Electric Vehicles, Solar Panels, and more as DERs:
Distributed energy resources, or DERs, are electricity supplies interconnected to the grid station. DERs encompass a spread of physical and virtual assets. They’re electricity generation assets and are usually located close to the center of loads and may be used personally or in aggregate to offer a grid with a value.
DERs are typically under 10 MW in ability and may consist of diesel, gas generators, microturbines, solar arrays, small wind farms, distributed storage (DS), and more. They can be owned and operated by electric software, independent electricity manufacturers, or local businesses. The software directs its operation equally that it controls big meaningful energy plant life operation, requesting starts and stops as needed.
Electric Vehicle Charging Stations:
Electric-powered cars, residential solar panels, industrial backup generators, and greater are all DERs just ready to be “harvested” by using an aggregator. Below the proper regulatory framework and with the capabilities outlined above, aggregating one-hundred megawatts of DERs may be more straightforward, inexpensive, and faster than building an equal-length power plant.
The application may have direct control over the batteries; and feature the choice to apply them for any quantity of offerings, which includes voltage manipulation, frequency manipulation, and height shaving. Virtual power plants and virtual DERs are a hastily evolving area.
A destiny milestone for this sector might be to discover a way to aggregate electric vehicles into virtual power plants referred to as vehicle to Grid (V2G) technology. Most people with EVs spend most of their time parked and plugged in—in other words, linked to the grid.
Therefore, the wondering goes, can EV batteries be used as DERs? Because the amount of lithium-ion batteries established in electric automobiles exceeds the number of batteries used in desk-bound strength programs by one or two, the capability benefit of harnessing EVs is massive.
There are many challenges to overcome before DERs can be deployed to their full ability. But, they’re considered one of the most important possibilities to fulfill destiny needs inside the strength quarter.
Electric Vehicle Objective Function:
With the backward forward load flow analysis of the IEEE 33 Bus system, use the Particle Swarm Optimization, PSO algorithm on MATLAB into the distribution network to optimize the placement of Electric Vehicle (EV) Chargers on the IEEE-33 Bus Systems which I have split into 5 regions. The optimal location of the Electric Vehicle EVs is considered along with voltage deviation consideration.
To learn more about the relevant article, load flow analysis using Matlab code click on the link.
So I split them into regions because I want to depict them as different areas. So each node for the ‘residential areas’ will represent a household (flat). I will be assuming default 40 kW spot loads represent 78 households default 20 kW spot loads represent 39 households, assuming that 35.3% of each flat owns a car.
So I need enough EV chargers for them. So for optimization, I want to optimize the number of EV chargers installed in each ‘region’ without exceeding the load and this will be done using PSO. The main contributions of this project are EV integration and improvement in the voltage profiles.
As for the malls and industrial (nodes 7,8,24,25,30), I will be ignoring them. Placing EV chargers for the public, to charge their vehicles (private vehicles)
Case Study & Other Details:
Load flow: backward forward sweep
Test System: IEEE 33
Optimization: Particle Swarm Optimization (PSO)
Matlab Code Explainer:
Question: Does this mean each electric vehicle charging station has the same number and chargers rating?
Answer: (No, The Number of chargers in the station depends on the number of cars in each region).
Question: May I know the rating and how many fast and slow AC/DC chargers are in each station?
Answer: (In the proposed approach we suppose we can install 1 DC Charge and the remaining capacity covered by AC Charger).
Question: How about regions 2, 3, 4, 5, and 6? How did you determine the real power of the EV charging station to be 54.6, 149.7, and so on?
Problem Formulation: PS(round(EV(n),0))=PS(round(EV(n),0))+Ps(n)
Explanation: The station’s value is fixed, but the location is optimized. So the value of Ps and Qs is fixed. PS & QS is manual calculation.
How to choose these values:
Ps=[133.2 54.6 149.7 54.6 129.9];
Qs=[84.1 34.5 94.5 34.5 82];From your graph
In Region 1, it has 9 cars
Each station has 1 DC Charger =120 kw (It’s enough for 5 cars in one hour)
And the remaining 4 cars, should have 4 AC Charger=3.3 kw.
So total =120+3.3*4=133.2 kw.
Reactive power=133.2/PF*sin(COS-1 (PF)). PF=0.85.
Question: Is there any way to automatically produce the value of the chargers in the station instead of fixing it?
Answer: Fixed because the number of the car in each region is fixed.
Question: What do L and C stand for in terms of wording? (Location of Charger Station)
PSO File, Line 22
LC=[2 19 20 21 22 22 22 22 22
3 23 24 25 25 25 25 25 25
4 5 6 26 27 28 29 31 33
9 10 11 12 12 12 12 12 12
13 14 15 16 17 18 18 18 18];
Specification of Chargers as per Regions
Region 1: 9 Cars, 120 (1 DC charger) + (3.3×4) (4 AC Chargers) = 133.2
Region 2: 4 Cars, 54.6kW/3.3kW = 16 AC chargers
Region 3: 14 Cars, 120kW (1 DC charger) + (3.3kW x 9) (9 AC Chargers) = 149.7kW
Region 4: 4 Cars, 54.6kW/3.3kW = 16 AC chargers
8 Cars, 120kw + (3.3kW x 3) (3 AC Chargers) = 129.9kW
Question: What’s the benefit of the construction coefficient?
Explanation: To get better results. And it’s used in PSO Formula. If you want to use Constriction Coefficients for PSO, In Matlab code, uncomment the following block and comment on the above set of parameters.
% % Constriction Coefficients
We didn’t use it because the solution is worse than conventional PSO. In some problems, the construction coefficient improves the results, but in our problem solution becomes worse.
Permitted Reactive and Real Power Constraint:
Load flow 1, 2:
The first file is to find the load flow solution with the backward forward sweep load flow. The second file is to make the load flow for the best solution and give the power loss and voltage profile result for each bus.
We need two files because the function’s input and result differ.
Question: How did you decide on ‘1’ and ‘9’ in the proposed method?
VarMin=[1 1 1 1 1]; % Decision Variables Lower Bound Size of DG
VarMax=[9 9 9 9 9]; % Decision Variables Upper Bound Size of DG
Answer: Because the maximum number of buses in the region is 9 that exists in region 3
Question: What does this line represent and mean?
Answer: Each particle in the PSO Method goes to the best location by calculated velocity, and this velocity has a limited value and can’t be exceeded.
Question: What does this line mean, and how are velmax and velmin determined as 0.8 and -0.8?
Answer: The limited velocity depends on the maximum and minimum location values (Distance), and the standard of PSO is 0.1 from that distance.
Hire Matlab Expert:
This project is tested through load flow Matlab and interested to learn more, looking into research assistant, click on the link Matlab Expert System.
Other relevant projects are IEEE 69 Bus System Data and FACTS Devices.
D. S. Kumar, H. Binte Ahmad Jefri, A. Sharma and W. L. Woo, “Planning of Distributed Energy Resources in Singapore,” 2019 IEEE Innovative Smart Grid Technologies – Asia (ISGT Asia), 2019, pp. 3953-3958, doi: 10.1109/ISGT-Asia.2019.8881383.