High Penetration Of Electric Vehicles Research Articles

Mitigation of transformers’ loss of life in power distribution networks with high penetration of electric vehicles

This paper presents a fuzzy-logic-based framework for aging mitigation of distribution transformers. At the first stage, a model for loading estimation of a transformer installed in the head of a power distribution network was developed in the MATLAB-Simulink environment. The model considers several factors that negatively affect the transformer's operation and lead to overheating of its windings, including high ambient temperatures, harmonic voltage distortions generated by nonlinear loads, reverse power flows, overloads due to high penetration of plug-in electric vehicles. At the second stage, a tuning algorithm was developed and measures aimed at optimizing the transformer's load level and power factor were elaborated. The developed model analyzes the parameters and factors that affect the normal operation of the transformer, and warns of the presence of dangerous factors that pose a threat to reliability and can lead to malfunction. Moreover, the effectiveness of photovoltaic generation units, shunt capacitor banks and battery energy storage systems, installed at the secondary voltage side, for distribution transformers' lifetime preserving were analyzed and discussed. The proposed measures allowed to reduce the transformer's loss of life more than 7 times, compared to the initial scenario.

Three-phase infeasibility analysis for distribution grid studies

With the increase of distributed energy resources in the distribution grid, planning to ensure sufficient infrastructure and resources becomes critical. Planning at the distribution level is limited by the complexities of optimizing unbalanced systems. In this paper we develop a three-phase infeasibility analysis that identifies weak locations in a distribution network. This optimization is formulated by adding slack current sources at nodes in the system and minimizing their norm subject to distribution power flow constraints. Through this analysis we solve instances of power flow that would otherwise be infeasible and diverge. Under conditions when power flow is feasible, our approach is equivalent to standard three-phase power flow; however, for cases where power flow fails, the nonzero slack injection currents compensate for missing power to make the grid feasible. Since an uncountable number of injected currents can provide feasibility, we further explore the optimization formulation that best fits the solution objective through use of both a least squares and an L1 norm objective. Our L1 norm formulation localizes power deficient locations through its inherent sparsity. We show the efficacy of this approach on realistic unbalanced testcases up to 8500 nodes and for a scenario with a high penetration of electric vehicles.

Joint power distribution and charging network design for electrified mobility with user equilibrium decisions

AbstractRapid adoption of electric vehicles (EVs) requires the development of a highly flexible charging network. The design and management of the charging infrastructure for EV‐dominated transportation systems are intertwined with power grid operations both economically and technically. High penetration of EVs in the future can increase the charging loads and cause a wide range of operational issues in power distribution networks (PDNs). This paper aims to design an EV charging network with an embedded PDN layout to account for energy dispatch and underlying traffic flows in urban transportation networks supporting electric mobility in the near future. A mixed‐integer bilevel model is proposed with the EV charging facility location and PDN energy decisions in the upper level and user equilibrium traffic assignment in the lower level considering an uncertain charging demand. The objective is to minimize the cost of PDN operations, charging facility deployments, and transportation. The proposed problem is solved using a column and constraint generation (C&CG ) algorithm, while a macroscopic fundamental diagram concept is implemented to estimate the arc travel times. The methodology is applied to a hypothetical and two real‐world case study networks, and the solutions are compared to a Benders decomposition benchmark. The east‐coast analysis results indicate a 77.3% reduction in the computational time. Additionally, the benchmark technique obtains an optimality gap of 1.15%, while the C&CG algorithm yields a 0.61% gap. The numerical experiments show the robustness of the proposed methodology. Besides, a series of sensitivity analyses has been conducted to study the impact of input parameters on the proposed methodology and draw managerial insights.

