Introduction

Remote areas, which are geographically isolated and distant from different services, are becoming a topic of increasing international interest since providing electricity to these areas is often challenging (Shenoy et al., 2020). In Brazil, 97% of the rated capacity that supplies remote areas comes from diesel power stations (EPE, 2019), representing 8% of the total emission from electricity generation in the country (EPE, 2020).

Distributed Energy Generation (DEG) arrives as a sustainable solution for supplying remote areas and off-the-grid buildings (stand-alone zero energy buildings that are not connected to an off-site energy utility facility) since it is a small electrical system that generates electricity within the area that it will be used. These microgrids can use several renewable technologies for generation, such as photovoltaic (PV), wind, biomass power, and hybrid power systems, which use more than one source type (Capehart, 2016). In this way, they arrive at a key to carrying out Conference of the Parties (COP) 26 outcomes, providing a significant environmental benefit and supporting the rise in the global average temperature limited to 1.5 degrees. The Government of India invests millions in microgrid projects, making them an integral part of its net-zero goal at COP26 (Proctor 2022).

According to Wolsink (2020) and Alanne and Saari (2006), DEG systems have additional advantages that point to a growth in the acceptance of these kinds of power generation, such as reliability, efficiency, flexibility, and economic benefits. Since most renewable resources are intermittent, a battery energy storage system (BESS) should be considered to provide additional electricity reserves. It plays an integral role in enhancing overall electric grid efficiency and reliability, integrating renewable resources while reducing dependence on fossil fuel generation (Proctor 2020).

Although distributed energy is growing in many countries, adapting to this kind of generation requires new policy designs and regulations due to technical and operational challenges, causing more research in this field (PWC 2017). Many techniques are well established in the literature to manage energy systems with higher efficiency and lower costs. Ho et al. (2012) presented an Electricity System Cascading Analysis (ESCA) method for designing and optimizing DEG systems that use non-intermittent power generators, such as biomass and energy storage. Later, the authors extend their study by applying the same technique to PV systems (Ho et al., 2014). Singh et al. (2018) also performed ESCA on PV systems, analyzing over an entire year.

Other studies were conducted to design optimal PV systems. Vides-Prado et al. (2018) used a software tool called HOMER to design a PV system for indigenous communities in the Colombian Guajira. Sambor et al. (2020) used a mixed-integer linear optimization model (FEWMORE) to power container farms integrated with a remote Arctic community microgrid. Irfan et al. (2019) developed a systematic method to assess the potential and economic viability of utilizing an off-grid solar PV system for rural electrification in the Punjab province of Pakistan. In this last case, the available area for generation capacity is fixed, so the DEG system has no optimal design.

Most studies in the literature do not determine the generator energy capacity to meet the demand of a system; they generally already have the area available, in PV systems case, and simulate how much electricity can be produced. Blackledge and Kearney (2012) evaluated the viability of installing PV systems in existing commercial buildings in Dublin by considering an economic analysis. Sunanda (2018) did a similar study on home PV systems in Pangkalpinang City. Otherwise, Rachchh et al. (2016) presented a different analysis in this context.

Since renewable sources usually depend on environmental conditions, optimizing hybrid power systems is more interesting than stand-alone systems with only one power source. Hiendro et al. (2013) used HOMER software to perform the techno-economic feasibility of the PV/wind hybrid system in a remote Indonesian area. The same power sources were used in an optimal size of a hybrid power system by Fathy (2016), but the author considers fuel cells as storage systems to meet a specific load of a remote area in Egypt. Razmjoo et al. (2019) used the same tool to investigate renewable energy sustainability for two cities in southeast Iran until 2030. In this case, the hybrid system comprises solar panels, wind turbines, and diesel.

A previous work by de Lira Quaresma et al. (2021) presented the Hybrid System Sources Diagram (HSSD) used in this context but considering an on-grid system, so the grid electricity is used to supply the load when renewable sources are unavailable. This paper presents an extension of HSSD, called HSSD off-grid, to DEG systems design with energy storage considering off-grid systems. The objective is to determine the capacity of an intermittent or non-intermittent power generator and battery storage that allows the system to run in a steady state without using an external electricity source.

