├── Clear_Sky_IT.m
├── D.mat
├── DG_module.m
├── Data_ElectricNetworkBarryIsalnd.m
├── Data_intro_ElectricThermal_NetworkBarryIsalnd.m
├── HPP.m
├── MC_HEATPOWER.m
├── MarkovFailure.m
├── NHPP.m
├── OPF.m
├── PowEl2PowTh.m
├── STP.m
├── TempMonteCarlo.m
├── Weather_Simulator.m
├── figs
├── CombinedGridTopology.jpg
├── ExampleSimulationResults.jpg
└── readme.rm
└── readme.md
/Clear_Sky_IT.m:
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https://raw.githubusercontent.com/Roberock/Heat_and_Electric_powerNetwork/a442f6040a0888d8e8adc7ce14c582283eff5419/Clear_Sky_IT.m
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/D.mat:
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https://raw.githubusercontent.com/Roberock/Heat_and_Electric_powerNetwork/a442f6040a0888d8e8adc7ce14c582283eff5419/D.mat
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/DG_module.m:
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1 | classdef DG_module
2 | % DG_module class:
3 | % to simulate the power produced by distributed generators
4 | %% START Proprieties
5 | properties
6 | % PV Panel
7 | PVn=1% PVn: number of solar technologies
8 | PVIPmax=1.32% PVIPmax: current at max power [A]
9 | PVItc=1.4% PVItc: current temperature coef. [mA/Celsius]
10 | PVIsc=1.8% PVIsc: short circuit current [A]
11 | PVTa=30% PVTa: ambient temperature [Celsius]
12 | PVTno=43% PVTno: nominal operating temperature [Celsius]
13 | PVVPmax=38% PVVPmax: voltage at max power [V]
14 | PVVtc=194% PVVtc: voltage temperature coef. [mV/Celsius]
15 | PVVoc=55.5% PVVoc: voltage open circuit [V]
16 |
17 | % WIND TURBINE
18 | WPrated =50 % WPrated: rated power [kW]
19 | WWSci =3.8000 % WWSci: cut-in wind speed [m/s]
20 | WWSrated =9.5 % WWSrated: rated wind speed [m/s]
21 | WWSco =23.8 % WWSco: cut-out wind speed [m/s]
22 |
23 | %Storage
24 | STPpeak=0.28; % storage peak power?
25 | STEs=0.0400; % storage electric power rating?
26 | STn=1; % STn: number of storage technologies
27 |
28 | % ON-OFF HeatPump (model Maneurop SH 140-4)
29 | a1=0.8387; a2= 0.08744; % Fitting Coefficient HP on-off (Dongelline et al)
30 | b1=0.01142; b2= 0.001204 ;
31 | c1=30.68/35; % [KW/deg C]
32 | c2= 3.615/35;
33 | end
34 |
35 |
36 | methods
37 | %% CONSTRUCTOR
38 | function PVobj = DG_module(varargin) % method to define an object of type Scenario_Based_Reliability
39 | if nargin==0; return % Create an empty object
40 | end
41 | for k=1:2:length(varargin)
42 | switch lower(varargin{k})
43 | case {'data', 'del', 'd', 'datastructure'}
44 | % PV
45 | PVobj.PVn=varargin{k+1}.PVn; PVobj.PVIPmax=varargin{k+1}.PVIPmax;
46 | PVobj.PVItc=varargin{k+1}.PVItc; PVobj.PVIsc=varargin{k+1}.PVIsc;
47 | PVobj.PVTa=varargin{k+1}.PVTa; PVobj.PVTno=varargin{k+1}.PVTno;
48 | PVobj.PVVPmax=varargin{k+1}.PVVPmax; PVobj.PVVtc=varargin{k+1}.PVVtc; PVobj.PVVoc=varargin{k+1}.PVVoc;
49 | % WT
50 | PVobj.WPrated=varargin{k+1}.WPrated;
51 | PVobj.WWSci=varargin{k+1}.WWSci;
52 | PVobj.WWSrated=varargin{k+1}.WWSrated;
53 | PVobj.WWSco=varargin{k+1}.WWSco;
54 | % HeatPump On-Off
55 | PVobj.WWSco=varargin{k+1}.a1;
56 | PVobj.WWSco=varargin{k+1}.a2;
57 | PVobj.b1=varargin{k+1}.b1;
58 | PVobj.b2=varargin{k+1}.b2;
59 | PVobj.c1=varargin{k+1}.c1;
60 | PVobj.c2=varargin{k+1}.c2;
61 | end
62 | end
63 | end % of constructor
64 |
65 | %% power method for the PV pannels
66 | function Pow = Power_PV(obj,s,Text,NDn)
67 | % s sun irradiance
68 | % Text [Celsius] external air temperature
69 | % NDn number of nodes
70 | % Output
71 | % Pow: solar power produced by module(s)
72 | Idx=ones(NDn,1);
73 | tc = Text+s(:,ones(1,obj.PVn)).*(obj.PVTno(Idx,:)-20)/0.8;
74 | iy = s(:,ones(1,obj.PVn)).*(obj.PVIsc(Idx,:)+obj.PVItc(Idx,:).*(tc-25)/1000);
75 | vy = obj.PVVoc(Idx,:)-obj.PVVtc(Idx,:).*tc/1000;
76 | ff = (obj.PVVPmax(Idx,:).*obj.PVIPmax(Idx,:))./...
77 | (obj.PVVoc(Idx,:).*obj.PVIsc(Idx,:));
78 | Pow = ff.*vy.*iy/1000;
79 | end
80 |
81 | %% power method for the Wind Turbines
82 | function Pow = Power_WT(obj,ws,NDn)
83 | % ws: wind speed [m/s]
84 | Pow = zeros(NDn,1);
85 | % Case 1: cut in wind speed < wind speed <= rated wind speed
86 | clas1 = all((obj.WWSci(ones(NDn,1),:) < ws(:,ones(1,1))).*(ws(:,ones(1,1)) <= obj.WWSrated(ones(NDn,1),:)) == 1);
87 | % Case 2: rated wind speed < wind speed <= cut out speed
88 | clas2 = all((obj.WWSrated(ones(NDn,1),:) < ws(:,ones(1,1))).*(ws(:,ones(1,1)) <= obj.WWSco(ones(NDn,1),:)) == 1);
89 | if clas1
90 | Pow = obj.WPrated(ones(NDn,1),:).*(ws(:,ones(1,1))-obj.WWSci(ones(NDn,1),:))./...
