(657g) Designing Thermal Energy Storage through Dynamic Optimization Using Process Data | AIChE

(657g) Designing Thermal Energy Storage through Dynamic Optimization Using Process Data


Nakama, C. S. M. - Presenter, University of São Paulo
Mdoe, Z. N. - Presenter, Norwegian University of Science and Technology
Knudsen, B. R., SINTEF Energy Research
Tysland, A. C., Norwegian University of Science and Technology
Jäschke, J., Norwegian University of Science and Technology
District heating (DH) systems that utilize industrial waste heat can play an important role for increasing efficiency in heating of residential and commercial buildings, which leads to financial savings and emission reduction [1]. Thermal energy storage (TES) can further improve this process by storing extra waste heat for later use when heat demand surpasses waste heat availability. However, determining the optimal capacity of a TES unit, and consequently, required investment, can be challenging task as it constitutes a trade-off in cost, size, and utility.

In this talk, we address the problem of finding the optimal capacity for a centralized short-term TES for DH systems using highly variable industrial waste heat. Taking operation conditions into account when designing a TES tank can prevent suboptimal volumes and improve the economic, energetic, and environmental benefits of TES [2]. Nevertheless, the difference in the timescales of the payback period for the tank and the DH system operation poses a challenge for designing a well-posed optimization model for obtaining the optimal volume of a TES tank.

Common approaches for sizing TES tanks can be roughly categorized as iterative, energy-balance approaches [3,4], simulation-based approaches [2], and optimization-based approaches [5]. The first category consists of methods that are simple to implement, but only consider heat amounts and do not take into consideration time-varying temperatures in the process. Simulation-based methods can account for dynamic models and control structures; however, they rely on exhaustive search of sampled values for the volume, resulting in a time consuming with no guarantee of optimality. Finally, while optimization-based approaches find optimal values for the capacity of a TES tank by formulating an optimization problem, they often adopt significant simplifications in the component models and the dynamic interactions within the DH plant.

We present an optimization-based approach that formulates a single optimization model based on dynamic models which describe the operation of the DH plant. To deal with the competing goals of the TES tank, we use a multi-objective formulation seeking to minimize the payback period and discarding of waste heat. We handle the issue with the different timescales by considering a two-step solution to the model. The long time-horizon is first discretized into short-term periods, and the model is solved for each period to evaluate potential heating savings. Then the model is reformulated with all the relevant periods for energy saving and re-solved to find an optimal size of the TES tank. A trade-off analysis of the conflicting objectives is conducted to evaluate how their assigned weights reflect on the optimal design. The proposed method is demonstrated on a case study with historical data and a simplified model of a DH plant in Mo i Rana, Norway, that recovers waste heat from a ferrosilicon plant.

We find that the single multi-objective dynamic optimization model can be systematically used to evaluate decrease of peak heating consumption in short-term periods and to study the trade-off of the different goals set to short-term TES tanks when calculating their optimal size. The proposed method has the advantage of being simple to implement and being able to consider moderately complex dynamic models to describe the process. Compared with iterative, steady-state or simulation-based approaches, an optimal TES volume can be identified from a continuous space of solutions, avoiding exhaustive investigation of discretized TES size samples, and enabling better precision in the decision process for the TES capacity.


[1] D. Connolly, H. Lund, B. V. Mathiesen, S. Werner, B. Möller, U. Persson, T. Boermans, D. Trier, P. A. Østergaard, and S. Nielsen, Heat roadmap Europe: Combining district heating with heat savings to decarbonise the EU energy system, Energy policy, 65 (2014) 475–489.

[2] B. R. Knudsen, D. Rohde, H. Kauko, Thermal energy storage sizing for industrial waste-heat utilization in district heating: A model predictive control approach, Energy, 234 (2021) 121200.

[3] M. Labidi, J. Eynard, O. Faugeroux, Optimal design of thermal storage tanks for multi-energy district boilers, in: 4th Inverse Problems, Design and Optimization Symposium (IPDO-2013), 2013.

[4] H. Kauko, D. Rohde, B. R. Knudsen, T. Sund-Olsen, Potential of thermal energy storage for a district heating system utilizing industrial waste heat, Energies, 13 (2020) 3923.

[5] B. Morvaj, R. Evins, J. Carmeliet, Optimising urban energy systems: Simultaneous system sizing, operation and district heating network layout, Energy, 116 (2016) 619–636.