Herding, R;
Ross, E;
Jones, WR;
Charitopoulos, VM;
Papageorgiou, LG;
(2023)
Stochastic programming approach for optimal day-ahead market bidding curves of a microgrid.
Applied Energy
, 336
, Article 120847. 10.1016/j.apenergy.2023.120847.
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Abstract
The deregulation of electricity markets has driven the need to optimise market bidding strategies, e.g. when and how much electricity to buy or sell, in order to gain an economic advantage in a competitive market environment. The present work aims to determine optimal day-ahead market bidding curves for a microgrid comprised of a battery, power generator, photovoltaic (PV) system and an electricity load from a commercial building. Existing day-ahead market bidding models heuristically fix price values for each allowed bidding curve point prior to the optimisation problem or relax limitations set by market rules on the number of price–quantity points per curve. In contrast, this work integrates the optimal selection of prices for the construction of day-ahead market bidding curves into the optimisation of the energy system schedule; aiming to further enhance the bidding curve accuracy while remaining feasible under present market rules. The examined optimisation problem is formulated as a mixed integer linear programming (MILP) model, embedded in a two-stage stochastic programming approach. Uncertainty is considered in the electricity price and the PV power. First stage decisions are day-ahead market bidding curves, while the overall objective is to minimise the expected operational cost of the microgrid. The bidding strategy derived is then examined through Monte Carlo simulations by comparing it against a deterministic approach and two alternative stochastic bidding approaches from literature.
| Type: | Article |
|---|---|
| Title: | Stochastic programming approach for optimal day-ahead market bidding curves of a microgrid |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1016/j.apenergy.2023.120847 |
| Publisher version: | https://doi.org/10.1016/j.apenergy.2023.120847 |
| Language: | English |
| Additional information: | © 2023 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
| Keywords: | Mixed-integer linear programming, Stochastic programming, Optimisation under uncertainty, Microgrid, Bidding curve, Day-ahead market |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Chemical Engineering |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10167043 |
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