# Long-Term Performance Analysis Using TRNSYS Software of Hybrid Systems with PV-T

## Abstract

**:**

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Location and Meteorological Data

^{2}are recorded between the end of May and the beginning of September. On the other hand, from 1 April to 30 September, the amount of energy from solar radiation constitutes as much as 76% of 1041 kWh/m

^{2}of total horizontal radiation available per year.

#### 2.2. Transient Models with PV-T

#### 2.3. Parameters of PV-T

^{−1}to even 0.0063 K

^{−1}[35]. In this paper, changes of $\beta $ value in a PV-T cell were analysed in the range of 0.003–0.006 K

^{−1}with a 0.001 K

^{−1}step.

## 3. Results and Discussion

^{2}of the PV-T surface area was shown for the discussed models and various $\beta $ values. In each case, the highest values were obtained for $\beta =0.003$ K

^{−1}, and the lowest—for $\beta =0.006$ K

^{−1}. Application of a photovoltaic cell with a higher parameter $\beta $, in the absence of heat reception from the PV-T, can reduce the annual electricity production by up to 7%. Models B-D, in which heat reception from the PV-T was used, generated more electricity in comparison to the base Model A.

^{2}(Figure 3). Compared to Model A, Model D gives from 3–6% more electrical energy for the same $\beta $ value. The highest impact of $\beta $ on electricity production is visible in the months from May to September, i.e., during the highest daily total horizontal radiation achieved and the highest air temperatures (Figure 1). In other months, at ambient temperatures close to STC, the impact of$\beta $ is less important. It may be observed that the better the PV quality connected with a lower value of $\beta $ parameter, the weaker the impact of the installation type on electricity production.

^{−1}than for $\beta $ = 0.003 K

^{−1}. This should be explained by the fact that the solar fluid in Model B reached high temperatures in the summer, which resulted in an equally high temperature of the PV cell. At a higher temperature, the PV cells transformed a lesser amount of solar radiation into electrical energy, generating a larger amount of heat simultaneously. The highest average annual efficiency of solar radiation energy conversion was obtained for Model D for $\beta =0.003$ K

^{−1}and it amounted to 28.3% in relation to total horizontal radiation (i.e., 18.8% more than for Model A).

^{−1}and other parameters enabling an easier interpretation of the presented simulation results. Cell temperatures in the PV-T for a solar irradiance value above 300 W/m

^{2}were the lowest for Model D among all discussed models. It was achieved due to, among other things, a lower average fluid temperature in the tank (Avg. Tank D) in comparison to Model B. The reason for this is that the average temperature of the water in Tank D is controlled by a differential controller at a level not exceeding 50 °C. In Model B, there is no such limitation, therefore the temperature of the fluid is higher since each portion of the heat generated in PV-T is stored in the tank. Averaged temperature for Tank C is not included in Figure 6 since the tank is loaded with the energy produced by the heat pump. Including averaged temperature in Tank C in the graph could lead the reader to erroneous conclusions. The decrease in the average temperature in tanks B and D around 6:00 a.m. and 7:00 p.m. was caused by DHW consumption set in the Type 14b component. As was emphasized above, during high solar irradiance values, the temperature of the cells in the PV-T in Model C was slightly lower than in Model A, since the heat pump used in the installation was characterized by a constant cooling power. In future studies, a heat pump with power modulated in a broader range should be considered.

^{−1}for Models B and D is similar to the daily total horizontal radiation distribution shown in Figure 1. Model B of the installation with PV-T makes much less use of the available solar radiation in the September–March period than Model D. On the other hand, in the April–August period, the heat generation values are lower by several kWh. The completely different nature of the installation in Model C is visible in the course of heat generation. In this installation, the heat pump runs continuously in the set time intervals, acquiring heat from the PV-T, which leads to a relatively constant amount of heat generation of approx. 3 kWh/day. During a lower daily total horizontal radiation, the obtained values of heat generation are lower. Noteworthy, in this model, is that heat can be obtained from the PV-T solar fluid even in a situation when solar radiation does not fall on the PV-T. During the winter period, it leads to a decrease in the temperature of the PV-T fluid, and in that of the photovoltaic cells, below the ambient temperature, which is evident in Figure 5 and Figure 6. Assuming that approx. 6.5 kWh of heat should be supplied daily to maintain the appropriate temperature in the DHW tank, the PV-T in the Model B installation was able to fully meet this demand for only five days a year. On the other hand, the Model D installation allowed for meeting this demand for 56 days.

