#371

Committed 14 Sep 2023 02:22AM UTC coverage: 0.271%. First build

Build # #371

Build Type

Pull #546

travis-ci

Pull Request Pull Request #546: Thermal+storage (fusion, maintenance)

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/src/write_outputs/write_emissions.jl

@doc raw"""
        write_emissions(path::AbstractString, inputs::Dict, setup::Dict, EP::Model)

Function for reporting time-dependent CO$_2$ emissions by zone.

"""
function write_emissions(path::AbstractString, inputs::Dict, setup::Dict, EP::Model)
    T = inputs["T"]     # Number of time steps (hours)
    Z = inputs["Z"]     # Number of zones

    scale_factor = setup["ParameterScale"] == 1 ? ModelScalingFactor : 1

    if (setup["WriteShadowPrices"] == 1 || setup["UCommit"] == 0 ||
        (setup["UCommit"] == 2 && (setup["OperationalReserves"] == 0 ||
          (setup["OperationalReserves"] > 0 && inputs["pDynamic_Contingency"] == 0)))) # fully linear model
        # CO2 emissions by zone

        if setup["CO2Cap"] >= 1
            # Dual variable of CO2 constraint = shadow price of CO2
            tempCO2Price = zeros(Z, inputs["NCO2Cap"])
            if has_duals(EP) == 1
                for cap in 1:inputs["NCO2Cap"]
                    for z in findall(x -> x == 1, inputs["dfCO2CapZones"][:, cap])
                        tempCO2Price[z, cap] = (-1) *
                                               dual.(EP[:cCO2Emissions_systemwide])[cap]
                        # when scaled, The objective function is in unit of Million US$/kton, thus k$/ton, to get $/ton, multiply 1000
                        tempCO2Price[z, cap] *= scale_factor
                    end
                end
            end
            dfEmissions = hcat(DataFrame(Zone = 1:Z),
                DataFrame(tempCO2Price, :auto),
                DataFrame(AnnualSum = Array{Float64}(undef, Z)))
            auxNew_Names = [Symbol("Zone");
                            [Symbol("CO2_Price_$cap") for cap in 1:inputs["NCO2Cap"]];
                            Symbol("AnnualSum")]
            rename!(dfEmissions, auxNew_Names)
        else
            dfEmissions = DataFrame(Zone = 1:Z, AnnualSum = Array{Float64}(undef, Z))
        end

        emissions_by_zone = value.(EP[:eEmissionsByZone])
        for i in 1:Z
            dfEmissions[i, :AnnualSum] = sum(inputs["omega"] .* emissions_by_zone[i, :]) *
                                         scale_factor
        end

        if setup["WriteOutputs"] == "annual"
            total = DataFrame(["Total" sum(dfEmissions.AnnualSum)], [:Zone; :AnnualSum])
            if setup["CO2Cap"] >= 1
                total = DataFrame(
                    ["Total" zeros(1, inputs["NCO2Cap"]) sum(dfEmissions.AnnualSum)],
                    [:Zone;
                     [Symbol("CO2_Price_$cap") for cap in 1:inputs["NCO2Cap"]];
                     :AnnualSum])
            end
            dfEmissions = vcat(dfEmissions, total)
            CSV.write(joinpath(path, "emissions.csv"), dfEmissions)
        else# setup["WriteOutputs"] == "full"
            dfEmissions = hcat(dfEmissions,
                DataFrame(emissions_by_zone * scale_factor, :auto))
            if setup["CO2Cap"] >= 1
                auxNew_Names = [Symbol("Zone");
                                [Symbol("CO2_Price_$cap") for cap in 1:inputs["NCO2Cap"]];
                                Symbol("AnnualSum");
                                [Symbol("t$t") for t in 1:T]]
                rename!(dfEmissions, auxNew_Names)
                total = DataFrame(
                    ["Total" zeros(1, inputs["NCO2Cap"]) sum(dfEmissions[!,
                        :AnnualSum]) fill(0.0, (1, T))],
                    :auto)
                for t in 1:T
                    total[:, t + inputs["NCO2Cap"] + 2] .= sum(dfEmissions[:,
                        Symbol("t$t")][1:Z])
                end
            else
                auxNew_Names = [Symbol("Zone");
                                Symbol("AnnualSum");
                                [Symbol("t$t") for t in 1:T]]
                rename!(dfEmissions, auxNew_Names)
                total = DataFrame(
                    ["Total" sum(dfEmissions[!, :AnnualSum]) fill(0.0,
                        (1, T))],
                    :auto)
                for t in 1:T
                    total[:, t + 2] .= sum(dfEmissions[:, Symbol("t$t")][1:Z])
                end
            end
            rename!(total, auxNew_Names)
            dfEmissions = vcat(dfEmissions, total)
            CSV.write(joinpath(path, "emissions.csv"),
                dftranspose(dfEmissions, false),
                writeheader = false)
        end
        ## Aaron - Combined elseif setup["Dual_MIP"]==1 block with the first block since they were identical. Why do we have this third case? What is different about it?
    else
        # CO2 emissions by zone
        emissions_by_zone = value.(EP[:eEmissionsByZone])
        dfEmissions = hcat(DataFrame(Zone = 1:Z),
            DataFrame(AnnualSum = Array{Float64}(undef, Z)))
        for i in 1:Z
            dfEmissions[i, :AnnualSum] = sum(inputs["omega"] .* emissions_by_zone[i, :]) *
                                         scale_factor
        end