91 | (obj.WWSrated(ones(NDn,1),:)-obj.WWSci(ones(NDn,1),:));
92 | elseif clas2
93 | Pow = obj.WPrated(ones(NDn,1),:);
94 | end
95 | end
96 |
97 | %% DGs Storage systems (ST)
98 | function Pow = Power_ST(obj,NDn)
99 | STlev = rand(NDn, obj.STn).*obj.STEs(ones(NDn,1),:);
100 | Pow = STlev;
101 | STPaux = obj.STPpeak(ones(NDn,1),:);
102 | Pow(Pow > STPaux) = STPaux(Pow > STPaux);
103 | end
104 |
105 | %% power method for the Heat Pumps Turbines
106 | function [Pow_hp,COPdc]=Power_HP_OnOff(obj,Text,Tw)
107 | %Onn-Off Air-to_Water Heat Pumps
108 | % Input
109 | % Text = External Air Temperature (e.g. Random Variable)
110 | % Tw = Hot Water Temperature (e.g. 35 deg Celsius)
111 | % Output
112 | % Powhp= Thermal Power Output delivered by the Heat Pump
113 | % COPdc = Coefficent of Performance at full load
114 | % Power and Coeff of performance at full load
115 |
116 | Pow_hp=obj.a1.*Text+obj.b1.*Text.^2+obj.c1.*Tw;
117 | %COPdc=3.42;
118 | COPdc=obj.a2.*Text+obj.b2.*Text.^2+obj.c2.*Tw;
119 | % if the temperature is outside the operative ranges
120 | Pow_hp(Text<-10 | Text>20)=0; COPdc(Text<-10 | Text>20)=0;
121 | end
122 | end
123 | end
124 |
125 |
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/Data_ElectricNetworkBarryIsalnd.m:
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1 | clc
2 | clear variables
3 | %% Barry island Electric Network Data
4 | Del.NDn=8;
5 | Del.FDn=7;
6 | Del.MSn=3;
7 | Del.MStyp=[1;2;1]; % 1 is Gas Turbine, 2 is steam Turbine
8 | Del.MSnod=[2,7,8];
9 | Del.DGtn=4;
10 | Del.FDX=0.08; % Reactance Ohm/km
11 | Del.FDR=0.164; % Reactance Ohm/km
12 | Del.Lengths=[260;170;230;320;200;160;260]; %line Lenght
13 | Del.muLDth=[0.2;0;0.5;0.5;0.2;0.2;0;0]; % load Demanded
14 | Del.stdLDth=[0.02;0;0.05;0.05;0.02;0.02;0;0]; % load Demanded
15 | Del.Cm=[1.3;1/0.79];
16 | Del.Z=8.1; %
17 | Del.MSmu=[20;10;20];
18 | Del.MSsig=[2;1;2];
19 | Del.MScap=[40;40;50];
20 | Del.Vnom=11; % base voltage in [kV]
21 | Del.FDks=[1;2;3;4;8;5;6]; % start node
22 | Del.FDke=[2;3;4;8;5;6;7]; % end node
23 | Del.FDAmp=[400;400;400;400;400;400;400];
24 | Del.MSCo=0.1450; % Main Generators cost
25 | Del.PVCo=3.7670e-05; % Potovolt cost
26 | Del.WCo=0.0390; % Wind Turbine cost
27 | Del.EVCo=0.0210; % El Vehicles cost
28 | Del.STCo=4.6284e-5; % Storage cost
29 | Del.VGCo=1e9; % Virtual generators cost
30 | Del.pckg=[50,1,1,50];% pakaging factors
31 | Del.VGcap=10000; % Virtual generators capacity
32 | Del.ts=1; % 1h simulation time
33 | % Storages
34 | Del.STn=1;
35 | Del.STEs=0.04;
36 | Del.STPpeak=0.2800;
37 | %% Barry island: Now Heat District Network Solution
38 | load('D.mat')
39 |
40 |
41 | %% Sample External Environmental Conditions
42 | Tamb=normrnd(6,3); % Heating Temperature Normally Distributed
43 | % Compute Thermal power Demand
44 |
45 | %% 1) Start With Electric Network Economic Dispatch
46 | % The Generators Allocation Matrix
47 | x=zeros(Del.NDn,Del.DGtn+1);
48 | x(Del.MSnod,1)=1;
49 | % the meccanical states
50 | FDmecst=ones(Del.FDn,1); %feeders meccanical state
51 | NETmecst=ones(Del.NDn,Del.DGtn+1); %feeders meccanical state
52 | % Loads
53 | LD=normrnd(Del.muLDth,Del.stdLDth); % MWel of the 5 lumped electrical loads
54 | % MS
55 | MSPp = normrnd(Del.MSmu, Del.MSsig);
56 | MSPav = MSPp(ones(Del.NDn,1),:);
57 | % DGs Storage systems (ST)
58 | ik=1;
59 | STlev = rand(Del.NDn, Del.STn).*Del.STEs(ones(Del.NDn,1),:);
60 | STPav = STP(STlev, x(:,1+ik), Del);
61 | STPav = STPav.*NETmecst(:,1+ik);
62 | % MCHP
63 | % DGPav=[..Pav MCHPav STPav]
64 | DGPav=x(:,2:end);
65 | NETPavM=[MSPav DGPav]; %Matrix of electric power Available
66 | [Pgen,ENS, NETminCo, PNETu, PDGu, ef] = OPF(x, NETPavM, LD, FDmecst, Del);
67 | PowTh=PowEl2PowTh(Pgen,Del);
68 |
69 |
70 |
71 |
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/Data_intro_ElectricThermal_NetworkBarryIsalnd.m:
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1 | clc; clear variables
2 | % see [1] for the original description of the heat-electric power network
3 | % case study and [2] for an application within a stochastic optimization framwork
4 | % [1] Xuezhi Liu et al "Combined analysis of electricity and heat networks ", Applied Energy,
5 | % Volume 162, 15 January 2016, Pages 1238-1250
6 | % DOI https://doi.org/10.1016/j.apenergy.2015.01.102
7 |
8 | % [2] R.Rocchetta and E.Patelli, "Stochastic analysis and reliability-cost optimization
9 | % of distributed generators and air source heat pumps", 2017 2nd International Conference on System Reliability and Safety (ICSRS)
10 | % https://strathprints.strath.ac.uk/72133/1/https://ieeexplore.ieee.org/document/8272792.pdf
11 |
12 | %% Example of how to define a data structure for the electric network
13 | % System topology clc; clear variables
14 | % see [1] for the original description of the heat-electric power network
15 | % case study and [2] for an application within a stochastic optimization framwork
16 | % [1] Xuezhi Liu et al "Combined analysis of electricity and heat networks ", Applied Energy,
17 | % Volume 162, 15 January 2016, Pages 1238-1250
18 | % DOI https://doi.org/10.1016/j.apenergy.2015.01.102
19 |
20 | % [2] R.Rocchetta and E.Patelli, "Stochastic analysis and reliability-cost optimization
21 | % of distributed generators and air source heat pumps", 2017 2nd International Conference on System Reliability and Safety (ICSRS)
22 | % https://strathprints.strath.ac.uk/72133/1/https://ieeexplore.ieee.org/document/8272792.pdf
23 |
24 | % Temp=load('D.mat');
25 | % Del= Temp.D.Del; % electrical grid data
26 | % Dth= Temp.D.Dth; % thermal grid data
27 | %% Example of how to define a data structure for the electric network
28 | % System topology
29 | Del.NDn=8; % number of nodes
30 | Del.FDn=7; % number of feeders (links between the nodes)
31 | Del.FDks=[1;2;3;4;8;5;6]; % start node
32 | Del.FDke=[2;3;4;8;5;6;7]; % end node
33 | Del.FDAmp=[400;400;400;400;400;400;400]; % Ampacity [A], maxiumum current a cable can carry without exceeding its thermal limit
34 |
35 | % Nodes parameters (generators positions and propreties
36 | Del.MSn=3; % number of main generator sources
37 | Del.MStyp=[1;2;1]; % Generators Type: one is Gas Turbine(2) and two is-steam Turbines (1)
38 | Del.MSnod=[2,7,8]; % nodes where the main generating sources are located
39 | Del.DGtn=4; %
40 |
41 | % links (power cables) parameters
42 | Del.FDX=0.08; % Reactance [Ohm/km]
43 | Del.FDR=0.164; % Resistance [Ohm/km]
44 | Del.Lengths=[260;170;230;320;200;160;260]; % Lenght of the links
45 |
46 | % Nodes parameters (random power demanded, produced and base voltage)
47 | Del.muLDth=[0.2;0;0.5;0.5;0.2;0.2;0;0]; % mean Load Demand
48 | Del.stdLDth=[0.02;0;0.05;0.05;0.02;0.02;0;0]; % standard deviation of the Demand
49 | Del.MSmu=[20;10;20]; % mean power produced by the main generators
50 | Del.MSsig=[2;1;2]; % standard deviation of the power produced by the main generators
51 | Del.Vnom=11; % base voltage in [kV]
52 |
53 | % Cost data
54 | Del.Cm=[1.3;1/0.79]; % Cost for Gas Turbine(2) and two is-steam Turbines (???)
