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Lipton_v4_4_Excess_Storage_only.m
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Lipton_v4_4_Excess_Storage_only.m
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% Origin: Tuesday 8 Feb 2022
% Author: Mayk Thewessen
% Department: Strategy - Research
% Intent: Electricity market NL analysis for Vehicle-to-Grid in 2030
close all
clear
clc
format short eng
set(groot,'defaultLineLineWidth',2)
% %% import 'NL Power usage (Load)' as an CSV file:
% M = readtable('merit_order.891876.csv');
%
% %% import xlsx file
% Load_xlsx_readtable = readtable('Total Load - Day Ahead _ Actual_202101010000-202201010000.xlsx');
%
% %% Import Electrical Load of NL in 2021 whole year - actual - XML
% Load_readtable_1year = readtable('ACTUAL_TOTAL_LOAD_202101010000-202201010000.xml');
% plot(Load_readtable_1year.quantity)
%
% plot(Load_readtable_1year.quantity(:))
%
% %%
% A_column_vector = (Load_readtable_1year.quantity(:));
% A_row_vector = (Load_readtable_1year.quantity(:)');
%
% %% option 2
% Load_readtable_1day = readtable('ACTUAL_TOTAL_LOAD_202102040000-202102050000.xml');
%% initiliaze plot
h0 = figure('Name','Electricity market NL 2030','pos',[0 0 2000 1200]); % width and height start and end points
subplot_size_x = 4; % height - number of rows
subplot_size_y = 2; % width - number of columns
subplot_count = 0;
%% Ch.1 Energietransitiemodel input
% import 'NL Power usage (Load)' as an CSV file:
merit_order_ETM_rawimport = readtable('merit_order.904566.csv'); % ETM model - column 2 till 76 is .output - column 77 to 182 is .input powers - all in MW
% converter to matrix for datetime and matrix for doubles
time_array = merit_order_ETM_rawimport{:,1};
merit_order_ETM_data = merit_order_ETM_rawimport{:,2:end};
% split producer from consumer:
m_o_producer = merit_order_ETM_rawimport(:,2:75);
m_o_consumer = merit_order_ETM_rawimport(:,76:end);
% Find largest contributors to producers
merit_prod_summed_per_type = sum(m_o_producer{:,:});
[merit_prod_max, merit_prod_pos] = sort(merit_prod_summed_per_type,'descend'); % prod max in MWh per year, position is 1 column number less than in rawimport
find_top = 10; % generators that contribute yearly most volume in MWh/year
merit_prod_pos_top = merit_prod_pos(:,1:find_top)+1;
merit_prod_max_top = merit_prod_max(:,1:find_top);
% A = convertCharsToStrings(merit_order_ETM_rawimport.Properties.VariableNames{merit_prod_pos_top+1})
% Find largest contributors to producers
merit_cons_summed_per_type = sum(m_o_consumer{:,:});
[merit_cons_max, merit_cons_pos] = sort(merit_cons_summed_per_type,'descend'); % prod max in MWh per year, position is 1 column number less than in rawimport
find_top = 10; % generators that contribute yearly most volume in MWh/year
merit_cons_pos_top = merit_cons_pos(:,1:find_top)+1+74;
merit_cons_max_top = merit_cons_max(:,1:find_top);
%% Construct demand curve 2030
Produce_curve = sum(m_o_producer{:,2:end},2); % [MW] data per hour
%% Construct consume curve
Consume_curve = sum(m_o_consumer{:,2:end},2);
%% Calculate installed power per type of generator:
% Import production capacities
production_parameters = readtable('production_parameters.904566.csv'); %
%production_parameters.installed_power = production_parameters{:,2} .* production_parameters{:,3};
production_parameters.installed_power = production_parameters{:,"number_of_units"} .* production_parameters{:,"electricity_output_capacity_MW_"};
