Several algorithms have been developed for building-attached photovoltaic system (BAPV) planning in educational institute based on PV capacity. Fewer studies on optimization algorithms for BAPV system planing on campus have been reported which considers a technoeconomic assessment. Therefore, a well-known robust algorithm is used as an optimization technique of BAPV system and considers technoeconomic assessment on campus. This paper presents a combination of analytical hierarchy process (AHP) with fuzzy theory (fuzzy AHP) for selecting a suitable and optimal design of BAPV system on academic campus. The BAPV system design is based on roof area and load profile at the project site. Five BAPV systems have been designed using five different types of PV. The design was comprehensively assessed by experts through a questionnaire with pairwise comparison model. Fuzzy AHP used to consider the qualitative and quantitative assessments that can affect the selection process. The comprehensive assessment in criteria consists of sizing systems, technical, economic, and environmental perspectives as criteria. The perspective is divided into 13 subcriteria. The results show degree of importance from the criteria-based fuzzy AHP as follows: technical > economic > environment > sizing system. Based on the assessment of criteria and subcriteria, design with monocrystalline is most suitable and polycrystalline as the least suitable design for BAPV system connected to grid and battery energy storage system in case study.

Global primary energy consumption increased by 2.2% in 2017 [

Building-attached photovoltaic (BAPV) and building-integrated photovoltaic (BIPV) are two of the innovative ways to implement solar photovoltaic technology. Several countries have been implemented the BIPV and BAPV systems. Germany adopted the “Rooftop Solar Electricity Program” law to encourage the development of BAPV. Japan subsidized the cost of installing PV system in residential buildings. PV systems in American and California increased to 62 MW and 36.5 MW in 2004 [

Although the BAPV system is better than the BIPV system, not all BAPV systems are feasible to be implemented. There are many factors that can affect BAPV system performance. Optimization of design, PV type, arrangement array of PV (in a roof or in facade), initial cost of system, cost of operation, and maintenance can affect the performance [

Thus, a technoeconomic assessment is needed to determine the feasibility of PV system performance. In [

One consideration in implementation BAPV system is required a priority factor chosen. Multicriteria decision-making (MCDM) algorithm can be used to determine the main priorities of several existing considerations. MCDM is widely used in several cases related to photovoltaic technology. In previous works, MCDM has been used to select the suitable PV modules [

Based on research studies that have perspectives, the feasibility of a BAPV system is determined by an assessment of technoeconomic factors. However, fewer studies about optimization algorithms for BAPV system planning on campus consider a technoeconomic assessment. Therefore, optimization technique with a well-known robust algorithm is used to get the best BAPV system design and considers technoeconomic assessment on campus. In this paper, combination of analytic hierarchy process with fuzzy theory is presented to choose the optimal and suitable BAPV system design in academic campus. Parameter assessment consists of a sizing system factor, technical factor, economic factor, and environment factor. Five existing PV technologies are used as an alternative design. The system design is simulated using PV

Department of Electrical Engineering in Universitas Negeri Semarang is located in Sekaran, Gunung Pati, Semarang City, with the tropical climate in coordinates 7.05° south latitude, 110.40° east longitude, and an altitude of 187 m above sea level [^{2} and 504 m^{2}. E11 Building is divided into 4 roof areas with a total available area of 1,060.44m^{2}. Roofs tilt in all buildings are 35°, except the flat roof in south-oriented of E11 Building is 16° [

Detail location and building of the project site experiment.

Predicted hourly energy demand in project site with PV

Daily irradiation and temperature in the project site.

The BAPV system was designed to reducing energy used from grid. This paper shows 2 scenarios of BAPV system. The first scenario is 73.5 kWp capacity of BAPV system connected to grid and battery energy storage system (BESS) for E11. BESS was added to E11 Building because it has high productivity in college activities. In addition, BESS can reduce high peak demand and improve the quality of the power system. The optimization of the BAPV system can be increased using the appropriate BESS capacity [

Scheme of BAPV connected to grid and BESS for E11 Building as scenario 1.

Scheme of BAPV connected to the grid for E6+E8 Buildings as scenario 2.

