A Transformer Attention‐Based Convolutional Network for Solar PV Integrated EV Charging Demand Prediction

In this paper, based on forecasting short-term PV generation, demand, and EV charging, we propose a forecast-based optimal V2G scheduling strategy of Energy management system for minimizing the operating cost of an EV charging station and increasing the use of renewable energy. The significance of the developed method is constructing the detailed forecast model consisting of PV generation, regional load demand, and EV charging demand and analyzing the effectiveness of the scheduling participation rate in the case study. Demand types of office and residential areas were considered to establish a realistic EV charging station operation scheme, and short-term electricity demand forecasts were made based on EV charging data. By integrating these various data, efficient power management is possible. The forecast-based scheduling results show that PV self-consumption has increased, and the main grid dependence and operating cost have decreased.

In line with sustainable energy and environmental targets, the share of electric vehicles in the automobile market is expected to reach 80%. On the other hand, unplanned electric vehicle charging station installation for EV charging demands and high dependency rates on the distribution grid may adversely affect grid reliability, energy costs, and environmental targets. This article investigates whether electric vehicle charging stations can achieve carbon neutrality through strategic techno-economic integration with solar renewables. Furthermore, an exhaustive analysis investigated achieving carbon neutrality via integrating energy storage systems with photovoltaics, factoring in investment costs and carbon taxes. The findings revealed a substantial potential to reduce grid energy dependency by up to 54.3%. Implementing a more stringent carbon tax has facilitated a notable enhancement in energy storage system capacity, elevating the self-consumption rate by 72% and declining carbon emissions by 25.55%. Self-consumption decreases by 30% during high charging demand in the morning and evening hours, leading to increased dependency on the grid and highlighting the pressing need for improved strategies to reduce carbon emissions. Notwithstanding the significant investment required for energy storage systems, a levelised increase in carbon taxes has effectively curtailed grid energy demands by up to 34.7%. Moreover, decreases in storage costs can increase the decline in grid dependency by an additional 18%. Despite the integration of energy storage systems, the ambitious zero carbon target remains unattainable due to the existing installation area constraints of EV charging stations. This study can help policies that align with global efforts to mitigate climate change, enhance energy security, optimize costs, drive technological innovation, and meet increasing demands for sustainable policies.

Transitioning from internal combustion engines to electric vehicles (EVs) is critical for fighting climate change. This requires widespread adoption of Electric Vehicle Charging Stations (EVCSs). Integrating EVCSs and renewable energy sources (RESs) into distribution networks (DNs) is vital for a sustainable transportation system while enhancing power generation in an environmentally friendly manner. This review explores challenges and opportunities of EVCS and RES integration, concentrating on EV charging-demand uncertainty modeling, forecasting algorithms, planning techniques, and the impacts on DN. It discusses forecasting algorithms in terms of learning-based and non-learning-based methods. EVCS planning algorithms are also discussed, involving deterministic and stochastic methods. The technical, environmental, reliability, and economic impacts of EVCS-RES on DNs are discussed. It explores optimization strategies to minimize these impacts, incorporating them as objective functions. Additionally, the survey examines the methods of incorporating EVs and RES in DN, optimizing EVCS allocation while addressing EVCS impacts on voltage regulation, power loss, and network reliability. The importance of energy management systems and advanced forecasting techniques in balancing power fluctuation and improving efficiency is emphasized. Finally, it identifies open problems and future directions for forecasting and optimizing EVCS-RES integration in the networks. These findings are highly relevant for designing resilient and efficient modern power systems that leverage RES and EVCS in the grids.

Optimal expansion planning of electric vehicle fast charging stations

Toward social equity access and mobile charging stations for electric vehicles: A case study in Los Angeles

This work aims to showcase a control scheme for a solar photovoltaic (PV) panel interfaced to the grid supporting a three-phase electric vehicle charging system. It uses a multifarious harmonic elimination (MHE) technique for the operation of the control infrastructure. Peak power is extracted from the PV array and is delivered towards the grid side, where it fulfills the demand of the EV charging dynamics. The MHE based control is employed to exquisitely harvest the fundamental component out of the nonlinear current waveforms. Moreover, the dynamic variations experienced by an EV charging station are satisfactorily fulfilled. The overall system's power quality is maintained throughout the operation. Moreover, the multifarious aspect of the control corresponds to the extraction of third, fifth, seventh and ninth order harmonics and eliminating them from the input signal. The perennial power after the demand of EV charging is transferred to the utility. The performance is tested on developed hardware experimental model. The results unveil the prodigious potency of the presented control scheme.