55 | Del.MSCo=0.1450; % Main Generators operational cost
56 | Del.PVCo=3.7670e-05; % Potovolt operational cost
57 | Del.WCo=0.0390; % Wind Turbine operational cost
58 | Del.EVCo=0.0210; % El Vehicles operational cost
59 | Del.STCo=4.6284e-5; % Storage operational cost
60 | Del.VGCo=1e9; % Virtual generators operational cost
61 | Del.Z=8.1; % t.b.d.
62 | Del.MScap=[40;40;50]; % t.b.d.
63 |
64 | % Distributed generators data and production cost
65 | Del.pckg=[50,1,1,50];% pakaging factors
66 | Del.VGcap=10000; % Virtual generators capacity (virtually inf)
67 | Del.ts=1; % 1-h simulation time step
68 |
69 | Del.STn=1; % storages number of technologies
70 | Del.STEs=0.04; % storage parameter
71 | Del.STPpeak=0.2800; % storage peak output
72 |
73 | Del.PVn=1; % PV number of technologies
74 | Del.PVTa=30; % PV parameters
75 | Del.PVTno=43; %
76 | Del.PVIsc=1.8; %
77 | Del.PVItc=1.4;%
78 | Del.PVVoc=55.5; %
79 | Del.PVVtc=194;%
80 | Del.PVVPmax=38;%
81 | Del.PVIPmax=1.32;%
82 | Del.EVPp=6.73;% EV power
83 | Del.SCn=300;%
84 | Del.Inc=0.0240; %
85 | Del.MScm=1.3;%
86 | Del.SCn=7e2;%
87 |
88 | Del.WWSrated=9.5; % rated Wind
89 | Del.Eprice=@(L)-5.13717E-09*L^2+5.31538E-05*L; % price of the electricity from the total load demanded L
90 |
91 | %% Barry island: Load the Heat District Network data
92 | Temp=load('D.mat');
93 | Dth=Temp.D.Dth; % load the structure of the 'thermal' network
94 |
95 | %% show topology of the two networks (not linked)
96 | G_th=graph(Dth.From_Node,Dth.To_Node);
97 | G_th.plot;
98 | hold on; grid on
99 | G_el=graph(Del.FDks,Del.FDke);
100 | G_el.plot
101 | legend('Thermal nodes', 'Electrical nodes')
102 |
103 | %% Sample External Environmental Conditions
104 |
105 | long=-3.27; % Barry Island UK Longitude
106 | lat=51.3; % Barry Island UK Latitude
107 | SCn= 200; % Number of samples
108 | Ndays=5; % number of days
109 | [ITot,ws,~,~]=Weather_Simulator(SCn,long,lat,24*Ndays); % Generate weather history
110 | % ITot clear sky irradiance
111 | % ws wind speed
112 | % s total 'real' irradiance
113 | a= 0.2579; b= 0.7264; % distribution parameters
114 | s=betarnd(a , b,size(ITot)).*ITot;
115 | Tamb=normrnd(6,3); % Example of normal distributed Temperature
116 | subplot(2,1,1)
117 | % display weather samples
118 | plot(ITot); hold on;
119 | xlabel('IX of the simulated hour'); ylabel('irradiance')
120 | plot(s,'.-r'); legend('clear sky irr', 'with cloud cover')
121 | subplot(2,1,2)
122 | plot(ws); hold on;
123 | xlabel('IX of the simulated hour'); ylabel('wind speed [m/s]')
124 | %% 1) Start With Electric Network Economic Dispatch
125 | % The Generators Allocation Matrix
126 | %x=zeros(Del.NDn,Del.DGtn+1); % zero generators over the NDn nodes and for the Del.DGtn+1 technologies
127 | % user-defined el-grid allocation matrix
128 | % MS PV WT EV ST
129 | x=[ 0,11, 0, 0, 13 % node 1
130 | 0, 0, 1, 0, 15 % node 2
131 | 0, 4, 4, 0, 5 % node 3
132 | 0, 0, 0, 0, 5 % node 4
133 | 0, 4, 0, 0, 20 % node 5
134 | 0, 0, 0, 0, 2 % node 6
135 | 0, 1, 1, 0, 0 % node 7
136 | 0, 9, 1, 0, 40]; % node 8
137 |
138 | x(Del.MSnod,1)=1; % add main generators in the first colum
139 |
140 | % the meccanical states
141 | FDmecst=ones(Del.FDn,1); %feeders meccanical state
142 | NETmecst=ones(Del.NDn,Del.DGtn+1); %feeders meccanical state
143 | % Loads
144 | LD=normrnd(Del.muLDth,Del.stdLDth); % MWel of the 5 lumped electrical loads
145 | % MS (Main power sources)
146 | MSPp = normrnd(Del.MSmu, Del.MSsig);
147 | MSPav = MSPp(ones(Del.NDn,1),:);
148 |
149 | % Distirbuted generators (DG)
150 | DGmdl=DG_module();% define generic DG module
151 |
152 | % Photovoltaic PV
153 | Sun= 1; % irradiance
154 | Text=29; % ambient temperature
155 | PVPav=DGmdl.Power_PV(Sun,Text,Del.NDn); % available power from PV pannles
156 | PVPav = PVPav.*NETmecst(:,2).*x(:,2);
157 |
158 | % Wind turbines WT
159 | WSpeed=15; % wind speed
160 | WPav=DGmdl.Power_WT(WSpeed,Del.NDn); % available power from wind turbines
161 | WPav = WPav.*NETmecst(:,3).*x(:,3);
162 |
163 | % Electric Vehicles EV (to do)
164 | %..
