This post is the first in a series applying machine learning techniques to an energy problem. The goal of this series is to develop models to forecast the UK Imbalance Price.
What is the Imbalance Price?
The Imbalance Price is what generators or suppliers pay for any unexpected imbalance.
In the UK generators and suppliers (known as Parties) contract with each other for the supply of electricity. Generators sell electricity to suppliers who then sell power to end use customers.
As System Operator National Grid handles real time balancing of the UK grid. Parties submit details of their contracts to National Grid one hour before delivery. This allows National Grid to understand the expected imbalance.
National Grid will then take actions to correct any predicted imbalance. For example the Balancing Mechanism allows Parties to submit Bids or Offers to change their position by a certain volume at a certain price.
National Grid also the ability to balance the system using actions outside the Balancing Mechanism. Examples include:
- Short Term Operating Reserve power plants.
- Frequency Response plants used to balance real time.
- Reserve Services.
More drastic scenarios National Grid may call upon closed power plants or disconnect customers. National Grid will always reduce the cost of balancing within technical constraints.
Parties submit their expected positions one hour before delivery – but they do not always meet these contracted positions!
A supplier may underestimate their customers demand. A power plant might face an unexpected outage. The difference between the contracted and actual position is charged using the Imbalance Price.
ELEXON uses the costs that National Grid incurs in correcting imbalance to calculate the Imbalance Price. This is then used to charge Parties for being out of balance with their contracts. ELEXON details the process for the calculation of the Imbalance Price here.
What data is available?
ELEXON make available a significant amount of data online. This includes data for the Imbalance Price calculation as well as data related to the UK grid. We will make use of the ELEXON API to access data.
The first iteration of this model will be auto-regressive. We will use only the previous values of the Imbalance Price to predict future values.
As we continue to develop the model we will add more data and explain it’s relevance to the Imbalance Price. Adding data iteratively will allow us to understand what value the more data has to the model.
The next post will be the Python code used to scrape data using the Elexon API. We will then do some visualization to analyze the Imbalance Price data.
Posts after that will be developing models in Python to predict the Imbalance Price.