TY  - INPR
N1  - © 2024 The Authors. Published by Elsevier Ltd. under a Creative Commons license (http://creativecommons.org/licenses/by/4.0/).
AV  - public
Y1  - 2024/06/22/
TI  - Risk-aware microgrid operation and participation in the day-ahead electricity market
A1  - Herding, Robert
A1  - Ross, Emma
A1  - Jones, Wayne R
A1  - Endler, Elizabeth
A1  - Charitopoulos, Vassilis M
A1  - Papageorgiou, Lazaros G
KW  - Mixed-integer linear programming
KW  -  Stochastic programming
KW  -  Scenario reduction
KW  -  Microgrid
KW  -  Conditional value-at-risk
KW  -  Day-ahead market
JF  - Advances in Applied Energy
UR  - http://dx.doi.org/10.1016/j.adapen.2024.100180
PB  - Elsevier BV
N2  - This work examines the daily bidding problem of a grid-connected microgrid with locally deployed resources for electricity generation, storage and its own electricity demand. Trading electricity in energy markets may offer economic incentives but exposes the microgrid to financial risk caused by market commitments. Hence, a multi-objective, two-stage stochastic mixed integer linear programming (MILP) model is formulated, extending a prior work of a risk-neutral microgrid bidding approach. The multi-objective model minimises both expected total cost of day-ahead microgrid operations and financial risk from bidding measured by conditional value-at-risk (CVaR). Bidding curves derived as first stage decisions are always feasible under present market rules - including a limitation on the number of break points per submitted curve - while being near optimal for the microgrid?s day-ahead recourse schedule. The proposed optimisation model is embedded in a variant of the 
?-constrained method to generate bidding curve candidates with different trade-offs between the two conflicting objectives. Moreover, scenario reduction is used to compromise accuracy of the uncertainty set for better computational performance. Particularly, the marginal relative probability distance between initial and reduced scenario set is suggested to make a decision on the extent of scenario reduction. The proposed solution procedure is tested in a computational study to demonstrate its applicability to generate optimal microgrid bidding curve candidates with different emphasis between total cost and CVaR in reasonable computational time.
ID  - discovery10193785
ER  -