{ "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", "# in case eao is not installed\n", "import os\n", "import sys\n", "# in case eao is not installed, set path\n", "myDir = os.path.join(os.getcwd(), '../..')\n", "sys.path.append(myDir)\n", "addDir = os.path.join(os.getcwd(), '../../../..')\n", "sys.path.append(addDir)\n", "\n", "import eaopack as eao\n", "\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\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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", 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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": "opt", "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": "3.12.5" }, "orig_nbformat": 2 }, "nbformat": 4, "nbformat_minor": 2 }