Securing a Renewable Energy Supply for a Single-Family House Using a Photovoltaic Micro-Installation and a Pellet Boiler

Abstract

Photovoltaic (PV) micro-installations producing renewable electricity and automatic pellet boilers producing renewable heat energy are promising solutions for single-family houses. A single-family house equipped with a prosumer 7.56 kWp PV micro-installation and a 26 kW pellet boiler was analyzed. This study aimed to analyze the production and use of electricity and heat over three successive years (from 1 January 2021 to 31 December 2023) and to identify opportunities for securing renewable energy supply for the house. Electricity production by the PV was, on average, 6481 kWh year⁻¹; the amount of energy fed into the grid was 4907 kWh year⁻¹; and the electricity consumption by the house was 4606 kWh year⁻¹. The electricity supply for the house was secured by drawing an average of 34.2% of energy directly from the PV and 85.2% from the grid. Based on mathematical modeling, it was determined that if the PV installation had been located to the south (azimuth 180°) in the analyzed period, the maximum average production would have been 6897 kWh. Total annual heat and electricity consumption by the house over three years amounted, on average, to 39,059 kWh year⁻¹. Heat energy accounted for a dominant proportion of 88.2%. From a year-round perspective, a properly selected small multi-energy installation can ensure energy self-sufficiency and provide renewable energy to a single-family house.

1. Introduction

In 2023, in Poland, 58.7% of the population lived in houses, of which as much as 53.7% in detached houses, 41.2% in flats in blocks, and 0.1% in other housing units [1]. Under the climatic conditions of Poland, households are very important beneficiaries of energy consumption. In 2022, they consumed 29.2% of final energy consumption, compared to the European Union (EU) average of 26.9% [2,3]. Polish households spend the most energy on heating. Although this indicator decreased from 71.3 to 65.1% over 20 years (2002–2021), it should be recognized as still being high [4]. The decrease in this type energy consumption results from the thermal upgrading of buildings and replacing old, low-efficiency boilers with newer ones. However, over these 20 years, the energy consumption for water heating, powering electrical appliances, and preparing meals increased, and in 2021, it accounted for 17.3, 9.0, and 8.5%, respectively. It should also be mentioned that in the structure of household energy consumption in Poland in 2021, hard coal was still of major importance (21.9%), whereas in the EU, it only accounted for 2.5% [5]. In contrast, the use of solid biomass in this structure in the EU and Poland was 17.3 and 22.4%, respectively. However, natural gas was the most important fuel in the structure of household energy consumption in the EU, with a share of 33.5%. In Poland, this indicator was also high and amounted to 20.6%.

Given the above, searching for suitable solutions for providing renewable energy to residential properties, including single-family houses, is still relevant. The role of renewable energy sources (RESs) is also becoming increasingly important because of the challenges of fossil fuel depletion, environmental degradation, climate change, and ensuring energy supply stability [6,7,8,9]. Of all the different renewable energy source technologies, two of them appear to be promising in the context of RES development, including their application in single-family houses: photovoltaic (PV) installations producing renewable electricity and automatic biomass boilers (including pellets as a biofuel source) producing renewable heat energy [10,11,12,13,14]. It should be noted that in the years 2017–2023, Poland saw a very rapid increase in the number of prosumer PV micro-installations, from approximately 21,000 units to approximately 1.4 million units [15,16]. In December 2023, in Poland, the total capacity of these PV installations was 11,206.9 MW. Solid biofuel boilers are becoming increasingly popular in the Polish market. An increased proportion of biomass boilers, including pellet boilers, can be observed, as biomass boilers accounted for approximately 65–70% of solid fuel boilers sold in 2020 [17]. In contrast, in 2021 in Poland, 42,000 pellet boilers were sold. It must be pointed out that the pellet boiler segment saw the highest growth in 2021, mainly thanks to sales in three leading countries, i.e., Poland, Germany, and France [18]. Burning pellets in automatic boilers is considered an efficient use of biomass to produce household heat energy [10,19]. This is why interest in pellets as a local and renewable energy source is increasing among consumers in the residential sector. This results from the current political situation as well as concerns about disruptions in the prices and availability of fossil fuels [16,19,20,21].

In view of the above, providing houses with RESs through the use of various devices and systems for generating energy (both electrical and thermal) is the subject of research and analysis. Various types of devices and their systems were analyzed, such as wood pellet boilers and a heat pumps [22], solar power and battery storage [23], photovoltaic (PV) systems with a small wind turbine, solar panels and biomass boilers [24], air heat pumps, solid fuel boilers, and photovoltaic installation [25]. The cited studies demonstrated the validity of using renewable energy to power the studied facilities from an economic, technical, and ecological point of view. On the other hand, the novelty of our research is a three-year analysis of the use of a multi-energy installation (PV micro-installation for electricity generation and automatic pellet boilers for heat energy generation), in which the devices used constituted a technical system that has not been described in the literature so far. Therefore, our research contributes new knowledge and extends the previously described systems for using RESs in single-family homes. Moreover, a novelty in this research was the modeling performed, which established prediction of the maximum average electricity production. Therefore, based on the literature data and the existing information, a research hypothesis was formulated: An appropriately selected multi-energy installation can ensure the renewable energy self-sufficiency of a single-family house in terms of year-round billing. Therefore, this study aimed to analyze renewable energy generation by a multi-energy installation over three successive years by evaluating the following: (1) electricity production by the PV micro-installation; (2) the amount of electricity consumed by the house and fed into the electricity grid; (3) pellet consumption for heat generation; (4) total energy consumption; and (5) the level of securing a renewable energy supply for the single-family house.

2. Materials and Methods

2.1. The Location and Characteristics of the Study Facility

This study was conducted in a single-family house equipped with a prosumer photovoltaic (PV) micro-installation for electricity generation and an automatic pellet-fueled heating system for heat generation [16]. These installations were placed in a house in north-eastern Poland in the Warmińsko-Mazurskie Voivodeship, Olsztyn district, in the city of Olsztyn (Figure 1). The main walls of the house were built using sand-lime bricks and autoclaved aerated concrete, and 15 cm thick polystyrene foam was used as external insulation. The attic of the house was insulated with 15 cm thick mineral wool, and the roof was covered with metal roofing tiles. The main heated area of the house was 247 m². Heat energy was distributed by convector heaters comprising 205 aluminum fins. In addition, nine aluminum fins were used to lightly heat a utility room with an area of 40 m².

