Joint optimization of sales-mix and generation plan for a large electricity producer

TL;DR

This paper presents a mixed-integer linear programming model for a large electricity producer to jointly optimize sales-mix and generation planning, accounting for market influence, risk, and uncertainties in demand, renewables, and fuel costs.

Contribution

It introduces a MILP formulation that captures the interaction between sales decisions and generation planning, considering market impact and risk management for large power producers.

Findings

The model effectively captures market influence and risk trade-offs.

Simulation results demonstrate the approach's applicability to real market data.

The approach improves profit management under uncertainty.

Abstract

The paper develops a typical management problem of a large power producer (i.e., he can partly influence the market price). In particular, he routinely needs to decide how much of his generation it is preferable to commit to fixed price bilateral contracts (e.g., futures) or to the spot market. However, he also needs to plan how to distribute the production across the different plants under his control. The two decisions, namely the sales-mix and the generation plan, naturally interact, since the opportunity to influence the spot price depends, among other things, by the amount of the energy that the producer directs on the spot market. We develop a risk management problem, since we consider an optimization problem combining a trade-off between expectation and conditional value at risk of the profit function of the producer. The sources of uncertainty are relatively large and encompass demand, renewables generation and the fuel costs of conventional plants. We also model endogenously the price of futures in a way reflecting an information advantage of a large power producer. In particular, it is assumed that the market forecast the price of futures in a naive way, namely not anticipating the impact of the large producer on the spot market. The paper provides a MILP formulation of the problem, and it analyzes the solution through a simulation based on Spanish power market data.