{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Discrete supply decisions - example battery in continuous intraday markets\n", "\n", "We often face discrete execution decisions in energy. Those may be shippings (e.g. LNG) or order books in futures markets. Another example are continuous intraday power or gas markets. Let us focus on the latter in this example of optimizing a battery directly against an intraday power order book.\n", "\n", "Our example: The main market targeted by battery storage (besides reserve markets) is the power intraday market. When power prices are low, the battery is charged, to be discharded at higher power prices. When optimizing a battery against the intraday market, a typical simplification is to assume there is a price for each time interval (such as 15min in central Europe). Following this optimization, we pass the position to our autotrader, that targets to close the position at the assumed price curve (or better, naturally). \n", "\n", "This simplification is often good, but does not account for significant features if the market: \n", "* Orders may have to be executed all or nothing. If orders are large as compared to the battery, this cannot be neglected\n", "* Bid ask spread. There may be a significant price gap between buying and selling\n", "* Limited liquidity. We may not be able to trade the full dispatch potential of the battery. Particularly for shallow markets and large assets this may have a significant effect\n", "\n", "Note how we consistently solve for an exact match between battery dispatch and order execution. If a direct link to an autotrader is established, this enables us to do a direct execution. Naturally there are many aspects to be accounted for in real life from this static demo to a live solution with a changing order and position tracker." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Some prerequisites\n", "\n", "Import relevant packages and set links. Note that we set a random seed for the generation of our randomized order book." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import datetime as dt\n", "import eaopack as eao\n", "import matplotlib.pyplot as plt\n", "np.random.seed(6924)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Setup (1) Basics \n", "\n", "Defining start, end and time grid. Here we optimize over a full day in an hourly granularity. That's an artificial choice for sake of visual clarity, of course." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "node = eao.assets.Node('power')\n", "S = dt.date(2021,1,1)\n", "E = dt.date(2021,1,2)\n", "timegrid = eao.assets.Timegrid(S, E, freq = 'h') # using hours for better visualization" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### (2) Order book\n", "\n", "As the first ingredient we generate a randomized order book with orders of various prices, capacities and duration" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# create larger number of orders\n", "## some time steps, buy & sell\n", "ob = pd.DataFrame(columns = ['start', 'end', 'capa', 'price']) # alternative to dict is DataFrame ... converted in asset\n", "r = dict() # row\n", "### orders\n", "# orders with bid/ask spread on base signal\n", "# base signal\n", "bs = (30*np.sin(timegrid.I/24*2*np.pi*2)+40).round(0) + 0*np.random.randn(timegrid.T).cumsum()\n", "prices = {'av': bs}\n", "for ii in timegrid.I:\n", " tp = timegrid.timepoints[ii]\n", " # sell (they sell)\n", " for i in range(0,5):\n", " r['start'] = tp\n", " r['end'] = tp + pd.Timedelta(np.random.randint(1, 4), 'h')\n", " r['capa'] = np.random.randint(1, 4)\n", " r['price'] = bs[ii] + np.random.randint(10, 50)\n", " ob.loc[len(ob)] = r\n", " # buy (they buy)\n", " for i in range(0,5):\n", " r['start'] = tp\n", " r['end'] = tp + pd.Timedelta(np.random.randint(1, 4), 'h')\n", " r['capa'] = -np.random.randint(1, 4)\n", " r['price'] = bs[ii] + np.random.randint(-10, 10)\n", " ob.loc[len(ob)] = r \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The artificial order book shown with their price against time of delivery. The thickness of the orders indicates their sice" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/png": 