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Electric vehicle (EV) load integration and environmental concerns in recent years have motivated energy users to step forward in reducing carbon footprints by promoting energy trading. In the energy trading market, different energy stakeholders participate in trading energy with each other to maximize their utilities and improve trading efficiency. The social welfare (SW) of the energy market plays a significant role in keeping the market going and motivating new users to engage in energy trading. The SW of the energy market depends on its structure, where energy flows from producer to end user in a fair way. This paper introduces a hierarchical energy trading framework, formulated as a three-stage Stackelberg game, to enhance SW. In the first stage of this hierarchy, the retailer, acting as the sole leader, seeks to maximize profits through optimized energy sales to charging stations (CS). Subsequently, in the second stage, each CS operates as a leader for EVs, aiming to maximize profits by minimizing energy trading costs, while also acting as a follower in response to the retailer's strategies. The third and final stage involves EVs collaboratively acting as a unified strategic agent to minimize their trading costs through optimized charging and discharging schedules. This paper also introduces an average price penalty function to further enhance SW in the energy market. The proposed model, addressing the complexities of energy trading interactions, is formulated as a non-linear optimization problem with constraints. It has been implemented in a Python environment and solved using the Gurobi optimization solver, demonstrating its potential to improve the efficiency, fairness, and overall social welfare of the energy trading market. • Introduces a hierarchical energy trading framework using a three-stage Stackelberg game to enhance market social welfare. • Incorporates renewable energy at the retailer level, diversifying energy sources and boosting ecological relevance. • Establishes comprehensive pricing functions across all levels, including reciprocal EV discharge signals, for dynamic market interaction. • Implements an average price penalty at charging stations to preserve social welfare and promote efficient energy use. • Demonstrates model efficiency and practical applicability through tests on the IEEE 33 bus system, showing superior profitability and convergence. [ABSTRACT FROM AUTHOR]

Integrating electric vehicles (EVs) into the electric power system poses significant challenges to grid operation and planning due to the potential constraints on the power system. To minimize grid congestion, the best option is to increase the production of photovoltaic (PV) energy for domestic use and EV charging stations. This paper addresses the nexus of technical, financial, and environmental effects of customer involvement in economic development and load management. In this paper, a new model design of solar-powered EV charging stations is proposed and implemented in HOMER Grid, and a case study has explored how economic, technical, and energy management benefits can be achieved through customer energy involvement and the integration of PV-based charging stations. The proposed PV-based charging stations contribute toward the energy management of the region, and the study observes the real-time optimal charging and discharging strategy of PV-based grid-connected charging stations. The case study results show that the investigated area can produce 1,070,804.096 MWh/year of energy through maximum customer involvement, consequently reducing overall energy costs. Moreover, the study demonstrates that the selected region can produce 86,961,688 kWh/year through the PV system, achieving 363,899 charging sessions per year, offering maximum advantages and facilitation for EV charging. The proposed model applicability shows that large-scale customer involvement can bring significant techno-economic benefits and attract regional investments. • This paper proposes a new design of a grid-tied renewable energy system. • It investigates the energy economics and environmental effects for a practical location. • The research examines energy management including PV systems and electric vehicles. • The results of proposed model are compared with other models. [ABSTRACT FROM AUTHOR]

Abstract This paper presents a significantly improved optimization model for the planning of the charging infrastructure for electric-drive vehicles, where the optimization objective function is the minimization of overall (installation, maintenance, operation) placement costs of charging stations with regards to a charging technology. The constraints involve the electric power system reliability check, ensuring charging reliability and the required quality of service of the charging infrastructure. In ensuring the charging reliability, at least one candidate location must be selected within the driving range of electric vehicles and suitable charging technologies placed to accommodate the disposable charging times of electric vehicle users for the requested quality of service. The proposed optimization model presents an upgrade of an existing optimization formulation since it includes a power system reliability check based on a DC power flow model. To show the general applicability and significance of the model, a test 10 × 10 grid road network and a standard six-bus test power system are considered. Numeric results illustrate the optimal charging stations placement layout and overall costs placement for different driving ranges and the required quality of service level by including a power system reliability check, to serve both the charging infrastructure investors and electric power system operators. Highlights • Including the electric power system reliability check, to derive an optimal charging stations placement solution. • New constraints in the charging stations placement optimization model, to serve both the investors and network operators. • Lines outage criticality ranking, to provide network operators valuable information for the network expansion planning. [ABSTRACT FROM AUTHOR]

