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The large‐scale penetration of electric vehicles (EVs) into the power system will provoke new challenges needed to be handled by distribution system operators (DSOs). Demand response (DR) strategies play a key role in facilitating the integration of each new asset into the power system. With the aid of the smart grid paradigm, a day‐ahead charging operation of large‐scale penetration of EVs in different regions that include different aggregators and various EV parking lots (EVPLs) is propounded in this study. Moreover, the uncertainty of the related EV owners, such as the initial state‐of‐energy and the arrival time to the related EVPL, is taken into account. The stochasticity of PV generation is also investigated by using a scenario‐based approach related to daily solar irradiation data. Last but not least, the operational flexibility is also taken into consideration by implementing peak load limitation (PLL) based DR strategies from the DSO point of view. To reveal the effectiveness of the devised scheduling model, it is performed under various case studies that have different levels of PLL, and for the cases with and without PV generation.

A conventional electric vehicle charger (EVC) charges only one EV concurrently. This leads to underutilization whenever the charging power is less than the EVC-rated capacity. Consequently, the cost-effectiveness of conventional EVCs is limited. Reconfigurable EVCs (REVCs) are a new technology that overcomes underutilization by allowing multiple EVs to be charged concurrently. This brings a cost-effective charging solution, especially in large car parks requiring numerous chargers. Therefore, this paper proposes an optimal planning strategy for car parks deploying REVCs. The proposed planning strategy involves three stages. An optimization model is developed for each stage of the proposed planning strategy. The first stage determines the optimal power rating of power modules inside each REVC, and the second stage determines the optimal number and configuration of REVCs, followed by determining the optimal operation plan for EV car parks in the third stage. To demonstrate the effectiveness of the proposed optimal planning strategy, a comprehensive case study is undertaken using realistic car parking scenarios with 400 parking spaces, electricity tariffs, and grid infrastructure costs. Compared to deploying other conventional EVCs, the results convincingly indicate that the proposed optimal planning strategy significantly reduces the total cost of investment and operation while satisfying charging demands.

The rapid growth of Electric Vehicles (EVs) risks causing grid congestion. Smart charging strategies can help to prevent overload while ensuring timely charging, thereby reducing the need for costly infrastructure upgrades. We study EV charging from a scheduling perspective, assuming an aggregator manages charging while respecting network cable capacities. In our model, vehicles depart only after charging is complete, so delays are possible. Our aim is to minimize these delays. We consider a network of parking lots, some of which are equipped with solar panels, where the demand that can be served is limited by the cables connecting them to the grid. We propose novel scheduling strategies that combine an online variant of well-known schedule generation schemes with a destroy-and-repair heuristic. We evaluate their effectiveness in a case study with data from the city of Utrecht. Without control, network cables would be overloaded 60–70% of the time. Our strategies completely eliminate overload, introducing an average delay of just over 1.5 min per EV in high-occupancy scenarios. This demonstrates that scheduling can significantly increase the number of EVs charged without compromising grid safety at the cost of a rather small delay. We also highlight the importance of accounting for grid topology and show the benefits of using flexible charging rates.

The rapid global adoption of electric vehicles (EVs) necessitates the development of advanced EV charging infrastructure to meet rising energy demands. In particular, community parking lots (CPLs) offer significant opportunities for coordinating EVs’ charging. By integrating energy storage systems (ESSs), renewable energy sources (RESs), and building prosumers, substantial reductions in peak load and electricity costs can be achieved, while simultaneously promoting environmental sustainability. This paper presents a novel three-stage real-time Energy Management System (EMS) designed to coordinate EV charging in CPLs, integrating solar photovoltaics, wind energy, ESSs, and building backup units. The proposed EMS operates in three stages: (1) day-ahead scheduling of energy generation and consumption, (2) real-time power management to address deviations between forecasted and actual power generation and demand, and (3) priority-based EV charging, which considers EV state of charge (SOC) and owner preferences. The system is evaluated through MATLAB ® simulations under four different scenarios and based on six performance indices: daily electricity bills, cost savings, self-sufficiency, self-consumption, carbon emissions, and fairness in EV charging. The results demonstrate that the proposed EMS can reduce electricity bills for parking lot operators (PLOs) by up to 45%, with a corresponding decrease in carbon emissions by 40% compared to uncoordinated charging scenarios. Additionally, the EMS improves the self-sufficiency ratio by up to 75% and increases the self-consumption ratio to 85%. The system also ensures fairness in charging, achieving a fairness index of 0.82, thus addressing the needs of both PLOs and EV owners. This research underscores the potential of CPLs to optimize energy use, lower costs, and contribute to broader sustainability goals by integrating renewable energy and intelligent charging strategies.

The economic operation of an electric vehicle (EV) parking lot under different cases are explored in the paper. The parking lot is equipped with EV charging stations with a vehicle-to-grid (V2G) function, renewable energy sources (RESs), and energy storage system (ESS). An optimisation problem is formulated to maximise the profit of the parking lot from EV charging and feed-in energy to the grid under various charging modes while considering the uncertain factors, ESS degradation, and diverse EV parking conditions. The electricity market price, solar radiation and wind speed are considered as uncertain factors, and the scenred toolbox of MATLAB is used to generate scenarios. Based on the parking time of different EVs, the model classifies the EVs entering the charging station and dynamically determines the charging price according to their charging demand through a linear price-demand relationship. The efficacy of the proposed model is verified by the comparison with two other models under three different cases. It is shown that the proposed model gains the most profit based on the proposed V2G services and dynamic charging price.

The increasing penetration of electric vehicles (EVs) brings challenges and opportunities for power systems. One particular opportunity concerns the use of parked EVs to provide energy and associated services to the grid. In this work, the potential energy storage capacity of parking lots (PLs) of EVs is computed using the proposed stochastic model which considers the sporadic nature of the EV’ behaviours (i.e. arrival/departure, battery degradation, travel pattern, charge/discharge rates). The analysis was performed for two types of PLs with very different occupancy distributions, i.e. a shopping centre PL, and a workplace PL. In both cases, the available energy storage capacity of EVs was estimated hourly using real household travel data, i‐MiEV data and car park occupancy records. The results show that the aggregated energy storage capacity closely follows the occupancy of EVs in the PLs, and is substantial, with little sensitivity to charging rate. The proposed stochastic modelling considered the variations in energy consumption, battery degradation, and user behaviour, predicted 13.4% less peak capacity than deterministic modelling. Moreover, the authors conclude that the shopping centre PL is a viable energy resource to the grid, with their scale and throughput compensating for the relatively low occupancy.

Summary Optimal placement of parking lots with optimal scheduling in power systems to maximize the benefit, considering different peak load states and different electricity prices for each state, is studied in this paper. The effectiveness of the proposed technique is tested on the IEEE 33‐bus distribution test system with considering the power system constraints and using genetic algorithm. This algorithm selects the optimal sites and sizes (number of electric vehicles) of these parking lots and shows the optimal results. The results of these simulations with genetic algorithm demonstrate that the economic problem of parking lots placement depends on many items such as number of the electrical vehicles (EVs) in each parking lot (capacity of parking lot), state of charge of EVs, as well as the electricity price in peak/off‐peak periods. Moreover, it is suggested that the existence of parking lots, considering adequate incentives for EVs owners, has economic benefit for distribution network operators, reduces total power loss, and can improve the distribution system voltage profile. Also, the number of parking lots to be allocated in a distribution system supports the distribution system at peak load hours because it is more interesting from economic point of view to install more parking lots. Copyright © 2016 John Wiley & Sons, Ltd.