To achieve a sizable and self-sustaining market for grid-connected, customer-sited photovoltaic (PV) systems, solar will likely need to be competitive with retail electricity rates. In this report, we examine the impact of retail rate design on the economic value of commercial PV systems in California. Using 15-minute interval building load and PV production data from 24 actual commercial PV installations, we compare the value of the bill savings across 20 commercial customer retail rates currently offered in the state. We find that the specifics of the rate structure, combined with the characteristics of the customer's underlying load and the size of the PV system, can have a substantial impact on the customer-economics of commercial PV systems. Key conclusions for policymakers that emerge from our analysis are as follows: {sm_bullet} Rate design is fundamental to the economics of commercial PV. The rate-reduction value of PV for our sample of commercial customers, considering all available retail tariffs, ranges from $0.05/kWh to $0.24/kWh, reflecting differences in rate structures, the revenue requirements of the various utilities, the size of the PV system relative to building load, and customer load shapes. For the average customer in our sample, differences in rate structure, alone, alter the value of PV by 25% to 75%, depending on the size of the PV system relative to building load. {sm_bullet} TOU-based energy-focused rates can provide substantial value to many PV customers. Retail rates that wrap all or most utility cost recovery needs into time-of-use (TOU)-based volumetric energy rates, and which exclude or limit demand-based charges, provide the most value to PV systems across a wide variety of circumstances. Expanding the availability of such rates will increase the value of many commercial PV systems. {sm_bullet} Offering commercial customers a variety of rate options would be of value to PV. Despite the advantages of energy-focused rates for PV, requiring the use of these tariffs would disadvantage some commercial PV installations. In particular, for PV systems that serve less than 25-50% of annual customer load, the characteristics of the customer's underlying load profile often determine the most favorable rate structure, and energy-focused rate structures may not be ideal for many commercial-customer load shapes. Regulators that wish to establish rates that are beneficial to a range of PV applications should therefore consider allowing customers to choose from among a number of different rate structures. {sm_bullet} Eliminating net metering can significantly degrade the economics of PV systems that serve a large percentage of building load. Under the assumptions stipulated in this report, we find that an elimination of net metering could, in some circumstances, result in more than a 25% loss in the rate-reduction value of commercial PV. As long as annual solar output is less than roughly 25% of customer load and excess PV production can be sold to the local utility at a rate above $0.05/kWh, however, elimination of net metering is found to rarely result in a financial loss of greater than 5% of the rate-reduction value of PV. More detailed conclusions on the rate-reduction value of commercial PV include: {sm_bullet} Commercial PV systems can sometimes greatly reduce demand charges. Though energy-focused retail rates often offer the greatest rate reduction value, commercial PV installations can generate significant reductions in demand charges, in some cases constituting 10-50% of the total rate savings derived from PV installations. These savings, however, depend highly on the size of the PV system relative to building load, on the customer's load shape, and on the design of the demand charge itself. {sm_bullet} The value of demand charge reductions declines with PV system size. At high levels of PV penetration, the value of PV-induced demand charge savings on a $/kWh basis can drop substantially. As a result, the rate-reduction value of PV can decline by up to one-half when a PV system meets 75% rather than 2% of total building load. Thus, for rates with significant demand charges, the drop in demand charge savings dramatically reduces the overall rate reduction value of PV as system size increases relative to customer load. {sm_bullet} The ability of PV to offset demand charges is highly customer-specific. Customers with loads that peak in the afternoon are often able to receive significant demand charge savings across a wide variety of circumstances, at least at lower levels of PV output relative to building load. In contrast, facilities with flat or inverted load profiles will often not earn much demand charge reduction value, regardless of PV system size. {sm_bullet} The type of demand charge can impact the ability of PV to offer savings. Time-of-day (TOD)-based demand charges are found to be more favorable to PV under a broad range of (Abstract truncated)

