├── .gitignore
├── Instructions_MESM_project.odt
├── Instructions_MESM_project.pdf
├── .ipynb_checkpoints
├── Untitled-checkpoint.ipynb
└── testing-checkpoint.ipynb
├── README.md
├── Plotting.py
├── testing.ipynb
└── data
└── inflow
├── Hydro_Inflow_AT.csv
└── Hydro_Inflow_CH.csv
/.gitignore:
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1 | *.pyc
2 | data_extra/
3 | TODO
4 | solution.ipynb
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/Instructions_MESM_project.odt:
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https://raw.githubusercontent.com/martavp/MESM_project/HEAD/Instructions_MESM_project.odt
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/Instructions_MESM_project.pdf:
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https://raw.githubusercontent.com/martavp/MESM_project/HEAD/Instructions_MESM_project.pdf
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/.ipynb_checkpoints/Untitled-checkpoint.ipynb:
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1 | {
2 | "cells": [],
3 | "metadata": {},
4 | "nbformat": 4,
5 | "nbformat_minor": 2
6 | }
7 |
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/README.md:
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1 | # MESM_project
2 | Macro Energy System Modelling (MESM) course project - Fall 2023 Semester Aarhus University.
3 |
4 | The file [Instructions_MESM_project](https://github.com/martavp/MESM_project/blob/master/Instructions_MESM_project.pdf) guides you through the installation of *Python for Power System Analysis* (PyPSA) which you will be using for your course project. Follow these instructions, and test if you have installed it correctly by running the Jupyter notebook [testing.ipynb](https://github.com/martavp/MESM_project/blob/master/testing.ipynb).
5 |
6 | For the project instructions, see [MESM_project.ipynb](https://github.com/martavp/RES_project/blob/master/RES_project.ipynb). Before you can run this, you need to download hourly capacity factors for [onshore wind](https://zenodo.org/record/3253876#.XSiVOEdS8l0) and [solar PV](https://zenodo.org/record/2613651#.XSiVOkdS8l0) (select 'optimal' for PV and onshore for wind). Create a new folder in the root of this repository, name this new folder "data_extra" and move the .csv files with hourly capacity factors to here.
7 |
8 | In the data folder, the electricity_demand and heating_demand data is provided in MWh.
9 |
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/Plotting.py:
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1 | #!/usr/bin/env python
2 | # coding: utf-8
3 |
4 | import pypsa
5 | import cartopy
6 | import cartopy.crs as ccrs
7 | import cartopy.mpl.geoaxes
8 | from matplotlib.patches import Circle, Ellipse
9 | from matplotlib.legend_handler import HandlerPatch
10 | import matplotlib.pyplot as plt
11 | import numpy as np
12 | import pandas as pd
13 |
14 | def assign_location(n):
15 | for c in n.iterate_components(n.one_port_components | n.branch_components):
16 |
17 | ifind = pd.Series(c.df.index.str.find(" ", start=3), c.df.index)
18 |
19 | for i in ifind.value_counts().index:
20 | # these have already been assigned defaults
21 | if i == -1:
22 | continue
23 |
24 | names = ifind.index[ifind == i]
25 |
26 | c.df.loc[names, 'location'] = names.str[:i]
27 |
28 | def rename_techs(label):
29 |
30 | # prefix_to_remove = ["residential ","services ","urban ","rural ","central ","decentral "]
31 | # rename_if_contains = ["CHP","gas boiler","biogas"]
32 | # rename_if_contains_dict = {"battery" : "battery storage"}
33 | rename = {"solar" : "solar PV",
34 | "offshorewind" : "offshore wind",
35 | "offshorewind-ac" : "offshore wind (AC)",
36 | "offshorewind-dc" : "offshore wind (DC)",
37 | "onshorewind" : "onshore wind",