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AQBgRdAMAgJinmVtUH+8XAFQfQTcAAIh5lEoDAOqKkT55AAAAAACED0E3AAAAAAB1hKAbAAAAAIA6QtANAAAAAEAdoZEavGV5TVXWLwUAAACgCLrhLdZUBQAAAGAdQTe8ZXmNUMtjAwDsqLhYZMsWMalhQ5GEhKBHAQBbnXDCCdK5c2eZNGlS0EPxBkE3vEX5NgCgtmjArdOWrO7v0tODHgUAYHcRdAMAgJin2WSrJ3N1bAAAfxF0w1uWG6mhemg4B8AKLd8mm+x3GT52jakKhr35pkhJSdCjEImPFznllF0+rKioSEaNGiX//Oc/JSkpSS699FL585//LHFxce7y0ksvyeDBg0sf37hxY1eOfv7558uJJ54oRx55pNx///2l9//www9ywAEHyJtvvin9+/eXsCHohrdopAYAwN5luQwfu8ZUBcM04LYQdFfTk08+KRdeeKG8//778uGHH8rIkSOldevWcvHFF+/yZy+66CIXsP/973+X5G2NkJ566ikXdGtAHkYE3fCWxWZln34qUlgoJiUliXTsKKZY3IYAAD/L8LFrTFVAbWnVqpXcc889Lqt9+OGHy2effeauVyfoHjJkiAu6X375ZTnzzDPdbU888YTLguvzhRFBN7xldadvNfuekiJy0klBjwIA4DPK8AGo4447rlyA3KNHD5e5LtY5KLuQkpIif/jDH+Txxx93QfdHH30ky5Ytk1deeUXCiqAbiJHMreWxAQAAIBw0GI9EIuVuK6xQCqol5rrs2HfffSfTpk1zZeUHHXSQhBVBNxAD2XcAgH8NwiyOyWpDLmvjAcJu8eLF5a4vWrRIDjvsMElISJBmzZrJunXrSu/78ssvJTc3t9zjjzrqKOnWrZtMnTpVpk+fXq6pWhgRdAMAgJhnsUFYTo7I8uViUvv2IqmpYgYNwuA97Rru0TjWrl0rY8aMkUsuucSVh993332uvFxp1lqDaC0513Lz66+/3nU4r6qhWmpqqpxxxhkSZgTdAAAg5llsEKaZ7p49xSRrmWUahMF71Vimy5LzzjtPfvnlFzn22GNddvuqq65yHcyVBt8jRoyQ3r17y/777y/33nuvLFmyZIfnOPvss2X06NHuq87zDjOCbgAAEPOsNghr0iToEQBAefPmzSv9fsqUKTvcr4H2zJkzy922efPmHR73448/Sl5enlt6LOwIugEAAAAAe0VhYaH89NNPcvPNN7su6EcffbSEHUE3dkobD1pdd7osnSYS5LJ+Ft8ni2OyuO2sjwdA7PHl8xu7xj4F2NGCBQukX79+0rZtW3nhhRckFhB0Y6d0p1+hOsSkjAyRevWC+/8tvk86pqVLxbwuXbYelFgR9O8SAFjcp2D3sE8BdnTCCSfssKRY2BlpkwcAAAAAQPgQdAMAAAAAUEcoLwdCrqREpKBAzMrNFUk08kkU8tUqAACAMSV6oIbQv09xkVgrqK9Edna2pKenS1ZWlqSlpQU9HFN8aeYSdKMSi+9TdExZWSLvvitmacBtpclM794iTZvaGQ+A2GRxnwI/j09gO4j88ssv3RrXzZo1k3r16kkcvyw70FC1oKBAfvjhBykuLpbDDjtM4uPjvYvljOSXYJX+7dMAxN/3KTlZpH59kZNPDnokfmjYkIMjAMGzuk8BUHs0cGzTpo2sW7dOvv/++6CHY16DBg2kdevWOwTcviDoNigzUyQ/P+hRYE+D3T59xISEBJH09KBHAQAAgLI0u62BZFFRkcvionJaDZCYmOh1JUCgQXdmZqbcddddsmTJEneW56WXXpLBgweXKycYP368TJ06VTZv3iy9evWSKVOmuLKCqP/9739yxRVXyKuvvurOfAwdOlTuvfdeaagpK09pwJ2XF/QoAAAAANQlDSSTkpLcBeEVaNCdk5MjnTp1kgsuuECGDBmyw/133nmnTJ48WZ588klXfnHLLbdIRkaGrFixQlK2dTwaNmyYC9hnzZolhYWFMmLECBk5cqRMnz5dfM6Swm9sQwAAAACmGqnpWZ6ymW4d1v777y9XX321XHPNNe42nRzfokULeeKJJ+Sss86Szz//XI488kj54IMPpFu3bu4xM2bMkEGDBsl3333nfr46fJh8DwAAAADwL5YzOxN9zZo1sn79ehkwYEDpbfpmdu/eXRYuXOiu69fGjRuXBtxKH69l5osXLw5k3AAAAAAAmG+kpgG30sx2WXo9ep9+bd68ebn7dZJ9kyZNSh9Tmfz8fHcpe3YEAAAAAICYyXTXpYkTJ7qsefTSqlWroIcEAAAAAAghs5nuli1buq8bNmyQ/fbbr/R2vd65c+fSx2zcuLHcz2nLfe1oHv35yowdO1bGjBlTLtNN4L1zLGPm75JhAAA/Wd73sp8DgBAE3dqtXAPn2bNnlwbZGhzrXO1LL73UXe/Ro4dbSkyXHOvatau7bc6cOVJSUuLmflclOTnZXVB9LGMGAMDexb4XAMIh0KB7y5Yt8t///rdc87SPP/7YzcnWheJHjx4tt912m1uXO7pkmHYkj3Y4b9eunZx88sly8cUXy0MPPeSWDBs1apTrbF7dzuWoHs5R1AzvFwAgzPsSy2MDAGsCXTJs3rx50q9fvx1uHz58uFsWTIc2fvx4eeSRR1xG+/jjj5cHH3xQ2rZtW/pYLSXXQPvVV191XcuHDh3q1vZu2LBhqNrMAwAAAAD8i+XMrNMdJB82FAAAAADAv1guJruXAwAAAAAQ043UYIPWQRQWinlJSSJxcUGPAgCAcPLleMAajk8AKIJu7JTuYGfOFPMyMkTq1Qt6FAAAhJMvxwPWcHwCQBF0AyFneZ1X7Bpr4QIAAPiNoBsIOdZ5BQAAAIJD0G2QpcyklpMtXSrmJSRsnTdlhaXsJGup+o3tBwAA4DeCboMsZSa1cUqHDmJeUZFIcXHQo7DJSvAPAPCXntjW+cmoGUsJAQDBIeg2iMyW/9iGAIAw0Q7cNAQDgN1D0G0QmUkAAAAACIf4oAcAAAAAAEBYkelGteh86S1bxKSGDbc2UgMAAAAAawi6US0acGtXdSvN3bRxWlTv3iLp6WKyeYrOgQMAAAAQuwi6Ue1sspW55gUFIrNnb7++YIHNTLd2eaXpDAAAABDbCLpRLRrUWskma9Cdmhr0KAAAu0srp3R5TFR/RQwrJ74BADVH0A0AAPYqDbjz8oIeBQAAewdBN7y1bJlIYaGYrg7Qed1B+/RTu++Tvj8dOwY9CtvIcCGsv9eoPt4vAPAbQTe8o4GazpfWoNZypkSbvWnX96Dl5NgOui1vQwC13whTP4+OOy7okfj53tGcEwD8RNAN7+hBhzYo0+ZuFjLJ1un8d8tBd0pK0KOwjQwXwkQ/i2bODHoUfqI5JwD4i6Ab3rJUchvN3ljUt+/271nGDAAAANi7CLqBGMrekCkBAAAA9q74vfz/AQAAAAAQM8h0AwCAvdoIEzVHDxMA8BdBNwAA2KuNMAEAiCUE3UAMZW/IlAAAAAB7F0E3UAvI3gAAAACoDI3UAAAAAACoIwTdAAAAAADUEYJuAAAAAADqCHO6DYlERAoLxaT580Xy88WsxMSt86pRteRkkT59gh4FAMBHlo9RyjYL5VgAgEUE3YbozmzmTDFp6VLbO9suXejMDQBALB6jROkqIjQ1BWARQXcZWVlbz+QGpaBAJCdn+/WUFJGEBDHBekCr75X1MVrIdAMAAADYuwi6y3jvPZEGDYI9i7x8+fbr7duLpKaKCR06iGkDBnB2GwAQzrJpC6XTPrxPQSZOAGBnCLrL6NlTJC0t2Ey3XspmbwEAQGyXTVsondb3SaeaWTZwIFVdAGwi6C4jPT3YoFvP0A4ZEtz/7zNKy6tWXCyyZUvQo8DuatjQzjQTIIx8yOCqsiflg3qfKla9WWtiyrEAAKsIug3RHRcl0qhtGnBnZgY9Cuwu7TivJwQBxG4G10JQqe/TsmW2m5haOgEAAGURdAO1SINba0uraaZbL9g1PenVq5eYy3QDiE0lJdsz3NpoNcgAt6hIJC9v+3WSBABQfQTdQC3SgLvsQYkVlCdXjx7QklUGYu/vXjO2FmmgvWLF1u9Xrgy+kVrZoL9du61zqC0F35ay7gBQFkE3UIto4OI3th8QezSQtRqsNWok0qmTmKTNXjXgthR0A4BVBN1ALc+/BQD4QwNu7QyOmrN6siIovjTlsyjoJfGAmA66i4uL5dZbb5WnnnpK1q9fL/vvv7+cf/75cvPNN0vctr/MSCQi48ePl6lTp8rmzZulV69eMmXKFDnssMOCHj4AADCOJqaIteXnLAp6STwgpoPuO+64wwXQTz75pLRv314