{ "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": 1, "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": 2, "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": 2, "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": 70, "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": 71, "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": 72, "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": 73, "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": 74, "metadata": {}, "outputs": [], "source": [ "model.generators_built = Var(techs, years, within=NonNegativeReals)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$c_{s,a}$$" ] }, { "cell_type": "code", "execution_count": 75, "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": 76, "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": 77, "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": 78, "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": 79, "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": 80, "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": 81, "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": 82, "metadata": {}, "outputs": [], "source": [ "opt = SolverFactory(\"ipopt\")" ] }, { "cell_type": "code", "execution_count": 83, "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": 84, "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": 85, "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": 86, "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": 87, "metadata": {}, "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", "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": 88, "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": 89, "metadata": {}, "outputs": [ { "data": { "image/png": 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kcTzrwJz1YNbuMMbg8ssvx2te8xpce+21AICf/vSn+MY3voHJyUnccccd8Pl8GB8ff8IymJtvvhmDg4O4+uqr8eEPfxjXXHNNXe9nc3IMcIlFQ5JJ7zyhhuxxLefqDHzFO9157zYhMHWvQ+Z41oE568Gs3XH48GEEg0G87nWvW9z2rGc9C52dnRgZGVlcM7x9+/YVn3R4/vnnLz5Wuh7sg+xBrfz0QaqfWzkH8t+BoObKe7eTepdZcDzrwJz1YNbueOCBB3D22Weftv3SSy/FLbfcgj179uBd73oX7r333hW//jvf+Q6e+cxn2i6zbpwgN4C/vtHBrZz59LzmqLcXMsezDsxZD2btLdu3b8edd96Jd7/73fD5fLj00ktx++23L+5/xStegT179mBubg5//ud/XvdxbS+x4BrkBkxNTWHnzp1ul0GWuZKzKcBfuM3Z92xT9V5B5njWgTnrwazdsXv3btx8880r7guHw7j44otx8cUXIxqN4tvf/jYuvPBCAL9ag7xR5XIZ4XB4UzWvhVeQG9Dd3e12CeQAN3L2F26HmJzj79uW6nzcNMezDsxZD2btjj179qBYLOK6665b3Hb06FF8//vfx+TkJACgVqvh/vvvx44dOzb9frbb+fEKMpGH+HNs79YsG3ncNBFRO6m3LVsziQj+/d//He985zvxkY98BOFwGGeccQb27t2Ld73rXSgWiwCA5zznObjqqqscr2+jOEFuQCaTaejXAdRaHM/ZGPjz33Hu/dpcvUssOJ51YM56MGv3jI6O4nOf+9xp2//kT/5kxdffc889Db+X7T7IXGLRgHqbWFNrczpnX+ku+GrTjr5nO5M6l1hwPOvAnPVg1joEg0Grx+cEuQHxeNztEsgBTufM7hVNVucSC45nHZizHsxah0qlYvX4nCA3wPbzv8kbnM6Zj5durnqXWHA868Cc9WDW1AycIDdgYGDA7RLIAU7mLOWH4Sv/3LH306DeJRYczzowZz00Zi0iKJVKbpfhqI12sSiVShv64Yk36TUgHo+zx6ICTubM5RXNV28XC45nHZizHhqz7u7uRiaTQaFQcLsUx2SzWXR1ddX9ehHZUAtATpAb0Nvb63YJ5AAnc+byCgvqXGLB8awDc9ZDY9Yigp6eHrfLcFS1WrWaNZdYNKBarbpdAjnAsZyrSfiKdzrzXorUewWZ41kH5qwHs9bBds6cIDcgm826XQI5wKmcA/nvQsBv6M0mJguY9dfkcTzrwJz1YNY62M6ZE+QGjIyMuF0COcCpnLn+2KI6lllwPOvAnPVg1jrYzpkT5AbEYjG3SyAHOJKzKcJfuNX++yhVzzILjmcdmLMezFoH2zlzgtwA209vIW9wImd/4fD8UgCyop5WbxzPOjBnPZi1DnySngf19fW5XQI5wImc/exeYVU9DwvheNaBOevBrHWwnbMjE2QR+ayITIvIfUu2fUlEfrLw51ER+cnC9jNFJL9k3yedqHEjZmZm3C6BHGA9Z2Pgzx+0+x7a1TFB5njWgTnrwax1sJ2zU32Q/w3ARwF8/tQGY8yrT30sIh8EsHSx4DFjzLMdqm3D+NOpDtZ/Oi2OwVflWjmb6lmDzPGsA3PWg1nrYDtnRybIxphDInLmSvtk/rl/fwDgIidqaQZtj3PUynbOgcwXrB6f6luDzPGsA3PWg1nrYP3/0VaPXp8XApgyxvxyybYni8jdANIA3mWMObzSF05PT2P//v0IBAKoVqvYt28fDhw4gFgshq6uLvj9fqTTaUSjUSSTSRhjEI1GMTU1tfi4wUwmg+HhYcTjcYgIBgYGEI/H0dvbi2q1imw2i5GREcRiMQSDQfT19SEWiyEUCqFUKiGfzy/uD4VC6OnpQSKRQH9/P/L5PAqFwuL+jo4ORCIRzM7OYnBwEHNzcyiVSov7I5EIQqEQUqkUhoaGkEqlUC6XF/fbPKeZmRn09fXxnJacUywWQ2dnp51zMnkUJ0PY0bUVk9k98EkZ0cgRTGb3YEv4QVRMBJnSTuzoPogTmUsQ9M9hIHwfpnLnY6DjPhSqA8iVty3uD/uT6A0fQzx3HgYjdyNX3o58Zevi/khgGp3BcSTy5yDaeQTp4i4UqwOL+zuDE+jwJ5EsnIXhzjEki2ehXO1Z3N8dOo6A5HGyuBujXYcQz5+HmglitOsQxjMXoTd0DACQLu3C9u5bPXNOAZ8fMyePr5lTLBZDd3e3p/7tteN4cvucYrHY4vfvdjmndsypGedUrVaRz+fb6pzaMafNntOpnDd7TqsRY0zdM9nNWLiC/C1jzFnLtn8CwEPGmA8ufB4G0G2MSYjI/wJwI4BfN8aklx9zbGzM7N6923rtyxWLRYTDYcffl5xlM+fA3OcQnv0rK8emXyl3vRqlwY+t+RqOZx2Ysx7MWodm5Xz06NG79u7de+7y7a52sRCRAIB9AL50apsxpmiMSSx8fBeAYwB+zZ0KV8YeizrYzDmQ/Q9rx6Zfker6Syw4nnVgznowax3avQ/ySwA8aIw5cWqDiERFxL/w8VMAPA3Awy7Vt6JQKOR2CeQAWzn7SvfBX/qJlWPTE4lZ/yY9jmcdmLMezFoH2zk71ebtegBjAJ4uIidEZP/CrssAXL/s5XsA3Csi9wD4KoA3GGOSTtRZr56eHrdLIAfYyjmQ4dVjp9TTB5njWQfmrAez1sF2zk51sXjNKtv/eIVtXwPwNds1bUYikVhc9E3ty0rOpohA7qvNPSatro4JMsezDsxZD2atg+2c3V5i0ZL6+/vdLoEcYCNnf+4/62o9Rs1RTx9kjmcdmLMezFoH2zlzgtyAfD7vdgnkABs5B9n72FFiCkBt7Rw5nnVgznowax1s58wJcgMKhYLbJZADmp2zVI7DV1yxpTdZtN46ZI5nHZizHsxaB9s5c4LcgJGREbdLIAc0O+dA5osQONN3nJZYZ4LM8awDc9aDWetgO2dOkBvAHos6NDVnU0Mge0Pzjkd1W+8KMsezDsxZD2atQ7v3QW5JHR0dbpdADmhmzv7C/8BXHW/a8ah+602QOZ51YM56MGsdbOfMCXIDIpGI2yWQA5qZc4A357lmvQkyx7MOzFkPZq2D7Zw5QW7A7CzbdGnQtJyrM/Dnb2nOsWjj1pkgczzrwJz1YNY62M6ZE+QGDA4Oul0COaBZOQeyX4ag3JRj0catdwWZ41kH5qwHs9bBds6cIDdgbm7O7RLIAc3KOZjlo6XdtN7DQjiedWDOejBrHWznzAlyA0qlktslkAOakbOveAS+8s+bUA01ar0nF3I868Cc9WDWOtjOmRPkBrDHog7NyJk353nAOleQOZ51YM56MGsd2AfZg9hjUYdN51zLIJC7sTnFUMPYB5kA5qwJs9aBfZA9iC1kdNhszoHcTRCTbVI11Kj1llhwPOvAnPVg1jqwzZsHhUIht0sgB2w250CGN+d5wXo36XE868Cc9WDWOtjOmRPkBqRSa/8Pl9rDZnKW8i/gL93ZxGqoYessseB41oE568GsdbCdMyfIDRgaGnK7BHLAZnIO8uY8zxBUgFpm1f0czzowZz2YtQ62c+YEuQH86VSHhnM2ZQSyX25uMbQpa92ox/GsA3PWg1nrwCvIHlQu86loGjSasz9/EFKbaXI1tBlrTZA5nnVgznowax1s58wJcgPYY1GHRnNm72MPWmOCzPGsA3PWg1nrwD7IHsQeizo0krNUJuAv3GqhGtqMta4gczzrwJz1YNY6sA+yB3V1dbldAjmgkZwD2eshqFmohjZjrQkyx7MOzFkPZq2D7Zw5QW6A3+93uwRywIZzNgaBzBftFEObs8YEmeNZB+asB7PWwXbOnCA3IJ1Ou10COWCjOfsKt8NXPW6pGtqMtR4WwvGsA3PWg1nrYDtnTpAbEI1G3S6BHLDRnEPpD1uqhDZrrcdNczzrwJz1YNY62M6ZE+QGJJNJt0sgB2wkZ19hDP7i9y1WQ5ux1hpkjmcdmLMezFoH2zlzgtwAY4zbJZADNpJzKP1Bi5XQpq2xxILjWQfmrAez1sF2zpwgN4C/vtGh3px9xR/DX7jNbjG0KVxiQcxZD2atA5dYeNDU1JTbJZAD6s05mPpHy5XQZq11kx7Hsw7MWQ9mrYPtnDlBbkB3d7fbJZAD6snZV7wbgcJ/O1ANbcZaa5A5nnVgznowax1s58wJMtEmBNO8etwSaimA6xKJiKhOnCA3IJPJuF0COWC9nH2lnyKQP+hQNbQZghpgVu6ZyfGsA3PWg1nrYDtnTpAbMDw87HYJ5ID1cg6m2Lmilay2zILjWQfmrAez1sF2zpwgNyAej7tdAjlgrZyl9AD8+f90sBrarNUmyBzPOjBnPZi1DrZz5gS5ASLidgnkgLVyDqX/EQKuaW0pq0yQOZ51YM56MGsdbOfMCXIDBgYG3C6BHLBazlL+Bfy5bzpcDW3WaleQOZ51YM56MGsdbOfsyARZRD4rItMict+Sbe8VkXER+cnCn99Zsu8dIvKQiPxcRC5xosaN4K9vdFgt51Dqg/M3fVFL4RIL3ZizHsxah3ZZYvFvAH5rhe0fNsY8e+HPtwFARJ4J4DIAv77wNR8XEb9Dddalt7fX7RLIASvlLOVj8OdudKEa2iyprjxB5njWgTnrwax1sJ2zIxNkY8whAMk6X/5KADcYY4rGmEcAPATgudaKa0C1WnW7BHLASjkH0x+GgPm3pFWuIHM868Cc9WDWOtjOOWD16Ot7o4hcAeDHAP7CGDMLYDuAHy55zYmFbaeZnp7G/v37EQgEUK1WsW/fPhw4cACxWAxdXV3w+/1Ip9OIRqNIJpMwxiAajWJqamrxCSyZTAbDw8OIx+MQEQwMDCAej6O3txfVahXZbBYjIyOIxWIIBoPo6+vDxMQEAoEASqUS8vn84v5QKISenh4kEgn09/cjn8+jUCgs7u/o6EAkEsHs7CwGBwcxNzeHUqm0uD8SiSAUCiGVSmFoaAipVArlcnlxv81zmpmZQV9fH89pyTlNTEygo6Nj8ZxGoxVMjPvQFdyDDn8SycJZGO4cQ7J4FsrVHuzoPogTmUvQHTqOgORxsrgbo12HEM+fh5oJYrTrEMYzF6E3dAwAkC7twvbuWzGZ3QOflBGNHMFkdg+2hB9ExUSQKe1cPGbQP4eB8H2Yyp2PgY77UKgOIFfetrg/7E+iN3wM8dx5GIzcjVx5O/KVrYv7I4FpdAbHkcifg2jnEaSLu1CsDizu7wxOtP05xWcGUUgfP+3f3sTEBDo7Oz31b68dx5Pb5zQxMYGenp62Oqd2zKkZ53TqOO10Tu2Y02bP6VQdmz2n1Yhx6OlSInImgG8ZY85a+HwYwAwAA+BqAKPGmCtF5GMAxowxX1h43bUAvm2M+dryY46NjZndu3c7Uv9SxWIR4XDY8fclZy3POZR4M4LZ/3CxItqMSuQVKEY/e9p2jmcdmLMezFqHZuV89OjRu/bu3Xvu8u2udbEwxkwZY6rGmBqAf8WvllGcAPCkJS/dAWDC6frWEovF3C6BHLA0Z6k8jkD2yy5WQ5sltdkVt3M868Cc9WDWOtjO2bUJsoiMLvn0VQBOdbi4GcBlIhIWkScDeBqAO52uby3BYNDtEsgBS3MOpj8CQdnFamjTVlmDzPGsA3PWg1nrYDtnR9Ygi8j1AF4EYEhETgD4WwAvEpFnY36JxaMA/hQAjDE/E5EvA7gfQAXAAWOMp1bc9/X1uV0COeBUzlKZQCDzRZeroc2SWmrF7RzPOjBnPZi1DrZzdmSCbIx5zQqbr13j9e8H8H57FW3OzMwMurq63C6DLDuVczD9zxCU3C6HNmm1JRYczzowZz2YtQ62c+aT9BrAn0516Ovrg1RjCGS/4HYp1AwmA6zwyyiOZx2Ysx7MWgfbOXOC3IBSiVcTNSiVSgim/wViCm6XQk0gMMAKyyw4nnVgznowax1s58wJcgPy+bzbJZAD8rkEApnPu10GNdFKj5vmeNaBOevBrHWwnTMnyA0YGRlxuwRywBmdX4EYfqNtJytNkDmedWDOejBrHWznzAlyA9hjsf35Sj/FZGzG7TKoyVaaIHM868Cc9WDWOrRtH+RWFgqF3C6BbDI1hJJ/gQ4/J8htZ4UJMsezDsxZD2atg+2cOUFuQE9Pj9slkEUcIPxRAAAgAElEQVSBzGfhLx1Fb/iY26VQk610BZnjWQfmrAez1sF2zpwgNyCRSLhdAlkilUmETs634I7nznO5Gmq2lSbIHM86MGc9mLUOtnPmBLkB/f39bpdAloRm3wkxcwCAwcjdLldDzbbSBJnjWQfmrAez1sF2zpwgN4AtZNqTP38Qgfw3Fz/Plbe7WA1ZsUIfZI5nHZizHsxaB7Z586BCgQ+OaDu1LELJtz9hU76y1aViyJaVHjfN8awDc9aDWetgO2dOkBvAHovtJ5T6B/iqjz9h247ugy5VQ7awD7JezFkPZq0D+yB7EHssthdf6acIzH36tO0nMpe4UA1ZtcISC45nHZizHsxaB/ZB9qCOjg63S6BmWeh5LKictisSmHahILJppSUWHM86MGc9mLUOtnPmBLkBkUjE7RKoSU71PF5JZ3Dc4WrINlnhCjLHsw7MWQ9mrYPtnDlBbsDs7OlXoaj1LO15vJJE/hwHqyEniMkCpvyEbRzPOjBnPZi1DrZz5gS5AYODg26XQE2wtOfxSqKdRxyshhyzbJkFx7MOzFkPZq2D7Zw5QW7A3NzqkypqDct7Hq8kXdzlUDXkpOXLLDiedWDOejBrHWznzAlyA0qlktsl0Gas0PN4JcXqgAPFkNOWt3rjeNaBOevBrHWwnTMnyA1gj8XWtlLP45WwD3J7Wj5B5njWgTnrwax1YB9kD2KPxda1Ws/jlbAPcptatgaZ41kH5qwHs9aBfZA9iC1kWtQaPY9X0hmcsFwQuWH5GmSOZx2Ysx7MWge2efOgUCjkdgnUgEDmc6v2PF5Jhz9psRpyy/IlFhzPOjBnPZi1DrZz5gS5AanU6Q8bIG+T0s8QOvm+DX1NsnCWpWrITcsnyBzPOjBnPZi1DrZz5gS5AUNDQ26XQBtRO4mOmdfOPyRiA4Y7xywVRK5aNkHmeNaBOevBrHWwnTMnyA3gT6ctxNQQnvkT+CqPbvhLk0VeQW5HvIKsE3PWg1nrwCvIHlQul9d/EXlCMPV+BAq3NvS15WpPk6shL1h+kx7Hsw7MWQ9mrYPtnDlBbgB7LLYGf+5mhNIfafjr2Qe5PcmyNm8czzowZz2YtQ7sg+xB7LHofVJ6AOHEmzZ1DPZBblPLllhwPOvAnPVg1jqwD7IHdXV1uV0CraWWQsfMFRu+KW+57tDxJhVEXrJ8iQXHsw7MWQ9mrYPtnDlBboDf73e7BFqNqSE886fwVR7Z9KECkm9CQeQ1YgpA7VfZcjzrwJz1YNY62M6ZE+QGpNNpt0ugVQRTf49A4b+bcqyTxd1NOQ55z9JOFhzPOjBnPZi1DrZz5gS5AdFo1O0SaAX+3LcQTP9T04432nWoaccij1myzILjWQfmrAez1sF2zpwgNyCZ5COIvUbKP0c4cQAC07RjxvPnNe1Y5C1LryBzPOvAnPVg1jrYzpkT5AYY07xJGDVBLY2O+OZvyjvtsCbY1OORdyxt9cbxrANz1oNZ62A758BqO0TkyjqPUTHGfL5J9bQE/vrGQ4xZuCnvWNMPzSUW7Uu4xEId5qwHs9bBzSUWnwZweR1/Pr7em4jIZ0VkWkTuW7Lt/4rIgyJyr4h8Q0S2LGw/U0TyIvKThT+fbPjsLJmamnK7BFoQTF2DQOG7Vo49nrnIynHJA5YsseB41oE568GsdbCd86pXkAHkjTEvXu8AIjK73msA/BuAjwJYeqX5uwDeYYypiMg/AHgHgLct7DtmjHl2Hcd1RXd3t9slEAB/7tsIpj9k7fi9oeZflSZvWLrEguNZB+asB7PWwXbOa11Bfk6dx1j3TiZjzCEAyWXbvmOMqSx8+kMAO+p8PyL4ikcRTvzvpt6UR3osf1gIERHRUqteQTbG/FJEthpjptc6gDHmoSbUcSWALy35/MkicjeANIB3GWMOr/RF09PT2L9/PwKBAKrVKvbt24cDBw4gFouhq6sLfr8f6XQa0WgUyWQSxhhEo1FMTU0t/uSRyWQwPDyMeDwOEcHAwADi8Th6e3tRrVaRzWYxMjKCWCyGYDCIvr4+jI+Pw+/3o1QqIZ/PL+4PhULo6elBIpFAf38/8vk8CoXC4v6Ojg5EIhHMzs5icHAQc3NzKJVKi/sjkQhCoRBSqRSGhoaQSqVQLpcX99s8p5mZGfT19bXEOflrJzBafSuOZV6GLeEHUTERZEo7saP7IE5kLkHQP4eB8H2Yyp2PgY77UKgOIFfetrg/7E+iN3wM8dx5GIzcjVx5O/KVrYv7I4FpdAbH8Wj6lQgHkkgXd6FYHVjc3xmcQIc/iWThLAx3jiFZPAvlas/i/u7QcQQkj5PF3RjtOoR4/jzUTBCjXYcwnrlo8cp0urQL27tvxWR2D3xSRjRyBJPZPVbPKZE/B9HOI+rP6bGpMxCuzcDv92N8fByRSETteNJyThMTE+ju7m6rc2rHnJpxTtVqFZlMpq3OqR1z2uw5ncp5s+e0GlnrLkARqQH4BYBDC39uN8Y8vuoXrEFEzgTwLWPMWcu2/w2AcwHsM8YYEQkD6DbGJETkfwG4EcCvG2NO6wg9NjZmdu92/mEOhUIBHR0djr8vAb7CD9ARf03TO1asJF/ZikhgzZ8PqUVVOl6C4tYbAHA8a8Gc9WDWOjQr56NHj961d+/ec5dvX6/N2w4A7wVQxvz64EdF5BERuU5E9ovI0zZTlIi8FsDLAPx/ZmGmbowpGmMSCx/fBeAYgF/bzPs021o/cZA9vsIhdMQvc2RyDACT2T2OvA85b2kfZI5nHZizHsxaB9s5r3WTHowxEwBuWPgDEekH8EIAewC8H0AUQEMPwxaR38L8pPtCY0xuyfYogKQxpioiTwHwNAAPN/IetoiI2yWo48/fivDMFRBTcOw9fVJ27L3IWUsnyBzPOjBnPZi1DrZzXnOCvJSInI35ifGFAF4AYBrA1+r82usBvAjAkIicAPC3mO9aEQbw3YWT/KEx5g0L7/E+EakAqAJ4gzHGU4/FGRgYcLsEVfz5gwjHr4Sg6Oj7RiNHHH0/cs7Sm/Q4nnVgznowax1s57zmBFlE/hLzE+JzATwE4A4AnwPwemPMybW+diljzGtW2HztKq/9GuqceLslHo9j586dbpehgj/3nwjPvB4C56/mTmb34KlbbnD8fckBy5ZYcDy3P+asB7PWwXbO611B/gCABwC8B8B3jTGPWqukhfT29rpdggr+7I0IJ94AQWX9F1uwJfygK+9L9gnKQC0D+Lo5npVgznowax1s57zeBHkH5pc8vBDAn4lIH+avIh8GcNgYc99aX9yuqtWq2yW0PX/2Kwgn3giBe/+tKybi2nuTfVJLwfi6OZ6VYM56MGsdbOe8ZhcLY8yEMeYGY8wBY8yzAJyN+Rv2dmF+7XDCanUelc0600VBq0DmeoQTB1ydHANApsRf0bWzU0/T43jWgTnrwax1sJ1zIzfpnfqzBcBdlurytJGREbdLaFuBzHUIJf/SE0/I29F90O0SyKaFdcgczzowZz2YtQ62c17zCrKI/KWIfFNEkgDGAOwDcD+APwSwxRjzfKvVeVQsFnO7hLYUmPuMZybHAHAic4nbJZBFpzpZcDzrwJz1YNY62M55vSvIL8H8E/T+AcCdxpiS1WpaRDAYdLuE9mKqCKb+AaH0h9yu5AmC/jm3SyCLTvVC5njWgTnrwax1sJ3zeg8K+S2r796i+vr63C6hbUh1CuGZP4G/+H23SznNQFjlPah6LKxB5njWgTnrwax1sJ3zekssfk1EDiz5/BYRuXXJn6dbrc6jZmZm3C6hLfgKh9Ax+WJPTo4BYCp3vtslkEWnllhwPOvAnPVg1jrYznnNCTKAtwPILPn8+QD+Y+HPzxb2q8OfTjfJ1BBM/V90TP8efLVpt6tZ1UAHryC3s1NLLDiedWDOejBrHWznvN4a5D0A3rLk86ox5loAEJEeAEdtFeZlpRKXYjesOoOOxBvgL9zmdiXrKlT5uNJ2dqrNG8ezDsxZD2atg+2c15sgbzXGpJd8fsWpD4wxcyIybKcsb8vn826X0JJ8hTGEE1fBV22NO4xz5W1ul0A2LSyx4HjWgTnrwax1sJ3zekss5kTkzFOfGGO+eepjEXkKnrj8Qg32WNwgYxBMfQQd05e2zOQYYB/kdifsg6wKc9aDWevgah9kAP8J4OpV9v2fhf3qsMfiBlRnEY7/IUKpq11/Mt5GsQ9yezs1QeZ41oE568GsdXC7D/J7APxARO4G8A0AMQCjAC4F0A/geVar86hQKOR2CS3BVzyC8Mzr4auOu11KQ8L+pNslkEWnJsgczzowZz2YtQ62c16vD3JMRM4F8FYAvw1gCEACwLcBfMgYk7BanUf19PS4XYK3mRKC6Y8jmPoHCMpuV9Ow3vAxt0sgm2opwBiOZyWYsx7MWgfbOa93BRnGmCSAdy38IQCJRALd3d1ul+FJ/twtCJ18D3yVh90uZdPiufPQF+IkuV0JaoCZQyIxy/GsAL9v68GsdbCd86prkEXkpfUcQEQubl45raG/v9/tEjxHyr9EePoP0DHzR20xOQaAwcjdbpdAlkltluNZCeasB7PWwXbOa92k99U6j/GlZhTSSthCZolaGqHZv0Fk8oUIFG51u5qmypW3u10CWSa1kxzPSjBnPZi1DrZzXmuJRbeIPLbO1wuAcBPraQmFQsHtEtxnaghkv4DQyb+D1NrzsZ75yla3SyDbaikUClvcroIcwO/bejBrHWznvNYE+cV1HqPWjEJaifYei77CDxGafSf85XvdLsUq9kFuf1KbxcjIb7pdBjlA+/dtTZi1DrZzXnWCbIy53eo7t7BYLIadO3e6XYbjpDKB0Mm/RSD3DbdLccSJzCV46pYb3C6DLJLaScTiOsezNlq/b2vErHWwnfO6XSzodB0dHW6X4KzqLIKZf0Uw/VGIybldjWMigWm3SyDLpJbSN56VYs56MGsdbOfMCXIDIpGI2yU4Qsq/RHDuUwhkv6xqYnxKZ7A1H3BCG1A7qWY8a8ec9WDWOtjOeb1HTdMKZmdn3S7BKl/hdoSnL0Nk8vkIZv5N5eQYABL5c9wugSyT6mzbj2eax5z1YNY62M65rivIIvIKAN82xlSsVtMiBgcH3S6h+UwRgexXEZz7FHzl+92uxhOinUfcLoEsk1qqPccznYY568GsdbCdc71XkK8GMCkiHxUR9bd8z83NuV1C81TjCKY+gM7xZyOcfDMnx0uki7vcLoEsk9rJ9hrPtCrmrAez1sF2znVdQTbGnC0iZwP4IwBfE5EsgH8H8AVjzKMW6/OkUqnkdgmbJqUHEJz7JALZr0JQdLscTypWB9wugWyrzbbFeKb1MWc9mLUOtnOu+yY9Y8w9AO4Rkb8GsBfABwH8HxH5PoBPAbjeGKOiJ3Kr9liU8iMI5G6GP3cj/OWful2O57EPcvuTWqplxzNtDHPWg1nrYDvnDd2kJyK7ALwHwCcAdCx8/K8A3oj6H03d8mKxmNsl1E3KDyOY+id0TL4YnZPnIZS6mpPjOp3IXOJ2CWSZ1E621HimxjFnPZi1DrZzrvcmvQMALgfwVABfBnC5MeaHS/Z/DYCaprFebyEj5YcXrhTfxMnwJnQGJ9wugWwzc4hE2DNVA69/36bmYdY62M653iUWv435JRU3GWNOW/RhjMmJyL6mVuZhoVDI7RJOI+VjC5PimzkpbpIOf9LtEsgygUEowPWKGnjx+zbZwax1sJ1zvUssbjPGfGX55FhE3nrqY2PMd5pamYelUim3S4BUxuHPfhmhxFsQmXguOid/E6HU+zk5bqJk4Sy3SyAHpFMJt0sgB3jh+zY5g1nrYDvneq8gvwfAP66w/V0APtS8clrD0NCQ4+8plePwF34AX/EH839XjztegzbDnWNul0AOiG5he3cN3Pi+Te5g1jrYznnNCbKIXHTqdSLyYgCyZPdTAKhsNphKpdDV1WX1PaT8EPzFH8BfGIOv+AP4qnzssdOSxbPQE+IPIu0ulc4g0u92FWSbE9+3yRuYtQ62c17vCvK1C3+HAXx2yXYDIAbgTTaK8rpyudy8g9Xy8JUfWPhzP3yl++ErPwCpzTTvPagh5WqP2yWQA8pl9gHXoKnft8nTmLUOtnNec4JsjHkyAIjI540xV1itpIU01HvPGEjlkYWJ8M/gK83/LZVHIVDRPrrlsA+yDtsG2BJKA/bG1YNZ62A753qfpLepybGIfBbAywBMG2POWtg2AOBLAM4E8CiAPzDGzC7seweA/QCqAP7MGOOpmUosFsPOnTtP31HLQ6qPw1d5DFJ5DFJ5HL7q/Me+8i8gJut8sdSwE5lL8NQtN7hdBlk2OdOBHXxoYttb9fs2tR1mrYPtnFedIIvIA8aYZyx8/Djml1WcxhhzRh3v828APgrg80u2vR3A94wx14jI2xc+f5uIPBPAZQB+HcA2AP8tIr9mjKnW8T6O6A7FEJj73pIJ8OPwVR6H1OJul0ZN1M31xyp0hzluNeCaVD2YtQ62c17rCvJVSz7+o828iTHmkIicuWzzKwG8aOHj6wDcBuBtC9tvMMYUATwiIg8BeC4Az7QU6Ch8CeHy59d/IbW0gOTdLoEc4Efa7RLIAX6/3+0SyCHMWgfbOa86QTbG3LHk49stvPewMWZy4fiTIrJ1Yft2AD9c8roTC9tOMz09jf379yMQCKBarWLfvn04cOAAYrEYurq64Pf7kU6nEY1GkUwmYYxBNBrF1NQUuru7AQCZTAbDw8OIx+MQEQwMDCAej6O3txfVahXZbBYjIyOIxWIIBoPo6+vDw4nzIX2PoFTtQ7Y8jB3dh3Ei80KE/Sn0ho4jnv8NDHbcj1xlGPnK4OL+SCCBzsAUEoVnIhq5F+nSThSrfYv7u4JTCPlTmC38GoY7jyJZeDrKta7F/d3BcQR8RZwsPgUjXUcwk38WaiaIka47MZE5H72hxwAA6dIZ2NY9hlj2ufBJGUORnyKWPQ9bwg+jUgsjU96+eMygL4uBjp9jKvcc9Hf8gue05JyOpS+F3x9sq3Nqx5yWn9OTug9ifO4CdAYn0OFPIlk4C8OdY0gWz0K52oMd3QdxInMJukPHEZA8js/twq6B40jO5lAzBlsHuzEVn0N3d3jhe0QRw9EeTCcy8IlgoL8T8ZkMens6UK3WkM2VMDLci9hUGoGgH1t6OzCTyKKvN4JSuYJ8vry4PxTyo7urA8nZLPq3dCKfL6FQrCzu7wgHEImEMHsyh4H+LmSyBZRK1cX9kUgQoWAAqXQeQ4NdOJkuoFL+1f6uzhD8fh/ScwVEh7p5TkvOaTyWQld4uK3OqR1zasY5Vao1zJ2caKtzaseclp9TMg0MDkYxNzeHUqm0OPeKRCIIhUJIpVIYGhpCKpVCuVxGtVpFOp3e9HxvNWLMiisnnvgika8D+LAx5vCSbS8E8GZjzO+te4D5158J4FtL1iCfNMZsWbJ/1hjTLyIfAzBmjPnCwvZrAXzbGPO15cccGxszu3fvruftm+qGG27AL37xC8ffl5zV2dmJXC7ndhm0Qa/93TieGb2+7tdny9vQxceKtz3mrAezbk250TGY4NPqf30uh87Ozk2/79GjR+/au3fvucu31/skvQsB/GDZtjEAL95ETVMiMgoAC39PL2w/AeBJS163A4Cn/qXzOe86DA4Oul0CNSBbDG/o9fH8eZYqIS9hznowax2SyaTV49c7QS4AWL4auhvAZprQ3QzgtQsfvxbATUu2XyYiYRF5MoCnAbhzE+/TdCKy/ouo5fl89Q4P8pJsPrih19fMxl5PrYk568GsdahnBcRm1DsDOAjgUyLSCwALf38UwC31fLGIXI/5K85PF5ETIrIfwDUALhaRXwK4eOFzGGN+BuDLAO5fOP4BL3WwAIBslu3aNJiamnK7BGrAXG5jN26Mdh2yVAl5CXPWg1nrEI1GrR6/3gnyXwDoBZAUkWkASQB9AN5SzxcbY15jjBk1xgSNMTuMMdcaYxLGmL3GmKct/J1c8vr3G2N2GWOeboz5r42elG2nFnxTexsdHXW7BGrAXK6u9u6LxjMXWaqEvIQ568GsdbB9EaveB4XMAvjdhbXCOwA8boxR+/ipYpGPptUgnWb7r1Z0cm5jS6B6Q8csVUJewpz1YNY62L5YuaFLLQvt2GIARER8C9v4nGQi8ozUBifIREREy9W1xEJEtonIN0QkAaCC+ZvzTv1RJxze2F3y1Jp6e3vdLoEacHLOwNS9egxIl3ZZrIa8gjnrwax1yGQyVo9f7/9FPgWgBGAvgAyA52C+28QbLNXlabZDIW+YnJx0uwRqiMBI/T/cbO++1WIt5BXMWQ9mrcPw8LDV49c7QX4+gCuNMT8BYIwx9wDYj/mb99Thc951sD34yJ4q6p8gT2b3WKyEvII568GsdVjrKXjNUO8EuYr5pRUAcFJEogCyWOUR0O3Odu898oZajcvrW1XV9NT9Wp+oXCmmDnPWg1nrYPuZFPVOkH8E4HcWPj4I4EsAvg7gxzaK8rp8Pu92CeSARCLhdgnUoHKt/rubo5EjFishr2DOejBrHQYGBqwev94J8uUAbl/4+C0A/gfAfQD+0EZRXsclFjpwiUXrKlXrH6P8dawOzFkPZq2D7SUW9fZBPrnk4zyAq61V1ALYB1mHVCrldgnUoGI5AtT5tNkt4QftFkOewJz1YNY62O40VW+bt5CIvE9Efiki2YW/rxaRDqvVeZTtdS/kDX7/xh5ZTN6RL9f/raliIhYrIa9gznowax2q1arV49e7xOITAC4C8GcAzlv4+0IAH7dUl6eFQiG3SyAH8JHirStfrH+CnCnttFgJeQVz1oNZ65DNZq0ev94n6V0KYNeSpRb3i8iPADwE4EorlXnY3Nyc2yWQA8bHx90ugRqUK9b/Q+yO7oMWKyGvYM56MGsdRkZGrB6/3ivIMQCdy7ZFAKh8kkJPT/0tpKh1bd+usothW8jk6v3ZHziRucRiJeQVzFkPZq1DLBazevx6/y/y7wBuEZF/AXACwJMAHADweRG56NSLjDEqHl/D/rg6lMvspdmq0huYIAf9/I2QBsxZD2atQzBY553YDar3/yJ/uvD3O5dtfwN+9bhpA+ApzSjK69gHWYfZ2Vm3S6AGpbP132A5EL7PYiXkFcxZD2atQ19fn9Xj19vm7clWq2gx7IOsw9atW/HII4+4XQY1IJ2pd/UYMJU7Hz2h4xarIS9gznowax1mZmaszsfq/78ILSoUCm6XQA7gFeTWNZuu/7UDHbzapAFz1oNZ6+CJK8gi0gvgvZhv7TYEYLERsDHmDCuVeRj74+oQDofdLoEalMkBBgEIKuu+tlC1+7hS8gbmrAez1qFUKlk9fr1XkD8O4DkA3gdgAMCbADwG4MOW6vI02wvDyRs6O5c3bqFWYqS+qwu58jbLlZAXMGc9mLUOtu8Hq/cmvZcCeIYxJiEiVWPMTSLyYwDfhMJJMvsg68A+yK2tih74kFj3deyZqgNz1oNZ6+CVPsg+AKmFjzMisgXzPZCfaqUqj2MfZB3YB7m1VUxvXa9jz1QdmLMezFoHr/RBvgfz64+/B+AwgI8ByAD4haW6PM3287/JG4rFotsl0CaUq12I1HG7QNiftF8MuY4568GsdQiF6n9iaiPqvYJ8FYBHFz7+MwB5AFsAXGGhJs/jxEkHLqVpbaVqfWvIe8PHLFdCXsCc9WDWOtj+bX5dE2RjzMPGmGMLH8eNMa83xrzaGHO/1eo8ijdv6TA0NOR2CbQJhXKkrtfFc+dZroS8gDnrwax1SCTWv8dkM+qaIIvIP4vI85dte76I/JOdsryNT9LTwfbgI7vqnSAPRu62XAl5AXPWg1nr0N/fb/X49S6xeA2AHy/bdheAP2xuOa2Bbd504G8KWluuWF8f61yZN2NqwJz1YNY62L5YWe8E2azwWv8Gvr6tBAL13ttIrSwSqe8KJHlTtlDfD7L5ylbLlZAXMGc9mLUOtp9qXO8E9zCA/19EfACw8Pd7F7arw5u3dGAf5NaWydU3QWbPVB2Ysx7MWgev9EF+M4CXAJgUkTsBTAC4GPNP1FOHfZB1YB/k1jaXq+83PeyZqgNz1oNZ6+CJPsjGmBMi8hwAzwXwJACPA7jTGFOzWZxXVSoVt0sgB/BmzNaWztbRBBlAJDBtuRLyAuasB7PWoaOjw+rx615MuzAZ/uHCH9XK5bLbJZADcrmc2yXQJqTqXAnVGeRSGg2Ysx7MWgfb9wmpvMlus3jzlg6Dg4Nul0CbkEzX97pE/hy7hZAnMGc9mLUOs7OzVo/PCXIDeGVRh5mZGbdLoE0oFAUG67d6i3YecaAachtz1oNZ62D7IhYnyA0Ih+vrr0qtjTdjtr6a9K77mnRxlwOVkNuYsx7MWgfbHcU4QW6A31/fzT/U2viDUOurYv0JcrE64EAl5DbmrAez1qFUKlk9PifIDWAfZB3YB7n1Vcz6vwVgz1QdmLMezFoHr/RBtkJEni4iP1nyJy0ibxGR94rI+JLtv+NmncvxV+86sA9y6ytXu9Z9DXum6sCc9WDWOniiD7ItxpifA3g2AIiIH8A4gG8AeB2ADxtj/tHF8lbFNm868GbM1lesdALrrIjqDE44Uwy5ijnrwax10NTmbS+AY8aY424Xsp5qtep2CeSAYrHodgm0ScVK57qv6fAnHaiE3Mac9WDWOoRCIavH99IE+TIA1y/5/I0icq+IfFZE+t0qaiW2n95C3tDf76l/dtSAfHH9Gy2ThbMcqITcxpz1YNY6pFIpq8d3dYnFKSISAvAKAO9Y2PQJAFcDMAt/fxDAlcu/bnp6Gvv370cgEEC1WsW+fftw4MABxGIxdHV1we/3I51OIxqNIplMwhiDaDSKqakpdHd3AwAymQyGh4cRj8chIhgYGEA8Hkdvby+q1Sqy2SxGRkYQi8UQDAbR19cHv9+PLVu2IBwOo7OzE+Pj49i+fTuKxSLm5uYwNDSERCKBzs5ORHvxpM8AACAASURBVCKRxf35fB65XA6Dg4OYmZlBT08PwuHw4v5cLodisYj+/n5MT0+jv78fwWBwcX8mk0G1WkVfXx+mpqYwODgIn8+HqakpjI6OIp2efzJCb28vJicnMTw8jFqthkQigeHhYaRSKfj9fnR3dy8es1wuY3Z2Flu3bsXs7CzPack5BYNB9PT0tNU5tWNOa53TyYIfD528DMOdY0gWz0K52oMd3QdxInMJukPHEZA8itU+ZMvbEM+fh5oJYrTrEMYzF6E3dAwAkC7twvbuWzGZ3QOflBGNHMFkdg+2hB9ExUSQKe1cPGbQP4eB8H2Yyp2PgY77UKgOIFfetrg/7E+iN3wM8dx5GIzcjVx5O/KVrYv7I4FpdAbHkcifg2jnEaSLu1CsDizu7wxOoMOfRLJw1prndLK4G6Ndh3hOS86pWO1DodLfVufUjjk145z6wz/DQycva6tzaseclp9T7EQGA9EM5ubmUCqVFudekUgEoVAIqVQKQ0NDSKVSKJfL6Ovrw/Hjxzc931t1bmqMqX8ma4mIvBLAAWPMS1fYdyaAbxljTvuRcGxszOzevdt+gcvcdNNNuOeeexx/X3LWjh07cOLECbfLoE14xYVZvODJ1675muNzv4udPf/pUEXkFuasB7NuTbnRMZjg0+p+/cTEBLZt27bp9z169Ohde/fuPXf5dq8ssXgNliyvEJHRJfteBeA+xytag8/nlf9sZFMwGHS7BNqkTH79X5KVq+xKowFz1oNZ62C7YYLrSyxEpBPAxQD+dMnmD4jIszG/xOLRZftcxz7IOrAPcutLZ9d/qA97purAnPVg1jq0dR9kADDG5Iwxg8aY1JJtlxtjnmWM+Q1jzCuMMZNu1rgc+yDrwD7IrS+dWX+CzJ6pOjBnPZi1Drb7ILs+QW5Fth9vSN6QyWTcLoE26eScrPua7pDnO0tSEzBnPZi1Dl1d6z8IajM4QW6AF25sJPvY77r1naxjNVRA8vYLIdcxZz2YtQ5+//q/IdwMTpAbEA6v31uVWl9fX5/bJdAmlcqAkbWftnSy6HwnHHIec9aDWetwqr2oLZwgNyCbzbpdAjlgamrK7RKoCWrYsub+0a5DDlVCbmLOejBrHaLRqNXjc4LcANvP/yZvGBwcdLsEaoIK1r6pNp4/z6FKyE3MWQ9mrUMyafeR4pwgN0Bk/Rt/qPWx33V7qNS619xfM+x3rQFz1oNZ62D7fjDOABrAJRY6cIlFeyhV177Tmb+O1YE568GsdeASCw869Vxvam+jo6Prv4g8r1TtXHP/eOYihyohNzFnPZi1DrYvYnGC3IBiseh2CeQA23fIkjMKpbXvGegNHXOoEnITc9aDWetg+2IlJ8hE1NbyJbZlJCKijeEEuQHsg6xDb2+v2yVQE+SKa4/XdGmXQ5WQm5izHsxaB9tPu+UEuQF8BLEOk5OTbpdATZAthNbcv737VocqITcxZz2YtQ7Dw8NWj88JcgNsP/+bvMH24CNnzOUCa+6fzO5xqBJyE3PWg1nrEI/HrR6fE+QG2O69R95Qq9XcLoGaIJ3xr7nfJ2WHKiE3MWc9mLUOtp9JwQlyA/L5vNslkAMSiYTbJVATpLNrf5uLRo44VAm5iTnrwax1GBgYsHp8TpAbwCUWOnCJRXs4uU63Pv46VgfmrAez1oFLLDyIfZB1SKVSbpdATTA7Bxis/qu4LeEHHayG3MKc9WDWOtjuNMUJcgNsr3shb/D71167Sq2hVhMYWb2hfMWs/SARag/MWQ9mrUO1WrV6fE6QGxAKrd02itoDHynePmpY/UpDprTTwUrILcxZD2atQzabtXp8TpAbMDc353YJ5IDx8XG3S6AmqZqeVfft6D7oYCXkFuasB7PWYWRkxOrxOUFuQE/P6v+zpfaxfft2t0ugJimb1X8bcCJziYOVkFuYsx7MWodYLGb1+JwgN4D9cXUol9lLs12Uqqt3ngn6+RshDZizHsxah2AwaPX4nCA3gH2QdZidnXW7BGqSYqVz1X0D4fscrITcwpz1YNY69PX1WT0+J8gNYB9kHbZu3ep2CdQkhdLqd7VP5c53sBJyC3PWg1nrMDMzY/X4nCA3oFAouF0COYBXkNtHvhRedd9AB682acCc9WDWOvAKsgexP64O4fDqkypqLbni6lkWqnYfV0rewJz1YNY6lEolq8fnBLkBtheGkzd0dq6+bpVaSyYfWHVfrrzNwUrILcxZD2atg+37wThBbgD7IOvAPsjtYy67+gSZPVN1YM56MGsd2AfZg9gHWQf2QW4fc7nVJ8jsmaoDc9aDWevAPsgeZPv53+QNxWLR7RKoSVJzsuq+sD/pYCXkFuasB7PWIRQKWT0+J8gN4MRJBy6laR8n15gg94aPOVgJuYU568GsdbD923xOkBvAm7d0GBoacrsEapKTcwZmlW938dx5DldDbmDOejBrHRKJhNXjc4LcAD5JTwfbg4+cJDCy8tWGwcjdDtdCbmDOejBrHfr7+60enxPkBrDNmw78TUF7qaF3xe25Mm/G1IA568GsdWCbNw8KBFa/I57aRySy+uOJqfVUzMoT5HyFjxTXgDnrwax1sP1UY06QG8Cbt3RgH+T2Uq51rbidPVN1YM56MGsd2AfZg9gHWQf2QW4vperKE2T2TNWBOevBrHWw3QfZ9bUCIvIogDkAVQAVY8y5IjIA4EsAzgTwKIA/MMbMulXjcpVKxe0SyAG8GbO9FMudwAq3D0QC084XQ45jznowax06OjqsHt8rV5BfbIx5tjHm3IXP3w7ge8aYpwH43sLnnlEul90ugRyQy+XcLoGaqFBe+ZtpZ5BLaTRgznowax1s3yfklQnycq8EcN3Cx9cBuNTFWk7Dm7d0GBwcdLsEaqJcMbzi9kT+HIcrITcwZz2YtQ6zs3YXFri+xAKAAfAdETEAPmWM+TSAYWPMJAAYYyZFZMVbUqenp7F//34EAgFUq1Xs27cPBw4cQCwWQ1dXF/x+P9LpNKLRKJLJJIwxiEajmJqaQnd3NwAgk8lgeHgY8XgcIoKBgQHE43H09vaiWq0im81iZGQEsVgMwWAQfX198Pv92LJlC8LhMDo7OzE+Po7t27ejWCxibm4OQ0NDSCQS6OzsRCQSWdyfz+eRy+UwODiImZkZ9PT0IBwOL+7P5XIoFovo7+/H9PQ0+vv7EQwGF/dnMhlUq1X09fVhamoKg4OD8Pl8mJqawujoKNLpNACgt7cXk5OTGB4eRq1WQyKRwPDwMFKpFPx+P7q7uxePWS6XMTs7i61bt2J