38 | "ror" : "hydroelectricity",
39 | "hydro" : "hydroelectricity",
40 | "PHS" : "hydroelectricity",
41 | "AC": "transmission"}
42 |
43 | # for ptr in prefix_to_remove:
44 | # if label[:len(ptr)] == ptr:
45 | # label = label[len(ptr):]
46 |
47 | # for rif in rename_if_contains:
48 | # if rif in label:
49 | # label = rif
50 |
51 | # for old,new in rename_if_contains_dict.items():
52 | # if old in label:
53 | # label = new
54 |
55 | for old,new in rename.items():
56 | if old == label:
57 | label = new
58 | return label
59 |
60 |
61 | def rename_techs_tyndp(tech):
62 | tech = rename_techs(tech)
63 | # if "heat pump" in tech or "resistive heater" in tech:
64 | # return "power-to-heat"
65 | # elif tech in ["methanation", "hydrogen storage", "helmeth"]:
66 | # return "power-to-gas"
67 | if tech == 'OCGT':
68 | return 'gas'
69 | # elif tech in ["CHP", "gas boiler"]:
70 | # return "gas-to-power/heat"
71 | elif "solar" in tech:
72 | return "solar PV"
73 | # elif tech == "Fischer-Tropsch":
74 | # return "power-to-liquid"
75 | # elif "offshore wind" in tech:
76 | # return "offshore wind"
77 | else:
78 | return tech
79 |
80 | def make_legend_circles_for(sizes, scale=1.0, **kw):
81 | return [Circle((0, 0), radius=(s / scale)**0.5, **kw) for s in sizes]
82 |
83 | def make_handler_map_to_scale_circles_as_in(ax, dont_resize_actively=False):
84 | fig = ax.get_figure()
85 |
86 | def axes2pt():
87 | return np.diff(ax.transData.transform([(0, 0), (1, 1)]), axis=0)[
88 | 0] * (72. / fig.dpi)
89 |
90 | ellipses = []
91 | if not dont_resize_actively:
92 | def update_width_height(event):
93 | dist = axes2pt()
94 | for e, radius in ellipses:
95 | e.width, e.height = 2. * radius * dist
96 | fig.canvas.mpl_connect('resize_event', update_width_height)
97 | ax.callbacks.connect('xlim_changed', update_width_height)
98 | ax.callbacks.connect('ylim_changed', update_width_height)
99 |
100 | def legend_circle_handler(legend, orig_handle, xdescent, ydescent,
101 | width, height, fontsize):
102 | w, h = 2. * orig_handle.get_radius() * axes2pt()
103 | e = Ellipse(xy=(0.5 * width - 0.5 * xdescent, 0.5 *
104 | height - 0.5 * ydescent), width=w, height=w)
105 | ellipses.append((e, orig_handle.get_radius()))
106 | return e
107 | return {Circle: HandlerPatch(patch_func=legend_circle_handler)}
108 |
109 | def plot_map(network, tech_colors, threshold=10,components=["links", "stores", "generators"],
110 | bus_size_factor=1e6, transmission=True):
111 |
112 | fig, ax = plt.subplots(subplot_kw={"projection": ccrs.PlateCarree()})
113 | n = network.copy()
114 | assign_location(n)
115 |
116 | preferred_order = pd.Index(["transmission","onshore wind","offshore wind",
117 | "solar PV","gas","H2"])
118 |
119 | # Drop non-electric buses so they don't clutter the plot
120 | n.buses.drop(n.buses.index[n.buses.carrier != "AC"], inplace=True)
121 | costs = pd.DataFrame(index=n.buses.index)
122 | capacity = pd.DataFrame(index=n.buses.index)
123 |
124 | if "stores" in components:
125 | legend_size = n.stores.e_nom_opt.max()
126 | unit_string = 'GWh'
127 | plot_item = 'storage'
128 | else:
129 | legend_size = n.generators.groupby('bus').sum().p_nom_opt.max()
130 | unit_string = 'GW'
131 | plot_item = 'power'
132 |
133 | for comp in components:
134 | df_c = getattr(n, comp)
135 | if len(df_c) == 0:
136 | continue # Some countries might not have e.g. storage_units
137 | df_c["nice_group"] = df_c.carrier.map(rename_techs_tyndp)
138 | attr = "e_nom_opt" if comp == "stores" else "p_nom_opt"
139 | capacity_c = ((df_c[attr])
140 | .groupby([df_c.location, df_c.nice_group]).sum()
141 | .unstack().fillna(0.))
142 | costs_c = ((df_c.capital_cost * df_c[attr])
143 | .groupby([df_c.location, df_c.nice_group]).sum()
144 | .unstack().fillna(0.))
