{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Endogenous Learning Curves in Multi-Horizon Dynamic Investment Optimisation\n", "\n", "Consider a long-term multi-year investment problem where **CSP (Concentrated Solar Power)** has a learning curve such that\n", "\n", "$$LCOE = c_0 \\left(\\frac{x_t}{x_0}\\right)^{-\\gamma} + c_1$$\n", "\n", "where $c_0$ is cost at start, $c_1$ is material cost and $x_t$ is cumulative\n", "capacity in the investment interval $t$. Thus, $x_0$ is the initial cumulative CSP capacity.\n", "\n", "Additionally, there are **nuclear** and **coal** generators for which there is no potential for reducing their LCOE.\n", "\n", "We build an optimisation to minimise the cost of supplying a flat demand $d=100$ GW with the given technologies between 2020 and 2050, where a CO$_2$ budget cap is applied.\n", "\n", "> **Hint:** Problem formulation is to be found further along this notebook.\n", "\n", "**Task:** Explore different discount rates, learning rates, CO$_2$ budgets. For instance\n", "* No learning for CSP and no CO$_2$ budget would result in a coal-reliant system.\n", "* A CO$_2$ budget and no learning prefers a system built on nuclear.\n", "* A CO$_2$ budget and learning results in a system with CSP.\n", "\n", "**NB** The learning curve coupling makes the problem non-linear, so you need to install the non-linear interior-point solver ipopt:\n", "\n", "conda install -c conda-forge ipopt\n", "\n", "### Licence\n", "\n", "Copyright 2019 Tom Brown (KIT)\n", "\n", "This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3 of the License, or (at your option) any later version.\n", "\n", "This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***\n", "## Imports" ] }, { "cell_type": "code", "execution_count": 231, "metadata": {}, "outputs": [], "source": [ "from pyomo.environ import ConcreteModel, Var, Objective, NonNegativeReals, Constraint, Suffix, exp\n", "from pyomo.opt import SolverFactory\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "plt.style.use('bmh')\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Parameters" ] }, { "cell_type": "code", "execution_count": 232, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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coalnuclearCSP
current annuity131400.0569400.01314000.000
potential annuity131400.0569400.0306600.000
learning parameter0.00.00.333
marginal cost35.010.00.000
specific emissions1.00.00.000
lifetime40.040.030.000
existing age20.00.00.000
existing capacity100.00.00.000
current LCOE50.075.0150.000
potential LCOE50.075.035.000
\n", "
" ], "text/plain": [ " coal nuclear CSP\n", "current annuity 131400.0 569400.0 1314000.000\n", "potential annuity 131400.0 569400.0 306600.000\n", "learning parameter 0.0 0.0 0.333\n", "marginal cost 35.0 10.0 0.000\n", "specific emissions 1.0 0.0 0.000\n", "lifetime 40.0 40.0 30.000\n", "existing age 20.0 0.0 0.000\n", "existing capacity 100.0 0.0 0.000\n", "current LCOE 50.0 75.0 150.000\n", "potential LCOE 50.0 75.0 35.000" ] }, "execution_count": 232, "metadata": {}, "output_type": "execute_result" } ], "source": [ "techs = [\"coal\",\"nuclear\",\"CSP\"]\n", "colors = [\"#707070\",\"#ff9000\",\"#f9d002\"]\n", "parameters = pd.DataFrame(columns=techs)\n", "parameters.loc[\"current annuity\"] = [15.*8760,65.*8760,150.*8760] # EUR/MW/a\n", "parameters.loc[\"potential annuity\"] = [15.*8760,65.*8760,35.*8760] # EUR/MW/a\n", "parameters.loc[\"learning parameter\"] = [0.,0.,0.333]\n", "parameters.loc[\"marginal cost\"] = [35.,10.,0.] #EUR/MWhel\n", "parameters.loc[\"specific emissions\"] = [1.,0.,0.] #tcO2/MWhel\n", "parameters.loc[\"lifetime\"] = [40,40,30] #years\n", "parameters.loc[\"existing age\"] = [20,0,0] #years\n", "parameters.loc[\"existing capacity\"] = [100,0,0] #GW\n", "\n", "parameters.loc[\"current LCOE\"] = parameters.loc[\"current annuity\"]/8760 + parameters.loc[\"marginal cost\"]\n", "parameters.loc[\"potential LCOE\"] = parameters.loc[\"potential annuity\"]/8760 + parameters.loc[\"marginal cost\"]\n", "\n", "parameters" ] }, { "cell_type": "code", "execution_count": 233, "metadata": {}, "outputs": [], "source": [ "#discount rate\n", "rate = 0.05\n", "\n", "#demand in GW\n", "demand = 100.\n", "\n", "# considered years\n", "years = list(range(2020,2070))\n", "\n", "\n", "#scenario = \"no_co2-no_learning\"\n", "#scenario = \"co2-0p2-no_learning\"\n", "scenario = \"co2-0p2-learning\"\n", "\n", "\n", "if \"no_learning\" in scenario:\n", " parameters.loc[\"learning parameter\"] = 0\n", "else:\n", " parameters.at[\"learning parameter\",\"CSP\"] = 0.333\n", "\n", " \n", "# carbon budget in average tCO2/MWh_el \n", "if \"no_co2\" in scenario:\n", " co2_budget = 2.