Use cases

Guarantee Scheme for the Long-Term FNCER Auction

We model the long-term contracting auction in the Wholesale Energy Market of the Colombian Ministry of Mines.

Seeking to facilitate the entry of complementary energies to those existing in the market, we calculated the risk that different guarantee schemes would bring to the different agents involved. This analysis was carried out jointly for the five types of guarantees: seriousness for generators and marketers, start-up, compliance and payment.

Approach

For the development of this research it is necessary to identify the Risk Factors to which the agents - energy generators and traders - participating in the auction are exposed, these are the IPP, IPC, Exchange Rate, interest rates, energy price, replacement price and power factors of solar and wind power plants.
Subsequently, assumptions are made about the guarantee scheme faced by the agents that want to participate in the auction and based on this, the number of agents that effectively participate in the auction is estimated, as a function of the defined scheme. This function was calibrated based on the result of the first auction of this style, which was declared successful. Additionally, it is defined that the number of traders depends exclusively on the payment guarantees, while the number of generators depends on the payment and compliance guarantees. Once the number of agents is defined, the result of the auction is approximated, based on the result of the first auction.
However, the decisions made by the generators and traders have an impact on other generators, traders, the Government and the consumers that make up the system. How are the guarantees scheme optimally chosen?
First, Risk Factor simulations are used to determine in which cases the generators and marketers default on their obligations acquired in the auction, and when an agent defaults, the cost incurred by its counterpart to replace the contract is quantified.
Second, the risk faced by the Nation is limited to consider the effective installed capacity, considering only guarantee schemes that allow an effective installed capacity above a threshold defined by the Ministry of Mines and Energy.
Finally, the guarantee scheme with the lowest aggregate risk is chosen, understanding the latter as the sum of the risks faced by each agent in the system.

Results

We were able to model the long-term contracting auction, calculating the risk of the different agents involved in the guarantee schemes. Developing a scheme to optimally choose the collateral scheme.

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