Impact of EV Interfacing on Peak-Shelving &amp; Frequency Regulation in a Microgrid

doi:10.21203/rs.3.rs-4822996/v1

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Impact of EV Interfacing on Peak-Shelving & Frequency Regulation in a Microgrid

https://doi.org/10.21203/rs.3.rs-4822996/v1

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A "vehicle-to-grid system" enables electric vehicles to be charged by the grid and to discharge electricity back into it when required. This paper presents the performance of a microgrid under the condition of interfacing the electric vehicle with a typical microgrid, which consists of solar, wind, and diesel power generation along with residential and industrial loads. In this research, the V2G effect on chopping the peak of load and on the regulation of frequency is observed. Diverse practical situations are considered to find the effect under a wide range of scenarios. The complete Phasor model is modelled and simulated in MATLAB/Simulink. Simulation results show that for a wide range of inputs V2G effectively regulated the grid frequency and reduced the peak of power demand.

Physical sciences/Energy science and technology

Physical sciences/Engineering

V2G

electric vehicle

PEV

smart Grid

wind energy

frequency regulation

peak shelving

Microgrid

Microgrids have gained some new-found popularity as a potential solution to many of the problems with traditional centralized power grids that we are experiencing right now and in recent years. More efficient and reliable small-scale power systems, like microgrids also frequently use renewable energy sources in addition to implementing both energy storage devices and management strategies. Microgrids are scalable and can operate in isolation or as part of the larger grid. Finally, the deployment of microgrids can help to reduce greenhouse gas emissions and increase energy independence.

Electric vehicles (EVs) are the latest addition to the market that potentially provide a critical mass for microgrid-minded operators. With this technology, EVs can be used as a mobile bank of energy storage that can implement bi-directional charge or discharge activity in the microgrid state based workload and provide additional resources to those available from renewable sources. This could inturn result more efficient use of energy and hence would ultimately decrease the amount of standby generators or other power supply that is required.

Incorporating EVs can give rise to the system stability and power quality problems. Variation of voltage and frequency due to intermittent charging and discharging the EVs might disrupt performance for other connected microgrid loads/utilities. The penetration of EVs into the power system network is potentially crucial in this respect.

The focus of this research paper is to analysis battery storage from movable plug in electric vehicles (PEVs) on peak shelving and frequency control within a distribution network. Distribution network is enabled to store the energy and use it in peak demand hours also known as Peak-shelving. Frequency Regulation - The capacity of the microgrid to reliably maintain a consistent level of frequency during and following larger shifts in power supply or demand [1]

The work presented here, models a micro grid system and forms several scenarios of charging/discharging EVs with analysing the interaction among different sources on the distribution system network [2][3].

In microgrid operation and control research, there are continuous efforts being made to improve the performance of these systems. One decentralised control method proposed is to use local controllers which can supervise the distributed energy resources (DERs) within the microgrid [2, 4]. There are other people who propose a more coordinated multi-agente based distributed energy management system to alleviate the microgrid performance from fluctuations by managing supply / demand trends in real time [1]. Additionally, the methods of EV charging and discharging in microgrids have been investigated [14], including their influence on stability/power quality in conjunction to peak-shelving/frequency regulation with individual/fully DPV penetrated configuration. A two-layer hierarchical control strategy for microgrids was recommended in another study [3] which considers the operation of multiple distributed energy resources, comprising wind turbines, PV modules and as well as ESS. The above approach can provide more security and reliability in the microgrid with proper scheduling.

Figure 1 is a pie chart showing the breakdown of references used in the research paper. It visually represents the proportion of references from different years, with the majority (40%) being from 2024, followed by 20% from 2023, 16% from 2021, 12% from 2022, and smaller percentages from earlier years like 2020, 2019, 2016, 2014 and 2007. This chart helps illustrate the recency and distribution of the sources cited in the study.

Figure 2 depicts the configuration of the microgrid system connected with a Vehicle-to-Grid (V2G) setup that is being studied in the research paper.

