with the result being that the EENS decreased by 8.35 %. The information shared was the demand shape of all consumers for a given day. Lastly, an algorithm that will be called Grid and Demand Side Management (GDSM) by this thesis proved to be even more successful, having the EENS reach a value of 29.85 MWh/year, a decrease of 21.55 %. The GDSM scheduling algorithm needs the grid to share not only the consumer behavior but also the expected generator behavior. Based on these results, recommendations are made to the electric utility in terms of the benefits it may reap if it expands and develops a communication infrastructure that includes information of generator availability

    Smart electric vehicle charging strategy in direct current microgrid

    Get PDF
    This thesis proposes novel electric vehicle (EV) charging strategies in DC microgrid (DCMG) for integrating network loads, EV charging/discharging and dispatchable generators (DGs) using droop control within DCMG. A novel two-stage optimization framework is deployed, which optimizes power flow in the network using droop control within DCMG and solves charging tasks with a modified Djistra algorithm. Charging tasks here are modeled as the shortest path problem considering system losses and battery degradation from the distribution system operator (DSO) and electric vehicles aggregator (EVA) respectively. Furthermore, a probabilistic distribution model is proposed to investigate the EV stochastic behaviours for a charging station including time-of-arrival (TOA), time-of-departure(TOD) and energy-to-be-charged (ETC) as well as the coupling characteristic between these parameters. Markov Chain Monte Carlo (MCMC) method is employed to establish a multi-dimension probability distribution for those load profiles and further tests show the scheme is suitable for decentralized computing of its low burn-in request, fast convergent and good parallel acceleration performance. Following this, a three-stage stochastic EV charging strategy is designed to plug the probabilistic distribution model into the optimization framework, which becomes the first stage of the framework. Subsequently, an optimal power flow (OPF) model in the DCMG is deployed where the previous deterministic model is deployed in the second stage which stage one and stage two are combined as a chance-constrained problem in stage three and solved as a random walk problem. Finally, this thesis investigates the value of EV integration in the DCMG. The results obtained show that with smart control of EV charging/discharging, not only EV charging requests can be satisfied, but also network performance like peak valley difference can be improved by ancillary services. Meanwhile, both system loss and battery degradation from DSO and EVA can be minimized.Open Acces

    Planning of PEVs Parking Lots in Conjunction With Renewable Energy Resources and Battery Energy Storage Systems

    Get PDF
    The last few decades have seen growing concerns about climate change caused by global warming, which is cause primarily by CO2 emissions. Thus, the reduction of these emissions has become critically important. One of the effective methods for achieving this goal is to shift towards green electricity energy resources and green vehicles in transportation. For these reasons, the goal of the work presented in this thesis was to address the challenges associated with the planning of plug-in electric vehicles (PEVs) parking lots in combination with renewable energy sources (RES) and battery energy storage systems (BESS) in power distribution networks. This thesis introduces a new planning technique that aims to minimize the overall capital and operational costs, taking into consideration the operational aspects of distribution networks, such as 1) coordinated PEV charging, 2) smart inverter control of renewable distributed generation (DG) units, and 3) smart scheduling of BESS. Moreover, a new model for the PEV coordinated charging demand is introduced in this work. Due to the complexity of the proposed planning approach, a combination between metaheuristic technique and deterministic optimization techniques have been utilized to manage both the planning and operational aspects respectively

    Coordination of EVs Participation for Load Frequency Control in Isolated Microgrids

    Get PDF
    Increasing the penetration levels of renewable energy sources (RESs) in microgrids (MGs) may lead to frequency instability issues due to intermittent nature of RESs and low inertia of MG generating units. On the other hand, presence of electric vehicles (EVs), as new high-electricity- consuming appliances, can be a good opportunity to contribute in mitigating the frequency deviations and help the system stability. This paper proposes an optimal charging/discharging scheduling of EVs with the goal of improving frequency stability of MG during autonomous operating condition. To this end, an efficient approach is applied to reschedule the generating units considering the EVs owners’ behaviors. An EV power controller (EVPC) is also designed to determine charge and discharge process of EVs based on the forecasted day-ahead load and renewable generation profiles. The performance of the proposed strategy is tested in different operating scenarios and compared to those from non-optimized methodologies. Numerical simulations indicate that the MG performance improves considerably in terms of economy and stability using the proposed strategy
    • …
    corecore