Impact of Electric Vehicle Load Demand and Energy Storage Device in Integrated Renewable Energy Sources

ABSTRACT The intermittent nature of renewable-based generation may cause the dip or rise generation and load imbalances. Besides, the higher penetration of Electric Vehicles can affect the voltage profile and imbalances. The main contribution of the proposed work is to determine (i) the realistic load model of electric vehicle (EV) charging station (ii) the size of battery energy storage (BES) considering the EV load demand with the most realistic ZIP load. (iii) the cost-benefit analysis and the probabilistic modeling of EV load demand are considered for BES sizing. (iv) minimization of the daily energy loss (v), maximizing the total benefit of the system, (vi) the nature of the electric vehicle load demand has been modeled for size determination of energy storage devices. The fuel cost of the thermal generator has been minimized, and the operating and maintenance cost of the Solar Photovoltaic (PV), and Wind Turbine (WT) have been considered in the objective function. The daily energy loss has been reduced to 30.085%. The voltage deviation has been reduced to 1.165%. The analysis has been carried out for the IEEE-33 bus test system. The iterative Monte Carlo Simulation and Nonlinear Programming have been used to solve the multi-objective problem.

Stackelberg-Game-Based Demand Response for Voltage Regulation in Distribution Network with High Penetration of Electric Vehicles

With the development of the economy, electricity demand continues to increase, and the time for electricity consumption is concentrated, which leads to increasing pressure on the voltage regulation of the distribution network. For example, a large number of electric vehicles charging during a low-price period may cause the problem of under-voltage of the distribution network. On the other hand, the penetration of distributed power generation of renewable energy may cause over-voltage problems in the distribution network. This study proposes a Stackelberg game model between the distribution system operator and the load aggregator. In the Stackelberg game model, the distribution system operator affects the users’ electricity consumption time by issuing subsidies to decrease the frequency of voltage violations. As the representative of users, the load aggregator helps the users schedule the demand during the subsidized period to maximize profits. Case studies are carried out on the IEEE 33-bus power distribution system. The results show that the time of the subsidy can be optimized based on the Stackelberg game model. Both the distribution system operator and the load aggregator can obtain the optimal economic profits and then comprehensively improve the operating reliability and economy of the power distribution system.

A hybrid strategy for mitigating unbalance and improving voltage considering higher penetration of electric vehicles and distributed generation

An intelligent hybrid strategy for reducing unbalance and improving voltage considering higher penetration of electric vehicles (EVs) and distributed generation (DG) is proposed in the paper. The proposed hybrid strategy is the combination of binary version of equilibrium optimization (BEO) and Chaos Net strategy and hence it is known as BEO-CNS. The main objective of this work is: (1) presenting a multi-objective optimization issues to reduce network unbalance, voltage profile including loss of energy network; (2) presenting an enhanced control method that secure DSOs effectively use the capacity of available network and enhance the comfort level for EV users. The proposed BEO-Chaos Net strategy used in distribution system and it is connected with EV to optimize the phase sequence and optimal dispatch of DGs for enhancing the network's operation and solve the multi-objective optimization issues. The proposed strategy is implemented in MATLAB/Simulink site and the performance is examined and compared with several existing methods. The results demonstrate that the superiority of the proposed technique over the other existing technique in terms of PV irradiation and speed, PV and wind power, cost, EVs charging demand, summer and winter voltage in node 12 and node 8, during summer and winter.

Real Time Congestion Management Using Plug in Electric Vehicles (PEV’s): A Game Theoretic Approach

This paper presents a game-theoretic approach for solving network congestion in the Smart Grid. A smart grid environment with high penetration of Electric Vehicles (EVs) for a bidirectional power trade provides an opportunity to use the EVs for congestion management. Optimal power injected or drawn by the EV charging stations can be obtained to mitigate the congestion problem by solving a constrained optimization problem to minimize the change in the power injected at every bus while satisfying several constraints. EV charging stations will be requested to inject or draw optimal power as obtained from the optimization problem. This paper proposes a game-theoretic approach (Greed Game) to develop a smart pricing model and design a mechanism to remove congestion in the network. A unique Nash equilibrium is achieved in the game, which optimally fulfills the line power congestion management problem and yields maximum benefit for the EVs. The algorithm was applied to 16-machine 68-bus test systems, and it has been found that the proposed mechanism maximizes the gain of the EVs and solves the congestion problem in the Grid.

