Grid Impact Research Articles

Mitigating power grid impact from proactive data center workload shifts: A coordinated scheduling strategy integrating synergistic traffic - data - power networks

The widespread adoption of cloud computing has markedly escalated the demand for Internet services, rendering Internet Data Centers (IDCs) a critical component in the consideration for demand response (DR) initiatives and grid energy management. This work proposes a flexible scheduling strategy integrating data, traffic, and power networks (DN-TN-PN) to mitigate the impact of proactive IDC workload transfers on power system balance. The spatial-temporal transfer characteristics of EV charging and discharging loads are utilized to track the proactive spatial-temporal transfer of IDC workloads. Considering the influence of weather factors and traffic congestion on EV travel time, an incentive pricing model for EV users is introduced to alleviate bus load fluctuation and line congestion in the distribution network. Numerical simulations demonstrate that the proposed EV pricing strategy, which accounts for the coupled DN-TN-PN, reduces IDC-induced load fluctuations by over 20 % and decreases transmission line occupancy by approximately 15 %, opening up more opportunities for the implementation of DR programs.

Unveiling electric vehicle (EV) charging patterns and their transformative role in electricity balancing and delivery: Insights from real-world data in Sweden

Accurately estimating the charging behaviours of electric vehicles (EVs) is crucial for various applications, such as charging station planning and grid impact estimation. However, the analysis of EV charging behaviours using real-world data remains limited due to (confidential) data availability constraints. Furthermore, while existing modelling studies have demonstrated EVs as effective tools for electricity balancing and delivery between locations, their potential remains unexplored empirically. This study aims to bridge the research gap by studying EV charging behaviours and their capacity for electricity balancing and delivery. Using data from 179,665 real-world charging sessions in Sweden, we employed statistical and clustering analysis to scrutinize charging behaviours comprehensively. Synthetic weekly charging load profiles are generated for both residential areas and workplaces, considering varying charging power levels, which can be used as inputs for large-scale EV charging load modelling. Furthermore, performance indicators are proposed to quantify the potential of EVs for electricity balancing and delivery. Results show that EVs exhibit significant potential for electricity balancing (up to 51.5 kWh daily). Many EV owners underutilize their EV battery capacity, providing an opportunity for active electricity delivery across locations. This study can help understand EV charging behaviours and recognize their significant potentials for electricity regulation and integrating more renewables in the future power system.

High-Resolution Monitored Data Analysis of EV Public Charging Stations for Modelled Grid Impact Validation

As electric vehicle adoption grows, understanding the impact of electric vehicle charging on electricity grids becomes increasingly important. Accurate grid impact modelling requires high-quality charging infrastructure data. This study examined the electric vehicle recharging infrastructure and usage patterns in a region of the Italian Alps over a three-year period from 2021 to 2023. The primary objectives were to analyze the growth and distribution of electric vehicle charging stations, assess energy consumption, and evaluate charging behaviours across various recharging points. The research involved collecting empirical data from 411,800 recharging sessions and simulated data using the emobpy tool to model energy consumption and charging behavior. Key findings reveal a substantial increase in the number of recharging points, from 673 in 2021 to 970 in 2023, with the total energy delivered increasing from 938 MWh in 2021 to 4133 MWh in 2023. The data showed distinct temporal trends: AC points were primarily used during the day, while DC points saw higher usage during morning and late afternoon peaks, aligning with travelling times. The study’s validation of simulation results against empirical data emphasized the importance of high-quality input for accurate grid impact assessments. These findings suggest the necessity for strategic placement of recharging infrastructure and provide practical insights for policymakers, urban planners, and utility companies to support sustainable electric vehicle integration.

Unlocking Grid Flexibility: Leveraging Mobility Patterns for Electric Vehicle Integration in Ancillary Services

The electrification of mobility has introduced considerable challenges to distribution networks due to varying demand patterns in both time and location. This underscores the need for adaptable tools to support strategic investments, grid reinforcement, and infrastructure deployment. In this context, the present study employs real-world datasets to propose a comprehensive spatial–temporal energy model that integrates a traffic model and geo-referenced data to realistically evaluate the flexibility potential embedded in the light-duty transportation sector for a given study region. The methodology involves assessing traffic patterns, evaluating the grid impact of EV charging processes, and extending the analysis to flexibility services, particularly in providing primary and tertiary reserves. The analysis is geographically confined to the Lombardy region in Italy, relying on a national survey of 8.2 million trips on a typical day. Given a target EV penetration equal to 2.5%, corresponding to approximately 200,000 EVs in the region, flexibility bands for both services are calculated and economically evaluated. Within the modeled framework, power-intensive services demonstrated significant economic value, constituting over 80% of the entire potential revenues. Considering European markets, the average marginal benefit for each EV owner is in the order of 10 € per year, but revenues could be higher for sub-classes of users better fitting the network needs.

