Economic model predictive control of combined thermal and electric residential building energy systems

Kuboth, Sebastian; Heberle, Florian; König-Haagen, Andreas; Brüggemann, Dieter

doi:10.1016/j.apenergy.2019.01.097

Cited by 91 publications

(34 citation statements)

References 26 publications

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“…Nevertheless, due to the need to combine both planning and tracking problems as well as the increasing interest of improving the economic performance of the CHP systems, the Economic Model Predictive Control (EMPC) has gained attention during the last years with great applications in systems such as the boiler-turbine system [12], residential building energy systems [13], and mechanical pulping processes [14]. The main advantage of EMPC with respect to MPC is that the former directly optimizes an economic cost function of the process, from which both market constraints and time-varying price profiles could be considered into both the cost function and the constraints of an optimization problem [15,16].…”

Section: Computational Time T Smentioning

confidence: 99%

Optimal operation of combined heat and power systems: An optimization-based control strategy

Diaz

Ocampo‐Martínez

Panten³

et al. 2019

Energy Conversion and Management

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The use of decentralized Combined Heat and Power (CHP) plants is increasing since the high levels of efficiency they can achieve. Thus, to determine the optimal operation of these systems in dynamic energy-market scenarios, operational constraints and the time-varying price profiles for both electricity and the required resources should be taken into account. In order to maximize the profit during the operation of the CHP plant, this paper proposes an optimization-based controller designed according to the Economic Model Predictive Control (EMPC) approach, which uses a non-constant time step along the prediction horizon to get a shorter step size at the beginning of that horizon while a lower resolution for the far instants. Besides, a softening of related constraints to meet the market requirements related to the sale of electric power to the grid point is proposed. Simulation results show that the computational burden to solve optimization problems in real time is reduced while minimizing operational costs and satisfying the market constraints. The proposed controller is developed based on a real CHP plant installed at the ETA research factory in Darmstadt, Germany.

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Section: Computational Time T Smentioning

confidence: 99%

Optimal operation of combined heat and power systems: An optimization-based control strategy

Diaz

Ocampo‐Martínez

Panten³

et al. 2019

Energy Conversion and Management

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“…Also, the same authors [7] studied that residential building energy cost savings in a year for TOU, hourly, and real time pricing, achieving 42% saving for building energy management in real time pricing. The authors in [8] also introduced a MPC method on a single family home to control a hot water and space heating simultaneously along with a SB. The model proposed showed a reduction of 11.6% in the operation cost per year when compared with a proportional-integral-derivative (PID) controller.…”

Section: Introductionmentioning

confidence: 99%

Energy Management of a PV-SB HPGS Based on Model Predictive Control

Al‐Gabalawy

Samir

2020

Preprint

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Abstract Background Hybrid Power Generation Systems (HPGSs) always introduces the solutions for many problems of the power systems such as; poor power quality and the highly generation costs. Usually, these systems have high opportunity of utilizing the renewable sources, where they are installed so near to the customers. In some times, the customers whom have installed these systems, have the ability to purchase or sell the required or the surplus energy, respectively. Therefore, the concept of the energy management should be implemented in these situations, which means providing energy to the consumers while respecting the demand and optimizing the production cost. Supplying power has become more and more complex in a growing network. Several ways exist to adapt to the steadily increasing energy demand. The energy efficiency of the buildings/generators could be improved, or new electric supply units could be built. However, smarter techniques of managing the grid from the demand side receive an increasing attention by research and industry for a promising economic potential. Methods The proposed HPGS in this paper is the combination of the Photovoltaic (PV) and the Storage Battery (SB). The Demand Response (DR) is the main philosophy for the energy management for PV-SB HPGS, while the Model Predictive Control (MPC) is applied to control the energy from the HPGS to the smart home, which was incorporated with an electrical heater in each room. The DR strategy has also been applied using other control techniques, such as thermostat and Proportional-Integral (PI) controller, for the purpose of comparing between the techniques. Results Simulation results for the three controllers were implemented. A comparison between the results was carried out. to verify the proposed system, which proved the superiority of the MPC. Conclusions A smart method for optimizing the comfort temperature inside the rooms by using a MPC strategy with a PV panel and a battery was proposed. DR allowed benefits on both system operation and market efficiency. MPC evidenced to provide economic benefits with respect to other compared controllers.

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“…In order to increase self-sufficiency, and therefore reduce energy costs, PV home systems are often equipped with a battery, in most cases based on the lithium-ion technology. Heat pumps are usually coupled with a heat storage device [3][4][5] or use the building mass as energy storage [6]. Therefore, they are a typical example of flexible sector coupling.…”

Section: Introductionmentioning

confidence: 99%

“…In practice, these control systems can be divided into 2 of 16 two groups, rule-based and optimization-based concepts [7]. The simplest approach that has previously been implemented in real systems is a rule-based concept for self-consumption optimization, which simply charges the storage device when there is a PV power surplus and discharges the storage device when the load power is higher [4,5,10,11]. On sunny days, this often leads to the storage device being fully charged very early.…”

Section: Introductionmentioning

confidence: 99%

“…However, as forecasts are never perfectly correct, some error correction mechanism has to be implemented or performance may be very poor. Finally, there is the possibility of using model predictive control (MPC), which re-evaluates the optimization problem in regular intervals in order to correct for forecast errors [5,6,11,[19][20][21][22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Optimization-Based Control Concept with Feed-in and Demand Peak Shaving for a PV Battery Heat Pump Heat Storage System

2019

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The increasing share of renewable energies in the electricity sector promotes a more decentralized energy supply and the introduction of new flexibility options. These flexibility options provide degrees of freedom that should be used optimally. Therefore, in this paper, a model predictive control-based multi-objective optimizing energy management concept for a hybrid energy storage system, consisting of a photovoltaics (PV) plant, a battery, and a combined heat pump/heat storage device is presented. The concept’s objectives are minimal operation costs and reducing the power exchanged with the electrical grid while ensuring user comfort. In order to prove the concept to be viable and its objectives being fulfilled, investigations based on simulations of one year of operation have been carried out. Comparisons to a simple rule-based strategy and the same model predictive control scheme with ideal forecasts prove the concept’s viability while showing improvement potential in the treatment of nonlinear system behavior, caused by nonlinear battery losses, and of forecast uncertainties.

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Economic model predictive control of combined thermal and electric residential building energy systems

Cited by 91 publications

References 26 publications

Optimal operation of combined heat and power systems: An optimization-based control strategy

Optimal operation of combined heat and power systems: An optimization-based control strategy

Energy Management of a PV-SB HPGS Based on Model Predictive Control

Optimization-Based Control Concept with Feed-in and Demand Peak Shaving for a PV Battery Heat Pump Heat Storage System

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