To make a more precise comparison of this paper with state-of-the-art, Table 1 summarizes what each method considers concerning the main attributes of a DEG system.

Table 1 Summary of developed methods for DEG systems design
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Unlike other methods in the literature, HSSD off-grid is a tool that does not use complex optimization resources to check the feasibility of installing a system that considers more than one type of source available and identifies the generator size and storage capacity, which are key factors in achieving technical-economical feasibility of an isolated renewable energy system, enabling the possibility of obtaining several scenarios allow the designers to choose the best alternative for their systems. It uses pinch analysis principles and can be easily associated with uncertainties analysis, supporting risk comprehension and decision-making. In addition, the main feature of all Source Diagram methods (da Silva Calixto et al., 2020) is that the HSSD off-grid yields a simple visual diagram during algorithm application that can be used as a first step by any user who does not have much knowledge about this subject to generate some viable scenarios to install a DEG system toward to a more sustainable environment. In this way, the methodology elaborates a preliminary study to evaluate available scenarios that will be studied in more detail in a future step, has the advantage of visual and control during the analysis process, and provides insights into the design problem.

HSSD off-grid is presented by solving four case studies: the first one to a DEG system composed of a non-intermittent biomass generator, the second and the third ones to a DEG system operating with intermittent solar and wind generators, respectively. The last case study is applied to a commercial building connected to the grid at the Universidade Federal do Rio de Janeiro (UFRJ). In order to evaluate the technical feasibility of making the building self-sustaining, a hybrid power system composed of PV, wind, and biomass sources with battery storage will be proposed, and the HSSD off-grid will be used to determine biomass generator capacity. The area is considered occupied by solar panels, and the number of wind turbines is kept fixed. Also, many studies in the literature do not consider uncertainties about the availability of intermittent sources, which depends on environmental conditions. To consider this situation, a Monte Carlo simulation is performed to account for these uncertainties. Also, HSSD on-grid (de Lira Quaresma et al., 2021) is applied on the first and second days of the four case studies to ensure that the system will run off-grid, showing if the system is or not run in a steady state and is or not independent of the grid.

HSSD off-grid offers DEG systems design that use renewable sources to generate electricity and can supply remote areas without electricity access. In this way, this work helps the development of two United Nations goals: Goal 7 – affordable and clean energy, and Goal 11 – sustainable cities and communities (UN 2022).

The article is organized as follows: The “Method” section shows how the method works by describing the heuristics rules and equations that should be used. The Results and Discussion” section presents two case studies with results and discussions. The first is to validate the method, and the second is to show an actual case application with uncertainties analysis. Concluding remarks are given in the “Conclusion” section 4, and the acknowledgements are highlighted after the “Conclusion” section. Also, there is Supplementary information with two more case studies and other details.

Method

The methodology presented in this article is derived from the original tool HSSD on-grid (de Lira Quaresma et al., 2021), a heuristic-algorithmic approach composed of six steps that enable the identification of the best source scheme allocation to reach the lowest outsourced electricity target. It is achieved by considering the distinct characteristics and availability of the various energy sources and the presence or absence of batteries. Additionally, the HSSD offers a unified framework for managing start-up and daily operations, energy losses, and intermittent behaviors of sources and demands, simplifying the overall management process.

To aim for the new objectives, the HSSD on-grid (de Lira Quaresma et al., 2021) will undergo a slight change in two items that compose step 6 and the inclusion of three new steps at the end of the algorithm, making the new method iterative. The heuristics of HSSD on-grid state that if there are no available sources or energy stored in the system’s battery to meet the load, an external source, such as energy from the grid, must be used. On the other hand, when this scenario occurs when applying HSSD off-grid, the heuristics state that an additional battery must be considered to meet the load. It happens because the energy from this additional battery represents the electricity from the system’s battery when operating in a steady state. In this way, the additional battery can be considered as a fictitious source to execute the HSSD off-grid and determine the generator size. Besides, it will be necessary to make iterations until the generator size is established.

