Presence Of Plug-in Electric Vehicles Research Articles

Optimal Distributed Generation Allocation in Practical Distribution System in the Presence of Plug-in Electric Vehicles

Optimal Distributed Generation Allocation in Practical Distribution System in the Presence of Plug-in Electric Vehicles

Optimal economic-emission planning of multi-energy systems integrated electric vehicles with modified group search optimization

The present paper aims to present a comprehensive multi-objective optimization model for energy management in local multi-energy systems in the presence of plug-in electric vehicles (PEVs), seeking to achieve the maximized profit of the operators of the local multi-energy systems and minimized CO2 emission at the same time. This problem involves technology transfer in local multi-energy systems and finding the PEVs charge/discharge optimization strategies in order to maximize the operators' profit and, in the meantime, reduce the CO2 emission. It can be solved by formulating a multi-purpose objective and can be dealt with by formulating a multi-objective programming problem through accurate modeling of mutual dependencies between the energy carriers. To solve this problem, a Modified Group Search Optimization (MGSO) algorithm is used based on the decomposition system. By the proposed structure, the local and global search is improved significantly. As indicated by the results of the effectiveness test of the optimization framework for maximizing the operator's profit and, meanwhile, reducing the CO2 emission, this objective is achievable through optimal coordination of multiple energy carriers in local multi-energy systems and effective management of the flexibility collected at both supply and demand sides.The simulation has been investigated in different scenarios. Obtained numerical analysis shows that the base case signifies the best case in terms of maximization of the local multi-energy systems operator’s profit through the optimized charging and discharging approaches of the PEVs. Also, in all scenarios, the profit of operator reductions by 5%–15% as compared with the base case. On the other hand, the best case scenario from an environmental point of view is provided by a scenario categorized with electric vehicles excluding under environmental optimization. In fact, CO2 emissions are reduced 7% compared to the emissions of base case.

Managing plug-in electric vehicles in eco-environmental operation optimization of local multi-energy systems

Local multi-energy systems (LMES) have been recently recognized as a promising alternative to centralized energy supply systems to meet local energy needs, since they promote efficient use of the available energy thanks to the coordination of heat and power technologies, storage, flexible demand and plug-in electric vehicles (PEVs). In this framework, PEVs represent loads to satisfy in the grid-to-vehicle (G2V) mode, while also serving as distributed storage when equipped with vehicle-to-grid (V2G) technology, and can provide both economic and environmental benefits if properly managed. The contribution of this paper is to present a comprehensive multi-objective optimization model for the energy management of an LMES in the presence of PEVs, with the aim to combine maximization of LMES operator’s profit with the minimization of CO2 emissions. The LMES supplies electricity, heat and cooling to a building cluster with PEVs, which can operate in both G2V and V2G modes. The problem consists of dispatching technologies in the LMES and finding the optimized charging/discharging strategies of PEVs in order to maximize the operator’s profit while also reducing CO2 emissions, and it is addressed by formulating a multi-objective linear programming problem with the detailed modeling of interdependencies among energy carriers. The weighted sum method is used to represent the eco-environmental optimization problem, and it is solved by using CPLEX solver and considering a cluster of office buildings located in Italy as end-user of the LMES with PEVs owned by the offices’ employees. Testing results demonstrate the effectiveness of the optimization framework to maximize the operator’s profit while also reducing the CO2 emissions, thanks to the optimal coordination of the multiple energy carriers in the LMES and the effective management of the flexibility collected at both supply and demand sides. Moreover, it is found that through the optimized charging and discharging strategies, the PEVs, acting as distributed energy storage, allow the provision of demand response services by also complementing renewable power to improve energy efficiency. In detail, under the economic optimization, most of flexibility collected from PEVs is sold into the wholesale market in order to maximize the operator’s profit, whereas, under the environmental optimization, the power discharged from PEVs is exploited for self-use in the LMES to minimize environmental impacts by using a carbon-free source.

Improved-GWO designed FO based type-II fuzzy controller for frequency awareness of an AC microgrid under plug in electric vehicle

The paper proposes to use an improved Grey Wolf Optimization (GWO) optimized Fractional Order (FO) based type-II fuzzy controller for frequency regulation of an AC microgrid in presence of plug-in electric vehicles. The AC microgrid comprises various renewable-based energy sources such as wind turbine generator (WTG), photovoltaic cell (PV), microturbine (MT), aqua electrolyzer based fuel cell (FC) and diesel engine generator (DEG) along with various storage devices (ESD) like battery energy storage (BES), flywheel energy storage (FES) and electric vehicles (EV). The uncertain nature of solar, wind technologies and load demand makes the system more complicated and introduces the frequency oscillations in the system. It is highly needed to establish power balance among generation by designing an appropriate controller for frequency regulation. This paper proposes a robust fractional order type-II fuzzy PID (FO-T2-FPID) where the parameters of the FO-T2-FPID controllers are optimized by suggesting an improved GWO (I-GWO) algorithm. To demonstrate the capability of proposed I-GWO algorithm few comparative analyses have been performed over particle swarm optimization (PSO) and grey wolf optimization (GWO) algorithm. It is also observed that for the same FO-T2-FPID controller structure, the percentage improvement in the objective function (ITAE) under load uncertainty by I-GWO compared to GWO and PSO are 29.74% and 65.94% respectively. Finally, it is conferred that proposed I-GWO optimized FO-T2-FPID controller significantly improves microgrid dynamic performance under various operational conditions.

A Stochastic Voltage Stability Constrained EMS for Isolated Microgrids in the Presence of PEVs Using a Coordinated UC-OPF Framework

Microgrids are increasingly affected by volatile wind energy due to the high penetration of this kind of renewable energy sources. This study investigates the impact of wind energy uncertainty on voltage stability and energy management system (EMS) of isolated microgrids in the presence of plug-in electric vehicles (PEVs). A stochastic voltage stability constrained microgrid energy management system (SVSC-MEMS) architecture based on a coordinated unit commitment-optimal power flow (UC-OPF) methodology, which simultaneously considers the UC and OPF constraints, is proposed. The proposed model guarantees the system security from the voltage stability standpoint, considering a specific loading margin. Besides, the role of PEVs in the microgrid EMS is examined. In order to validate the performance of the introduced SVSC-MEMS, a CIGRE benchmark test system is used. The numerical results, obtained from the implementation of the proposed model in the general algebraic modeling system (GAMS) environment, demonstrate the importance of voltage stability and wind power uncertainty on the microgrid EMS, and furthermore, the key role of PEVs in the emerging microgrids.

Optimal Allocation of Inverter-Based WTGS Complying With Their DSTATCOM Functionality and PEV Requirements

Recently, the integration of inverter-based wind turbine generation systems (WTGS) and plug-in electric vehicles (PEV) has remarkably been expanded into distribution systems throughout the world. These distributed resources could have various technical benefits to the grid. However, they are also associated with potential operation problems due to their stochastic nature, such as high power losses and voltage deviations. An optimization-based approach is introduced in this paper to properly allocate multiple WTGS in distribution systems in the presence of PEVs. The proposed approach considers 1) uncertainty models of WTGS, PEV, and loads, 2) DSTATCOM functionality of WTGS, and 3) various system constraints. Besides, the realistic operational requirements of PEVs are addressed, including initial and preset conditions of their state of charge (SOC), arriving and departing times, and various controlled/uncontrolled charging schemes. The WTGS planning paradigm is established as a bi-level optimization problem which guarantees the optimal integration of multiple WTGS, besides optimized PEV charging in a simultaneous manner. For this purpose, a bi-level metaheuristic algorithm is developed for solving the planning model. Intensive simulations and comparisons with various approaches on the 69-bus distribution system interconnected with four PEV charging stations are deeply presented considering annual datasets. The results reveal the effectiveness of the proposed approach.

Two‐stage scenario‐based DEP incorporating PEV using Benders’ decomposition

This study proposes a novel scheme for dynamic distribution expansion planning (DEP) in the presence of plug-in electric vehicles (PEVs). The model considers investment, production and maintenance cost and identifies the substations and feeders to be built, reinforced or replaced. Owing to the increasing penetration of PEVs into the distribution network, traditional strategies to expand the network have to be updated to cope with the new uncertainties incurred by the PEVs integration. In this regard, a two-stage scenario-based strategy is presented, in which the uncertainties related to the PEVs are modelled via stochastic optimisation using Monte Carlo simulation. In the first stage, the binary decision variables (here and now decision variables) are determined, whereas in the second stage, the optimal production of substations and optimal charging of PEVs (wait and see decision variables) are identified in a day-ahead electricity market. Moreover, the daily electricity price and load volatilities have also been taken into account. The DEP problem is formulated as a mixed-integer linear programming problem and is solved using the efficient Benders’ decomposition algorithm. The results of the case study based on a 24-node distribution system show the feasibility, tractability and effectiveness of the proposed model.