Data-Driven Study of Low Voltage Distribution Grid Behaviour With Increasing Electric Vehicle Penetration

In this paper, the impact of Electric Vehicle (EV) uncontrolled charging with four levels of EV penetration in overall 21 real low voltage distribution grids in two seasons are analysed. The employed real grid data is provided by distribution system operators from three European countries: Austria, Germany and the Netherlands. At least six grids in each country were considered and they are categorised into three types, namely rural grids, suburban grids and urban grids. The EV charging data used in this study is based on real measurements or surveys. The seasonal and the weekday-weekend factors are also considered in the EV charging impact research. Three key congestion indicators, the transformer loading, line loading and node voltage as well as several other evaluation indexes are studied. The results reveal that the majority of the simulated grids had no or minor moments of mild overloading while the rest grids had critical issues. Among all the grids, suburban grids are most vulnerable to massive EV integration. Out of the evaluated grids, those who are located in Germany have the highest redundancy for high EV penetration accommodation. Overall, the impact of uncontrolled EV charging depends on the combination of EV charging demand as well as the grid inherent features.

Local Electricity Markets for Electric Vehicles: An Application Study Using a Decentralized Iterative Approach

Local electricity markets are emerging solutions to enable local energy trade for the end users and provide grid support services when required. Various models of local electricity markets (LEMs) have been proposed in the literature. The peer-to-peer market model appears as a promising structure among the proposed models. The peer-to-peer market structure enables electricity transactions between the players in a local energy system at a lower cost. It promotes the production from the small low–carbon generation technologies. Energy communities can be the ideal place to implement local electricity markets as they are designed to allow for larger growth of renewable energy and electric vehicles, while benefiting from local transactions. In this context, a LEM model is proposed considering an energy community with high penetration of electric vehicles in which prosumer-to-vehicle (P2V) transactions are possible. Each member of the energy community can buy electricity from the retailer or other members and sell electricity. The problem is modeled as a mixed-integer linear programing (MILP) formulation and solved within a decentralized and iterative process. The decentralized implementation provides acceptable solutions with a reasonable execution time, while the centralized implementation usually gives an optimal solution at the expense of reduced scalability. Preliminary results indicate that there are advantages for EVs as participants of the LEM, and the proposed implementation ensures an optimal solution in an acceptable execution time. Moreover, P2V transactions benefit the local distribution grid and the energy community.

Mitigating unbalance and improving voltage considering higher penetration ofEVsandDGusing hybrid optimization technique

In this manuscript, an efficient hybrid method is proposed aimed at mitigating imbalance, improving the voltage in the higher penetration of electric vehicles (EVs) and distributed generation (DG). The proposed hybrid method is the combination of the mayfly algorithm (MA) and the radial basis function neural network (RBFNN), called the mayfly algorithm-radial basis function neural network (MA-RBFNN) method. These major contributions of the proposed work mitigate network unbalancing and voltage profile with the loss of network energy, utilize obtainable network capacity and enlarge the comfort level for EV consumers. The MA is utilized for solving the multi-objective optimization issue of coordinating EV together with DG in the distribution grid. The RBFNN agrees with the distribution system operators to mutually enhance the phase sequence with the optimum dispatch of DGs for improving the performance of the network. The proposed system allows distribution system operators to mutually enhance the phase sequence with an optimum DG dispatch for recovering the performance of the network and solve the multi-objective optimization issue of coordinating EV and DG on the distribution network. This proposed technique is implemented on the MATLAB/Simulink platform as well as evaluated for efficiency, likened to the existing methods such as genetic algorithm (GA), particle swarm optimization (PSO) and MA technique. The performance of the proposed method was analysed in terms of quality of solution comparison, integral of the time-weighted absolute error (ITAE), integral squared error (ISE) and loss reduction (%). These comparative outcomes display the eminence of the proposed method more than any other existing technique.

