Operational Optimization of Microgrids Integrating Electric Vehicles and Vehicle‐to‐Grid Impact

This work develops a dual-layer energy management (DLEM) model for a microgrid (MG) consisting of a community, distributed energy resources (DERs), and a grid. It ensures the participation of all these energy entities of MG in the market and their interaction with each other. The first layer performs the scheduling operation of the community with the goal of minimizing its net-billing cost and sends the obtained schedule to the DER operator and grid. Further, the second layer formulates a power scheduling algorithm (PSA) to minimize the net-operating cost of DERs and takes into account the load demand requested by the community operator (COR). This PSA aims to achieve optimal operation of MG by considering solar PV power, requested demand, per unit grid energy prices, and state of charge of the battery energy storage system of the DER layer. Moreover, to study the impact of electric vehicles (EVs) load programs on DLEM, the advanced probabilistic EV load profile model is developed considering practical and uncertain events. The EV load is modelled for grid to vehicle mode, and a new mode of EV operation is introduced, i.e., vehicle to grid with EV demand response strategy (V2G_DRS) mode. The solar PV and load demand data are obtained from the MG setup installed and buildings present at the university campus. However, a scenario reduction technique is used to deal with the uncertainties of the obtained data. In order to evaluate the efficacy of the developed DLEM, its results are compared to previously reported energy management models. The results reveal that DLEM is superior to the existing models as it decreases the net-billing cost of COR by 13% and increases the profit of the DER operator by 17%. Further, it is found that for the highest EV penetration, i.e., 30 EVs, the V2G_DRS mode of EV operation reduces the total energy imported by COR by 11.39% and the net-billing cost of COR by 7.88%. Therefore, it can be concluded that the proposed model with the introduced V2G_DRS mode of EV makes the operation of all the entities of MG more economical and sustainable.

This work aims to investigate the impact of electric vehicles (EVs) charging/discharging strategies on the optimal operation of islanded microgrid (MG). For this purpose, firstly, load model of EVs under four different charging/discharging strategies, namely, uncoordinated charging mode, maximum renewable mode, valley filling mode and charging discharging mode is established. Thereafter, impact of these strategies on the optimal operation of MG is investigated. A 2m point estimate method is used to study the impact of forecast errors associated with load demand and renewable energy sources on the MG total cost under different EV charging/discharging strategies. Finally, a sensitivity analysis of variations of battery parameters on the MG total cost is also presented.

This study proposes an integrated framework for coordinated optimisation of the interdependent microgrid (MG) and electric vehicle (EV) fleet entities using the normalised normal constraint approach. By considering the active/reactive power management option of the bidirectional charger enabled EVs in the proposed model, the authors investigate the effectiveness of EV's integration in the presence of the techno‐economical objective functions. This work concentrates on the trade‐off analysis of two conflicting objectives, including the economic objective of the MG's operation cost minimisation and the technical objective of the MG's voltage deviation. Besides, they consider several uncertainty sources, e.g. wind, EV and solar panel (PV) power provision, as well as market price fluctuations in the proposed model affecting the aforementioned techno‐economic trade‐off solution. The proposed model is a stochastic multi‐objective mixed‐integer non‐linear programming problem where the authors apply the designed integrated framework on a modified IEEE 18‐bus test case in GAMS software. Through numerical results, they demonstrate MG optimal operation changes due to different MGO priorities and study the positive effects of EVs integrated energy management on the bi‐objective operation.

The rapid depletion of fossil fuel resources and increase in demand of electricity has renewed interest in micro‐grids (MGs). Incorporating renewable energy sources (RESs) and storage devices into MG could play a vital role in enhancing the reliability of the system. However, due to the intermittent nature of RESs, voltage fluctuations may occur in the system. Therefore, storage devices can be the most viable option to handle the intermittent issues of RESs. Nowadays, handling a group of batteries with different ratings in the MG has been a major focus of research. The batteries with different ratings used for storage as well as grid support is termed as a distributed energy storage system. This work mainly emphasises on optimal power management of an MG by controlling the charging and discharging rates of individual battery. A droop‐based controller is proposed for optimal power management of batteries. An aggregator has been suggested at the MG which distributes the power among the various charging stations (CSs) and each CS finally distributes the power among the individual batteries based on droop participation factor. Moreover, the charging and discharging rates of batteries are controlled to achieve the desired power flow between the MG and CSs. The simulation results show the efficacy of the controller to meet the desired power at the MG. The proposed system is compared with an existing system to prove the efficacy of the proposed controller. Moreover, all the critical cases have been considered, such as the sudden failure of any generating unit. It has been observed that due to sudden failure of any generating unit, CSs manages power of the MG by altering charging and discharging rate of the batteries.