Multi-objective optimal short-term planning of renewable distributed generations and capacitor banks in power system considering different uncertainties including plug-in electric vehicles

The increasing penetration of solar distributed generations (SDGs) and wind distributed generations (WDGs) together with plug-in electric vehicles (PEVs) will lead to a promising amount of reduction in greenhouse gas emissions. Nevertheless, they bring about adversities such as uncertainty in production-load sides, augmented power loss, and voltage instability in the power system, which should be carefully addressed to increase the reliability. In this concern, this paper proposes a multi-objective optimization methodology for sizing and siting of SDGs, WDGs, and capacitor banks (CBs) in the power system considering uncertainties stemmed from PEVs load demand, solar irradiance, wind speed, and the conventional load. The understudy objectives are the voltage stability index, green-house gas emissions, and the total cost. An unconventional point estimate method (PEM) is used to handle the related uncertainties, and chance-constrained programming method is deployed to deal with smooth constraints. The corresponding probability distribution functions of output variables are estimated by the maximum entropy method. Furthermore, robustness analysis is made by Monte Carlo simulation (MCS). The proposed methodology is applied to a typical radial distribution network. The results show that the presence of PEV’s significantly increases the load demand, which results in voltage collapse in the distribution system without the presence of distributed generations. However, the proposed probabilistic method ensures the safe operation of the distribution system with the optimal allocation of renewable distributed generations and CBs. Moreover, the results of deterministic and probabilistic cases are compared under different penetration levels of PEVs. The best tradeoff solution of the Pareto front is selected by the fuzzy satisfying method.

Optimal integration of DGs into radial distribution network in the presence of plug-in electric vehicles to minimize daily active power losses and to improve the voltage profile of the system using bio-inspired optimization algorithms

PurposeThe increase in plug-in electric vehicles (PEVs) is likely to see a noteworthy impact on the distribution system due to high electric power consumption during charging and uncertainty in charging behavior. To address this problem, the present work mainly focuses on optimal integration of distributed generators (DG) into radial distribution systems in the presence of PEV loads with their charging behavior under daily load pattern including load models by considering the daily (24 h) power loss and voltage improvement of the system as objectives for better system performance.Design/methodology/approachTo achieve the desired outcomes, an efficient weighted factor multi-objective function is modeled. Particle Swarm Optimization (PSO) and Butterfly Optimization (BO) algorithms are selected and implemented to minimize the objectives of the system. A repetitive backward-forward sweep-based load flow has been introduced to calculate the daily power loss and bus voltages of the radial distribution system. The simulations are carried out using MATLAB software.FindingsThe simulation outcomes reveal that the proposed approach definitely improved the system performance in all aspects. Among PSO and BO, BO is comparatively successful in achieving the desired objectives.Originality/valueThe main contribution of this paper is the formulation of the multi-objective function that can address daily active power loss and voltage deviation under 24-h load pattern including grouping of residential, industrial and commercial loads. Introduction of repetitive backward-forward sweep-based load flow and the modeling of PEV load with two different charging scenarios.

Exact approach for charging of PEVs with V2G capability to improve micro‐grid reliability

Nowadays, the major proportion of fossil fuels is consumed in the transportation systems. Plug-in electric vehicles (PEVs) can play a significant role in reducing the petrol consumption. A large amount of additional electrical power demand needed for the charging of PEVs may adversely affect the electric grid reliability. Using the charging station in the micro-grids (MGs) to minimise the total system operation cost in the presence of PEVs with vehicle to grid (V2G) capability is becoming vitally important. In this work, a practical model for charging strategy of PEVs based on typical charge and discharge profiles given by the battery producers is considered. The problem is constrained by the loss of load probability index of reliability. The proposed strategy makes it possible to handle the generation shortage due to the outage of conventional units and intermittency of wind turbine units. Mixed Integer Non-linear programming is used for the optimal planning of MG in the presence of PEVs. Results show that an appropriate charge and discharge strategy may lead to a significant improvement in the reliability of the considered MG as well as a reduction in the operational cost of the total system.