% Sum up the renewable contributors
P_solar = sum( production_parameters{[14,95,120],"installed_power"} );
% pv households = 120
% pv buildings = 14
% pv solar parks = 95
P_wind = sum( production_parameters{110:112,"installed_power"} );
% wind onshore 111
% wind coastal 110
% wind offshore 112
% Scale solar and wind to:
%P_zon_prognose_2030 = 33000; % [MW] laag scenario - als er veel grid congestie is - eprijs dempt flink - curtailment issues in overheidsregeling
P_zon_prognose_2030 = 46200; % [MW] hoog scenario - pv cost down
zon_scale = P_zon_prognose_2030 / P_solar
%P_wind_prognose_2030 = 8800 + 16700; % [MW] laag scenario 8.8GW onshore + 16.7GW offshore
P_wind_prognose_2030 = 8800 + 21300; % [MW] hoog scenario 8.8GW onshore + 21.3GW offshore reeds aangekodigd door overheid, plannen die dit bewerkstelligen
wind_scale = P_wind_prognose_2030 / P_wind
%% Simulate different years
jaren = [2022; 2030];
for jaar = 1:2
%% Step 2: Calculate price
% 2 = PV buildings rooftop solar (industry)
% 44 = PV large scale solar
% 57 = coastal wind energy
% 58 = inland wind energy
% 59 = offshore wind energy
% 61 = PV households
% A) subtract no marginal cost from power usage curve: construct residual load curve
PV_sum_producers = merit_order_ETM_rawimport{:,2}+merit_order_ETM_rawimport{:,44}+merit_order_ETM_rawimport{:,61} ;
Wind_sum_producers = merit_order_ETM_rawimport{:,57}+merit_order_ETM_rawimport{:,58}+merit_order_ETM_rawimport{:,59} ;
%% Scale production for 2030
if jaren(jaar) == 2030
P_solar(jaar) = zon_scale .* P_solar;
P_wind(jaar) = wind_scale .* P_wind;
PV_sum_producers = zon_scale .* PV_sum_producers;
Wind_sum_producers = wind_scale .* Wind_sum_producers;
end
renewable_producers = PV_sum_producers + Wind_sum_producers;
residual_load_curve = Consume_curve - renewable_producers;
residual_fossil_production = residual_load_curve;
residual_fossil_production(residual_fossil_production<0) = 0;
% price_electricity = 21.486.*exp(residual_load_curve.*1e-4) ; % v2: y = 21,486e0,0001x, v1: y = 27,775e4E-05x
price_electricity = 21.486.*exp(residual_load_curve.*1e-4) - (residual_load_curve<0)*21.486; % [€/MWh] and if residual < 0 than €0/MWh if 0 fossil production or negative residual = excess reneawble energly production
price_electricity_raw = price_electricity;
price_electricity_only_pos = price_electricity(price_electricity>0);
price_electricity(price_electricity<0) = 0; % set electricity price to zero when residual load is negative = excess energy
%% Statistics
% Production volumes
Prod_annual = sum(Produce_curve)/1000 % [GWh electricity]
Cons_annual = sum(Consume_curve)/1000 % [GWh electricity]
Wind_annual = sum(Wind_sum_producers)/1000
Solar_annual = sum(PV_sum_producers) / 1000
Prod_wind_perc = Wind_annual / Prod_annual
Prod_solar_perc = Solar_annual / Prod_annual
% Electricity Prices
Price_avg = mean(price_electricity)
Price_max = max(price_electricity)
Price_min = min(price_electricity)
Price_sigma = std(price_electricity)
Price_zero_hours = length(find(price_electricity==0))
Price_subzero_hours = length(find(price_electricity<0))
Price_only_pos_avg = mean(price_electricity_only_pos)
%% Calculate Storage methods
Storage = zeros(length(time_array),1);
P_V2G_discharge = Storage;
P_V2G_charge = Storage;
for a = 1:(length(time_array)-1)
if residual_load_curve(a) < 0 % thus excess energy, than storage activated