Excellent energy management techniques in EV charging stations have become necessary because of fast EV adoption to establish reliable, sustainable, and efficient charging systems. The expanding number of EV charging stations makes it difficult to properly bind these systems to existing energy grids that cannot handle the variable energy needs of EV charging operations. This study examines different methods to enhance renewable power utilization while strengthening power grid stability and offering economical solutions to consumer and utility provider needs. The study investigates existing methods and new technological solutions to determine the best strategies for up-and-coming EV charging infrastructure. This paper starts by exploring different energy management methods for EV charging stations. Three key technical approaches for managing charging demand consist of load management techniques, demand response efforts, and the implementation of storage systems. Technical load management systems enable multiple charging stations to share energy resources to prevent peak electrical usage, which would overwhelm the power grid. The implementation of demand response programs rewards EV users for charging their vehicles during specific times, including off-peak periods and when renewable energy generation reaches peak capacity. The implementation of energy storage systems serves to accumulate surplus renewable-based energy, which can later supply EV charging requirements. The implemented approaches make charging infrastructure more efficient and stabilize the entire energy grid performance. The paper examines the stakeholder effects of these energy management strategies between consumers, utility providers, and policymakers. Knowledgeable energy management produces benefits that reduce station operators' operational expenses, consumer electricity savings, and strengthen electrical grid stability. Rapid EV adoption requires all stakeholders to create a charging infrastructure to fulfill upcoming needs effectively. This study researches to establish beneficial energy management strategies that will enable EV charging infrastructure to evolve successfully.

A comprehensive planning framework for electric vehicles fast charging station assisted by solar and battery based on Queueing theory and non-dominated sorting genetic algorithm-II in a co-ordinated transportation and power network

Hierarchical Operation of Electric Vehicle Charging Station in Smart Grid Integration Applications — An Overview

With the increasing penetration of plug-in electric vehicles (EVs), peak demand charges can make up a considerable portion of the electricity bills of industrial and commercial sites that host an EV charging station. Typically, daily peak demand minimization-based EV charging scheduling strategies are used to avoid significant demand charges. However, these approaches may lead to suboptimal results for reducing total electricity cost as they do not account for the energy charges and the historical peak values within a billing period. In this work, a receding horizon control-based EV charging scheduling methodology is proposed to simultaneously reduce the demand and energy charges of an industrial/commercial site with an EV charging station. As a salient feature, the methodology maintains data privacy as the confidential power system- and EV-related data are not circulated. Case studies on a workplace charging setting reveal that the proposed methodology is able to minimize the demand and energy charges to a greater extent when compared to conventional strategies.

With the widespread adoption of electric vehicles, the distinct features of their charging and switching technologies have a significant impact on the grid load. This paper focuses on the operation mechanism of charging and switching technologies, and after analyzing the temporal and spatial patterns of load variation in EV charging and switching modes, a Monte Carlo simulation method based on the momentary charging likelihood is adopted to establish a charging load prediction model to investigate the temporal–spatial distribution patterns of charging demand under different day categories, vehicle types, charging and switching modes, and charging areas. Example analysis shows that the model can accurately simulate the user's travel pattern, capturing the temporal and spatial variation in charging and battery-swapping demand for electric vehicles across different driving and parking states. The charging load distribution of various vehicle categories varies greatly, while the charging loads of private cars and cabs account for a higher proportion, with higher regulation potential; compared with double holidays, the charging demand fluctuates more on weekdays, with a higher total demand; the charging demand also has obvious seasonal characteristics, with a greater demand in winter and summer, and the peak appears in an earlier time. The study offers a reference for the planning and development of electric vehicle charging facilities as well as for assessing their impact on the power grid.