The photovoltaic (PV) micro-installation for electricity generation with a total peak power of 7.56 kWp was installed on the house’s roof in February 2020. The PV micro-installation was connected to the overall electricity grid (on-grid) and comprised the following components: Longi Solar 315 Wp photovoltaic panels, Full Black (24 pcs); a Solar Edge SE5k inverter, 5 kW (1 pc.); a P370 power optimizer (24 pcs.); DC surge protectors (1 set); AC surge protectors (1 set); a DC cable (1 × 4 mm²); an AC cable (5 × 4 mm²); AC/DC switchgear; an aluminum frame; a Smart Meter energy meter; and transformers (1 set). For technical and organizational reasons, the photovoltaic panels were arranged on three different roof slopes of the house: the azimuth of 18 panels was 250°, the azimuth of three panels was 160°, and three were 70°. The azimuth was expressed in degrees (°) and indicated the angle between north and the plane of the panels. It should be stressed that this “on-grid” PV micro-installation was billed under the system known as “net-metering”. This system was introduced in Poland in July 2016, and it has contributed to the high interest in photovoltaics, particularly micro-installations installed by prosumers. The word “prosumer” was coined by combining the words “producer” and “consumer”. A prosumer can be an individual who owns a registered RES installation and uses electricity produced by the operation of their RES installation, e.g., a PV micro-installation. According to the Act on RES [26], a renewable energy prosumer is a final consumer who generates electricity exclusively from renewable energy sources for their own needs, using a micro-installation. The “net-metering” system allows the prosumer to “store” surplus electricity produced by a PV micro-installation in the electricity grid for one year. Prosumers can withdraw the energy stored in the electricity grid at a ratio of 1:0.8 (energy fed into the grid: energy withdrawn from the grid) using micro-installation with an installed peak power of up to 10 kWp. The choice of the tested PV micro-installation was mainly due to its purchase cost, technical possibilities of installation, and visual effect on a specific roof of a house.

The heating system for generating heat energy (central heating water and hot tap water) was installed in the boiler room of the house in April 2018. The heating system was fueled with pine wood pellets and comprised the following components: KIPI pellet burner, type ROT-POWER, 26 kW (Suchy Las, Poland); Viadrus Hercules U22 cast-iron boiler, 29.1 kW (Bohumin, Czech Republic); PLUM controller, ecoMAX 850 P2; and Salus 091FLRF (TX) thermostat. The solid biofuel was fed using a screw feeder, which drew pellets from an approximately 2.5 m³ tank and transported them to the KIPI burner. Hot tap water was stored in a 250 dm³ tank. In this particular case, the KIPI pellet burner, type ROT-POWER, was chosen due to its construction and operating characteristics, including mainly the cylindrical shape, the rotating nature of the work, and the self-cleaning of the burner during operation. These features make this burner very efficient in burning various types of pellets and require minimal maintenance from the user. An additional boiler powered by electricity and a generator were installed in the analyzed house in order to increase the energy security of the facility in the event of failure of the pellet boiler or a lack of electricity supply from the power grid.

2.2. Data Collected and the Scope of This Study

The study period covered three years, from 1 January 2021 to 31 December 2023. To carry out the electricity study, the following data was collected from the mySolarEdge application: electricity production by the PV micro-installation; electricity consumption by the house directly from the PV micro-installation, i.e., electricity self-consumption; electricity from the PV micro-installation fed into the electricity grid; and electricity withdrawn from the electricity grid. Further electricity indicators were then calculated, including the following:

Total household electricity consumption (1):

THCe = SCe + EfG

(1)

where

THCe—total household electricity consumption (kWh), i.e., the energy drawn directly from the PV installation and from the power grid (at a time when there was no energy from the PV installation);

SCe—electricity self-consumption (kWh);

EfG—electricity withdrawn from the electricity grid (kWh).

Securing electricity supply for the house through self-consumption (2):

SeSC = SCe ∙ 100/THCe

(2)

where

SeSC—Securing electricity supply for the house through self-consumption (%).

Securing electricity supply for the house resulting from energy generation by the PV micro-installation (3):

SeP = PPV ∙ 100/THCe

(3)

where

SeP—securing energy supply for the house from the energy produced by the PV micro-installation (%);

PPV—electricity production by the PV micro-installation (kWh).

Average electricity generation per 1 kWp of an installed peak power (4):

AG = PPV/TpPV

(4)

where

AG—average generation per 1 kWp (h);

TpPV—total PV micro-installation peak power (kWp).

Average self-consumption per 1 kWp of installed peak power (5):

ASC = SCe/TpPV

(5)

where

ASC—average self-consumption per 1 kWp (h).

Energy withdrawable from the electricity grid by the prosumer (80%) after considering the “net-metering” billing system (6):

EfG = EtG ∙ 0.8

(6)

where

EfG—energy withdrawable from the grid by the prosumer (kWh);

EtG—electricity from the PV micro-installation, fed into the electricity grid (kWh);

0.8—a correction factor used in the “net-metering” billing system for micro-installations with a peak power of up to 10 kWp, which allows the prosumer to withdraw, from the grid, 80% energy fed into the grid by their PV micro-installation.

Securing electricity supply for the house resulting from the amount of energy withdrawn from the electricity grid after incorporating the assumptions of the prosumer program into the “net-metering” billing system (7):

SePr = EfG ∙ 100/THCe

(7)

where

SePr—securing electricity supply for the house after incorporating the prosumer program into the “net-metering” billing system (%).

Total security of electricity supply for the house through self-consumption and the energy withdrawable from the electricity grid (8):

TSE = SeSC + SePr

(8)

where

TSE—total security of electricity supply for the house (%).

The next stage was a sensitivity analysis based on mathematical modeling. Based on the amount generated by each panel and the azimuth of the position, extreme values of energy production were determined for each analyzed plane. On this basis, a statistically significant polynomial function model was built, describing the effect of the panel position on the amount of electricity production from PV. Based on the model, a prediction of the maximum average electricity production was made in the analyzed solar conditions of the analyzed period. The scope of second modeling was performed to determine the relationship between the energy production and radiation intensity based on a regression analysis. A statistically significant linier model was obtained. Radiation intensity reflected weather condition and was measured for Olsztyn in the period of study.

To carry out the heat energy generation study, analyses of selected elemental composition and thermophysical properties of the pine pellets purchased were performed. Each year, representative pellet samples were collected for laboratory analyses. The tests were carried out at the UWM laboratory in triplicate to determine the following: moisture content, solid contents, volatile matter content, ash content, carbon (C) content, sulfur (S) content, hydrogen (H) content, chlorine (Cl) and nitrogen (N) content, higher heating value (HHV), and lower heating value (LHV), using procedures and standards presented in the literature [27]. Over the three years of this study, each charge of pine pellets to the storage hopper was weighed in order to determine the actual consumption of this solid biofuel in each month, starting from January 2021 and ending in December 2023. Based on the amount of pine pellets consumed (Mg) and its LHV (GJ Mg⁻¹), heat consumption (GJ) in the house was determined. Each month under study was also assessed regarding the heating degree-day (HDD) indicator [28].