Efficient methods of charging from - and discharging to - microgrids will need to be created for EVs, if they are ever going to work their way into the mix. Several works have been published on techniques for the EV charging and discharging as a means in microgrids. For instance, in [6] the authors proposed a practical model for real-time pricing of EV charging inside an energy-aware microgrid. This approach reduces the energy costs for EV owners and enables more effective operation of a microgrid, according to the proposal. In a related work, [7] proposed an EV charging and discharging strategy based on the electricity consumption times in different loads with existing state of charge (SOC) level of consuming EVs. It can effectively balance the load and decrease the peak demand of microgrid, which conforms to our recommended strategy.

The participation of electric vehicles (EVs) into microgrids can have an influence on the system stability and power quality. This has made EVs are an interesting object of study on the two from stability and quality aspect in microgrids worldwide. For example, may have investigated how EV charging affects a microgrids voltage stability [2]. Voltage fluctuations in the microgrid that could have implications for linked loads and generators are caused by EV charging, according to the study. A different paper [6] investigated the impact of EVs on frequency stability for a microgrid. The study noted that if too much charging or discharging took place, it could affect oscillations in frequency and therefore lead to instability without optimizing the using of EV for grid stabilization.

[8] developed a new peak-time rebate based demand-responsive microgrid energy management.

In sum, the literature highlights that proper charging and discharging protocols as well as an integrated assessment of microgrid stability and power quality under EV interfacing are needed in order to introduce EVs within microgrids. The importance of researching how the EV interface might impact with peak-shelving and frequency control in a microgrid was glowing [5], hence valuable insights can be glean-able for Micro-grid operators, and electric vehicle manufacturers on best practices to integrate EVs into microgrids.

The study employs simulation of a microgrid with EV interfacing via MATLAB/Simulink environment. The EV interfacing system is included in the microgrid that contains a wind turbine, PV array and diesel generator as well as an energy storage system(ESS). The bi-directional converter along with battery pack as energy storage component of microgrid and EV interfacing system is considered to store the power when not utilized from Grid or in case of Vehicle by using this available resource. The parameters are used in the simulation model to represent a real microgrid system. The vehicle-to-grid system illustrated above helps manage a microgrid's frequency response to events unfolding over the course of an entire day. Phasor mode in Power Systems is used to quickly reproduce the situation and assess its impacts.

An electric vehicle uses power from the grid to charge itself, and can discharge power into the grid if necessary using technology called a "vehicle-to-grid system. OnPower uses vehicle-to-grid technology to help regulate the frequency of a microgrid (a measure of how quickly or slowly electricity is being supplied and taken up) in such situations. The electrical system shown in Fig. 3 is a schematic of the microgrid for power supply to neighborhood with an order of one thousand houses. The microgrid comprises of a quartet of vitally important parts that all synergize together in the production and disbursementof electricity.

The first component of the microgrid is a diesel generator. This generator which acts as the main power jet kicks in when renewable energy resources become unsuccessful to generate enough electricity to meet up community needs. The next part of the microgrid is composed by a PV (photovoltaic) farm and windfarm. Using these two renewable energy sources together generates enough electricity for the neighbourhood. A V2G (vehicle-to-grid) system comprises the third component of the microgrid. Here, the system is installed beside load of grid i.e., where electricity gets directly consumed in our community. That way, electric vehicles can be charged during off-peak hours and deliver the electricity they have stored on to power providers in peak demand times through V2G system. It deals with the problem of unbalance between supply and demand in microgrid electricty. The microgrid's final piece is the load of the grid, which essentially stands for energy consumption by locality. The microgrid would have the capacity to serve approximately 1,000 homes on a low-demand day in spring or fall. To further confirm the 1-to-10-cars-per-household ratio, the base model consists of at least a hundred EV. This is only a realistic future because more and more communities are being run with renewable energy sources providing the power for these cars which would make easily accessible fuel intake at home.