\n", "else:\n", " co2_budget = 0.2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Build Model\n", "> **Note:** We use [`pyomo`](https://pyomo.readthedocs.io/en/stable/) for building optimisation problems in python. This is also what `pypsa` uses under the hood." ] }, { "cell_type": "code", "execution_count": 201, "metadata": {}, "outputs": [], "source": [ "model = ConcreteModel(\"discounted total costs\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Generator capacity available for tech $s$ in year $a$\n", "$$G_{s,a}$$" ] }, { "cell_type": "code", "execution_count": 202, "metadata": {}, "outputs": [], "source": [ "model.generators = Var(techs, years, within=NonNegativeReals)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Generator dispatch for tech $s$ in year $a$\n", "$$g_{s,a}$$" ] }, { "cell_type": "code", "execution_count": 203, "metadata": {}, "outputs": [], "source": [ "model.generators_dispatch = Var(techs, years, within=NonNegativeReals)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "New capacity built for tech $s$ in year $a$ \n", "$$Q_{s,a}$$" ] }, { "cell_type": "code", "execution_count": 204, "metadata": {}, "outputs": [], "source": [ "model.generators_built = Var(techs, years, within=NonNegativeReals)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$c_{s,a}$$" ] }, { "cell_type": "code", "execution_count": 205, "metadata": {}, "outputs": [], "source": [ "model.fixed_costs = Var(techs, years, within=NonNegativeReals)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The objective is to minimise the system costs:\n", "\n", "$$\\min \\quad \\sum_{s\\in S, a\\in A} \\frac{1}{10^6\\cdot (1+r)^{a}} \\left( o_{s,a} \\cdot g_{s,a} \\cdot 8760 + \\sum_{b} c_{s,b} Q_{s,b} \\mathbb{I}(a \\geq b) \\mathbb{I}(a < b+L_s) \\right) $$" ] }, { "cell_type": "code", "execution_count": 206, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "171.94111609602413\n" ] } ], "source": [ "# in billion EUR\n", "\n", "# annuities from existing generators\n", "# in billion (MW to GW *1e3, then devide by 1e9)\n", "constant =sum(parameters.at[\"existing capacity\",tech]*parameters.at[\"current annuity\",tech]/1e6/(1+rate)**(year-years[0]) for tech in techs for year in years if year < years[0] + parameters.at[\"lifetime\",tech] - parameters.at[\"existing age\",tech])\n", "print(constant)\n", "\n", "model.objective = Objective(expr=constant +\n", " sum(model.generators_built[tech,year]*model.fixed_costs[tech,year]/1e6*sum(1/(1+rate)**(yearb-years[0]) for yearb in years if ((yearb>= year) and (yearb < year + parameters.at[\"lifetime\",tech])))\n", " for year in years\n", " for tech in techs) + \n", " sum(model.generators_dispatch[tech,year]*parameters.at[\"marginal cost\",tech]*8760/1e6/(1+rate)**(year-years[0])\n", " for year in years\n", " for tech in techs))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Add a constraint such that demand is met by generator dispatch:\n", "\n", "$$\\forall a\\in A: \\quad d = \\sum_{s \\in S} g_{s,a}$$" ] }, { "cell_type": "code", "execution_count": 207, "metadata": {}, "outputs": [], "source": [ "def balance_constraint(model, year):\n", " return demand == sum(model.generators_dispatch[tech,year] for tech in techs)\n", "model.balance_constraint = Constraint(years, rule=balance_constraint)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$ g_{s,a} \\leq G_{s,a} $$" ] }, { "cell_type": "code", "execution_count": 208, "metadata": {}, "outputs": [], "source": [ "def generator_constraint(model, tech, year):\n", " return model.generators_dispatch[tech,year] <= model.generators[tech,year]\n", "model.generator_constraint = Constraint(techs, years, rule=generator_constraint)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Add a constraint on carbon dioxide emissions:\n", "\n", "$$\\sum_{s\\in S, a\\in A} G_{s,a} \\cdot e_{t} \\leq \\hat{e} \\cdot |A| \\cdot d$$" ] }, { "cell_type": "code", "execution_count": 209, "metadata": {}, "outputs": [], "source": [ "def co2_constraint(model):\n", " return co2_budget*len(years)*demand >= sum(model.generators_dispatch[tech,year]*parameters.at[\"specific emissions\",tech] for tech in techs for year in years)\n", "model.co2_constraint = Constraint(rule=co2_constraint)" ] }, { "cell_type": "code", "execution_count": 210, "metadata": {}, "outputs": [], "source": [ "def fixed_cost_constraint(model,tech,year):\n", " if parameters.at[\"learning parameter\",tech] == 0:\n", " return model.fixed_costs[tech,year] == parameters.at[\"current annuity\",tech]\n", " else:\n", " return model.fixed_costs[tech,year] == parameters.at[\"potential annuity\",tech] + (parameters.at[\"current annuity\",tech]-parameters.at[\"potential annuity\",tech])*(1+sum(model.generators[tech,yeart] for yeart in years if yeart < year))**(-parameters.at[\"learning parameter\",tech])\n", "model.fixed_cost_constraint = Constraint(techs, years, rule=fixed_cost_constraint)" ] }, { "cell_type": "code", "execution_count": 211, "metadata": {}, "outputs": [], "source": [ "def build_years(model,tech,year):\n", " if year < years[0] + parameters.at[\"lifetime\",tech] - parameters.at[\"existing age\",tech]:\n", " constant = parameters.at[\"existing capacity\",tech]\n", " else:\n", " constant = 0.\n", " \n", " return model.generators[tech,year] == constant + sum(model.generators_built[tech,yearb] for yearb in years if ((year>= yearb) and (year < yearb + parameters.at[\"lifetime\",tech])))\n", "model.build_years = Constraint(techs, years, rule=build_years)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "> **Hint:** You can print the model formulation with `model.pprint()`" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Solve Model" ] }, { "cell_type": "code", "execution_count": 212, "metadata": {}, "outputs": [], "source": [ "opt = SolverFactory(\"ipopt\")" ] }, { "cell_type": "code", "execution_count": 213, "metadata": {}, "outputs": [], "source": [ "results = opt.solve(model,suffixes=[\"dual\"],keepfiles=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Results" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Optimised cost (in billion euros NPV):" ] }, { "cell_type": "code", "execution_count": 214, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1019.7276625042617\n" ] } ], "source": [ "print(model.objective())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The