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Add descriptions of winning algos

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pjbull committed Jun 12, 2018
1 parent b529407 commit e9f22d280454b33c16076a8629cf1241dd0152c2
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### For more about this repository, see the competition page:
### https://www.drivendata.org/competitions/53/optimize-photovoltaic-battery/
Flexibility can be defined as "the ability of a resource, whether any component or collection of components of the power system, to respond to the known and unknown changes of power system conditions at various operational timescales".1 The exploitation of flexibility is essential to avoid costly reinforcements of the power system and maintain security of supply while increasing the penetration of renewable (and intermittent) sources of energy.
Flexibility can be produced in different manners. It might come from generation options, from energy storage or from energy demand. In some cases, generation can also be proposed through alternative dispatchable assets such as Combined Heat and Power (CHP). Storage is valid for both electricity and heat. Energy storage is an easy way to increase building flexibility, provided there is a business case for such an investment. The present challenge is focused on making a good usage of an installed storage system.
This repository contains the example implementation for how the optimization challenge code will be executed at the end of the competition. Competitors are required to only implement a single method `propose_state` within the file `battery_controller.py`.
Viewed from the demand side, in the case of smart buildings, time of use tariffs incite to use energy when it is the most available. Given such a tariff, the goal is to buy more energy when its price is the lowest, and buy less (or possibly sell) energy when its price is the highest.
This code exists to make it easy for competitors to test their solutions.
The goal in this competition is to build an algorithm that controls a battery charging system and spends the least amount of money over a simulation period.
## Final Results
Place |Team or User | Score | Summary of Model
--- | --- | --- | ---
1 | VietNam national ORlab | -0.201322 | We considered the problem as a dynamic optimization problem. The problem at each step was modeled as a linear programming (LP). We selected Ortools to solve LP model optimally because it seemed to be the fastest and easy to install on docker.
2 | ironbar | -0.199243 | My solution is based on simplificatoin of the period, dynamic programming, and intelligent pruning of the actions.
3 | Helios | -0.198155 | Formulate a linear programming model for the optimization problem at each step with forecast data and use an open-source tool, which can be installed by pip to solve it. Scatter the energy charged (or discharged) among steps to avoid buying superfluous energy due to the uncertainty of next forecasts.
#### [Interview with winners](http://drivendata.co/blog/power-laws-optimization-winners/)
## Requirements
- Docker
- Python 3.6 (for local execution instead of on Docker)
This repository contains the example implementation for how the optimization challenge code will be executed at the end of the competition. Competitors are required to only implement a single method `propose_state` within the file `battery_controller.py`.
This code exists to make it easy for competitors to test their solutions.
## Running the simulation
1. Clone this repository
2. Add the data from the competition to the `data` folder. The script expects a folder named `submit` and a file called `metadata.csv` in the `data` directory.

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