2.3. Statistical Analysis

Data from the photovoltaic installation and the automatic heating system were first prepared and collated in Microsoft Excel for analysis. The statistical analyses of the results obtained and the indicators for electricity, e.g., production by the PV micro-installation, energy fed into the grid, self-consumption, energy withdrawn from the grid, total energy consumption, average generation per 1 kWp of peak power, average self-consumption per 1 kWp of peak power, securing energy supply, and selected indicators for heat energy production, were performed based on the analysis of variance with repeated measurements. For identification of homogeneous groups (h.g.), the Tukey’s test of significance (HSD) was used at the significance level of p < 0.05. Statistical analysis covered also calculations of descriptive statistics such as the means and the standard deviations, the median, the minimum and maximum values, a lower quartile, an upper quartile, and the variation coefficient, computed for all the analyzed characteristics and indicators. The next step covered an analysis of the relationships between the variables under study. The r-Pearson’s correlation coefficients were determined and a linear regression analysis between variable, such as the monthly briquette consumption and the number of HDDs, was determined as well, which identified r in terms of heat energy production. The last part of the relationships analysis was a cluster analysis (CA). This agglomerative multidimensional method was conducted for the variables related to electricity production and use by the PV micro-installation for the successive months. Before performing the analyses, the input data were standardized in columns. For agglomeration, the Ward’s method was used; for distance measure, the Euclidean method was used. The clusters were distinguish based on Sneath’s criterion at 1/3 D_max and at 2/3 D_max, where D_max was calculated as the maximum distance D. Statistica 13 software (TIBCO Software Inc., Palo Alto, CA, USA) was used for all statistical analyses.

3. Results

3.1. Electricity Production by the PV Micro-Installation and Electricity Consumption by the Studied Facility

3.1.1. Average Monthly Indicators

All of the studied characteristics concerning electricity production by the PV micro-installation and its use by the single-family house on an average monthly basis were differentiated by the main factor (p < 0.001), i.e., month (data is not presented in the table due to identical p values). Average monthly (averaged over the three years of 2021–2023) electricity production by the PV micro-installation was the highest in June—1071.6 kWh (h.g. “a”) (Figure 2). The second intermediate h.g. “ab” contained electricity production in July and May, which was 9.4% and 10.7% lower, respectively, compared to the production achieved in June. Regarding this indicator, August, April, September, March, and October were ranked in lower positions. The values obtained were lower and ranged from 24.7 to 64.3%. On the other hand, electricity production in February (intermediate h.g. “fg”) was 81% lower than in June. However, the last h.g., “g”, contained electricity production in November, January, and December, with these values being 89%, 94.8%, and 97.1% lower, respectively. It should, therefore, be stressed that electricity production by the PV micro-installation varied greatly between the months over the three-year study period. This was even more evident based on the minimum (23.7 kWh) and maximum (1125.5 kWh) values obtained for the entire dataset (

Table A1. Selected descriptive statistics for average monthly electricity production indicators under study (N valid = 36).

Indicator	Mean	Minimum Value	Maximum Value	Standard Deviation	Coefficient of Variation (%)
Electricity production by the PV micro-installation (kWh)—PPV	540.1	23.7	1125.5	372.6	69.0
Electricity self-consumption (kWh)—SCe	131.2	18.4	222.7	65.4	49.8
Electricity from the PV micro-installation fed into the electricity grid (kWh)—EtG	408.9	4.8	910.5	311.9	76.3
Electricity withdrawn from the electricity grid (kWh)—EfG	252.6	118.4	473.0	106.5	42.1
Total household electricity consumption (kWh)—THCe	383.8	302.2	492.0	53.7	14.0
Securing energy supply through self-consumption (%)—SeSC	36.2	3.9	64.5	19.6	54.1
Securing energy supply from energy produced (%)—SeP	153.2	4.8	337.6	113.8	74.3
Average energy generation per 1 kWp of installed peak power (h)—AG	71.4	3.1	148.9	49.3	69.0
Average self-consumption per 1 kWp of installed peak power (h)—ASC	17.4	2.4	29.5	8.7	49.8

). The variation coefficient for this characteristic was high and amounted to 69%. The average monthly electricity produced by the PV micro-installation was significantly positively correlated with the self-consumption (0.94) and energy fed into the grid (1.00). It significantly negatively correlated with the energy withdrawn from the grid (−0.95) and total energy consumption (−0.74) (

Table A2. Pearson’s correlation coefficients for average monthly electricity production indicators under study.

Indicator	PPV	SCe	EtG	EfG	THCe	SeSC	SeP	AG	ASC
PPV	1.00	0.94 *	1.00 *	−0.95 *	−0.74 *	0.98 *	0.99 *	1.00 *	0.94 *
SCe		1.00	0.91 *	−0.91 *	−0.59 *	0.97 *	0.88 *	0.94 *	1.00 *
EtG			1.00	−0.94 *	−0.75 *	0.97 *	0.99 *	1.00 *	0.91 *
EfG				1.00	0.87 *	−0.97 *	−0.95 *	−0.95 *	−0.91 *
THCe					1.00	−0.75 *	−0.81 *	−0.74 *	−0.59 *
SeSC						1.00	0.96 *	0.98 *	0.97 *
SeP							1.00	0.99 *	0.88 *
AG								1.00	0.94 *
ASC									1.00

* Significant values (p < 0.05); the explanation of abbreviations for the studied indicators are presented in the Section 2 and Table A1.

).

The average monthly electricity self-consumption by the house was the highest in May and June, 206–207 kWh (h.g. “a”) (Figure 3). The second intermediate h.g. “ab” contained electricity self-consumption in July and April, and it was approximately 10.6% lower as compared to the highest self-consumption in May. In terms of this indicator, March, August, September, and October were ranked lower, and the values obtained were 20–41% lower than the highest self-consumption. Electricity self-consumption in the other months was below 100 kWh, with 89.6 kWh in January (h.g. “e”), and it was 56.7% lower than the highest value. However, the last h.g., “f”, contained electricity self-consumption in November, January, and December, with these values being 76%, 81.9%, and 89.2% lower, respectively, compared to the value obtained in May. Similar to the production from the PV installation (Pearson’s correlation coefficient was statistically significant and amounted to 0.94), the variation in electricity self-consumption by the house between months was consistent with the production from PV and was also very high (Table A2). The minimum value for the entire dataset was 18.4 kWh, and the maximum was 222.7 kWh (Table A1). The variation coefficient for this characteristic was 49.8%. In addition, the average monthly self-consumption of electricity withdrawn by the house directly from the PV installation was positively correlated with the energy fed into the grid (0.91) and negatively correlated with the energy withdrawn from the grid (−0.91) (Table A2).