unoptimized cost (where everything is supplied by coal) is:" ] }, { "cell_type": "code", "execution_count": 215, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2190.0\n" ] } ], "source": [ "print(8760*demand*parameters.at[\"current LCOE\",\"coal\"]*len(years)/1e6)" ] }, { "cell_type": "code", "execution_count": 216, "metadata": {}, "outputs": [], "source": [ "dispatch = pd.DataFrame(0.,index=years,columns=techs)\n", "for year in years:\n", " for tech in techs:\n", " dispatch.at[year,tech] = model.generators_dispatch[tech,year].value" ] }, { "cell_type": "code", "execution_count": 217, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "\n", "fig.set_size_inches((10,6))\n", "\n", "dispatch.plot(kind=\"area\",stacked=True,color=colors,ax=ax,linewidth=0)\n", "ax.set_xlabel(\"year\")\n", "ax.set_ylabel(\"dispatch [GW]\")\n", "\n", "fig.tight_layout()\n", "\n", "fig.savefig(\"{}-dispatch.pdf\".format(scenario),transparent=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plotting the development of the technology mix of the optimal solution over time:" ] }, { "cell_type": "code", "execution_count": 218, "metadata": {}, "outputs": [], "source": [ "capacities = pd.DataFrame(0.,index=years,columns=techs)\n", "for year in years:\n", " for tech in techs:\n", " capacities.at[year,tech] = model.generators[tech,year].value" ] }, { "cell_type": "code", "execution_count": 219, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "\n", "fig.set_size_inches((10,6))\n", "\n", "capacities.plot(kind=\"area\",stacked=True,color=colors,ax=ax,linewidth=0)\n", "ax.set_xlabel(\"year\")\n", "ax.set_ylabel(\"capacity [GW]\")\n", "\n", "fig.tight_layout()\n", "\n", "fig.savefig(\"{}-capacity.pdf\".format(scenario),transparent=True)" ] }, { "cell_type": "code", "execution_count": 220, "metadata": {}, "outputs": [], "source": [ "build_years = pd.DataFrame(0.,index=years,columns=techs)\n", "for year in years:\n", " for tech in techs:\n", " build_years.at[year,tech] = model.generators_built[tech,year].value" ] }, { "cell_type": "code", "execution_count": 221, "metadata": {}, "outputs": [ { "data": { "image/png": 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fpCciLwRwCoCfARhUSh0EFjvRAI6KOh5qbCJiO4Rlvgv0wDnItriUH+Qe5gf5cSk3vMKjEPhMN2XbYkyki/REpBPAtwC8Wyk1EyYBDx8+jDPOOAPJZBKlUgkXXHABLrvsMoyMjKCjowOJRAIzMzMYGBjA5OQklFIYGBjA6Ojo8irUTCaDwcFBjI2NQUTQ19eHsbExdHd3o1QqYW5uDkNDQxgZGUEqlUJPTw/Gx8fR09ODfD6PbDa7/Hw6nUZXVxcmJibQ29uLbDaLXC63/Hxrayva2towNTWF/v5+zM7OIp/PLz/f1taGdDqN6elpbN68GdPT0ygUCsvP8zNV95lyuRwOHz7sxGfy5ko4JnUU9mfORnd68Wp/Jn8ctnbegv2Zl6FUeK5pf0+2PlMymcTU1FSsPlMcf0+2PlNrayuGh4dj9Zni+Huy8Zk6OzsxPDzsxGeaHx9GaebNOKbzJjyXeR3aUwfQmpjEZO4EDLbfiZH9z6FUVmhpaQEAeJ4HEUGpVFruOwGL+0oUCoXl6mClUgmpVGr5Tn0ikUCxWEQikYBSCuVyGalUCoVCASIS+vlkMolyuXzE857nwfO85edLpRKUUkc8vzrmjZ6v9jPl83lMT0+jpaXliN+Tb581qt1iRCQF4EYANymlPrH02GMAXqWUOigiWwDcppT65ZXvu+2229R6O7MQAcDw8LAztSpbR85BIn/vus+VUy9BdsuuiCMil/KD3MP8ID8u5UZ66nKkZq/yfX7umKegpAuZTMbJHQBdISLo7Oxcc3dgz5499+zYsePU1a+PqoqFAPgigEcqneMlNwD4AwBXLv393dXvZR1kCtLd3W07hEWqDK/wWMDzvA1mgzP5QU5ifpAfl3LDd4HeElHzgNeNrq6uiCJqDlFNsTgDwEUAHhCR+5Ye+yAWO8bfFJE/AvAsgDdFFA/FhCtlAKX4VHClCi6ksMKV/CA3MT/Ij0u5EbS+BcBi+8KxRO0i6SArpe4A4DfheEfQe11KUnLP3NwcNm/ebDuMDb/AWObNDlfyg9zE/CA/ruSGlEYh5Q02/eAdSiOc32qadZApyNDQkO0QAABePqDEG8AvMEtcyQ9yE/OD/LiSGxtNrwA4AGOK8x3kQqFgOwRy2MjIiO0QAIQYQUYJUAsRRUMVruQHuYn5QX5cyQ0Jqq9fwQEYI5zvILtUi5Dc48odhjBX+ZyHHD1X8oPcxPwgP67kRqgR5LJb203HhfMdZFaxoCBBNQwjU56FlJ7d8GXcLCR6TuQHOYv5QX5cyY0NF+gBHEE2xPkOMreapiDj4xssXoiAV3gEghC1JzlPLHIu5Ae5i/lBfpzIDVWEV3h8w5dx8MUM5zvIHEGmIC5c5W+4QG8JF1JEz4X8IHcxP8iPC7khhScgyG/8QrYtRjjfQeauMBQknw/x5WFYqFtgAG+DWeBCfpC7mB/kx4Xc8MIs0ANHkE1xvoNcLpdth0AOy2btfzGE7SALF+lFzoX8IHcxP8iPC7kRevCFi/SMcL6D7MpKUnKT9VqVSoWrYAFwBNkC6/lBTmN+kB8XciP89D22LSY430FmHWQKYrtWpZSehajZcC9WvMqPmu38ILcxP8iPC7kRdooF5yCb4XwH2fOcD5EsSqfTVs8f9gof4FW+Dbbzg9zG/CA/1nOjNAmvFLKTzrbFCOd7n+wgU5Curi6r5w89RwzgRiEW2M4Pchvzg/zYzo3Qo8fg+hZTnO99sg4yBZmYmLB6/tDzj8ERZBts5we5jflBfmznRjVtC0eQzXC+g5xMJm2HQA7r7e21ev5qrvI5Tyx6tvOD3Mb8ID+2c6Oau5PC9S1GON9BZpk3CmK1FE85Cyk+Hfrl3Cgkei6UaiJ3MT/Ij+3cqG7whXlsAjvI1NByuZy1cy9uMV1FfvJLLHI284Pcx/wgP1ZzQ5XhFR4L/XJO3zPD+Q4y6yBTEJu1KqtaoAcupLDBhVqm5C7mB/mxmRtS3Ftdp5dtixHOd5BZB5mC2KxVWdUiCoAjyBa4UMuU3MX8ID9W25ZqB1/YthjhfAeZZd4oSGtrq7VzVzVHDOAiPQts5ge5j/lBfqy2LVUPvrBtMcH53ic7yBSkra3N2rm9wiNVvZ5X+dGzmR/kPuYH+bHbtlQ7+JIFlDITTBNzvvfJOsgUZGpqysp5pXgQUp6s7k1lluKJmq38oMbA/CA/NnOj2hFkgeIUPgOc7yCzDjIF6e/vt3Leqq/wwRFkG2zlBzUG5gf5sZYb5VlIaV/172P7op3zHWSWeaMgs7OzVs5b7SIKAJwnZoGt/KDGwPwgPzbbFkH10yVYZ18/dpCpoeXzeSvn9fIcQW4EtvKDGgPzg/w0UtsCgCPIBjjfQWYdZApiq1ZlbSPI/AKLGuvcUhDmB/lpqLYFHEE2wfkOMusgUxArtSpVHlJ4suq3icoBindEosQ6txSE+UF+bOVG1SXeKrhZiHbOd5BZ5o2C2CjF4xUeh6DGCzde5UeKZbwoCPOD/FjJDaWqLh9awSl8+jnf+xQR2yGQw9LpdOTnrPUWGAB2kCNmIz+ocTA/yI+N3JDSsxBV4+JAti3aOd9BLpVKtkMgh01PT0d+Tql1EQV4lR81G/lBjYP5QX5s5EbNC/TAtsUE5zvIrINMQTZv3hz5OesaQeY8sUjZyA9qHMwP8tN4bQs3otLN+Q4yR5ApCK/yKQhHCCkI84P82Glb6pm+x7ZFN+c7yIr7i1OAyKuclMbhlQ/V/n7Fq/wosQoOBWF+kB8buVHLDq0VLPOmn/MdZNZBpiBR16qs6xYYOIIcNda5pSDMD/ITeW6Us5Di07W/n22Lds53kHmFT0GirlVZz/QKAJyDHDHWuaUgzA/yE3nbUngEgtrr5AvbFu2c7yAnEgnbIZDDOjo6Ij0fR5AbS9T5QY2F+UF+Gq1tYZk3/ZzvIBMFifoCqu4RZHaQI8ULbArC/CA/0bctHHxxjfMdZFaxoCAzMzPRnUyV4BUeq+sQwkV6kYo0P6jhMD/IT9S54RUerO8AHEHWzvkOMhfpUZCBgYHIziWFJyBYqO8gvMqPVJT5QY2H+UF+Is0NpTh9z0HOd5CLxaLtEMhhk5OTkZ0rkb+7/oNwIUWkoswPajzMD/ITZW5I8UlI+XB9B2Hbop3zHWSiIFHWyfYWdtd9DF7lR4t11CkI84P8RJkbCQ1tC+9O6ud8B5lbTVOQKG+D6fkS41V+lHgLnYIwP8hPlLmhZ/CF61t0c76DzDrIFGR0dDSaE5UPQ4pP1H0Y7nYUrcjygxoS84P8RJkbiTxHkF3kfAeZZXgoSGdnZyTnSSzcDYGGW26cJxapqPKDGhPzg/xElhvlGUid1ZEAbhRigvMdZCIX6LgFBnAOMhERPU/b4AvbFu2c7yCzDjIFyWQykZxHy/xjgF9iEYsqP6gxMT/IT1S5oW3wBQVAseqXTs53kFkHmYIMDg6aP4kqwcvv0XIozkGOViT5QQ2L+UF+osoNLfOPK9i+aOV8B5l1kCnI2NiY8XN4hUcgStNoAkeQIxVFflDjYn6Qn0hyQ5XhLdyj7XAcgNHL+Q4yURARMX4Ob+EufQfjQopIRZEf1LiYH+QnityQwqMQNavvgGxftHK+g8w6yBSkr6/P+Dm0zT8Gr/CjFkV+UONifpCfaNoWjYMvAO9QauZ8B5l1kClIJFMsdGwxXcEvsEjxFjoFYX6Qn4ZrW8ABGN2c7yCzDjIF6e7uNnuC0hi84tPaDicoAWpB2/EomPH8oIbG/CA/UeQGR5Dd5nwHmSiI6TKAOqdXLOM8sciwTCQFYX6QH+O5UZqAV3xK6yGlzO2mdXK+g8wvMAoyN2f2C0FXjcqVuFlIdEznBzU25gf5MZ0bRgZf2LZo5XwHmXWQKcjQ0JDR42utUVmh2ChHxXR+UGNjfpAf07mhe/4xwDnIujnfQeYiPQoyMjJi7uCqAC9/v/bDcgQ5Okbzgxoe84P8mM4N7fOPAY4ga+Z8B5l1KimIyTsMXv4BM51ZfolFhnegKAjzg/wYzQ1VhJe/T/thOfiil/MdZFaxoCA9PT3Gjm1i/jEACBfpRcZkflDjY36QH6NtS+FBM9MhuEhPK+c7yNxqmoKMj48bO7aR+ccAR5AjZDI/qPExP8iPydzwFvTPPwY4gqyb8x1kjiBTkEYcQeYivehwhJCCMD/Ij8ncMDL/GAC4SE8r5zvISinbIZDD8vm8keNK8QC80n4zx+ZVfmRM5QfFA/OD/JjMDXODL2xbdHK+g1wul22HQA7LZs18IXimplcA3CgkQqbyg+KB+UF+TOWGlEbglfaZOTbbFq2c7yBzlTEFMVWr0tgtMHAEOUqsc0tBmB/kx1RuGBs9BjiCrJnzHWTWQaYgpmpVmlpEAYDzxCLEOrcUhPlBfkzlhpEd9JYI17do5XwH2fOcD5EsSqfT+g+qcvDyD+g/7hLudhQdI/lBscH8ID+mcoMjyI0j6feEiOwMeYycUuq1muJZgx1kCtLV1aX9mF7+fggMLt7hl1hkTOQHxQfzg/wYyQ2Vh5f/uf7jLuH0Pb18O8gATgPw9g3eLwA+pS+ctVgHmYJMTEygs7NT6zFNzj8GuJAiSibyg+KD+UF+TOTG4uDLgtZjHoFti1ZBHeSfKqWu3egAIvK/NMazRjIZFCI1u97eXu3HNDr/GOAIcoRM5AfFB/OD/JhpWwxOrwBHkHXznb+glNoR5gAmp1cALPNGwUyU4jH9JcZFetFhGS8KwvwgPyZyw+QCPQBsWzQLnOArIu8TkVeIiLVhXHaQKUgul9N6PCkOwysf0nrMNefgVX5kdOcHxQvzg/yYyA2j9fWBxbuT3FxNm41WwL0GwH8BOCwi/yUiHxKRs0SkJYLYALAOMgXTXavSMzz/GABQZimeqLDOLQVhfpAf3bkhxX3wSmbLCgoUp/BpFNhBXpo+sQnA2QD+E8CvAfgWFjvMu0Tkb0wHyDrIFER3rUrjt8DAEeQosc4tBWF+kB/duRHJ4AvADrJGG9ZQU0qVlFJ3KaX+USl1HoAXA/gwgF8E8H+MB8gybxSgtbVV6/GMzz8GOE8sQrrzg+KF+UF+dOdGwvTi7yWss6/PhnOLRaQfwPYVf7YAuBPAxwHsMhod2EGmYG1tbfoOVp6DV3hY3/F8cAQ5Olrzg2KH+UF+dOeGl+cIcqMJ7CCLyEMA2gDcDuAOAFcppR6LIrAK1kGmIFNTU+ju7tZyLC+/B4KSlmMF4hdYZHTmB8UP84P8aM2N8jy8/EN6jrUBUfPgMj09NhqePQCgA8A2AC8AcIyItFd7EhG5RkQOiciDKx67QkT2i8h9S39+e733sg4yBenv79d2rCjmHwOAqBygWJ0lCjrzg+KH+UF+dOaGl78XgogG+7hZiDYbLdI7B8DRAN4PYBbAOwE8JSI/E5H/KyK/G/I8Xwbwm+s8/kml1K8u/fnBem9kmTcKMjs7q+1Ykcw/ruA8sUjozA+KH+YH+dGZG1HNPwY4hU+nsIv0dq9YpHc8FitZ/D6Ab4c5iVJqJ4DJWgJkB5mC5PN5PQdSCon8PXqOFep87CBHQVt+UCwxP8iPztyIbP4xwLZFo1oW6Z2ExakXN2NxbnI93iEiFwO4G8CfK6WmVr9genoaZ5xxBpLJJEqlEi644AJcdtllGBkZQUdHBxKJBGZmZjAwMIDJyUkopTAwMIDR0dHlfdQzmQwGBwcxNjYGEUFfXx/GxsbQ3d2NUqmEubk5DA0NYWRkBKlUCj09PRgfH0dPTw/y+Tyy2ezy8+l0Gl1dXZiYmEBvby+y2Sxyudzy862trWhra8PU1BT6+/sxOzuLfD6//HxbWxvS6TSmp6exefNmTE9Po1AoLD/Pz1TdZyqVSjh8+HDdnylzeC/U5GtxTOdNeC7zOrSnDqA1MYnJ3AkYbL8TkwsnoFDqWn6+Mz2MpGRxeOF4bOnYibHsaSirFLZ07MT+zNnoTu8FAMzkj8PWzltwcG47PClgoG03Ds5tR1v7JIrwmub3ZOszpdNpTE1NxeozxfH3ZPMzDQ8Px+4zxfH3FPVn6u3txfDwsJbPJBMJzOffvNx+tCQm0d2yF2Pzp6G/7V7MF7YiWzxq+fm25CG0p/ZjInsKBtp3Y2bhOCyU+kK1T88cSKJt03hBenEGAAAgAElEQVTT/J50fCY/ogJ2XVlapHc8gKcB7MRih3inUupp3zf5H+uFAG5USp2w9O9BAOMAFICPAtiilHrr6vfddttt6uSTT672dNQkhoeHsW3btrqPk8x8DS2Tf6YhonDmh3ZBpV8S2fmala78oHhifpAfXbkhhb1oP/gKDRGFs9D7Dyh2XRLZ+eJgz5499+zYsePU1Y9vNIL8UQC3K6UO6g5IKTVa+VlEPg/gxvVexzJvFERXKZ5I5x9jcZ4YVxqbxzJeFIT5QX505UaU848BsEqSRoEdZKXU11f+W0ReAEBWPP9srScWkS0rOt7nA3jQ53W1noKaQDqd1nKcRD7aDjIUt5uOgq78oHhifpAfXbkR6fxjcKMQnQKHZ0XkQhG5ZsVDj2NxusUzAJ4Wkf8R5iQich0WNxf5ZRF5TkT+CMDHReQBEfk5gFcDeM967y2VIqhLSw1renq6/oOUZyCFSMt7c6VxRLTkB8UW84P86MoNjyPIDWujKRb/G8DlK/6dA9C59PMZAP4aixUtAiml3rLOw18MFSDrIFOAzZs3132MxMLdkKgnPLBWZSR05AfFF/OD/GjJjfIsvMIj9R+nCsK2RZuNJvgej8WR34rMUtm3EhZ31vslY5Et4QgyBdFxle8tRHsLDOAIclQ4QkhBmB/kR0/bcg8EEZeq5RQLbTbqIPcAWJ6Io5R6wYrn0gA2mQhqpaAqG0SFQqHuYyTyEd8CA3gbLCI68oPii/lBfvS0LRGvbQEHX3TaqIP8JBanUqznTAB79YazViqVMn0KamBDQ0P1HUCV4S1EuEHIEuEivUjUnR8Ua8wP8qMjN6KujgSAI8gabdRB/jyAq0TklJUPisjLAHwOwNWmAqvgFT4FGRkZqev9UngUoixsN8ur/EjUmx8Ub8wP8lN3biiFhJXBF7YtumxU5u0zIvLLAHaLyLMARgBsAXAMgH9RSn3GdICJRML0KaiBdXR01PX+hIX5xwC4SC8i9eYHxRvzg/zUmxtSeAyiLMxxZ9uizYYlIpRS7xSRTwM4G8BmABMAblFKPW46OKKN1HsB5dmYfwxe5UeFF9gUhPlBfurNDRvzjwHw7qRGoWqoKaWeAPCE4VjWxSoWFGRmZga9vb01v9/aCDLniUWi3vygeGN+kJ96c8PK/GNwfYtOvnOQReTaMAcQkS/pC2ctLtKjIAMDA7W/uTQBr/iUvmCqwN2OolFXflDsMT/IT725kbDUQeYIsj5BI8hvFJEvY8XW0j7OB3CJtohWKRaLpg5NMTA5OYn29vaa3mvtCwzgPLGI1JMfFH/MD/JTV26UpiDFJ/UGFBI3CtEnqIM8BuCagOcruAyYrKmnTrat+ccA5yBHhXXUKQjzg/zUkxuJvIXdWSvYtmjj20FWSr0wwjh8catpClLPbTBr848BfolFhLfQKQjzg/zUkxu25h8DgKAAqCIg7DvVa6M6yNaxDjIFGR0dre2Nqggvf5/eYKrAOcjRqDk/qCkwP8hPPblhdfAF4CJwTZzvILMMDwXp7Oys6X1e4UG7nVSOIEei1vyg5sD8ID8154YqwcvfqzeYKnEARg/nO8hEJti8BQaAi/SIiGLIKzxkv9Qa2xctnO8gsw4yBclkMjW9z2oFC/AKPyq15gc1B+YH+ak1N7wFe4u/l/EOpRahOsgiMunz+CG94azFOsgUZHBwsPo3KQUvd4f+YKqKgV9gUagpP6hpMD/IT625kcjt1BxJ9TgAo0fYEeQ1vVQRSQEwPkGYdZApyNjYWNXv8QoPwCsbv7YLJCgBasFqDM2glvyg5sH8ID815YYqOtFB5gCMHoF1QERkFwAFoFVEVv/WjwHwU1OBEYUhstE+NmslsrcYiKQG5Xkg0WI7ilirJT+oeTA/yE8tueEt7IaoGQPRVEfK3G5ah40K5X0BizvpnQbgiyseVwBGARjvabAOMgXp6+ur+j2J3M0GIqmeqCwUem2HEWu15Ac1D+YH+amtbXFk8IUjyFoE9j6VUtcCgIj8t1Lq0WhCOhLrIFOQsbExbNu2LfwbyrNuLKIAANsrnZtA1flBTYX5QX5qyY1E1pXBF85B1sG3gywib131719f73VKqTDbUdeMdZApSHd3d1WvT+R2Lu405IDFEWQyqdr8oObC/CA/VedGaQxe4QEzwVSLI8haBI0gXxTi/QqA0Q4yUZBqywC6Mr0CAL/EIsAykRSE+UF+qs2NZO5WiCNDHsK2RQvfDrJS6tVRBuKHX2AUZG5uDps3bw79+kT2VoPRVEdYzN24avODmgvzg/xU37Y4NPjCRXpaBE2xEKWUWvrZtxycUqpsIrAK1kGmIENDQ6FfK4XH4ZX2GYymSrzKN66a/KDmw/wgP1XlhiojkbvNWCzV4giyHkF1kKdX/FwEUFj1p/KYUVykR0FGRkZCv9apK3yAi/QiUE1+UPNhfpCfanLDy98HKU8YjKZKXKSnRdAc5Jeu+PlFpgPxwzqVFKSaOwzOlOBZwqt883gHioIwP8hPI7ctvDupR9Ac5H0rfh6OJpy1WMWCgvT09IR7YTmLxMKdZoOpFucgGxc6P6gpMT/ITzW54czmU0u4vkWPULtwiMhXgfWXZyqlLtYa0SrcapqCjI+Po6OjY8PXJRZ+AlG5CCIKjyPI5oXND2pOzA/yEzo3ytPw