Scheduling with mixed fleets to improve the feasibility of electric minibus taxis: A case scenario of South Africa

The path to electrification of the informal and organic transport sector – minibus taxis in South Africa’s case – is fraught with challenges. These stem mainly from shorter ranges and slower re-energising of electric vehicles, compounded by their unscheduled nature and day-variant routing. However, the transformation also presents opportunities for improving infrastructure and vehicle utilisation. Planning for electrification becomes a complex spatio-temporal interdependence between mobility, infrastructure (civil and electrical), vehicle specifics, and driver behaviour. This paper evaluates two scenarios using mixed fleet analysis: (1) striving to achieve the same mobility patterns with equal-sized electric vehicles and (2) scheduling for passenger demand, which was extracted from existing mobility patterns. The paper’s main contributions lie in a proposed method to bridge the gap between these two scenarios through trip extraction, as well as introducing the concept of mixed fleets as a way to achieve electrification in the paratransit industry. The results show that using passenger demand to place the charging infrastructure and schedule the vehicles and their charging, results in a reduced number of vehicles, a higher proportion of electric vehicles in the fleet, a reduced number of chargers required, a reduced grid impact, and only a fractional increase in total fleet distance covered. Consequently, we propose to leverage the transformational opportunity presented by electrification to structure sub-Saharan Africa’s paratransit.

A Review on Electric Vehicles for Holistic Robust Integration in Cities: History, Legislation, Meta-Analysis of Technology and Grid Impact

Electric vehicle technology is transitioning from mobility based on fossil fuel combustion to one based on vehicle electrification, in which the primary energy is increasingly renewable, and the generation of pollutants and CO2 emissions is being reduced. This paper provides a tour of the key aspects of these systems, reviewing their most important historical, legislative, and grid impact topics. For this purpose, a literature review of publications up to 2022 is conducted. The last decade is the subject of a deeper analysis, shedding light on the essential characteristics of this technology and fundamentally focusing on its integration into electrical distribution networks. This work is carried out based on a review of a selection of articles written by authors worldwide who have researched these topics. We ordered and analyzed the temporal evolution of the defined categories, obtaining their research line direction. A meta-analysis of grid impact was also carried out, prompting clear conclusions about the state of the art and potential future works.

Grid impact analysis on wind power plant interconnection in strengthening electricity systems

The Timor system is one of the large systems in the East Nusa Tenggara region. Based on the general plan for electricity supply for 2021-2030, there is a plan to interconnect a 2x11 MW wind power plant. The addition of wind power plants will pose a considerable threat to the system due to the intermittency nature of renewable energy plants. Therefore, a comprehensive grid impact study is needed to convince network managers that adding wind farms will not cause disruptions to the system either locally or in general and is expected to strengthen the electricity system. The power flow simulation results, installing a 2x11 MW wind farm on the Timor system can improve voltage quality and reduce losses on both 70 and 150 kV systems. For transient stability, the frequency value on the Timor system still meets the grid code requirements. In addition, the simulation results of the intermittency impact of the wind power plant output show that the Timor system is still in a stable condition. The stability of the rotor angle of the existing power plant when the transient stability simulation is carried out shows that it is still in a balanced condition.

Impact of Multi-Energy System and Different Control Strategies on a Generic Low-Voltage Distribution Grid

The rising electricity costs, cost of space heating, and domestic hot water end up driving consumers toward reducing expenses by generating their electricity through devices like photovoltaic systems and efficient combined heat and power plants. When coupled with thermal systems via an energy management system (EMS) in a Multi-Energy System (MES), this self-produced electricity can effectively lower electricity and heating bills. However, MESs with EMSs can serve various purposes beyond cost reduction via self-consumption, such as reacting to variable electricity prices, meeting special grid connection conditions, or minimizing CO2 emissions. These diverse strategies create unique prosumer profiles, deviating significantly from standard load profiles. The potential threat to the power grid arises as grid operators lack visibility into which consumers employ which control strategies. This paper investigates the impact of controlled MESs on the power grid compared to average households and answers whether new control strategies affect the planning strategies of low voltage grids. It proposes a comprehensive four-step toolchain for the detailed simulation of thermal–electrical load profiles, MES control strategies, and grid dynamics. It includes a new method for the grid impact analysis of extreme and average bulk values. As a result, this study identifies three primary factors influencing distribution power grids by MESs. Firstly, the presence and scale of photovoltaic (PV) systems significantly affect extreme values in the grid. Secondly, MESs incorporating combined heat and power (CHP) and heat pump (HP) units impact the overall grid performance, mainly reflected in bulk values. Thirdly, the placement of an MES with heating systems, especially when concentrated in one feeder, plays a crucial role in grid dynamics. Despite the three distinct factors identified as impactful on the power grid, this study reveals that the various control strategies, despite leading to vastly different grid profiles, do not exhibit divergent impacts on buses, lines, or transformers. Remarkably, the impact of MESs remains consistently similar across the range of control strategies studied. Therefore, different control strategies do not pose an additional challenge to the grid integration of MESs.

