【V2G】电动汽车接入电网优化调度研究（Matlab代码实现）

%% Figure 16/22
figure(2)
plot(planning_periods, total_x_1st, planning_periods, total_x_2nd, 'r', planning_periods, total_x_3rd, 'g')
title('Figure 16/22')
xlabel('Time')
ylabel('Electricty Units')
legend('Scenario 1','Scenario 2', 'Scenario 3')
%% Figure 17/23
figure(3)
plot(planning_periods, total_y_1st, planning_periods, total_y_2nd, 'r', planning_periods, total_y_3rd, 'g')
title('Figure 17/23')
xlabel('Time')
ylabel('Electricty Units')
legend('Scenario 1','Scenario 2', 'Scenario 3')
%% Figure 18/27
figure(4)
yyaxis left
plot(planning_periods, total_x_3rd, planning_periods, total_y_3rd)
title('Figure 18/27')
xlabel('Time')
ylabel('Electricity Units')
yyaxis right
plot(planning_periods, Ct_available_EU)
ylabel('Price (cents)')
legend('Charging','Discharging','Price')
%% Figure 19/24
[list_of_total_z_1st, z_index_1st] = sort(list_of_total_z_1st);
z_freq_temp_1st = frequency_of_z_1st;
for i = 1:length(z_freq_temp_1st)
    frequency_of_z_1st(i) = z_freq_temp_1st(z_index_1st(i));
    if ~ismember(list_of_total_z_1st(i),list_of_total_z_2nd)
        list_of_total_z_2nd = [list_of_total_z_2nd list_of_total_z_1st(i)];
        frequency_of_z_2nd = [frequency_of_z_2nd 0];
    end
    if ~ismember(list_of_total_z_1st(i),list_of_total_z_3rd)
        list_of_total_z_3rd = [list_of_total_z_3rd list_of_total_z_1st(i)];
        frequency_of_z_3rd = [frequency_of_z_3rd 0];
    end
end
[list_of_total_z_2nd, z_index_2nd] = sort(list_of_total_z_2nd);
z_freq_temp_2nd = frequency_of_z_2nd;
for i = 1:length(z_freq_temp_2nd)
    frequency_of_z_2nd(i) = z_freq_temp_2nd(z_index_2nd(i));
end
[list_of_total_z_3rd, z_index_3rd] = sort(list_of_total_z_3rd);
z_freq_temp_3rd = frequency_of_z_3rd;
for i = 1:length(z_freq_temp_3rd)
    frequency_of_z_3rd(i) = z_freq_temp_3rd(z_index_3rd(i));
end
plot_param = zeros(length(frequency_of_z_1st),3);
for i = 1:length(frequency_of_z_1st)
    for j = 1:3
        if j == 1
            plot_param(i,j) = frequency_of_z_1st(i);
        elseif j == 2
            plot_param(i,j) = frequency_of_z_2nd(i);
        else
            plot_param(i,j) = frequency_of_z_3rd(i);
        end
    end
end
figure(5)
bar(list_of_total_z_1st, plot_param)
title('Figure 19/24')
xlabel('Z')
ylabel('Frequency')
legend('Scenario 1','Scenario 2', 'Scenario 3')
%% Figure 20/25
z_values_1st = [];
z_values_2nd = [];
z_values_3rd = [];
EVs = [];
for i = 1:N
    z_values_1st = [z_values_1st EV_1st(i).z];
    z_values_2nd = [z_values_2nd EV_2nd(i).z(periods)];
    z_values_3rd = [z_values_3rd EV_3rd(i).z(periods)];
    EVs = [EVs i];
end
figure(6)
plot(EVs, z_values_1st, EVs, z_values_2nd, 'r', EVs, z_values_3rd, 'g')
xlabel('EV Number')
ylabel('Electricity Units')
title('Figure 20/25')
legend('Z1','Z2','Z3')
%% Figure 21/28
figure(7)
plot(planning_periods, total_x_3rd, planning_periods, cpt_available_EU)
title('Figure 18/27')
xlabel('Time')
ylabel('Electricity Units')
legend('Charging','Capacity')
%% SOC vs distance plot of test EV taking home trip
%Assuming an initial SOC of 100% (i.e., of either 10 EU or 15 EU) can cover
%400 miles at full charge. First determine whether the test EV is PHEV or
%BEV
distance_per_EU = [];
distance_per_period = [];
total_periods = [];
home_vehicle_IDs = [];
for i = 1:N
    if ismember(i,home_vehicles)
        distance_per_EU = [distance_per_EU 400/EV(i).mc]; %Distance covered per EU depending on the type
        distance_per_period = distance_per_EU; %Assume 1 EU is spent in one period of travelling