8veYD6gabFu0CLtN3ZOr/j0E4I0A/k1vOGtxBJmChL3Kd+0KHwDniUWAI4QUhPlBfkK3LbnbIHCr2pZwfYsWoTrISqm/Xv2YiHwRwIe1R7T23KZPQQ0sn8+Hep1T9Y+XcLcj88LmBzUn5gf5Cd22ODn4whFkHYKqWGzkPgBn6QrET7lstIocNbhsduMvAikOwyvujSCaKvFLzLgw+UHNi/lBfsLmhnML9MDpe7qEnYN89qqH2gG8GcDD2iNahauMKUiYWpXOlXdbwoUU5rHOLQVhfpCfMLkh+YfhlQ5GEE2V2LZoEXYE+Yur/ly59PhbTAS1EusgU5AwtSpdvMIHwBHkCLDOLQVhfpCfMLmRdHDqHsARZF3CzkG2VgfZ8+qZBUJxl06ng1+gCkjkdkUTTLU4B9m4DfODmhrzg/yEyQ0n5x8DbFs0CVvFAiKyCcDvADgawAEAP1BKTZkKrIIdZArS1dUV+Ly38N/OrujlVb55G+UHNTfmB/nZMDfKc/AWfhZNMNVSWUApgBut1SVU73NpDvIzAP4MwGkA3gngaRHZYS60RayDTEEmJoK393T2Ch8Aym523ONko/yg5sb8ID8bti25XRC4WQVFoDiFT4OwI8ifAfA2pdQ3Kw+IyJsAfBbA8SYCq0gmQw9yUxPq7e0NfN7Z+cfgCHIUNsoPam7MD/LTyG0LgKUOcrvtKBpa2PkLRwP41qrHrsfihiFGscwbBQkqxSOlESQKD0UYTZU4T8w4lvGiIMwP8rNRbrjeQWad/fqF7SB/BcBlqx77k6XHjWIHmYLkcv7bRzs9vQIcQY5CUH4QMT/IT1BuSGEvvOIz0QVTC7YvdfOdvyAiuwBUtrHzAPyJiLwPwH4AWwEMAvhv0wGyDjIFCapV6eLueUdgrUrjWOeWgjA/yE9w2+L24AsASHkO3Ie4PkETfL+w6t+fNxmIH9ZBpiAjIyPYtm3b2idUCYnczugDqoJgAVBlQFipxRTf/CAC84P8BeWGq5tPHYEjyHXz7SArpa6NMhA/LPNGQVpbW9d93MvvgZSNVyGsn5oHpNN2FLHllx9EAPOD/PnmhlpAYuGn0QZTA07hq5/zvU92kClIW1vbuo83xBU+wIV6hvnlBxHA/CB/frnh5e5sjAVwjRCj45zvfbIOMgWZmlp/lLgR5ogBvMo3zS8/iADmB/nzyw1Xt5dejW1L/ZzvILMOMgXp7+9f+2BpEl7+vuiDqQUX6hm1bn4QLWF+kB+/3GiUwRduRFW/sDvpWfsWYZk3CjI7O7vmsUTuVggaI294lW/WevlBVMH8ID/r5YYU98MrPGYhmhqwbalb2BHkfSLyXRF5o4ikjUa0CjvIFCSfX7vVZ8Nc4QOA4lW+SevlB1EF84P8rN+2NMb0CoAbhegQtoO8DcDNAN4PYERErhaRM82F9TzWQaYga2pVKoVE9lY7wdSAI8hmsc4tBWF+kJ/1csP1zaeOwLalbqE6yEqpMaXUp5VSpwE4HcAhAF8VkadE5CMiYqyQJOsgU5CRkZEj/u0VHoBXPmQpmhpwDrJRq/ODaCXmB/lZkxuq6Hxt/ZWEbUvdalmkN7T0pxvAXizuqneviHxAZ2AVLPNGQVaX4mmoK3xwBNk0lvGiIMwP8rM6N7yF3RA1YymaGnCKRd1ClYgQkZcC+H0AvwcgA+BaACcppfYvPf9RAD8HcKXuAEVE9yEpRtLpI6fEN9IcMQC8DWbY6vwgWon5QX7Wti0cfGk2YYdndwLoAvBGpdSvKKX+vtI5BgCl1DMA/p+B+FAqlUwclmJienr6+X+UZ+Et3G0vmBoIF+kZdUR+EK3C/CA/q3OjYTafquAIct3CFhk+Xym1ZvKNiLxcKXUXACil/kprZEtYB5mCbN68efnnRG4nBA02Z51X+UatzA+i1Zgf5OeI3CiNwSs8YC+YGnAEuX5hR5Bv9Hn8h7oC8cMRZAqy8iq/4aZXAFykZxhHCCkI84P8rMyNZO5WCJTFaGrAtqVugcOzIuIBkMUfRZZ+rjgOgPF9oJVqsKSkSK2sctJI5d0qeJVvFqvgUBDmB/k5sm1prPnHAHh3UoON5i8UgeXLptWd4TKAv9Ue0Sqsg0xBKrUqpfAEvNI+y9HUgPPEjGKdWwrC/CA/y7mhFBK526zGUguub6nfRlMsXoTFkeLnAPzCij8vAtCtlLrCaHTgFT4Fq9SqbLgFFEu425FZrHNLQZgf5KeSG17+Pkh53HI0NeAIct0CR5CVUsNLPxrbCGQjiUTC1qmpAXR0dABovBI8yzhPzKhKfhCth/lBfhq9beFGIfXz7SCLyNVKqbct/fwVv9cppS42ERhRGIlEAihnkVj4qe1QasI5yGbxApuCMD/ITyU3GrWDzBHk+gWNID+94ue9pgPxwyoWFGRmZgabW/dAVM52KLXhFAujZmZm0NvbazsMchTzg/zMzMygt8druNr6FYICoAqAcB1XrXw7yEqpj634+a+jCWctLtKjIAMDA0jkrrYdRs04gmzWwMCA7RDIYcwP8rPYtvwXBA08SKey7CDXIVQdZBH5gIictuqxl4vI+8yE9bxi0XglOWpgk5OTDbtADwBvgxk2OTlpOwRyGPOD/DR82wIuAq9X2I1C3gXg4VWPPQzg3XrDIaqOKk3BKz5pO4zacSGFUayjTkGYH+RHKYVErvFq6x+B7UtdwnaQ08CaPXzzAFr1hrMWt5qmIEOdjTk/rIJX+GbxFjoFYX6Qn8FN4/BKB2yHUR/eoaxL2A7yPQD+dNVjbwewR284a7EOMgU5NMIvMPI3OjpqOwRyGPOD/Bw6cL/tEOrGAZj6hB2efQ+AH4vIRVisaPFiAIMAzjEVWAXL8JAfKTyNTYk7bIdRF0EJUAuAtNgOJZY6Oztth0AOY37QulQZm7wf2Y6ifhyAqUuoDrJS6iER+SUArwfwAgDfBnCjUipjMjiiIMn5b9kOQY/yPJBgB5mIyAXewk8gpSnbYdRNytxuuh5hp1hAKZVRSn1dKfUPS39H0jlmHWTyk5z7Fmbyx9kOo24s9WZOJsNrePLH/KD1xKVt4QhyfUKNIItIEotzkM8CsBmAVJ5TSm03E9oi1kGm9Xj5++EVn8DWzmnbodRP8SrflMHBQdshkMOYH7SGyiM5/z1s7Wz8u3qcg1yfsCPInwTwvwHsBPBrAL4F4CgAxvdgZB1kWk9ybnF6xcE5o9dnkeAIsjljY2O2QyCHMT9otUT2vyBqOhZtC0eQ6xO2g3wBgN9SSn0KQHHp7/MAvNpYZER+VBmJ+esBAJ7EoMoJv8SMEZGNX0RNi/lBq1XWtsShbeHgS33CdpDbAexb+jkrIu1KqUcBnGImrOexDjKt5i38FF7pIABgoG235WjqJyzmbkxfX5/tEMhhzA86QnkWiexi9Yo4tC3gIr26hO0gPwKgstX03QCuEJHLAew3EtUKrINMq1WmVwDxmGLBEWRzeAudgjA/aKXk/A+WR13j0LZwBLk+1Ww1XZkM/F4ALwNwLoC3hXmziFwjIodE5MEVj/WJyI9F5Imlv3vXey/rINMRVB7J7I3L/9zU8qjFYDThIj1juru7bYdADmN+0EqJFaVD49G28O5kPUJ1kJVSu5VSe5Z+fkIp9Rql1CuUUrtCnufLAH5z1WMfAHCzUuoXAdy89G+iQInszZDy8/Upi6rNYjR68CrfHJaJpCDMD1pWGkMid/vyP+PQtvDuZH1C10EWkbNF5PMi8v2lv3eEfa9SaieAyVUP/y6Aa5d+vhaLi/7W4BcYrZSc/48j/p3Jb7MUiUacg2zM3BxH58kf84MqkvPfWdzZdEkc2haub6lP2DrI78XiCO+XANwL4FgAXxORjyul/rHGcw8qpQ4CgFLqoIgctd6LpqenccYZZyCZTKJUKuGCCy7AZZddhpGREXR0dCCRSGBmZgYDAwOYnJyEUgoDAwMYHR1d3kY0k8lgcHAQY2NjEBH09fVhbGwM3d3dKJVKmJubw9DQEEZGRpBKpdDT04Px8XH09PQgn88jm80uP59Op9HV1YWJiQn09vYim80il8stP9/a2oq2tjZMTU2hv78fs7OzyOfzy8+3tbUhnU5jenoamzdvxvT0NAqFwvLz/EwBnykzhRep3Xhm9s1IJWbR1/IgiuVWTOZOQK7Uh/nC0Tim8yY8l3kdWhKT6G7Zi7H509Dfdi/mC1uRLR61/Hxb8hDaU/sxkT0FA+27MbNwHBZKfcvPt6cOoDUxicncCRhsvxOTCyegUOpafr4zPYykZHF44Xhs6diJsexpKKsUtnTsxP7M2ehO7wUAzOSPw9bOW3Bwbjs8KWCgbTcOzm3HppZHUVRtyOS3YbC9iH3Dw/H5PTmUe+l0GlNTU7H6THH8Pdn8TMPDw7H7THH8PZn+TPMHxrC19ejl7/LNrXfjycNvruq7vNI+VNqn0fnT0df6oLX2aTJzIno75mP1ezKRe35EKbVhT1ZE9gN4nVJq5RzilwL4sVLq6A0PsPj6F2Jxe+oTlv59WCm1acXzU0qpNfOQb7vtNnXyySeHOQXFXHLum2iZ+NMjHnvy8Jvx4k1ftxSRHvnu96Cw6S9thxFLw8PD2Lat8UeCyAzmBwGAFIfRfuDXjngsDm1LqeV05Aa/ZzsM5+3Zs+eeHTt2nLr68dBTLAA8uerfTwHYuHftb1REtgDA0t+H1nsR61RSRWJF9YqKVGLWQiR6cbcjc7gTJwVhfhBwZGWkiji0LZyDXJ+wHeQrAHxRRH5RRNpE5JcAXA3gwyLiVf5Uee4bAPzB0s9/AOC7672IVSwIAFAaP2IBRUVfy4PrvLjBcJ6YMUG3z4iYHwQ8vznISnFoWzgHuT5hO7X/AuAtAB4DkAHwKIDfw2InuYDFEnC+BYtF5DoAdwL4ZRF5TkT+CMCVAM4RkScAnLP07zW41TQBQHL+uxCszYXR+dMtRKMXq1iYMz4+bjsEchjzg7z8g/AKj615PA5tC0eQ6xN2m7oX1XMSpdRbfJ7asBIGR5AJAJJz/7Hu432tjX+Vzy8xczhCSEGYH5SIcdvCwZf6hOogK6WGTQcScG5bpyZHSHEYifz6237mSjHYKpZzkI3J5/O2QyCHMT+anFJIzl+/7lNsWyjsCDJE5A0AzgKwGcDyyjml1MUG4lpWLpdNHp4aQHLu277PzRdCFVFxGq/yzclm+d+W/DE/mpu3cCe80v51n4tD2wKVBZQCWOygJqHmIIvIh7E4D9kD8CYAEwBeB+CwudAWcZUxrbeAouKYzpsijMSQMjcrMGVoaMh2COQw5kdzi3vbIlCcwleHsIv03grgHKXUewDkl/4+F8ALTQVWUSj4rv2jJiD5h+AVHvV9/rnM6yKMxgyOIJszMjJiOwRyGPOjiakCkvP+NYLj0LYAYAe5DmE7yJtWbBKSF5GUUuouLE65MMrzqq0eR3ESdIUPAC2J1TuYNyDOEzMmnU7bDoEcxvxoXoncLZCyf/sRi7YFrLNfj7BzkPeKyEuVUg8BeBDAn4jIFIApc6EtYge5iSkVOP8YALpb9kYUjDkcQTanq6vLdgjkMOZH81pvc5CV4tC2AOAIch3C9j4vB9C/9PMHAPwZgH8A8OcmglqJdZCbl7fw3/BKzwW+Zmz+tIiiMYjF3I2ZmJiwHQI5jPnRpMpzSGR/GPiSWLQtAIRrXGoWtszbD1b8fBeAFxuLaJVkMnShDYqZjaZXAEB/270RRGKWYAFQZaDqzShpI729vbZDIIcxP5pTIvuDDacexKFtAcAR5DqErWJxsYictOqxk0XkIjNhPY9l3pqUKiA5f8OGL5svbI0gmAhwnpgRLONFQZgfzWmj6RVAfNoWTuGrXdghq48C2LfqsX0A/kZvOGuxg9ycErlbAxdQVGSLR0UQTQTYQTYil8vZDoEcxvxoQqUJJHK3bfgyti0UtoPcDWBm1WPTADbpDWct1kFuTmGu8IF41KoEeJVvCuvcUhDmR/NJzn8Xgo3XNrFtobAd5IcB/I9Vj50P4BG94azFOshNqDy/4QKKitjUquRCPSNY55aCMD+aT3I+uDJSRXzaFi7Sq1XYFXDvB/ADEbkQwF4sLtLbAeC3TQVWwTJvzSeR/U+ICvc/dVvykOFooiEqC2U7iBhqbW21HQI5jPnRXKT4HLyFn4V6bVzaFi7Sq12o3qdS6g4ALwWwG0AHgLsAnKCU+onB2ACwg9yMknP/Efq17an9BiOJUMgLAqpOW1ub7RDIYcyP5pKc+9bi9sshxKVt4UYhtQtdQ00ptQ/AlQZjWRfrIDeZ0mSoBRQVE9lT0NvymLl4IsJ5YmZMTU2hu7vbdhjkKOZHcwlTOrQiLm0LR5Br5/zwLOsgN5fFBRTh550PtO82GE2EOAfZiP7+/o1fRE2L+dE8JP8IvMLDoV8fl7ZF2LbUzPkOMsu8NZdqrvABYGbhOEORRIsjyGbMzs7aDoEcxvxoHs3atrDMW+3YQSZnSHF/6AUUFQulPkPRRIxfYkbk83nbIZDDmB/NIzkXrnpFRVzaFs5Brl3YnfRO2vhVZrAOcvNIzodfQFERn1qV/BIzgXVuKQjzozl4C3fBKz1b1Xvi0rZwDnLtwo4g3ygiEyLyHRF5j4i8TETEaGRLWAe5eSRCbg6yUmxqVfJLzAjWuaUgzI/mUE1lpIq4tC2cvle7sGXejgVwGoDvADgJwL8DmBKRGw3GBoBl3pqFl7sTicJDVb+vPXXAQDQWcCGFESzjRUGYH02gPIvk3DerfhvbFqqmzNtTIpIEkF7685sAjG9WHtFANVmWmr2qpve1JiY1R2IHr/LNSKfTtkMghzE/4i+Z+SpEZap+X1zaFt6drF3YOchfF5F9AL4C4BcA/BuAFyqlXm4yOAAolUqmT0GWSfEZJLL/WdN7J3MnaI7GEs5BNmJ6etp2COQw5kfMqRJSs5+v6a1xaVvC7kpLa4Wdv3AqgBKA+5f+3KeUiqQ+Dusgx19q9vMQ1FatZLD9Ts3R2MFFemZs3rzZdgjkMOZHvCWy34dX2lfTe+PStnAEuXZh5yC/GMArAdwC4EwA/ykij4vIF0wGB3AEOfbKs0hm/q3mt08uxOMqn/PEzOAIIQVhfsRbaqa2qXtAfNoWbhRSu9Ar4JRSIwAeA/AkgGcADAH4LTNhHXFe06cgi2qdH1ZRKHVpjMYezkE2g1VwKAjzI768hT1I5O+q+f1xaVs4gly7sHOQbxCRSQDfBfAyAN8D8GtKqa0mgwNYBznW6pgfVhGfWpW8yjeBdW4pCPMjvlKz/1zX++PStggKgOKFYC3CjiB/G4sd4m1KqYuUUl9QSj1hMrAKXuHHVz3zwypYq5KCsM4tBWF+xJMU9yMx/726jhGXtgUAR5FrFHYO8pcBPCcivyEiFwKAiHSISIfJ4AAgkUiYPgVZkpqp7wofADrTwxoicQC/wIzo6DD+FUUNjPkRT8nZz0NQrOsYsWlbwEXgtQo7xeJEAI8D+DyALy49fBaAawzFRTHnLdyDRH533cdJSkw6llxIYQQvsCkI8yOGynNIZb5a92Fi07YAbF9qFHaKxT8D+Cul1PEAKnMebsdiRQujWMUinuqdH1ZxeOF4LcexjVf4ZszMzNgOgRzG/Iif5Nx1EFV/dZK4tC0AeIeyRmE7yC8F8K9LPysAUErNATC+TycX6cXP4vwwPbuUb+nYqeU41vELzIiBgQHbIZDDmB8xo8pIzf6LlkPFpm0BB2BqFbaD/AyAX1v5gIi8HIsl34wqFuubR0Tu0TE/rGIse5qW49gmKAFqwXYYsTM5GZPtYskI5ke8JLI3wSs+reVYcWlbAHAApkZht6n7EIDvi8hVANIi8n8AvB3ApcYio3jSND9s+XAqRncYyvNAosV2FLHCOuoUhPkRL6nZ2jcGWS1ObYuUud10LcJWsbgRi5uCDGBx7vE2ABcopX5kMDYA3Go6bnTND6uI120wXuXrxlvoFIT5ER9e/gEkFn6i7Xhxals4glybanbS26OU+lOl1O8opd6ulLrHZGAVrIMcIxrnh1Xsz5yt9XhWKV7l6zY6Omo7BHIY8yM+khpHj4F4tS2cg1ybUMOzIpIG8IcAfhVA58rnlFIX6w/reSzDEx8654dVdKf3aj2eTaKy4A1fvTo7Ozd+ETUt5kc8SGkUybnrtR4zTm0LR5BrE3b+wrUATsbiFtO85Kaa6JwfFkv8EiMiqlpy9hoI8rbDcBan79UmbAf5NwG8SCl12GQw62Ed5HjQPT+sYiZ/HI5qr3/DERcIi7lrl8lk0N/fbzsMchTzIwZUDqnMl7UfNvDdZ28AACAASURBVE5tC7hIryZh5yA/C8DK8nrWQY4H3fPDKrZ23mLkuFbwKl+7wcFB2yGQw5gfjS85901IeUL7cePUtnAEuTZhO8hfAfBdEXmLiJy98o/J4ADWQY4DKY1onx9WcXBuu5HjWsFFetqNjY3ZDoEcxvxocEppX/hdEa+2hXcnaxF2isU7lv7+u1WPKwC/oC8ciiOT88M8iU+VE17l6ycitkMghzE/Glsidwu8wmNGjh2ntoV3J2sTqoOslHqR6UD8sA5ygytnjcwPqxhoi8kcMWBxoxDSqq+vz3YI5DDmR2MzNXUPiFfbwvUttQldB9kW1kFubMn5b0LK5rZzjdNtMI4g68db6BSE+dG4JP8okrlbjR0/Tm0LR5Br43wHmXWQG5hSSM2YmR9WsanlUaPHjxTniWnX3d1tOwRyGPOjcZkuGxqntkW4vqUmzneQqXElcrfAKz5u9BxF1Wb0+FHibkf6sUwkBWF+NKjSOJLz/2H0FHFqWziCXBvnO8j8AmtcJueHVWTy24yfIzKcJ6bd3BxHTsgf86MxpTJfhqic0XPEqW3hHOTaON9BZh3kxmR6fljFMZ03GT9HVDgHWb+hoSHbIZDDmB8NSOWRnL3G+Gni1LZwBLk2zneQuUivMUW1rfRzmddFcp5I8EtMu5GREdshkMOYH40nOfdteOVDxs8Tp7aFgy+1cb6DzDqVDSiC+WEVqcRsJOeJBOcga8c7UBSE+dF4opi6B7BtoQboILOKReNJzX7e+Pywir6WByM5TxR4la9fT0+P7RDIYcyPxpLI3opEIZrv/Di1LVBZQCnbUTQc5zvI3Gq6sUhpJLLpFQAwOn96ZOcyrswFQ7qNj4/bDoEcxvxoIKqM1OG/jux0cWpbBIpT+GrgfAeZI8iNJXX4Y5HWXOxrjc9VPkeQ9eMIIQVhfjSO5Nw3Ihs9BuLVtgBgB7kGzneQFW8LNAzJP4zk3HWRnjNXitFWsZwnpl0+n7cdAjmM+dEgylmkpv8u0lPGqm0B6+zXwvkOcrlcth0ChZQ+fAUE0f6+5gtHR3o+kziCrF82y/+m5I/50RhSs5+DVzoY6Tnj1LYA4AhyDZzvIHOVcWNIZG9FMndL5OeNVa1KFnPXjnVuKQjzowGUDiE18+nITxurtgWAcI1L1ZzvILMOcgNQZaQOX2Hl1LGqVYkFQPGOiU6sc0tBmB/uS0//faTrWiri1LYA4AhyDZzvIHue8yE2veTcdUgUHrJy7pbEpJXzGsN5Ylql02nbIZDDmB9uk8LjSGb+1cq549a2cApf9ZzvfbKD7LjyPFLTH7N2+u6WvdbObQQ7yFp1dXXZDoEcxvxwW3rqwxCUrJybbQs53/tkHWS3pWY/C69k7zbl2Pxp1s5tAq/y9ZqYmLAdAjmM+eEuL7cTydyPrZ2fbQs530FOJpO2QyAfUhpFauYzVmPob7vX6vm140I9rXp7e22HQA5jfjhKKaSnPmw1hPi1LVykVy3nO8gs8+au1OErrSyeWGm+sNXq+XXjVb5eLONFQZgfbkrOfROJwgNWY4hb28JFetVjB5lqIvlHkZz7mu0wkC0eZTsEvSxfcMRNLpezHQI5jPnhIAubgqwnbm0LNwqpnvMdZNZBdtPipiB2Fk+sFLtalbzK14p1bikI88M9qdmr4JX22w4jdm0LR5Cr53wHmXWQ3ePlbkcy91+2wwAQw1qVnIOsFevcUhDmh2NKY0jNfMp2FADi17YI25aqOd9BZpk3x6iy9cUTK7UlD9kOQSuOIOvV2tpqOwRyGPPDLenpj0NUxnYYAOLXtrDMW/Wc732yg+yW5Nw3kCg8aDuMZe0p+7fitOKXmFZtbW22QyCHMT/cIYUnkMx81XYYy+LWtnAOcvWc732yDrJDHFk8sdJE9hTbIWjFLzG9pqambIdADmN+uGNxXYs77X3c2hbOQa6e8x1k1kF2R2r2c/BKB22HcYSB9t22Q9CLX2Ja9ff32w6BHMb8cIOXuwPJrFuL4uLWtnD6XvWc7yCzzJsjSoeQmvm07SjWmFk4znYIenEhhVazs7O2QyCHMT8coBTSh91Z11LBtoXYQaZQ0tN/b31TkPUslPpsh6AVr/L1yufztkMghzE/7EvM/zsS+ftth7FG3NoW3p2snvMdZNZBtk8KjyGZ+VfbYawrfrUqeZWvE+vcUhDmh2Uqh/Thv7Udxbri1ra4OMDlOuc7yKyDbF966sNObAqyntjVqmQHWSvWuaUgzA+7UjNubAqynri1LRxBrp71FXAi8gyAWQAlAEWl1Kkrn2eZN7u83E5nNgVZT3vqgO0Q9OI8Ma1YxouCMD8sKo07synIeuLWtnCjkOpZ7yAvebVSany9J0Qk6liowrFNQdbTmpi0HYJWnIOsVzqdth0COYz5YU96+h8gyt1FknFrWziCXD3nh2dLJTdv7TeD1OxnkSg8YDuMQJO5E2yHoBenWGg1PT1tOwRyGPPDDi/330hmvmQ7jEBxa1sEBUBxymo1XBhBVgB+JCIKwL8opa5e+eThw4dxxhlnIJlMolQq4YILLsBll12GkZERdHR0IJFIYGZmBgMDA5icnIRSCgMDAxgdHUVnZycAIJPJYHBwEGNjYxAR9PX1YWxsDN3d3SiVSpibm8PQ0BBGRkaQSqXQ09OD8fFx9PT0IJ/PI5vNLj+fTqfR1dWFiYkJ9Pb2IpvNIpfLLT/f2tqKtrY2TE1Nob+/H7Ozs8jn88