2dpbntOScQqEQenp62uqc2jGnes8pWRjCQycvw47ugziRuQTdoeMISB6Faj+y5W2I589DzQQx2nUI45mL0BuafxpXurQL27tvxWR2D3xSRjRyBJPZPdgSfhAVE0GmtHPxmEH/HAbC92Eqdz4GOu5DoTqAXHnb4v6wP4ne8DHEc+dhMHI3cuXtyFe2Lu6PBKbRGRxHIn8Oop1HkC7uQrE6sLi/MziBDn8SycJZGO4cQ7J4FsrVntPO6WRxN0a7DvGclpxTodqPQqW/rc6pHXNqxjn1hX+Oh05e1lbn1I45LT+n2IkMBqIZzM3NoVQqLc69IpEIQqEQUqkUhoaGkEqlUC6X0dvbi+PHj296vrcaMcZsaDbbbCKyzRgzsTAJ/i6ANwG42RizZclrZo0xp3WEHhsbM7t373aw2nk33ngj7r33Xsffl5y1bds2TExMuF0GNckl5+dx0dP/X3v3GhvZWd4B/P94PDNre2yvb+t1nJDQbWiAJRDIpREltFkRQtqqXIoIKiRS4UMrkKAtFFr1A6illKiFilIJCVEpbdMilVxIm6UBsREh1Zak2Vx2w6aQjdhmHd/tnbvn5qcf5tg7vkS7Puv3vGfm+f+k0Y7PzLz7zP41zpMz5zznG1u2v5S/BZf1f89DRRQl5mwHs25PpYmj0OSVF/z86elpTExMXPTfe+zYsScPHTp07ebt3g+xUNWXgz/nANwP4HoAsyIyAQDBn7E64j6RSPgugSKQTm//lTy1p3x5+y/MKo3hiCshH5izHczahmq16nR9rw2yiPSJNK//KiJ9AG4BcALAgwDuDJ52J4Dv+Klwe5yDbAPnIHeWXGH7X3ecmWoDc7aDWdvQ6XOQxwE8JiLPAHgcwEOq+p8A/grAO0TkZwDeEfwcG5yDbAPnIHeWbGH7b344M9UG5mwHs7aho+cgq+qLAN64zfZFAIeir+jCcMybDRzz1lmWc9tv703yOHMLmLMdzNoGq2PeYq3RaPgugSJQqVR8l0C7qFACdJt9AnsSSx6qoagxZzuYtQ2pVMrp+myQQ3B99RaKh6GhLYNTqM2pDG7ZtrRy0EMlFDXmbAeztiGbzTpdnw1yCMVi0XcJFIG5uVgNT6Fd0MDW8wfGe496qISixpztYNY2jI6OOl2fDXIIvJKeDdyD3HnqurVBXqpwb5MFzNkOZm0D9yDHUFcX/9ksSCaTvkugXVZb7du6rcGpNBYwZzuYtQ2uByaw0wuBc5Bt4BzkzlOtb22QOTPVBuZsB7O2odPnILclzkG2gXOQO0+l3rtlG2em2sCc7WDWNrieg8wGOQTXlzekeCgUCr5LoF1Wrm69fHgmddpDJRQ15mwHs7ahr2/rN4K7iQ1yCKrquwSKAOddd55SZeuIxm4pe6iEosac7WDWNiQS218ddbewQQ4hnd66F4o6z+Dg1pm51N6KK1tPvDxbucpDJRQ15mwHs7Yhl3uFy6PuEjbIIXAOsg2zs7O+S6BdVixvbZAn+h71UAlFjTnbwaxtGBsbc7o+G+QQOAfZhpGREd8l0C7LlbZ+JTdfvs5DJRQ15mwHs7ZhacntJcXZIIcgIr5LoAhw3nXnyRW6t2xbVc67toA528GsbXB9Phg7gBB4iIUNPMSi82S3GWHOr2NtYM52MGsbeIhFDGUyGd8lUAQmJiZ8l0C7bHmbczqmCjdHXwhFjjnbwaxtcL0Tiw1yCJVKxXcJFAHXZ8hS9MoVgWLjFJqB1ClP1VCUmLMdzNoG1zsr2SATkSmrMuC7BCIiijk2yCFwDrINAwNspDpRAxtzzVUPeKqEosSc7WDWNri+2i0b5BB4CWIbpqenfZdADtR149dyk5kjniqhKDFnO5i1DePj407XZ4Mcguvrf1M8uP7wkR+1xsYGebp4k6dKKErM2Q5mbcP8/LzT9dkgh+B69h7Fw+rqqu8SyIFqo3fDz11S81QJRYk528GsbXB9TQo2yCGUy2XfJVAEFhcXfZdADqzUNl4Jc6znCU+VUJSYsx3M2obh4WGn67NBDoGHWNjAQyw6U7myZ8PP/DrWBuZsB7O2gYdYxBDnINuQzWZ9l0AOlKobp9DsTT/vqRKKEnO2g1nb4HrSFBvkEFwf90LxkEgkfJdADhTLyQ0/17XnFZ5JnYQ528GsbWg0Gk7XZ4McQiqV8l0CRYCXFO9MhU0NcqF6uadKKErM2Q5mbUOxWHS6PhvkEPL5vO8SKAJTU1O+SyAHcsWN3wxcmnnYUyUUJeZsB7O2Yf/+/U7XZ4McQn9/v+8SKAKTk5O+SyAHNjfIZwrv9FQJRYk528GsbZiZmXG6PhvkEDgf14ZajbM0O9HZ3Mafkwl+I2QBc7aDWduQTCbP/6SLwAY5BM5BtmF5edl3CeTA2fzGk2yH0yc8VUJRYs52MGsbBgcHna7PBjkEzkG2Yd++fb5LIAeqNUDl3Fnus6UbPVZDUWHOdjBrGxYWFpyuzwY5hJWVFd8lUAS4B7lzreLcnofhPdzbZAFztoNZ28A9yDHE+bg2pNPp8z+J2lId5wbMrzTcXq6U4oE528GsbahWq07XZ4McgusDwykeent7fZdAjtRXz824LtUu8VgJRYU528GsbXB9Phgb5BA4B9kGzkHuXLXVc+cRcGaqDczZDmZtA+cgxxDnINvAOcidq1I/9+0AZ6bawJztYNY2dPQcZBG5TEQeEZGTIvKciHwi2P45EZkSkaeD220+69zM9fW/KR4qlYrvEsiRleq5KRbpxJLHSigqzNkOZm1DKpVyun6309XPrw7gj1T1mIj0A3hSRL4fPPYVVf1rj7W9IjZONvBQms5Vru5Zvz+QPuWxEooKc7aDWdvg+tt8r3uQVXVaVY8F9/MATgKI/ffaPHnLhtHRUd8lkCOlyrk9D/Ol6zxWQlFhznYwaxsWFxedru97D/I6EbkCwDUAfgzgrQA+LiJ3APgfNPcybxlKOzc3h4985CPo7u5Go9HAe9/7XnzsYx/DzMwM+vr6kEgkkMvlMDY2hqWlJagqxsbGMDs7i0ymeRZ7oVDA+Pg45ufnISIYHh7G/Pw8BgYG0Gg0UCwWsX//fszMzCCZTGJwcBDd3d3Yu3cv0uk0ent7MTU1hcnJSVQqFeTzeYyOjmJxcRG9vb3o6elZf7xcLqNUKmFkZAQLCwvo7+9HOp1ef7xUKqFSqWBoaAhzc3MYGhpCMplcf7xQKKDRaGBwcBCzs7MYGRlBV1cXZmdnMTExgVyueQ3dgYEBTE9PY3x8HKurq1hcXMT4+Diy2SwSiQQymcz6mrVaDcvLy9i3bx+Wl5f5nlreUzqdRn9/f0e9p07MKcx7Wq4exEzpRnRLGeXGCIq1SzBfvg6rmsRE36OYKtyMgVRzL1SuegCTmSOYLt6ELqlhrOcJTBdvwt7086hrDwrVy3Fp5mGcKbwTyUQew+kTmC3diOE9J7DSGEapdsn64+nEEgbSpzBfug4jPU+hVJtEub5v/fGe7jn0JqewWL4GY71PIFc5gEpjeP3x3uTL2JNYwtLKQYz3HsVS5SBqjf71xzOp0+iWMs5WrsJE36N8Ty3vqdwYwUp9qKPeUyfmtBvvqT/1Al44e3tHvadOzGnze5o5U8DwWAH5fB7VanW99+rp6UEqlUI2m8Xo6Ciy2SxqtRoymQxOnz590f3eK/alqrqzTtYBEckA+CGAL6jqfSIyDmABgAL4cwATqvq7m1939OhRveqqq6ItFsD999+P48ePR/73UrTWPpzUed5+bRW3Hfw6AGCqcDMmM0c8V0SuMWc7mHV7Kk0chSavvODnz87OYnx8/KL/3mPHjj156NChazdv9z7FQkSSAO4FcI+q3gcAqjqrqg1VXQXwDQDX+6xxs+7u2Ox4J4d6enrO/yRqS/niuYv9lOu8pLgFzNkOZm2D66sa+55iIQC+CeCkqn65ZftEy9PeAyBW143kyVs2cA5y58oWZP0+Z6bawJztYNY2dPoc5LcC+DCAmzeNdLtLRI6LyLMAfg3AH3itchPOQbaBc5A719n8uV99nJlqA3O2g1nb4PoQSK/HCqjqYwBkm4cOR13LTtTrdd8lUARcX8aS/Mm2fAnU0z3nrxCKDHO2g1nbsGfPnvM/6SL43oPclmq1mu8SKAKlUsl3CeRIvQGoNM9s7k3yUBoLmLMdzNoG1+cJsUEOgSdv2TAyMuK7BHKogUEAwGL5Gs+VUBSYsx3M2obl5S3Tf3cVG+QQuGfRhoWFBd8lkEMNNM8lGOt9wnMlFAXmbAeztsH1Tiw2yCGk02nfJVAEeDJmZ6utNg+xyFUOeK6EosCc7WDWNrieKMYGOYREInH+J1Hb4/8IdbZaow8AUGkMe66EosCc7WDWNlSrVafrs0EOgXOQbeAc5M62Uu8FwJmpVjBnO5i1DZ0+B7kt8at3GzgHubNVas0RQZyZagNztoNZ2+B6DjIb5BA45s0GnozZ2UqV5iE0vcmXPVdCUWDOdjBrGzjmLYYajYbvEigClUrFdwnkUKnS3IO8J7HkuRKKAnO2g1nbkEqlnK7PBjkE11dvoXgYGhryXQI5VCg3LyS6tHLQcyUUBeZsB7O2IZvNOl2fDXIIxWLRdwkUgbk5Xq60kxVKzQZ5vPeo50ooCszZDmZtw+joqNP12SCHwCvp2cA9yJ0tVwz2IFe4t8kC5mwHs7aBe5BjqKuL/2wWJJNJ3yWQQ9lC83Nca3AqjQXM2Q5mbYPrgQns9ELgHGQbOAe5s53NNf/kzFQbmLMdzNoGzkGOIc5BtoFzkDtbrgAoujgz1QjmbAeztoFzkGPI9eUNKR4KhYLvEsihVQVU+pFJnfZdCkWAOdvBrG3o6+tzuj4b5BBU1XcJFAHOu+58qxhAt5R9l0ERYM52MGsbEomE0/XZIIeQTqd9l0ARGBwc9F0COVbXfpytXOW7DIoAc7aDWduQy+Wcrs8GOQTOQbZhdnbWdwnkWG01g4m+R32XQRFgznYwaxvGxsacrs8GOQTOQbZhZGTEdwnkWLXRh/nydb7LoAgwZzuYtQ1LS24vKc4GOQQR8V0CRYDzrjtfpd6DVeW8awuYsx3M2gbX54OxAwiBh1jYwEMsOt9KtYdfxxrBnO1g1jbwEIsYymQyvkugCExMTPgugRwrVdKYKtzsuwyKAHO2g1nb4HonFhvkECqViu8SKAKuz5Al/0qVFAZSp3yXQRFgznYwaxtc76xkg0xEZhXKPFaRiIi2YoMcAucg2zAwMOC7BHIsX+pGrnrAdxkUAeZsB7O2wfXVbtkgh8BLENswPT3tuwRyLF9MYDJzxHcZFAHmbAeztmF8fNzp+myQQ3B9/W+KB9cfPvLvbF4wXbzJdxkUAeZsB7O2YX5+3un6bJBDcD17j+JhdXXVdwnk2Nkc0CU132VQBJizHczaBtfXpGCDHEK5XPZdAkVgcXHRdwnkWL4kGO15yncZFIGxnid8l0ARYdY2DA8PO12fDXIIPMTCBh5iYcN06ZDvEigC/NrdDmZtAw+xiCHOQbYhm836LoEi0J/myZgW7E0/77sEigiztsH1pCk2yCG4Pu6F4iGRSPgugSJQXR30XQJFoK49vkugiDBrGxqNhtP12SCHkEqlfJdAEeAlxW3IVy7zXQJFoFC93HcJFBFmbUOxWHS6PhvkEPL5vO8SKAJTU1O+S6AIDPX81HcJFIFLMw/7LoEiwqxt2L9/v9P12SCH0N/f77sEisDk5KTvEigCM/k3+S6BInCm8E7fJVBEmLUNMzMzTtePdYMsIreKyP+KyAsi8lnf9aw5coRX6bHgRz/6ke8SKAL3/wdHQllw70MnfJdAEWHWNjzwwANO149tgywiCQB/D+BdAF4H4IMi8jq/VTU98sgjvkugCDz22GO+S6AIPHj4cd8lUATu/XdONrCCWdtw3333OV0/tg0ygOsBvKCqL6pqFcC3APyW55oAAF1dcf5no92STCZ9l0ARaKzypFsL6qs86dYKZm1DvV53ur7E9bLJIvLbAG5V1Y8GP38YwA2q+vG15xw+fDg/PT293q0ODAzMDw8PL7iubW5u7jU9PT2cAdbhisVid19fn9tPIHlXq+Rx6XjVdxnk2MJyo3t0KMHPswHMuj2VG5ecUqQu+Jfx0tLS6C71fJcfOnRobPPG7l1Y2JXthg1v6OZvu+02ni1HRERERLsqzscKnAHQOqD0UgAve6qFiIiIiIyIc4P8BIArReTVIpICcDuABz3XREREREQdLraHWKhqXUQ+DuBhAAkA/6Cqz3kui4iIiIg6XJz3IENVD6vqa1T1gKp+wdXfIyKXicgjInJSRJ4TkU8E24dF5Psi8rPgz6Fg+ztE5EkROR78eXPLWm8Jtr8gIl8Vke2OpSYPQuR8vYg8HdyeEZH3tKzFnGNqpzm3vO5VIlIQkU+1bGPOMRXi83yFiJRbPtNfb1mLOcdYmM+0iFwtIkeD5x8XkT3BdmYdUyE+07/T8nl+WkRWReRNwWMXn7Oqmr8BmADw5uB+P4Cfojl7+S4Anw22fxbAl4L71wC4JLh/EMBUy1qPA7gRzZMMvwvgXb7fH2+hc+4F0N3y2rmWn5lzTG87zbnldfcC+DcAn2rZxpxjegvxeb4CwIlXWIs5x/gWIutuAM8CeGPw8wiABLOO9y3s7+5g+xsAvNjy80XnHOs9yFFR1WlVPRbczwM4CWASzbnLdwdPuxvAu4PnPKWqaycMPgdgj4ikRWQCwICqHtVmQv+49hryL0TOJVVdGxW0B8EUFeYcbzvNGQBE5N0AXkTz87y2jTnHWJict8Oc4y9E1rcAeFZVnwles6iqDWYdbxf5mf4ggH8Fdu8zzQZ5ExG5As09xD8GMK6q00AzOAD7tnnJ+wA8paoVNIM80/LYmWAbxcyF5iwiN4jIcwCOA/i9oGFmzm3iQnIWkT4AnwHw+U0vZ85tYge/t18tIk+JyA9F5G3BNubcRi4w69cAUBF5WESOicgfB9uZdZsI0Yt9AEGDjF3KObYn6fkgIhk0v2b9pKrmznfIioi8HsCX0Py/VeACZjeTfzvJWVV/DOD1IvJaAHeLyHfBnNvCDnL+PICvqGph03OYcxvYQc7TAF6lqosi8hYADwS/w5lzm9hB1t0AfgXAdQBKAH4gIk8CyG3zXGYdMyF6sRsAlFT1xNqmbZ6245y5BzkgIkk0A7lHVdcu8D0b7Kpf22U/1/L8SwHcD+AOVT0VbD6D5rzmNZzdHDM7zXmNqp4EUETzmHPmHHM7zPkGAHeJyM8BfBLAn0pzgg5zjrmd5KyqFVVdDO4/CeAUmnsamXMb2OFn+gyAH6rqgqqWABwG8GYw69gL+d/o23Fu7zGwSzmzQQYQnN34TQAnVfXLLQ89CODO4P6dAL4TPH8vgIcA/Imq/tfak4Nd/3kR+eVgzTvWXkP