V2G Smart Grid Integration Simulation
Software Tools & Platforms UsedIndustry-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.
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).
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.
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.
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.
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.
%% 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');
%% 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
%% 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');
%% 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;
%% 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;
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