Dual grid energy management strategy for electric vehicles in hybrid microgrid utilizing matrix pencil method

Abstract This study introduces a cutting-edge Matrix Pencil-based Dual Grid Energy Management System (MP-DEMS) aimed at seamlessly integrating electric vehicles (EVs) into power grids while enhancing power quality (PQ) and reliability (PR). Operating within an AC-DC hybrid microgrid (HMG) framework, the MP-DEMS reduces the necessity for additional conversion devices, simplifying the integration of EVs and batteries. The core of the proposed DEMS comprises three key modules: the Wind Energy Management Module (WEMS), Smart Storage and EV Power Coordination (SS-EVPC), and Coordinated Power Conversion Control (CPCC). These modules work together to optimize energy usage, leverage renewable sources effectively, and manage EV charging/discharging schedules to minimize grid impact. An innovative aspect of the MP-DEMS is its use of Matrix Pencil-based techniques, offering several advantages over traditional Discrete Fourier Transform (DFT) methods. These advantages include swift dynamic response, resilience to voltage variations, and precise voltage phase estimation. Software simulations and Hardware-in-the-Loop (HIL-402) testing validate the efficacy of the MP-DEMS approach, showcasing enhanced power transfer capabilities, improved harmonic performance, and superior voltage/frequency regulation within EV-HMS applications. Overall, the MP-DEMS presents a promising solution for advancing the integration of EVs into microgrid systems, contributing to a more sustainable and reliable energy future.

Navigating the Present and Future Dynamics of Electric Vehicle Fast Charging and its Impact on Grid

As electric vehicle (EV) adoption surges worldwide, the need for fast charging infrastructure becomes paramount. Fast charging technology alleviates range anxiety and bolsters EV practicality. This comprehensive analysis delves into current challenges and future prospects surrounding EV fast charging and its grid impact. Examining the state of fast charging technology, it scrutinizes charging standards, connector types, and power levels across global regions. Emphasis is placed on key market players and the imperative for interoperability and standardization to seamlessly integrate EVs into the power grid. Voltage fluctuations, harmonics, and power factor issues stemming from rapid, high-power charging pose technical hurdles. Solutions like advanced power electronics, grid management tactics, and enhanced communication between EVs and the grid are explored to uphold stability and power quality. Consideration is given to energy sources fueling fast chargers, including renewable integration and environmental repercussions. Economic facets, encompassing infrastructure deployment costs and grid upgrade analyses, are examined amidst escalating electricity demand and potential grid expansion needs. A forward-looking perspective addresses future challenges and opportunities. This includes advancements in battery tech, smart grid solutions, and bidirectional power flow potential between EVs and the grid. Index Terms: Battery electric vehicles (BEVs), Grid, Fast Charging, plug-in hybrid electric vehicles (PHEVs).

Enabling large-scale integration of electric bus fleets in harsh environments: Possibilities, potentials, and challenges

This paper presents a comprehensive and detailed investigation of electric bus transit systems, focusing on their feasibility in harsh environmental areas. The aspects that combine economics, operation, long-term performance, and reliability are all addressed and discussed in both short and long-term horizons. This work covers most of the “important to consider” criteria, compares the strategies applied in E-bus energy management, and recommends the best practices to follow in the presence of many system variables. Due to the significant impact of the ambient temperature, characterizing the battery performance under hot ambient temperatures is performed considering the existing Li-ion battery technologies. Moreover, the grid impact of large-scale charging systems is identified and classified by identifying the factors affecting the grid hosting capacity and permissible interaction levels. This work includes developing a comprehensive picture of the state of the art, emphasizing the gaps found in the literature, and proposing new venues for future research that help address the identified issues to enable reliable E-transportation plans.

