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IEEE 2026 · Multi-EV Aggregator · Renewable Co-Simulation · ISO 15118 · Demand Response · Bangalore

V2G Smart Grid Integration MATLAB Simulation

The definitive guide to Vehicle-to-Grid Smart Grid Integration in MATLAB/Simulink — multi-EV fleet aggregation, renewable (PV/wind) co-simulation, ISO 15118 communication latency, demand-response scheduling, primary frequency & voltage support, and microgrid islanding. Includes annotated Simulink block diagrams, multi-agent control architecture and ready-to-run MATLAB scripts for EEE, ECE and EV-stream final-year projects in Bangalore. Full delivery: .slx multi-EV model, scripts, IEEE 2026 paper, report, PPT and viva coaching.

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Smart-Grid Circuits
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Sample Scripts
IEEE
2026 Papers
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Projects Delivered

V2G Smart Grid Integration Simulation

Software Tools & Platforms Used

Industry-standard environments for V2G–smart-grid co-simulation used at VTU, Anna University, NIT, IIT and leading research labs for multi-EV aggregator, renewable integration and demand-response studies.

MATLAB R2024b Simulink / Simscape Simscape Electrical Python (control, cvxpy) GridLAB-D OpenEMS PSCAD / EMTDC PLECS CYME / DigSILENT HOMER Pro
V2G Smart-Grid Architecture — System Layers
Distribution Feeder · EVSE Fleet · Aggregator · DSO Interface · Renewable Plants · Communication Stack

How V2G Fits Inside a Modern Smart Grid

V2G Smart Grid Integration treats every connected EV as a controllable distributed energy resource (DER). A hierarchical control stack coordinates hundreds of bidirectional chargers so that the fleet collectively provides frequency regulation, voltage support, peak shaving and renewable smoothing while respecting driver mobility constraints and battery health.

In the MATLAB/Simulink environment the architecture is decomposed into four layers:

  • Physical Layer: Distribution feeder (11 kV / 400 V), PV arrays, wind turbines, transformer, line impedance models, and N individual EV charger subsystems (battery + DC-DC + VSI + LCL filter).
  • Local Control Layer: Per-charger PLL, d-q current controllers, SOC estimator and local mode logic (V2G / G2V / Standby).
  • Aggregator Layer: Central dispatch engine that receives DSO set-points, renewable forecasts and per-EV SOC/departure data, then allocates power references every 5–15 min.
  • Communication Layer: ISO 15118 / OCPP 2.0 message latency and packet-loss models implemented as Transport Delay and Random Number blocks.

Key performance indicators tracked in simulation: grid frequency deviation (±0.2 Hz), voltage profile at PCC (±5 %), total fleet THD, renewable self-consumption ratio, and cumulative battery degradation (Ah-throughput).

Simulink Tip: Use Simscape Electrical → Specialized Power Systems for the feeder and power-electronics blocks. Model the aggregator as a MATLAB Function block or Stateflow chart that runs at a slower sample time (Ts_agg = 1–5 s) while the power stage runs at Ts = 5 µs.
Multi-EV Aggregator Control
Fleet Dispatch · Priority Rules · SOC Fairness · Departure-Time Constraints · Virtual Power Plant (VPP)

Aggregator Logic Implemented in Simulink

The aggregator turns a heterogeneous fleet into a single Virtual Power Plant (VPP). Every control cycle it solves:

  • Power balance: Σ P_EV,i + P_renewable − P_load = P_grid_setpoint
  • SOC fairness: Prefer discharging high-SOC vehicles and charging low-SOC vehicles so that no single battery is over-cycled.
  • Departure-time constraint: Vehicles leaving within the next T hours must reach a user-defined minimum SOC; the aggregator reserves charging capacity for them.
  • Priority hierarchy: (1) Battery protection (SOC < 15 %), (2) Frequency support (Δf outside dead-band), (3) Voltage support (Q injection), (4) ToU cost minimisation / renewable absorption.

