We consider a simple home energy system consisting of a (net) load, an energy storage device, and a grid connection. We focus on minimizing the cost for grid power that includes a time-varying usage price and a tiered peak power charge that depends on the average of the largest N daily powers over a month. When the loads and prices are known, the optimal operation of the storage device can be found by solving a mixed-integer linear program (MILP). This prescient charging policy is not implementable in practice, but it does give a bound on the best performance possible. We propose a simple model predictive control (MPC) method that relies on simple forecasts of future prices and loads. The MPC problem is also an MILP, but it can be solved directly as a linear program (LP) using simple enumeration of the tiers for the current and next months, and so is fast and reliable. Numerical experiments on real data from a home in Trondheim, Norway, show that the MPC policy achieves a cost that is only 1.7% higher than the prescient performance bound.
@article{pineiro2023hem,title={Home energy management with dynamic tariffs and tiered peak power charges},author={P{\'e}rez-Pi{\~n}eiro, David and Skogestad, Sigurd and Boyd, Stephen},journal={arXiv preprint arXiv:2307.07580},year={2023},}
2021
Supervisory control design for balancing supply and demand in a district heating system with thermal energy storage
Cristina Zotică, David Pérez-Piñeiro, and Sigurd Skogestad
This paper presents a systematic comparison between three alternatives to design the supervisory control layer of a district heating network composed of a waste heat boiler, an electric boiler, a dump, a hot water storage tank, and a set of consumers. The three alternatives are split range control, controllers with different setpoints, and model predictive control. We evaluate the closed-loop performance in the face of time-varying supply and demand, and constant electricity prices. All alternatives were found to give similar performance. Controllers with different setpoints is the easiest to implement, while model predictive control is the most difficult.
@article{zotica2021supervisory,author={Zotic{\u{a}}, Cristina and P{\'e}rez-Pi{\~n}eiro, David and Skogestad, Sigurd},journal={Computers \& Chemical Engineering},volume={149},pages={107306},year={2021},publisher={Elsevier},}
2020
Optimal operation and control of a thermal energy storage system: Classical advanced control versus model predictive control
Cristina Zotica, David Pérez-Piñeiro, and Sigurd Skogestad
The objective of this work is to define the optimal operation and control for a thermal storage system with heat sources and a consumer, which exchange utilities using one hot water thermal energy storage tank. In this work, we compare a decentralized control structure using classical advanced control with PID controllers and logic blocks (split-range control and selectors) and a centralized control structure (model predictive control) to implement optimal operation for a simple thermal energy storage system, which is a multivariable system with constraints. We analyze a varying heat supply profile over a horizon of 24 hours. We show that the supply and demand can be balanced, and we achieve optimal operation by using the energy stored in the tank while minimizing the heat from the market.
@incollection{zotica2020optimal,title={Optimal operation and control of a thermal energy storage system: Classical advanced control versus model predictive control},author={Zotica, Cristina and P{\'e}rez-Pi{\~n}eiro, David and Skogestad, Sigurd},booktitle={Computer Aided Chemical Engineering},volume={48},pages={1507--1512},year={2020},publisher={Elsevier},}
Self-optimizing control of a continuous-flow pharmaceutical manufacturing plant
David Pérez-Piñeiro, Anastasia Nikolakopoulou, Johannes Jäschke, and 1 more author
This article considers the real-time optimization under uncertainty of a compact reconfigurable system for on-demand continuous-flow pharmaceutical manufacturing. Self-optimizing control is employed, which optimizes operation in the presence of uncertainty by controlling a carefully chosen combination of measurements to a constant setpoint. The method is applied to a simulated plant based on the physical process. The closed-loop simulations indicate that this simple policy is able to maintain the process operation close to optimality despite disturbances, sensor noise, and parametric model uncertainty.
@article{pineiro2020self,title={Self-optimizing control of a continuous-flow pharmaceutical manufacturing plant},author={P{\'e}rez-Pi{\~n}eiro, David and Nikolakopoulou, Anastasia and J{\"a}schke, Johannes and Braatz, Richard D},journal={IFAC-PapersOnLine},volume={53},number={2},pages={11601--11606},year={2020},publisher={Elsevier},}