+/PBDGTFihKSnp8uVV17pHnPnnXfK5MmT3WPatGkjt9xyi2RkZMiKFSskpYYLXc+ZI5KaKqZKXcmcAgAAAIC/TAfd7733npx++uly6qmnuusHH3ywPPPMM/L++++XZrknTZrkMt/6OPWPf/xDWrRoIf/5z3/krLPOqnETLBpOAQAAAABqS7wY1rNnT5k9e7asWrXKXf/kk0/k3XfflVNOOcVdX7NmjSs7HzBgQOnPaBa8e/fusnDhwt3KLGty3MqFpk4AAAAA4DfTme4bbrhBsrOz5YgjjpCEhAQ3x/svf/mLDBs2zN2vAbfSzHZZej16X2Xy8/PdJUr/D3XiiSJpaXX0YhBqvjRPoVEJAAuKi0W2bBGTGjak6g27h6Z8u4+mfAg700H3c889J08//bRMnz7dzen++OOPZfTo0a6h2vDhw3f7eSdOnCgTJkyo1bEitvnSPIVGJQAs0IA7M1NM0l4q6elBjwI+oikfAC+D7muvvdZlu6Nzs4866ij55ptvXNCsQXfLli3d7Rs2bJD99tuv9Of0eufOnat83rFjx8qYMWPKZbpbtWpVp68FAIAgWMwq65isrj9dv77YYXHjGZO5IEHyC+K2zsszVqJAQ1wAXgTdubm5Eh9fftq5lpmXlJS477VbuQbeOu87GmRrAL148WK59NJLq3ze5ORkd4GnLNZyF2i624O67YKImEPNOxCzWWWLNMttJlvJxtul/KVNJa8wQaR9+8CXoNHDk6Ki7df1UKVAjw+wSxwKIOxMB92/+c1v3Bzu1q1bu/LypUuXyt133y0XXHCBu1/X6tZy89tuu82tyx1dMkzLzwcPHhz08BFLtdwacC9tLvZtFEkyFnhT8w7U+Rxlsm01e7/MYOPtUrJmt0sz3cEfnnz8cflAUosVsGscCiDsTAfd9913nwuiL7vsMtm4caMLpi+55BIZN25c6WOuu+46ycnJkZEjR8rmzZvl+OOPlxkzZtR4jW6lZyM5I7lrnI3cUVJiRDK6bBQfxgkgtmhMwhxlT7HxdqnPIDFDjyEJsgFUJi6ii13HOC1J16XGnn02Sxo0oH25+bORulezlumGx79QAADsOQ5Pdh+HAqiNWC4rK0vSjC5FZTrTHauWLbM3ZTmK5TAAAAAAoPoIug3SgNtq0G0CkX+4sDgnACAEODzZfRwKIOwIug2y/MFjYmwshAkAAIzh8ARAVQi6y+jfX8TCNABTZ0krWZ7LNb8OsuGcxSXDfOk4Z3FMAABv1ukuHZJ2Vje2LrY1vEUAogi6y9Czk5yhrKCgUGSusa4gGnAvXSrmdelipDSgDDqVAIA/DK7TvSUnUTKX72tiXWzrdLU3ms8DUATdAAAAFhlcp7thsUifnvpN8OtiW2dqzXcAgSLoBmqr5L2kpPxtFkvgWSEQAPxhcJ1ujbPTmwQ9CgDwC0E3UBs04P7qqx0D3ERjf2IDB4okJwc9CgAAACBmGIsIYI7F9S80mNXg0ZKCApG33xZZsUKkqGjrbfHx9pqWvfOOiTndrhFPXuLWsfTqFfRwUEM0BwJiuGHo/Pki+flBj8IfevLd0rGAnng3NmUBiAUE3fBz/Qtr2VoNuuvXLx9o60GTtXJuPVCqWAYfgK2NeNK3ntQpDno0qCmaAwF1TAPumcaamEZpI1OrJwQssthUFcBeR9AN1CbrO9aUFBNjbJgk0qdrzrZMd9CjQU3RHAh7ShtyW02WkgjcBS1zycsLehT+0NIuPSFvhbVpb0CM4C8PqM0yfGul+EbX6XaNeIIeBIDAaMBN3OapX/1KZPnyoEfh10kKS/Nxjj466BEAMYmgGwhzGT4AGGRthpAvYzNTMaVrdKN6eve2dXxAqRIQCIJu+F8LaBH1id71BoKXRRPwlKmPx8o+mAok+DH161f+Nv7oqm7MuUXs7uMiSSIFhrbb/+z9KtGcE7GAoBvVQy0gQt4bCDWjMyksJW+A0H8w8UdXqS1ZIpmLxMyvkvaZs85abzeacyIWEHSjeqi3qxneLwAA9kqW1ErlhC5kopfPP7dd1WWtt9vChfYy3RQswlTQ/eWXX8rcuXNl48aNUlJhGaJx48aJb6IflqjEcds/eayVJQEAgNikwZqVLKkeQ6ambh2TgdU5ventpicoLJ+kAAINuqdOnSqXXnqpNG3aVFq2bClxZaIw/d7HoHv2bJEGDYIehX1UuAEAAFTOUum24dVDTaNgEbUtLhLRtg81d9BBB8lll10m119/vfguOztb0tPT5dlns6RBg7Sgh2MeQTd2F43UwoOKF4SGLx9M/NGZx68SYoGl5oVlY7nWrdMlKytL0tLSwpXp3rRpk/zud7+r3dEACDVWVgNgDh9MqCX8KiEWaMCtixpZkpsr5u120K0B91tvvSV//OMfa3dEAAAAgKdZN8tYngthal4YlZ0t4Qq6J0+eXPr9oYceKrfccossWrRIjjrqKEmqMDnkyiuvFN/07y9itCLBFOYBAQB2B+W3qO3fpawskfnzgx6NP3r3ttN4riz+5vxhqXlhlA+/OzWa092mTZvqPWlcnKxevVp8m9NteR4AAAC+0+7OLImN2vxd0kx3Xl7Qo/GHNlGzmOnmbw5hj+VqlOles2aNhNmcOVuXekDVWLfQ8/QNKt1s89+Nk/zocoEJieZOmSbaG5I5lj6bKHfdeaCUkyPmgxIzLP4yWRmTfmb/UF80fkyNbjhLi09bjnAB2J/TretyH3/88TuUk4dBfj6fjdhNGrn5kL5BOYWFcTJzaXNZujpNCou2HawdcsjWKNeQLl2Y1uETi01mLH1ULl8uprVvL3ZY/GXSsyYWNmJxgshXh26/3rq1sTMm236ZyOYA2J2gu3///pKSkiLHHXec9OvXz130+0RjB6m7mymx9nltjal1C62cba8sfcPZ7Z3KXNZE8guDz0gUFsW5gHvRysbbg+4f40SCH1o5331nK4GjH/fVnG201+hx7UkniQkWm8xYoR+VerHM1HGAxV8m3ff27Gkj051Zf/t1q5luANiddbq/+eYbmTNnjrzzzjsyb948Wbt2rTRo0EB69epVGoQfc8wxEm/tg8/zeQCohHZPsZIB0PTN0qXbr3N2e6dmLW0qeYXBn5QoLI6TpV+ly+r1DaSoZFv9douW5k6YWEu+a9Zds+/Wjm2tBN3wfyYOTZ3sKynxY5kgVE+DBvbOmcAf2R7EcjUOuivShmkafOtFA/HvvvtOGjVqJJs3bxZf+LCh4EGme/bs7dfJdHuT6f7wq8by9cb6Uly87Qh7/wPMbbu2bW0NSQOSjh3FFEtzugHUPV+a8qF6aKSGsMdye5w7OeSQQyQhIcF1LNfLf/7zHymwXjuGcLC0ZoGeuxoyJOhReKNPhpjZbAMqZt2S4kWMZbjIugEAAPhrt4JuLSnXzLY2VdOvP/74o/Ts2VN69+4tr732mnTv3r32RwpYphERp2i9o3FsPUt9CgAAABA6ibuT2d60aZObw92nTx+55JJLpFu3bqFopGaFTlPWTurwF6WuAKywNBOnsj5hlqZOwKNfKC2qzDF+7MlUMwDb1PjT6pdffnFftVGaBtq6dJiWl6P2aMCdlxf0KAAAYWBx1akoPTlpZZYQ/PqFSoqIZNQzPu+mV29+wauJZTERdjUOutetWydffPFFaXn5nXfeKXl5eW7t7hNOOEH69u0rXbt29ap7uTWmluXCbmEbArDC4qpTZccGzxj5hXLTg8Q4V8oR9CAAhKJ7ufr8889L53e/9dZb7ja6l4dkLRXGFK71L+jIBQCVY25XzTCPynwFvi+YZoLQxHI7sceTYTZs2CCffvqpu3zyySfuRSeT5ts9GkhaW/+i4vrTVvZmX30l5g0dKlK/vpjCmhwAUDnmdiFcFfjeYJoJYkGNg+6NGzeWrsut2e1Vq1a5ed3HHnusnHXWWdKvXz/p0aNH3YwWKGvjRpGSEjFFKzyixSP/+Y+tSUp6GlkvlsZEpgSI6QSuqY8AEgY1w/tlvQLfG0wz8fPz25KcHAlf0N2yZUsXZGvH8qFDh7ogW5cLq28to+cji2XTOp6iovK3aeBmoURZA25rQbdm4aNj0k9JvW6FrjCgWRxLYwJC9HFpdUYHCdxqIkpCLdHDJDK3qA18flePDycmahx0v/nmm65pWmpqat2MKJZZLeVevbr8bYccsjWAC5q1+dLRPW10XJoFsJRV1rHp8iWWxkSmBCGajWN1RoflPzPLYwOAoPEZWT0+5JNqHDll6BEE6oYGQ126iLl0UqdO5W876aRgjyItp7j