Dynamic online optimization of a house heating system in a fluctuating energy price scenario

Abstract

SummaryThe main focus of this thesis is to find implementation strategies for the optimal operation of processes during transients. That is, we do not focus on the algorithms to solve a given dynamic optimization problem, but on how to implement the solution in practice using control. The underlining theme is based on the general idea of finding feedback policies that give acceptable performance even in the presence of model uncertainties and disturbances. By 'acceptable' we mean that important constraints should always be satisfied and the economic performance should be near the optimal. In this thesis we considered different classes of applications, each one with their own particularities and challenges.The first part of the work deals with the optimal operation of thermal energy storage systems. We consider the optimal operation of energy storage in buildings with focus on the optimization of an electric water heating system. The optimization objective is to minimize the energy costs of heating the water, with the requirement that we should satisfy the uncertain demand at any time. The main complications in this problem are the time varying nature of the electricity price and the unpredictability of the future water demand. First, we present a detailed problem formulation which may also be suitable similar problems. Many insights into the optimization problem formulation are given and guidelines on implementation strategies including feedback control structures are proposed.Next, we use the hot water system as an example to illustrate our proposed implementation strategy based on hierarchical decomposition of the optimization-control problem. In our approach, economic objectives and control objectives are decoupled using a two-layer cascade feedback structure. We show that the decomposed optimization problem can be written as a simple linear program (LP) which can be solved very efficiently. The main result is that great economical benefits can be obtained at a very low computational cost and suitable for low cost embedded hardware.Part two of the thesis is dedicated to an intelligent anti-slugging coni Summary trol system for offshore oil production maximization. Existing anti-slug control systems are not robust and tend to become unstable after some time, because of inflow disturbances or plant dynamic changes, thus, requiring constant supervision and retuning. A second problem is the fact that the ideal setpoint is unknown and we could easily choose a suboptimal or infeasible operating point. Here we present a method to tackle these problems. Our complete control solution is composed of an autonomous supervisor that seeks to maximize production by manipulating a pressure setpoint and a robust adaptive controller that is able to quickly identify and adapt to changes in the plant. Our proposed solution has been tested in a experimental rig and the results are very encouraging. An analysis of the robustness and optimality of different linear controllers for slug mitigation is also carried out in this part of ...

Improved Model for Solar Heating of Buildings

“…However, it is clear that the energy consumption is reduced with the new model: this is a result of a combination of ventilation heat integration and allowing for temperature distribution in the storage tank 4 . The fact that the temperature 4 Temperature distribution/stratification implies less entropy loss due to mixing, and thus a higher exergy = available energy.…”

“…However, it is clear that the energy consumption is reduced with the new model: this is a result of a combination of ventilation heat integration and allowing for temperature distribution in the storage tank 4 . The fact that the temperature 4 Temperature distribution/stratification implies less entropy loss due to mixing, and thus a higher exergy = available energy. Proceedings of the 56 th variation increases in the improved model simply reflects the fact that for the given conditions, the solar energy is too plentiful to allow good control: to improve the control, it is necessary to be able to reduce the heat flow from the collector.…”

“…In [13], a relatively complete model structure for online use is given. With respect to on-line use of models, [4] use a simple model of a building together with prediction of outdoor temperature and varying electric spot prices to study possibilities for cost savings through advanced control, with emphasis on efficient algorithms, while [10], [17] and [14] discuss the use of Model Predictive Control of a system for thermal capturing of solar energy with auxiliary energy supply.…”

Proceedings of the 56th Conference on Simulation and Modelling (SIMS 56), October, 7-9, 2015, Linköping University, Sweden

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