In Simulink the aggregator is realised as an Embedded MATLAB Function that receives vectors of SOC, Ibat, departure flags and DSO set-points, then outputs a vector of Iref commands. A communication delay block (50–200 ms) emulates ISO 15118 message latency.

Renewable Co-Simulation with V2G
PV Array · Wind Turbine · Curtailment Avoidance · Self-Consumption · Islanded Microgrid Mode

PV + Wind + V2G Fleet Co-Simulation

High renewable penetration creates net-load ramps and reverse power flow. The V2G fleet acts as a fast-acting buffer: excess solar midday is absorbed by controlled G2V charging; evening ramp-down is smoothed by coordinated V2G discharge.

  • PV model: Simscape PV Array block with irradiance and temperature profiles (1-min resolution). MPPT is assumed ideal; power is fed through a DC-AC inverter into the same 400 V bus as the EV chargers.
  • Wind model: Wind Turbine + Doubly-Fed Induction Generator (DFIG) or simple PMSG model driven by a wind-speed time series.
  • Curtailment metric: Any renewable energy that would otherwise be curtailed is first offered to the aggregator. The aggregator increases G2V current references until either the renewable surplus is absorbed or fleet SOC limits are reached.
  • Islanded microgrid mode: When the grid breaker opens, the aggregator switches to isochronous frequency control (or droop with secondary restoration) and maintains voltage via reactive-power sharing among the EV inverters.
ISO 15118 Communication & Latency Modelling
PLC / Wi-Fi / Cellular · Message Latency · Packet Loss · OCPP 2.0 · Secure Session

Why Communication Delay Matters in V2G Control

Primary frequency response must act within 1–2 s. ISO 15118 Power-Line Communication (PLC) or Wi-Fi/cellular links introduce 50–300 ms of latency plus possible packet loss. Ignoring this delay can make a perfectly tuned PI controller unstable when the fleet is large.

  • Latency model: Transport Delay block (fixed) or Variable Transport Delay driven by a Random Number source (50–250 ms uniform).
  • Packet loss: Zero-Order Hold + Random Boolean switch that occasionally freezes the last received Iref.
  • Session establishment: A simple Stateflow chart models the ISO 15118 handshake (SECC discovery → TLS → contract authentication) before the charger is allowed to accept aggregator set-points.
  • OCPP 2.0: Higher-level messages (SetChargingProfile, NotifyEVChargingNeeds) are emulated as discrete events every 15 min; the continuous current control loop remains at the local DSP level.
Simulink Circuit Diagrams — Smart-Grid V2G Models
Multi-EV Aggregator · Feeder Interface · Renewable Bus · Communication Delay · Islanded Microgrid
Circuit 1 — Multi-EV Aggregator Smart-Grid Overview (Fleet + Feeder + Renewables)
V2G Smart-Grid Integration — Multi-EV Aggregator Topology DIST. GRID 11 kV / 50 Hz 3-Phase Source XFMR 11kV/400V Δ-Y 400 V BUS PV ARRAY 50–200 kW WIND TURBINE 30–100 kW EV CHARGER 1 Bat + DC-DC + VSI 7.4 kW · SOC EV CHARGER 2 Bat + DC-DC + VSI 11 kW · SOC EV CHARGER N Bat + DC-DC + VSI 22 kW · SOC AGGREGATOR Dispatch Logic SOC Fairness Freq / Volt Support ToU + DR DSO / AGC Set-points P*, Q* ISO 15118 Latency 50–250 ms delay model Power flows: Grid ↔ Bus ↔ Renewables / EV Fleet · Control signals: DSO → Aggregator → Chargers (dashed) N chargers share the 400 V bus; aggregator allocates Iref every 5–15 s while local PLL runs at 5 µs
Distribution Grid / Transformer
400 V Common Bus + EV Chargers
PV Array
Wind Turbine
Aggregator Dispatch Engine
DSO / AGC Set-points
Circuit 2 — Single EV Charger Subsystem with Communication Delay & Local Control
Iref from Aggregator COMM DELAY Transport Delay 50–250 ms MODE LOGIC SOC + Δf + Iref → V2G / G2V Ibat_ref PI CURRENT Kp, Ki Duty / Idq PWM + POWER DC-DC + VSI LCL Filter Battery 350 V fsw = 10 kHz 400 V BUS PCC / Grid SOC feedback (coulomb counting / EKF) Local Control Loop with Communication Latency Emulation Iref is delayed → Mode Logic decides V2G/G2V → PI tracks Ibat → PWM drives power stage
Aggregator Iref Command
ISO 15118 Communication Delay
Local Mode / SOC Logic
PI Current Controller
Power Stage (DC-DC + VSI + LCL)
400 V PCC / Grid Interface
Circuit 3 — Islanded Microgrid Mode (Grid Breaker Open · V2G Frequency & Voltage Support)
UTILITY GRID (disconnected) OPEN µG BUS PV Island mode LOAD Critical EV FLEET Isochronous / Droop Control P + Q sharing f = 50 Hz V = 400 V AGGREGATOR Secondary f restore Q sharing weights SOC priority ISLANDED STATUS Δf < ±0.1 Hz ΔV < ±3 % When grid breaker opens, aggregator switches EV inverters to voltage-forming mode (isochronous or droop) and restores frequency via secondary integral control
Utility Grid (disconnected)
Open Circuit Breaker
Microgrid Bus + EV Fleet (voltage-forming)
Local PV Generation
Critical Load
Aggregator Secondary Control
Power Quality, Frequency & Voltage Support
Primary Frequency Response · Reactive Power Sharing · THD · IEEE 519 · Voltage Profile at PCC