0rz5a46LVH0GeGKj4Pmn9lgbdQafdKirQxVZhPl0Kb5HA9biOU/cpVtNcmsrt1y/oUQChxud39WRnS/iC7hNPPLFaj5szZ47Uhv/7v/+T66+/3mXYc3Nz5dBDD5Vp06a58nalzdfHjx8vU6dOdR3Te/XqJVOmTJHDDjtMvKMH1ZaykFEVA2wN3oJO3ViVlSWyaJGYrJxo316EChU/WUiXAmFnsY5TT+IuXy4mde0a9AgAwBs1Drq1gdpBBx0kp556qpvbXZc2bdrkgmidN65Bd7NmzeTLL7+UffbZp/Qxuk745MmT5cknn5Q2bdrILbfc4rLxK1askBTN6qF2LFu2PWtqrRmXtayElRqXsvPLlXZ879gxyBEBgF0W6zh1X2s1uGXSMgDU3Trdd911l8s0//TTTzJs2DC54IILpEOHDlIXbrjhBlmwYIHMnz+/0vt16Pvvv79cffXVcs0117jbdH22Fi1ayBNPPOG6qXu1tpvlsum3396eAdD53JS62lcx021x+gIq/xgoqvD3NXAgme5qfFxarMK3OKZAWd7PWccvk5+/S2w3oM6ZieVqM+iOWrhwoTz++OPy3HPPyeGHH+6C73POOadWX+iRRx7pstbfffedvPPOO3LAAQfIZZddJhdffLG7f/Xq1fKrX/1Kli5dKp07dy79ub59+7rr9957b2g2VOAsznXDzunByIcflt/x19EJMtSegsI4mbm0efkb9WQJ1SW7RBW+B7SHgw8d8CziF9zP3yW2G1DnfIjldrsFta7FrRcNbJ9//nl54IEHXLb5+++/r7UXq0G1zs8eM2aM3HjjjfLBBx/IlVdeKfXq1ZPhw4fL+vXr3eM0s12WXo/eV5n8/Hx3KbuhTNGy4C1bxJSKzdQs0TnmWvKO8vR82oABQY8CNeX6ylXIilhYLQDeMrVLKRBJLLOeqs4CC/zjWz8rKy6NadHu5UgAAAbs8ZHcRx995LLQn3/+uSszr8153iUlJa5h2l//+ld3vUuXLrJs2TJ56KGHXNC9uyZOnCgTJkwQs/ToSDPLqH5rR+aW7UjL2Ti77ieS2gjpLiWuUKT5cmP9HTXgtrZcZ2V0monFeecAgLoJujWbrXOm9aJZ4nPPPVcWL17sysFr03777bfDc7Zr107+/e9/u+9btmzpvm7YsME9Nkqvly03r2js2LEuex6lr6FVq1Zixkcf2WnG5QN9v1i2BIjp1YuiiwdYOs9kpQhHx2Fm2RnNdJdZJdBUv9NvvrG97509W6RBAzFDTwCY+cUCEJPVU9tYK1qulaB70KBBMnfuXBk4cKBrqqZdzBPrqPRRO5evXLmy3G2rVq1y3dOVdivXwHv27NmlQbYG0HoC4NJLL63yeZOTk93FLD3rbuFIzRc+lAXCJIt9eHRMVs8haVC7YIHdiveFC231K7JShKO7EwvjcCJJIkMyxNzcYBUfv/17q/s6y2e99jatrNT50tbRjwMhY6l6Kio3V8yr8WHTjBkzXFZ57dq1rkS7qjJtLTvfU3/605+kZ8+errz8zDPPlPfff18eeeQRd1FxcXEyevRoue2229y63NElw7Sj+eDBg8Vblk8IWMT7hd2kAbcPfXisKLsKXe/etrLKFmmGGZ5Me9HAyMQE852oX99WABf0vtfq7xIQcqaqp8Kc6R43bpwLdveGY445Rl566SVXDv7nP//ZBdWTJk1yS5VFXXfddZKTkyMjR46UzZs3y/HHH+9ODHi9Rre132T4zWIdkBWa1MqxlbJ1PZ3KVrgm2woEose4evzP8e6u//Q0eWop+46dZEytZ01ZegqAAaaqp7bx4aOxxkuG5ebmSgNLc4pipM08sEc1wdbqgCwvzxWwwuI4WfpVmb1Z69bGJr5uxSpm1cNqQQAA1C0fYrkap3iaNm0qJ554opx22mnuEm1mhpCzvE43zVz8qwOylOkusJXpFm1RUFgms12PyBYhZHmfYhH7OYS46M1K00mgLtX4aFOXBnvllVfkueeec2tmd+rUqTQAP+qoo+pmlAieHhzRwMVPFuuAjEiKiGQMEVNcHycPioksNlKDR9inABLrza+sNZ0E6lKND5u0c/gVV1zhLprCf+ONN+Tll192ncybNGlSGoD37dtXEjhtFR5BN0zxdWwwzWofHsq2EXp8btcM7xdCXPRG00nEghrP6a5KYWGhzJs3z2XB9fLzzz/LfffdV67pmVU+zAMAEBssLmOG3UfvK4QOUwP8xlQFL3AsUPNYrlkz27FcrRUIJiUlyUknneQukydPlo8//liKWD8ZAEKRfQcAh6kBQJ1jSdOaCeU63bvy4osvyq233iqffvppbT81AAAIA7Kl/tLjO40ItIyjY8egR4OaYqoC4E/Q/fDDD8usWbOkXr16ctVVV0n37t1lzpw5cvXVV8uqVavkvPPOq/2RAgBguPTO4pgq0hU/de3wwJEt9Vfbtlu/6lKGJ50U9GjgIR8+Ky00VfXhPdKmqkyhqqOg+/bbb5dx48ZJx44d5YsvvnBN1G666SY3f1sD8EsuuUT22Wefmj4tAABel97pmJYuFdOuv95I0yKybf5jGyJEn9/W+LA/UV260Py1zoLuadOmydSpU2X48OEyf/5816X8vffek//+97+Smppa06eDj6f+6AwEAFUqKdm29JsxpnoF0MgJtYUFqBFCmkHWgNa6gQNt7Fuys4MeQR0E3WvXrpUTTzzRfd+7d2/XQG3ChAkE3LF06i8jw8ZfGAAYpAH32rViTuvWQY8AqAMsQI0Q0tyWDxlkDQcshAT1DIyh1oPu/Px8SdF5PNvovG5dnxsAfO+hZGklFcvJGyuBbU6OmKILdug0ZS1aatFi6wGTifnTFg9KrH4IwL8PTBagBhDWRmq33HKLNNBuLO7Ap0Buu+02t851WXfffXftjBBA6NBDye/kjQUa2JoKIrfNvOnWbfv1448PNslV2YwlLVm0UPoet4UPgZqiYVEVtHybbLJX9LNSiybhPx+y8d4G3X369JGVK1eWXu/Zs6esXr263GPi2CsA8LD/jqVxWU7ewI/pnBpcz5snJjVZlizxHrQwsURP6Jg6wLX0gQmvaJhg7aQpUNfiIhE9Fx7bsrOzXaY+KytL0tLSghsIjdQAGGa55D3oANciDbp9aBOC6qGdiqfHTBZxHIeQybYSy9V2eTnqCKf+ABhmueSdfkVAjPGl+axFnMEB7AfdQ4YMqfR2PbvQtm1bueiii6RZs2a1MTYAgCGWS94t9Suy0iPMh3VeNeHWoUPQo4D3JTj0B6iZrCxbQTelSogBNQ66KzZMi9q8ebNbv/uuu+6SzMxM6cBeFABChX5FfjUK1Opb67tiXQxlwICgR+EHU/O5LdE/tuXLgx6FXzTgtvQLRamSFyzP6Cgw0CC01oPuadOmVXlfSUmJXHzxxTJ27Fh59dVX93RsAAB4h/5SNXuvLCXc4CE9c9O+fdCj8Evv3rb+8CyVKsHLGR25uWJerc7pjo+PlyuvvFJOOeWU2nxaACE7JVmuIZehhi5UuKE2WC3BNyVag69l+LOCHowHrKyJbQnrTu0+Q/td+MfijI7cWAu6VWpqquT68MqBsDN8SnJLTqJkLt9365UuXcyUuVHhBsRYDT78RfNZIBAWZ3TkG+ijsteD7lmzZrmGagBQlYYpRdKn/U9br/SOiBg5bqLCDdhLqMGvGd4vAEZ89ZVOKRZTSoyNp1aC7ldeeaXS23VdtCVLlsijjz7qLgCw04ZcqUVbr2hm2UjQDWAvoVQaALyc0aHHcNYKlXJzQxh0Dx48uNLbGzVqJIcffrgLuM8666zaGBsAAAAAwMiMDq0KNDIrsFR8vIQv6NYO5aH17bd69kDMsNjVyeKY4HeTGWuf3PCpJ1859Aaqwty5W9fltUibKPTrF/Qo4LNynTmN4ZgJIWSxUCk7W8IXdA8aNEieeeaZ0vW6b7/9dvnjH/8ojRs3dtd/+ukn6d27t6xYsUK88+STtuZN6RIYqaliCp2m/EGTGYS/J185eo6JX/lKaMC9ZImY1LVr0COA7zTg1m74FnHMBGB3g+4ZM2ZIfpkWcX/961/lzDPPLA26i4qKZOXKleKl4cPJdO8KnaYAwC960G81uCUgQW0cl1hMvSmOmQDUVvfyiNYdhkWrViJpaUGPAoitmmBrqFFG2FC+jdB35uTkDcJ/yMThid9qfckwAEb4UhNsDTXKAADEFB8OmTg8ibGgOy4uzl0q3gYANWp8Y229ibLzXy3t1QxMM6EnHwAAwF4MurWc/Pzzz5fkbQ3H8vLyXCO11G0Nv8rO9waASmnAvXy5mKQBt6XozUAjHnryAQAA7MWge7g2Gyvj3HPP3eEx55133h4MCUDos8oacOfmijkabOv7ZWnBx48+Yk4uAAB7wbJldud2a9GbpZyAJTk5Er6ge9q0aXUzEgB1UxOs5dLz54spGtRaTJ3qMn1axWNpykxRUdAjAAAgJg6ZNLC1lqcoezigeQHsyIdCaxqpAWEVrQlu0kSkf38xJSVFpKBAzNFxWVumb9tUHgAAULeHTNpGhWyyf4o9OBlB0F3WnDki2+amYycBgNX1MOHPciqDBgU9Ai92IFu2bLuSFfBgPGCg3xwAbJWZaS71lvlpY8kvNDR1qiyNcjt2FEsz8jQA79Ur6NGgurKzxTyC7rL0A5KjNgAGaMCtx23wpt8cAGw/njRWo5yfUyR5hUaPcZPiRfLszA3WtjPRFi/wQ67BNkEVEXRXzOJqeSmqRqkrsNcytxSV1Oz9AgATDB4rJacmipjNdCeIGDn81mC7a1cy3b7J9iDTHRfRNcBiXHZ2tqSnp0tWVpakpaUFPRwAgId0b2q1661lepBrqXchsNv4ENh9fBAg5LEcmW4AAGqBHmvPnBn0KPyjHYMtLmYA1BgfAruPDwKEHEE3AMDX3kDmjreXLhXTiaQOHYIeBYDAmnPm2Tzsb5hSJEZnmwO1xuZfXxVuv/12GTt2rFx11VUyadIkd1teXp5cffXV8uyzz0p+fr5kZGTIgw8+KC1atAh6uAAQKgZ7A5kLuqksBWCRBtyZy/cVi/q0/0nog4mw8ybo/uCDD+Thhx+WjhWWFPjTn/4kr7/+ujz//POuln/UqFEyZMgQWbBgQWBjBYAwMtgbyBRd/MLy+q6WxwaESnTdKUM+Wt5cinPzt34QxNtqqLbwi8aS8HaciKHPKCsr5JZbPhReN1LzIujesmWLDBs2TKZOnSq33XZb6e06Wf6xxx6T6dOny4knnuhumzZtmrRr104WLVokxx13XICjBoBwsXAAYrlfkY7J0nvki0QvjkSAatCAVucmZ2WJzJ8vlhTF15OEevEi7duJNGggluhHeWFRoghLdO2A5UOrhyXDasnll18up556qgwYMKBc0L1kyRIpLCx0t0cdccQR0rp1a1m4cCFBNwCEFP2KwoP+SQgN7b6tv8xNmoj07y+WJKckiBTEbV0aV8ty4EVlF8uHVg+Z7lqgc7U/+ugjV15e0fr166VevXrSuHHjcrfrfG69ryo691svZdvMAwAAALVSglO/vljSpzQ/pelkUsrVEgl+GTM9P5LOhPdd8mG1OdNB97fffuuaps2aNUtS9MxcLZk4caJMmDCh1p4PAAAAMY4SnHChDAexEnRr+fjGjRvl6KOPLr2tuLhYMjMz5f7775eZM2dKQUGBbN68uVy2e8OGDdKyZcsqn1c7oI8ZM6ZcprtVq1Z1+EoAwH+WGroUFIjk5Gy/TsXkThQXS2KekQ1XQVFKQ83lBD0MIPTMLxnGxwBCzuZf3zb9+/eXzz77rNxtI0aMcPO2r7/+ehcoJyUlyezZs2Xo0KHu/pUrV8ratWulR48eVT5vcnKyuwAA/GzoolWcZRMQvXpRglelrC0SN9/Ihqsg0quPJCWx4QCJ9SXDUouCHgYQu0F3o0aNpEOHDuVuS01NlX333bf09gsvvNBlrZs0aSJpaWlyxRVXuICbJmoAEDsNXXRsZEqq0KShSH/DG86DuXiA7zSbrMGt1bEBYWc66K6Oe+65R+Lj412mW5ujZWRkyIMPPhj0sAD4UqeMatOY1uUkiXD9QiceYO8uGWb589vgko9mW7tpI7WCoAdh/1fehyZmFsRFIvqrH9t0Tnd6erpb91uz5QDqmK5haqVOGTWn6W6COADAbvbkoN9cOFjpNZftQSznfaa71gMBzkH4h6ybfyzXKaN62w8A4F81F8dMQCAIust67z2RBg2CHgVqiqybPwyvYYoaHlDGx1NTBgDWu05WxDETEAiC7rJ69hQxWpKAnSDr5g/WMA0PKzVlAGCN5WoujpmAQBB0l6Vn/gi6AQBhohm3/PygR4E9ocucWg3isCOaF/rabw67sS1RPQTdAACEmQbceXlBjwIAHJ2ZRKEUYg1Bd1k0UvMTTUH8s2xZ+fVCAlZcEidbCmweATSsVyAJ8cY+l0hT+Jclhd/YhkBMolCpenJyxDyC7rJopOYnmoL4Ixqs6UkSQ5m3LTmJkrmiqVig5/2KSrY3KOt92I+S3qBITElJkaTEJKGNmicoSwYAL/vOak8+Q4dLZuXminkE3WXRSM1PNAXxr6ZMt5mhiUANG4j06W7jVHJBYZzM/rhJ6fUF36RIQrzYkpQkGafEUR4IAEAd9p01VhhoVp4HJyYIusuikRoQk5k3nZxgpVaioEAkNSXoUfjD8nK42DVmBwFA1Tp0CHoEfsgl0w0AQGwuh4tdY3YQACAWEHQDALxleTlc7Bqzg4C9hLKgmqEMB7WMoBsADPGlMbiVKfkshwsA1UBZkFdlOCaPBaLd3QzKzi4Q6wi6AcAQ1i8FANQ6yoK8KsMxeSxQUCgyd6ZYVM+DSd0E3QAAhJnlhV51/WkCASCmyoIMJ0y3KxZJit8a/AK1gaAbAIAw04Dbh/VUAMTUcljWaXm3uWwzvEXQDQBAmDPLn35qN62Umipy0klBjwIAgDpF0A0A8I7l8sS4Ldszy4mJBsoT27YVs1JYlB4AvGCyu9s22dliHUE3AMA7lssTmyxLlvhtJwS6dbPT6d3snG4AgH0mu7ttY3VcZRB0A4hdltOl2DldHaSwQgrZRFpZ5H8dtjcGiwzQg4FAhwMAplhOmJbFCVPUJoJuALHLcroUO6cB99Lm5W/r0oWjJAAwznLCFKgrBN0AsGwZGe+a0MC2Q4egR2FXcbEk5m3Z+n0Wme5droWrSxkBAPxQXCyyZds+zops5nQDgH0acBN0eyUpMSIZXTaWv3FgJNgANzpdIStL4t6d725KzNS0jthhpAS/lK7RbWTtYABANWjArat0WJKbK9YRdAMAJcnevV+uPDEpUv5GDbiDDLoLCkXmztyaBYgUbDuhI7b07m2rrlMz3QAAf+jntp4wtSSbTDcA2O/m4kNHF3hxMsDRcmldf9oizSpbCroBwAAf+qrqLs5EoZLu46xVKMVZeGN2jqAbQOyimwsAADHPh76qmh/gkMVf8UEPAAAAAACAsCLTDQBArCw+a6UEHwCAGELQDQBAbWC6AgAAqARBNwAAAICYbVqmY+rXz2jjMiuFShY3XFTBthVDDCPoBgAAALBX+NC0TNG4zKMNl8s63QBgX2amSH5+0KPwR3KyvTU6AQAAjCLoBgANuPPygh4FAAAAQoigGwA0c4vq4/0CAACotrhIRGfFx7bs7GxJT0+XrKwsSUtLC3o4APa24mKRLVuCHgX2RMOGIgkJQY8CAFDNflzz59ue2ZWYaKuRWuAiEYkrKix3/r13b7ETyzVrZjqWI9MNABpw67xu+EvnmKenBz0KAEA1V1csKdl6ztsqy2MLhp6B2N5ZLlJS7mqw6lkZSNUIugFAs6Q0BvN/GwIAvKnoSi5MECmOE0lJMVOp9OmnNlfF0uXCOnYUU5hpVjME3QCgO3uypACAsDJY0eVOdWus3cNWpZLFvqp6XuKkk4IeBfYEQTcAAAAQZpYrugxVKlnN3lodF6qPoBsAgNrsDmSZ1ijSGQiIzc+l+vXFbOm7kQnUfY4TuyJGPr8NTlWQ7GyxznTQPXHiRHnxxRfliy++kPr160vPnj3ljjvukMMPP7z0MXl5eXL11VfLs88+K/n5+ZKRkSEPPvigtGjRItCxAwBijB7YzpwppmVkeNFwBkAMfS7Br89vg1MVJDdXrDMddL/zzjty+eWXyzHHHCNFRUVy4403ysCBA2XFihWSmprqHvOnP/1JXn/9dXn++efdsl+jRo2SIUOGyIIFC4IePgAgls64FxSI5OSIfP65SFHR1qxyfLyY61+g47JUM2m15BUA4MdUhWwy3XtkxowZ5a4/8cQT0rx5c1myZIn06dPHrcX22GOPyfTp0+XEE090j5k2bZq0a9dOFi1aJMcdZ7lGBAAQqjPumlFavlxk9eqtQXfr1lu731iiHYKMlHECADxksflsnIGye5+D7oo0yFZNmjRxXzX4LiwslAEDBpQ+5ogjjpDWrVvLwoULCboBIOwsnXHXTLdeNLttNdOtJwGsZboBAAg5b4LukpISGT16tPTq1Us6dOjgblu/fr3Uq1dPGjduXO6xOp9b76uKzv3WS1S2ByUJAGKs8dUDD3gxR8mMBg1ERo8O9my3/j4NGSKm0UgNiC36N69zgS1Nxans5KmRdbpNs3TCFOENunVu97Jly+Tdd9+tlQZtEyZMqJVxAfCY5QYzH39sc7FQqzSDq9szyCYzGsxaaHIDABU/l7RadNEiMUmrlayVKwOxGHRrc7TXXntNMjMz5cADDyy9vWXLllJQUCCbN28ul+3esGGDu68qY8eOlTFjxpTLdLdq1aoOXwEA1BBltzXD+wUAVdPlwjp1EpM0C79tCimqQDWA90wH3ZFIRK644gp56aWXZN68edKmTZty93ft2lWSkpJk9uzZMnToUHfbypUrZe3atdKjR48qnzc5OdldAMCs3/wm6BEAAMLil19EPvkk6FHYpdODtBeHVb17izRtyvQgjyVaLynXzuQvv/yyNGrUqHSeti4Nput269cLL7zQZa21uVpaWpoL0jXgpokaAAAAYKzppEXaBHP2bDFLl0IeNIgpTB4zHXRPmTLFfT3hhBPK3a7Lgp1//vnu+3vuuUfi4+Ndplubo2VkZMiDDz4YyHgBeNpgBuFAkxkA8GeZJ2tBd2pq0KNAiMVFtIY7xumcbs2a65Jkmi0HAAAAEENBt9XGqlGaJCDT7W0sZ2wBUQAAAAAAwsN0eTkAAAAASKxPN2MKldcIugEAAADEruh65kAdIegGAKvrlm7ZIiZo64/Cwu3XWS+0ZpkJlngBAOzJftcCi2OKsnK8tBME3QBgdQeSmSkm6E526dLt19u3p8trddH4BgCwO/tda43dKh4LWJKfL9YRdAOARZbWVNWurnqJSkkJcjQAAMSGZcvsZJeLikRWrxaTCo28RztB0A0AFllaU5X1SwEACCaYtBJQ6rQ3DbwtKjI6rjIIugEAAADAGksdy7U/SaLR0DESEeviIhEPRlnHfFhQHQACY7F5isUx+dBIzdp4AAB+7OMsjqlsLHfAAaZjOaOnKwAAZlhcSkVL3ufODXoU/qGxGwDYZ3G/q5KTxaSSErGOoBsA4N8yZhp05+SUb+7GMmYAAMAggm4AgJ/LmC1fvv06y5gBAACjCLoBANXDMmYAAAA1RtANAPBvGTOdv/XrXwc9Cv9oI5yyJyssoLkbEJu0L0dWlpik+7p+/YIeBUKEoBsA4B9dk5NGauFAczcgNmnAvWSJmNS1a9AjQMgQdAMAAADYu77+2m4DTB0bUIsIugEAAADsXdoAMy9PTKJPCGoZQTcAAACAvcvqms/WxwYvEXQDAPyjzbd0LjDCsS0BxFZDR1328bjjxDQLTSeLi7cu12mx6aSuaGJ1eoBBBN0AAP/ogQfNtwDAPxpwz5wZ9Cj8kJMjsnz51u+7dLF1klKXELWyookHCLoBAP7KzBTJzxez5YlW1jUHAPhHg+w2bbZ+36mTraBbs/BWlnzLzhbrCLoBAP7SgNtqIx4AQNXl5dYlJgZfzq3v05o1W79v3NhW0G1Jbq5YR9ANAPCX5WY3lscGAEEGkkuXinkWyrm1i7p2eVe9ezOtqipkugEAockAWGzoouV2llkpvVM0vQFgge4/NKC1buBAW0Gulf2uRXH23xeCbgCwxHKDGcsNXbBrNL0BYCVA8mH/oQG3paAbXiPoBgD4W+ZWNvtujbXMso4HAADsdQTdAIDq0QAyNXXr95oxtRB0a/n2J5+ISWSWAWBHmuXOyBDzfMjGwxsE3QAAf2n21uqyXGSWAaDy8nILJ22BvYigGwAsZgAslk3rmHRet1q3zovGJYGqX99WeTkAYOcyM7cuRWlxNQyrJ5hRLQTdAGAxA6Bl04sWidlGati1iy8Wad066FEAAKpLA+68vKBHgRAi6AYAiyyWTWumu2fPoEfhj5Ytgx4BAKCmGWWLrI4L1UbQDQAWaVmyxSZcTZoEPQIAAOqGtZPdCI34oAcAAAAAAEBYkekGAAAAAGssNlW1KDtbrCPoBgAAAABrNODWjurYudxcsY6gGwAAAACssdhU1aJsMt0AAAAAgLA0VbW43KpxNFIDAAAAAKCOEHQDAAAAAFBHQhN0P/DAA3LwwQdLSkqKdO/eXd5///2ghwQAAAAAiHGhmNP9r3/9S8aMGSMPPfSQC7gnTZokGRkZsnLlSmnevHnQwwOAcNAOqvn5YlJyMs1mAADh3c+hajk5Yl0ogu67775bLr74YhkxYoS7rsH366+/Lo8//rjccMMNQQ8PAMJBD0Ty8oIeBQAAtSsSESks3LpEl9X9XGKiFw3DApFv/0SJ90F3QUGBLFmyRMaOHVt6W3x8vAwYMEAWLlwY6NgAIFQ0m2yV5bEBAGzTgHvmTJFly7Z+b1G3biJJSUGPwqbiYrHO+6D7xx9/lOLiYmnRokW52/X6F198UenP5Ofnu0tUtgdruwFA4CjfBgCEWbt2djPdxxwjUq9e0KOwKdt+LOd90L07Jk6cKBMmTAh6GAAAAACs0IB7+XIxSQNuMt2Vy80V67wPups2bSoJCQmyYcOGcrfr9ZYtW1b6M1qKro3Xyma6W7VqVedjBQAAAGBUSopI+/ZiUu/eZLqrQqa77tWrV0+6du0qs2fPlsGDB7vbSkpK3PVRo0ZV+jPJycnuAgAAACDGaQY5I0PMj5FGapXz4H3xPuhWmrUePny4dOvWTY499li3ZFhOTk5pN3MAAAAAqDJoI4uMOhSKoPv3v/+9/PDDDzJu3DhZv369dO7cWWbMmLFDczUAAAAAAPamuEhEF6aLbTqnOz09XbKysiQtLS3o4QAAAAAAQhLLxQc9AAAAAAAAwoqgGwAAAACAOkLQDQAAAABAHSHoBgAAAACgjhB0AwAAAABQRwi6AQAAAACoIwTdAAAAAADUkcS6emKfRJcq1zXeAAAAAAB+yN4Ww0VjOosIukXkp59+cl9btWoV9FAAAAAAALsR06Wnp4tFBN0i0qRJE/d17dq1ZjcUKj+rpSdKvv32W0lLSwt6OKgBtp2f2G5+Yrv5i23nJ7abv9h2fsrKypLWrVuXxnQWEXTrxPb4rVPbNeDmD8w/us3Ybn5i2/mJ7eYntpu/2HZ+Yrv5i23nd0xnkd2RAQAAAADgOYJuAAAAAADqCEG3iCQnJ8v48ePdV/iD7eYvtp2f2G5+Yrv5i23nJ7abv9h2fkr2YLvFRSz3VgcAAAAAwGNkugEAAAAAqCME3QAAAAAA1BGCbgAAAAAA6ghBNwAAAAAAsRR0P/DAA3LwwQdLSkqKdO/eXd5///1y9z/yyCNywgknuEXr4+LiZPPmzdV63rVr18qpp54qDRo0kObNm8u1114rRUVFpfevW7dOzjnnHGnbtq1bXH306NG18rxq3rx5cvTRR7uueoceeqg88cQTEjZh227vvvuu9OrVS/bdd1+pX7++HHHEEXLPPfdI2IRtu6n8/Hy56aab5KCDDnJ/c/r6Hn/8cQmbMG47fU3t2rVzf3OHH364/OMf/5Cw8W27XXnlldK1a1f3t9S5c+cd7tf92+mnny777befpKamusc8/fTTEkZh23Zff/21G2fFy6JFiyRMwrbd1MyZM+W4446TRo0aSbNmzWTo0KFue4aNT9vuk08+kbPPPltatWrl9mG6L7v33nvLPWZ3fyd8E9R2e/HFF+Wkk05yfxP63D169HB/K7vy6aefSu/evd14dfvdeeedOzzm+eefd7GAPuaoo46SN954Q7wOuv/1r3/JmDFjXNv3jz76SDp16iQZGRmycePG0sfk5ubKySefLDfeeGO1n7e4uNhtpIKCAnnvvffkySefdIHvuHHjyh2o60a6+eab3f9bW8+7Zs0a95h+/frJxx9/7P7ALrroomr9EvgijNtNDx5HjRolmZmZ8vnnn7vn14t+UIRFGLebOvPMM2X27Nny2GOPycqVK+WZZ55xAVyYhHHbTZkyRcaOHSu33nqrLF++XCZMmCCXX365vPrqqxIWvm23qAsuuEB+//vfV3qf/n8dO3aUf//73+7AZcSIEXLeeefJa6+9JmESxm0X9fbbb7tgIHrRgC8swrjd9LhST3SdeOKJ7rhSjyd//PFHGTJkiISJb9tuyZIlLhB86qmn3D5MT/7rPu3+++/fo+f1TZDbLTMz0wXdGhDr9tDY6ze/+Y0sXbq0yufNzs6WgQMHukSN/sxdd93ljkPKHu/r/6cnVC688EL3XIMHD3aXZcuWVf+NiRhz7LHHRi6//PLS68XFxZH9998/MnHixB0eO3fuXF3uLLJp06ZdPu8bb7wRiY+Pj6xfv770tilTpkTS0tIi+fn5Ozy+b9++kauuuqpWnve6666LtG/fvtzP/f73v49kZGREwiKM260yZ5xxRuTcc8+NhEUYt9ubb74ZSU9Pj/z000+RMAvjtuvRo0fkmmuuKfdzY8aMifTq1SsSFr5tt7LGjx8f6dSpU7UeO2jQoMiIESMiYRLGbbdmzRo3zqVLl0bCKozb7fnnn48kJia61xL1yiuvROLi4iIFBQWRsPB520VddtllkX79+lV63548r2VWtlvUkUceGZkwYUKkKg8++GBkn332Kfcc119/feTwww8vvX7mmWdGTj311HI/171798gll1wSqS5TmW49c6FnGAYMGFB6m5Ze6PWFCxfu0XPrz2spQIsWLUpv07MuenZDz0bV5fPqY8q+puhj9vQ1WRHW7VaRntnSM119+/aVMAjrdnvllVekW7durjTogAMOcCVc11xzjfzyyy8SFmHddpoB0LKtsrRET8vSCgsLxXc+brfdlZWVJU2aNJGwCPu2O+2001yG7vjjj3efoWER1u2mlQj6OqZNm+ayf/r39s9//tO9rqSkJAmDsGy7sH0W+rbdSkpK5Oeff97pNtDn7dOnj9SrV6/c82ql5KZNm2otljMVdGtpjH54lH0zlV5fv379Hj23/nxlzxu9ry6ft6rH6C9JGAKBsG63qAMPPNDNq9JATktddWpAGIR1u61evdrNx9eSn5deekkmTZokL7zwglx22WUSFmHddroDe/TRR90OOxKJyIcffuiua8Ctr9l3Pm633fHcc8/JBx984MrMwyKs265hw4by97//3c1VfP31113QrSWTYQm8w7rd2rRpI2+99ZYrzdXjk8aNG8t3333n/vbCIgzbThM1Wmo9cuRIiRXWttvf/vY32bJli5t2uCfPW9VjavKaTAXdteWUU05xOxK9tG/fPujhwPPtNn/+fHfw/9BDD7kATucHw+5207Oa2pRDGzkde+yxMmjQILn77rvd3J8wnOQK87a75ZZb3Ji0OZBma3TO4vDhw0vPlMPmditr7ty5LtieOnWqubFZYG3bNW3a1M291EZHxxxzjNx+++1y7rnnujmNsLvd9ED/4osvdp+PeoLrnXfecVm63/72t+6EJYLfdnriX/dhOq9Z5wtj72+36dOnu94wejJKK3mCliiG6Id/QkKCbNiwodzter1ly5bVfh7NjEQPrqNlNvrzFTvnRf+fmjx3RdV5Xv1a2WvSrnpaOum7sG63smeUlZa06GO0uYI2U/BdWLebdlDWsvL09PTSx2gHUT0Q0UzAYYcdJr4L67bTz0PtMv/www+7+3RbaiOTaHde3/m43WpCD/y1YY2u8qCN1MIk7NuuLA3AZ82aJWEQ1u2mnaF1H1e2w7I279Kuy4sXL3YnLn3n87ZbsWKF9O/f32W4tWFaLLGy3Z599llXmapVPBXLwiuqKk4r+7xVPaYmr8lU6kDP0uk8Fe06XDZrpde15Xt16QG3LsulF+1Ep/TnP/vss3Kd83SnooHvkUceudtjrs7z6mPKvqboY2rymiwL63arjL4unXcaBmHdbrrM2/fff+/KiaJWrVrlMqU6VSAMwrrtonQHq9tKd9y64/z1r38diky3j9utunTZMO0qe8cdd4SylDLM264i7YatJ7zCIKzbTTs/V/xM1M/L6OsLA1+3nc4t1o7ZWoXwl7/8RWKNhe32zDPPuIor/ar7pV3R59Wu52V7x+jz6qo3++yzT+3FchFjnn322UhycnLkiSeeiKxYsSIycuTISOPGjct1qlu3bp3rtDl16lTX8S4zM9Nd31m34qKiokiHDh0iAwcOjHz88ceRGTNmRJo1axYZO3Zsucfp8+ila9eukXPOOcd9v3z58j163tWrV0caNGgQufbaayOff/555IEHHogkJCS4x4ZFGLfb/fff77qBrlq1yl0effTRSKNGjSI33XRTJCzCuN1+/vnnyIEHHhj57W9/657rnXfeiRx22GGRiy66KBImYdx2K1eujPzzn/90f2+LFy92qzw0adLEdVgOC9+2m/ryyy/d47RLa9u2bUufI9rpdc6cOW4fp/+Xjj16CdsKAmHcdvpapk+f7o5N9PKXv/zFdQd+/PHHI2ERxu02e/Zs16lcOzLr5+WSJUvcijgHHXRQJDc3NxIWvm27zz77zD2PrnJT9rNw48aNe/S8vglyuz399NOus7/GWmW3webNm6t8Xr2vRYsWkT/84Q+RZcuWufHrPu3hhx8ufcyCBQvc8/7tb39zn5W6skBSUpLb5tVlLuhW9913X6R169aRevXqubbzixYtKne/vlDdQBUv06ZN2+nzfv3115FTTjklUr9+/UjTpk0jV199daSwsLDcYyp7Xv0Q29Pn1Zb4nTt3dq/pkEMO2eVYfRS27TZ58mS31Jv+4elyBF26dHHLCpRdoiMMwrbdlH4gDhgwwD1GA3BddipMByJh3Xa6c9bPSb1f/+ZOP/30yBdffBEJG9+2my5rU9nPRU+GDB8+vNL79efCJmzbTg+K27VrV7qf09eky1GFTdi2m3rmmWfccUlqaqoLPE477TS37wsbn7ZdVWOp+DO78zvhm6C2W98q/nZ0P7Uzn3zySeT44493JwsOOOCAyO23377DY5577jl3Ekxfk8YHr7/+eo3ekzj9p/p5cQAAAAAAUF3+T5IDAAAAAMAogm4AAAAAAOoIQTcAAAAAAHWEoBsAAAAAgDpC0A0AAAAAQB0h6AYAAAAAoI4QdAMAAAAAUEcIugEAAAAAqCME3QAAAAAA1BGCbgAAAAAA6ghBNwAAAAAAdYSgGwAAAAAAqRv/H5DkQCvLtGemAAAAAElFTkSuQmCC", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.rcdefaults()\n", "fig, ax = plt.subplots(1,1,figsize=(10,4), tight_layout = True)\n", "#out['dispatch'].loc[:,'battery'].plot(ax = ax)\n", "for i,r in ob.iterrows():\n", " if r['capa']>0 : ax.plot([r['start'], r['end']], [r['price'],r['price']],'b-',linewidth = abs(r['capa']), alpha = 0.3)\n", " elif r['capa']<0: ax.plot([r['start'], r['end']], [r['price'],r['price']],'r-',linewidth = abs(r['capa']), alpha = 0.3)\n", "ax.set_title('Order book')\n", "ax.set_ylabel('EUR/MWh')\n", "# for legend only\n", "ax.plot([S, S], [0,0],'b-',linewidth = 5, alpha = 0.3, label = 'sell')\n", "ax.plot([S, S], [0,0],'r-',linewidth = 5, alpha = 0.3, label = 'buy')\n", "ax.legend(loc = 'upper right')\n", "ax.set_xlim(S, E)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### (3) Assets\n", "\n", "Order book: We utilize a specific asset in EAO to implement the behaviour of the order book. Note that the asset has a parameter \"full_exec\". Setting it to False, the optimizer is allowed to execute parts of an order -- and the optimization problem is continuous and thus very fast (on my laptop 1.4s). In reality most of the orders will still be executed fully due to the nature of the problem setup. If required, the parameter may be set to True, resulting in a more complex MIP as orders must be executed fully (6.6s).