There are five BAPV designs for each scenario with five different PV types from German manufacturing, which are heterojunction PV (HIT), cadmium telluride PV (CdTe), copper indium selenium PV (CIS), monocrystalline PV (m-Si), and polycrystalline PV (p-Si). PV was chosen because it has high efficiency of 15-20%. Table

Modules PV used of design BAPV system.

Design 1 | Design 2 | Design 3 | Design 4 | Design 5 | |
---|---|---|---|---|---|

PV type | HIT | CdTe | CIS | m-Si | p-Si |

Nominal power (Wp) | 250 | 420 | 175 | 350 | 250 |

Weight of PV (kgs) | 15 | 36 | 20 | 18.6 | 19 |

Efficiency | 19.8% | 18% | 14.2% | 20.6% | 15.37% |

Temperature coefficient (%/°C) | -0.28 | -0.32 | -0.31 | -0.25 | -0.40 |

Sizing inverter capacity is based on the BAPV system capacity. BAPV system is connected to the grid, so a grid-tie inverter type is chosen because the inverter type can adjust the voltage and frequency of the grid [

Cosf of the designed BAPV system main components.

Component | Unit price |
---|---|

Module m-Si | $320 |

Module p-Si | $243 |

Module CIS | $162 |

Module CdTe | $445 |

Module HIT | $239 |

Inverter 50 kWp | $5,265 |

Inverter 25 kWp | $3,185 |

Inverter 15 kWp | $2,939 |

AC battery 13 kWh | $8,750 |

The development of BAPV system is supported by various PV types. Five types of PV that are chosen in Table

Hierarchy for fuzzy AHP in BAPV system.

Fuzzy AHP algorithm is divided into 4 main stages. The first stage begins by constructing the hierarchy including goal, criteria, and subcriteria. Criteria are built from the perspective of sizing system, technical, economic, and environment. Subcriteria is built from detailed assessment of each criteria. The second stage is designing and assessing five alternative designs of BAPV systems for project experiment. The third stage is making an assessment of the hierarchy by an expert with questionnaire. An expert was needed to give opinion about comparison the criteria, subcriteria, and alternative using numeric scale. After getting the experts’ opinion, the last stage is calculating the experts’ opinion using fuzzy AHP algorithm and find the best option. The detailed explanation of the main stage algorithm is shown as a flowchart in Figure

Construct the hierarchy

The framework of fuzzy AHP in the proposed project.

Criteria and subcriteria were built with different level as a hierarchical model to evaluate the alternative and achieve the goal [

Each alternative design of BAPV system uses a different type and nominal rating of PV, so sizing each design has difference. Each BAPV system design has own advantages and different performances according to installation conditions and location [

Detailed assessment of sizing system such as type of PV, amount of PV, weight of PV, covered area with PV, and unit price of PV is used as subcriteria. Different types of PV can affect the amount of PV that needs to be used and the total weight of the PV. For example, alternative design 1 uses a PV-type HIT 250 Wp; it takes 294 modules to get a capacity of 73.5 kWp. Different from alternative design 2 that uses PV-type CIS 175 Wp, it needed 420 modules to get the same capacity with design 1. Each design alternative has a different total PV weight and covered area by PV, depending on the type of the PV used.

The technical assessment is affected by the performance of BAPV system in real conditions. In this paper, the BAPV system design was simulated in location of project site using software PV

Examples of the shading analyses with PhotoPlan 3D in PV

Initial investment cost, life cycle cost (LCC), and cost of energy (COE) are calculated as subcriteria of economic. Initial investment cost is calculated with summing the cost of components system (PV, battery, inverter, balance of system (BOS) [

Detail initial investment cost of BAPV system design 1 in scenario 1.

Cost of PV | $70,266 |

Cost of inverter | $8,450 |

Cost of battery | $26,250 |

Cost of installation | $10,496 |

Cost of cabling | $15,745 |

Cost of shipping | $79,307 |

Total initial investment cost | $210,515 |

LCC is the total cost of investing in the BAPV system during the lifetime (25 years). LCC is calculated with summing the cost of initial investment, cost of operation and maintenance (O&M), and cost of replacement components. Cost of O&M is assumed at 2% of the PV cost in each year multiplied by the discount factor (

Detail LCC of BAPV system design 1 in scenario 1.

