TY - JOUR
T1 - Distributed management of energy communities using stochastic profile steering
AU - López, Juan Camilo
AU - Pappu, Aditya
AU - Hoogsteen, Gerwin
AU - Hurink, Johann L.
AU - Rider, Marcos J.
N1 - Publisher Copyright:
© 2024 The Authors
PY - 2024/7
Y1 - 2024/7
N2 - This paper presents a novel approach for distributed management of energy communities. The proposed method utilizes a stochastic profile steering algorithm as a greedy heuristic. The optimization process considers random parameters such as local forecasted demand, photovoltaic (PV) production, and the initial energy of electric vehicles (EVs) as embedded scenarios. Profile steering coordinates the flexible electricity assets within an energy community by determining the contribution of each prosumer's profile to the average value of the objective function. It iteratively selects the prosumer that contributes the most until no further improvements can be made. This process scales linearly with the number of controllable prosumers and can achieve various community-level objectives, such as maximizing self-sufficiency or minimizing aggregated cost-of-energy, even when dealing with non-convex optimization problems for modeling each prosumer's local energy management system. The outcome of the proposed method optimizes the average value of the community's objective while ensuring that grid limitations are met within a specified probability. The proposed method is evaluated through simulations involving small-scale communities (5 households) and large-scale communities (100 households). The results demonstrate the efficiency, flexibility, and scalability of the proposed method, as well as its ability to reschedule the aggregated demand to ensure that grid limits are not violated with at least a 95% probability.
AB - This paper presents a novel approach for distributed management of energy communities. The proposed method utilizes a stochastic profile steering algorithm as a greedy heuristic. The optimization process considers random parameters such as local forecasted demand, photovoltaic (PV) production, and the initial energy of electric vehicles (EVs) as embedded scenarios. Profile steering coordinates the flexible electricity assets within an energy community by determining the contribution of each prosumer's profile to the average value of the objective function. It iteratively selects the prosumer that contributes the most until no further improvements can be made. This process scales linearly with the number of controllable prosumers and can achieve various community-level objectives, such as maximizing self-sufficiency or minimizing aggregated cost-of-energy, even when dealing with non-convex optimization problems for modeling each prosumer's local energy management system. The outcome of the proposed method optimizes the average value of the community's objective while ensuring that grid limitations are met within a specified probability. The proposed method is evaluated through simulations involving small-scale communities (5 households) and large-scale communities (100 households). The results demonstrate the efficiency, flexibility, and scalability of the proposed method, as well as its ability to reschedule the aggregated demand to ensure that grid limits are not violated with at least a 95% probability.
KW - UT-Hybrid-D
KW - Energy communities
KW - Flexible energy assets
KW - Probabilistic grid limits
KW - Stochastic profile steering
KW - Distributed energy management system
UR - http://www.scopus.com/inward/record.url?scp=85189434983&partnerID=8YFLogxK
U2 - 10.1016/j.ijepes.2024.109973
DO - 10.1016/j.ijepes.2024.109973
M3 - Article
AN - SCOPUS:85189434983
SN - 0142-0615
VL - 158
JO - International Journal of Electrical Power and Energy Systems
JF - International Journal of Electrical Power and Energy Systems
M1 - 109973
ER -