+hcj51SLSHdy/HMAvAfg5c463neasqm9T1StU9QoAfwvgL1X1a8w53kJ8nsdEJBHc/wUAV6J5Ug9zjrmdZo3meNirRaQ3+B3+dgA/YdbxFiJniEgXgPcD+Nbatl3Leadn9XXiDc2vYhTNs16fDm63oXnm6w8A/Cz4czh4/p+huTfx6ZbbvuCxawGcQHPvxNcAiO/3x1vonD+M5klbTwM4BuDdLWsx55jedprzptd+DhunWDDnmN5CfJ7fF3yenwk+z7/JnNvjFuYzDeBDxC5PbAAAAnFJREFUQd4nANzFrON/C5nzrwL4723WuuicJViIiIiIiIjAQyyIiIiIiDZgg0xERERE1IINMhERERFRCzbIREREREQt2CATEREREbVgg0xERERE1IINMhERERFRCzbIREQEAFi7ciQRkXVskImI2oCIfFpE7t207e9E5G9FZFBEviki0yIyJSJ/0XJZ5QMickREFkVkQUTuEZG9LWv8XEQ+IyLPAiiySSYiYoNMRNQu/hnArWvNbdDIfgDAPwG4G0AdwC8CuAbALQA+GrxOAHwRwCUAXgvgMjQvqd3qgwB+HcBeVa07fRdERG2ADTIRURtQ1WkAjwJ4f7DpVgALAM4AeBeAT6pqUVXnAHwFwO3B615Q1e+rakVV5wF8GcDbNy3/VVV9SVXLUbwXIqK441dpRETt424Avw/gGwA+hObe48sBJAFMi8ja87oAvAQAIrIPwFcBvA1Af/DY8qZ1X3JdOBFRO+EeZCKi9vEAgKtF5CCA3wBwD5rNbQXAqKruDW4Dqvr64DVfBKAArlbVATQba9m0rkZTPhFRe2CDTETUJlR1BcC3AfwLgMdV9f+CQy++B+BvRGRARLqCE/PWDqPoB1AAcFZEJgF82kvxRERthA0yEVF7uRvAG9A8vGLNHQBSAH6C5uET3wYwETz2eQBvBpAF8BCA+yKrlIioTYkqv1kjImoXIvIqAM8D2K+qOd/1EBF1Iu5BJiJqEyLSBeAPAXyLzTERkTucYkFE1AZEpA/ALIDTaI54IyIiR3iIBRERERFRCx5iQURERETUgg0yEREREVELNshERERERC3YIBMRERERtWCDTERERETU4v8BqWJEqUKc1WUAAAAASUVORK5CYII=\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": 90, "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": 91, "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", "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": 92, "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": 93, "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": 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ff39VTqXI5XLE4/FVv85SQjsHWUTOBp50zv1qn+tpHwrcu+D2juJ9+xkeHuaSSy4hGo3i+z7nnnsul112GYODg7S2thKJRJiamqKvr4/x8XGcc/T19TE0NFQ6iX9mZob+/n5GRkYQEbq7uxkZGaGjowPf95mdnWVgYIDBwUFisRidnZ3s3r2b9vZ2/PnDOHzPYNK/YtfOJ2nv6GRsbIyuri5SqRTpdLr0/EQiQTKZZGJigp6eHqanp8lkMqXtyWSSeDzO5OQkvb29TE5Oks1mS9uD3KfR0VE6OzvJZDKkUqnS9ng8Tnt7u8p92r17N11dXU21T83YabX7tHv3bnp6eppqn5qx02r3affu3axbt66p9qkZO1Vjn5xzbN++van2ablOHR0dzM/P43keIoLv+6W5kXOOWCxGNps94HYoLJmWzWZLH3zzfZ9YLEYuV7hScCQSIZfLEYlECteCyOdLrykipe033HAD733ve/nnf/5nEokEhx12GC996Ut5z3vew/z8PCLCc57zHC688ELy+TzOObLZLL7vk8vlyh7znrWul9rnpfbJ932eeOKJRZ2WnKfWakHt4hHkbznnniUia4D/As5wzk2KyP8CJzjnRkXkM8BW59wXis+7Dvi2c+7WfV9z69atbtOmTTUZ/0Jzc3OsWbMGnGPNk8cg+cJfa3Prf4KLPaXm4zHBKHU2Tc0662Cd9dDYempqio6OjrCHUVO+7694JYsD/d9p27Zt923ZsuWEfR8b1jJvTwGOBH5VnBwfBmwTkQEKR4wX/tvIYcDOmo/wIEp/cYjgx+2Des3qYH9ZmuZhnXWwznpYax32HN0OSigTZOfcb5xz65xzRzjnjqAwKX6uc24QuB04X0RaRORI4GnAT8MY51IW/vVh5yE3L21/jWtlnXWwznpYax2CXge5Vsu8fZnCh+yOFpEdInLJUo91zv0OuBm4H7gDuKyeVrCAxUuLLLxgSMSOIDeVoJeQMfXBOutgnfWw1joEfYpwTSbIzrnXO+fWO+dizrnDnHPX7bP9COfc6ILbH3bOPcU5d7Rz7ju1GONKzM7Oln7e7whyjc7pNsFb2Nk0L+usg3XWQ2NrESGTyYQ9jJrK5/Mrenwmk2GfRSEOyq6kV4GBgYHSzy6yAed1IfkJxE0i/uO46MYQR2eqZWFn07yssw7WWQ+Nrdva2piZmSGdToc9lJrxfZ/5+fmyHy8iK7oMuU2QKzA4OMjGjcVJsAj5+LOJpAtXhfEyv8K3CXJTWNTZNC3rrIN11kNjaxGhvb097GHU1Pbt2wPtHNYqFg1t36u3+DH7oF4zCvoqPaY+WGcdrLMe1lqHoDvbBLkCnZ2di27nbam3prRvZ9OcrLMO1lkPa61D0J1tglyB0dHFl3Fc+EG9iH1Qr2ns29k0J+usg3XWw1rrEHRnmyBXYN+/Wlz0SJwU1l2U/BjiPxnGsEyV2VEIHayzDtZZD2utgx1BrkP7LaUiQj5+bOmmnYfcHLQtmaOVddbBOuthrXUIurNNkCuQSqX2u8/OQ24+B+psmo911sE662GtdQi6s02QK3CgNRb9RRcMsQlyM9C4lqZG1lkH66yHtdYh6M42Qa7A4ODgfvctPoJsH9RrBgfqbJqPddbBOuthrXUIurNNkCsQj8f3u89Fn4KTVgC8/DDi2y9ooztQZ9N8rLMO1lkPa61D0J1tglyBA16tRrx9Pqhnp1k0Om1XJdLKOutgnfWw1joE3dkmyBUYGxs74P2LTrPI2koWjW6pzqa5WGcdrLMe1lqHoDvbBLkCXV1dB7w/b5ecbipLdTbNxTrrYJ31sNY6BN3ZJsgVWGppEd+WemsqtlSQDtZZB+ush7XWwZZ5q0PpdPqA97vYU3GSBMDzd4E/XMthmSpbqrNpLtZZB+ush7XWIejONkGuwJJr70mUfOxZpZsRO82iodlamjpYZx2ssx7WWgdbB7kOHWztvXzczkNuFraWpg7WWQfrrIe11sHWQa5DiURiyW12yenmcbDOpnlYZx2ssx7WWoegO9sEuQLJZHLJbXYEuXkcrLNpHtZZB+ush7XWIejONkGuwMTExJLb8rGjcbQA4PlPgD9eq2GZKjtYZ9M8rLMO1lkPa61D0J1tglyBnp6epTdKjHz8GaWbdhS5cR20s2ka1lkH66yHtdYh6M42Qa7A9PT0QbcvPM0iYuchN6zlOpvmYJ11sM56WGsdgu5sE+QKZDKZg263S043h+U6m+ZgnXWwznpYax2C7mwT5Aost/aefVCvOdhamjpYZx2ssx7WWgdbB7kOLbf2Xj52DI4YAF7uMchP1mJYpspsLU0drLMO1lkPa62DrYNch5ZdWkRayMc2lW7aUeTGZEsF6WCddbDOelhrHWyZtzoUj8eXfYxdMKTxldPZND7rrIN11sNa6xB0Z5sgV2BycvlTJhavZGFHkBtROZ1N47POOlhnPay1DkF3tglyBXp7e5d9TD7+nNLPdgS5MZXT2TQ+66yDddbDWusQdGebIFegrCPIsWNwRADwco9A3tZlbDR2FEIH66yDddbDWutgR5DrUDabXf5BXnKfD+r9JsARmSCU1dk0POusg3XWw1rrEHRnmyBXoNy19xavh/yLoIZjAmJraepgnXWwznpYax1sHeQ6VO7ae/mWE0o/R9M/DGo4JiC2lqYO1lkH66yHtdbB1kGuQ62trWU9zk+cUfrZS/+PnYfcYMrtbBqbddbBOuthrXUIurNNkCsQiUTKepyLrsePFU6zELJE7ChyQym3s2ls1lkH66yHtdYh6M42Qa7A1NRU2Y/1k3uPIkdS3wtiOCYgK+lsGpd11sE662GtdQi6s02QK9DX11f2Y/3kmaWfo6nvg/ODGJIJwEo6m8ZlnXWwznpYax2C7lyTCbKIXC8iwyLy2wX3/aOI/F5Efi0iXxeRtQu2vUtEHhaRB0XkzAO/anjGx8fLfmw+fhx5bx0Akh/Dy9wX1LBMla2ks2lc1lkH66yHtdYh6M61OoJ8A/Dyfe67E3iWc+7ZwB+AdwGIyDOA84FnFp9zjYjU1QlFzrnyHyzePqdZfDeAEZkgrKizaVjWWQfrrIe11iHozjWZIDvn7gbG97nve865XPHmvcBhxZ9fDXzFOTfvnHsMeBh4QS3GWa6VHtZfOEGO2nnIDcP+mU4H66yDddbDWusQdOdooK9evj8H/rP486EUJsx77Cjet5/h4WEuueQSotEovu9z7rnnctlllzE4OEhrayuRSISpqSn6+voYHx/HOUdfXx9DQ0O0tbUBMDMzQ39/PyMjI4gI3d3djIyM0NHRge/7zM7OMjAwwODgILFYjM7OTh555BEOP/xwMpkMqVSqtD0ej9Pe3s7Y2BhdXV2kUinS6TQD/S8i7uJ4ksHLPsDO7Vvp6DmW6elpMplM6fnJZJJ4PM7k5CS9vb1MTk6SzWZL24Pcp9HRUTo7O8vfp+L2RCJBMplkYmKCnp6eptqn//3f/+Woo45qqn1qxk6r3acnnniCpz71qU21T83YabX7tHPnTo4++uim2qdm7FSNffJ9n0gk0lT71IydVrtPezqvdp+WIrX6pwgROQL4lnPuWfvc/x7gBOBc55wTkc8AW51zXyhuvw74tnPu1n1fc+vWrW7Tpk373h24sbExenp6VvScluHziaa/D8B819Xk2v8iiKGZKqqks2k81lkH66yHtdahWp23bdt235YtW07Y9/5QV7EQkYuAs4A/c3tn6juADQsedhiws9ZjqzY7D9kYY4wxpjGENkEWkZcD7wDOds7NLdh0O3C+iLSIyJHA04CfhjHGpczMzKz4OYsmyOkf21X1GkAlnU3jsc46WGc9rLUOQXeu1TJvXwa2AkeLyA4RuQT4NNAO3CkivxSRzwI4534H3AzcD9wBXOZcfS0e3N/fv+LnuOhh+LHC2SVChkj6rmoPy1RZJZ1N47HOOlhnPay1DkF3rtUqFq93zq13zsWcc4c5565zzj3VObfBOfec4tebFjz+w865pzjnjnbOfacWY1yJg53UfTB2mkVjqbSzaSzWWQfrrIe11iHoznYlvQqISEXPW3xVvTvB5as1JBOASjubxmKddbDOelhrHYLubBPkCnR3d1f0vHz8eJxXWLdP8qN4mW3VHJapsko7m8ZinXWwznpYax2C7mwT5ApUfFhfPHLJ00s3I3bRkLpm/0yng3XWwTrrYa11sFMs6lBHR0fFz114moWdh1zfVtPZNA7rrIN11sNa6xB0Z5sgV8D3K19Uw0+cgiMOQCT7OyS3o1rDMlW2ms6mcVhnHayzHtZah6A72wS5ArOzs5U/2WvDT2wu3bTTLOrXqjqbhmGddbDOelhrHYLubBPkCgwMDKzq+YuXe7MJcr1abWfTGKyzDtZZD2utQ9CdbYJcgcHBwVU9f/FV9e6BvP21W49W29k0Buusg3XWw1rrEHRnmyBXIBaLrer5Lno4+dgxAAjzdlW9OrXazqYxWGcdrLMe1lqHoDvbBLkCnZ2dq36NnK1mUfeq0dnUP+usg3XWw1rrEHRnmyBXYHR0dNWvsfg8ZLuqXj2qRmdT/6yzDtZZD2utQ9CdbYJcgWr81ZKPPw/n9QDg5YfxMr9c9Wua6rKjEDpYZx2ssx7WWgc7glyHMpnM6l9EIuSSp5Vu2mkW9acqnU3ds846WGc9rLUOQXe2CXIFUqlUVV5n8VX1bLm3elOtzqa+WWcdrLMe1lqHoDvbBLkC1Vp7z0+8FEfhU5iR7G+Q3M6qvK6pDltLUwfrrIN11sNa62DrINehqq2957WTT7y4dNOOItcXW0tTB+usg3XWw1rrYOsg16F4PF6117Ll3upXNTub+mWddbDOelhrHYLubBPkCrS3t1fttRYt9zZ/D+TnqvbaZnWq2dnUL+usg3XWw1rrEHRnmyBXYGxsrGqv5aJHkI8dDYC4NJH03VV7bbM61exs6pd11sE662GtdQi6s02QK9DV1VXV18slFhxFTtt5yPWi2p1NfbLOOlhnPay1DkF3tglyBaq9tMh+y705V9XXN5WxpYJ0sM46WGc9rLUOtsxbHUqn01V9vXzLCTiv8JeQ5w/iZX9d1dc3lal2Z1OfrLMO1lkPa61D0J1tglyBqq+9J1H8hF1Vr97YWpo6WGcdrLMe1loHWwe5DgWx9l5u4WoWNkGuC7aWpg7WWQfrrIe11sHWQa5DiUSi6q/pJ1+GIwpAJPMrJLer6u9hViaIzqb+WGcdrLMe1lqHoDvbBLkCyWSy+i/qdZJvObF0M5K+s/rvYVYkkM6m7lhnHayzHtZah6A72wS5AhMTE4G87sKr6kXnbg/kPUz5gups6ot11sE662GtdQi6s02QK9DT0xPI6/pr/giHABBJ/wjJPhzI+5jyBNXZ1BfrrIN11sNa6xB0Z5sgV2B6ejqQ13XRwxddejo2fX0g72PKE1RnU1+ssw7WWQ9rrUPQnW2CXIFMJhPYa+fa/qL0c3T2y5C3X/SwBNnZ1A/rrIN11sNa6xB0Z5sgVyDItff8xCnko08FQNw00dmvBvZe5uBsLU0drLMO1lkPa62DrYNchwJde088su2XlG7GZq6zS0+HxNbS1ME662Cd9bDWOtg6yHUo6KVFcq3n46QVAC/7IN78PYG+nzkwWypIB+usg3XWw1rrYMu81aF4PB7sG3jt5FrPL92MTV8b7PuZAwq8s6kL1lkH66yHtdYh6M42Qa7A5ORk4O+x8DSLSOoOJPdE4O9pFqtFZxM+66yDddbDWusQdGebIFegt7c38PdwsafjJ04BQMgTnbkh8Pc0i9WiswmfddbBOuthrXUIurNNkCtQq79Os20LP6z