5.1 Diesel Generator

The diesel generator, which serves as a gap filler in both directions; the frequency deviation of microgrid is determined by monitoring rotor speed while measuring synchronous machine.Diesel generator are commonly used for emergency power supply and not common in some location without connections to the national grid. Further, they can also be used for versatile functions such as peak-shelving or in grid supporting and export to the grid. One of the common challenges like low load and shortages power arising as a result diesel generators being undersized. The specifics of modern electronics - in particular, non-linear loads - is such that it can be quite a challenge. From sizes of about 50 MW and up, open-cycle gas turbines are much more compact as well as efficient while diesel ranges might be made use of for either base load or peak hours demans because they have higher part-load performances than straight-fuel burning engines. The diesel generator is of importance for power output and consumption equilibrium, also the frequency deviation in microgrid can be detected via monitoring rotor speed of synchronous machine. Diesel generators are widely used for many applications, and proper sizing is very necessary to achieve the anticipated power-related problems. Open-cycle gas turbines may be more efficient and portable for larger power requirements, but diesel arrays will still likely remain the preferred choice on average part-loading because of their higher efficiency.

5.2 Renewable Energy

The microgrid under consideration has two renewable energy sources PV and Wind turbines. First, size of area the PV farm covers, irradiance data, and the efficiency of the solar panels all affect how much electricity the farm produces. Figure 3 depicts the variation of solar power throughout the day.

The power generated from wind turbines depends upon the consistency and velocity of wind and the swept area of the rotor.

Named a top technology of 2020, the V2G system provides grid energy supply-demand balancing and reduced transportation related GHG emissions alongside greater renewable integration opportunities. The V2G which stands for vehicle-to-grid allows the electric vehicles to be charged by thags electrical grid that in then can discharge electricity back into them as needed The two main functions of a V2G system within this context are:

Control of connected battery charging – the V2G system will manage when and for how long those batteries in attached electric vehicles are recharged or discharged from. So it can control how much energy is going to and from the batteries that way only keep charge when need be without over charging.
- DayTime event: for such a daytime, we also can use the V2G system as grid control. One such system is the V2G, which can order EVs that are plugged in to discharge power and try filling a gap if there's a sudden surge of electric consumption on the grid.

The V2G block has five types of car-user profiles. These profiles could be the result of different driving styles, differing charging routines or any other factors that may influence how well V2G will work.

Profile 1: Work charging at all times of dayThis profile likely represents those that are able to charge their electric vehicles while they're at work during the day. This implies they will most likely return at home battery charged - maybe even fully or partially [5], [7], [9–16].

Profile 2 - Long Commuter (may charge at their place of work): This is similar to profile 1 but simply has an expanded commute. The upshot is that the battery of an electric vehicle could be a low at point 2, but still help top off and recharge if he or she can park while working.

Profile 3: Single occupancy electric vehicle commute drivers that do not have the option to charge them at their job This describes people who can drive TO work in an EV no problem but they are unable to recharge while AT WORK. This means by the time they make it home in the evening, their car batteries are more likely to be depleted.

People staying at home - This profile most likely includes those who do not normally commute to work and are spending the daytime in their homes. Profile 4 They will be fully charged, or not completely discharged depending on their usage profile.

Car-user profile 5: Night shift workers They are also possibly not driving during the day, so they could have different charging behaviors than the former profiles. There might be traits of this profile...the list goes on...

1) Charging Schedules

People who work during the night can charge their electric vehicle in daylight while they are sleeping or at work. As a consequence, the V2G system may have to provide power on occasions that are different from those with previous pro- files [5].

2) Discharging Patterns

An EV owner returning from a long night shift with an electric battery car may find it completely discharged, or half run-down, depending on how many hours he has worked. This means that the V2G system might have to support the grid in what can possibly be a time of reduced electricity consumption at early hours [1].

3) Driving Habits

Night workers may still have to commute to and from work, but their routines can be more erratic than those of the typical 9 − 5 worker. For example, if they live farther from the workplace than they do today, then perhaps that means lower traffic or a longer commute.

Profile five increases the total complexity of an V2G system as it requests a intelligent structure to control and share the power for that different charging and discharging patterns [1].

