Toward Transactive Control of Coupled Electric Power and District Heating Networks

TL;DR

This paper introduces a transactive control method for the coordinated operation of coupled electric power and district heating networks, leveraging model predictive control to optimize social welfare and system efficiency.

Contribution

It develops a novel transactive control framework using nonlinear model predictive control for coupled energy networks, considering operational limits and privacy, with real-time applicability.

Findings

Improved system efficiency and reduced losses in coupled networks.

Enhanced coordination of flexible producers and consumers.

Demonstrated benefits in a realistic test system.

Abstract

Although electric power networks and district heating networks are physically coupled, they are not operated in a coordinated manner. With increasing penetration of renewable energy sources, a coordinated market-based operation of the two networks can yield significant advantages, as reduced need for grid reinforcements, by optimizing the power flows in the coupled systems. Transactive control has been developed as a promising approach based on market and control mechanisms to coordinate supply and demand in energy systems, which when applied to power systems is being referred to as transactive energy. However, this approach has not been fully investigated in the context of market-based operation of coupled electric power and district heating networks. Therefore, this paper proposes a transactive control approach to coordinate flexible producers and consumers while taking into account the operational aspects of both networks, for the benefit of all participants and considering their privacy. A nonlinear model predictive control approach is applied in this work to maximize the social welfare of both networks, taking into account system operational limits, while reducing losses and considering system dynamics and forecasted power supply and demand of inflexible producers and consumers. A subtle approximation of the operational optimization problem is used to enable the practical application of the proposed approach in real time. The presented technique is implemented, tested, and demonstrated in a realistic test system, illustrating its benefits.