165 | EVav=zeros(Del.NDn,1);
166 |
167 | % Storages (ST)
168 | STPav=DGmdl.Power_ST(Del.NDn); % available power from storages
169 | STPav = STPav.*NETmecst(:,5).*x(:,5); % remove failed comp and use allocation matrix
170 |
171 | %Matrix of electric power Available
172 | NETPavM = [MSPav PVPav WPav EVav STPav].*[ones(Del.NDn,1) Del.pckg(ones(Del.NDn,1), 1:4 )];
173 |
174 | Del.NDn=8; % number of nodes
175 | Del.FDn=7; % number of feeders (links between the nodes)
176 | Del.FDks=[1;2;3;4;8;5;6]; % start node
177 | Del.FDke=[2;3;4;8;5;6;7]; % end node
178 | Del.FDAmp=[400;400;400;400;400;400;400]; % Ampacity [A], maxiumum current a cable can carry without exceeding its thermal limit
179 |
180 | % Nodes parameters (generators positions and propreties
181 | Del.MSn=3; % number of main generator sources
182 | Del.MStyp=[1;2;1]; % Generators Type: one is Gas Turbine(2) and two is-steam Turbines (1)
183 | Del.MSnod=[2,7,8]; % nodes where the main generating sources are located
184 | Del.DGtn=4; %
185 |
186 | % links (power cables) parameters
187 | Del.FDX=0.08; % Reactance [Ohm/km]
188 | Del.FDR=0.164; % Resistance [Ohm/km]
189 | Del.Lengths=[260;170;230;320;200;160;260]; % Lenght of the links
190 |
191 | % Nodes parameters (random power demanded, produced and base voltage)
192 | Del.muLDth=[0.2;0;0.5;0.5;0.2;0.2;0;0]; % mean Load Demand
193 | Del.stdLDth=[0.02;0;0.05;0.05;0.02;0.02;0;0]; % standard deviation of the Demand
194 | Del.MSmu=[20;10;20]; % mean power produced by the main generators
195 | Del.MSsig=[2;1;2]; % standard deviation of the power produced by the main generators
196 | Del.Vnom=11; % base voltage in [kV]
197 |
198 | % Cost data
199 | Del.Cm=[1.3;1/0.79]; % Cost for Gas Turbine(2) and two is-steam Turbines (???)
200 | Del.MSCo=0.1450; % Main Generators operational cost
201 | Del.PVCo=3.7670e-05; % Potovolt operational cost
202 | Del.WCo=0.0390; % Wind Turbine operational cost
203 | Del.EVCo=0.0210; % El Vehicles operational cost
204 | Del.STCo=4.6284e-5; % Storage operational cost
205 | Del.VGCo=1e9; % Virtual generators operational cost
206 | Del.Z=8.1; % t.b.d.
207 | Del.MScap=[40;40;50]; % t.b.d.
208 |
209 | % Distributed generators data and production cost
210 | Del.pckg=[50,1,1,50];% pakaging factors
211 | Del.VGcap=10000; % Virtual generators capacity (virtually inf)
212 | Del.ts=1; % 1-h simulation time step
213 |
214 | Del.STn=1; %
215 | Del.STEs=0.04; %
216 | Del.STPpeak=0.2800; %
217 |
218 | Del.PVn=1; %
219 | Del.PVTa=30; %
220 | Del.PVTno=43; %
221 | Del.PVIsc=1.8; %
222 | Del.PVItc=1.4;%
223 | Del.PVVoc=55.5; %
224 | Del.PVVtc=194;%
225 | Del.PVVPmax=38;%
226 | Del.PVIPmax=1.32;%
227 | Del.EVPp=6.73;%
228 | Del.SCn=300;%
229 | Del.Inc=0.0240; %
230 | Del.MScm=1.3;%
231 | Del.SCn=7e2;%
232 |
233 | Del.WWSrated=9.5; % rated Wind
234 | Del.Eprice=@(L)-5.13717E-09*L^2+5.31538E-05*L; % price of the electricity from the total load demanded L
235 |
236 | %% Barry island: Load the Heat District Network data
237 | Temp=load('D.mat');
238 | Dth=Temp.D.Dth; % load the structure of the 'thermal' network
239 |
240 | %% show topology of the two networks (not linked)
241 | figure
242 | G_th=graph(Dth.From_Node,Dth.To_Node);
243 | G_th.plot;
244 | hold on; grid on
245 | G_el=graph(Del.FDks,Del.FDke);
246 | G_el.plot
247 | legend('Thermal nodes', 'Electrical nodes')
248 |
249 |
250 | %% 1) Start With Electric Network Economic Dispatch
251 | % The Generators Allocation Matrix
252 | x=zeros(Del.NDn,Del.DGtn+1);
253 | x(Del.MSnod,1)=1;
254 | % the meccanical states
255 | FDmecst=ones(Del.FDn,1); %feeders meccanical state
256 | NETmecst=ones(Del.NDn,Del.DGtn+1); %feeders meccanical state
257 | % Loads
258 | LD=normrnd(Del.muLDth,Del.stdLDth); % MWel of the 5 lumped electrical loads
259 | % MS
260 | MSPp = normrnd(Del.MSmu, Del.MSsig);
261 | MSPav = MSPp(ones(Del.NDn,1),:);
262 | % DGs Storage systems (ST)
263 | ik=1;
264 | STlev = rand(Del.NDn, Del.STn).*Del.STEs(ones(Del.NDn,1),:);
265 | STPav = STP(STlev, x(:,1+ik), Del);
266 | STPav = STPav.*NETmecst(:,1+ik);
267 | % MCHP
268 | % DGPav=[..Pav MCHPav STPav]
269 | DGPav=x(:,2:end);
270 | NETPavM=[MSPav DGPav]; %Matrix of electric power Available
271 | [Pgen,ENS, NETminCo, PNETu, PDGu, ef] = OPF(x, NETPavM, LD, FDmecst, Del);
272 | PowTh=PowEl2PowTh(Pgen,Del);
273 |
274 |
275 |
276 |
277 |
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/HPP.m:
--------------------------------------------------------------------------------
1 | %% HPP
2 | function T=HPP(lambda,Tmax)
3 | % lambda=input('Enter The lambda:'); % arrival rate
4 | % Tmax=input('Enter maximum time:'); % maximum time
5 | % clear T;
6 | T(1)= 0;
7 | i=1;
8 |
9 | while T(i) < Tmax,
10 | U(i)=rand(1,1);
11 | T(i+1)=T(i)-(1/lambda)*(log(U(i)));
12 | i=i+1;
13 | end
14 |
15 | T(i)=Tmax;
16 | end
17 | %
18 | % stairs(T(1:(i)), 0:(i-1));
