/MarketSim

Electricity market simulation where RL agents learn a bidding strategy

Primary LanguagePython

MarketSim

This repository contains a PIP package which is an OpenAI environment for simulating an enironment in which electricity products get sold.

Installation

Install the OpenAI gym.

Then install this package via

pip install -e .

Usage

import gym
import marketsim

env = gym.make('MarketSim-v0')

See https://github.com/matthiasplappert/keras-rl/tree/master/examples for some examples.

The Environment

Imagine an electricity generator that sells electricity on the Intraday Continous Market shortly before delivery, for example one hour, thats 4 timeslots of 15-minutes. The generator places trade-offers on the market and can update the offered price every timeslot before delivery. The probability buyers will buy the electricity product is given by equation where x is the offered price by the generator. If the generator can not sell the electricity before delivery, the reward is the negative generation costs z. If the generator succesfully sells the electricity the reward is x - z.