145 | costs = pd.concat([costs, costs_c], axis=1)
146 | capacity = pd.concat([capacity, capacity_c], axis=1)
147 | plot = capacity.groupby(capacity.columns, axis=1).sum()
148 |
149 | plot.drop(columns=plot.sum().loc[plot.sum() < threshold].index,inplace=True)
150 | technologies = plot.columns
151 | plot.drop(list(plot.columns[(plot == 0.).all()]), axis=1, inplace=True)
152 | new_columns = preferred_order[preferred_order.isin(plot.columns)]
153 | plot = plot[new_columns]
154 | for item in new_columns:
155 | if item not in tech_colors:
156 | print("Warning!",item,"not defined in tech_colors")
157 | plot = plot.stack() # .sort_index()
158 | to_drop = plot.index.levels[0].symmetric_difference(n.buses.index)
159 | if len(to_drop) != 0:
160 | print("dropping non-buses", to_drop)
161 | plot.drop(to_drop, level=0, inplace=True, axis=0)
162 | # make sure they are removed from index
163 | plot.index = pd.MultiIndex.from_tuples(plot.index.values)
164 | # PDF has minimum width, so set these to zero
165 | line_lower_threshold = 100
166 | line_upper_threshold = 1000
167 | linewidth_factor = 50
168 | ac_color = "gray"
169 | dc_color = "m"
170 | links = n.links
171 | lines = n.lines
172 | line_widths = lines.s_nom_opt - lines.s_nom
173 | link_widths = links.p_nom_opt - links.p_nom
174 | if transmission:
175 | line_widths = lines.s_nom_opt
176 | link_widths = links.p_nom_opt
177 | linewidth_factor = 50
178 | line_lower_threshold = 0.
179 |
180 | line_widths[line_widths < line_lower_threshold] = 0.
181 | link_widths[link_widths < line_lower_threshold] = 0.
182 | line_widths[line_widths > line_upper_threshold] = line_upper_threshold
183 | link_widths[link_widths > line_upper_threshold] = line_upper_threshold
184 |
185 | fig.set_size_inches(16, 12)
186 | n.plot(bus_sizes=plot / bus_size_factor,
187 | bus_colors=tech_colors,
188 | line_colors=ac_color,
189 | link_colors=ac_color,
190 | line_widths=line_widths / linewidth_factor,
191 | link_widths=link_widths / linewidth_factor,
192 | ax=ax,
193 | boundaries=(n.buses.x[n.buses.x>0].min()-5,
194 | n.buses.x[n.buses.x>0].max()+5,
195 | n.buses.y[n.buses.y>0].min()-5,
196 | n.buses.y[n.buses.y>0].max()+5),
197 | color_geomap={'ocean': 'lightblue', 'land': "palegoldenrod"})
198 |
199 | for i in technologies:
200 | ax.plot([0,0],[1,1],label=i,color=tech_colors[i],lw=5)
201 | fig.legend(loc='center right', frameon=False,borderaxespad=1)
202 | fig.suptitle('Installed ' + plot_item + ' capacities and transmission lines',y=0.92,fontsize=15)
203 |
204 | handles = make_legend_circles_for(
205 | [legend_size/10,legend_size], scale=bus_size_factor, facecolor="white")
206 | str1 = [" {:10.0f} ".format(s) for s in (legend_size/10,legend_size)]
207 | labels = [x + unit_string for x in str1]
208 | l1 = ax.legend(handles, labels,
209 | loc="upper left", bbox_to_anchor=(0.05, 0.96),
210 | labelspacing=4,
211 | frameon=False,
212 | title='',
213 | handler_map=make_handler_map_to_scale_circles_as_in(ax))
214 | ax.add_artist(l1)
215 | handles = []
216 | labels = []
217 | for s in (1000, 100):
218 | handles.append(plt.Line2D([0], [0], color=ac_color,
219 | linewidth=s / linewidth_factor))
220 | labels.append("{} MW".format(s))
221 | l2 = ax.legend(handles, labels,
222 | loc="upper left", bbox_to_anchor=(0.3, 0.96),
223 | frameon=False,
224 | labelspacing=4, handletextpad=1.5,
225 | title='')
226 | ax.add_artist(l2)
227 |