Key Performance Metrics in the Smart-Grid V2G Simulation

  • Primary frequency response: Aggregator computes ΔP = −(1/R)·Δf for the whole fleet, then allocates the total power among available EVs proportional to their remaining SOC headroom. Response time < 1 s (including communication delay).
  • Reactive power (voltage) support: Each charger’s Iq reference is set by a local voltage-droop law or by an aggregator Q-setpoint. Voltage at the 400 V bus is kept within ±5 % of nominal.
  • THD: LCL filters per charger keep individual current THD < 3 %. Fleet-level harmonic cancellation further reduces aggregate distortion at the PCC.
  • IEEE 519 / IEC 61000 compliance: Verified with Powergui FFT Analysis and a post-processing MATLAB script that reports individual harmonic orders and total THD.

Grid Frequency (Hz)

Step disturbance of −0.3 Hz is restored by coordinated V2G discharge within 2–3 s. Dead-band ±0.1 Hz prevents unnecessary cycling.

Fleet Aggregate Power (kW)

Sum of all EV active-power contributions. Positive = G2V (charging), negative = V2G (discharging). Tracks the aggregator dispatch command with communication delay visible as a small lag.

Per-EV SOC Trajectories

High-SOC vehicles are preferentially discharged; low-SOC vehicles are charged. Departure-time constraints force certain EVs into G2V mode before they leave.

Renewable Self-Consumption

Percentage of PV/wind energy absorbed by the EV fleet instead of being exported or curtailed. Target > 80 % on sunny days.

PCC Voltage Profile

Phase voltages at the 400 V bus stay inside ±5 % during mode transitions and renewable ramps. Reactive-power support from the fleet is the primary control action.

Communication Latency Effect

Compare ideal (zero-delay) vs realistic (100–200 ms) dispatch. Large latency can produce overshoot; the script quantifies the degradation in frequency nadir.