Storage(a+1) = Storage(a) + -residual_load_curve(a);
end
end
Residual_excess = -residual_load_curve;
Residual_excess(Residual_excess<0) = 0;
Residual_excess_cumsum = cumsum(Residual_excess);
OBC_power = 11e-3; % [MW] bidirecitonal power transfer capability per EV
n_vehicles = 2.2e6; % [2030]
share_participate_V2G = 0.4; %[-]
n_vehicles_V2G = n_vehicles * share_participate_V2G; % number of vehicles participating in V2G
max_charge_power = n_vehicles_V2G * OBC_power; % [MW]
E_vehicle = 65e-3; %[MWh] storage per vehicle
E_vehicle_V2G_part = 0.5; %[-]
E_vehicle_V2G_fleet = n_vehicles_V2G * E_vehicle * E_vehicle_V2G_part; % [MWh]
Storage3 = zeros(length(time_array),1);
for a = 1:(length(time_array)-1)
if residual_load_curve(a) < 0 % thus excess energy, than storage is charged
Storage3(a+1) = Storage3(a) + -residual_load_curve(a);
P_V2G_charge(a) = -residual_load_curve(a); % If excess energy charge V2G
if residual_load_curve(a) > max_charge_power % check charge power limit
Storage3(a+1) = Storage3(a) + max_charge_power; % limit storage charging to max power and add energy stored
P_V2G_charge(a) = max_charge_power;
end
if Storage3(a) > E_vehicle_V2G_fleet % limit Storage capacity (270GWh is 0.9M EV's with 290kWh V2G volume/year)
Storage3(a) = E_vehicle_V2G_fleet;
Storage3(a+1) = E_vehicle_V2G_fleet;
P_V2G_charge(a) = 0; % if storage is fully charged - no more charging possible thus 0 MW;
end
elseif residual_load_curve(a) > 0 % thus shortage of energy - fossil back up required
Storage3(a+1) = Storage3(a); % make sure storage is kept neutral if not used
if Storage3(a) > 0 % make sure storage can not deplete more than was charged before
Storage3(a+1) = Storage3(a) - residual_load_curve(a); % export of stored energy
P_V2G_discharge(a) = - residual_load_curve(a); % Discharge V2G energy only when Storage Energy remaining is still >0.
else
Storage3(a) = 0; % make sure storage can not go below zero
Storage3(a+1) = 0;
end
end
end
%% Plot A - generation
subplot(subplot_size_x,subplot_size_y, (1 *subplot_size_y)-subplot_size_y + jaar) % hoogte * y + rij
plot(time_array,Produce_curve/1000)
hold on
xlabel('Time')
ylabel('Electrical Power [GW]')
title('Production')
title(sprintf('Production - Year: %.0f, Consumption: %.1f TWh, %.1f GW Wind, %.1f GWp PV, %.0f prct Wind, %.0f prct PV', jaren(jaar),Cons_annual/1000, P_wind(jaar)/1000 ,P_solar(jaar)/1000, Prod_wind_perc*100, Prod_solar_perc*100) )
% choose time3
%start_point = 2500; % 7 June 2030
start_point = 2900; %
days = 14;
xlim([time_array(start_point), time_array(start_point+days*24)])
ylim([0 55])
% Fossil residual
area(time_array, (Wind_sum_producers + PV_sum_producers + residual_fossil_production)/1000,'FaceColor','#A2142F') % Purple '#7E2F8E' , Red ,'#A2142F'
% V2G discharge
area(time_array, (Wind_sum_producers + PV_sum_producers + P_V2G_discharge)/1000,'FaceColor','#7E2F8E') % Purple '#7E2F8E' , Red ,'#A2142F'
% Solar
area(time_array,(Wind_sum_producers + PV_sum_producers)/1000,'FaceColor','#EDB120') % Yellow
% area(time_array, residual_load_curve+merit_order_ETM_rawimport{:,57}+merit_order_ETM_rawimport{:,58}+merit_order_ETM_rawimport{:,59})
% V2G Charge
area(time_array,(Wind_sum_producers + P_V2G_charge)/1000,'FaceColor','#FF0000') % Bright Red
% Wind: as last foreground color
area(time_array,(Wind_sum_producers)/1000,'FaceColor','#77AC30') % Grey
% ik wil graag overshot ook laten zien met stippelijn erboven over, of negatief?