3H5BP1eR9TYEdhdDBOutgnfWw1jrYEeQ6lM1ma/I+fvJM8pENAEh+nOjcN2ryvqagVp1NuKyzDtZZD2utQ9CdazJBFpHrRWRYRH674L5uEblTRB4qfu9asO1dIvKwiDwoImfWYowrUbM1FiVCrv3i0s3o9L/bkm81ZGtp6mCddbDOelhrHUJdB1lE/rzMrwuXeZ8bgJfvc987gR84554G/KB4GxF5BnA+8Mzic64RkUgF+xaYWq6xmG19A04SAESyv8bL/Lxm762draWpg3XWwTrrYa11CLpzdJn/YaRIAAAgAElEQVTtnwPKWYT3+cBNS210zt0tIkfsc/ergVOLP98I/Ah4R/H+rzjn5oHHRORh4AXA1jLGUROtra21e7NIN7k1ryU2+0WgsOTbfMvza/f+itW0swmNddbBOuthrXUIuvNyE+SUc+6ly72IiExU8N79zrldAM65XSKyrnj/ocC9Cx63o3jffoaHh7nkkkuIRqP4vs+5557LZZddxuDgIK2trUQiEaampujr62N8fBznHH19fQwNDdHW1gbAzMwM/f39jIyMICJ0d3czMjJCR0cHvu8zOzvLwMAAg4ODxGIxOjs7GRsbIxqNkslkSKVSpe3xeJz29nbGxsbo6uoilUqRTqdL2xOJBMlkkomJCXp6epieniaTyZS2J5NJ4vE4k5OT9Pb2Mjk5STab5dCeC+gsTpAjc7ezY/tf0t13TFX3aXR0lM7Ozprt057tQXZa7T6NjY2RSCSaap+asdNq92lsbIw1a9Y01T41Y6fV7tPY2Bjt7e1NtU/N2Kka+5RMJtm+fXtT7VMzdlrtPu3pvNp9Woq4g5zTKiJPc849tOQD9j7uqc65h5d5zBHAt5xzzyre3u2cW7tg+4RzrktEPgNsdc59oXj/dcC3nXO37vuaW7dudZs2bVpueFW3fft2Nm7cWNP3TAy+kkjmpwBkOt9BtvPymr6/RmF0NrVnnXWwznpYax2q1Xnbtm33bdmy5YR97z/oOcjlTI6Ljzvo5HgJQyKyHqD4fbh4/w5gw4LHHQbsrOD1A9PX11fz98y2/0Xp5+jMDeDsU7pBC6OzqT3rrIN11sNa6xB057JXsRCRuIj8pYhcIyI3Lfyq8L1vBy4q/nwRcNuC+88XkRYRORJ4GvDTCt8jEOPj4zV/T3/NWeS9wlkonj9EZO5bNR+DNmF0NrVnnXWwznpYax2C7rySZd5uBP4OmAYe2efroETkyxQ+ZHe0iOwQkUuAq4HTReQh4PTibZxzvwNuBu4H7gAuc875Kxhn4A52WkpgJE6u/Y2lm7GZ62o/BmVC6WxqzjrrYJ31sNY6BN15uQ/pLfRy4Ejn3O6Vvolz7vVLbNqyxOM/DHx4pe9TK2H9802u7UJikx9HyBGZvxcv8xvy8WNDGYsG9s90OlhnHayzHtZah7o5xQJ4HGgJaiCNZGhoKJT3dZEB/DWvKt2OTl8byji0CKuzqS3rrIN11sNa6xB054MeQRaRly24eRNwm4h8Elg0KufcDwMYW93as2RIGLLtlxKd+zoA0blbyaz9e4h0HfxJpiJhdja1Y511sM56WGsdgu683CkWBzrR9SP73HbAUdUZjllOPv58/NixRLK/QVya6OwXyXW8JexhGWOMMcY0jeWWeTuyjC91k+OZmZnw3lyE3IIl32LT10N9fYaxaYTa2dSMddbBOuthrXUIuvOy5yCLyBUi8kIRWckH+ppaf39/qO+fW3MuziucVuH5jxNJ3RnqeJpV2J1NbVhnHayzHtZah6A7l/MhvdOA7wO7ReT7InKliJwiImo/sHewSxPWhJck2/qG0s3YjH1YLwihdzY1YZ11sM56WGsdgu687ATZOXcGsBZ4GfAd4HnArRQmzPeIyIcCHWEdEpGwh0Cu/WJcMV8k/SMkW9ZFD80K1ENnEzzrrIN11sNa6xB057KWeXPO+c65nzrnPuacOwd4KvA+Cle5e1eQA6xH3d3dYQ8BFz0cP3lm6XZs2i4cUm310NkEzzrrYJ31sNY6BN25rAmyiPSIyGtE5BMich+Fq9y9EPgH4MQgB1iP6uWfb7ILPqwXnf0iknsyxNE0n3rpbIJlnXWwznpYax2C7rzsB+9E5HdAErgL+G/gs865BwMdVZ3r6OgIewgA5FtOXrDkW4r47g8y3/vZsIfVNOqlswmWddbBOuthrXUIunM5R5B3Aq3ARmADcJiIrAl0VHXO9+tkWTURMl17TwGPzt2CN//zEAfUXOqmswmUddbBOuthrXUIunM5H9I7HTgEeAcwDfwN8KiI/ERE/klEXh3oCOvQ7Oxs2EMoySdeQi659/LT8Yn3gHMhjqh51FNnExzrrIN11sNa6xB055V8SO9nCz6kt4nCShZvAL4W5ADr0cDAQNhDWCTT9T4ccQAimfuIzN0a8oiaQ711NsGwzjpYZz2stQ5Bd670Q3ojwGXAD4A3BznAejQ4OBj2EBZx0SPIdrypdDu++wOQnwtxRM2h3jqbYFhnHayzHtZah6A7l3Mlvd8Bw8A/Ap3Ap4CnO+c2Ouf+zDn3uUBHWIdisVjYQ9hPtuNtOK8PAM/fSWz60yGPqPHVY2dTfdZZB+ush7XWIejO5RxB/iBwmHPuqc65P3fO3eiceyzQUdW5zs7OsIewP6+dzNp3l27Gpv4Fye0McUCNry47m6qzzjpYZz2stQ5Bdy7nQ3pfcc7tCnQUDWZ0dDTsIRxQrvVP8WPHApSWfTOVq9fOprqssw7WWQ9rrUPQncs5xcI/yFdeRNStp1K3f51KhEzX3klxdO6rePP3hTigxla3nU1VWWcdrLMe1lqHoDsve6EQYAwYB24EbgPmAx1RA8hkMmEPYUn5xGZyybOIpr4FFJZ9S/d/B+za9CtWz51N9VhnHayzHtZah6A7l3MO8nrgcuC5FFatuAIYcM49sucryAHWo1QqFfYQDiqzduGybz8nMqduJb6qqPfOpjqssw7WWQ9rrUPQncs5B9l3zn3TOffHFNY/3gb8XxH5g4gcE+jo6lS9r7HoYkeSbf+r0m1b9q0y9d7ZVId11sE662GtdaiLdZAXcMUvgEiVx9IwGmGNxWzn2xcs+/YkselrQh5R42mEzmb1rLMO1lkPa61DPayD7InIH4nIzcAfgBOAdznnnuKceyDQ0dWpeDwe9hCW57WTWfuu0s3Y1KeQnC1GshIN0dmsmnXWwTrrYa11CLpzOUeQnwT+Cfgl8DLgauBJETlqz1eQA6xH7e3tYQ+hLLnWP8OPPRMAcXPEbNm3FWmUzmZ1rLMO1lkPa61D0J3LmSD3A0cDHwJ+AzwEPLzg66HARlenxsbGwh5CeSRCputDpZuxuZvx5reFOKDG0jCdzapYZx2ssx7WWoegO5fzIT1vwVek+LXovkBHWIe6urrCHkLZ8omTyCX/qHQ7PvEecO4gzzB7NFJnUznrrIN11sNa6xB053LOQf5vEXm3iBwX6EgaSKMtIZNZ+/c4Ctcsj2R+RmTu6yGPqDE0WmdTGeusg3XWw1rrEPoybxTWQE4C14vIEyLy7yLyGhFpC3RkdSydToc9hBXZf9m390Pe/gdkOY3W2VTGOutgnfWw1joE3bmcUyy2OueudM49D3g+sBX4M+AxEfm+iLxdRDYFOso604hrLBaWfesFbNm3cjViZ7Ny1lkH66yHtdahrtZBds4NOueud869jsIV9j5c/H6riFwRxADrUUOuseh17LPs2yeRrLrPV65IQ3Y2K2addbDOelhrHUJfB3kpzrmcc+6/gB85554JfKJ6w6pviUQi7CFUJNf6hkXLvrWMXgrO/ilqKY3a2ayMddbBOuthrXUIunNZE2QReZqIvHbhB/VE5GwRuQ/4PIBzLhvQGOtOMpkMewiVkQiZnk/jaAEgkv0t8YmrQh5U/WrYzmZFrLMO1lkPa61D0J3LWcXijcD9wDXAfSLytyLyDeCfgeuBjYGOsA5NTEyEPYSK5ePHkunae8GQ2Mz1ROa+GeKI6lcjdzbls846WGc9rLUOQXcu5wjyO4CznXP9wLnAx4BHgKOdc59xzqlbDqGnpyfsIaxKru1icslXlW63jL0VyW0PcUT1qdE7m/JYZx2ssx7WWoegO5czQT7EOfed4s/fBHzgnZpOqdjX9PR02ENYHRHme/6ZfOTwwk03VTwfORPywOpLw3c2ZbHOOlhnPay1DkF3LmeCLHt+cM45YE7z5Bggk2mCiaTXyXzvv+OIAhDJbCO++0PLPEmXpuhslmWddbDOelhrHYLuXM4EuVVEHt/zBXQuvF28T5VmWWMx3/I8MmuvLN2OTV9DJPW9EEdUX5qlszk466yDddbDWutQD+sgvwy4YMHXvrcvCGx0daqZ1ljMtb+ZXOL00u2WsbcguZ0hjqh+NFNnszTrrIN11sNa6xB05+hyD3DO3RXkAETkbcBfAA74DXAxsAb4T+AI4H+BP3HO1c3HUptqCRnxmO/5NN7gqXj+LiQ/TsvYX5Je9w2QZf/zaGpN1dksyTrrYJ31sNY6BN152RmQiNx0gLuzwHbgFufc/ZW+uYgcCvwt8AznXEpEbgbOB54B/MA5d7WIvBN4J4XVNOpCPB4PewjVFelhvuffSAyfg5AnMn8vscl/JLvgynsaNV1nc0DWWQfrrIe11iHozuWcYvHIAb52AUcDW0Xkj1Y5hiiQFJEohSPHO4FXAzcWt98InLPK96iqycnJsIdQdfnEi8l27v0bJDb1cbz03SGOKHzN2NnszzrrYJ31sNY6BN25nFMs3r/UNhE5Dbga+H+VvLlz7kkR+SfgcSAFfM859z0R6XfO7So+ZpeIrDvQ84eHh7nkkkuIRqP4vs+5557LZZddxuDgIK2trUQiEaampujr62N8fBznHH19fQwNDdHW1gbAzMwM/f39jIyMICJ0d3czMjJCR0cHvu8zOzvLwMAAg4ODxGIxOjs7yWaz7N69m0wmQyqVKm2Px+O0t7czNjZGV1cXqVSKdDpd2p5IJEgmk0xMTNDT08P09DSZTKa0PZlMEo/HmZycpLe3l8nJSbLZbGl7kPs0OjpKZ8cb6fZ+SDL/EwRHbOhSnojdwpqOIxt3nzo7K+6UzWaZmZlpqn1qxk6r3adsNsvc3FxT7VMzdlrtPmWzWebn55tqn5qxUzX2qbOzk+3btzfVPjVjp9Xu057Oq92npUhh5bbKiIgA4865rgqf3wXcCpwH7Aa+CtwCfNo5t3bB4yYO9B5bt251mzZtqmjsq7Fz504OOeSQmr9vLYg/SHLXS5F84T8aP3Eq6b6bQcq6KnlTaebOZi/rrIN11sNa61Ctztu2bbtvy5YtJ+x7/2pnPYdQmNhW6jTgMefcSHFt5a8BLwaGRGQ9QPH78CrHWVXZbPMuA+0iA6R7/hVXXP46kv4RsalPhTyqcDRzZ7OXddbBOuthrXUIuvOyE2QROeoAX0eLyJnAV4CbV/H+jwMnisia4tHoLcADwO3ARcXHXATctor3qLpmX2MxnzyVbMfflW7HJj+Kl743xBGFo9k7mwLrrIN11sNa61AP6yA/DDxU/L7n67fANcA9wPsqfXPn3E8onFKxjcISbx7wOQrnNZ8uIg8Bpxdv1w0NayxmO9+B3/JCAASflrG/BL+uDuQHTkNnY521sM56WGsd6mEd5EBPPnXOvY/9J9nzFI4m16XW1tawhxA8iTLf8zmSg6ci+Qk8fyeJ4T8h3X8beJ1hj64mVHQ21lkJ66yHtdYh6M7lnGJx0GPYIvK86g2nMUQikbCHUBMueijzPdfgiv+ZRLK/JTHyp5CfDXlktaGls3bWWQfrrIe11iHozuUcHf7DwhvF0x4W+q/qDacxTE1NhT2EmvGTp5Pp3vshvcj8T2gZvRhcJsRR1YamzppZZx2ssx7WWoegO5czQZZ9bvcus73p9fX1hT2Emsq1nc/82g+XbkfTP6Rl7M3g/BBHFTxtnbWyzjpYZz2stQ5Bdy5ngrzvQsnL3W564+PjYQ+h5nIdf0Wm84rS7ejcbcTH3w6rWEe73mnsrJF11sE662GtdQi6s76rP1TBai6u0siyHZeTbf+r0u3Y7BeJ735f006StXbWxjrrYJ31sNY6BN152VUsgDUicveC2+0LbguQrP6w6lfXTd1UdNnAJuEA/2ghv75wZk1s+hoSv/o0kcfDHVcQNHfWxDrrYJ31sNaNbeLC8o4MB32KRTkT5Ev2uX3dPrevrdJYTAMQIPIHh4uC6ytMkv2jPMjliewMd2zGGGOM0WFoaIiNGzcG9vrlrIN8Y2DvbhqSOIje78gdC667OEl+ugd+nshQyIMzxhhjTNNra2sL9PUPOkEWkTOcc99b7kVE5HTn3J3VG1b9mrhwnLGxMXp6esIeSvjyMySGX0ck83MAcsfEmD3pBvw1rwh5YNVhnXWwzjpYZz2stamG5T6kd0uZr/Ofqx1II5mZmQl7CPXBayPd92XysWcAxUtSj/4FXvqekAdWHdZZB+usg3XWw1rrEHTn5U6xaBOR5T5+JUBLlcbTEPr7+8MeQv2IdJFe91USQ2fh5R5DmCcx8gbS675OvuW5YY9uVayzDtZZB+ush7XWIejOy02QX1rm6+RXO5BGMjIywoYNG8IeRt1wkX7S624lMfRKPH8QcbMkRs4jve5r5OPHhj28illnHayzDtZZD2utQ9CdDzpBds7dFdg7NzARdRcPXJaLHk563a0kh16F5MeR/ASJobOY770OP3la2MOriHXWwTrrYJ31sNY6BN3ZLhRSge7u7rCHUJdc7GjSff+Jkw4AxM3SMvJnRGcacyEU66yDddbBOuthrXUIurNNkCswMjIS9hDqVr7leFL93yYfOQwofnBv/P8jtvsD4BrrTBzrrIN11sE662GtdQi6s02QK9DR0RH2EOqai28iPXAHfuzZpfviU5+iZexScOkQR7Yy1lkH66yDddbDWusQdOdVT5BFpLcaA2kkvu+HPYS65yIDpPtvJ5c4o3RfdO42EsOvBX8sxJGVzzrrYJ11sM56WGsdgu687ARZRMb3uf2DfR7yaFVH1ABmZ2fDHkJj8NqY7/sPsm17r1Yemf8JyaFXINn6/8/GOutgnXWwznpYax2C7lzOEeTYPreP3+e2uo+LDgwMhD2ExiERMl1XM7/2g7jifype7lGSQy/Hm/9pyIM7OOusg3XWwTrrYa11CLpzORNkt8rtTWdwcDDsITQWEXIdb2a+9/M4SRTuyo+TGHoNkbnbQh7c0qyzDtZZB+ush7XWIejO9iG9CsRi+x5UN+Xw15xFet03cF7htHVhnsToJcSm/gVc/f2dZZ11sM46WGc9rLUOQXde7kp6AAkRuWnB7dZ9bqu6zDRAZ2dn2ENoWPmWE0j1f4fEyPl4uUcAiO9+P5LbTqbrapBy/pOsDeusg3XWwTrrYa11CLpzOUeQPww8suDrIwe4rcro6GjYQ2hoLnYkqf478FtOLN0Xm7mBxPC5SO7JEEe2mHXWwTrrYJ31sNY6BN152cN1zrn3BzqCBmR/nVZBpIv0ultpGfsbonNfK9w1/z8kB09hvvvj+GvODnmA1lkL66yDddbDWutQD0eQEZGoiLxRRL4oIt8tfr9YRFSe6JPJZMIeQnOQFuZ7Pkum8x244n+Kkt9NYvTPiY+9FfLhLtVjnXWwzjpYZz2stQ5Bdy5nHeRO4H+A/wtkgW3F71cD/1PcrkoqlQp7CM1DPLKdl5Nedzv5yIbS3bHZL5IcfBle5pehDc0662CddbDOelhrHYLuXM4R5I8CI8BRzrk3Oufe5Zx7I3AUMFzcroqtsVh9+cSJpNb/iNya15Tu83KPkBh8RXGVi3zNx2SddbDOOlhnPay1DvWwDvI5wJudc4v+vbt4+zLgNQd8VhOzNRYD4nUy3/M55rs/g5NWAIQs8d3vJzH8OiS3q6bDsc46WGcdrLMe1lqHelgHuRNYammBHUBH9YbTGOLxeNhDaF4i5NrOIzXwX/jxvRdtjMzfTXLwFCJz367ZUKyzDtZZB+ush7XWIejO5UyQHwFetsS2LcCj1RtOY2hvbw97CE3PxY4i3f9tMh1vK12iWvLjJEYvJD7+fyA/F/gYrLMO1lkH66yHtdYh6M7lTJA/DtwkIq8VEQ9ARDwReR1wQ3G7KmNjY2EPQQeJkV37HtLrvkE+ckjp7tjMDSQHT8PL/DrQt7fOOlhnHayzHtZah6A7LztBds7dAPwThclwWkR2Amng88DHnXOfD3KA9airqyvsIaiST7yE1MBd5JJ710b2cn8gMXga8fF3Q34qkPe1zjpYZx2ssx7WWoegO5e1DrJz7mPAIcCrgMuL3w91zv1jgGOrW7aETAgiXcz3Xsd89z/jZA0AQp7YzOdI7jyR6OzN4FxV39I662CddbDOelhrHUJf5k1EukXk5c65aefcd51zXyx+nxKRl4uIuj/V0ul02EPQSYRc2xsKH+BLnFK628sP0zL21ySGX4Vk7q/a21lnHayzDtZZD2utQ9CdyzmC/F7geUtsOx54T/WG0xhsjcVwudhTSPfdQrr3OvKR9aX7I/P3khx8KfGJ90B+etXvY511sM46WGc9rLUO9bAO8lnAvy2x7XPAq6s3nMZgayzWARH8Na8mtX4rmfa34IgW7sYnNv1vJHe+kMjsV1d12oV11sE662Cd9bDWOtTDOsgDzrnRJbaNA/1VHE9DSCQSYQ/B7OG1ke36e1Lr78Jv2bz37vwwibE3kxg+G8k8UNFLW2cdrLMO1lkPa61D0J3LmSBPiMjRS2x7OrC7iuNpCMlkMuwhmH242NGk132ddM/nyEf2/s0Wmd9KcvBU4hNXrfi0C+usg3XWwTrrYa11CLpzORPkrwOfEpFFIyne/gRwy2oGICJrReQWEfm9iDwgIi8qfjDwThF5qPi9rj4IODExEfYQzIGI4LeeS2r9vWTb/xpHpHA3PrHpa1iz8wVEpz4LrrwT+62zDtZZB+ush7XWIejO5UyQrwS6gUdF5PMi8hER+TyFK+z1AO9b5Rg+CdzhnNsEHAc8ALwT+IFz7mnAD4q360ZPT0/YQzAH47WT6foAqYEf4be8uHS35Edo2f1ekjufT3T6enCZg76MddbBOutgnfWw1joE3bmcC4VMAy+mMFFOACcUv18JnFTcXhER6QBOBq4rvlfGObebwgf/biw+7EbgnErfIwjT06tfIcEEz8WPIb3uNtI9n1202oXn76Jl4gqSO19IdOYL4LIHfL511sE662Cd9bDWOgTdOVrOg5xzWeDa4leJiERE5APOuasqfP+jgBHg8yJyHHAf8Fag3zm3q/jeu0Rk3YGePDw8zCWXXEI0GsX3fc4991wuu+wyBgcHaW1tJRKJMDU1RV9fH+Pj4zjn6OvrY2hoiLa2NgBmZmbo7+9nZGQEEaG7u5uRkRE6OjrwfZ/Z2VkGBgYYHBwkFovR2dnJyMgIyWSSTCZDKpUqbY/H47S3tzM2NkZXVxepVIp0Ol3ankgkSCaTTExM0NPTw/T0NJlMprQ9mUwSj8eZnJykt7eXyclJstlsaXuQ+zQ6OkpnZ2eT7tPJpHO3cnjH92mZ+iRRKVye0vOfoGX87/DGP8awu5Rk/xsZHBop7dPIyAjt7e11uk/N2CmcfRoZGaGzs7Op9qkZO612n0ZGRuju7m6qfWrGTtXYJ9/32b59e1PtUzN2Wu0+7em82n1airhVLIMlIi3AnHMuUuHzTwDuBV7inPuJiHwSmAL+xjm3dsHjJpxz+52HvHXrVrdp06YKR1+5+fl5Wlpaav6+pgryc0Rnric+9S9IfvF13PPRp5HpvAJ/zatBPOushHXWwTrrYa11qFbnbdu23bdly5YT9r2/rEtNL0NW8dwdwA7n3E+Kt28BngsMich6gOL34dUNsbpsjcUG5q0h1/EW5g75OZnO9+C8tXs35R4iMXYpycFTicz9PwYHd4U4UFMr9vusg3XWw1rrUA/rIC+n4kPQzrlB4IkFy8htAe4HbgcuKt53EXDbqkZYZbaETBPw2sl2vo25Q7aR6bgcJ+17N2XvJzF6EU/xLiAye8uS5yib5mC/zzpYZz2stQ5Bd172HGQRedlBNserMIa/Ab4oInHgUeBiChP3m0XkEuBx4I+r8D5VE49XY7dNXfA6yK59B9n2vyQ2/Rli0/+OuFkAWtwDMPYm8rv/nlzbX5BtuxAi3SEP2FSb/T7rYJ31sNY6BN25nA/pXbfM9sdXMwDn3C8prIyxry2red0gTU5Osnbt2uUfaBpHpIvs2veSbf8r4lP/QnTmeqS4XrLnDxKf/BCxqY+Ra/0Tsu1vwsWeFvKATbXY77MO1lkPa61D0J2XnSA7544M7N0bVG9vb9hDMEGJ9JHp+gCZjr/FjX+ONfNfwMsXToEXlyI2cyOxmRvJJU4j2/4m8olTQFZzGr4Jm/0+62Cd9bDWOgTduRrnIKszOTkZ9hBM0CK97Mq+kdShv2C++zP4sWMXbY6mv09y5HUkB08iOvMfkE+FNFCzWvb7rIN11sNa6xB0Z5sgVyCbtQ9taZDNZkFayLWdR3rgh6TW3UYu+QrcgoVbvOzvaRl/G2t2PofY7o8iuSdCHLGphP0+62Cd9bDWOgTd2SbIFRgYGAh7CKYGFnUWIZ94CfN9/0Fq/U/Itv0lTlr3bs6PEZ/6GMmdzyUx/Fois7faUeUGYb/POlhnPay1DkF3tglyBWyNRR2W6uxiR5Hp/ghzh/6a+bXvJx/ZUNomOCLpu0iM/RVrnnwW8fEr8DK/hFVckMcEy36fdbDOelhrHRphHWR1Wltbl3+QaXjLdvY6yXVcRuqQn5Hu/Ty5xJZFp1+ImyQ2cz3JwdNIDp5CdOqz4I8GPGqzUvb7rIN11sNa6xB0Z5sgVyASqejK2qbBlN1ZovhrXsX8uv8kdcgvyXS+m3z0iEUP8bL307L7vax58lhaRi4mkroTXK76gzYrZr/POlhnPay1DkF3tglyBaampsIegqmBSjq76KFkO99Oav1PSa27jWzreTjZe7UfIUs09U0SI68nufM5xCfeizf/czsFI0T2+6yDddbDWusQdGebIFegr68v7CGYGlhVZ/HIJ15CpuczzB16P/Pdn8CPP3/RQzx/kNj0Z0kOvbw4Wb7SJsshsN9nHayzHtZah6A72wS5AuPj42EPwdRA1Tp77eTaLiA98B3m1v8Pmfa3kPfWLX6I/ySx6X8tTpaPJz5xFd78fTZZrgH7fdbBOuthrXUIurNNkAROZ1gAAB+PSURBVCvgbNKiQhCdXezpZLv+ntShvyLddzPZ1j/DeYsvlen5O4hNX0Ny6EySO59LbOJ9ePPbbLIcEPt91sE662GtdQi6s02QK2D/fKNDoJ0lhp98GZmeTzJ36AOk+/5zicnyE8SnP0Ny6AySO59LfOI9eOm7wWWCG5sy9vusg3XWw1rrYKdY1KGhoaGwh2BqoGadJYaf3FKcLN9fnCz/KU46Fz3M858gNv1vJIfPZc2Op9My8kaiM18Cf7g242xS9vusg3XWw1rrEHTnaKCv3qTa2trCHoKpgVA6Sxw/uaUwYe7+JyLpu4nM3UZ07tuI23vdeXEzRFPfIpr6Fi2AHz8eP3k6fvIM8rFng9jfvuWy32cdrLMe1lqHoDvbBNmYeiVx/ORp+MnTyHR/jEj6v4mk7iSS+h6ev33RQyOZXxDJ/AIm/4G8t640WfYTJ4PXHtIOGGOMMY3JDjNVYGZmJuwhmBqoq84SL5yz3P1RUof8vLAaxtr34be8GMfixdK9/DCx2S+SGL2INTuOIrlrM/GxtxKduREv81u7QMk+6qqzCYx11sNa6xB0ZzuCXIH+/v6wh2BqoG47ixRWw4g9nWzH30B+N5HUfxFJ3Uk0/X0kv3fpG8Eh2d/jZX8Ps18EwMka8vFn48efR77leeTjz8NFDgGRpd6xqdVtZ1NV1lkPa61D0J1tgrxCH/jAB8IegjFLGEDkT9nQO8Qxhz3G0YdtZ33XKJ63eCkccXNE5u8lMn8vTBfum5pr5fGRfnaM9jO0u5uh3T2Mz3TgnP0jkzHGmNq56qqrynrcyMgIGzZsCGwcNkE2pok45/H4yHoeH1nPd3/xYmLRLIf1DLOhd5DD+wbZ0DfE2tb9/1mqY80sz9r4KM/a+GjpvqwfYWSyi+Hd3Qzv7mZosvB9dKqTvIvs9xrGGGNMrUjA/+ppE2Rjmlg2F+OxoUN5bOjQ0n3tyRkO7xtiQ98gh/cOcljvMC2x7H7PjUV8Duke5ZDu0UX3+3mP0cm1pQnz+EwH49OdjE93MDXXhkPnqRrGGGNqp7u7O9DXtwnyCl111VVs376djRs3hj0UEzAtnXPOx8/+gUjmPrzM/Xi5B5Hsg3j+4AEfH/Hy9HeN09+1/2U+HXFcdAP56EZcdGPp+56f8ToP8Irh0tJZO+ush7XWYWRkJNDONkGuQEdHR9hDMDWgprNEcPFjyMWPWXx/fhIv+we87IN42QeRPT/7O5Z+KTJI7hG83CMH3O6kk3z0MFxkfeEregguUvjKF28j7TX9wKCazspZZz2stQ5Bd7YJcgV83w97CKYG1Hf2Osm3PJ98y/MX35+fxss+hJf9A5J7CC+3Hck9XvieHzvoS4qbJJKdhOzvlnyMk9biBPoQ8tFDcJF+nNeHi6zDRfpKt/HWVmUirb6zEtZZD2utQ9CdbYJcgdnZWXp7e8MehgmYdV6C106+5bnkW567/7b89N7Jcm774u/+44hLL/vy4maR3MOQe5jI/NKPc8SKE+Y+nLeuOIFeh/N6cZFuKH53Xg/O6wZvzQFfxzrrYJ31sNY6BN3ZJsgVGBgYCHsIpgascwW8dlz8mfjxZ+6/zeWR/AiS24n4u4pfO/FKt4vfXaqstxKyiL8T/J1lPd7JGpzXXfiK9IDXjYv0ckRbO5HpPpy3FrwunLe2+NUFXgeIrdjRDOz3WQ9rrUPQnW2CXIHBwUH7AIAC1rnKxCucHhHpB44/8GOcAzeJ5Hbi7Zkw+8PFrxEkP4z4Q4Wf3cquoiRuDvHnwN8BCxbtiAHMLjEcBKQDF+nCeZ3FCXRnYQItHeB14ryO4raO4rbOwnO8DpA1ai/AUm/s91kPa61D0J1tglyBWCwW9hBMDVjnEIiArMXF1+LzjIM/Nj9XOCK9Z/LsDxV+zo8j+VHEH4f8OJIfQ/wxhP2Xslt2OOyZsE9WtDuOaHHi3IGTdvDait/bcV77op+R4n1eO0gbzmsDaS19R+x/rlfDfp/1sNY6BN3Z/he3Ap2d9bdUlak+61znvDU4r7CE3LKcAzdTnCyPl76THyM3P0Q8MovkJ5D8bvAnkPxk4babWtUQhVxxkr7/kngr5SRZmjA7aQOvtfi9DSetIGtwXvG7tBb+77Po/tYF968pHt1OqjnCbb/PelhrHYLubBPkCoyOjtLa2hr2MEzArHMTEdl7hDZ6xKJNB10z1eUgP4nkdxe/JiA/geSnkfxUYZubLP48VZxY77l/uuzzqcvaBZcCl0Lyo8s/eAX2TJadJIuT52TxdvF+b89EOlncVvzuJXGSKE20Cz8nC8/zEiCJ4uMTQDz0ibj9PuthrXUIurNNkCtgf53qYJ11OGhniUKkBxfpwVXy4m6+OHGeRtx0YZWP/DS4aSQ/s+Dn4jZXnHi7GSQ/W/w+A262cLpHAMTNgZsrXP8woFWTCudyJ4uT5n0mz4t+biltL/ycLH5vWfDYlgXbF/xMCxRvF+6PF7cV/t+c/T7rYa11sCPIdSiTyYQ9BFMD1lmHQDtLC+xZim41r+NcYRLrZiA/W/w+g7jZ4veZwjnZeya7+dnCpNrNFu+fXXD/ntupspbdq4bCudwLJuI15IiAJEi4KDKbABIg8QWT6MJE2hEvTrD3bCv+zJ7HtOBk72Ngz+343uew8Ha8dNtJrPA8YsXt9v96g2T/261D0J3tt7QCqVT1/tnU1C/rrENDdBYpnENMK0So3rFkl987cXWp4mQ6VZxcL7w9V5xQ7znNI7Xo9oG3pYvb0oVzsUMi+OBmiQD4lX3YstocXnGSvWACTRwkduDbpYl1rDhhjxW+2DP5jgPRBc+NlZ4LseJjYks8JrrPa+65b+FzYkAk9NNkytUQv9P/f3v3HlvnXd9x/PP1cbyY+JI4cZ3QWygtLbduXEaZGAItApXSja6MUTSgf4Q/tgWJauNSEH8MaYyLNECMSZM2JmWjG9ugK5UoYqgwpk0ZbA2FtmRbW5SQ0NhxYtf2cewc++S7P85z7MeOW+LHeS7H3/dLOvLxc26/p2+d9Ovjcx5jw/LuzICcAcdYjIHOMYTubF2S9UnqWxq6c3kjhy8uD8t+LjVAz6fOt7efk/xcMnA/2/n5peu2tifn1d7eaN1fTm9N2QjT+da+a15yFf6qehYuk1YNzW7dag/Uy4P4GtvaQ7Z1S+pObVu+rq+6zvKQ3r3ium611P3Xkh8auldc7/Kdrq7GTPI4tdTj1JYfJ71dXR0z/GMZx0GuII6xGAOdY6BzAaxbsn5J/fkO4qu5S1qUfF4njj+pKy+/LBmiG6uG68Ya2xvLl6kheSM1eJ+TtJD6PrlMC6nv07drfW19f66SQ/vP01pzez9U6cG+N8NtlofsmpYH9FrqB4LV27vX2N6t1rC98jJfGuaXryvVlrevuL+uVWuppe4nfd2V51v31ZV63NryDwRL95G6rdVa+7F0/a5V99uV4b9isTgOcgX19PSUvQQUgM4x0HkTs+VXPWs9I/LuPUsXlTqi+mJqYF6Q+cLS+QuH62To1kIysC8m55Prps63hvbk+