19 | % title(['A Sample path of the Poisson process with arrival rate ', num2str(lambda)])
20 | % xlabel(['Time'])
21 | % ylabel(['Number of Arrivals in [0,', num2str(Tmax), ']',])
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/MC_HEATPOWER.m:
--------------------------------------------------------------------------------
1 | function [RES] = MC_HEATPOWER(X, D)
2 | % The Generators Allocation Matrix
3 | % the Data structure (contains 2 structures 1 for the heat and one for the
4 | % electrci network)
5 | Wea=D.Weather; % the electric network data
6 | Del=D.Del; % the electric network data
7 | Dth=D.Dth; % the heat network data
8 | % The decision variables
9 | x_el=X.x_el; % the DGs atrucutre (PV,WT,EV,ST) in the electric network
10 | x_th=X.x_th; % the DGs in the heat network
11 | %% Define a Distributed Generator Object
12 | % DGmdl=DG_module('D',Del); # --> not yet available, need flexible constructor
13 | DGmdl=DG_module();
14 | %% Scenarios cycle
15 | [pdfENS, pdfCg,NETPav,NETuP, DGPu, ef] = deal(zeros(Del.SCn,1));
16 | [nENS] = deal(zeros(Del.SCn,Del.NDn));
17 | tic
18 | % random initial time state
19 | mnth = randi(12); % the random month of the year
20 | day = 1;
21 | td = randi(24); % random hour of the day td
22 | %% Generate weather History
23 | % ITot clear sky irradiance
24 | % ws is the wind speed profile [m/s]
25 | % Beta distributed solar irradiance [kW/m^2] the total 'real' irradiance
26 | long=-3.27; % Barry Island UK Longitude
27 | lat=51.3; % Barry Island UK Latitude
28 | [ITot,ws,~,~]=Weather_Simulator(Del.SCn,long,lat,mnth*day);
29 | s=betarnd(Wea.PVa(1) , Wea.PVb(1),size(ITot)).*ITot;
30 | Weather.s=s; % save weather samples
31 | Weather.ws=ws;% save weather samples
32 |
33 |
34 | for i = 1:Del.SCn
35 |
36 | % try a time-sequential simulation of the system state
37 | td=td+1; % add 1 h
38 | if td>24; td=1;day=day+1; end
39 | if day>30; day=1; mnth=mnth+1; end
40 | if mnth>12; mnth=1; end
41 |
42 | % mnth = randi(12); % the random month of the year
43 | % td = randi(24); % random hour of the day td
44 | %% The Meccanical State of the component
45 | % to include failures change a state from 1 to 0
46 | FDmecst=ones(Del.FDn,1); % meccanical state of the cables (1= healthy)
47 | NETmecst=ones(Del.NDn,Del.DGtn+1); % meccanical state of the generators (1= healthy)
48 |
49 | %% A simple failure model
50 | D.GenFailRates=[0.01 0.05 0.05 0.05 0.05]; % failure probability generators (probability of 1 --> 0 in 1-h)
51 | D.FDFailRates= 0.01; % failure probability links
52 | D.GenRecoveryRates= [0.6 0.3 0.3 0.3 0.3]; % recovery probability generators (probability of 0 --> 0 in 1-h)
53 | D.FDRecoveryRates= 0.8; % recovery probability links
54 | [FDmecst,NETmecst]=MarkovFailure(FDmecst,NETmecst,D);
55 | MecStates.FDmecst(i,:)=FDmecst;
56 | MecStates.NETmecst(i,:)=NETmecst(:);
57 | %% Random electric and thermal loads
58 | % electric loads
59 | LDel = normrnd(Del.muLDel(:,td).*1.5, Del.stdLDel(:,td)); % in [KWel]
60 | LDel(LDel<0)=0;
61 | % thermal loads
62 | LDth = normrnd(Dth.LDth(:,td).*1.5,(Dth.LDth(:,td).*1.5)/20); % expressed in [KWth]
63 | LDth(LDth<0)=0;
64 | LDtots.el(i)=sum(LDel);
65 | LDtots.th(i)=sum(LDth);
66 | %% Main supply generators
67 | MSpow=Del.MSmu; % this can be the control variable
68 | % MSpow = normrnd(Del.MSmu, Del.MSmu/10); % random power generated by the main sources
69 | MSPav = MSpow.*NETmecst(:,1).*x_el(:,1);
70 | % random external temperature conditioanl to month
71 | Text=normrnd(Wea.muText(mnth),Wea.stdText(mnth)); % external environmental conditions
72 | Weather.Text(i)=Text;
73 | %% Power DGs
74 | % PV
75 | PVPav=DGmdl.Power_PV(s(i),Text,Del.NDn); % available power from PV pannles
76 | PVPav = PVPav.*NETmecst(:,2).*x_el(:,2);
77 | % WT
78 | WPav=DGmdl.Power_WT(ws(i),Del.NDn); % available power from wind turbines
79 | WPav = WPav.*NETmecst(:,3).*x_el(:,3);
80 |
81 | % ST
82 | STPav=DGmdl.Power_ST(Del.NDn); % available power from storages
83 | STPav = STPav.*NETmecst(:,5).*x_el(:,4);
84 |
85 | %% Heat generated by Heat Pumps (function of the extenral tamperature)
86 | Twater=35; % target water temperature
87 | % [P_onoffhp,COPdc_onoffhp]=OnOff_HP(Text,Twater);
88 | [P_onoffhp,COPdc_onoffhp]=DGmdl.Power_HP_OnOff(Text,Twater);
89 | % [P_idhp,COPdc_idhp]=ID_HP(Text,Twater,psi);
90 | % Ncomp=randi(2); [P_mchp,COPdc_mchp]=MC_HP(Text,Twater,Ncomp);
91 | HPav=P_onoffhp.*x_th;
92 | Lel_onoffhp=HPav./COPdc_onoffhp;
93 | Lel_onoffhp(isnan(Lel_onoffhp))=0;
94 | LDel=LDel+Dth.LumpedThLoadsMatrix*Lel_onoffhp;
95 |
96 | %% Simple Power-Heat-Balance
97 | % solve flow-pression equations for the heat network
98 | NeededPowTh=(LDth-HPav); % the thermal power needed is the load - HP available from the Heat Pumps
99 | NeededPowTh(NeededPowTh<0)=0; % if there is a thermal production excedence, then it goes wasted!!
