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runBenchmarkAlgorithm.m
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function [Output] = runBenchmarkAlgorithm(timeStepSize, endTime, kwargs)
% Analysis of the solution found by the shortestpath solver for the *ground
% truth* demand (rather than using demand estimates). Identical to the
% shortest path algorithm of (Rist et al., 2017)
%% Handling inputs:
arguments
timeStepSize (1,1) double {mustBePositive} = 15 % length of a timestep in [s]
endTime (1,1) double {mustBePositive} = 24; % final time in [h]
kwargs.PriceIndex (1,1) double {mustBePositive} = 1;
kwargs.BuildingType (1,1) BuildingType {mustBePositive} = BuildingType.ResidentialHIGH;
kwargs.dataPath (1,1) string = "../Data"
kwargs.transitionPenalty (1,1) double = 0.01;
kwargs.powerScalingFactor (1,1) double = NaN;
end
% Constants
ABSOLUTE_CERTAINTY_ALPHA = 0;
% Unpack kwargs:
transitionPenalty = kwargs.transitionPenalty;
iP = kwargs.PriceIndex;
tB = kwargs.BuildingType;
pD = kwargs.dataPath;
psf = kwargs.powerScalingFactor;
%% Load Parameters
[FUEL_MAP, HEAT_MAP, HEAT_TARIFF, MDOT_FUEL_SU, NODES_CONNECTED_TO_ARTIFICIAL_START, ...
NUM_WINDOWS, POWER_MAP, PRICE_kg_f, RoundHourIndices, SV_states, ~, ...
demands_true, elecTariffs, g, nTimesteps, nTotalNodes, sol_select, stateFromMap, ...
stateToMap, stepsPerHour, timeFrom, transitionPenaltyFlag] = ...
loadParametersForRobustAlgorithms(endTime, tB, pD, psf, timeStepSize);
% Calling this in a loop is bad, because many files are read from the hard-drive inside
% NWI = 3; %Debug
NWI = NUM_WINDOWS-1; % Number of Windows of Interest
% Explanation of the "-1" above:
% The {averaging} windows are used to predict "the next day".
% The last "next day" happens to be in the next year (01/01/2005).
% We are not interested in predicting that day even though we have enough data for it,
% because we have no ground-truth to compare it with later on.
%=> As a result we use NUM_WINDOWS-1 to reflect this
%% Initialize Variables
% Output Data
Output.Power_Generation = zeros(NWI,endTime); %Does not save the power generation at all times, but just at ``XX:00" times.
Output.Heat_Generation = zeros(NWI,endTime);
Output.Fuel_Consumption = zeros(NWI,endTime);
Output.EstimatedCost = zeros(NWI,1);
Output.TrueCost = zeros(NWI,1);
Output.AlgorithmType = AlgorithmType.Benchmark;
Output.AlgorithmParameters{1} = [];
%% Run Algorithm
START_DATE = datetime(2004,1,15); END_DATE = dateshift(START_DATE, 'end', 'year');
DATA_DATES = (START_DATE:END_DATE).';
CURRENT_DAY_OFFSET = +1;
isWeekend = weekday(DATA_DATES) == 7 | weekday(DATA_DATES) == 1;
fuelPrice = PRICE_kg_f(iP);
heatTariff = HEAT_TARIFF(iP);
for iW = 1:NWI
%% Computation using the forecast demand
% <Skipped in the benchmark case because the demand is known exactly at all times.>
% d = demands_estimate(iW);
% ...
%% Computation using the true demand
d = demands_true(iW + CURRENT_DAY_OFFSET); % _true == non-averaged
mElec = 1e3*d.valMean(:,1, 1+isWeekend(iW)); %1e3* - conversion from kWh to W.
mHeat = 1e3*d.valMean(:,2, 1+isWeekend(iW));
sElec = 1e3*d.valStd(:,1, 1+isWeekend(iW)); % should be zero anyhow.
sHeat = 1e3*d.valStd(:,2, 1+isWeekend(iW)); % should be zero anyhow.
decided_costs = assignCostsInternal(...
sol_select, stateFromMap, stateToMap, nTotalNodes, ...
mElec, zeros(size(sElec)), mHeat, zeros(size(sHeat)), ABSOLUTE_CERTAINTY_ALPHA, ...
elecTariffs(iW), heatTariff, fuelPrice,...
POWER_MAP, HEAT_MAP, FUEL_MAP, MDOT_FUEL_SU, ...
transitionPenaltyFlag, transitionPenalty, ...
timeFrom, nTimesteps, stepsPerHour, timeStepSize);
g = digraph(g.Edges.EndNodes(:,1), g.Edges.EndNodes(:,2), decided_costs);
[path_MGT, path_cost, path_edge] = shortestpath(g, 1, max(g.Edges.EndNodes(:,2)), 'Method', 'acyclic');
[power_MGT, heat_MGT, mdot_MGT] = extractPath(path_MGT, POWER_MAP, HEAT_MAP, FUEL_MAP, SV_states);
%% Check Performance of the Schedule on True Demand
% Replace old cost calculation method, which uses a ZOH for the demands
% and generations, with a new edge-based method that linearly
% interpolates the demand and generation.
%We run this again to avoid transition penalty.
true_cost = sum(assignCostsInternal(...
sol_select(path_edge), stateFromMap(path_edge), stateToMap(path_edge), NODES_CONNECTED_TO_ARTIFICIAL_START, ...
mElec, zeros(size(sElec)), mHeat, zeros(size(sHeat)), ABSOLUTE_CERTAINTY_ALPHA, ...
elecTariffs(iW), heatTariff, fuelPrice,...
POWER_MAP, HEAT_MAP, FUEL_MAP, MDOT_FUEL_SU, ...
transitionPenaltyFlag(path_edge), transitionPenalty, ...
timeFrom(path_edge), nTimesteps(path_edge), stepsPerHour, timeStepSize));
%% "Parse" Output Data
Output.Power_Generation(iW,:) = power_MGT(RoundHourIndices);
Output.Heat_Generation(iW,:) = heat_MGT(RoundHourIndices);
Output.Fuel_Consumption(iW,:) = mdot_MGT(RoundHourIndices);
Output.EstimatedCost(iW) = path_cost;
Output.TrueCost(iW) = true_cost;
end
end
function [decided_costs] = assignCostsInternal(...
sol_select, stateFromMap, stateToMap, nTotalNodes, ...
elecDemandMean, elecDemandStd, heatDemandMean, heatDemandStd, demandStandardEnvelope, ...
elecTariff, heatTariff, fuelPrice,...
powerMap, heatMap, fuelMap, mdot_fuel_SU, ...
transitionPenaltyFlag, transitionPenalty, ...
time_from, nTimesteps, stepsPerHour, timeStepSize)
% Apply alpha:
% 1kWh = 3.6e6J
elecDemand = elecDemandMean + demandStandardEnvelope * elecDemandStd;
heatDemand = heatDemandMean + demandStandardEnvelope * heatDemandStd;
% Assign edge costs
decided_costs = assignCosts(...
timeStepSize, powerMap, heatMap, fuelMap, ...
mdot_fuel_SU, nTotalNodes, sol_select, time_from, nTimesteps,...
stateFromMap, stateToMap,...
elecDemand, heatDemand, elecTariff, heatTariff, fuelPrice, ...
transitionPenaltyFlag, transitionPenalty, stepsPerHour);
end