This research proposal is aimed towards the development of charging stations which are powered by renewable energy sources. The two sources used to replace traditional fuels are the wind turbines and solar photovoltaic cells. The goal is to perform mathematical analysis for economical and eco-friendly charging station for electric vehicles. With the increase in use of electric vehicles, the use of EV chargers also increases, and enough chargers should be available throughout the globe for promoting this trend. The EV chargers are powered through grids and conventional energy synchronized with wind turbine, solar power and battery packs. The solar and wind energy provide the energy to the EV charger for charging of battery packs. But when they are completely charged or not in use, the excess generation is sent back into the local distribution system. This not only lowers the load on our distribution system but also reduces the losses experienced from GENCOs to DISCOs. The reverse metering is also enabled to earn from the investment. Through this no generation is wasted and we can get credit from the electricity company. The wind and solar power are not constant and vary according to the location and time, so a model is designed to optimize the system size for wind, solar and battery pack. The cost is optimized to keep the annual cost of the charging station to a minimum. A total of ten cities are chosen around the globe having different wind and solar characteristics. A mathematical model is prepared which estimates the source capacity. The simulation results justify that we can achieve zero carbon footprint for charging electric vehicles where sunny weather or winds prevail.

Could EV charging be a remedy for grid congestion rather than a cause? The rapid adoption of EVs, heat pumps, and photovoltaic systems has increased electricity flows, often peaking at specific times and causing congestion. Smart EV charging, which entails shifting demand to periods of low grid usage, is widely seen as a potential solution. Yet, the most effective ways to harness this flexibility, especially from a system-wide perspective, remain unclear. This dissertation examines mechanisms grid operators can use to promote smart charging and explores two major barriers to its effective implementation. The first three chapters investigate how different mechanisms, such as capacity-limitation systems and alternative grid tariffs, can help grid operators mitigate congestion through smart EV charging. Chapter 2 focuses on a capacity-limitation product, where EVs optimize charging costs and/or emissions while keeping total load within transformer limits. It compares the costs and emissions of upgrading a transformer with the additional savings from smart charging under higher capacity. Across nearly all scenarios, the upgrade’s costs and emissions outweigh the savings, suggesting it is not optimal from a system value perspective. Chapter 3 examines how replacing traditional grid tariff structures can help mitigate congestion. It analyzes how EV charging patterns respond to various alternative tariffs and evaluates them using regulatory principles. Most alternatives reduce the need for grid investments and lower overall system costs, with only a small effect on charging costs. They also lead to a fairer distribution of grid costs between households and EV charging stations. Chapter 4 analyzes a case where a shared EV fleet operated by a car-sharing company helps mitigate grid congestion through a capacity-limitation system, without requiring private EVs to adjust their charging schedules. The results show that a small number of shared EVs can eliminate all congestion in the studied grid, with only a modest increase in their charging costs. Chapters 5 and 6 address two main barriers to implementing smart charging for grid congestion mitigation. Chapter 5 introduces a framework to reduce uncertainties in charging demand, timing, and session counts, which often lead to overly conservative strategies. By aggregating fleet data into three parameters for each 15-minute interval, the framework achieves high forecasting accuracy (R² up to 0.98). Chapter 6 examines technical barriers, drawing on large-scale tests that show many EV models cannot pause charging and require a constant minimum of 6 amperes, even during high grid load. Simulations reveal this limitation can halve the effectiveness of smart charging in reducing costs and congestion, and the chapter proposes several solutions. In conclusion, this thesis shows that the studied mechanisms for promoting smart charging can effectively reduce grid reinforcement needs and lower system costs, making smart charging a key solution to grid congestion. Grid operators and policymakers should therefore prioritize mechanisms that enable its widespread adoption. The thesis also identifies significant barriers to implementation and offers practical solutions to address them.

The owners of PV systems and EV charging station are probably different. Combining PV systems with EV charging station can contribute to more benefit, according to previous research, but also lead to some problem. In order to solve the problem of energy management and profit distribution, a multi-party energy management for EV charging station cooperated with PV systems in smart grid is proposed. Firstly, the feasible model of EV charging energy demand is established based on starting charging time, departure time and SOC, which is varied with the PV power. The actual charging power can be then decided according to the relationship between charging demand and PV power. Finally, the operation profit is distributed to each project using Shapley value method, which considers the contribution of each project. A comprehensive result obtained from simulation tests has shown that the proposed energy management can obviously increase the profit of each party in cooperation mode compared to independent mode, which can be utilized to practical application.