TY - GEN
T1 - Modified Worst-Case Excess Minimization for Value Sharing in Energy Communities
AU - Safdarian, Amir
AU - Divshali, Poria Hasanpor
AU - Baranauskas, Marius
AU - Keski-Koukkari, Antti
AU - Kulmala, Anna
N1 - Funding Information:
ACKNOWLEDGMENT This work was supported by Academy of Finland under project DisMa (Distributed management of electricity system), funding decision 323696, and by Business Finland under project ProCemPlus (Prosumer Centric Energy Communities - towards Energy Ecosystem), funding decision 8211/31/2018. Helen Sähköverkko Oy is also acknowledged for providing data associated with an apartment building in the southern part of Finland in the metropolitan area.
Publisher Copyright:
© 2022 IEEE
PY - 2022
Y1 - 2022
N2 - Worst-case excess minimization provides a stabilizing value sharing mechanism for energy communities (ECs). It, however, takes much time to solve the problem especially in larger ECs with tens of prosumers. To alleviate the issue, this paper proposes a modified worst-case excess minimization problem which is faster to solve. In this paper, the problem is formulated for individual time slots, e.g., hour by hour, instead of solving the problem for the whole study horizon. This enables modifying the problem by defining new constraints as well as removing unnecessary variables and constraints. By doing so, the minimization problem can be solved faster without sacrificing the accuracy of the simulation. The proposed method is simulated, and the achieved results are compared with the original worst-case excess minimization problem. Based on the results, the proposed method performed significantly better than the original.
AB - Worst-case excess minimization provides a stabilizing value sharing mechanism for energy communities (ECs). It, however, takes much time to solve the problem especially in larger ECs with tens of prosumers. To alleviate the issue, this paper proposes a modified worst-case excess minimization problem which is faster to solve. In this paper, the problem is formulated for individual time slots, e.g., hour by hour, instead of solving the problem for the whole study horizon. This enables modifying the problem by defining new constraints as well as removing unnecessary variables and constraints. By doing so, the minimization problem can be solved faster without sacrificing the accuracy of the simulation. The proposed method is simulated, and the achieved results are compared with the original worst-case excess minimization problem. Based on the results, the proposed method performed significantly better than the original.
KW - energy community
KW - minimization
KW - optimization
KW - payoff allocation
KW - prosumer
KW - value sharing
KW - worst-case excess minimization
UR - http://www.scopus.com/inward/record.url?scp=85127024226&partnerID=8YFLogxK
U2 - 10.1109/PESGRE52268.2022.9715951
DO - 10.1109/PESGRE52268.2022.9715951
M3 - Conference contribution
AN - SCOPUS:85127024226
BT - PESGRE 2022 - IEEE International Conference on "Power Electronics, Smart Grid, and Renewable Energy"
PB - IEEE
T2 - IEEE International Conference on Power Electronics, Smart Grid, and Renewable Energy
Y2 - 2 January 2022 through 5 January 2022
ER -