Sample MATLAB Scripts for V2G Smart-Grid Integration
Fleet Parameters · Aggregator Dispatch · Renewable Profiles · Latency Sweep · Results Export
Script 1 — SmartGrid_V2G_Parameters.m  ·  Multi-EV Fleet & Feeder Initialisation MATLAB
%% SmartGrid_V2G_Parameters.m
% Run before opening the multi-EV Simulink model
% Projectsatbangalore | IEEE 2026 V2G Smart-Grid Integration

clear; clc;

%% ── FLEET DEFINITION ────────────────────────────────────
N_EV          = 8;                    % Number of EVs in the aggregator
P_rated_EV    = [7.4 7.4 11 11 22 7.4 11 22]*1e3; % W
Cbat_Ah       = [40 50 60 60 80 45 55 75];   % Ah
Vbat_nom      = 350*ones(1,N_EV);      % V
SOC_init      = [0.35 0.55 0.70 0.45 0.80 0.25 0.60 0.90];
SOC_min       = 0.15;
SOC_max       = 0.95;
departure_hr  = [2 4 6 3 8 1.5 5 7]; % hours from t=0

%% ── GRID / FEEDER ───────────────────────────────────────
Vgrid_ll      = 400;                  % V (line-line RMS)
f0            = 50;                   % Hz
S_base        = 500e3;                % VA (feeder rating)
R_line        = 0.02;                 % pu
X_line        = 0.08;                 % pu

%% ── RENEWABLE PLANTS ────────────────────────────────────
P_pv_rated    = 150e3;                % W
P_wind_rated  = 80e3;                 % W
% Simple irradiance / wind profiles (1-s resolution, 2 h)
t_prof        = 0:1:7200;
irr           = 800 + 200*sin(2*pi*t_prof/3600);  % W/m²
wind_spd      = 8 + 3*sin(2*pi*t_prof/1800);   % m/s

%% ── AGGREGATOR / CONTROL ────────────────────────────────
R_droop       = 0.04;                 % 4 % droop
deadband_f    = 0.1;                  % Hz
Ts_agg        = 5;                    % Aggregator sample time (s)
Ts_power      = 5e-6;                 % Power-stage sample time
comm_delay_ms = 120;                  % Typical ISO 15118 latency (ms)

%% ── LCL FILTER (per charger) ────────────────────────────
L1_filt       = 1.5e-3;
L2_filt       = 0.4e-3;
Cf_filt       = 15e-6;
Rd_filt       = 2.0;

disp('✔ Smart-Grid V2G parameters loaded. Open SmartGrid_V2G_Model.slx');
Script 2 — Aggregator_Dispatch.m  ·  Fleet Power Allocation with SOC Fairness MATLAB
%% Aggregator_Dispatch.m — Embedded MATLAB Function for Simulink
% Inputs : SOC (1xN), f_grid, P_renewable, P_load, departure_flag (1xN)
% Outputs: Iref (1xN), mode (1xN)   mode: 0=standby, 1=G2V, 2=V2G

function [Iref, mode] = Aggregator_Dispatch(SOC, f_grid, P_ren, P_load, dep_flag)
    N      = length(SOC);
    Iref   = zeros(1,N);
    mode   = zeros(1,N);
    f0     = 50;
    R      = 0.04;
    db     = 0.1;
    P_rated = [7.4 7.4 11 11 22 7.4 11 22]*1e3;
    V_nom  = 230;

    % ── 1. Compute required fleet power from frequency ──
    delta_f = f_grid - f0;
    if abs(delta_f) < db
        P_freq = 0;
    else
        P_freq = -(1/R) * (delta_f/f0) * sum(P_rated);
        P_freq = max(min(P_freq, sum(P_rated)), -sum(P_rated));
    end

    % ── 2. Net power that fleet must supply ─────────────
    P_net = P_freq + (P_load - P_ren);   % +ve = need discharge