The concern about the complete depletion of fossil fuels along with the negative environmental impact of most of the current energy sources are significant reasons to think of a different way to produce electrical energy and, at the same time, satisfy the necessities for urban mobility. In this context, microgrids (MGs) are power networks which, among other properties, allow more flexible demand consumption, and also help with, the efficient integration of renewable resources and electric vehicles (EVs). However, the effect of the incorporation of EVs has to be taken into consideration with care, since that situation may drive to unfeasible operations in grids which have not been designed to support these particular elements. In this paper, a system composed of different agents is used to develop a market-oriented operation in a MG with several EV charge stations. It is expected that the system takes to an optimal management allowing a technically feasible operation considering the behaviour of every agent involved. The demand shifting and the flexible operation of EVs, spatial and temporal, are features that reduce costs and thus improve the benefit of the participants.

The electrification of transportation has become a sustainable solution to the global economic and environmental challenges associated with the fossil fuel-dependent transportation. Substantial electric vehicle (EV) integration has resulted in the recent past owing to the distinct advantages and various incentives provided by governments. It can be expected that EV penetration will further accelerate along with technological advancements. This strategic shift of primary source of transportation energy from oil pipelines to power grids will inevitably bring numerous challenges to power grids around the world. The potential impacts of EV integration on power grids are yet to be discovered. This research concentrated on modelling the EV charging load, evaluating its impact on power system stability and identifying remedies. Further, two computationally efficient indexes were developed to identify voltage stability and oscillatory stability prudent charging solutions. A comprehensive EV charging infrastructure planning strategy was also developed. This research investigated the probable grid impacts associated with EV charging by comprehensively reviewing the available literature. Even though there were number of system studies on wide variety of grid impacts, scant attention has been paid to impact of EV charging on system stability. Electrification of transportation brings significant load integration to the grid. Hence, it is important to understand the EV charging impact on power system stability. The extant literature is largely based on conventional load characteristics rather than actual EV load behaviours, due to the unavailability of proper load models. Hence, this study develops static and dynamic EV load models following analytical and numerical methodologies, as an essential basis for accurate system stability studies. It is identified that the static load model of a power electronically controlled EV load can be best described by a combination of constant power and negative exponential load models. The factors affecting the load model parameters are also evaluated. Subsequently, a dynamic load model is derived, considering the dynamics of the EV charger and the battery. The EV charging load dynamics could be described by an eleventh order dynamic model. The developed static and dynamic load models are then utilised to evaluate the impacts of EV charging on power system voltage stability and oscillatory stability. Power system static voltage stability and oscillatory stability studies with EV charging loads are carried out on different test systems, under several practical scenarios. The results show that the EV is an onerous load, and the existing studies which have modelled EVs with conventional load characteristics have resulted in conservative outcomes. Further investigations are performed iv | P a g e to identify the factors affecting the voltage stability and oscillatory stability of the power grid in the presence of the EV charging load. Remedies which can be implemented to mitigate impact of EV charging load on static voltage stability are investigated. Effectiveness of load bus voltage control to mitigate the voltage stability impact of EV charging load, is tested and verified. In addition, importance of giving priority to implement public charging stations to ease the grid impacts caused by distributed home based chargers in the distribution system, is highlighted in this research. It is found that proper planning can be done to minimise the grid impacts, proactively. Two computationally efficient indexes are derived to identify the relative static voltage stability and oscillatory stability status of the power system in different planning cases. The developed indexes having good physical interpretations are proven to be simple and easy to incorporate in distribution system planning exercises to obtain a stability preserved planning solution. A metaheuristic technique has been incorporated to facilitate EV charging infrastructure planning. Several planning cases are discussed to represent different planning requirements. A comprehensive EV charging infrastructure planning framework is developed by incorporating consumer, investor and power grid requirements within the planning objectives and constraints. This could identify solutions which cause less impact on the grid in terms of losses, voltage regulation, asset overloading, grid voltage stability and oscillatory stability, while optimally satisfying the EV customer and investor requirements. The optimal capacity and placement of a fixed capacitor to minimise grid impacts through effective reactive power compensation have also been identified. Further, the possibility of enhancing EV charging facilities while minimising the grid impacts is proven with optimum utilisation of renewable energy sources. Overall, the contribution made by this thesis promotes greener transportation with fewer associated grid impacts.