        if setup["WriteOutputs"] == "annual"
            total = DataFrame(["Total" sum(dfEmissions.AnnualSum)], [:Zone; :AnnualSum])
            dfEmissions = vcat(dfEmissions, total)
            CSV.write(joinpath(path, "emissions.csv"), dfEmissions)
        else# setup["WriteOutputs"] == "full"
            dfEmissions = hcat(dfEmissions,
                DataFrame(emissions_by_zone * scale_factor, :auto))
            auxNew_Names = [Symbol("Zone");
                            Symbol("AnnualSum");
                            [Symbol("t$t") for t in 1:T]]
            rename!(dfEmissions, auxNew_Names)
            total = DataFrame(["Total" sum(dfEmissions[!, :AnnualSum]) fill(0.0, (1, T))],
                :auto)
            for t in 1:T
                total[:, t + 2] .= sum(dfEmissions[:, Symbol("t$t")][1:Z])
            end
            rename!(total, auxNew_Names)
            dfEmissions = vcat(dfEmissions, total)
            CSV.write(joinpath(path, "emissions.csv"),
                dftranspose(dfEmissions, false),
                writeheader = false)

            if setup["OutputFullTimeSeries"] == 1 && setup["TimeDomainReduction"] == 1
                write_full_time_series_reconstruction(path, setup, dfEmissions, "emissions")
                @info("Writing Full Time Series for Emissions")
            end
        end
    end
    return nothing
end