Average monthly energy fed into the grid by the PV micro-installation was the highest in June (865.7 kWh) (h.g. “a”) (Figure 4). The same h.g. contained the value of this characteristic obtained in July, although it was 9.3% lower. The next intermediate h.g. (“ab”) contained energy fed into the grid in May, with this value being 13.4% lower than the value obtained in June. Regarding this indicator, August, September, April, March, and October were ranked at lower positions. Average monthly energy fed into the grid in February, November, January, and December was classified into the last h.g. “fg” and “g”. The average monthly energy fed into the grid in December was 99% lower than the value obtained in June. The high variation in this characteristic over the entire study period is also demonstrated by the high variation coefficient (76%) (Table A1). For the entire dataset from the three years, the average monthly energy fed into the grid was 408.9 kWh. However, the span of this characteristic was very wide, as the maximum value was 910.5 kWh, and the minimum value was 4.8 kWh. The energy fed into the grid was significantly positively correlated with production (1.00) and self-consumption (0.91) and significantly negatively correlated with the energy withdrawn from the grid (−0.94) and its total consumption (−0.75) (Table A2).

The average monthly electricity withdrawn by the house from the electricity grid was the highest in January, i.e., 431.9 kWh (h.g. “a”) (Figure 5). The same h.g. contained the value of this characteristic in December, even though it was 2.9% lower. The next intermediate h.g. (“ab”) contained electricity withdrawn from the grid in November. The average monthly electricity withdrawn from the grid in February and October was 30.6% and 36.9% lower, respectively, as compared to January and was classified into the group “bc”. The next h.g., “bcd” and “de”, contained the values of this characteristic obtained in March and April. The next group, “ef”, contained September. The average monthly electricity withdrawn from the grid in June, July, August, and May was classified into the last group, “f”. Therefore, the average monthly electricity withdrawn from the grid in June was 70% lower than the January value. For the entire dataset from the three-year period, the average monthly electricity withdrawn from the grid was 252.6 kWh (Table A1). However, the range of this characteristic was very wide, as the maximum value was 473.0 kWh, and the minimum value was 118.4 kWh. Energy withdrawn from the grid was significantly positively correlated with total energy consumption (0.87) and significantly negatively correlated with production (−0.95), self-consumption (−0.91), and energy fed into the grid (−0.94) (Table A2).

The total average monthly electricity consumption by the house was the highest in January (469.5 kWh) (h.g. “a”) (Figure 6). The intermediate h.g. “ab” contained total energy consumption in December and March. The next h.g., “abc”, contained the values of this characteristic, which were obtained in November and April. The average monthly energy consumption in October, February, and May were 16.1%, 17.1%, and 20.7% lower, respectively, as compared to January, and were classified as the group “bcd”. The next h.g., “cd”, contained the value of this characteristic, which was obtained in June. The last group, “d”, contained the July, September, and August values. The total average monthly energy consumption in August was 39.1% lower than in January. This characteristic exhibited the lowest variation coefficient (14.0%) of all the indicators under analysis (Table A1). For the entire dataset from the three-year period, the total average monthly electricity consumption was 383.8 kWh. However, the span of this characteristic ranged from 302.2 to 492.0 kWh. The total energy consumption was positively correlated with the energy withdrawn from the grid (0.87) and negatively correlated primarily with the energy fed into the grid (−0.75) and production (−0.74) (Table A2).

The highest average monthly security of energy supply for the house through self-consumption was noted in June (61.7%) and was classified into the h.g. “a” (

Table 1. Selected average monthly indicators of securing electricity supply for the house by the PV micro-installation, average values for the years and months.

Month	Securing Energy Supply Through Self-Consumption (%)	Securing Energy Supply from Energy Produced (%)	Average Energy Generation per 1 kWp of Installed Peak Power (h)	Average Self-Consumption per 1 kWp of Installed Peak Power (h)
Jan	8.2 ± 4.1 g	12.3 ± 8.1 ef	7.4 ± 4.5 f	5.0 ± 2.3 f
Feb	23.2 ± 6.3 f	52.7 ± 16.7 ef	26.9 ± 7.7 ef	11.9 ± 2.8 e
Mar	38.6 ± 6.8 de	128.9 ± 61.3 cd	72.0 ± 28.6 cd	21.9 ± 2.3 bc
Apr	46.1 ± 2.6 cd	168.1 ± 22.3 cd	89.1 ± 8.5 c	24.5 ± 0.8 ab
May	55.6 ± 2.3 ab	256.4 ± 20.4 ab	126.5 ± 14.7 ab	27.4 ± 2.1 a
Jun	61.7 ± 2.4 a	321.1 ± 18.8 a	141.8 ± 6.4 a	27.2 ± 1.3 a
Jul	57.0 ± 1.4 ab	300.6 ± 28.3 a	128.4 ± 3.5 ab	24.5 ± 2.4 ab
Aug	51.1 ± 2.8 bc	253.1 ± 39.4 ab	106.7 ± 14.2 bc	21.6 ± 1.1 bc
Sep	44.0 ± 1.9 cd	209.7 ± 45.2 bc	88.2 ± 12.9 c	18.7 ± 0.9 cd
Oct	31.0 ± 3.3 ef	98.8 ± 25.5 d	50.7 ± 8.9 de	16.1 ± 1.0 de
Nov	12.4 ± 1.7 g	29.3 ± 3.0 ef	15.6 ± 0.2 f	6.6 ± 0.5 f
Dec	5.1 ± 1.2 g	7.1 ± 1.5 f	4.1 ± 0.7 f	3.0 ± 0.6 f

a, b, c, etc.—h.g. for the months, separate for each characteristic; ±SD.

). After analyzing the average daily security of energy supply through self-consumption in the individual months, it was found that the months of July (57.0%) and May (55.6%) were included in the intermediate group “ab”. The next group, “bc”, contained securing energy supply through self-consumption in August (51.1%). Considering this indicator, the months of April, September, March, and October were ranked at lower positions, and the values of this indicator ranged from 46.1 to 31.0%. In the other months, securing energy supply through self-consumption was very low and accounted for less than 24%. In February, it was 23.2%; in November, 12.4%; in January, 8.2%; and in December, only 5.1%.