Scheduling of a Microgrid with High Penetration of Electric Vehicles Considering Congestion and Operations Costs

This paper reviews the impact that can be presented by the immersion of generation sources and electric vehicles into the distribution network, with a technical, operational and commercial approach, given by the energy transactions between customer and operator. This requires a mathematical arrangement to identify the balance between congestion and the operating cost of a microgrid when the operation scheduling of the system a day ahead of horizon time it is required. Thus, this research is directed to the solution, using heuristic algorithms, since they allow the non-convex constraints of the proposed mathematical problem. The optimization algorithm proposed for the analysis is given by the Multi-Object Particle Swarm Optimization (MOPSO) method, which provides a set of solutions that are known as Optimal Pareto. This algorithm is presented in an IEEE 141-bus system, which consists of a radial distribution network that considers 141 buses used by Matpower; this system was modified and included a series of renewable generation injections, systems that coordinate electric vehicles and battery storage, and the slack node was maintained and assumed to have (traditional generation). In the end it can be shown that the algorithm can provide solutions for network operation planning, test system robustness and verify some contingencies comparatively, always optimizing the balance between congestion and cost.

Optimal Sizing of Electric Vehicle Charging Stations Considering Urban Traffic Flow for Smart Cities

Achieving high penetration of electric vehicles (EVs) is one of the objectives proposed by the scientific community to mitigate the negative environmental impact caused by conventional mobility. The limited autonomy and the excessive time to recharge the battery discourage the final consumer from opting for new environmentally friendly mobility alternatives. Consequently, it is essential to provide the urban road network with charging infrastructure (CI) to ensure that the demand generated by EV users is met. The types of terminals to be considered in charging stations (CS) are fast and ultra-fast. The high energy requirements in these types of terminals could stress the electrical systems, reducing the quality of service. To size and forecast the resources needed in CI, it is of great interest to model and predict the maximum number of EVs, in each hour, that each CS will have to serve according to the geographic area in which they are located. Our proposal is not based on an assumed number of vehicles to be served by each CS, but rather it is based on the analysis of vehicular traffic in geo-referenced areas, so that the load managers can design the topology of the CS. The maximum vehicular concentration is determined by some considerations such as the road system, direction of the route, length of the road segment, the intersections, and the economic zone to which it belongs. The topology of the road map is freely extracted from OpenStreetMap to know the latitude and longitude coordinates. Vehicular traffic will be modeled through the topology obtained from OpenStreetMap and other microscopic variables to understand the traffic engineering constraints. In addition, the Hungarian algorithm is used as a minimum cost decision tool to allocate demand to the CS by observing vehicular traffic as a cost variable. The multi commodity flow problem (MCFP) algorithm aims to make commodities flow through the road network with the minimum cost without exceeding the capacities of the road sections. Therefore, it is proposed to solve the transportation problem by directing the vehicular flow to the CS while minimizing the travel time. This situation will contribute significantly to the design of the topology of each CS, which will be studied in future research.

State of Charge Influence on the Harmonic Distortion From Electric Vehicle Charging

There is a general interest from manufacturers, users, regulation institutions, and research around electric vehicles development and its relation with the grid infrastructure. The penetration of electric vehicles in distribution networks brings considerable challenges in terms of power quality, particularly harmonic emissions. Such challenges are more evident in scenarios with high penetration of electric vehicles which consider the clustering effect in the charging process. Methodologies such as hosting capacity model electric vehicles as a constant power load with constant harmonic distortion along the entire charging process. This article presents the relationship between the charging algorithms of the electric vehicle chargers, the state of charge of the batteries, and the harmonic current distortion generated during the charging process. This model proposes that the harmonic current distortion depends on the state of charge of the battery and the charging algorithm of the vehicle. As this study would change meaningfully, the current studies of power quality related to electric vehicles, practical measurements in two charging stations, and offline simulations of the identified on-board chargers are shown to support the content of this article.