HSSD off-grid is applied in this paper considering energy losses from an inverter, rectifier, charging/discharging the battery, temperature for solar generation, and depth of discharge (DoD) to approximate the case study to real conditions, avoiding an under-sized one system’s design discussed in de Lira Quaresma et al. (2021). Steps 1 to 6 are the same as the HSSD on-grid, except for a slight change in the heuristics, as shown in de Lira Quaresma et al. (2021).

  1. 1.

    Data extraction

  2. 2.

    Definition of time intervals

  3. 3.

    Addition of power demand appliances and battery

  4. 4.

    Determination of the energy required in each interval of each power demand appliance

  5. 5.

    Representation of power sources in the diagram

  6. 6.

    Determination of the power sources to meet demand appliances in each interval. In this step, the allocation of the available sources to satisfy each demand begins using the following heuristics:

  1. i.

    preferably, meet AC demand appliances with AC power sources and DC demand appliances with DC power sources. If more than one is available in an interval, allocate first the intermittent power source that has the shortest end time;

  2. ii.

    satisfy all the demands of appliances in an interval with the sources available, meeting their energy required (step 4). If there is an excess, allocate it in the battery (as batteries store only DC, if necessary, do the AC/DC conversion) to be available at the next interval;

  3. iii.

    if there are not enough AC power sources available to meet an AC demand appliance, consider the following options in order to satisfy it:

    1. a.

      convert directly the surplus of DC power sources at the time interval to AC;

    2. b.

      discharge from the system’s battery and convert to AC;

    3. c.

      discharge from the additional battery that must have the same power losses as the system’s battery and convert to AC;

  4. iv.

    if there are not enough DC power sources available to meet a DC demand appliance, consider the following options in order to satisfy it:

    1. a.

      convert the surplus of AC power sources at the time interval to DC;

    2. b.

      discharge from the system’s battery;

    3. c.

      discharge from the additional battery must have the same power losses as the system's battery.

HSSD off-grid changes occur in heuristics iii.c and iv.c, when compared to HSSD on-grid.

The equations to determine the actual energy amount that a power source should deliver to the demand appliance, considering energy losses, are as follows:

  • AC power source to meet AC demand and DC power source to meet DC demand

    $${E}_i^n={\varepsilon}_{j,n}$$
    (1)

  • DC power source to meet AC demand

    $${E}_i^n=\frac{\varepsilon_{j,n}}{h_I}$$
    (2)

  • AC power source to meet DC demand

    $${E}_i^n=\frac{\varepsilon_{j,n}}{h_R}$$
    (3)

Moreover, processes that involve the system or additional batteries must be considered. The appropriate equations for each case are as follows:

  • DC power source to DC battery

    $${E}_{C, battery}^{n+1}=\dot{E_i}\left({t}_{final,n}-{t}_{inicial,n}\right){h}_C={E}_i^n{h}_C$$
    (4)

  • AC power source to DC battery

    $${E}_{C, battery}^{n+1}=\dot{E_i}\left({t}_{final,n}-{t}_{inicial,n}\right){h}_R{h}_C={E}_i^n{h}_R{h}_C$$
    (5)

  • DC battery to DC demand appliance

    $${E}_{D, battery}^n=\frac{E_j^n}{h_D}$$
    (6)

  • DC battery to AC demand appliance

    $${E}_{D, battery}^n=\frac{E_j^n}{h_I{h}_D}$$
    (7)

where \({E}_i^n\) is the amount of energy delivered by a power source i in interval n; hI is the inverter efficiency; hR is the rectifier efficiency, \({E}_{C, battery}^{n+1}\) is the actual energy stored by the battery in interval n+1; hC the battery charging efficiency; \({E}_{D, battery}^n\) is the actual energy provided by the battery in interval n; \({E}_j^n\) is the electricity required by power demand appliance j in interval n; and hD is the battery discharging efficiency.