The average monthly securing of energy supply from the produced energy was the highest in June and July and amounted to 321.1% and 300.6%, respectively (h.g. “a”) (Table 1). The second intermediate h.g., “ab,” contained security of energy supply from the energy produced in May (256.4%) and August (253.1%). Considering this indicator, September, April, and March were ranked at lower positions. October was the month in which this indicator was below 100%. Even lower security from the energy produced was noted in February, November, and January. December, with this indicator value of 7.1%, was last, in h.g. “f”.

The highest average monthly energy generation per 1 kWp was noted in June (141.8 h) and was classified into the h.g. “a” (Table 1). After analyzing the average monthly energy generation per 1 kWp in individual months, it was found that July and May were included in the intermediate group “ab”. Considering this indicator, August, April, September, March, and October were ranked at lower positions. The lowest average monthly energy generation per 1 kWp of 4.1 h was noted in December (h.g. “f”). Moreover, the same group also contained values for January and November.

Average monthly self-consumption per 1 kWp of an installed peak power reached its highest value in May (27.4 h) and June (27.2 h) and was classified into the h.g. “a” (Table 1). The intermediate h.g. “ab” contained July and April, and the next group, “bc”, contained August and March. In September, the value of this indicator was already below 20 h (h.g. “cd”). The lowest average monthly self-consumption per 1 kWp of installed peak power (3.0 h) was noted in December (h.g. “f”). The same h.g. also contained the values for January and November.

A cluster analysis carried out for the analyzed indicators of electricity production by the PV micro-installation allowed two clusters to be separated already at a cut-off of 1/3 D_max (Figure 7a). The energy withdrawn from the grid and total energy consumption showed similar variation and formed one cluster. In contrast, the remaining ten indicators were found in the second cluster. Regarding the analyzed months, it was found that at a cut-off of 2/3 D_max, two clusters were separated (Figure 7b). A single cluster contained the late-autumn and winter months (November, December, January, and February). The second cluster contained the remaining eight (spring and summer) months. When the accuracy of the analysis was increased, three clusters were separated. The late autumn and winter months continued to form their separate cluster. On the other hand, the spring and summer months were separated into two additional clusters, with March, April, and October, i.e., the early spring and autumn months, forming a separate cluster. The remaining months (May, June, July, August, and September) constituted the third cluster.

3.1.2. Average Annual Indicators

After analyzing average annual indicators of electricity production by the PV micro-installation and its consumption by the house, it was found that the highest electricity production from the photovoltaic micro-installation was achieved in 2022 and amounted to 6846 kWh (Figure 8). The highest values were also noted for energy fed into the grid (5170 kWh), total energy consumption (4641 kWh), and self-consumption (1675 kWh). In 2023, energy production was 4.7% lower compared to 2022. Similarly, in 2023, energy fed into the grid, total energy consumption, and self-consumption were 3.6%, 0.7%, and 8.2% lower, respectively, than in 2022. The average annual energy withdrawn from the grid outside the operation of the PV micro-installation in 2023 was 3.4% higher than in 2022. The lowest selected average annual indicators of electricity production by the PV micro-installation and of its consumption by the house were noted in 2021 when the production amounted to 6076 kWh. In 2021, the lowest indicator values were also demonstrated for total energy consumption (4569 kWh), energy fed into the grid (4566 kWh), energy withdrawn from the grid (3059 kWh), and self-consumption (1510 kWh). Given the above, average annual indicators (from the three successive years) were as follows: electricity production by the PV micro-installation—6481 kWh; energy fed into the grid—4907 kW; total energy consumption—4606 kWh; energy withdrawn from the grid—3031 kWh; and self-consumption—1574 kWh.

Differences in the obtained values were noted when analyzing the average annual securing of electricity supply through self-consumption over the three years. The highest value of this indicator was found in 2022 when it amounted to 36.1% (

Table 2. Selected average annual indicators of securing electricity supply for the house by the PV micro-installation.

Year	Securing Energy Supply Through Self-Consumption (%)	Securing Energy Supply from Energy Produced (%)	Average Generation per 1 kWp (h)	Average Self-Consumption per 1 kWp (h)	Energy Withdrawable from the Grid (kWh)	Securing Energy Supply from Energy Withdrawn from the Grid (%)	Total Securing of Electricity Supply (%)
2021	33.0	133.0	803.7	199.7	3652.9	80.0	113.0
2022	36.1	147.5	905.5	221.6	4136.2	89.1	125.2
2023	33.4	141.6	862.7	203.4	3987.4	86.5	119.9
Average	34.2	140.7	857.3	208.2	3925.5	85.2	119.4

). In 2023, this indicator fell to 33.4%, representing a decline compared to the previous year. However, in 2021, securing energy supply through self-consumption was 33.0%, representing the lowest value obtained from the three-year period. The average value of this indicator for the entire study period was 34.2%, indicating a general trend towards maintaining a relatively high level of securing energy supply for the house through self-consumption.

After analyzing the average annual security of electricity supply from the energy produced by the PV micro-installation over the three years, it was noted that the highest value (147.5%) was obtained in 2022. In 2023, it was 141.6%, whereas in 2021 it, was 133.0% (Table 2). The average value of this indicator amounted to 140.7%.

Electricity generation per 1 kWp of an installed peak power over the three-year period was, on average, 857.3 h (Table 2). The highest value (905.5 h) was obtained in 2022; in 2023, it was 862.7 h, and in 2021, it was 803.7 h. The highest average annual electricity self-consumption per 1 kWp of installed peak power was noted in 2022, with a value of 221.6 h. This was the highest value noted over all three years of this study. In 2023, this value amounted to 203.4 h, whereas in 2021, it was 199.7 h, representing a decrease compared to 2022. The average value of this indicator for the entire study period was 208.2 h.

As far as prosumers are concerned, the important factor is the amount of energy withdrawable from the electricity grid, which has previously been fed into it (as surplus production from a PV). In the case of the house under analysis, it accounted for 80% of the energy fed into the grid. Therefore, the highest value of this indicator, amounting to 4136.2 kWh, was noted in 2022 (Table 2). In the next year, 2023, a slightly lower value of 3987.4 kWh was noted. However, in 2021, this value was even lower and amounted to 3652.9 kWh. The average value of this indicator for the entire study period was 3925.5 kWh.

In view of the above, securing electricity supply for the house only from the energy withdrawn from grid (80% energy fed into the grid from PV) amounted to (averaged over three years) 85.2% and ranged from 80 to 89% (Table 2). However, when the energy withdrawn directly from the PV installation was taken into account, it appeared that, on an annual basis, the total securing of electricity supply for the house was, on average, 119.4%. The highest value of this indicator (125.2%) was obtained in 2022, and it reached the lowest level (113.0%) in 2021. This means that, on an annual basis, in all the years under study, the test facility was self-sufficient in electricity produced by the PV installation. In addition, each year, its surplus was noted, which was possibly used by other users of the electricity grid and its operator.