100 | % assume 100% efficent (if is an electric backup system is quite colose to 100%)
101 | LDelbackup= (Dth.LumpedThLoadsMatrix*(NeededPowTh));
102 | LDel=LDel+ LDelbackup./Del.MScm; % MScm conversion efficiency
103 | LDtots.th_to_elbackup(i)=sum(LDelbackup./Del.MScm);
104 | LDtots.LumpedThLoads(i)=sum(Dth.LumpedThLoadsMatrix*Lel_onoffhp);
105 | %% Run an OPTIMAL Power Flow, Here we have the Network Solver
106 | NETPavM = [MSPav PVPav WPav STPav].*[ones(Del.NDn,1) Del.pckg(ones(Del.NDn,1),[1:2,4])]; %Matrix of electric power Available
107 | NETPav(i) = sum(sum(NETPavM)); % Total available power produced
108 | DGPp(i,:) = sum(NETPavM(:, 2:end));% Total production for each generator type
109 | [Pgenerators(i,:) ,pdfENS(i), NETminCo, NETuP(i), DGPu(i), nENS(i,:), ef(i)] = OPF(x_el, NETPavM, LDel, FDmecst, Del);
110 |
111 | %% Cost Model
112 | %ep= electricity price is a function of the electric load
113 | %NETCi= investment costs (fixed for x) prorated in thn years
114 | %NETminCo= operational cost of the electrical network
115 | %inc_th= incentives for producing clean thermal power (ASHP 2.61p/kWh incentive in pounds 2017 UK)
116 | %Del.Inc= incentives for producing clean electric power in p/kWh
117 | inc_th=0.0261;
118 | ep = Del.Eprice(sum(LDel));
119 | DGCi = Del.DGCi.*Del.pckg;
120 | NETCi = (sum(sum(x_el).*[Del.MSCi DGCi([1:2,4])])+sum(x_th)*Dth.Cost_onoff)/(365*24*Del.thn);
121 | HPPowused=sum(LDth-NeededPowTh);
122 | NetGainDG_el=(Del.Inc + ep)*(DGPu(i))*Del.ts; % gain from producing renwable electric Energy
123 | NetGainDG_th= inc_th*HPPowused*Del.ts; % gain from producing renwable electric Energy
124 | pdfCg(i) = NETCi + NETminCo + - NetGainDG_el - NetGainDG_th;
125 | % try to add a cost of the energy not supplied (which must be bought from the grid at a cost ep)
126 | pdfCg(i) = pdfCg(i) + pdfENS(i)*ep;
127 |
128 | %% Incentives 2017 UK
129 | % ASHP 2.61 p/kWh
130 | % NETPpM = [MSPp PVPp WPp EVPp STPp].*[ones(D.NDn,D.MSn) D.pckg(ones(D.NDn,1),:)];
131 | % NETPp(i) = sum(sum(NETPpM));
132 | % DGPavM = NETPavM(:, 2:end);
133 | % DGPav(i) = sum(sum(DGPavM));
134 | % TLD(i) = sum(LDel);
135 | end
136 |
137 | pdfCg(isnan(pdfCg))=[];% get rid of the NaN simulations
138 | pdfENS(isnan(pdfENS))=[];% get rid of the NaN simulations
139 |
140 | Pgenerators(isnan(pdfENS),:)=[];
141 | RES.pdfPgenerators=Pgenerators; % power
142 | RES.NETPav=NETPav;
143 | RES.pdfnENS=nENS; % Enrgy not supplied per node
144 | RES.pdfDGP_RES=DGPu; % Total renewable power electric used
145 | RES.pdfNETuP=NETuP; % real used power in the net
146 | RES.pdfDGPp=DGPp; % real used power of the dgs
147 | RES.pdfLDel=LDtots; % total electric thermal and combined loads
148 | RES.pdfENS=pdfENS;
149 | RES.pdfCg=pdfCg;
150 | RES.Weather=Weather; % samples of the weather
151 | RES.MecStates=MecStates;% save mechanical states
152 | % Some statistics
153 | RES.EENS=mean(pdfENS);
154 | RES.ECg=mean(pdfCg);
155 | RES.CovENS=std(pdfENS)/RES.EENS;
156 | RES.CovCg=std(pdfCg)/RES.ECg;
157 | Alpha=0.95*100;
158 | RES.p95ENS=prctile(pdfENS,Alpha);
159 | RES.p95Cg=prctile(pdfCg,Alpha);
160 | RES.Supequantile95_ENS=mean(pdfENS(pdfENS>=prctile(pdfENS,Alpha)));
161 | RES.Supequantile95_ENSCg=mean(pdfCg(pdfCg>=prctile(pdfCg,Alpha)));
162 | end
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/MarkovFailure.m:
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1 | function [FDmecst,NETmecst] = MarkovFailure(FDmecst, NETmecst, D)
2 | apply_random_failure_model = 0;
3 | if apply_random_failure_model==1 % recovery model to be implemented
4 | % Mechanical states of nodes --> 0 failure --> 1 operative
5 | NETmecst=zeros(D.Del.NDn,D.Del.DGtn+1); % generators meccanical states are all healthy
6 | NETf = D.GenFailRates;
7 | %NETr = D.GenRecoveryRates;
8 | %ppt = NETf./(NETf+NETr);
9 | NETrnd = rand(D.Del.NDn, D.Del.DGtn+1);
10 | NETmecst(NETrnd> NETf) = 1;
11 | %NETmecst(find((x~=0).*NETrnd > (x~=0).*ppt(ones(D.Del.NDn,1),:))) = 1;
12 |
13 | % Feeders mecanical state --> 0 failure --> 1 operative
14 | FDmecst = zeros(D.Del.FDn,1);
15 | FDmecst(rand(D.FDn, 1) > D.FDFailRates) = 1;
16 |
17 | else % example - all feeders and busses are operative
18 | FDmecst=ones(D.Del.FDn,1); % feeders meccanical states are all healthy
19 | NETmecst=ones(D.Del.NDn,D.Del.DGtn+1); % generators meccanical states are all healthy
20 | end
21 |
22 | end
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/NHPP.m:
--------------------------------------------------------------------------------
1 | %% NHPP
2 | function NHPP(lambda,Tmax)
3 | lambda=input('Enter The arrival Rate:'); % arrival rate
4 | Tmax=input('Enter maximum time:'); % maximum time
5 | clear T;
6 | T(1)= 0;
7 | a=input('Enter constant a>0:');
8 | b=input('Enter constant b<1:');
9 | S(1)=0;
10 | i=1;
11 |
12 | while T(i) < Tmax,
13 | U(i)=rand(1,1);
14 | T(i+1)=T(i)-(1/lambda)*(log(U(i))); % Homogeneous Poisson Distr.
15 | lambdat=a*(T(i+1))^(-b);
16 | u(i)=rand(1,1);
17 | if u(i)<=lambdat/lambda % <-- Here is my doubt/question
18 | i=i+1;
19 | S(i)=T(i); % <-- Here is my doubt/question
20 | end
21 | end
22 | stairs(S(1:(i)), 0:(i-1));
23 | title(['A Sample path of the non homogeneous Poisson process']);
24 | xlabel(['Time interval']);
25 | ylabel([' Number of event ']);
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/OPF.m:
--------------------------------------------------------------------------------
1 | function [Pgen,ENS, NETminCo, PNETu, PDGu, nENS, ef] = OPF(x, NETPavM, LD, FDmecst, D)
2 | %% OPF.m: Optimal power flow function considering virtual generators
3 | %
4 | % Inputs
5 | % x: allocation matrix for the distributed generators
6 | % NETPavM: power available in the net
7 | % LD: loads
8 | % FDmecst: mechanical states of the feeders
9 | % D: struct with the needed data for the evaluation of fns
10 | % Output
11 | % ENS: Energy Non Supplied
12 | % NETminCo: minimum operating cost of the net
13 | % PNETu: real used power in the net
14 | % PDGu: real used power of the dgs
15 | % ef: exitflag of the solver of the OPF
16 | %
17 | % OPF problem definition
18 | %
19 | % min NETco x xNET
20 | % s.t.
21 | % Ainq x xnet <= binq
22 | % Aeq x xnet = beq
23 | % lb <= x <= up
24 | %
25 | % xNET: decision vector of the minimization problem. Contains the
26 | % DGs, the virtual generators and the feeders present in
27 | % the network. The optimal gives the real power used from the
28 | % DGs, the load shedding (power virtualy generated to satify
29 | % the demand) and the power flow in the feeders.
30 | % binq: containts the max power available for each generator
31 | % (NETPavM) and the limit of power that is allowed to flow by each feeder
32 | % beq: is equal to the load in each node
33 | % _________________________________________________________________________
34 | %
35 | % DEVELOPED BY 2112 inc.