Geospatial optimisation of wind and solar energy generation: A framework for optimised resource allocation

Wind and solar power generation profiles exhibit a high degree of power output variability and uncertainty compared to conventional fossil fuel generation plants. However, it is generally accepted that this variability and uncertainty can be mitigated by geographic siting and dispersion of the renewable energy plants, particularly with the view to leverage aggregation effects. In this context, this paper proposes a framework for the geospatial optimisation of wind and solar PV generation for scenarios with a high penetration of wind and solar renewable energy. The proposed optimisation framework represents a probabilistic risk-based approach that seeks to minimise the number of events where high residual load values require ancillary service interventions to maintain power balance. In this approach, the wind and solar resources are categorised in terms of the statistical properties of the associated spatiotemporal wind and solar power profiles for a given set of daily and seasonal Time of Use periods. It is thereby recognised that the resource characteristics and grid impact of wind and solar generation profiles can be interpreted with reference to the daily and seasonal cycles exhibited by the demand profiles, wherein some Time of Use periods are more important than others. The proposed framework is implemented for a number of baseline case studies and optimisation case studies, using wind and solar resource data sets covering South Africa. Overall, it is concluded that this approach can leverage geospatial site optimisation to substantially reduce the risks associated with high residual load events. Furthermore, the proposed framework is highly flexible in that the formulation of the minimum and maximum allocation constraints allows application for real-world scenarios whereby stringent capacity allocation constraints can be applied.

A Stochastic Methodology for EV Fast-Charging Load Curve Estimation Considering the Highway Traffic and User Behavior

The theoretical impact of the electric vehicle (EV) market share growth has been widely discussed with regards to technical and socioeconomic aspects in recent years. However, the prospection of EV scenarios is a challenge, and the difficulty increases with the granularity of the study and the set of variables affected by user behavior and regional aspects. Moreover, the lack of a robust database to estimate fast-charging stations’ load curves, for example, affects the quality of planning, allocation, or grid impact studies. When this problem is evaluated on highways, the challenge increases due to the reduced number of trips related to the reduced number of charger units installed and the limited EVs range, which influence user anxiety. This paper presents a methodology to estimate the highway fast-charging station operation condition, considering regional and EV user aspects. The process is based in a block of traffic simulation, considering the traffic information and highway patterns composing the matrix solution model. Also, the output block estimates charging stations’ operational conditions, considering infrastructure scenarios and simulated traffic. A Monte Carlo simulation is presented to model entrance rates and charging times, considering the PDF of stochastic inputs. The results are shown for the aspects of load curve and queue length for one case study, and a sensibility study was conducted to evaluate the impact of model inputs.

Load configuration impact on energy community and distribution grid: Quantifying costs, emissions and grid exchange

Energy communities are emerging across Europe, and each country is currently in the process of forming regulations for their integration into the electricity grid. The efficacy of energy communities depends upon various factors, including member demographics, technological aspects, load profiles, solar irradiation, and spot prices within the community’s geographical location. Notably, existing studies on energy communities predominantly focus on residential load profiles, with limited exploration into their impact on the distribution grid. This article aims to contribute to the existing literature by investigating the benefits of energy communities and their grid impact under diverse member configurations. Our approach involves the development of an optimisation model incorporating battery energy storage and shiftable loads, aimed at minimising the operational costs of energy communities over a one-year period. Case studies in Norway and Spain, with different load configurations: residential, commercial, and mixed load, are undertaken, utilising real hourly measurements to identify operational variations influenced by geographical location and seasonal fluctuations in load and photovoltaic (PV) generation. Additionally, we quantify the costs, CO2 emissions, and self-consumption rates for energy communities. Furthermore, we assess the distribution grid impact in terms of import and export dynamics. The results underscore the substantial influence of load configurations on member benefits and distribution grid impacts, attributable to the inherent correlation between load and PV generation. In the context of energy community benefits, commercial loads demonstrate the best outcomes in Norway, whereas residential loads exhibit superior results in Spain. Conversely, concerning distribution grid impact, commercial loads prove most advantageous in Norway, while mixed loads yield the best results in Spain. Overall, our findings indicate that Spanish energy communities consistently achieve more substantial reductions in costs and CO2 emissions compared to their Norwegian counterparts, irrespective of the load configuration. This study contributes valuable insights for policymakers, researchers, and industry stakeholders involved in the development and regulation of energy communities across Europe.

