Stochastic Model Predictive Control Approaches applied to Drinking Water Networks

Control of drinking water networks is an arduous task given their size and the presence of uncertainty in water demand. It is necessary to impose different constraints for ensuring a reliable water supply in the most economic and safe ways. To cope with uncertainty in system disturbances due to the stochastic water demand/consumption, and optimize operational costs, this paper proposes three stochastic model predictive control (MPC) approaches, namely: chance-constrained MPC, tree-based MPC, and multiple scenarios MPC. A comparative assessment of these approaches is performed when they are applied to real case studies, specifically, a sector and an aggregate version of the Barcelona drinking water network in Spain

Uncertainty management in peer-to-peer energy trading based on blockchain and distributed model predictive control

This work presents a distributed energy management platform based on a smart contract displayed on a blockchain network to optimize the behavior of an energy community under stochastic disturbances, such as solar irradiance and agents’ energy demands. Disturbances are modeled as probability distributions and are handled by a distributed model predictive control scheme based on chance constraints. The performance of the proposed algorithm is assessed across various simulations