Therefore, the actual amount of energy stored in the system’s battery in interval n+1, after considering charging and discharge efficiencies that may occur in interval n, is determined by Eq. (8):

$${E}_{battery}^{n+1}={E}_{battery}^n+\sum\limits_i{E}_i^n{h}_R{h}_C-\sum\limits_j\frac{E_j^n}{h_I{h}_D}$$
(8)
  1. 7.

    Determination of the new capacity of the power generator

The equation used in this step is different according to the source that will be recalculated through iterations:

$$\textrm{Biomass}:{S}_{i+1}={S}_i-\frac{\left({E}_{excess}-{\sum}_n{E}_{ad. bat}^n\right)}{T}$$
(9)
$$\textrm{PV}:{A}_{i+1}={A}_i-\frac{\left({E}_{excess}-{\sum}_n{E}_{ad. bat}^n\right)}{\sum_n{R}_n\eta }$$
(10)
$$\textrm{Wind}:{NT}_{i+1}={NT}_i-\frac{\left({E}_{excess}-{\sum}_n{E}_{ad. bat}^n\right)}{\sum_n{P}_n^W\eta }$$
(11)

where Si + 1 is the capacity of the biomass power generator in iteration i+1, Si is the capacity of the biomass power generator in iteration i, Eexcess is the electricity stored after the last interval, \({E}_{ad. bat}^n\) is the energy from the additional battery used in interval n, T is the total time duration, Ai + 1 is the area covered by the solar panel in iteration i+1, Ai is the area covered by the solar panel in iteration i, Rn is the average solar radiation, η is the efficiency of the generator related to the equation, NTi + 1 represents the number of wind turbines in iteration i+1, and \({P}_n^W\) is the power output in interval n.

  1. 8.

    Determination of the percentage change (P):

Percentage change is determined by Eq. (12)

$$P=\frac{\mid {S}_{i+1}-{S}_i\mid }{S_i}x\ 100$$
(12)
  1. i.

    if the percentage change is greater than 0.05%, go back to step 6, considering the new capacity;

  2. ii.

    if the percentage change is less than 0.05%, the capacity is the last one determined in step 7.

Note that the value of 0.05% is chosen as a tolerance to ensure the conformity of the results. It can be changed as needed by the project.

  1. 9.

    Determination of minimum battery capacity

In previous work (de Lira Quaresma et al., 2021), the minimum battery capacity is the highest amount stored at the battery line added in step 3. Since the additional battery represents the electricity that will come from the battery when the system is operating in a steady state in the HSSD off-grid, new cumulative energy in the storage in each interval must be calculated before the pinch point (higher time of the last interval, where the additional battery is used) via the following equation:

$$E_{new,battery}^{n} = \begin{cases} E_{excess} + E_{battery}^n - E_{ad.bat.}^{n-1} &\rightarrow \text{until first $E_{battery}^n=0$}\\ E_{new,battery}^{n-1} - E_{ad.bat.}^{n-1} &\rightarrow \text{after first $E_{battery}^n=0$}\\ \end{cases}$$
(13)

where \({E}_{new,\kern0.5em battery}^n\) is the new cumulative energy in the battery in interval n and \({E}_{new,\kern0.5em battery}^{n-1}\) is the new cumulative energy in the battery in intervals n−1.

Therefore, minimum battery capacity is the highest amount considering the

$$E_{battery}^{min} = \begin{cases} E_{new,battery}^{n} &\rightarrow \text{before pinch point}\\ E_{battery}^{n} - E_{ad.bat.}^{n-1} & \rightarrow\text{after pinch point}\\ \end{cases}$$

A new line to show the adjusted cumulative energy in the battery to determine the minimum battery capacity is added in the last iteration.

Considering DoD loss

$${E}_{battery}^{\mathit{\min}, real}=\frac{E_{battery}^{min}}{DoD}$$
(14)

where \({E}_{battery}^{min}\) is the minimum battery capacity disregarding DoD loss, determined by the highest amount stored at an interval in the diagram.