Years | Discount factor | Initial investment cost | Cost of replacement | Cost of O&M | Total | |
---|---|---|---|---|---|---|

Battery | Inverter | |||||

0 | 1.0000 | $210,515 | $210,515 | |||

1 | 0.9434 | $3,972 | $3,972 | |||

2 | 0.8900 | $3,747 | $3,747 | |||

3 | 0.8396 | $3,535 | $3,535 | |||

4 | 0.7921 | $3,335 | $3,335 | |||

5 | 0.7473 | $6,314 | $6,314 | |||

6 | 0.7050 | $2,968 | $2,968 | |||

7 | 0.6651 | $2,800 | $2,800 | |||

8 | 0.6274 | $2,642 | $2,642 | |||

9 | 0.5919 | $2,492 | $2,492 | |||

10 | 0.5584 | $14,657 | $4,718 | $19,376 | ||

11 | 0.5268 | $2,218 | $2,218 | |||

12 | 0.4970 | $2,092 | $2,092 | |||

13 | 0.4688 | $1,974 | $1,974 | |||

14 | 0.4423 | $1,862 | $1,862 | |||

15 | 0.4173 | $3,526 | $3,526 | |||

16 | 0.3936 | $1,657 | $1,657 | |||

17 | 0.3714 | $1,564 | $1,564 | |||

18 | 0.3503 | $1,475 | $1,475 | |||

19 | 0.3305 | $1,392 | $1,392 | |||

20 | 0.3118 | $8,184 | $2,635 | $10,820 | ||

21 | 0.2942 | $1,238 | $1,238 | |||

22 | 0.2775 | $1,168 | $1,168 | |||

23 | 0.2618 | $1,102 | $1,102 | |||

24 | 0.2470 | $1,040 | $1,040 | |||

25 | 0.2330 | $981 | $981 | |||

$210,515 | $22,843 | $17,193 | $45,255 | $295,806 |

COE is the cost of electrical energy produced per kWh by a BAPV system during the lifetime. The energy produced in the system lifetime is affected by the degradation of PV (

Reducing CO_{2} and improving human living standard is one of the advantages of utilizing rooftop for BAPV system [_{2} reduction is related to the amount of energy that can be produced by the BAPV system.

Construct the alternative

Five alternatives of BAPV system were designed to be chosen using the fuzzy AHP method. Evaluation criteria of the five BAPV system designs have been carried out. Details and assessments of five BAPV system designs for scenario 1 and scenario 2 are shown in Tables

Expert opinion

Alternative design for 73.5 kWp BAPV system in E11 Building.

Alternative | Type of PV | Number of PV | Weight of PV | Price of PV | Covered area | PR | Energy spent to load | Total energy in 25 years | Grid feed-in | LCC | LCOE | Reduction CO_{2} | ||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|

PV | Battery | Grid | ||||||||||||

(unit) | (kg) | $ | m^{2} |
(%) | (%) | (MWh/25 years) | (%) | ($) | ($) | (kg/year) | ||||

1 | HIT | 294 | 4410 | $239 | 370.7 | 71.6 | 71.8 | 15.4 | 12.8 | 2,274.28 | 42.3 | $295.806 | $0.130 | 79,551 |

2 | CdTe | 175 | 6300 | $445 | 433.1 | 73.9 | 72.4 | 15.1 | 12.2 | 2,328.51 | 43.7 | $333.223 | $0.141 | 81.963 |

3 | CIS | 420 | 8400 | $162 | 515.8 | 74.3 | 72.7 | 15.1 | 12.2 | 2,368.65 | 44.0 | $359.109 | $0.152 | 82,565 |

4 | Mono | 210 | 3906 | $320 | 342.2 | 76.0 | 74.1 | 14.7 | 11.2 | 2,508.66 | 46.2 | $262.271 | $0.105 | 86,960 |

5 | Poly | 294 | 5586 | $243 | 478.3 | 71.1 | 72.5 | 15.4 | 12.1 | 2,214.63 | 42.5 | $301.372 | $0.136 | 80,547 |

Alternative design for 31.5 kWp BAPV system in E6+E8 Buildings.

