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Stochastic programming approach for optimal day-ahead market bidding curves of a microgrid

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  • Herding, Robert
  • Ross, Emma
  • Jones, Wayne R.
  • Charitopoulos, Vassilis M.
  • Papageorgiou, Lazaros G.

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.

Suggested Citation

  • Herding, Robert & Ross, Emma & Jones, Wayne R. & Charitopoulos, Vassilis M. & Papageorgiou, Lazaros G., 2023. "Stochastic programming approach for optimal day-ahead market bidding curves of a microgrid," Applied Energy, Elsevier, vol. 336(C).
    • Handle: RePEc:eee:appene:v:336:y:2023:i:c:s0306261923002118
    DOI: 10.1016/j.apenergy.2023.120847
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    References listed on IDEAS

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