## 4. Conclusions

^{−1}on the effectiveness of the PV-T.

- The absence of heat received from the solar fluid, particularly in the summer period, leads to cell temperatures even several times higher than the outside temperature;
- Models C and D, in which the PV-T cooperated with a heat pump, allow for obtaining more heat and electricity from the PV-T in the long run than Model B, in which heat energy from the PV-T is transferred directly to the DHW tank;
- Intensification of the heat collection process from the PV-T using a heat pump resulted in an increase in electricity production by 6% compared to the base model A.
- For each installation model, the highest values of electricity production were obtained for $\beta =0.003$ K
^{−1}, and the lowest—for $\beta =0.006$ K^{−1}. - The type of cell used may reduce the production of electricity from PV-T by up to 7% on an annual basis.
- In temperate oceanic climate (Dfb sub-group in Köppen–Geiger climate classification system) the highest impact of $\beta $ on electricity production in PV-T is visible in the months from May to September, i.e., during the highest daily total horizontal radiation achieved and the highest air temperatures.
- A key element to improve the efficiency of the heat pump and increase the value of the seasonal COP is the appropriate adjustment of the devices in the hybrid installation.

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## Nomenclature

$\beta $ | Temperature coefficient, K^{−1} |

COP | Coefficient Of Performance |

DHW | Domestic Hot Water |

$\eta $ | PV electrical efficiency |

${\eta}_{{T}_{ref}}$ | is the module’s electrical efficiency at the reference temperature |

PV | Photovoltaic Panels |

PV-T | hybrid PhotoVoltaic-Thermal collector |

STC | Standard Test Conditions |

${T}_{cell}$ | PV cell temperature |

${T}_{ref}$ | reference temperature |

## Appendix A

Component | Short Description |
---|---|

Equa | The equations statement allows variables to be defined as algebraic functions of constants, previously defined variables, and outputs. |

Type 2b | Differential controller generates a control function (1 or 0) chosen as a function of the difference between upper and lower temperatures, compared with two dead band temperature differences. |

Type14b | The time-dependent forcing function specifies the value of the water drawn at various times throughout one cycle. |

Type14h | Time-dependent forcing function has a behavior characterized by a repeated pattern. It is responsible for PV-T working priority during the day. |

Type 15-6 | Weather data processor allows reading data at regular time intervals from an external weather data file and making it available to other TRNSYS components. |

Type 24 | This component integrates a series of specified quantities over a period of time. |

Type 50d | It simulates a PV-T with high complexity in the heat losses calculation. |

Type 65a | The online graphics component displays chosen system variables during the simulation. Additionally, data sent to the online plotter are automatically once per time step saved in a defined external file. |

Type 114 | These component models a single (constant) speed pump that is able to maintain a constant fluid outlet mass flow rate. |

Type 122 | This component models a fluid boiler (auxiliary heater). |

Type 156 | It simulates a fluid-filled, vertical, cylindrical, constant volume storage tank with an immersed coiled-tube heat exchanger. |

Type 927 | These component models a single-stage water-to-water heat pump based on user-supplied data. |

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**Figure 5.**Changes of average cell temperature for$\beta $ equal 0.005: (

**a**) Models A and B; (

**b**) Models C and D.

Parameters | Value | Unit |
---|---|---|

Collector area | 6 | m^{2} |

Collector efficiency factor | 0.7 | - |

Collector plate absorptance | 0.9 | - |

Number of glass covers | 1 | - |

Loss coefficient for bottom and edge losses | 5.56 | W/(m^{2}·K) |

Collector slope | 34 | ° |

Packing factor | 0.6 | - |

Cell efficiency at reference conditions | 0.2 | - |

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**MDPI and ACS Style**

Pater, S.
Long-Term Performance Analysis Using TRNSYS Software of Hybrid Systems with PV-T. *Energies* **2021**, *14*, 6921.
https://doi.org/10.3390/en14216921

**AMA Style**

Pater S.
Long-Term Performance Analysis Using TRNSYS Software of Hybrid Systems with PV-T. *Energies*. 2021; 14(21):6921.
https://doi.org/10.3390/en14216921

**Chicago/Turabian Style**

Pater, Sebastian.
2021. "Long-Term Performance Analysis Using TRNSYS Software of Hybrid Systems with PV-T" *Energies* 14, no. 21: 6921.
https://doi.org/10.3390/en14216921