1	@doc raw"""
2	write_emissions(path::AbstractString, inputs::Dict, setup::Dict, EP::Model)
3
4	Function for reporting time-dependent CO$_2$ emissions by zone.
5
6	"""
7	function write_emissions(path::AbstractString, inputs::Dict, setup::Dict, EP::Model)	×
8	T = inputs["T"] # Number of time steps (hours)	×
9	Z = inputs["Z"] # Number of zones	×
10		×
11	scale_factor = setup["ParameterScale"] == 1 ? ModelScalingFactor : 1	×
12
13	if (setup["WriteShadowPrices"] == 1 \|\| setup["UCommit"] == 0 \|\|	×
14	(setup["UCommit"] == 2 && (setup["OperationalReserves"] == 0 \|\|
15	(setup["OperationalReserves"] > 0 && inputs["pDynamic_Contingency"] == 0)))) # fully linear model
16	# CO2 emissions by zone	×
17
18	if setup["CO2Cap"] >= 1
19	# Dual variable of CO2 constraint = shadow price of CO2	×
20	tempCO2Price = zeros(Z, inputs["NCO2Cap"])
21	if has_duals(EP) == 1	×
22	for cap in 1:inputs["NCO2Cap"]	×
23	for z in findall(x -> x == 1, inputs["dfCO2CapZones"][:, cap])	×
24	tempCO2Price[z, cap] = (-1) *	×
25	dual.(EP[:cCO2Emissions_systemwide])[cap]	×
26	# when scaled, The objective function is in unit of Million US$/kton, thus k$/ton, to get $/ton, multiply 1000
27	tempCO2Price[z, cap] *= scale_factor	×
28	end
29	end
30	end
31	dfEmissions = hcat(DataFrame(Zone = 1:Z),	×
32	DataFrame(tempCO2Price, :auto),	×
33	DataFrame(AnnualSum = Array{Float64}(undef, Z)))	×
34	auxNew_Names = [Symbol("Zone");
35	[Symbol("CO2_Price_$cap") for cap in 1:inputs["NCO2Cap"]];	×
36	Symbol("AnnualSum")]
37	rename!(dfEmissions, auxNew_Names)
38	else	×
39	dfEmissions = DataFrame(Zone = 1:Z, AnnualSum = Array{Float64}(undef, Z))	×
40	end
41
42	emissions_by_zone = value.(EP[:eEmissionsByZone])	×
43	for i in 1:Z
44	dfEmissions[i, :AnnualSum] = sum(inputs["omega"] .* emissions_by_zone[i, :]) *
45	scale_factor	×
46	end	×
47		×
48	if setup["WriteOutputs"] == "annual"	×
49	total = DataFrame(["Total" sum(dfEmissions.AnnualSum)], [:Zone; :AnnualSum])	×
50	if setup["CO2Cap"] >= 1	×
51	total = DataFrame(
52	["Total" zeros(1, inputs["NCO2Cap"]) sum(dfEmissions.AnnualSum)],
53	[:Zone;	×
54	[Symbol("CO2_Price_$cap") for cap in 1:inputs["NCO2Cap"]];	×
55	:AnnualSum])	×
56	end	×
57	dfEmissions = vcat(dfEmissions, total)	×
58	CSV.write(joinpath(path, "emissions.csv"), dfEmissions)
59	else# setup["WriteOutputs"] == "full"
60	dfEmissions = hcat(dfEmissions,	×
61	DataFrame(emissions_by_zone * scale_factor, :auto))	×
62	if setup["CO2Cap"] >= 1
63	auxNew_Names = [Symbol("Zone");
64	[Symbol("CO2_Price_$cap") for cap in 1:inputs["NCO2Cap"]];
65	Symbol("AnnualSum");
66	[Symbol("t$t") for t in 1:T]]
67	rename!(dfEmissions, auxNew_Names)	×
68	total = DataFrame(	×
69	["Total" zeros(1, inputs["NCO2Cap"]) sum(dfEmissions[!,	×
70	:AnnualSum]) fill(0.0, (1, T))],
71	:auto)	×
72	for t in 1:T	×
73	total[:, t + inputs["NCO2Cap"] + 2] .= sum(dfEmissions[:,	×
74	Symbol("t$t")][1:Z])	×
75	end	×
76	else	×
77	auxNew_Names = [Symbol("Zone");
78	Symbol("AnnualSum");	×
79	[Symbol("t$t") for t in 1:T]]	×
80	rename!(dfEmissions, auxNew_Names)
81	total = DataFrame(	×
82	["Total" sum(dfEmissions[!, :AnnualSum]) fill(0.0,
83	(1, T))],
84	:auto)
85	for t in 1:T
86	total[:, t + 2] .= sum(dfEmissions[:, Symbol("t$t")][1:Z])
87	end
88	end
89	rename!(total, auxNew_Names)
90	dfEmissions = vcat(dfEmissions, total)
91	CSV.write(joinpath(path, "emissions.csv"),
92	dftranspose(dfEmissions, false),
93	writeheader = false)
94	end
95	## Aaron - Combined elseif setup["Dual_MIP"]==1 block with the first block since they were identical. Why do we have this third case? What is different about it?
96	else
97	# CO2 emissions by zone
98	emissions_by_zone = value.(EP[:eEmissionsByZone])
99	dfEmissions = hcat(DataFrame(Zone = 1:Z),
100	DataFrame(AnnualSum = Array{Float64}(undef, Z)))
101	for i in 1:Z
102	dfEmissions[i, :AnnualSum] = sum(inputs["omega"] .* emissions_by_zone[i, :]) *
103	scale_factor
104	end
105
106	if setup["WriteOutputs"] == "annual"
107	total = DataFrame(["Total" sum(dfEmissions.AnnualSum)], [:Zone; :AnnualSum])
108	dfEmissions = vcat(dfEmissions, total)
109	CSV.write(joinpath(path, "emissions.csv"), dfEmissions)
110	else# setup["WriteOutputs"] == "full"
111	dfEmissions = hcat(dfEmissions,
112	DataFrame(emissions_by_zone * scale_factor, :auto))
113	auxNew_Names = [Symbol("Zone");
114	Symbol("AnnualSum");
115	[Symbol("t$t") for t in 1:T]]
116	rename!(dfEmissions, auxNew_Names)
117	total = DataFrame(["Total" sum(dfEmissions[!, :AnnualSum]) fill(0.0, (1, T))],
118	:auto)
119	for t in 1:T
120	total[:, t + 2] .= sum(dfEmissions[:, Symbol("t$t")][1:Z])
121	end
122	rename!(total, auxNew_Names)
123	dfEmissions = vcat(dfEmissions, total)
124	CSV.write(joinpath(path, "emissions.csv"),
125	dftranspose(dfEmissions, false),
126	writeheader = false)
127
128	if setup["OutputFullTimeSeries"] == 1 && setup["TimeDomainReduction"] == 1
129	write_full_time_series_reconstruction(path, setup, dfEmissions, "emissions")
130	@info("Writing Full Time Series for Emissions")
131	end
132	end
133	end
134	return nothing
135	end

GenXProject / GenX / #371

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