Bear in mind these are kind of hypothetical profiles and might not perfectly represent behaviours/tendencies different drivers have. Different scenarios can be tested using several profiles in order to study the performance of V2G system and potential benefits.

4) Load

The microgrid load is a combination of residential consumption and an asynchronous machine, simulating industrial inductive effects. IM behaviour is a square relation between its rotor speed and the mechanical torque as compared to residential consumption following specific power factors, patternized. A day-long simulation of wind and sun (sun generally peaking at noon, while the strength of wind varies with multiple peaks and valleys) In practise, a household energy consumption profile can be approximated by the split of load in residential use since people consume more during day and less at night. There are three primary activities to be carried out during the day, and this will affect both of them or is explained [17–20].

For example, the loss of a big nuclear plant might see demand fall to 200GW but when up after two hours only reach halfway while locked down asynchronous machine pick-up which would push put over 70cO2 factories.Any partial shading in the afternoon will also decrease the frequency of solar power, but only for households on this system.

Third, the wind farm will be finally tripped at maximum output 22:00 hours of turbine. This lead to a sharp reduction of the wind farm generation and hence, further frequency decrease. With the assistance of specialist software and hardware systems that can model and simulate these types of events, they may even evaluate if/when we lose power in outbuildings (the stability of our microgrid), which will help them develop plans to reduce frequency changes during such events.

The simulation's findings shed important light on how the microgrid's generating systems work and how the Vehicle-to-Grid (V2G) technology affects peak regulation. The outcomes specifically show how much power is produced by each system, including the diesel generator, PV farm, and wind farm. It is feasible to ascertain the contribution of each generation system to the microgrid's overall power supply by evaluating this data.

The simulation findings also demonstrate how the V2G technology affects peak regulation. The V2G system contributes significantly to grid frequency control by controlling the charge of the batteries connected to it and utilizing the available electricity to balance the grid's power consumption during peak hours. It is feasible to evaluate the performance of the V2G system in preserving the grid's stability and averting frequency variations during peak periods using the simulation results. The design and operation of the microgrid can be optimized using this information, which will also raise the microgrid's overall performance.

Midday is when the solar intensity is at its peak according to a normal distribution. Throughout the day, wind exhibits multiple peaks and valleys, while a typical household's consumption pattern resembles the distribution of the residential load. Consumption is minimal during daylight hours, reaches a peak in the evening, and gradually diminishes throughout the night. This case study examines two partial shedding effects detailed in Table 1. The industrial load initiates at 3 Hr. The car profile selected for this scenario is outlined in Table 2. Additional insights into the simulation results will be elaborated below.

Table 1

Shedding Effect
Partial Shedding No.	Event Occur at	Duration	Factor
1	12 Hr	10 Min	0.7
2	14 Hr,	15 Min	0.5

Table 2

EV Profile
Profile Name	Number of Cars
Profile-1	35
Profile-2	25
Profile-3	10
Profile-4	20
Profile-5	10

7.1 Impact of EV Interfacing on Frequency Regulation

With V2G Enabled:

The frequency regulation improves as the number of connected electric vehicles (EVs) increases from 20 to 100 across Cases 1–5 (Figs. 4–8). The V2G system helps stabilize the grid frequency by controlling the charging and discharging of the connected EV batteries.

Case 1

Vehicle load = 100 *40 kW

Case 2

Vehicle load = 80 *40 kW

Case 3

Vehicle load = 60 *40 kW

Case 4

vehicle load = 40*40 kW

Case 5

Vehicle load = 20*40 kW

With the support of V2G, the frequency regulation is improved as shown in Figures for Case 1 to Case 6 shown in Fig. 4 to Fig. 8.

With V2G Disabled

Figure 9 shows that when EVs are not connected to the microgrid (V2G off), the frequency variation is significantly worse compared to when V2G is enabled. This highlights the important role V2G plays in maintaining grid stability.

7.2 Impact Of EV Interfacing On Peak-Shelving

Figure 10 depicts the variation in total electrical load on the microgrid over a 24-hour period, with consumption peaking in the evening hours.