Alternative | Type of PV | Number of PV | Weight of PV | Price of PV | Covered area | PR | Energy spent to load | Total energy 25 years | Grid feed-in | LCC | LCOE | Reduction CO_{2} | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|

PV | Grid | ||||||||||||

(unit) | (kg) | $ | m^{2} |
(%) | (%) | (%) | (MWh/25 years) | (%) | ($) | ($) | (kg/year) | ||

1 | HIT | 126 | 1890 | $239 | 158.9 | 72.3 | 74.69 | 25.31 | 996.43 | 59.1 | $104.302 | $0.105 | 35.28 |

2 | CdTe | 75 | 2700 | $445 | 185.6 | 74.8 | 75.54 | 24.46 | 1,023.0 | 59.9 | $125.001 | $0.122 | 36,373 |

3 | CIS | 180 | 3600 | $162 | 221.1 | 75.3 | 75.30 | 24.70 | 1,040.9 | 60.3 | $136.095 | $0.131 | 36,648 |

4 | Mono | 90 | 1674 | $320 | 146.7 | 75.9 | 75.90 | 24.10 | 1,055.4 | 60.3 | $98.108 | $0.093 | 36.929 |

5 | Poly | 126 | 2394 | $243 | 205.0 | 71.2 | 74.82 | 25.18 | 954.81 | 58.8 | $114.865 | $0.120 | 35,087 |

The fuzzy AHP algorithm processes the matrix derived from a questionnaire with pairwise comparison model. The questionnaire is filled with opinion from the experts. The questionnaire was made through Google Form and sent via email to the experts. This research involves 4 experts that were selected from related fields. The assessment of the four experts involved in this research has been able to determine the optimal and feasible system design.

Fuzzy AHP as decision-making of design BAPV system

Fuzzy AHP is the development of AHP using triangular fuzzy number (TFN) of fuzzy theory. Research in [

After the assessment from experts, a pairwise comparison matrix was made. The matrix must be made consistently by experts so it can be analyzing properly. Matrix of pairwise comparisons is made with Equation (

Comparison of criteria in a numerical scale of experts.

Criteria | Sizing | Technical | Economic | Environment | |
---|---|---|---|---|---|

Expert 1 | Sizing | 1 | 1/7 | 1 | 5 |

Technical | 7 | 1 | 5 | 7 | |

Economic | 1 | 1/5 | 1 | 9 | |

Environment | 1/5 | 1/7 | 1/9 | 1 | |

Expert 2 | Sizing | 1 | 1/5 | 1/6 | 1/5 |

Technical | 5 | 1 | 2 | 1/2 | |

Economic | 6 | 1/2 | 1 | 2 | |

Environment | 5 | 2 | 1/2 | 1 | |

Expert 3 | Sizing | 1 | 1/5 | 1/7 | 1/9 |

Technical | 5 | 1 | 1/4 | 1/7 | |

Economic | 7 | 4 | 1 | 1/5 | |

Environment | 9 | 7 | 5 | 1 | |

Expert 4 | Sizing | 1 | 3 | 5 | 7 |

Technical | 1/3 | 1 | 3 | 5 | |

Economic | 1/5 | 1/3 | 1 | 3 | |

Environment | 1/7 | 1/5 | 1/3 | 1 |

The matrix in Step 2 was in numerical scale. To proceed to Step 2, convert the numerical scale to a TFN scale. The TFN scale is shown in Table

Triangular fuzzy number.

Linguistic variable | Numeric scale | TFN scale ( |
Reciprocal ( |
---|---|---|---|

Equally strong | 1 | (1, 1, 1) | (1, 1, 1) |

Moderately strong | 3 | (1, 3/2, 2) | (1/2, 2/3, 1) |

Strong | 5 | (2, 5/2, 3) | (1/3, 2/5, 1/2) |

Very strong | 7 | (3, 7/2, 4) | (1/4, 2/7, 1/3) |

Extremely strong | 9 | (4, 4/5, 5) | (2/9, 2/9, 1/4) |

Intermediate | 2, 4, 6, 8 | (1/2, 1, 3/2); (3/2, 2, 5/2); (5/2, 3, 7/2); (7/2, 4, 9/2) | (2/3, 1, 2); (2/5, 1/2, 2/3); (2/7, 1/3, 2/5); (2/9, 1/4, 2/7) |

Pairwise comparison matrix in TFN scale of all respondents needs to be combined with the calculation of fuzzy geometric mean as follows Equation (

Matrix geometric mean of criteria weight.