A sensitivity analysis based on mathematical modeling allowed us to build a statistically significant polynomial function model (Figure 9). Based on the model, it was determined that if the installation had been located to the south (azimuth 180°) in the analyzed period, the maximum average production volume would have been 6897 kWh, which is 128 kWh more than if the entire installation had been located in the most favorable actual arrangement, i.e., 160°. Then, the average annual production would have been 6768 kWh. In particularly favorable solar conditions, the production volume would have been higher, as evidenced by the production volume in 2022, which in the analyzed arrangement, amounted to 6846 kWh (Figure 8).

Irradiation in Olsztyn was 1116.41 kWh m⁻² in 2021, 1142.81 kWh m⁻² in 2022, and 1092.22 kWh m⁻² in 2023. These are representative values for Poland, where the mean annual irradiation was 950–1150 kWh m⁻²; it is worth stressing that in the northern part of Poland where Olsztyn is located, the mean irradiation has the highest value [29]. Based on the statistically significant model, which explained the relationship between variables, the linier regression model was obtained (Figure 10). Energy production increased by 0.72 kWh kWp⁻¹ per 1 kWh m⁻² of irradiation.

3.2. Heat Electricity Production by a Pellet Boiler and Its Consumption by the House

Table A3. Characteristics of the selected properties of pine pellets—average values for the three-year period.

Year	Moisture Content (%)	Fixed Carbon (% d.m.)	Volatile Matter (% d.m.)	Ash Content (% d.m.)	Higher Heating Value (GJ Mg−1 d.m.)	Lower Heating Value (GJ Mg−1)	C (% d.m.)	H (% d.m.)	S (% d.m.)	N (% d.m.)	Cl (% d.m.)
2021	6.80	20.94	78.66	0.40	20.39 b	17.66	52.33 b	6.17	0.013	0.11	0.014
2022	6.76	20.91	78.68	0.41	20.40 b	17.68	51.74 c	6.15	0.011	0.10	0.015
2023	6.69	20.68	78.94	0.38	20.59 a	17.86	53.09 a	6.20	0.011	0.10	0.015
Average	6.75	20.84	78.76	0.40	20.46	17.74	52.39	6.17	0.011	0.10	0.014

a, b, c, etc.—h.g.

presents the elemental composition and thermophysical properties of the pine pellets used for heat energy production. The pellet characterized by a low moisture content (6.75% on average), a low ash content (0.40% DW), and a high LHV (17.74 GJ Mg⁻¹). Since the sulfur, nitrogen, and chlorine contents were also low, pine pellets were a very good-quality solid biofuel from an energy point of view.

Pellet consumption for heat energy production in successive months over the three successive years of 2021–2023 is presented in Figure A1. The highest pellet consumption for heat energy production was noted in 2021 and was particularly high in the winter months: in December (1290 kg) and in January (1260 kg). In February, 1100 kg was consumed, representing a slight decrease as compared to January. In the successive months of this year, from March to August, the pellet consumption decreased, and from September to December, it increased. In 2022, the pellet consumption was generally lower compared to 2021. Nevertheless, in the winter months, January and December, it was the highest as well. The year 2023 saw the lowest pellet consumption for heat energy production, yet the relationships between months were similar.

An analysis of average pellet consumption for heat energy production in the successive months, averaged over three years, in relation to average heating degree-days (HDDs) confirmed that the significantly highest consumption was noted in the winter months, in December (an average of 1140 kg) and January (on average, 1110 kg), and was classified into h.g. “a” and “ab” (Figure 11). These values accounted for 16.3% and 15.9% of the average annual pellet consumption, respectively. The number of HDDs for these months was the highest as well and amounted, on average, to 576 and 554, respectively. The second h.g., “bc”, contained pellet consumption in February (937 kg on average) and March (920 kg on average), which accounted for 13.4% and 13.1% of its average annual consumption, respectively. The consumption of this solid fuel from three years in November (on average, 795 kg) was classified into the h.g. “cd”. In April, however (on average, 610 kg), it was classified into the h.g. “de”. The month of October was classified into the h.g. “ef”, whereas May was classified into the group “fg”. The months of September, August, July, and June were classified into the last h.g., “g”, which accounted for 3.0%, 2.9%, 2.3%, and 2.0% of the average annual pellet consumption, respectively. This low pellet consumption in the summer months was due to its use mainly for hot tap water production. The production of central heating water was redundant or marginal. The total amount of pellets consumed during the heating period months (October–April), on average for the three years, accounted for 85.4% of the total consumption. Therefore, the remaining warmer months (May–September), in which mainly hot tap water was prepared, accounted for an average of 14.6% of all pellets to be consumed. A regression analysis demonstrated a linear relationship between the pellet consumption and number of HDDs, and the constructed model explained very well the relationship between pellet consumption and the number of HDDs in individual months over the three years under study (R² = 0.9643), (Figure A2). It was determined that pellet consumption increased by 1.7 kg, whereas the number of HDDs increased by one degree-day.

Average annual pellet consumption was also correlated with the total number of heating degree-days (HDDs) (Figure 12). Average pellet consumption over the period of three successive years was 6997 kg, and the number of HDDs was 3428. The highest pellet consumption (7560 kg) was noted in 2021 when the highest HDD value (3713) also occurred. In successive years, because total pellet consumption dropped to 7050 and 6380 kg, it was 6.7 and 15.6% lower, respectively. The total number of HDDs was also lower in the subsequent two years and amounted to 3426 and 3146, respectively.

The energy value of the pellets consumed for heat energy production in each year of the study, i.e., 2021, 2022, and 2023; its amount; and the LHV for a particular year, which amounted to 17.66, 17.68, and 17.86 GJ Mg⁻¹, respectively, were used (Table A3). Therefore, the highest total heat consumption (133.51 GJ year⁻¹) was noted in 2021 (Figure 13). In the subsequent years of 2022 and 2023, the value of this indicator was 6.6 and 14.7% lower, respectively. As with the number of pellets, the highest amount of energy (18–23 GJ) was consumed in the winter months, particularly in December and January of each year and in February of 2021.

Average monthly energy values of the pellets consumed for heat energy production in the successive months averaged over three years were the highest in December (20.21 GJ) and January (19.68 GJ) (h.g. “a” and “b”) (Figure 14). The second intermediate h.g., “bc”, contained the average monthly energy values of the biofuel consumed in February (16.60 GJ) and March (16.31 GJ). On the other hand, the lowest heat consumption (<4 GJ) was noted in the summer months of August, July, June, and September, i.e., h.g. “g”. This low consumption resulted from using this solid biofuel mainly to prepare hot tap water.

