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Electrically driven heat pumps offer in combination with thermal energy storage systems the potential to response to fluctuating renewable energy sources, e.g. photovoltaics. To fully exploit this flexibility and financial potential, smart predictive control strategies such as Model Predictive Control (MPC) are needed. For such a controller, weather forecast data are mandatory to perform the optimization. Several sources of weather forecast data are available with variable forecasting quality. In this study, the impact of the weather forecast quality on a realistic heat pump heating system is investigated in experiments and simulations. Therefore, the operation of a MPC strategy is carried out for a perfect forecast compared to two imperfect forecast scenarios over a consecutive period of 4 days on a Hardware-in-the-Loop test bench with a geothermal heat pump and a thermal energy storage system. In order to evaluate the benefits in real operation compared to rule-based controllers, a heat-controlled (HC) and a PV self-consumption optimized controller (PVC) are also operated on the test bench. In addition and as a validation process, all scenarios are simulated and compared to the measurement results. Compared to a standard rule-based HC strategy the PV self-consumption can be increased by using a PVC and MPC strategy by 6.2 % and 38.9 %, respectively. The accurately the weather forecasting quality is in general the higher the performance of the HP heating system. Thus, the PV self-consumption is reduced for high-quality and low-quality weather forecasts by 4.6 % and 11.1 %, respectively, compared to a perfect MPC. Even a MPC with low-quality weather forecast data can achieve higher system performance as a simple rule-based HC strategy. For achieving higher system performance by using a MPC instead of a rule-based control strategy like PVC the, forecasting quality has to be as accurate as possible.
The integration of heat pumps into a PV and battery system faces the challenge of sharing the available PV power among the actors. In order to coordinate this competition, a suitable energy concept, as well as the control, must match. In a cluster of eight terraced houses, an energy system has been implemented, which should represent a smart integration of heat pumps. To coordinate the interaction of the components, model predictive controls are used. The simulation shows very good results for the predictive controls, but the results cannot be replicated in total practice.
Evaluation and test operation of different model predictive control approaches for an energy system
(2021)
Heat pumps in combination with thermal energy storage systems offer the potential to response to fluctuating renewable energy sources, e.g. photovoltaics. To fully exploit this flexibility and financial potential, predictive control strategies are needed. Since an additional effort due to detailed knowledge and programming skills is required to create the model predictive control (MPC) strategies, a fast and easy implementation is prevented. Therefore, a second model-based approach is developed with a predictive but rule-based control. This simplified approach uses predictive models as well but energy balancing to determine the heat pump operation and the state of charge of thermal storage units throughout the day. In this paper, two predictive approaches were compared with two rule-based controls and evaluated for their potential for PV self-consumption and cost savings in annual simulations. In addition, one rule-based PV optimized control (PVC) and the predictive approaches, MPC and the simple predictive control (SPC), are implemented in the real operation in a plus energy building. In simulation, the best result is achieved by the MPC with a cost saving of 8.3 % due to a high PV energy consumption but mainly to the best efficiency with a SPF of 4.5. Despite the predictive approach of SPC, SPC and PVC achieve very similar results with cost savings of 2.5 % and 0.8 %. Since the costs of PV include taxes, these moderate cost savings are achieved. Excluding these taxes, there are significantly higher cost savings of up to 34 % for MPC. In real operation, differences between simulation results and measured data become apparent. This gap between the set point output of the simulation and the set point input of the real components poses a challenge to the implementation of efficient and cost-effective control like the MPC.
Since 2018, a terraced house complex with shared energy system is monitored and evaluated regarding PV selfconsumption and efficiency (Figure 3). Beside heat pumps and photovoltaics (PV), different kind of storage units are integrated and used to store PV surplus. Thermal storage units for heating and domestic hot water as well as an electrical storage are integrated into the higher-level control system and are charged selectively. However, since the capacity limits have been reached, additional storage options are interesting. The thermal mass of the building offers additional potential to store PV surplus energy. This paper evaluates the operation of thermal mass activation for the terraced house complex in order to increase the PV self-consumption and decrease the grid consumption. The simulation study of thermal mass activation is realized in TRNSYS. Increasing the room set point temperature of 20 °C by 2 K during PV production, aims to a reduction of the grid consumption by 47 % and an increase of PV self-sufficiency from 23 % up to 64 %. At the same time, the mean deviation of the room set temperature increased from 1.7 K to 2.0 K. The results are compared to real measured data from the terraced house.
Evaluation and test operation of different model predictive control approaches for an energy system
(2021)
The increasing number of predictive model control (MPC) approaches enables a wide variety of designs. However, the question arises which approach is suitable for real operation. This paper examines two different MPC approaches based on simple and detailed models as well as using different optimization algorithms. The MPC approaches are applied and evaluated in a real energy system controlling heat pumps of variable and constant compressor speed. Due to the different characteristics, two implementation processes are required. Both approaches ensure correct system operation, even if the GA approach had to be corrected four times by rule-based intervention. The power control of the MHPs could not be strictly implemented by any approach.
This study focuses on the numerical simulation of air flows in classrooms to investigate
comfort, infection control and energy efficiency. At first validation methods of a
computational fluid dynamics (CFD) simulation such as the mesh independence test and experimental validation are explained in detail. A mesh study is then carried out to determine the optimum mesh density for CFD simulations of the ventilation laboratory (room volume 123 m3). The experimental validation carried out shows that the simulation model reflects reality very well. Considering the measurement tolerance, the maximum deviation of the simulation from reality is 6.30% for the temperature and 8.29% for the air velocity. This validated CFD model is the basis for high-quality simulations of different ventilation scenarios. Specifically, the spread of airborne pathogens such as coronaviruses in classrooms will be investigated. This should help to implement future infection control measures in a more targeted manner.
In the course of the COVID-19 pandemic, indoor ventilation came into focus. In Germany, some schools were retrofitted with decentralized ventilation systems. This monitoring study compares the influence of mechanical and natural ventilation in classroom settings on indoor air quality (IAQ). Three different concepts are investigated: exclusively natural ventilation in combination with carbon dioxide (CO2) measuring devices and mechanical ventilation with decentralized air handling units (AHUs) and with a central ventilation system. This study examines eight classrooms across three elementary schools, continuously measuring CO2 concentrations and various other parameters. The evaluation of the monitoring data shows that the correct setting of ventilation systems is not a matter of course and is an important factor.
Since the feed-in tariff of photovoltaics (PV) decreases [1], PV selfconsumption becomes more profitable. Especially heat pumps, which are able to modulate the compressor speed, open up more flexibility for consuming fluctuating PV power. For exploiting the full potential of PV self-consumption smart control
strategies for modulating heat pumps (MHPs) are needed. In this research, the development and the real application of optimized control strategies for MHPs in plus energy terraced houses, built in 2017, are realized. Two control strategies, a PV rule based (PVC) and a model predictive control (MPC) are tested and compared in simulation. Results for the MPC of a 12 months simulation show potential for a moderate increasing PV self-consumption up to 2.1 % and significant reducing operating costs up to 36.1 %. In real application in the terraced houses, the PVC offers an increase of PV self-consumption by 4.1 % for a monitored week, compared to a heat controlled operation.