Criteria | Sizing | Technical | Economic | Environment | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|

Sizing | 1.00 | 1.00 | 1.00 | 0.48 | 0.62 | 0.82 | 0.52 | 0.75 | 0.99 | 0.77 | 0.96 | 1.19 |

Technical | 1.22 | 1.61 | 2.08 | 1.00 | 1.00 | 1.00 | 0.93 | 1.30 | 1.72 | 0.83 | 1.09 | 1.46 |

Economic | 0.91 | 1.30 | 1.69 | 0.61 | 0.83 | 1.19 | 1.00 | 1.00 | 1.00 | 1.03 | 1.39 | 1.79 |

Environment | 0.84 | 1.05 | 1.31 | 0.67 | 0.89 | 1.15 | 0.59 | 0.77 | 1.07 | 1.00 | 1.00 | 1.00 |

Fuzzy synthesis value (

Matrix geometric mean of fuzzy synthesis value.

Criteria | Sum of row from geometric mean | Fuzzy synthesis value | ||||
---|---|---|---|---|---|---|

Sizing | 2.770 | 3.325 | 3.997 | 0.135 | 0.201 | 0.298 |

Technical | 3.986 | 4.995 | 6.257 | 0.195 | 0.302 | 0.466 |

Economic | 3.559 | 4.513 | 5.671 | 0.174 | 0.273 | 0.423 |

Environment | 3.101 | 3.708 | 4.532 | 0.152 | 0.224 | 0.338 |

To obtain the value of

Degree possibility (

Defuzzification of criteria.

Criteria | ||
---|---|---|

0.50504 | 0.5050 | |

0.63291 | ||

0.86323 | ||

1.43906 | 1.000 | |

1.11061 | ||

1.32828 | ||

1.33354 | 0.8867 | |

0.88669 | ||

1.21884 | ||

1.12937 | 0.6475 | |

0.64750 | ||

0.77086 |

After defuzzification, the value of fuzzy vector weight is obtained using Equation (

Value of fuzzy vector weight of criteria.

Criteria | |
---|---|

Sizing | 0.16617 |

Technical | 0.32903 |

Economic | 0.29175 |

Environment | 0.21305 |

Normalized fuzzy vector weight is calculated using Equation (

Normalized of fuzzy vector weight of criteria and subcriteria for BAPV design scenario 1.

Criteria | _{Criteria} |
Subcriteria | _{Subcriteria} |
---|---|---|---|

Sizing (_{1}) |
0.16617 | Type of PV (_{11}) |
0.2409 |

Number of PV (_{12}) |
0.2340 | ||

Weight of PV (_{13}) |
0.1650 | ||

Unit price PV (_{14}) |
0.1872 | ||

Covered area (_{15}) |
0.1729 | ||

Technical (_{2}) |
0.32903 | Performance ratio (_{21}) |
0.3763 |

Energy spent to load (_{22}) |
0.2465 | ||

Total energy in 25 years (_{23}) |
0.1967 | ||

Grid feed-in (_{24}) |
0.1805 | ||

Economic (_{3}) |
0.29175 | Life cycle cost (_{31}) |
0.5000 |

Cost of energy (_{32}) |
0.5000 | ||

Environment (_{4}) |
0.21305 | Reduction of CO_{2} (_{41}) |
0.2130 |

Global priority is calculated by multiplying the fuzzy vector weight of the criteria, subcriteria, and alternatives assessed. The rank of alternative is obtained after ordering the highest value of global priority as the first priority and smallest value as the last priority.