The diesel generator output (Fig. 11) ramps up during these peak load times to help meet the increased demand.

Solar power generation (Fig. 12) follows a normal distribution, peaking around midday in line with maximum sunlight intensity.

Wind power (Fig. 13) fluctuates throughout the day with multiple peaks and valleys.

Figure 14 combines the power generation profiles from the PV farm, wind farm, diesel generator along with the total power and load. This illustrates the relative contributions of each source in meeting the microgrid's energy needs over the course of the day.

The results demonstrate:

V2G is highly effective at regulating microgrid frequency and improves dynamic performance, with more connected EVs providing better grid support.Optimal EV charging and discharging schedules can enable peak power shaving in the microgrid. The simulations quantify the daily variation in electricity demand and generation from solar, wind, and diesel sources - providing a holistic view of their interplay in powering the microgrid.These findings underscore the potential for EVs, when intelligently integrated via V2G, to enhance the effectiveness and efficiency of microgrids. As EV adoption grows exponentially, studying their impact when interconnected with larger power networks is a key area for future research.

This research explored the impact of electric vehicle (EV) integration on peak-shelving and frequency regulation in a microgrid using a MATLAB/Simulink model. The results show that Vehicle-to-Grid (V2G) operation effectively controls microgrid frequency, with regulation improving as the number of connected EVs increases from 20 to 100[1]. This highlights the potential for EVs to provide valuable grid support services when intelligently integrated via V2G. Moreover, proper EV charging and discharging schedules can enable peak power shaving in the microgrid, reducing reliance on diesel generation and enhancing overall efficiency. The simulations quantify the daily variation in electricity demand and generation from solar, wind, and diesel sources, illustrating their relative contributions in meeting the microgrid's energy needs.

In conclusion, this research underscores the immense potential for EV integration to enhance the effectiveness and efficiency of microgrids. As EV adoption grows, studying their impact when interconnected with larger power networks is crucial. Advanced control strategies, optimization algorithms, and market mechanisms should be developed to maximize V2G benefits while ensuring grid stability. The environmental and economic implications of widespread EV integration in microgrids also merit further investigation to provide valuable insights for policymakers and stakeholders.

Author Contribution

Sharika Shrivastava: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data Curation, Writing - Original Draft, Writing - Review & Editing, Visualization, Project administration.Saifullah Khalid: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data Curation, Writing - Original Draft, Writing - Review & Editing, Supervision.D.K. Nishad: Conceptualization, Methodology, Validation, Formal analysis, Investigation, Resources, Writing - Original Draft, Writing - Review & Editing, Supervision, Project administration, Funding acquisition.All authors contributed to the study's conception and design. Sarika Shrivastava developed the simulation model, performed the simulations, and analyzed the data. Sharika Shrivastava wrote the manuscript's first draft, and all authors commented on previous versions. All authors read and approved the final manuscript.Sharika Shrivastava and Saifullah Khalid were responsible for data collection, data analysis and interpretation. D.K. Nishad supervised the project and acquired funding. All authors were involved in conceptualizing the study, developing the methodology, validating results, and reviewing and editing the manuscript.

Data Availability

Data availability:The datasets used and/or analyzed during the current study available from the corresponding author on reasonable request.

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No competing interests reported.

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You are reading this latest preprint version

Impact of EV Interfacing on Peak-Shelving & Frequency Regulation in a Microgrid

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Version 1

Abstract

Figures

1. Introduction

2. Literature Review

3. Charging and Discharging of Grid Interfaced Electric Vehicle

4. Impact of EVs On Stability and Power Quality of The Microgrid

5. Methodology

5.1 Diesel Generator

5.2 Renewable Energy

6. Vehicle-To-Grid

1) Charging Schedules

2) Discharging Patterns

3) Driving Habits

4) Load

7. Result & Discussion

7.1 Impact of EV Interfacing on Frequency Regulation

7.2 Impact Of EV Interfacing On Peak-Shelving

8. CONCLUSION

Declarations

Author Contribution

Data Availability

References

Additional Declarations

Status:

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