A New Time-Decoupled Framework for PEVs Charging and Scheduling in Industrial Microgrids

Solving the optimal power flow (OPF) in industrial microgrids (IMGs) in presence of plug-in electric vehicles (PEVs) changes the structure of problem to a complex dynamic OPF (DOPF). The complication is due to energy and time related constraints of PEVs that requires the complex non-linear time-coupled DOPF problem to be solved over a long period of few hours (e.g., 24-h). This paper presents a novel time-decoupled framework for solving the generation scheduling problem in IMGs by relaxing the time correlated constraints of the DOPF. The relaxation is mathematically achieved by replacing the time-coupled constraints with their corresponding long term time averages. This will resolve the time coupling property of DOPF and results in a simpler solution approach. The relaxed DOPF problem can be solved in independent time periods while considering other constraints such as PEVs and security constraints of OPF as well as factories constraints. To examine the effectiveness of the proposed method, different simulation scenarios have been applied to an 18-bus IMG consisting of PEVs and 12 factories with electrical and thermal loads as well as combined heat and power systems.

Integration of Plug-in Electric Vehicles Into Microgrids as Energy and Reactive Power Providers in Market Environment

The concept of electricity markets in the deregulated environment generally refers to energy market and reactive power market is not paid attention as much as it deserves to. However, reactive power plays an important role in distribution networks to improve network conditions such as voltage profile improvement and loss reduction. Plug-in electric vehicles (PEVs) are mobile sources of active and reactive power, capable of being participated in energy market, and also in reactive power market without battery degradation. Active and reactive powers are coupled through the ac power flow equations and branch loading limits, as well as PEVs and synchronous generators capability curves. This paper presents a coupled energy and reactive power market in the presence of PEVs. The objective function is threefold, namely offers cost (for energy market), total payment function (for reactive power market), and lost opportunity cost, all to be minimized. The effectiveness of the proposed coupled energy and reactive power market is studied based on a 134-node microgrid with and without PEV participation.

An Interior Point Optimization Method for Stochastic Security-constrained Unit Commitment in the Presence of Plug-in Electric Vehicles

An Interior Point Optimization Method for Stochastic Security-constrained Unit Commitment in the Presence of Plug-in Electric Vehicles

Voltage Security Considerations in Optimal Operation of BEVs/PHEVs Integrated Microgrids

Nowadays, it is a common concern for modern electrical networks and energy management systems to derive an optimal operation management considering energy costs, pollutant emissions, and security, of the system in the presence of plug-in electric vehicles (PEVs). In this paper, a multiobjective optimization problem is solved to schedule power sources in a typical microgrid, while PEVs are viewed as a stochastic factor. PEVs residing in a city are considered as probable loads and/or generations depending on how they interact with the utility grid. A novel stochastic methodology is used to calculate the 24 h expected power demand and generation of vehicles aggregated in municipal parking lots. The objective functions of the problem are voltage security margin to be maximized and the total power losses, the total electrical energy costs, and the total emissions of power sources, which should be minimized. Based on different scenarios, the impact of PEVs penetration on base load, voltage profile, and also the value of objective functions, are assessed. Numerical results show the effect of different fleets of PEVs, as well as on the operation of future microgrids.

Predicting Voltage Unbalance Impacts of Plug-in Electric Vehicles Penetration in Residential Low-voltage Distribution Networks

Plug-in electric vehicles will soon be connected to residential distribution networks in high quantities and will add to already overburdened residential feeders. However, as battery technology improves, plug-in electric vehicles will also be able to support networks as small distributed generation units by transferring the energy stored in their battery into the grid. Even though the increase in the plug-in electric vehicle connection is gradual, their connection points and charging/discharging levels are random. Therefore, such single-phase bidirectional power flows can have an adverse effect on the voltage unbalance of a three-phase distribution network. In this article, a voltage unbalance sensitivity analysis based on charging/discharging levels and the connection point of plug-in electric vehicles in a residential low-voltage distribution network is presented. Due to the many uncertainties in plug-in electric vehicle ratings and connection points and the network load, a Monte Carlo-based stochastic analysis is developed to predict voltage unbalance in the network in the presence of plug-in electric vehicles. A failure index is introduced to demonstrate the probability of non-standard voltage unbalance in the network due to plug-in electric vehicles.