Optimising block bids of district heating operators to the day-ahead electricity market using stochastic programming
Abstract
The wide spread of district heating in Denmark offers a massive potential for flexibility in an energy system with intermittent renewable energy production. To leverage this potential, a cost-efficient power market integration of combined heat and power (CHP) units in district heating systems is important. We propose a stochastic program optimising block bids to the day-ahead market for CHP units in district heating systems under uncertain power prices. Block bids allow the internalisation of start-up costs. Based on the stochastic program, we develop a solution approach based on sample average approximation (SAA) to solve the stochastic program for a large number of price scenarios. We present results for a case study from Middelfart, Denmark. The system consists of two sub-networks that have lately been connected. We analyse the block bidding behaviour with and without connection using real data from different seasons. The results show that the bidding varies significantly depending on seasons and the layout of the network. Furthermore, the results show that the solution approach based on SAA reduces computation time significantly while maintaining solution quality.
Info
Journal Article, 2021
UN SDG Classification
DK Main Research Area
Science/Technology