In the contest of European and National choices regarding energy and mobility policies, the goal of this paper is to introduce reasonable guidelines for the local promotion of electric mobility in an area of central Italy and possibly to extend the methodology to other regions/countries. In fact, the path towards car electrification is started worldwide, but the rate of increase of electrically powered cars will depend primarily on charging stations availability. In this respect, utilities that manage/own local grids may be key actors by investing on infrastructures and thus locally creating the conditions for the spreading of electric cars. A techno-economic analysis of the investment of a local utility has been performed taking into account medium- and long term-forecasts of electricity and fuel prices, as well as electric cars market share and economic risk. The study proved that an investment in urban charging station infrastructure in Italy can be profitable even without incentives, with a payback period of 4–5 years. The results of the analysis have been exploited by an Italian utility which owns/manages the local electric grid in two municipalities. The first steps of the plan implementation, which started in 2014, are reported in the paper. [ABSTRACT FROM AUTHOR]

The massive deployment of plug-in electric vehicles (PEVs), renewable energy resources (RES), and distributed energy storage systems (DESS) has gained significant interest under the smart grid vision. However, their special features and operational characteristics have created a paradigm shift in distribution network resource allocation studies. This paper presents a combined model formulation for the concurrent optimal resource allocation of PEVs charging stations, RES and DESS in distribution networks. The formulation employs a general objective function that optimizes the total Annual Cost of Energy (ACOE). The decision variables in the formulation are the locations and capacities of PEVs charging stations, RES, and DESS units. A Markov Chain Monte Carlo (MCMC) simulation model is utilized to account for the uncertainties of PEVs charging demand and output generation of RES units. Also, in order to enhance the accuracy of the resource allocation problem, the coordinated control of PEVs charging, RES output power, and DESS charging/discharging are incorporated in the formulated model. The formulation is decomposed into two interdependent sub-problems and solved using a combination of metaheuristic and deterministic optimization techniques. A sample case study is presented to illustrate the performance of the algorithm. [ABSTRACT FROM AUTHOR]

India’s ever increasing population has made it necessary to develop alternative modes of transportation with electric vehicles being the most preferred option. The major obstacle is the deteriorating impact on the utility distribution system brought about by improper setup of these charging stations. This paper deals with the optimal planning (siting and sizing) of charging station infrastructure in the city of Allahabad, India. This city is one of the upcoming smart cities, where electric vehicle transportation pilot project is going on under Government of India initiative. In this context, a hybrid algorithm based on genetic algorithm and improved version of conventional particle swarm optimization is utilized for finding optimal placement of charging station in the Allahabad distribution system. The particle swarm optimization algorithm re-optimizes the received sub-optimal solution (site and the size of the station) which leads to an improvement in the algorithm functionality and enhances quality of solution. The genetic algorithm and improved version of conventional particle swarm optimization algorithm will also be compared with a conventional genetic algorithm and particle swarm optimization. Through simulation studies on a real time system of Allahabad city, the superior performance of the aforementioned technique with respect to genetic algorithm and particle swarm optimization in terms of improvement in voltage profile and quality. [ABSTRACT FROM AUTHOR]