The transition from internal combustion engines to electric vehicles (EVs) has received significant attention and investment due to its potential in reducing greenhouse gas emissions. The integration of EVs into electric and transport systems presents both benefits and challenges in energy management. The scheduling of EV charging can alleviate congestion in the electric system and reduce waiting times for EV owners. The use of renewable energy sources (RESs) for EV charging and supporting the grid can help mitigate the uncertainty of these energy resources. Vehicle-to-grid (V2G) technology can be used as an alternative approach in the event of sudden high consumption of the grid. Additionally, cost minimization through large-scale coordinated planning is crucial for the future of e-mobility systems. This review paper focuses on the latest trends considering the various approaches and features in coordinated EV scheduling, as well as the influence of different stakeholders, categorized as single- and multiple-charging stations (CS) and aggregator levels. By implementing coordinated EV scheduling, various methods are presented to better manage the needs and satisfaction of EV owners as well as the profit of CS and the market trends of e-mobility systems. In this regard, EV charging strategies considering V2G, uncertainty evaluation of parameters, coordinated charging management, congestion of CSs and electrical lines, route mapping, and technical and economic aspects of the system hierarchy, including consumers, CSs and aggregators, are reviewed and discussed.

For optimal microgrid (MG) operation, one significant challenge is the inherent randomness of renewable energy sources (RESs) within MG should be accommodated by it itself. Here, a robust model predictive control (MPC) by considering scenario optimisation is proposed for MG dispatching to achieve the accommodation of uncertain RES and electricity demand by utilising discharging/charging of electric vehicle (EV) with a certain constraints violation probability of EV allowed. The method is based on the iterated solution, at each step, of a finite‐horizon optimal control that takes into account a suitable number of randomly extracted scenarios of uncertain RES and electricity demand while the economic objective and operational constraints are included. The optimisation of a number of uncertain scenarios embedded in a receding horizon of MPC can provide a probabilistic guarantee of EV constraints satisfaction. Simulation on an MG case is implemented and the results demonstrate that with the proposed control strategy, the coordination between uncertain RES and EV can be achieved to make the MG be controllable as seen from the main grid.

Flexible Integration of EVs and PVs into the Electricity Grid

Grid integration of solar photovoltaic (PV) systems and electric vehicles (EVs) has been increasing in recent years, mainly with two motivations: reducing energy cost, and reducing emission. Several research studies focuses on the individual impact of grid integration of PVs and EVs. However, it is worth noting that with the increasing penetration of PVs and EVs, the power grid will be experiencing the combined impact of PV–EV integration. To present a thorough understanding, this study first presents a detailed study on the impact of grid integration of PVs and EVs individually, followed by combined impact of PV and EV, on the aspects of grid stability, power quality and energy economics. It has been identified from the literature review that individually PVs and EVs can negatively affect the grid stability and power quality due to the intermittent nature of PV energy and uncertainty of EV load. However, several research works have reported that coordinated operation of the PVs and EVs can negate the issues arising due to individual integration of PVs and EVs. Furthermore, large-scale penetration of PVs and EVs are expected in future energy market, and coordinated operation of them can potentially help lowering energy costs and carbon footprint.

It is expected that electric vehicles (EVs) will be important part of smart gird, not only in form of load but also as distributed energy source in Vehicle to Grid (V2G) system. As increase of EVs integration, V2G contributes to improve flexibility, reliability and stability of grid by providing ancillary services. These services, however, could accelerate degradation of battery whose price is almost half of EV. Thus, battery degradation cost must be considered while scheduling of EV charging. In this paper, a battery degradation cost model of EV lithium-ion batteries was incorporated in the optimal charging schedule of 400 EVs. EVs are located to 33 bus system in order to consider network losses in calculations. Heuristic algorithms such as Genetic Algorithm (GA), Differential Evolution (DE), Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC) are used for solving the associated optimization problem. The objective function aims to maximize user profit under dynamic pricing. Also, distribution system and EVs constraints are considered during optimization. The numerical results illustrate that each of the used heuristic algorithms able to mitigate peak loads and improve voltage levels. GA presents the most profitable charging scheduling in terms of EV owners.DOI: http://dx.doi.org/10.5755/j01.eie.24.6.22283