This algorithm, summarized in Fig. 1, is the same when applied to DEG systems that use biomass, solar, or wind sources, but the equation used in step 7 is different among the sources. The steps circled with dashed lines are new steps or steps that undergo any change concerning the HSSD on-grid (de Lira Quaresma et al., 2021). Also, Fig. 1 summarizes step 6 for a clearer understanding.

Fig. 1
figure 1

Flowchart of (a) HSSD off-grid algorithm and (b) step 6

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Results and Discussion

To avoid an extensive reading, case studies 2 and 3, relative to solar and wind generators, respectively, are evaluated in Supplementary information.

Case Study 1: Biomass Generator

The first case study to be analyzed was taken from Ho et al. (2012), which used ESCA methodology to design a DEG system composed of a non-intermittent biomass power generator and energy storage device. The goal is to determine the minimum generator capacity for the system to be off-grid and run in a steady state. Table 2 shows the daily load profile, which is AC type, as the biomass power source. To make the case study more realistic, the following energy losses are considered (Ho et al., 2012):

  • inverter efficiency: 90%

  • rectifier efficiency: 90%

  • charging efficiency: 88.3%

  • discharging efficiency: 88.3%

  • DoD: 80%

Table 2 Daily load profile (biomass case study)
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With the data extraction (step 1), the HSSD off-grid can be applied for iteration 1, considering the power source from biomass equal to 50 kW from 0 to 24 h. After applying steps 2 to 6, the resulting diagram for this iteration is shown in Fig. 2a.

Fig. 2
figure 2

HSSD off-grid for (a) iteration 1 and (b) iteration 5 (biomass case study)

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Using interval n = 7 as an example to resume step 6, 50 kWh from biomass and 155 kWh stored in the battery are available to meet the 35 kWh required from the demand appliance. According to heuristics i, ii, and Eq. (1), the demand will be satisfied by allocating 35 kWh from biomass, and the 15 kWh in excess must be stored in the battery. Since the power source from the biomass generator is AC type, Eq. (5) must be used to determine the electricity stored in the battery, resulting in 11.9 kWh. Thus, the energy stored available in the next interval (n = 8) is 166.9 kWh.

After the determination of the power sources to meet the demand appliance in each interval, the new capacity of the power generator must be calculated (step 7) by using Eq. (9). Note that the electricity stored after the last interval is the excess shown in Fig. 2a (219.53 kWh), and no energy from the additional battery was used. Considering this excess as available energy on the next day, it is possible to conclude that there will always be an increase in this amount as the days go by, which is not an ideal scenario since the system does not operate in a steady state in this way. Thus, a decrease in the power generator’s capacity is expected to set the steady state. It is confirmed by the value of 45.85 kWh obtained in the calculation of the new capacity by Eq. (9).

The following procedure determines the percentage change (P) between the previous and the new capacity. By using Eq. (12), the percentage change is 18.29%. Since this amount is larger than 0.05%, a second iteration is needed, and step 6 of the algorithm must be performed again, using the last calculated capacity of the biomass power generator.

Four iterations were executed to reach a less than 0.05% percentage change. The biomass generator capacity to make the system independent from the grid and running in a steady state is 39.76 kWh. A summary of their main results is shown in Table 3, and the final HSSD off-grid for the first period of 24h is presented in Fig. 2b.

Table 3 Iterations results (biomass case study)
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The fifth iteration was also executed to determine the real battery capacity since the value of power generation capacity used in the system is the new value calculated in the last iteration. In the following step (step 9), the new cumulative energy line is placed in the diagram (Fig. 2b) to recalculate the new cumulative energy in the battery.