In this study, the combination of fuzzy theory with AHP method has determined priority factors that can affect the selection of BAPV system design for campus areas. To support the work and result of fuzzy AHP algorithm, the classic AHP method is also used in this study. The priority weights of criteria, subcriteria, and alternatives have been obtained using both algorithms. Both of these algorithms show the same priority rank that shows in Figure

Priority weight of criteria.

Tables

The results of priority weights subcriteria with both algorithms are shown in Figure

Priority weight of subcriteria.

Reviewing the priority weight of criteria and subcriteria in Figures

The final stage is determining the priority weights of each alternative BAPV system design. The priority weight of alternative BAPV system design is calculated by adding up the normalized weights from the multiplication of priority weights from criteria and subcriteria. Priority weight of alternative design BAPV systems using fuzzy AHP and classic AHP is shown in Figure

Priority weight of alternative.

Alternative design 4 of a BAPV system with monocrystalline PV (represented as design 4) is the most feasible option to be implemented at case study location with many advantages. Considering the efficiency of each PV module, the m-Si PV has the highest efficiency than the others, which is 20.6%. Considering the temperature coefficient of each PV module, m-Si PV with temperature coefficient of -0.25%/°C has the best performance than others. Then polycrystalline PV (represented as design 5) with the higher temperature coefficient of -0.40%/°C has the lowest system performance. This is because the BAPV system was designed in tropical country with dominant hot weather. It proves PV modules with low-temperature coefficients have better performance than PV modules with high-temperature coefficients.

Another advantage of BAPV system design with m-Si is having the lowest life cycle cost even though the monocrystalline has not the lowest module unit price. The lowest life cycle cost with the best system performance makes the lowest cost of energy compared to other system designs. Therefore, monocrystalline is feasible to be implemented in case studies.

The design 4 alternative in this study uses monocrystalline PV type. BAPV system design for E11 Building (scenario 1) requires 210 PV modules with nominal rating 350 WP. BAPV system design for E6+E8 Buildings (scenario 2) requires 90 PV modules with the same series and nominal rating PV. Detail configuration of array and string is shown in Figures

Schematic design of BAPV system connected to grid and BESS for E11 Building.

Schematic design of BAPV system connected to the grid for E6+E8 Buildings.

Architectural from top view at project site (BAPV system design for E11 Building).

Architectural from top view at the project site (BAPV system design for E6+E8 Buildings).

A comprehensive evaluation of PV technology priority in the BAPV system design has been carried out based on a combination of fuzzy theory and AHP as an optimization technique. The optimization technique is used to find a suitable and optimal BAPV system design based on technoeconomic assessment. Evaluation is considered several perspectives such as system sizing, technical, economic, and environmental. Based on these perspectives, fuzzy AHP and classic AHP analyze the priority rankings of expert opinion as follows: technical > economic > environment > sizing system. This research has analyzed five BAPV system designs that were experimented in campus areas in tropical countries. The PV design is constructed with five existing PV technologies such as m-Si, p-Si, CdTe, CIS, and HIT. Based on both algorithms with considering the criteria and subcriteria, design of the BAPV system using monocrystalline (represented as alternative design 4) became the most optimal design. The second option is design of BAPV system using CdTe PV (represented as alternative design 2) for all scenario in project site. Scenario design of BAPV system connected to the grid and battery consists of 210 m-Si PV modules and 3 batteries with output energy system in lifetime is 2,508.66 MWh/25 years and cost of energy is $0.105 (E11 Building). Scenario design of BAPV system connected to the grid without battery consists of 90 m-Si PV modules with output energy system in lifetime is 1,055.4 MWh/25 years and cost of energy is $0.093 (E6 and E8 Buildings).

The data used to support the findings of this study are available from the first author and corresponding author upon request.

The authors declare that they have no conflict of interest with regard to publishing this article.

This work is partly supported by the Lembaga Penelitian dan Pengabdian Masyarakat UNNES under grant no. 71.13.5/UN37/PPK.3.1/2019 and previous grant funding and then the team of Smart Energy Project Study (UEESRG), Department of Electrical Engineering, Universitas Negeri Semarang.

_{p}grid- connected photovoltaic system for residential building in India: a comparative analysis of various PV technology