    % ── 3. Priority: protect low-SOC & respect departure ─
    avail_dis = (SOC > 0.25) & ~dep_flag;   % can discharge
    avail_chg = (SOC < 0.90);               % can charge

    if P_net > 0          % need V2G (discharge)
        weights = SOC .* avail_dis;         % prefer high SOC
        if sum(weights) > 0
            share = weights / sum(weights);
            P_alloc = share * P_net;
        else
            P_alloc = zeros(1,N);
        end
        for i = 1:N
            if avail_dis(i)
                mode(i) = 2;
                Iref(i) = -min(P_alloc(i)/V_nom, P_rated(i)/V_nom);
            end
        end
    else                 % need G2V (charge) or absorb surplus
        weights = (1-SOC) .* avail_chg;     % prefer low SOC
        if sum(weights) > 0
            share = weights / sum(weights);
            P_alloc = share * (-P_net);
        else
            P_alloc = zeros(1,N);
        end
        for i = 1:N
            if avail_chg(i)
                mode(i) = 1;
                Iref(i) = min(P_alloc(i)/V_nom, P_rated(i)/V_nom);
            end
        end
    end

    % ── 4. Force G2V for imminent departure ─────────────
    for i = 1:N
        if dep_flag(i) && SOC(i) < 0.60
            mode(i) = 1;
            Iref(i) = 0.5 * P_rated(i)/V_nom;  % half-rate charge
        end
    end
end
Script 3 — Latency_Sensitivity.m  ·  Communication Delay Sweep MATLAB
%% Latency_Sensitivity.m
% Sweeps ISO 15118 communication delay and records frequency nadir
% after a 0.3 Hz step disturbance. Quantifies control degradation.

SmartGrid_V2G_Parameters;   % load base params

delay_ms   = [0 50 100 150 200 300 500];
nadir_Hz   = zeros(size(delay_ms));
settle_s   = zeros(size(delay_ms));

for k = 1:length(delay_ms)
    % Set the Transport Delay block parameter via set_param
    set_param('SmartGrid_V2G_Model/CommDelay', 'DelayTime', ...
              num2str(delay_ms(k)/1000));
    
    % Run simulation (assumes model already configured for step Δf)
    simOut = sim('SmartGrid_V2G_Model', 'StopTime', '10');
    
    f_sig  = simOut.f_grid;           % timeseries from To Workspace
    nadir_Hz(k) = min(f_sig.Data);
    % settling time: first instant after which |f-50| < 0.05 Hz for 1 s
    err = abs(f_sig.Data - 50);
    idx = find(err < 0.05, 1, 'first');
    if ~isempty(idx)
        settle_s(k) = f_sig.Time(idx);
    else
        settle_s(k) = NaN;
    end
end

%% Plot
figure('Name','Latency Sensitivity');
subplot(2,1,1);
plot(delay_ms, nadir_Hz, 'o-', 'LineWidth', 2, 'Color', [0 .6 .6]);
ylabel('Frequency Nadir (Hz)'); grid on;
title('Effect of ISO 15118 Latency on Frequency Nadir');
subplot(2,1,2);
plot(delay_ms, settle_s, 's-', 'LineWidth', 2, 'Color', [.8 .3 0]);
xlabel('Communication Delay (ms)'); ylabel('Settling Time (s)');
grid on;
fprintf('Latency sweep complete. Results stored in nadir_Hz, settle_s\n');
Script 4 — Renewable_SelfConsumption.m  ·  PV/Wind Absorption Metrics MATLAB
%% Renewable_SelfConsumption.m
% Post-processing: calculates renewable self-consumption ratio
% and curtailment energy for a completed multi-EV simulation

% Assume To Workspace signals: P_pv, P_wind, P_fleet, P_grid
t      = P_pv.time;
P_ren  = P_pv.signals.values + P_wind.signals.values;
P_ev   = P_fleet.signals.values;   % positive = charging (absorbing)
P_exp  = max(P_grid.signals.values, 0);  % export to utility

% Energy calculations (trapezoidal integration)
E_ren  = trapz(t, P_ren) / 3.6e6;   % kWh
E_abs  = trapz(t, max(P_ev,0)) / 3.6e6;
E_curt = trapz(t, P_exp) / 3.6e6;