In interval n = 1, the amount is 11,45 kWh, representing electricity stored after the last interval. From interval n = 1 to n = 10, where electricity stored is zero for the first time, the first equation from Eq. (13) determines the new cumulative energy. Since the pinch point is at 13 h, from interval n = 11 to n = 13, the second equation from Eq. (13) is used, and after that, the new cumulative energy is the same as the electricity stored shown in the battery line for this iteration. Therefore, real battery capacity is the highest amount shown in that line, which occurs in interval n = 22. Considering the DoD, Eq. (14) gives 157,0 kWh as the actual battery capacity.

The HSSD on-grid can be applied to the first and second days to confirm that the system operates in a steady state. The resulting diagram is shown in Fig. 3a,b, respectively. From Fig. 3a,b, it can be noted the difference between HSSD off-grid and HSSD on-grid. While “AB” signed orange arrows represent the additional battery in the first one, purple arrows in the second represent an external source from the grid. The associated values also differ due to the power losses considered when energy from the additional battery is allocated.

Fig. 3
figure 3

HSSD on-grid for (a) first and (b) second day (biomass case study)

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Since the battery excess on the second day is almost the same as on the first day, and there is no use of an external source, the system operates in a steady state. As the tolerance of percentage change approximates to zero, the same excess in the battery at the end of each day is expected to remain the same over time. Also, it is interesting to note that the energy used from the additional battery in the last iteration of the HSSD off-grid (Fig. 3a) becomes the energy from the battery when the system runs at a steady state (Fig. 3b).

Comparing the results obtained from the HSSD off-grid with the results obtained by ESCA (Ho et al., 2012), it can be noticed that the biomass generator and battery capacity are the same, which validates the HSSD off-grid technique. The first- and second-days’ analyses will not be compared because no corresponding study was found in the literature for this example.

Case Study 4: Hybrid Power System

HSSD off-grid can also evaluate the pertinent parameters to install off-grid hybrid power systems. A real case study of a Brazilian commercial building located on the Central Campus of UFRJ is considered to show de application. Currently, this building is connected to the grid, and the main objective is to evaluate making it self-sustaining by installing an integrated PV-wind-biomass source with a battery storage hybrid system.

Supplementary Tables S8, S9, and S10 show the data to evaluate this case study.

HSSD off-grid methodology for hybrid systems is the same as one source systems. The only difference is that it is necessary to choose one source to be recalculated through iterations while the other remains with its fixed parameters.

The number of wind turbines and the area for solar panels are kept fixed at 16 m2 and 1000 m2, respectively, due to geographic constraints (area availability). Thus, the HSSD off-grid determines the biomass power generator’s capacity and the battery storage size. Since biomass is an AC-type non-intermittent source, it is expected to be available from 0 to 24 h, and a 500-kW power rating is initially considered.

Since the load is AC type, wind source was first allocated, followed by biomass and solar (see heuristics i). Following the algorithm, five iterations were needed to determine the biomass generator’s size, 1633.8 kW, to build an off-grid system. The battery capacity considering DoD loss is 16671.3 kWh, as shown in Table 4.

Table 4 Iterations results (hybrid case study)
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Diagrams for the first and the sixth iterations are presented in Fig. 4a,b. Also, Fig. 5a,b shows HSSD for start-up and daily operation, respectively. As seen in the other 3 case studies, the system is grid independent. From the second day, it can be noticed that there is a difference of 0.1 kWh between the excess at the last interval and the electricity stored at the beginning, which is irrelevant when compared to the excess magnitude. It is important to note that in interval n = 9, the energy from the wind source is zero because wind speed at this time is lower than the turbine cut-in speed.

Fig. 4
figure 4

HSSD off-grid for (a) iteration 1 and (b) iteration 6 (hybrid case study)

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Fig. 5
figure 5

HSSD on-grid for (a) first and (b) second day (hybrid case study)

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It is important to emphasize that there is no assurance that a project can be implemented using the generated results since the proposed methodology still presents technical and operational challenges to be considered to make a reliable model. Nevertheless, the values presented in Table 4 offer an initial insight into the required size of the battery and biomass power plant. If these values are technically infeasible, the project may be discarded, may be discarded after this initial assessment avoiding additional costs in more detailed studies.