3.3. Total Electricity and Heat Consumption by Study Facility

Figure A3 presents the total electricity and heat consumption by house in successive months over the three years of this study. As can be seen from the data presented, energy consumption in January and December of 2021 was the highest and exceeded 6600 kWh (Figure A3a). In contrast, the lowest values in these months (approximately 5500 kWh) were noted in 2023 (Figure A3c). An analysis of the total average monthly energy consumption by house in the successive months averaged over three years showed that the highest energy consumption was noted in the winter months, in December (an average of 6055 kWh, h.g. “a”) and January (on average, 5935 kWh, h.g. “ab”) (Figure 15). The second h.g. “bc” contained energy consumption in February (5000 kWh on average) and March (4965 kWh). On the other hand, total energy consumption from three years in November (on average, 4320 kWh) was classified into the h.g. “cd”. In contrast, the consumption in April was classified into the h.g. “de”. The month of October was classified into the h.g. “ef” and May into the group “fg”. The months of September, August, July, and June were classified into the last h.g. “g”. It is also worth noting that the presented data shows that heat consumption in the house was considerably higher compared to the electricity consumption. Heat energy averaged over three years accounted for 88.2%, and electricity only for 11.8% of total energy consumption. However, the ratio of heat to electricity consumption varied from month to month, and in the winter months, the share of electricity was less than 10%. However, in the summer months, especially in August and July, this indicator was approximately 30%.

On the other hand, total annual heat and electricity consumption by house over the period of the three successive years amounted, on average, to 39,059 kWh (Figure 16). The highest total energy consumption (41,655 kWh) was noted in 2021. In successive years, annual energy consumption dropped to 39,265 and 36,259 kWh, i.e., was 5.74 and 12.95% lower, respectively. However, the proportion of heat energy in the years under study was dominant and ranged from 87.3 to 89.0%. Total energy consumption per 1 m² was, on average, 158 kWh/year/m², and per capita, it was, on average, 6510 kWh/year/person (Figure 17).

4. Discussion

In the authors’ study, electricity generation by the PV micro-installation with a peak power of 7.56 kWp from the three years was, on average, 6481 kWh year⁻¹. Therefore, on average, electricity generation per 1 kWp of installed peak power of the micro-installation was 857.3 kWh and ranged from 803.7 kWh in 2021 to 905.5 kWh in 2022. In another study, an installation with a peak power of 13.5 kWp generated 13,134 kWh [30]; an PV with a peak power of 7.91 kWp generated 7124 kWh [31]; and an PV with a peak power of 3 kWp, located in Kraków, generated 3366 kWh over a year [32]. Considering the above, electricity generation per 1 kWp of an installed peak power in these micro-installations amounted to 972.9, 900.6 and 1122 kWh, respectively. It should, therefore, be concluded that in the authors’ study, under the conditions of north-eastern Poland, average electricity generation per 1 kWp of installed peak power was lower than the quoted study results. It should be emphasized that the value obtained in 2022 was higher than that obtained by [31].

In the authors’ study, average monthly (averaged over the three years of 2021–2023) electricity production by the PV micro-installation was the highest in June (1071.6 kWh), indicating that electricity generation per 1 kWp of an installed peak power in that month amounted to 141.8 kWh (a range of 136.3–148.9 kWh). In another study in the south of Poland [33] for a PV installation with a peak power of 5.04 kWp, the highest production (768 kWh) was achieved in July. Therefore, electricity generation per 1 kWp of an installed peak power was 152.4 kWh. In contrast, in a study carried out in the north of Poland (the city of Słupsk), involving an installation with a peak power of 5.5 kWp, the highest production (829.9 kWh, i.e., 150.9 kWh kWp⁻¹) was achieved in May [34]. The quoted values were 7.5 and 6.5% higher, respectively, compared to the authors’ study results. These differences could have resulted from the installation mentioned above facing south, whereas in the authors’ study, most panels faced west, three faced east, and only three faced south. In contrast, in a study conducted in France, the highest electricity production per 1 kWp of an installed peak power over one month (189.7 kWh) was noted in June [35], which was consistent with the authors’ study results. The above-mentioned studies used monocrystalline photovoltaic cells with an efficiency of 18.1%. The same was true for a study carried out in Ireland, which also noted the highest value of this indicator (111.7 kWh kWp⁻¹) in June [36]. The study also used monocrystalline cells with an efficiency of 17.2%. At this point, it should be noted that the value of this indicator, obtained in France, was 33.8% higher, whereas that obtained in Ireland was 21.2% lower compared to the value obtained in the authors’ study. These differences must have been due to the differences in climatic conditions, including solar radiation, in these countries. In contrast, in another study carried out in Tamil Nadu (a state in India), the highest electricity production from 1 kWp of an installed peak power over one month (153.1 kWh) was achieved in March [37]. This value approximated the average value from the authors’ study. Nevertheless, it should be noted that due to the tropical climate prevailing at the location of the installation and the slight differences between the summer and the winter period, the lowest production was achieved in November (106.3 kWh).

In the authors’ study, total annual heat and electricity consumption per occupant over the three successive years amounted to 6510 kWh year⁻¹ person⁻¹. In another study carried out for 12 years, total (heat and electrical) energy consumption per capita ranged from 5900 to 7103 kWh year⁻¹ person⁻¹ [10]. Thus, the value obtained in the authors’ study fell within the quoted range. The average value of this indicator, obtained in the authors’ study, was higher by 17%, than the average energy consumption in Polish households (5583 kWh year⁻¹ person⁻¹) [38]. However, having examined the three years under analysis, it was found that the value of this indicator, obtained in the authors’ study, was 8–24% higher in 2023 and 2021, respectively, than the average energy consumption in Polish households. In contrast, the average household energy consumption for the entire EU was 6389 kWh per year⁻¹ person⁻¹. Households in countries located in the south of the EU consumed less energy per capita, e.g., in Malta and Portugal, 2278 and 3278 kWh year⁻¹ person⁻¹, respectively. The highest household energy consumption is noted in the north of the EU, e.g., in Finland (11,833 kWh year⁻¹ person⁻¹). Lower energy consumption per household occupant in Finland (on average, approximately 8611 kWh year⁻¹ person⁻¹) was noted in a previous study [39], with slightly lower consumption observed in towns, as compared to the countryside. In contrast, in China, the average annual energy use per person in rural areas was considerably lower and amounted to 4139 kWh year⁻¹ person⁻¹ [40]. However, the value of the quoted indicator for small towns was 5139 kWh year⁻¹ person⁻¹, whereas for cities, it was 5278 kWh year⁻¹ person⁻¹. On the other hand, another study conducted in China showed wide variation in this indicator (ranging from 2889 to 24,056 kWh year⁻¹ person⁻¹), depending on the economic zone [41].