228 | def line_plot_generation(n,c):
229 | plt.plot(n.loads_t.p[c][0:96], color='black', label='demand')
230 | plt.plot(n.generators_t.p[c + ' onshorewind'][0:96], color='blue', label='onshore wind')
231 | plt.plot(n.generators_t.p[c + ' solar'][0:96], color='orange', label='solar')
232 | plt.plot(n.generators_t.p[c + ' OCGT'][0:96], color='brown', label='gas (OCGT)')
233 | plt.legend(fancybox=True, shadow=True, loc='best')
234 |
235 | def pie_chart_generation(n,c):
236 | labels = ['onshore wind',
237 | 'solar',
238 | 'gas (OCGT)']
239 | sizes = [n.generators_t.p[c + ' onshorewind'].sum(),
240 | n.generators_t.p[c + ' solar'].sum(),
241 | n.generators_t.p[c + ' OCGT'].sum()]
242 |
243 | colors=['blue', 'orange', 'brown']
244 |
245 | plt.pie(sizes,
246 | colors=colors,
247 | labels=labels,
248 | wedgeprops={'linewidth':0})
249 | plt.axis('equal')
250 | plt.title('Electricity mix ' + c, y=1.07)
251 |
252 |
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/testing.ipynb:
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1 | {
2 | "cells": [
3 | {
4 | "cell_type": "markdown",
5 | "metadata": {},
6 | "source": [
7 | "# Checking PyPSA and Gurobi installation\n",
8 | "\n",
9 | "This is a very simple script to test that PyPSA and Gurobi installations are working properly.\n",
10 | "\n",
11 | "It is based on the example \"Unit commitment\" available as a Jupyter notebook at https://pypsa.readthedocs.io/en/latest/examples/unit-commitment.html\n",
12 | "\n"
13 | ]
14 | },
15 | {
16 | "cell_type": "code",
17 | "execution_count": 1,
18 | "metadata": {},
19 | "outputs": [],
20 | "source": [
21 | "import pypsa"
22 | ]
23 | },
24 | {
25 | "cell_type": "markdown",
26 | "metadata": {},
27 | "source": [
28 | "Initiallize the network, set 4 time steps in the snapshots."
29 | ]
30 | },
31 | {
32 | "cell_type": "code",
33 | "execution_count": 2,
34 | "metadata": {},
35 | "outputs": [],
36 | "source": [
37 | "network = pypsa.Network()\n",
38 | "\n",
39 | "network.set_snapshots(range(4))"
40 | ]
41 | },
42 | {
43 | "cell_type": "markdown",
44 | "metadata": {},
45 | "source": [
46 | "Add one bus named \"bus\" to the network and add two generators (coal and gas).\n",
47 | "\n",
48 | "Include the data regarding nominal power (p_nom), marginal cost (marginal_cost), and minimum power (p_min_pu) that needs to be dispatched by every generator. To enable unit commitment on a generator, set its attribute committable = True.\n",
49 | "\n",
50 | "Add the load to the bus."
51 | ]
52 | },
53 | {
54 | "cell_type": "code",
55 | "execution_count": 3,
56 | "metadata": {},
57 | "outputs": [],
58 | "source": [
59 | "network.add(\"Bus\",\"bus\")\n",
60 | "\n",
61 | "\n",
62 | "network.add(\"Generator\",\n",
63 | " \"coal\",\n",
64 | " bus=\"bus\",\n",
65 | " committable=True,\n",
66 | " p_min_pu=0.3,\n",
67 | " marginal_cost=20,\n",
68 | " p_nom=10000)\n",
69 | "\n",
70 | "network.add(\"Generator\",\n",
71 | " \"gas\",\n",
72 | " bus=\"bus\",\n",
73 | " committable=True,\n",
74 | " marginal_cost=70,\n",
75 | " p_min_pu=0.1,\n",
76 | " p_nom=1000)\n",
77 | "\n",
78 | "network.add(\"Load\",\n",
79 | " \"load\",\n",
80 | " bus=\"bus\",\n",
81 | " p_set=[4000,6000,5000,800])"
82 | ]
83 | },
84 | {
85 | "cell_type": "markdown",
86 | "metadata": {},
87 | "source": [
88 | "Find the optimal solution using Gurobi as solver.\n",
89 | "\n",