36 | %__________________________________________________________________________
37 | %%
38 |
39 | V0 = D.Vnom; % per unit Voltage
40 | S0 = D.Vnom.^2; % per unit Apparent power
41 | Z0 = (V0.^2)/S0; % per unit Impedance
42 | Xpu = D.FDX/Z0; % per unit Reactance
43 | Bpu = (-1./Xpu); % per unit Susceptance
44 | LD0 = LD/S0; % per unit Susceptance
45 | FDPlim = D.FDAmp*D.Vnom; % feeders power limit
46 |
47 | spx = sparse(x); % sparse x
48 | nzn = nnz(spx); % number of non-zeros elements in x
49 | xvg = [x ones(D.NDn,1)]; % adding the virtual generators to x
50 | spxvg = sparse(xvg); % sparse xvg
51 | nznvg = nnz(spxvg); % number of non-zeros elements in xvg
52 |
53 | % UNTIL THE MOMENT THE COSTS OF THE FEEDERS ARE NOT CONSIDERED
54 |
55 | MSco = D.MSCo(ones(D.NDn,1),:);
56 | DGco = [D.PVCo(ones(D.NDn,1),:) D.WCo(ones(D.NDn,1),:) D.STCo(ones(D.NDn,1),:)].*D.pckg(ones(D.NDn,1),1:3);
57 | % DGco = [D.PVCo(ones(D.NDn,1),:) D.WCo(ones(D.NDn,1),:) D.EVCo(ones(D.NDn,1),:) D.STCo(ones(D.NDn,1),:)].*D.pckg(ones(D.NDn,1),:);
58 | MS_DG_VGco = [MSco DGco D.VGCo(ones(D.NDn,1),:)]; % add virtual gen
59 | NETco = [MS_DG_VGco(find(spxvg)); zeros(D.FDn,1)]; % [MS_DG_VGco FDco]
60 |
61 | MS_DGAeq = zeros(D.NDn,nznvg);
62 | [i,~,~] = find(spxvg);
63 | MS_DGAeq(i+(((1:nznvg)')-1)*D.NDn) = 1;
64 |
65 | FDAeq = zeros(D.NDn,D.FDn);
66 | FDAeq(D.FDks+(((1:D.FDn)')-1)*D.NDn) = Bpu.*FDmecst;
67 | FDAeq(D.FDke+(((1:D.FDn)')-1)*D.NDn) = -Bpu.*FDmecst;
68 |
69 | Aeq = [MS_DGAeq, FDAeq];
70 | beq = LD0;
71 | lb = [zeros(nznvg,1); FDPlim./Bpu]/S0;
72 | ub = [NETPavM(find(spx)); ones(D.NDn,1)*D.VGcap; - FDPlim./Bpu]/S0;
73 |
74 | % Solver
75 | Options.Display='off';
76 | [xNET, fval, ef] = linprog(NETco', [], [], Aeq, beq, lb, ub, Options);
77 | if ef~=1
78 | xNET=NETco.*NaN;
79 | fval=NaN;
80 | end
81 |
82 | % outputs
83 | %fdP = xNET(nzn+D.NDn+1:end).*Bpu*S0;
84 | %Angle=fdP./(Bpu*S0);
85 | ENS = sum(xNET(1+nzn:nzn+D.NDn))*S0/D.ts;
86 | NETminCo = fval*S0-ENS*D.VGCo(1);
87 | PNETu = sum(xNET(1:nzn))*S0;
88 | Pgen=xNET(1:nzn).*S0;
89 | PDGu = sum(xNET(D.MSn+1:nzn+D.MSn-1,1))*S0;
90 | nENS = xNET(1+nzn:nzn+D.NDn)*S0;
91 | % nPnetr = xNET.*[ones(nzn,1);zeros(D.NDn,1);zeros(D.FDn,1)]*S0;
92 | % fdP = xNET.*[zeros(nzn,1);zeros(D.NDn,1);ones(D.FDn,1)]*S0;
93 |
94 |
95 | end
96 |
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/PowEl2PowTh.m:
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1 | function PowTh=PowEl2PowTh(MGPgen,D)
2 | %% simple example model for converting residual thermal to electrical power demand
3 | Powelcon=0.6; % MWel power output of extration unit in full condensing mode
4 | Powthcon=0; % MWth power output of extration unit in full condensing mode
5 | PowTh=zeros(size(MGPgen));
6 | PowTh(D.MStyp==1)=MGPgen(D.MStyp==1).*D.Cm; % gas turbines
7 | PowTh(D.MStyp==2)=(Powelcon-MGPgen(D.MStyp==2)).*D.Z+Powthcon; % steam turbines
8 | end
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/STP.m:
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1 | function Pow = STP(STlev, kg, D)
2 |
3 | %Storage power output in kW
4 |
5 | STlev = STlev.*kg;
6 | Pow = STlev/D.ts;
7 | STPaux = D.STPpeak(ones(D.NDn,1),:).*kg;
8 | Pow(Pow > STPaux) = STPaux(Pow > STPaux);
9 |
10 | end
11 |
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/TempMonteCarlo.m:
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https://raw.githubusercontent.com/Roberock/Heat_and_Electric_powerNetwork/a442f6040a0888d8e8adc7ce14c582283eff5419/TempMonteCarlo.m
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/Weather_Simulator.m:
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1 | function [ITot,V,Wind_Events,Lightning_Events]=Weather_Simulator(Tseries,long,lat,g)
2 | ITot=[]; V=[];
3 | %% LIGHTNING OCCURRANCE
4 | %% sequence of HPP for Lightning (with multiple, constant occurance ratio)
5 | %SeasonsInt=[0 1400 2200 2900 3500 4300 5030 5800 6500 7200 8760]; % time of the year
6 | SeasonsInt=linspace(1,8760,13); % 12 months
7 | Light_occurance=[0 0 0.0005 0.0001 0.0085 0.019 0.022 0.015 0.002 0.001 0 0]; % frequency of occurance
8 | Lightning_Events=[];
9 | for i=1:length(Light_occurance)
10 | Tlight=HPP(Light_occurance(i),SeasonsInt(i+1)-SeasonsInt(i));
11 | Lightning_Events=[Lightning_Events Tlight(2:end-1)+SeasonsInt(i)];
12 | end
13 | %% WIND
14 | Wind_occurane=[0.011 0.012 0.0075 0.0065 0.005 0.006 0.003 0.0029 0.0045 0.0065 0.006 0.009]; % frequency of occurance
15 | Wind_Events=[];
16 | for i=1:length(Wind_occurane)
17 | Twind=HPP(Wind_occurane(i),SeasonsInt(i+1)-SeasonsInt(i));
18 | Wind_Events=[Wind_Events Twind(2:end-1)+SeasonsInt(i)];
19 | end
20 | %Transition data from Malaysian state
21 | %[] Shamshad et al. "First Order Markov Chain Models for Synthetic
22 | %Generation of Wind Speed Time Series" Energy, 2005, 10.1016/j.energy.2004.05.026
23 |
24 | % % wind speed upper and lower bound for the considered states
25 | Vl=[0 2 4 6 8 10 12 14 16 18 20 22];
26 | Vu=[2 4 6 8 10 12 14 16 18 20 22 25];
27 |
28 | %First order cumulative transition matrix
29 | Pwindcum=[...