vPt7W1IZ1OY3p6Gps3b8b09DQKhcLy8658poW5fXghrsKTmTejLXkI7an9mMiegoH23ZhZOA4LpT4c03kTnsu8Du2pA2hNTGIydwIG2+/E5MIJKJS6lp/vTA8jKVkcXjgeWzp2Yix7GsoqhS0dO7E/cza603sBADP547C18xYcnNsOTwoYaNuNg3PbsanlURRVGzL5bcvHTCVm0dfyIBZKPZjMnYBcqQ/zhaOXn29JTKK7ZS/G5k9Df9u9mC9sRbZ41PLzrn6mTR1z2D883NS5p/MzJZNJTE1NxeozxfH3ZOsztbe3Y3h4OFafyfnfUzGD/Oj38IL2niO+y0fnT0df64POfJf3pB/Dk4ffXFf75Nxn2jyN0bG55s09n8/kR5RStXZstRCRo5VSB0TkKAA/BvBOpdTOyvO33367Oumkk+wF2IzUAtpGXgOv8IjtSDY0PPs72Nb1fdthaFNOvhDZo++2HUZsHDhwAEcffbTtMMhRzI/opSf+DKm5r9kOY0Nxa1sAYH7rg1AJVm5Zbc+ePffs2LHj1NWPW59ioZQ6sPT3IQDXA3j5qudthNXU0of/piE6xwBQKHXZDkEvLqTQilVwKAjzI1qJ+e83ROcYiGHbArB9qZLVDrKIdIhIV+VnAK8F8ODK17AOcrS83E4kZ6+yHUZo8atVyS8wnVjnloIwP6IjpVG0TL7Xdhihxa1tAcCFelWyPYI8COAOEbkfwF0Avq+U+uHKF/AKP0LlabRMvBOCxhm1Z61KCsI6txSE+RGd9MS7IOUJ22GEFru2BRyAqZbVRXpKqacAnBz0mkQiEVE01DL5PmeLtvvpTA/bDkErQQlQC4C02A4lFjo6OmyHQA5jfkQjOXuN0/X01xO3tgUAB2CqZHsEmRyRmPs2kvPfsh1G1ZISw//hOU9MG15gUxDmh3lSeBLpw1fYDqNqcWxbpMztpqvhfAeZdZDNk+IBtEz+he0wanJ44XjbIWjHzUL0mZmZsR0COYz5YZgqomXiTxry1n4c2xaOIFfH+Q4yF+kZphRaJi6DqMYsmL+lY+fGL2o0ilf5ugwMDNgOgRzG/DArNf0PSOTvtR1GTeLYtjTihYpNzneQi8Wi7RBiLTl7FRILu2yHUbOx7Gm2Q9COI8j6TE7GbLtY0or5YY63cDdSM//Pdhg1i2PbwhHk6jjfQSZzJP8o0tN/azuMupRVDO8wcA6yNqyjTkGYH4aU5xanVqBxp0jGsW0Rti1Vcb6DnEy6sBt2DKk8WibeDlE525HUJZ63wXiVrwtvoVMQ5ocZ6anL4RWfth1GXeLYtoBTLKrifAeZdZDNSB3+GBKFBzd+oeP2Z862HYJ+7CBrMzo6ajsEchjzQ7/E/A+Rmvuq7TDqFse2hYMv1XG+g8wyPPp5uZ8iNftZ22Fo0Z3eazsE/bhIT5vOzk7bIZDDmB+alcbQMvke21FoEc+2hSPI1XC+g0yalWcXq1agbDsS8sGrfCJqRC2T74aUx2yHQX7YtlTF+Q4y6yDrlZ76ALzSPtthaDOTP852CPpxIYU2mUzGdgjkMOaHPsnMV5DM3mQ7DG3i2LZwkV51nO8gsw6yPon5G5Ca+4btMLTa2nmL7RC04wiyPoODg7ZDIIcxP/SQwlNIT33IdhhaxbFt4QhydZzvILMOsh5SPIiWyT+3HYZ2B+e22w5BP84T02ZsjLd7yR/zQwNVQsvEn0JitnYijm1L3H5HpjnfQSYNVGGxJmV5ynYk2nkSvyon3O1IHxGxHQI5jPlRv9T03yKRv9t2GNrFsW3hCHJ1nO8gsw5y/dIT70Ji4Q7bYRgx0Lbbdgj6cZ6YNn19fbZDIIcxP+qTnP0y0jOfth2GEXFsWzgHuTrOd5BZB7k+qcN/i9T8N22HYUw8b4PxKl8X3kKnIMyP2iWyNyE99X7bYRgTx7aFI8jVcb6DzDrItUtmrkV65pO2wzBqU8ujtkPQj19i2nR3d9sOgRzG/KiNt7AHLeOXNvRW0huJY9vCwZfqON9Bptoksj9CevJ9tsMwrqjabIegH+cga8MykRSE+VE9KT6D1rHfi/1aCbYt5HwHmV9g1fMW7o391X1FJr/Ndgja8Spfn7k5rtomf8yPKpUm0XrowqbYDCSObQtUFlDKdhQNw/kOMusgV2fx6v5/NU05l2M641OYflm5OX53URgaGrIdAjmM+VGFchatY78HrxjDLZjXEce2RaA4ha8KzneQuUivCk10dV/xXOZ1tkPQjiPI+oyMjNgOgRzG/AhJldEy8XYk8vGr7OAnjm0LAHaQq+B8B5l1KkNSObSO/X7TXN1XpBKztkPQj/PEtOEdKArC/AgnPfWXSGa/bzuMSMWybQHr7FfD+Q4yq1iEoMpoGX87Evm7bEcSub6WB22HoB1HkPXp6emxHQI5jPmxseTMZ5HKfN52GJGLY9sCgCPIVXC+g8ytpjeWnrocyeyNtsOwYnT+dNsh6Mdi7tqMj4/bDoEcxvwIlpi7HunDV9gOw4pYti0AhGtcQnO+g8wR5GDJmc8hlbnadhjW9LXG7ypfsACosu0wYoEjhBSE+eHPy/0ULRPvWFzY1YTi2LYA4AhyFZzvICuWJPGVmPsO0oc/bDsMq3KlmG4V2yRVSEzL5/O2QyCHMT/WJ4XH0Tp+8eLFepOKa9vCOcjhOd9BLpc5krYeL3cnWiYua9qr+4r5wtG2QzCDV/laZLP870j+mB9rSWlkqRrSYduhWMW2hZzvIHOV8VqLV/cXNfXVfUUca1UCXKinC+vcUhDmxyrlDFoOvQVeaZ/tSKxj20LOd5BZB/lIUjzIq/sVYlurkgv1tGCdWwrC/FhB5dEy/lYkCg/YjsQJ8W1bOH0vLOc7yJ7nfIiRkcITaB39bV7dr9CSmLQdghG8ytcjnU7bDoEcxvxYUp5F66ELkczdYjsSZ8S1beEUi/CStgPYCDvIi7yFuxe3kC7H9H/aGnW3xHRjFC7S06Krq8t2COQw5sfinOOWQ29GohDTqg01imvbwkV64Tnf+2QdZCCRvQmthy5g53gdY/On2Q7BCI4g6zExMWE7BHJYs+eHFJ5E68hvs3O8jri2LRxBDs/5DnIy6fwgt1HJzL+iZexiXvX56G+713YIZnAOsha9/7+9ew+SrCzvOP59+jq9u2xgYQUEAUM08YYBA2pKBaUimOBdUKsUUsZ4SUhEFERj4iUWKApaKF6iqCuXaJR1BReCeCkwiopgBGFRLlHY3dmd2bl2z0zP9OXJH31GmmVntmec7vf0Ob9P1RQ9Z043z1Y99b5Pn/O+z9lvv9AhSIylOT8ysz+ntPOvyTQeDB1KLCV1bjHNLR2LfYGc5jZv+YmPUhw9C6MROpTYmq4dEjqErtAV5JWhNl6ymLTmR3bmO7oruRdJnVvQxbaOxf7ybCoLZG9QGDuXfGVD6Ehib6b+mNAhdIcGsRVRrVZDhyAxlsb8yFWupDD6DgwtX1xMUucW3Y3uXOwL5NT1QfYqxV1vIjdzXehI+kJye1VqEFsJ6nMri0lbfuQnLqIwcUHoMPpCUucWrUHuXOyXWKSqD3JznIGhV6o4XoLE9qrUILYi1OdWFpOa/PAmhdFzVBwvQVLnFi3f61zsryCnpc2b1bcxMHwamdqvQ4fSV0q5odAhdIc2UqyIgYGB0CFIjKUiP7xKcdebyc1sDh1JX9HcIiqQY8DmtrSK48Zg6FD6zqr8ttAhdIW+5a+MUqkUOgSJscTnR3OcgeHXkZ39SehI+k5S5xbdnexc7KvPpPdBzlR/TGnnKSqOl2lk5ujQIXSH1iCviLGxsdAhSIwlOT+svp3SzlNUHC9TUucW00OoOhb7AjnJfZCz09cyMHQq5hOhQ+lb61fdGjqErtAmvZWx//77hw5BYiyp+WFz9zCw82QytXtCh9K3kjq36Apy52JfICeyzZvPUhj7F4q73oAxGzqavjY5e2ToELpD68RWRLlcDh2CxFgS8yNXuZzSzpPINLaHDqWvJXVu0YNCOhf7y7NJK5Bt7m4GRt5CpnZ36FASYbaxLnQIXaE1yCtjbm4udAgSY4nKj8YoxdGz1AVphSR1btEV5M7FvkBOTB9kd3Llz1EY/3ddNV5Bye1VqW/5KyFtfW5laZKSH9mZH1AYPZNMY2foUBIjqXOLUQOvgSWktuqi2C+xSEIfZGvsoDh8GsXx96o4XmHqVSmLSU2fW1mWvs8Pr7aW6w2fpuJ4hSV1bgF0FblDsb+C3O9t3rLTmymOvl3PvO+SVfmErrPTOrEVkfg2XvIH6ef8aC3XezOZ2pbQoSRSYucWwJpTeGZt6DBiL/YFspmFDmF5mlMUxt5DfurK0JEk2kA2oV889A1/RRQKhdAhSIz1ZX64kyt/lsL4h3RHsosSO7eA5pcOxf7ybKPRCB3CkmVmb6e04wQVxz0wWn1q6BC6Qm3eVsbEhFooysL6LT+sPsjA8KkUx/9VxXGXJXVuAVQgdyj2V5D7qg+yN8hPfoL8xEcxkv2Ak7g4cNUtoUPoDp+G5hRkVoeOpK8dcMABoUOQGOun/MhOf5vi6NlartcjiZ1bAGvuwkMH0Qd0BXmFWP1BBoZeQmHiAhXHPTQ6m8xv+UaTXOWK0GH0vX67Qii91Rf50axQGHkbA7v+VsVxDyV1bgHIlzeEDqEvxL5Ado/59xxvkqt8hdLg8WRnfxo6mtSpNfYJHULX5MufAdeXrT9EErrgSPfEPT8y1Zsp7Xi+lusFkOS5JTvzbax2f+gwYi/2BXKc+yBnZ75HaccJrdtenrwnMvWDpPaqBMg0tpKb3hg6jL6WlD630h1xzQ+r/Zri0GsoDb2CTP3/QoeTSkmeW4wm+fKlocOIvdgXyHH8hm9zd1EcOpWB4VfriXiBJbpXJZCf/GToEPpa3/e5la6KXX40hiiMnk1p8Hnkqt8NHU2qJX1uyVW+Bo2h0GHEWuwL5Gw2GzqE37P6IIWRf6a04/nkqj8IHY4Aawq/Cx1CV2VqW8jO3Bg6jL61erU2OcrCYpMfzRnyExexavtx5CtfweiPvTdJlvS5xZglX/6P0GHEWuwL5FhoTpEf/zClwWeRn7oKoxk6IonkLPntavKTl4QOoW/F6Qu2xE/w/PAmucpXKQ0e19rg7ZWw8cjvpWJuKX8JmloeupDYF8hBu1h4k1zl8tbgNfkxzKfCxSJ7ND77Z6FD6Lrs7C1kZn8eOoy+NDk5GToEibGQ+ZGp3szAjhMpjp5JpjEYLA7ZszTMLeYT5CrqaLGQ2BfIoTbpPbwB7+16xn2MHbz65tAh9ISuIi/P+vXrQ4cgMRYiP9o34GVrd/b8/y+dSc3cUv4c+FzoMGIp9gVyvd7bNlfagNdfhmeODR1CT2Rn/hur3Rs6jL4zOqq+sbKwnuaHNuD1lbTMLZnGILmpb4QOI5ZiXyCDk5+4EGt0d7dxpvpDisOv1wa8PtP0+LYBXElGk/zkp0KH0Xdi30ddwmmMkpm+AZvr7oUQq91LYfQcVm0/Vhvw+kha5haIuiVprHyU2BfIuVyWwsSFlLYdTXHX35Gp/njlPtyr5CpXMDB4PKWhl5ObuV4b8PpMWm6DAeSmvo7VtVZxKbTEQhZSHH0Hh+YuYtWO5zGw8xSyU98EX6G2ou5kZ75Hceg0SoN/Sb7yJe1h6TNpmlsy9XvJzlwfOozYiX2BXKu1llgYNXLT36I09JLWLaryl6C5vB2/Vh8kP34+q7Y9neLoWWRrd61kyNJD2yovCB1Czxhz5MqfCx1GX9m5U/sH5NFyla+Rm7n29+NHdvYnDIz8PaXtf05+/MPL/yLanCJX/iKlwWczMPxqctXvY+jKXD9K09wC6rm/J7EvkLPZR4eYqd1NcewcVm17GoXRd3e8NjMzexvFXW+itP0YCpMXY82RlQ5XemxtIV2Py8xXNkBTnRk6tWbNmtAhSMxY/SEKY+cBjx4/Mo2dFCY/Rmn70RSH30Cm+j8dfuaDFMb+jVXbjqI4di6Z+n0rHrf0VtrmluzcrWSqPwkdRqzEvkBejHmZfOXzrBp8NgM7X0F2ejP4buu7vEZ26moGdpxEaedJ5KY3YsTv6XwinWjl/JdDhyHSn7xJceQfMV+896tRJzdzDaWhl1EafA658hf32C82U/0RxeEzKG0/lnz505hPdCtyka5Tt6RHin2B3Gh0tiY4O3szA7vOoLT9GPITF2O135CfuJjS9qMZGHkz2bnbuhyphDA5d2ToEHouN/lZ8NnQYfSFSkUPXpCH5cqfJjv78D6WTsaPTO0eimPnRncs30Vm7lfkKlcxMPh8SkMvJTezWRvvEiiNc0u2eiM2d0/oMGIjFzqAvcnnlxZiprGNwsT5FCbO71JEEieHrPl+6BB6LtMcIjf1NeprTg8dSuwdeOCBoUOQmLC5uymMX/CIY0sZP8wr5CuXka9cttKhSQylcW4xnHz5k8ztf2noUGIh9leQe90HWfrL4NTzQocQRH7yUnB1XNmb4eHh0CFIHPgcxZG3Yjzyzktaxw/Zu7TmRm5qI1bfFjqMWIh9gQwWOgCJsYylcz15pn4/2ZnrQocRe2YaPwTy4xfssVtRWscP2bu05oZRI1/+TOgwYiH2BXJuD10sROatL90aOoRgtKFi79atWxc6BAksU72FfHnPt4zTPH7I4tKcG7nK5dAcDx1GcLGvPmt1bX6QhaX1NhhAdu72jttQpZWWWKRcs9zqWrHAA6DSPH7I4tKcG+ZT5Mtaax/7AnlPfZBF5u1bTPeOWz1+enFr164NHYIEVBh7D5nGgwv+Pe3jhyws7bmRL38BvBo6jKDiX33qIUSyiLqXQocQVK76XWxOT4JcSKOhO1BplZ3eTH7qPxc9J+3jhyws7blhzWFylatChxFU7AvkRlM79WVhlbnDQ4cQXEGPCF3Q1NRU6BAkhMYQxdGz93qaxg9ZiHID8uVPP/rhaykS+wJ5qX2QJV0OXXND6BCCy05vwuoPhQ4jlg466KDQIUgAxZG3Yc2RvZ6n8UMWotyATP23ZKevCR1GMLEvkGs19UGWhW2tnBQ6hOCMOvlJteXZkx07doQOQXosV/4yueqNHZ2r8UMWotxoyaf4DmXwAtnMTjazX5vZfWZ23u5/Hx9XqxFZ2NWbfxU6hFjITV0BjdHQYcTOpk2bQocgPWS1ByiMv6/j8zV+yEKUGy3Z2h1kqjeFDiOIoAWymWWBS4EXAU8GXmtmT24/RwWyLObqa9O903ie+TT5yhdChxE7GzduDB2C9Io3KI78A+adrzvX+CELUW48rJDSnvuhryAfB9zn7g+4+xzwVeCl7Se4uljIIurNNaFDiI18+TJoTocOI1b0qPr0yE9+nOzcz5f0Ho0fshDlxsOy1ZvIzP0ydBg9Zx6wAjWzVwEnu/sbo99fDzzT3c+cP+faa6+pjgxvz8//vv9+mfoB+2U16wkAu8YaOeXDw+aa67bXfe1E6DjiYnR09IB169btCh2HdJcxly9lB48EX9KzxTV+yEKUG49U99Xjc831g6Hj6JLDTzzxxPW7HwzdImJPg9kjKvYXv/glAz2KRUREREQk+BKLrcDj2n4/FNgeKBYRERERkeAF8q3AE8zs8WZWAF4DpLfpnoiIiIgEF3SJhbvXzexM4AYgC3zR3fXcXBEREREJpudXkM3scWb2AzPbYmZ3AU9w9ycCxwInmNm9Znajme0Xnf9XZnabmd0Z/fcFbZ/1jOj4fWZ2iZktaYOGxM/u+WFmb4uOr4vyYvf8OM7M/jf6+aWZvbzts5QfCbPU/Gh732FmVjGzd7YdU34kzDLGjyPMbKZtDPls22cpPxJkOWOHmR1lZrdE599pZgPRceVGGrh7T3+Ag4Fjotf7AL+h1QP5QuC86Ph5wEei10cDj41ePxXY1vZZPwOeTWuz3/XAi3r979FP8PxYBeTa3jvU9rvyI2E/S82PtvddDXwdeGfbMeVHwn6WMX4cAfxqgc9SfiToZxm5kQPuAJ4e/b4/kFVupOen51eQ3X3Q3W+PXpeBLcAhtPofb4hO2wC8LDrnF+4+v3HvLmDAzIpmdjCw1t1v8VbGfmX+PdK/lpEf0+4+34pngKgLivIjmZaaHwBm9jLgAVrjx/wx5UcCLSc/9kT5kTzLyI0XAne4+y+j94y4e0O5kR6hn6R3BK0rxD8FDnT3QWglMvCYPbzllcAv3H2WVmJvbfvb1uiYJESn+WFmz4yW69wJvCUqmJUfCddJfpjZauBdixAQdAAABGFJREFUwAd2e7vyI+GWML883sx+YWY3mdlzo2PKjwTrMDeeCLiZ3WBmt5vZudFx5UZKBNukZ2ZraN32PMvdJ/e2hMfMngJ8hNa3Ouigh7L0r6Xkh7v/FHiKmT0J2GBm16P8SLQl5McHgI+7e2W3c5QfCbaE/BgEDnP3ETN7BrApmmuUHwm1hNzIAc+htT9qGviemd0GTO7hXOVGAgUpkM0sTytBr3T3jdHhnWZ2sLsPRrcwhtrOPxT4JnC6u98fHd5Kq2/yPPVQToil5sc8d99iZlO01qorPxJqifnxTOBVZnYhsC/QNLNq9H7lRwItJT+iu5Gz0evbzOx+WlcONX4k0BLHjq3ATe6+K3rvdcAxwBUoN1IhRBcLAy4Dtrj7xW1/ugY4I3p9BvCt6Px9gc3Au939R/MnR7dCymb2rOgzT59/j/SvZeTH480sF70+HPhT4LfKj2Raan64+3Pd/Qh3PwL4BHC+u39K+ZFMyxg/1ptZNnr9x8ATgAeUH8mz1Nyg1X72KDNbFc0xxwN3KzfSw1przHv4PzR7DvBDWutFm9Hh99BaC/RfwGHAg8Cp7j5qZu8F3g3c2/YxL3T3ITP7C+DLQInWTtJ/8l7/g2RFLSM/Xk9r53EtOv+D7r4p+izlR8IsNT92e+/7gYq7fyz6XfmRMMsYP14JfBCoAw3gfe5+bfRZyo8EWc7YYWavo1V/OHCdu58bHVdupEDPC2QRERERkTgL/ahpEREREZFYUYEsIiIiItJGBbKIiIiISBsVyCIiIiIibVQgi4iIiIi0UYEsIiIiItJGBbKIiIiISBsVyCIiKTb/JEoREXmYCmQRkZgys3PM7Ordjn3SzD5hZn9kZpeZ2aCZbTOzD7U9NvlIM/u+mY2Y2S4zu9LM9m37jN+a2bvM7A5gSkWyiMgjqUAWEYmvK4CT54vbqJB9NXA5sIHWI5L/BDgaeCHwxuh9BlwAPBZ4EvA44P27ffZrgb8B9nX3elf/FSIifUYFsohITLn7IHAzcGp06GRgF7AVeBFwlrtPufsQ8HHgNdH77nP3G9191t2HgYuB43f7+Evc/SF3n+nFv0VEpJ/otpqISLxtAN4KfB54Ha2rx4cDeWDQzObPywAPAZjZY4BLgOcC+0R/G9vtcx/qduAiIv1KV5BFROJtE3CUmT0VOAW4klZxOwsc4O77Rj9r3f0p0XsuABw4yt3X0iqsbbfP9d6ELyLSf1Qgi4jEmLtXgW8AVwE/c/cHo6UX3wEuMrO1ZpaJNubNL6PYB6gA42Z2CHBOkOBFRPqUCmQRkfjbADyN1vKKeacDBeBuWssnvgEcHP3tA8AxwASwGdjYs0hFRBLA3HWXTUQkzszsMOAe4CB3nwwdj4hI0ukKsohIjJlZBjgb+KqKYxGR3lAXCxGRmDKz1cBO4He0WryJiEgPaImFiIiIiEgbLbEQEREREWmjAllEREREpI0KZBERERGRNiqQRURERETaqEAWEREREWnz/4+qpoXIlM7hAAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "\n", "fig.set_size_inches((10,6))\n", "\n", "build_years.plot(kind=\"area\",stacked=True,color=colors,ax=ax,linewidth=0)\n", "ax.set_xlabel(\"year\")\n", "ax.set_ylabel(\"new capacity built [GW]\")\n", "\n", "fig.tight_layout()\n", "\n", "fig.savefig(\"{}-new_capacity.pdf\".format(scenario),transparent=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plotting the development of the costs of the technology over time:" ] }, { "cell_type": "code", "execution_count": 222, "metadata": {}, "outputs": [], "source": [ "costs = pd.DataFrame(0.,index=years,columns=techs)\n", "for year in years:\n", " for tech in techs:\n", " costs.at[year,tech] = model.fixed_costs[tech,year].value/8760. + parameters.at[\"marginal cost\",tech]" ] }, { "cell_type": "code", "execution_count": 223, "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "\n", "fig.set_size_inches((10,6))\n", "\n", "costs.plot(color=colors,ax=ax,linewidth=3)\n", "ax.set_xlabel(\"year\")\n", "ax.set_ylabel(\"LCOE [EUR/MWh]\")\n", "ax.set_ylim([0,160])\n", "\n", "\n", "fig.tight_layout()\n", "\n", "fig.savefig(\"{}-lcoe.pdf\".format(scenario),transparent=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.7" } }, "nbformat": 4, "nbformat_minor": 2 }