wvuezHZ3t7Wvt1C6j7al6cfY1FSoyOH+vUwLSb7mqjwDwBFaP3GID1Udy0N2Z4euJeG6dryDw+p664czmtrXN69dHtXTQvbPyrvfu5FrTHvf7sZkDPo7+8vewkoAJ1joHMMleps7VcInyPpwmG9kqOoN7VyaF49aDeXB20tLn1dHtgXk2F8cen28mZqKE/dv5qp67Zv21y+v+Ty1v00k23L93u+uaBa1/nk+8UVX5cfP/XY1fwvXqrWf5P2f7dzrY3eviw/C4N3XfR1835OMyBncObMGfX19ZW9DOSMzjHQOQY6b9DSq4BblzZVdbB/1mObr8XPqzUwrxyorT1IrxiqF5MfBM6vuu6F15OayYdEF1ZsWx72k9ukti0P8ecvuGz5MdKPfT65fvu+zq94nLUva92HqZla1/nlx8nreI8X5eLf2pH3c5oBOYMdO3aUvQQUgM4x0DkGOsex7tbWpdav/Vf+6eJnGvir8oNArpaG8PRw3RqobWloT31Nhu/lofu8Vgz+Op8M+OnbpYZ5NWV+Xl4buegl5v2crsSAbGY1Sf8t6WfufquZDUn6B0l7JR2V9NvuPlneCleam5vTwMBA2ctAzugcA51joHMctL4ErEutQw1eeNGz/YBQ5A8PeXeuyscU3yfpSOr7uyU96O7XSXow+b4y5ueLObg+ykXnGOgcA53joHUMeXcufUA2syskvVnSX6U2v0XSweT8QUm3Fb2uZxP6uKmB0DkGOsdA5zhoHUOE4yB/TtIHJaU/jjji7iclyd1Pmtlla93w1KlT2r9/v7q7u9VsNnX77bfrwIEDGh0d1bZt21Sr1TQ9Pa3h4WFNTEzI3TU8PKyxsbGlN3bX63WNjIxofHxcZqahoSGNj49rYGBAzWZTs7Oz2r17t0ZHR7VlyxYNDg7qySef1FVXXaVGo6G5ubmly3t6etTf368zZ85ox44dmpub0/z8/NLlW7duVW9vryYnJ7Vz507NzMyo0WgsXd7b26uenh5NTU1p165dmpqa0sLCwtLlee7T6dOnNTg4yD6l9uno0aO65pprNtU+bcZOG92n48eP69prr91U+7QZO210n5566ildf/31m2qfNmOnS7FPzWZTtVptU+3TZuy00X1qd97oPj0Tcy/v7eZmdqukW9z9983s9ZLen7wH+Wl335663qS7X/Bu7EOHDvkNN9xQ4IpbxsbGNDJy8W8kR2eicwx0joHOcdA6hkvV+fDhww/t27fvlau3l/0K8msk/YaZ3SJpq6QBM/uSpDEz25O8erxH0qlSV7lKb2+Wv9ODTkPnGOgcA53joHUMeXcu9T3I7v5hd7/C3fdKukPSt939nZLul3RncrU7JX2tpCWuaXKyMgfUQI7oHAOdY6BzHLSOIe/OpX9I7xl8UtIbzOxxSW9Ivq+MnTt3lr0EFIDOMdA5BjrHQesY8u5cmQHZ3f/V3W9Nzp9x933ufl3ydaLs9aXNzMyUvQQUgM4x0DkGOsdB6xjy7lyZAbmTNBqNspeAAtA5BjrHQOc4aB1D3p0ZkDPgGIsx0DkGOsdA5zhoHUPenRmQMxgdHS17CSgAnWOgcwx0joPWMeTdmQE5Aw4hEwOdY6BzDHSOg9YxbOrDvHWqnp6espeAAtA5BjrHQOc4aB1D3p0ZkDOYmpoqewkoAJ1joHMMdI6D1jHk3ZkBOYNdu3aVvQQUgM4x0DkGOsdB6xjy7syAnAE/ncZA5xjoHAOd46B1DLyCXEELCwtlLwEFoHMMdI6BznHQOoa8OzMgZ8AxFmOgcwx0joHOcdA6Bo6DXEEcYzEGOsdA5xjoHAetY+A4yBW0bdu2speAAtA5BjrHQOc4aB1D3p0ZkDOo1WplLwEFoHMMdI6BznHQOoa8OzMgZzA9PV32ElAAOsdA5xjoHAetY8i7MwNyBsPDw2UvAQWgcwx0joHOcdA6hrw7MyBnMDExUfYSUAA6x0DnGOgcB61jyLszA3IG7l72ElAAOsdA5xjoHAetY8i7MwNyBvz6JgY6x0DnGOgcB61j4C0WFTQ2Nlb2ElAAOsdA5xjoHAetY8i7MwNyBn19fWUvAQWgcwx0joHOcdA6hrw7MyADAAAAKQzIGdTr9bKXgALQOQY6x0DnOGgdQ96dGZAzGBkZKXsJKACdY6BzDHSOg9Yx5N2ZATmD8fHxspeAAtA5BjrHQOc4aB1D3p0ZkDMws7KXgALQOQY6x0DnOGgdQ96dGZAzGBoaKnsJKACdY6BzDHSOg9Yx5N2ZATkDfn0TA51joHMMdI6D1jHwFosKGhgYKHsJKACdY6BzDHSOg9Yx5N2ZATmDZrNZ9hJQADrHQOcY6BwHrWPIuzMDcgazs7NlLwEFoHMMdI6BznHQOoa8OzMgZ7B79+6yl4AC0DkGOsdA5zhoHUPenRmQMxgdHS17CSgAnWOgcwx0joPWMeTdmQE5gy1btpS9BBSAzjHQOQY6x0HrGPLuzICcweDgYNlLQAHoHAOdY6BzHLSOIe/ODMgZnD59uuwloAB0joHOMdA5DlrHkHdnBuQM+Ok0BjrHQOcY6BwHrWPgFeQKajQaZS8BBaBzDHSOgc5x0DqGvDszIGcwNzdX9hJQADrHQOcY6BwHrWPIu3OpA7KZXWlm3zGzI2b2mJm9L9k+ZGbfMrPHk687ylznahxjMQY6x0DnGOgcB61j2OzHQV6U9Ifu/kJJr5Z0wMxeJOluSQ+6+3WSHky+rwyOsRgDnWOgcwx0joPWMWzq4yC7+0l3P5ycn5F0RNLlkt4i6WBytYOSbitnhWvr6ekpewkoAJ1joHMMdI6D1jHk3bk713tfBzPbK+llkr4nacTdT0qtIdrMLlvrNqdOndL+/fvV3d2tZrOp22+/XQcOHNDo6Ki2bdumWq2m6elpDQ8Pa2JiQu6u4eFhjY2Nqa+vT5JUr9c1MjKi8fFxmZmGhoY0Pj6ugYEBNZtNzc7Oavfu3RodHdWWLVs0ODioer2up59+Wo1GQ3Nzc0uX9/T0qL+/X2fOnNGOHTs0Nzen+fn5pcu3bt2q3t5eTU5OaufOnZqZmVGj0Vi6vLe3Vz09PZqamtKuXbs0NTWlhYWFpcvz3KfTp09rcHCQfUrtU71eV71e31T7tBk7bXSf6vW6zp49u6n2aTN22ug+1et1nTt3blPt02bsdCn2aWBgQMeOHdtU+7QZO210n9qdN7pPzziXuvs6xth8mFmfpO9K+ri732tmT7v79tTlk+5+wfuQDx065DfccEORS5UkHTt2TFdffXXhj4ti0TkGOsdA5zhoHcOl6nz48OGH9u3b98rV28t+D7LMbIukr0q6x93vTTaPmdme5PI9kk6Vtb617NhRqc8MIid0joHOMdA5DlrHkHfnso9iYZK+KOmIu38mddH9ku5Mzt8p6WtFr+3ZcAiZGOgcA51joHMctI4h785lvwf5NZLeJekRM3s42fYRSZ+U9I9mtl/STyW9raT1rWl+fr7sJaAAdI6BzjHQOQ5ax5B351IHZHf/d0n2DBfvK3It68ExFmOgcwx0joHOcdA6hs1+HOSOxDEWY6BzDHSOgc5x0DqGTX0c5E61devWspeAAtA5BjrHQOc4aB1D3p0ZkDPo7e0tewkoAJ1joHMMdI6D1jHk3ZkBOYPJycmyl4AC0DkGOsdA5zhoHUPenRmQM9i5c2fZS0AB6BwDnWOgcxy0jiHvzgzIGczMzJS9BBSAzjHQOQY6x0HrGPLuzICcQaPRKHsJKACdY6BzDHSOg9Yx5N2ZATkDjrEYA51joHMMdI6D1jFwHOQK4hiLMdA5BjrHQOc4aB0Dx0GuIA4hEwOdY6BzDHSOg9YxcJi3Curp6Sl7CSgAnWOgcwx0joPWMeTdmQE5g6mpqbKXgALQOQY6x0DnOGgdQ96dGZAz2LVrV9lLQAHoHAOdY6BzHLSOIe/ODMgZ8NNpDHSOgc4x0DkOWsfAK8gVtLCwUPYSUAA6x0DnGOgcB61jyLszA3IGHGMxBjrHQOcY6BwHrWPgOMgVxDEWY6BzDHSOgc5x0DoGjoNcQdu2bSt7CSgAnWOgcwx0joPWMeTdmQE5g1qtVvYSUAA6x0DnGOgcB61jyLszA3IG09PTZS8BBaBzDHSOgc5x0DqGvDszIGcwPDxc9hJQADrHQOcY6BwHrWPIuzMDcgYTExNlLwEFoHMMdI6BznHQOoa8OzMgZ+DuZS8BBaBzDHSOgc5x0DqGvDszIGfAr29ioHMMdI6BznHQOgbeYlFBY2NjZS8BBaBzDHSOgc5x0DqGvDszIGfQ19dX9hJQADrHQOcY6BwHrWPIuzMDMgAAAJDCgJxBvV4vewkoAJ1joHMMdI6D1jHk3ZkBOYORkZGyl4AC0DkGOsdA5zhoHUPenRmQMxgfHy97CSgAnWOgcwx0joPWMeTdmQE5AzMrewkoAJ1joHMMdI6D1jHk3ZkBOYOhoaGyl4AC0DkGOsdA5zhoHUPenRmQM+DXNzHQOQY6x0DnOGgdA2+xqKCBgYGyl4AC0DkGOsdA5zhoHUPenRmQM2g2m2UvAQWgcwx0joHOcdA6hrw7MyBnMDs7W/YSUAA6x0DnGOgcB61jyLszA3IGu3fvLnsJKACdY6BzDHSOg9Yx5N2ZATmD0dHRspeAAtA5BjrHQOc4aB1D3p0rPSCb2c1m9r9m9oSZ3V32etruu+++speAAtA5BjrHQOc4aB1D3p0rOyCbWU3Sn0t6k6QXSXqHmb2o3FW13HvvvWUvAQWgcwx0joHOcdA6hrw7V3ZAlvQqSU+4+0/cvSHpy5LeUvKaJEmLi4tlLwEFoHMMdI6BznHQOoa8O5u75/oAWZnZb0m62d3fk3z/Lkk3uft729d54IEHZk6ePLk05A8MDIwPDQ2dznttExMTu4p4HJSLzjHQOQY6x0HrGC5h56v37ds3vHpj9yW447ys9Ue2V0zzt9xyS39BawEAAEAQVX6LxQlJV6a+v0LSUyWtBQAAAEFUeUD+L0nXmdnzzKxH0h2S7i95TQAAANjkKvsWC3dfNLP3SvqmpJqkv3b3x0peFgAAADa5Kr+CLHd/wN1f4O7Pd/eP5/U4ZnalmX3HzI6Y2WNm9r5k+5CZfcvMHk++7ki2v8HMHjKzR5Kvv5a6r1ck258ws8+b2VrvpUYJMnR+lZk9nJx+aGa/mbovOlfUejunbneVmdXN7P2pbXSuqAzP571mNpd6Tv9F6r7oXGFZntNmdqOZHUqu/4iZbU2207qiMjynfyf1fH7YzM6b2S8ll228s7uHP0naI+nlyfl+Sf+n1rGXPy3p7mT73ZI+lZx/maTnJudfIulnqfv6vqRfUetDht+Q9Kay949T5s7PkdSduu2p1Pd0ruhpvZ1Tt/uqpH+S9P7UNjpX9JTh+bxX0qPPcF90rvApQ+tuST+S9IvJ9zsl1Whd7VPWf7uT7S+V9JPU9xvuXOlXkIvi7ifd/XByfkbSEUmXq3Xc5YPJ1Q5Kui25zg/cvf2BwcckbTWzXzCzPZIG3P2Qtwr9Tfs2KF+GzmfdvX2gxa1KjqJC52pbb2dJMrPbJP1EredzexudKyxL57XQufoytH6jpB+5+w+T25xx9yatq22Dz+l3SPp76dI9pxmQVzGzvWq9Qvw9SSPuflJqhZN02Ro3eaukH7j7ObVCnkhddiLZhoq52M5mdpOZPSbpEUm/mwzMdO4QF9PZzLZJ+pCkj626OZ07xDr+3X6emf3AzL5rZq9NttG5g1xk6xdIcjP7ppkdNrMPJttp3SEyzGJvVzIg6xJ1ruyH9MpgZn1q/Zr1Lnef/nlvWTGzF0v6lFo/rUoXcexmlG89nd39e5JebGYvlHTQzL4hOneEdXT+mKTPunt91XXo3AHW0fmkpKvc/YyZvULSfcm/4XTuEOto3S3pVyX9sqSzkh40s4ckTa9xXVpXTIZZ7CZJZ9390famNa627s68gpwwsy1qBbnH3dt/4Hsseam+/ZL9qdT1r5D0z5Le7e5PJptPqHW85jaO3Vwx6+3c5u5HJM2q9Z5zOlfcOjvfJOnTZnZU0l2SPmKtI+jQueLW09ndz7n7meT8Q5KeVOuVRjp3gHU+p09I+q67n3b3s5IekPRy0bryMv4/+g4tv3osXaLODMiSkk83flHSEXf/TOqi+yXdmZy/U9LXkutvl/R1SR929/9oXzl56X/GzF6d3Oe727dB+TJ0fp6ZdSfnr5Z0vaSjdK629XZ299e6+1533yvpc5L+xN2/QOdqy/B8HjazWnL+GknXqfWhHjpX3Hpbq3V42BvN7DnJv+Gvk/RjWldbhs4ysy5Jb5P05fa2S9Z5vZ/q24wntX4V42p96vXh5HSLWp98fVDS48nXoeT6H1Xr1cSHU6fLksteKelRtV6d+IIkK3v/OGXu/C61PrT1sKTDkm5L3RedK3pab+dVt/0jrTyKBZ0resrwfH5r8nz+YfJ8/nU6d8Ypy3Na0juT3o9K+jStq3/K2Pn1kv5zjfvacGdL7ggAAACAeIsFAAAAsAIDMgAAAJDCgAwAAACkMCADAAAAKQzIAAAAQAoDMgAAAJDCgAwAAACkMCADACRJ7b8cCQDRMSADQAcwsw+Y2VdXbfszM/ucmQ2a2RfN7KSZ/czM/jj1Z5Wfb2bfNrMzZnbazO4xs+2p+zhqZh8ysx9JmmVIBgAGZADoFF+SdHN7uE0G2bdL+ltJByUtSrpW0sskvVHSe5LbmaRPSHqupBdKulKtP6md9g5Jb5a03d0Xc90LAOgADMgA0AHc/aSkf5P0tmTTzZJOSzoh6U2S7nL3WXc/Jemzku5IbveEu3/L3c+5+7ikz0h63aq7/7y7H3f3uSL2BQCqjl+lAUDnOCjp9yT9paR3qvXq8dWStkg6aWbt63VJOi5JZnaZpM9Leq2k/uSyyVX3ezzvhQNAJ+EVZADoHPdJutHMXiLpVkn3qDXcnpO0y923J6cBd39xcptPSHJJN7r7gFqDta26Xy9m+QDQGRiQAaBDuPu8pK9I+jtJ33f3nyZvvfgXSX9qZgNm1pV8MK/9Nop+SXVJT5vZ5ZI+UMriAaCDMCADQGc5KOmlar29ou3dknok/Vitt098RdKe5LKPSXq5pClJX5d0b2ErBYAOZe78Zg0AOoWZXSXpfyTtdvfpstcDAJsRryADQIcwsy5JfyDpywzHAJAfjmIBAB3AzLZJGpN0TK1DvAEAcsJbLAAAAIAU3mIBAAAApDAgAwAAACkMyAAAAEAKAzIAAACQwoAMAAAApPw/ru9Ew35IvZEAAAAASUVORK5CYII=\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", "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 }