        total_periods = [total_periods EV(i).travel_time];
        home_vehicle_IDs = [home_vehicle_IDs i];
    end
end
%EV energy vs distance plot 
random_num = randi(length(total_periods));
total_periods_vector = zeros(1,total_periods(random_num));
count = 0;
for i = 1:total_periods(random_num)
    count = count + 1;
    total_periods_vector(i) = count*distance_per_period(random_num);
end
%Create a uniformly distributed distance vector
total_periods_vector = linspace(total_periods_vector(1),total_periods_vector(length(total_periods_vector)),total_periods(random_num)+1);
start_time = EV_1st(home_vehicle_IDs(random_num)).schedule(2);
end_time = EV_1st(home_vehicle_IDs(random_num)).schedule(3);
figure(8)
plot(total_periods_vector, EV_1st(home_vehicle_IDs(random_num)).soc(start_time:end_time).*(100.*(EV_1st(home_vehicle_IDs(random_num)).mc).^(-1)),total_periods_vector, EV_2nd(home_vehicle_IDs(random_num)).soc(start_time:end_time).*(100.*(EV_2nd(home_vehicle_IDs(random_num)).mc).^(-1)),total_periods_vector, EV_3rd(home_vehicle_IDs(random_num)).soc(start_time:end_time).*(100.*(EV_3rd(home_vehicle_IDs(random_num)).mc).^(-1)))
title('Energy vs. Distance plot for randomly chosen vehicle to recharge home')
xlabel('Distance in miles')
ylabel('State of Charge of EV in Percentage of Max Capacity')
legend('1st Scenario','2nd Scenario', '3rd Scenario')
figure(9)
plot(total_periods_vector, EV_1st(home_vehicle_IDs(random_num)).soc(start_time:end_time).*(100.*(EV_1st(home_vehicle_IDs(random_num)).mc).^(-1)),total_periods_vector, EV_2nd(home_vehicle_IDs(random_num)).soc(start_time:end_time).*(100.*(EV_2nd(home_vehicle_IDs(random_num)).mc).^(-1)))
title('Energy vs. Distance plot for randomly chosen vehicle to recharge home')
xlabel('Distance in miles')
ylabel('State of Charge of EV in Percentage of Max Capacity')
legend('1st Scenario','2nd Scenario')
figure(10)
plot(total_periods_vector, EV_2nd(home_vehicle_IDs(random_num)).soc(start_time:end_time).*(100.*(EV_2nd(home_vehicle_IDs(random_num)).mc).^(-1)),total_periods_vector, EV_3rd(home_vehicle_IDs(random_num)).soc(start_time:end_time).*(100.*(EV_3rd(home_vehicle_IDs(random_num)).mc).^(-1)))
title('Energy vs. Distance plot for randomly chosen vehicle to recharge home')
xlabel('Distance in miles')
ylabel('State of Charge of EV in Percentage of Max Capacity')
legend('2nd Scenario', '3rd Scenario')
figure(11)
plot(total_periods_vector, EV_1st(home_vehicle_IDs(random_num)).soc(start_time:end_time).*(100.*(EV_1st(home_vehicle_IDs(random_num)).mc).^(-1)),total_periods_vector, EV_3rd(home_vehicle_IDs(random_num)).soc(start_time:end_time).*(100.*(EV_3rd(home_vehicle_IDs(random_num)).mc).^(-1)))
title('Energy vs. Distance Plot for Randomly Chosen Vehicle to Recharge Home')
xlabel('Distance in miles')
ylabel('State of Charge of EV in Percentage of Max Capacity')
legend('1st Scenario', '3rd Scenario')
%% SOC vs time plot of test EV taking home trip
figure(12)
plot(planning_periods, EV_1st(home_vehicle_IDs(random_num)).soc.*(100.*(EV_1st(home_vehicle_IDs(random_num)).mc).^(-1)), planning_periods, EV_2nd(home_vehicle_IDs(random_num)).soc.*(100.*(EV_2nd(home_vehicle_IDs(random_num)).mc).^(-1)), planning_periods, EV_3rd(home_vehicle_IDs(random_num)).soc.*(100.*(EV_3rd(home_vehicle_IDs(random_num)).mc).^(-1)))
title('Energy vs Time Plot for Randomly Chosen Vehicle to Recharge Home')
xlabel('Time in periods')
ylabel('State of Charge of EV in Percentage of Max Capacity')
legend('1st Scenario', '2nd Scenario', '3rd Scenario')