Electric vehicles (EVs) play a vital role in the reduction of emission of the greenhouse gases by reducing the consumption of fossil fuel. This paper presents a fully developed integration of the EVs with a security-constrained unified active and reactive power dynamic economic dispatch of microgrids (MGs) to minimize the total operating cost or maximizes the profit. The formulation of the overall optimization problem considers the reactive power production cost and relevant constraints, the environmental costs, and the battery degradation cost. A comprehensive set of constraints including active and reactive security constraints, limitation of the greenhouse gases constraints, and constraints relevant to the integration of the EVs with the MG are considered as well. The bi-directional penetration of the EVs with the MG is modelled and incorporated with unit commitment (UC) optimization problem. The results show that the proposed approach of the integration of the EVs with the MG reduces the total operating cost and increases the profit.

This paper proposes the generation scheduling approach for a microgrid comprised of conventional generators, wind energy generators, solar photovoltaic (PV) systems, battery storage, and electric vehicles. The electrical vehicles (EVs) play two different roles: as load demands during charging, and as storage units to supply energy to remaining load demands in the MG when they are plugged into the microgrid (MG). Wind and solar PV powers are intermittent in nature; hence by including the battery storage and EVs, the MG becomes more stable. Here, the total cost objective is minimized considering the cost of conventional generators, wind generators, solar PV systems and EVs. The proposed optimal scheduling problem is solved using the hybrid differential evolution and harmony search (hybrid DE-HS) algorithm including the wind energy generators and solar PV system along with the battery storage and EVs. Moreover, it requires the least investment.

Coming years, the number of electric vehicles (EVs) shall increase significantly, so the demand for electricity for charging EVs will proportionately increase as well. Thus, the growing energy requirements for charging these EVs might put huge burden on the electricity generation and supply infrastructure. Such a huge load growth opportunity for utilities if integrated successfully, or if not, a significant challenge to operate and balance grid loads in the future. Customarily, the increase in adoption of EVs in recent years has yielded challenges to the utilities as the electricity demand of EVs occurs mostly during peak hours. In some cases, a sojourn time may be longer than a charging time, that means, EVs will be connected to the charging station without charging. However, the load shifting potential of EVs may be consequential and might subsequently be used to alleviate challenges to the electric grid system. Considering charging behaviors for EV scheduling is crucial, as they depend on uncertainties of EV availabilities (i.e., sojourn time and energy required). Such uncertainties would impact substantially on the deployment of feasible EV charging scheduling. To address above-mentioned issues, firstly, we define an idle time ratio, which is basically load shifting potential. Consequently, we develop a heuristic EV charging scheduling scheme with an emphasis on inevitable charging behaviors of the EV users. Such a scheduling incorporates priority determination using the idle time ratio and TOU period as well as priority-based time slot allocation. Moreover, accurate prioritization of EVs is realized by predicting the energy demand and idle time ratio. Minimization of charging cost is perhaps the most perceptive objective, such that, the EV charging scheduling is done when TOU tariff is low. Performance evaluation shows that the proposed flexible smart charging scheduling outperforms the baseline scheduling in terms of the charging power and charging cost.

The integration of electric vehicles (EV) into the global car fleet has seen a major shift boosted by the new environmental regulations. The adoption of this low-carbon vehicle is hampered by several constraints in emerging countries and particularly in Morocco, e.g. constraints related to the infrastructure (land purchasing for charging stations, integration with existing power infrastructures, funding and optimized deployment) [1], and constraints related to the lack of standards and regulatory frameworks [2]. For this purpose, our objective is to share with the community a dataset about EV exploitation in the Moroccan context. This dataset [3] could be used to improve the energy management system characterized by a limited driving range and restrictive charging infrastructures. Subsequently, several driving cycles have been done in three main trajectories using data collection in the region of Rabat-Salé-Kénitra (RSK). The collected data contains mainly the date, time, Battery State of Charge (SoC), speed, vehicle's position, weather information, traffic conditions and road speed limits. The dataset collection is done using an onboard developed electronic card that collects the vehicle's internal and external data. Collected data are pre-processes and then stored in a Comma Separated Values (CSV) file. The collected dataset could be used in applications that are related to EV management and planning, such as speed prediction, speed control strategies, rerouting and EV charging scheduling, vehicle-to-grid and grid-to-vehicle, and energy demand forecasting.