SCR = 100 * E_abs / max(E_ren, eps);   % Self-Consumption Ratio %

fprintf('\n── Renewable Integration Metrics ──────────────────\n');
fprintf('Total renewable energy : %8.2f kWh\n', E_ren);
fprintf('Absorbed by EV fleet  : %8.2f kWh\n', E_abs);
fprintf('Exported / curtailed  : %8.2f kWh\n', E_curt);
fprintf('Self-Consumption Ratio: %8.1f %%\n', SCR);
fprintf('───────────────────────────────────────────────────\n');

figure('Name','Renewable vs Fleet Power');
plot(t/3600, P_ren/1e3, 'g', 'LineWidth', 1.5); hold on;
plot(t/3600, P_ev/1e3,  'b', 'LineWidth', 1.5);
plot(t/3600, P_exp/1e3, 'r--','LineWidth', 1.2);
legend('P_{renewable}', 'P_{fleet} (charge)', 'P_{export}');
xlabel('Time (h)'); ylabel('Power (kW)');
title(['Renewable Self-Consumption = ' num2str(SCR,'%.1f') '%']);
grid on;
Script 5 — Islanded_Frequency_Restore.m  ·  Secondary Control after Islanding MATLAB
%% Islanded_Frequency_Restore.m
% Demonstrates secondary frequency restoration after islanding
% Uses a simple discrete PI on the aggregator side

% Parameters
f0      = 50;
Kp_sec  = 200;      % W/Hz  (secondary proportional)
Ki_sec  = 50;       % W/(Hz·s)
Ts      = 0.1;      % secondary sample time
Tsim    = 60;       % s

% Simulate a step load increase at t=5 s while islanded
t       = 0:Ts:Tsim;
P_load  = 30e3 * ones(size(t));   % 30 kW base
P_load(t>=5) = 45e3;             % +15 kW step

P_ev    = zeros(size(t));
f       = f0 * ones(size(t));
int_err = 0;
H_eq    = 3;                     % equivalent inertia (s)
D       = 0.02;                  % damping

for k = 2:length(t)
    % Primary droop response (already included in P_ev via R)
    delta_f = f(k-1) - f0;
    P_prim  = -(1/0.04) * (delta_f/f0) * 80e3;  % fleet rating 80 kW
    
    % Secondary PI
    int_err = int_err + delta_f * Ts;
    P_sec   = -Kp_sec * delta_f - Ki_sec * int_err;
    
    P_ev(k) = P_prim + P_sec;
    P_ev(k) = max(min(P_ev(k), 80e3), -80e3);
    
    % Simple swing equation
    dP = P_ev(k) - P_load(k);
    df = (dP/(2*H_eq*1e5) - D*delta_f) * Ts;  % scaled
    f(k) = f(k-1) + df;
end

figure('Name','Islanded Frequency Restoration');
subplot(2,1,1);
plot(t, f, 'b', 'LineWidth', 1.8);
yline(50, 'k--'); ylabel('Frequency (Hz)');
title('Secondary Control Restores Frequency after Load Step'); grid on;
subplot(2,1,2);
plot(t, P_ev/1e3, 'Color', [0 .6 .5], 'LineWidth', 1.8);
xlabel('Time (s)'); ylabel('Fleet Power (kW)'); grid on;
Recommended execution order: (1) Run SmartGrid_V2G_Parameters.m → (2) Open SmartGrid_V2G_Model.slx (Powergui discrete, Ts = 5e-6) → (3) Simulate 2–4 hours of real-time (or accelerated) → (4) Run Renewable_SelfConsumption.m → (5) Run Latency_Sensitivity.m for the communication study → (6) Run Islanded_Frequency_Restore.m for microgrid validation. All results are written to the MATLAB workspace for the project report.

V2G Smart Grid Integration — Complete MATLAB Project Support Bangalore

Looking for a complete V2G Smart Grid Integration MATLAB simulation in Bangalore? We deliver end-to-end IEEE 2025–2026 projects covering multi-EV aggregator control, renewable co-simulation (PV + wind), ISO 15118 communication latency, demand-response scheduling, primary frequency & voltage support, and islanded microgrid operation. Deliverables include the full .slx multi-EV Simulink model, all MATLAB scripts, IEEE base paper, annotated circuit diagrams, waveform results, university-format report (VTU / Anna University / NIT / IIT) and viva coaching for EEE, ECE and EV-stream students.

