A distributed economic model predictive control design for a transactive energy market platform

The electricity distribution grid is fundamentally changing due to the widespread adoption of variable renewable energy resources (VREs), network-enabled digital physical devices, and active consumer engagement. VREs are uncertain and intermittent in nature and pose various technical challenges to power systems control and operations which can limit their penetration. Effective demand-side control structures for social and retail market engagement of the individual electricity consumers can serve as a control lever to increase the utilization of VREs. This paper presents a novel transactive energy market case study for the City of Lebanon NH distribution grid where the grid is broken into various subgrids that coordinate to reach a consensus on the flows of power through the system. To achieve this, an artificially intelligent, distributed model predictive control (DEMPC) algorithm based on the augmented Lagrangian alternating direction inexact Newton (ALADIN) distributed control algorithm is used to maximize the social welfare of individual consumers. To account for the dynamic characteristics of the distribution system, the DEMPC algorithm solves the Alternating Current Optimal Power Flow (ACOPF) problem over a receding time-horizon to capture consumer preferences through explicitly time-varying functions. The methodology is demonstrated on actual data for a 13 and 30-node distribution feeders for the City of Lebanon, NH. The results illustrate that the methodology scales well with large numbers of energy resources while ensuring consensus in 5–27 iterations.