90 | "In this case, we are optimising the dispatch of every generator to minimise the total system cost.\n"
91 | ]
92 | },
93 | {
94 | "cell_type": "code",
95 | "execution_count": 4,
96 | "metadata": {},
97 | "outputs": [
98 | {
99 | "name": "stderr",
100 | "output_type": "stream",
101 | "text": [
102 | "INFO:linopy.model: Solve problem using Gurobi solver\n"
103 | ]
104 | },
105 | {
106 | "name": "stdout",
107 | "output_type": "stream",
108 | "text": [
109 | "Set parameter Username\n",
110 | "Academic license - for non-commercial use only - expires 2024-05-05\n",
111 | "Read LP format model from file /tmp/linopy-problem-iz7b60d1.lp\n",
112 | "Reading time = 0.03 seconds\n",
113 | "obj: 36 rows, 32 columns, 84 nonzeros\n",
114 | "Gurobi Optimizer version 10.0.3 build v10.0.3rc0 (linux64)\n",
115 | "\n",
116 | "CPU model: Intel(R) Core(TM) i7-8550U CPU @ 1.80GHz, instruction set [SSE2|AVX|AVX2]\n",
117 | "Thread count: 1 physical cores, 1 logical processors, using up to 1 threads\n",
118 | "\n",
119 | "Optimize a model with 36 rows, 32 columns and 84 nonzeros\n",
120 | "Model fingerprint: 0x0cec1f76\n",
121 | "Variable types: 8 continuous, 24 integer (24 binary)\n",
122 | "Coefficient statistics:\n",
123 | " Matrix range [1e+00, 1e+04]\n",
124 | " Objective range [2e+01, 7e+01]\n",
125 | " Bounds range [1e+00, 1e+00]\n",
126 | " RHS range [1e+00, 6e+03]\n",
127 | "Found heuristic solution: objective 356000.00000\n",
128 | "Presolve removed 36 rows and 32 columns\n",
129 | "Presolve time: 0.01s\n",
130 | "Presolve: All rows and columns removed\n",
131 | "\n",
132 | "Explored 0 nodes (0 simplex iterations) in 0.14 seconds (0.00 work units)\n",
133 | "Thread count was 1 (of 1 available processors)\n",
134 | "\n",
135 | "Solution count 1: 356000 \n",
136 | "\n",
137 | "Optimal solution found (tolerance 1.00e-04)\n",
138 | "Best objective 3.560000000000e+05, best bound 3.560000000000e+05, gap 0.0000%\n"
139 | ]
140 | },
141 | {
142 | "name": "stderr",
143 | "output_type": "stream",
144 | "text": [
145 | "WARNING:linopy.solvers:Dual values of MILP couldn't be parsed\n",
146 | "INFO:linopy.constants: Optimization successful: \n",
147 | "Status: ok\n",
148 | "Termination condition: optimal\n",
149 | "Solution: 32 primals, 0 duals\n",
150 | "Objective: 3.56e+05\n",
151 | "Solver model: available\n",
152 | "Solver message: 2\n",
153 | "\n"
154 | ]
155 | },
156 | {
157 | "data": {
158 | "text/plain": [
159 | "('ok', 'optimal')"
160 | ]
161 | },
162 | "execution_count": 4,
163 | "metadata": {},
164 | "output_type": "execute_result"
165 | }
166 | ],
167 | "source": [
168 | "network.optimize(solver_name='gurobi')"
169 | ]
170 | },
171 | {
172 | "cell_type": "markdown",
173 | "metadata": {},
174 | "source": [
175 | "The result ('ok', optimal') indicates that the optimizer has found an optimal solution. Hence, you have checked that it is working. \n",
176 | "\n",
177 | "Now, you can for example show the status of every generator (if they are working or not)"
178 | ]
179 | },
180 | {
181 | "cell_type": "code",
182 | "execution_count": 5,
183 | "metadata": {},
184 | "outputs": [
185 | {
186 | "data": {
187 | "text/html": [
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311 | " 0.0 | \n",
312 | "
\n",
313 | " \n",
314 | " | 2 | \n",
315 | " 5000.0 | \n",
316 | " 0.0 | \n",
317 | "
\n",
318 | " \n",
319 | " | 3 | \n",
320 | " 0.0 | \n",
321 | " 800.0 | \n",
322 | "
\n",
323 | " \n",
324 | "
\n",
325 | "