30 | 0.371 0.778 0.952 0.988 0.997 0.998 0.999 1.000 1.000 1.000 1.000 1.000
31 | 0.166 0.612 0.924 0.983 0.995 0.999 0.999 1.000 1.000 1.000 1.000 1.000
32 | 0.051 0.294 0.798 0.961 0.989 0.997 0.999 1.000 1.000 1.000 1.000 1.000
33 | 0.017 0.100 0.403 0.794 0.954 0.989 0.997 0.999 1.000 1.000 1.000 1.000
34 | 0.010 0.045 0.144 0.421 0.803 0.960 0.991 0.998 0.999 1.000 1.000 1.000
35 | 0.006 0.027 0.070 0.178 0.473 0.816 0.962 0.993 0.997 1.000 1.000 1.000
36 | 0.005 0.021 0.048 0.095 0.205 0.507 0.831 0.973 0.994 0.998 1.000 1.000
37 | 0.006 0.022 0.052 0.085 0.140 0.267 0.632 0.871 0.976 0.998 1.000 1.000
38 | 0.009 0.028 0.042 0.060 0.102 0.167 0.307 0.633 0.902 0.981 0.995 1.000
39 | 0.014 0.068 0.123 0.137 0.164 0.192 0.233 0.438 0.726 0.890 0.973 1.000
40 | 0.000 0.000 0.000 0.040 0.040 0.040 0.120 0.240 0.400 0.640 0.920 1.000
41 | 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.200 0.200 0.400 1.000 1.000];
42 | St=randi(12);%initial wind state
43 | [V]=zeros(1,Tseries);
44 | IT=zeros(round(Tseries/24),24);
45 |
46 | for t=1:Tseries
47 | %Random wind speed depending on the previous state
48 | V(t)=Vl(St)+rand()*(Vu(St)-Vl(St));
49 | % new state random consistent with the transition probability
50 | St= find(Pwindcum(St,:) > rand(), 1, 'first') ;
51 |
52 | %% IRRADIANCE CLEAR SKY
53 | gnew=ceil(t/24);%day of the year
54 | if g~=gnew %if we are analyzing a new day
55 | g=gnew;
56 | [It]=Clear_Sky_IT(g,long,lat); % return clear sky total irradiance for h=1 to h=24 for given day, and given latitude and longitude
57 | IT(g,:)=It;
58 | end
59 |
60 | %% External Temperature
61 |
62 | end
63 | IT=IT';
64 | ITot=IT(:)';
65 | end
66 |
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/figs/readme.rm:
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1 | ### Visualization examples for the simulation results
2 |
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/readme.md:
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1 | # **Simulation Code for Combined Heat and Electric Networks**
2 |
3 | This repository contains MATLAB code for simulating various operational states of a combined heat and electricity network, integrating distributed generators and heat pumps.
4 |
5 | ## **📄 Citation**
6 | If you use this code, please cite the following:
7 |
8 | ```bibtex
9 | @INPROCEEDINGS{8272792,
10 | author={Rocchetta, Roberto and Patelli, Edoardo},
11 | booktitle={2017 2nd International Conference on System Reliability and Safety (ICSRS)},
12 | title={Stochastic analysis and reliability-cost optimization of distributed generators and air source heat pumps},
13 | year={2017},
14 | pages={31-35},
15 | doi={10.1109/ICSRS.2017.8272792}
16 | }
17 | ```
18 |
19 |
20 | ## ⚙️ Functionality
21 |
22 | ### Core Functions
23 | - **`MC_HP.m`** → Power production model for multi-compressor air-to-water heat pumps
24 | - **`OnOff_HP.m`** → Power model for an on-off air-to-water heat pump (Maneurop SH 140-4)
25 | - **`HPP.m`** → Samples from a homogeneous Poisson Process (HPP)
26 | - **`MarkovFailure.m`** → Simulates random failures of network components
27 | - **`MC_HEATPOWER(X, D)`** → Monte Carlo simulation of the combined network
28 | - `X`: Allocation matrix
29 | - `D`: Data structure containing grid information
30 | - **`DG_module.m`** → Simulates power production from distributed generators (PV, EV, ST, WT, HP)
31 | - **`Weather_Simulator.m`** → Simulates weather conditions based on geo-location and day of the year
32 | - **`PowEl2PowTh.m`** → Converts electrical power to thermal power
33 | - **`OPF.m`** → Optimal power flow function considering virtual generators
34 | - **`Clear_Sky_IT.m`** → Computes clear sky irradiance
35 |
36 | ### Data Structures
37 | - **`D.mat`** → Contains key grid data:
38 | - **`Del`** → Electrical grid data
39 | - **`Dth`** → Thermal grid data
40 | - **`Weather`** → Weather data for simulations
41 |
42 | ---
43 |
44 | ## 📌 Examples
45 |
46 | ### Example 1: Monte Carlo Simulation for Resilience Assessment
47 | This example evaluates reliability and resilience (energy not supplied distribution) using Monte Carlo simulations for a given allocation matrix.
48 |
49 | ```matlab
50 | Temp=load('D.mat'); % load data
51 | D = Temp.D; # all data in the structure D
52 | Del= Temp.D.Del; % electrical grid data
53 | Dth= Temp.D.Dth; % thermal grid data
54 | Wtr= Temp.D.Weather; % weather data
55 | ```
56 |
57 |
58 | ```matlab
59 | %% show topology of the two networks (not linked and separatelly)
60 | figure(10)
61 | G_th=graph(D.Dth.From_Node,D.Dth.To_Node);
62 | G_th.plot;
63 | hold on; grid on
64 | G_el=graph(D.Del.FDks,D.Del.FDke);
65 | G_el.plot
66 | legend('Thermal nodes', 'Electrical nodes')
67 | ```
68 |
69 |
70 | 
71 |
72 |
73 |
74 |
75 | ```matlab
76 | % Electrical allocation matrix (MS, PV, WT, EV, ST)
77 | # MainSources PhotoV WindT ElVehicle StorageUnits
78 | x_el = [0,11, 0,0,13; % Node 1
79 | 1, 0,11,0, 1; % Node 2
80 | 0,11,23,0,10; % Node 3
81 | 0, 0,11,0,21; % Node 4
82 | 0,11, 0,0,11; % Node 5
83 | 0, 0, 0,0, 2; % Node 6
84 | 1,11,11,0, 0; % Node 7
85 | 1,11,11,0,11]; % Node 8
86 |
87 | # Aggregated Number of HeatPumps at the electrical nodes
88 | x_th = [5; % Node 1
89 | 0; % Node 2
90 | 0; % Node 3
91 | 0; % Node 4
92 | 0; % Node 5
93 | 14; % Node 6
94 | 0; % Node 7
95 | 0]; % Node 8
96 |
97 | X_candidate = [x_el, x_th];
98 | ```
99 |
100 |
101 | ```matlab
102 | % Run Monte Carlo simulation for data structure D and thermal-electrical allocation
103 |
104 | [RES] = MC_HEATPOWER(X_candidate, D);
105 | ```
106 |
107 |
108 |
109 | 
110 |
111 |
112 |
113 |
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