Reducing grid peak load through the coordinated control of battery energy storage systems located at electric vehicle charging parks

Both global climate change and the decreasing cost of lithium-ion batteries are enablers of electric vehicles as an alternative form of transportation in the private sector. However, a high electric vehicle penetration in urban distribution grids leads to challenges, such as line over loading for the grid operator. In such a case installation of grid integrated storage systems represent an alternative to conventional grid reinforcement. This paper proposes a method of coordinated control for multiple battery energy storage systems located at electrical vehicle charging parks in a distribution grid using linear optimization in conjunction with time series modeling. The objective is to reduce the peak power at the point of common coupling in existing distribution grids with a high share of electric vehicles. An open source simulation tool has been developed that aims to couple a stand alone power flow model with a model of a stand alone battery energy storage system. This combination of previously disjointed tools enables more realistic simulation of the effects of storage systems in different operating modes on the distribution grid. Further information is derived from a detailed analysis of the storage system based on six key characteristics. The case study involves three charging parks with various sizes of coupled storage systems in a test grid in order to apply the developed method. By operating these storage systems using the coordinated control strategy, the maximum peak load can be reduced by 44.9%. The rise in peak load reduction increases linearly with small storage capacities, whereas saturation behavior can be observed above 800kWh.

Electric Vehicles in Jordan: Challenges and Limitations

An increasing number of electric vehicles (EVs) are replacing gasoline vehicles in the automobile market due to the economic and environmental benefits. The high penetration of EVs is one of the main challenges in the future smart grid. As a result of EV charging, an excessive overloading is expected in different elements of the power system, especially at the distribution level. In this paper, we evaluate the impact of EVs on the distribution system under three loading conditions (light, intermediate, and full). For each case, we estimate the maximum number of EVs that can be charged simultaneously before reaching different system limitations, including the undervoltage, overcurrent, and transformer capacity limit. Finally, we use the 19-node distribution system to study these limitations under different loading conditions. The 19-node system is one of the typical distribution systems in Jordan. Our work estimates the upper limit of the possible EV penetration before reaching the system stability margins.

Linking energy sector and air quality models through downscaling: Long-run siting of electricity generators to account for spatial variability and technological innovation

Modeling the air pollution implications of long-term energy transitions requires a downscaling process as an intermediate step between national-scale energy models and fine-scaled air quality models. Traditional “Grow-in-Place” (GIP) downscaling methods assume that future patterns of generator siting and emissions will be similar to those in the past. However, rapid technological change and shifting policy might yield very different future spatial patterns of power emissions. Here, we propose a “Site-and-Grow” (SAG) downscaling framework to couple the Electricity Market Module (EMM) of the National Energy Modeling System (NEMS) with the Community Multi-scale Air Quality (CMAQ) model to simulate future changes in emissions from power sector. The SAG framework consists of two steps. First, we downscale regional energy information to subregions using a modified generation expansion model under the assumption that economic fundamentals drive decisions at that scale. Second, we use GIS-based screening to locate potential sites for new power plants, and specify the final county-level placement using a multicriteria value function, assuming that land use and environmental constraints are most influential. The method is implemented in one EMM region (Carolinas and Virginia) as a case study. We compare spatial and temporal variability of downscaled emissions using both GIP and SAG methods, as well as emissions differences among four NEMS scenarios (base case, high natural gas consumption, high penetration of electric vehicles, and marine vessel electrification in ports). The results indicate that coal power plant emissions such as SO2 and NOx continue to dominate emissions from all other traditional power plants even in 2040, which suggests that emission changes will mainly be determined by where old coal plants are retired. An ANOVA (analysis of variance) comparison of four energy scenarios with two downscaling methods shows that the choice of downscaling method can contribute as much to emissions patterns as much as the choice of scenario.

