An Optimal-Control Scheme for Coordinated Surplus-Heat Exchange in Industry Clusters

Abstract

Industrial plants organized in clusters may improve their economics and energy efficiency by exchanging and utilizing surplus heat. However, integrating inherently dynamic processes and highly time-varying surplus-heat supplies and demands is challenging. To this end, a structured optimization and control framework may significantly improve inter-plant surplus-heat valorization. We present a Modelica-based systems model and optimal-control scheme for surplus-heat exchange in industrial clusters. An industry-cluster operator is assumed to coordinate and control the surplus-heat exchange infrastructure and responsible for handling the surplus heat and satisfy the sink plants’ heat demands. As a case study, we use an industry cluster consisting of two plants with surplus heat available and two plants with heat demand. The total surplus heat and heat demand are equal, but the availability and demand are highly asynchronous. By optimally utilizing demand predictions and a thermal energy storage (TES) unit, the operator is able to supply more than 98% of the deficit heat as surplus heat from the plants in the industry cluster, while only 77% in a corresponding case without TES. We argue that the proposed framework and case study illustrates a direction for increasing inter-plant surplus-heat utilization in industry clusters with reduced use of peak heating, often associated with high costs or emissions.

Data-Driven Robust Optimal Operation of Thermal Energy Storage in Industrial Clusters

“…The NLPs (17) and (18) are modeled using the software JuMP [34] (version 0.18.5), within the framework of the Julia [35] (version 0.6.2). The solver used within this framework is Ipopt [36] (version 3.12.8), which uses interior-point algorithms to solve the NLPs.…”

“…These terms are quadratic costs associated with the other control inputs Q dump S (t) and q C (t), weighted with some positive penalty parameters << 1. This is similar to the objective function formulation used in [17].…”

“…To account for the mismatch between the model and the real system, state feedback information is incorporated by repeatedly solving problem (17). After every time step, problem (17) is solved with updated state information, and with a receding prediction horizon.…”

“…Predicted Uncertainty Using notation analogous to problem (17), the scenario-based multistage optimal control problem is formulated as follows:…”

“…Classical control techniques like the proportional-integral-derivative (PID) control have also been investigated, for instance in TES for concentrated solar polar [16]. An optimal control scheme was proposed by [17] for surplus heat exchange using TES in industrial clusters. A review of various types of TES and their applications can be found in [18,19], whereas a review of optimization and control for TES systems is given in [20].…”