Pine pellets used for heat generation for the house under study were characterized by very good energetic properties, with their high LHV being particularly important (on average, 17.74 GJ Mg⁻¹). In other domestic studies, the LHV of pine pellets was also high and amounted to 18.00 GJ Mg⁻¹ [27] or 17.69 GJ Mg⁻¹ [11]. Moreover, the values quoted were the highest among different types of agricultural and forest-origin pellets and their mixtures. A lower LHV (16.9 GJ Mg⁻¹) was noted for pine pellets in Portugal [42]. The ash content of pine pellets in the authors’ study was very low (on average, 0.40% d.m.), which was beneficial from a practical point of view. A very similar value of this characteristic (0.37% DW) was obtained in another study [11]. However, the lowest ash content (0.19% d.m.) in pine pellets was determined by Stachowicz and Stolarski [27]. The ash content of pine pellets originating from other countries ranged from 0.27% d.m. [43] to 0.60–0.81% d.m. [44].

When the consumed heat and electricity were taken into account, it appeared that total annual energy consumption by the house over the three successive years (2021–2023) amounted, on average, to 39,059 kWh year⁻¹. It averaged 158 kWh year⁻¹ m⁻² of the house area to be heated. In another study carried out for 12 years, total (heat and electrical) energy consumption per 1 m² of the house area ranged from 128 to 164 kWh year⁻¹ m⁻² [10]. Therefore, the above-mentioned value per area of 1 m² was similar to the results of the authors’ study. On the other hand, total energy consumption by a house under the climatic conditions of Sweden was, on average, approximately 147 kWh year⁻¹ m⁻² [45], whereas in Finland, this value was higher and amounted to an average of approximately 181 kWh year⁻¹ m⁻² [39]. It can, therefore, be concluded that the total average energy consumption per 1 m² in the authors’ study under Polish conditions was higher than the value in Sweden and lower compared to Finland. It must certainly be added that this indicator is strongly dependent on the technology and materials used to build the house and on the way occupants use the house.

It should also be added that it is essential to seek energy savings in both houses and all types of buildings. In addition, renewable sources should be increasingly used, and the use of fossil fuels reduced [16]. This necessity is also due to legislative changes, as the EU amended the Building Directive in 2024 [46], which is aimed at improving the energy performance of buildings, reducing energy use, and moving away from fossil fuels in construction. This applies to both newly built houses, including multi-family ones, public utility facilities, and existing properties. In practice, this will mean that coal and gas boilers will be discarded. In addition, the installation of fossil fuel boilers, including natural gas boilers, has already started to be banned starting in 2025. All fossil fuel boilers will be banned by the year 2040. This process is to be gradual, concerning coal, oil, and gas boilers; their use in new buildings will be banned by 2027; and their use in upgraded houses will also be banned by 2030. In addition, residential buildings should be thermally upgraded during this period to achieve a target reduction in primary energy consumption: 16% by 2030 and 20–22% by 2035. It follows from the conducted analyses that, in practice, it means that by 2030, the EU Member States will have to renovate 16% of non-residential buildings with the worst energy performance and 26% by 2033 [47].

In view of the above, it must be stressed that the planned changes will have an enormous impact on both civil engineering and the market for already existing properties. This also increases the costs concerning heating buildings and, generally, securing an energy supply for them. Therefore, the application of the solutions proposed in the presented study, involving the supply of electricity using a PV micro-installation and the supply of heat energy using automatic pellet-fueled systems, is one of the solutions that can become very attractive from the practical point of view. This study shows that solutions of this type can also be attractive from an energy point of view. It should also be added here that the analyzed house was equipped with an additional boiler powered by electricity and a power generator, which additionally increased the energy security of the building in the event of, for example, a failure of the pellet boiler. Another critical issue is to increase the energy efficiency of an analyzed system, which will increase the energy value of such systems, especially that energy efficiency increase reduces the demand for energy in households, which is important in reducing CO₂ emissions. Also important is the identification and understanding of the reasons that convince people to choose more efficient and environmentally friendly systems for generating energy, as there are still many barriers in this area [48,49]. Another important issue is the economic assessment of such systems. In [50], electricity costs were compared, specifically the costs of electricity from the power grid compared with the costs of electricity generation from a PV installation. Also, heat production costs were compared, with three comparisons performed: eco-pea coal with pellet, natural gas with pellet, and heating oil with pellet. Next, comparisons were made for multi-energy systems. The comparative analysis proved that a multi-energy RES system based on PV installation for electricity production and a boiler fired by pellet for heat generation was the most advantageous. In this system, the lowest total costs (EUR 41,265) among all the analyzed variants were achieved. The profitability indexes confirmed the analyzed system. The comparison of multi-energy systems was performed for three systems [50]. First was electricity from the electric power grid and eco-pea coal with PV installation and pellet. In this case, the net present value (NPV) was EUR 2335, the simple payback period (SPBP) was 11 years, and the discounted payback period (DPBP) was 15.3 years. The second comparison covered electricity from the grid and natural gas with PV installation and pellet. In this case, NPV was EUR 12,610, SPBP was 7.4 years, and DPBP was 8.9 years. The third comparison covered electricity from the grid and heating oil with PV installation and pellet. In this case, NPV was EUR 36,252, SPBP was 3.7 years, and DPBP was 4.0 years. This is important because any new solution for generating renewable electricity and heat in residential buildings should consider environmental, economic, and energy aspects.

5. Conclusions

This study demonstrated that an appropriately selected PV micro-installation and an automatic pellet-fueled heating system can result, on an annual basis, in a family house being self-sufficient and securely supplied with renewable energy. Under the conditions of north-eastern Poland, electricity production per 1 kWp of installed peak power of the PV micro-installation over the three years amounted to an average of 857.3 kWh. Annual electricity production by the PV micro-installation amounted to an average of 6481 kWh year⁻¹. Thanks to the “net-metering” billing system, such an amount of electricity was sufficient to secure electricity supply to the house on an annual basis at a level of 119.4% on average. Considerably more energy was used to produce heat energy (central heating water and hot tap water). Average annual pellet consumption for heat energy production (6997 kg year⁻¹) was linearly determined by the number of heating degree-days (HDDs = 3428), which translated into average annual energy consumption of 124,033 GJ year⁻¹ (34,453.7 kWh year⁻¹). Therefore, the heat energy in the total annual energy consumption was dominant and accounted for 88.2%, whereas electricity accounted for the remaining 11.8%. This is why it is necessary to constantly strive to save all forms of energy by improving the energy efficiency of each building.