Uncertainties

PV and wind sources are intermittent, meaning their availability depends on environmental conditions. Therefore, performing one scenario that uses only a fixed value related to intermittent sources does not yield great reliability. To bypass these circumstances and account for these variations, uncertainties can be introduced around the original value of meteorological variables, such as solar radiation and wind speed. Those variables have a historical record, and deviations can be approximated through a stochastic uncertainty model expressed by a probability distribution.

It can be assumed that a Gaussian probability distribution function can represent the variation of solar radiation and wind speed. Thus, assuming the values shown in Supplementary Table S10 are the mean and standard deviations are 25% (Zhou et al., 2013) for solar radiation and 0.15 m/s (Roberts et al., 2018) for wind speed, a Monte Carlo simulation can be made to show how variable the values are determined by HSSD off-grid for biomass generator and battery capacities. A thousand scenarios were carried out in the Python application using the “random.normal” method from the NumPy library. This method allows drawing random samples from a normal distribution by giving the mean and standard deviation. The resulting histograms for biomass generator and battery capacity are shown in Fig. 6.

Fig. 6
figure 6

(a) Histogram for biomass capacity. (b) Histogram for battery capacity considering DoD loss

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It can be noticed that most of the scenarios achieved a power capacity between 1632 and 1635 kWh, showing that the result determined by the HSSD off-grid is consistent. The same occurs with battery capacity. The value found was 16671.3 kWh using a mean parameter of solar radiation and wind speed as an input, and close values are the most frequent in the Monte Carlo simulation.

Technical feasibility

The area for solar panels and wind turbines to run this case study was considered available. Given the size of the biomass generator, it is interesting to determine if installing a biomass power plant near the building is possible. According to NREL (2022), a 1 MW biomass power plant occupies 3.5 acres. From the previous section, it takes 1.635 MW to run the DEG system, which requires 5.73 acres. Since there are 7.24 acres available on the block in front of the Incubadora building, it is possible to install the proposed DEG system. It is important to mention that the HSSD off-grid does not consider all details to ensure feasibility. Other features are mentioned in the next section to be considered as the next steps. Regardless, it is recommended to pursue a more complex and precise assessment before proceeding with the installation.

Conclusion

A new tool to design DEG systems with battery storage that runs in a steady state and provides complete independence from the grid was developed. It has the advantage that it can be applied to three different types of DEG systems according to the renewable source being used, and the difference among them is only the equation that determines the new capacity of the power generator during the iterative process. For the first and second case studies, this paper found the same capacity for biomass generator (39.76 kW) and the same area that should be covered by PV panels (7.977 m2) compared to the respective studies found in the literature. Although the HSSD off-grid was not validated by comparison with another technique for the wind generator case study, it can be assumed that it is a suitable method due to the successful results obtained by the other case studies and the HSSD on-grid verification.

Also, the technique can be applied to hybrid DEG systems, iterating the capacity of one power source and fixing the others. An actual case study of a Brazilian commercial building was carried out to implement a PV-wind-biomass hybrid system, which needs a biomass generator of 1633.8 kWh and a battery bank of 16671.3 kWh. A Monte Carlo simulation validated the consistency of those values, despite not being technically feasible. According to all case studies, the presented method is a remarkable tool for designing off-grid systems.

HSSD off-grid does not consider all the details to ensure the proposed system is entirely viable, but it generates viable scenarios considering the proposed conditions. The main proposal is to offer a simple and visual methodology that any user can use as an initial evaluation to install a DEG system toward a more sustainable environment, thereby addressing the challenge of managing the complexity associated with existing optimization and simulation models. A method that obtains the sizing of the DEG is important to ensure the cost-effective use of renewable sources at the desired conditions. In this way, the methodology elaborates a preliminary study to evaluate available scenarios that will be studied in more detail in a possible following step using more detailed models. For future work, in the context of the HSSD, new parameters such as tilt angle and frequency control should be considered to ensure a more realistic scenario as a preliminary scenario.