Decentralized cooperative scheduling of prosumer flexibility under forecast uncertainties. (15th May 2021)

• Record Type: Journal Article
• Authors: Mashlakov, Aleksei; Pournaras, Evangelos; Nardelli, Pedro H.J.; Honkapuro, Samuli
• Is Part Of: Applied energy. Volume 290(2021)
• Publication Date: 2021-05-15
• Subjects: Flexibility scheduling -- Forecast uncertainty -- Multiobjective optimization -- Prosumer motivations -- PV-battery systems -- Smart grid; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2021.116706
• Languages: English
• ISSNs: 0306-2619

Abstract

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