% negatief: opladen van batterij
plot(time_array, Consume_curve/1000,'k')
legend('Production total','Residual load (mainly fossil backup)','V2G discharge','PV solar (household+buildings+central)','V2G charge','Wind energy (inland, coastal, and offshore)','Consumption total')
grid
%% Plot B - Limited Storage only excess energy stored and fed back
subplot(subplot_size_x,subplot_size_y, (2 *subplot_size_y)-subplot_size_y + jaar) % x*sub_y bepaalt row, sub_y +x bepaalt welke column
% Histogram of storage size
% figure
% h1 = histogram(Storage/1e3);
% h1.BinWidth = 5;
% xlim([0 600])
% ylabel('Hours per year')
% hold on
% yyaxis right
% ylabel('Annual coverage [%]')
% h2 = cdfplot(Storage/1e3);
% xlabel('GWh storage capacity')
% ylim([0 1.0])
% legend('Histogram','Cumulative distribution function')
plot(time_array,Storage/1000)
hold on
%plot(time_array,Residual_excess_cumsum/1000)
plot(time_array,Storage3/1000)
xlabel('Time')
ylabel('Storage [GWh] positive is charging')
title('Unlimited Storage charging on excess residual load and discharging on shortage')
grid
ylim([-1 200])
legend('Unlimited storage charging on excess energy',sprintf('Energy storage in %.0f V2G EVs with a fleet storage of %.f GWh',n_vehicles_V2G,E_vehicle_V2G_fleet/1000))
%% Plot C - Electricity Price
subplot(subplot_size_x,subplot_size_y, ( 3 *subplot_size_y)-subplot_size_y + jaar) % hoogte + rij * x
% plot(time_array,Consume_curve)
% hold on
% plot(time_array,residual_load_curve)
% xlim([time_array(start_point), time_array(start_point+days*24)])
%ylabel('Electrical Power [MW]')
%yyaxis right
plot(time_array,price_electricity,'Color','#D95319')
hold on
legend('Consume curve','Residual load','Electricity price')
xlabel('Time')
ylabel('€/MWh')
grid
xlim([time_array(start_point), time_array(start_point+days*24)])
title('Electricity prices')
ylim([0 150])
% %
% % Electricity Prices
% Price_avg = mean(price_electricity)
% Price_max = max(price_electricity)
% Price_min = min(price_electricity)
% Price_sigma = std(price_electricity)
% Price_zero_hours = length(find(price_electricity==0))
% Price_subzero_hours = length(find(price_electricity<0))
% Price_only_pos_avg = mean(price_electricity_only_pos)
%% Plot D - histogram electricity price
subplot(subplot_size_x,subplot_size_y, ( 4 *subplot_size_y)-subplot_size_y + jaar) % hoogte + rij * x
h_elec = histogram(price_electricity);
h_elec.BinWidth = 5;
grid
xlim([0 150])
xlabel('Electricity price [€/MWh]')
ylabel('Occurance [hours per year]')
title('Probability distribution of Electricity price')
title(sprintf('Electricity prices - avg: %.1f, Std: %.1f, max: %.1f, avg fossil price: %.1f €/MWh',Price_avg, Price_sigma, Price_max, Price_only_pos_avg ) )
end
% Save figure as pdf
% save_fig(h0,'Lipton_PDF_v4_2'); % uses minimized edge borders
% Save figure as png
print -dpng -r300 Lipton_v4_4_Excess_Storage_only
%% Find names of largest producers
for j = 1:find_top
Producer_type_name(j,:) = convertCharsToStrings((merit_order_ETM_rawimport.Properties.VariableNames{merit_prod_pos_top(j)})); % first column: name
Consumer_type_name(j,:) = convertCharsToStrings((merit_order_ETM_rawimport.Properties.VariableNames{merit_cons_pos_top(j)})); % first column: name
end
Produced_MWh_year = merit_prod_max_top';
Consumed_MWh_year = merit_cons_max_top';
Largest_prod_cons_table = table(Producer_type_name,Produced_MWh_year,merit_prod_pos_top',Consumer_type_name,Consumed_MWh_year,merit_cons_pos_top');