Quad‐active‐bridge converter with flexible power flow based on LC series resonance decoupling for renewable energy charging stations

AbstractIntegration of photovoltaic panels (PV) with electric vehicle (EV) charging stations could reduce the grid impact and carbon footprint from the extensive fast and ultra‐fast charging. This paper introduces a decoupled quad‐active‐bridge converter (QAB) with multi‐directional power flow capability, which can integrate PV, energy storage (ES), grid, and EV in a charging station and rule the power among them. As the number of ports increases in the multiple‐active‐bridge converter, the complexity of control increases exponentially because of power decoupling. For the QAB, by tuning one port in series with LC units, the decoupling is achieved between the other three ports, reducing the control difficulty significantly. Further, the resonance decoupling method is extended to the n‐port converter. For different power flows, the system automatically switches between different resonance modes to form higher‐efficiency power flow channels. Three operating modes with decoupled closed‐loop control methods have been constructed for the QAB to be suitable for future charging stations: (1) Charging mode: PV, ES, and grid coordinate with each other to provide incessant and stable charging for EV. (2) Electricity sales mode: Gird is supplied by PV, ES, and EV flexibly. (3) Under no load, PV power is stored locally. Experimental waveforms were presented by a 1‐kW prototype, verifying the effectiveness of power decoupling and the feasibility of the three operating modes.

Decentralized Smart Charging of Electric Vehicles in Residential Settings: Algorithms and Predicted Grid Impact

Decentralized Smart Charging of Electric Vehicles in Residential Settings: Algorithms and Predicted Grid Impact

Performance analysis and capacity configuration of building energy system integrated with PV/T technology under different operation strategies

Implementing suitable operation strategies significantly enhances the overall performance of the energy network of solar buildings. Previous studies predominantly concentrated on dispatching energy from standalone photovoltaic or photothermal modules within buildings. The photovoltaic/thermal (PV/T) modules have garnered attention due to their higher energy efficiency. However, the investigations targeting the operation strategies of PV/T modules in building remain scarce. To fill this research gap, this study scrutinized the maximum self-consumption (MSC), time-of-use (TOU), and optimization-based (OPT) strategies within a building energy system integrated with PV/T modules. The investigation encompassed an analysis of overall performance, parameter sensitivity, and optimal energy storage capacity. The main results were: (1) The OPT strategy displayed superior flexibility and lower operation costs compared to other strategies but exhibited lower energy utilization efficiency and greater grid impact. (2) The MSC strategy demonstrated lower sensitivity concerning battery power and charge states when contrasted with other strategies. (3) The optimal capacities for battery and water tank configurations for the MSC, TOU, and OPT strategies were identified as 4 kWh/200 L, 2.5 kWh/200 L, and 4.5 kWh/100 L, respectively. This research aims to provide reference and insights for planning the integration of PV/T modules into building energy systems.

Research on a UAV spray system combined with grid atomized droplets.

UAVs for crop protection hold significant potential for application in mountainous orchard areas in China. However, certain issues pertaining to UAV spraying need to be addressed for further technological advancement, aimed at enhancing crop protection efficiency and reducing pesticide usage. These challenges include the potential for droplet drift, limited capacity for pesticide solution. Consequently, efforts are required to overcome these limitations and optimize UAV spraying technology. In order to balance high deposition and low drift in plant protection UAV spraying, this study proposes a plant protection UAV spraying method. In order to study the operational effects of this spraying method, this study conducted a UAV spray and grid impact test to investigate the effects of different operational parameters on droplet deposition and drift. Meanwhile, a spray model was constructed using machine learning techniques to predict the spraying effect of this method. This study investigated the droplet deposition rate and downwind drift rate on three types of citrus trees: traditional densely planted trees, dwarf trees, and hedged trees, considering different particle sizes and UAV flight altitudes. Analyzing the effect of increasing the grid on droplet coverage and deposition density for different tree forms. The findings demonstrated a significantly improved droplet deposition rate on dwarf and hedged citrus trees compared to traditional densely planted trees and adopting a fixed-height grid increased droplet coverage and deposition density for both the densely planted and trellised citrus trees, but had the opposite effect on dwarfed citrus trees. When using the grid system. Among the factors examined, the height of the sampling point exhibited the greatest influence on the droplet deposition rate, whereas UAV flight height and droplet particle size had no significant impact. The distance in relation to wind direction had the most substantial effect on droplet drift rate. In terms of predicting droplet drift rate, the BP neural network performed inadequately with a coefficient of determination of 0.88. Conversely, REGRESS, ELM, and RBFNN yielded similar and notably superior results with a coefficient of determination greater than 0.95. Notably, ELM demonstrated the smallest root mean square error.

Industrial Energy Communities: Energy Storage Investment, Grid Impact and Cost Distribution

Industrial Energy Communities: Energy Storage Investment, Grid Impact and Cost Distribution

Assessment of Electric Vehicles Charging Grid Impact via Predictive Indicator

Assessment of Electric Vehicles Charging Grid Impact via Predictive Indicator