Decentralized cooperative scheduling of prosumer flexibility under forecast uncertainties

Scheduling of prosumer flexibility is challenging in finding an optimal allocation energy resources for heterogeneous goals under various forecast uncertainties and operation constraints. This study addresses this challenge by introducing a bottom-up framework cooperative scheduling that relies on decentralized network agents to perform coordinated decision-making select subset households’ net load schedules fulfills the techno-socio-economic objectives resource modes ensures reliability grid. The terms alternative mathematically modeled with multiobjective optimization attains economic, environmental, self-sufficiency respect their relative importance. coordination achieved privacy-preserving collective learning algorithm aims reduce aggregated peak demand households considering prosumers’ willingness cooperate accept less preferred schedule. By utilizing real-world data, novel case demonstrated prosumers equipped solar battery systems community microgrid. findings show cooperation level decreases global costs shaving 83% while increasing local 28% comparison noncooperative scheduling. However, uncertainty parameters frequency containment reserve causes imbalances planned schedules. It suggested can be decreased if modeling takes into account variable specific levels uncertainty.