Optimal Charging Strategy of Electric Vehicles with Consideration of Battery Storage

The high penetration of electric vehicles (EVs) will increase burden of a power grid. However, the expansion of capacity of distribution facilities is not always possible, especially in some old residential community. This paper proposes to use an optimal charging strategy of EVs with additional battery energy storage (BES) to improve the charging capabilities in a residential community. By modeling the EV charging behavior, the required charging capacity is evaluated using Monte Carlo method and the BES size is determined as the difference between the required capacity and the distribution capacity. An optimal charging strategy is then proposed to reduce the charging cost and ensure the safe running of distribution network.

Micro-market Operation Strategy Based on Two-way Bidding of Electric Vehicles and Battery Energy Storage

To solve the problem that charging is constrained by the capacity of distribution facilities under high penetration of electric vehicles (EVs), this paper proposes to improve EV charging capacity through battery energy storage (BES) and presents the design of a locally organized market, namely micro-market, that manages the energy transactions between EVs and the BES. When the load of a distribution system approaches its limit, additional EV charging demand is met by the BES, and the price is determined in an automated two-way bidding process. This mechanism can increase EV charging capacity as well as provide an additional revenue stream for the BES in distribution systems. The presented micro-market design ensures the balance between revenue and expenditure of market participants. The organization and settlement process of the micro-market are demonstrated using an example case, and the effectiveness of the design is proved.

Coordination strategies in distribution network considering multiple aggregators and high penetration of electric vehicles

Given the current state of the society in which we live, in terms of energy pollution, several objectives have been set to try to reduce environmental problems. Some of these goals include an exponential increase in production through renewable energy, and Electric Vehicles (EVs) circulating on roads. Due to this high penetration of distributed energy resources in the electricity grid, several problems may exist: grid congestion, causing severe energy systems damage. Innovative coordination strategies must be developed to mitigate these situations. This paper proposes a methodology to minimize this problem in a smart grid with high penetration of Distributed Generation (DG) and EVs, taking into account multiple aggregators. Initially, the proposed model calculates each aggregator’s profit through some business models and analyzes the network without any congestion strategy. This analysis is then done in the presence of Distribution Locational Marginal Pricing (DLMPs), which the aggregator receives from the Distributed System Operator (DSO). The DSO gets these prices after running the Optimal Power Flow (OPF), where these prices involve the market price, the cost of losses, and the cost of congestion at a given point in the network. Here the aggregators react according to these costs, such as trying to buy flexibility from their customers. In this study, the results prove that dynamic prices are more viable for the power grid by reducing congestion by analyzing each aggregator’s profit.

Enhancing Transient Stability of Distribution Networks With Massive Proliferation of Converter-Interfaced Distributed Generators

High penetration of renewable energy sources and energy storage systems has considerably increased the flexibility in power distribution networks operation. However, employing converter-interfaced energy and storage sources may significantly reduce the mechanical inertia and as a result, the power grids may confront serious stability challenges during transient conditions. This article introduces a strategy for enhancing transient stability margin of active distribution networks with high penetration of electric vehicles (EVs). The proposed optimization strategy intends to control EVs contributions during transient stability conditions. The EVs contributions are controlled through a new index proposed based on the system's total corrected critical kinetic energy (TCCKE). The proposed procedure for TCCKE calculation is driven by a hybrid algorithm taking into account the equal area criterion and sensitivity analysis. The suggested procedure for TCCKE only depends on the during fault data and as a result, the proposed optimization strategy is useful to prevent transient instability in the case of first swing instability. The proposed optimization is applied and evaluated on the IEEE test systems. The results clearly demonstrate the applicability and efficacy during a multitude of fault and emergency conditions.