Battery swapping stations (BSSs) and charging stations (CSs), which provide electric vehicle battery refueling services, are important participants in the electricity and carbon markets. Battery swapping stations (BSSs) and charging stations (CSs), which provide electric vehicle battery refueling services, are important participants in the electricity and carbon markets. The coordination between BSSs and CSs offers a great opportunity for enhancing system flexibility and lowering operation costs, which is underexplored in the literature. Thus, this paper proposes a cooperative operation approach for a BSS, a CS, and a group of residential buildings (RBs) in a community microgrid to minimize their operation costs. These three entities can trade electricity and carbon allowance in the central and local markets. The local electricity and carbon trading problem is formulated using Nash bargaining theory to determine the amounts of electricity and carbon allowance to be traded and the corresponding payments. The Nash bargaining problem is decomposed into an electricity and carbon allowance scheduling problem (P–S1) and a payment bargaining problem (P–S2). The P–S1 is transformed into a mixed-integer linear programming (MILP) problem using a linearization technique, while the closed-form solution to the P–S2 is derived. Compared to the noncooperative benchmark, the proposed approach reduces operation costs of the BSS, CS, and RBs by 49.85%, 67.17%, and 77.24%, and their carbon emissions by 10.52%, 26.73%, and 27.82%. • Cooperation between a battery swapping station and a charging station is proposed. • Nash bargaining theory is used to formulate local electricity and carbon trading. • A linearization technique is employed to solve the Nash bargaining problem. • Impacts of different coalition combinations and market settings are investigated. [ABSTRACT FROM AUTHOR]

Fast charging stations for electric vehicles require a high-power demand, meaning that electricity distribution companies must define the connection locations within the distribution network to guarantee adequate power supply levels. Due to electric vehicle users' driving patterns and equipment's high costs, these stations must be concentrated in certain regions. This paper presents a methodology for assisting electricity distribution companies in identifying candidate connection points for fast charging stations to reduce new installations and network reinforcement investments. First, possible connection points are analyzed with graph theory to find the least costly connection; this strategy prioritizes the current network elements' unused capacity. As a second step, the electric distribution network is analyzed after fast-charging stations have been connected, evaluating the networks' operational limits. The methodology is applied in a Brazilian city combining spatial information with a realistic representation of the network and network total supply capability to connect new loads. Model outcomes are spatial maps that help identify suitable connection locations, determine new capacity values, and calculate the necessary investment. We compare the proposed methodology with other conventional approaches, demonstrating how the developed methodology can assist distribution companies in reducing overall investment and operational costs of fast charging stations for electric vehicles. • Two-stage method to assess EV fast-charging stations. • Novel local search algorithm based on graph theory and power-flow method. • Prioritizing unused network capacity can reduce expansion costs by up 17%. • Computational time reduces by 24% compared to conventional methods. [ABSTRACT FROM AUTHOR]

The development of policies for sustainable development has led to an increase in electric vehicles (EVs). This has given rise to new problems, such as the location and sizing of charging stations over the considered traffic network. The solution of such problems requires the application of analysis techniques in order to predict the joint allocation of transportation and energy demands. In this paper, an approach is proposed that extends the User Equilibrium (UE) principle in order to determine, besides to the flow over the network links, the service requests from the drivers to the various service stations. The application of this method is preliminary for the statement of any problem related to the optimization of location and sizing of service stations. The proposed approach is applied to a case study in Genoa Municipality (Liguria Region). • A new model for charging stations and electric vehicles. • Traffic assignment and energy demands assessment. • User Equilibrium (UE) principle for the case of electric vehicles. • A real case study is considered. [ABSTRACT FROM AUTHOR]

In recent years, as a policy to increase penetration of electric vehicles and reduce air pollution, the number of electric taxis has increased in the transportation networks. In contrast to private electric vehicles, electric taxis need to recharge in a short time due to their constant operation and different driving patterns. Therefore, fast charging stations are required to meet the demand for recharging electric taxis and should be located at strategic places. To improve the sustainable planning of urban infrastructures, this paper presents a methodology to help in decision making for installing fast charging stations, considering the criteria of all urban planners involved in the installation process. The result of the proposal is a spatial database that identifies locations for charging stations that meet the predefined criteria. Such database can be visualized in geographic information systems of the urban planning department or the electrical service concessionaire. The proposed methodology is tested in a Brazilian medium-sized city showing the importance of this information in decision making. The proposal is compared with another spatial methodology, showing that the proposed method creates a better spatial distribution of charging stations giving better options for the main agents interested in the city. • The proposed helps to determine the location for fast charging stations (Fast-CS). • The spatio-temporal requirements of the planners are considered. • The proposal considers several aspects that influence the location of Fast-CS. • The proposal models traffic flows and the state of charge of electric taxis. • The results of the proposal can be processed by any Geographic Information System. [ABSTRACT FROM AUTHOR]