✅ V2G Smart Grid Integration MATLAB
✅ Multi-EV Aggregator Simulink
✅ Renewable V2G Co-Simulation
✅ ISO 15118 Latency Modelling
✅ Demand Response V2G MATLAB
✅ Islanded Microgrid V2G
✅ Frequency Support Fleet Control
✅ Voltage Support Q-Sharing
✅ Self-Consumption Ratio Analysis
✅ Virtual Power Plant MATLAB
✅ OCPP 2.0 / ISO 15118 Simulink
✅ IEEE 519 THD V2G Fleet
✅ SOC Fairness Dispatch Algorithm
✅ Departure-Time Constrained Charging
✅ V2G MTech Project VTU Anna Univ
✅ Smart Grid EV Integration Bangalore

How to Get Your V2G Smart-Grid Project

A transparent 4-step process from topic selection to viva-ready submission for BE, BTech and MTech students in Bangalore.

01
Select Scope & IEEE Paper
Choose multi-EV aggregator, renewable co-simulation, islanded microgrid, latency study or demand-response focus. We shortlist the best IEEE 2026 base paper for your department.
02
Simulink Model Build
Complete .slx multi-EV model with feeder, PV/wind, N bidirectional chargers, aggregator logic, communication delay blocks and Powergui (discrete, Ts = 5 µs).
03
Results & Metrics
Frequency nadir, voltage profile, self-consumption ratio, per-EV SOC trajectories, THD spectrum and latency-sensitivity curves — all annotated for the project report.
04
Report, PPT & Viva
University-format report, IEEE-style PPT, script documentation and full viva coaching covering aggregator algorithms, ISO 15118, renewable integration and control theory.

Frequently Asked Questions — V2G Smart Grid Integration

What is V2G Smart Grid Integration in MATLAB/Simulink?
It models a fleet of electric vehicles as distributed energy resources coordinated by an aggregator. The Simulink environment co-simulates bidirectional chargers, a renewable plant (PV/wind), a distribution feeder and an aggregator control layer that dispatches active/reactive power for frequency regulation, voltage support and peak shaving while respecting ISO 15118 communication constraints and battery SOC limits.
Which Simulink blocks are used for multi-EV aggregator simulation?
Key blocks: multiple Battery + Bidirectional Converter subsystems, Three-Phase Source (distribution grid), PV Array / Wind Turbine, PLL and d-q current controllers per charger, Aggregator Logic (MATLAB Function or Stateflow), Communication Delay (Transport Delay), Powergui, Scope and To Workspace. Simscape Electrical supplies the specialised power-electronics library.
How does demand-response scheduling work in the model?
A MATLAB Function block receives ToU tariff, renewable forecast, aggregator set-points and per-EV SOC/departure-time constraints. It solves a simple linear programme or rule-based dispatch every 15 min, outputting Iref commands to each charger. The result minimises charging cost and maximises renewable self-consumption while keeping grid frequency within ±0.2 Hz.
How is communication latency modelled?
A Transport Delay (or Variable Transport Delay) block of 50–250 ms is inserted between the aggregator Iref output and each charger’s local controller. Packet loss can be added with a Random Boolean switch. The Latency_Sensitivity.m script sweeps delay values and records frequency nadir and settling time to quantify control degradation.
Can I get a complete V2G Smart-Grid project with report and viva support in Bangalore?
Yes. Projectsatbangalore provides the full multi-EV .slx model, all MATLAB scripts, IEEE 2026 base paper, annotated circuit diagrams, waveform results, university-format report (VTU, Anna University, NIT, IIT), PPT and viva Q&A coaching covering aggregator algorithms, ISO 15118, renewable integration and control theory. WhatsApp +91 95919 12372 for pricing and topic list.