\n", "\n", "Battery: Our main asset is a battery storage. We choose artificial parameters such as 95% efficiency and a size of 40 MWh as compared to a capacity of 10 MW (a 4h battery). Note that we need to specifc a start and a target fill level.\n", "\n", "Target fill level flex: The target fill level requires some additional thoughts. Left unpenalized, an optimizer will completely drain a battery at the end of the day (in case power prices are positive). In order to avoid this we need to define the value battery power has for us towards the end of the day. We do this by forcing the battery to be left at 50% fill level and adding an \"extra source\" for power with a market price above the order book. Power drawn from this \"extra source\" represents battery usage from this 50% target fill level.\n", "\n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "# order book asset\n", "order_book = eao.assets.OrderBook('orders', node, \n", " orders = ob, \n", " full_exec = True) # switch to enforce (or not) full order execution\n", "\n", "# battery\n", "efficiency = 0.95\n", "battery = eao.assets.Storage('battery', node, cap_in = 10, \n", " cap_out = 10,\n", " start_level = 20,\n", " end_level = 20,\n", " eff_in = efficiency,\n", " size = 40,\n", " no_simult_in_out = True) # at negative prices, we want to ensure the battery does not charge & discharge at the same time to \"burn\" power\n", "# last resort - battery end level. May allow battery not to be completely full, \"borrowing\" in last hours\n", "extra_power = eao.assets.SimpleContract('fill_level_adjust', node,\n", " max_cap = 10,\n", " min_cap = 0,\n", " start = timegrid.timepoints[-2],\n", " end = E,\n", " price = 'av',\n", " extra_costs = 20\n", " )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### (4) Setting up the portfolio\n", "\n", "In EAO we can easily link all assets in a portfolio. By refering to the same node we ensure their dispatch sums to zero." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "portf = eao.portfolio.Portfolio([battery, order_book, extra_power])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Perform the optimization\n", "\n", "Once the portfolio is set up, optimization can be called and the output extracted." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "op = portf.setup_optim_problem(prices=prices, timegrid=timegrid)\n", "res = op.optimize(solver = 'SCIP')\n", "out = eao.io.extract_output(portf= portf, op=op, res=res)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Create charts and interpret the results" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.rcdefaults()\n", "fig, ax = plt.subplots(1,1,figsize=(10,4), tight_layout = True)\n", "for i,r in ob.iterrows():\n", " if out['special'].loc[i,'value'] > 0.95: myal = 1\n", " else: myal = 0.05 # executed\n", " if r['capa']>0: ax.plot([r['start'], r['end']], [r['price'],r['price']],'b-',linewidth = abs(r['capa']), alpha = myal)\n", " elif r['capa']<0: ax.plot([r['start'], r['end']], [r['price'],r['price']],'r-',linewidth = abs(r['capa']), alpha = myal)\n", "ax.set_title('Executed orders')\n", "ax.set_ylabel('prices of orders EUR/MWh')\n", "ax.plot([S, S], [0,0],'b-',linewidth = 5, alpha = 1, label = 'executed sell - we buy')\n", "ax.plot([S, S], [0,0],'r-',linewidth = 5, alpha = 1, label = 'executed buy - we sell')\n", "ax.set_xlim(S, E)\n", "ax2 = ax.twinx() \n", "### show how to manually calculate fill level from dispatch\n", "# fill_level = -out['dispatch'].loc[:,'battery']\n", "# fill_level[fill_level>0] *= efficiency\n", "# fill_level = fill_level.cumsum()+20 \n", "### this is the automatic output\n", "fill_level = out['internal_variables']['battery_fill_level']\n", "ax2.plot(fill_level, label = 'battery fill level')\n", "ax2.set_ylabel('fill level battery in MWh')\n", "ax.legend(loc = 'upper left')\n", "ax2.legend(loc = 'upper right')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In bold color we can observe orders that have been executed. We have successfully combined a battery with a discrete order book consisting of orders that are partly overlapping and come with various prices and sizes." ] } ], "metadata": { "kernelspec": { "display_name": "my_env", "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": "3.12.9" }, "orig_nbformat": 2 }, "nbformat": 4, "nbformat_minor": 2 }