# Simulating Socio-Technical Transitions of Photovoltaics Using Empirically Based Hybrid Simulation-Optimization Approach

^{1}

^{2}

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## Abstract

**:**

## 1. Introduction

## 2. Modeling Energy Transitions of Photovoltaics

## 3. Empirically Based Hybrid Simulation-Optimization Approach

^{2}= 20.46; df = 33; p = 0.958). It indicates that the sample is well-represented by the population concerning provincial distribution. The model validation was conducted by comparing the simulation results with independent source. The empirical data used in model specification was different from that used for calibration, so that a self-evident model was avoided.

#### 3.1. Agent-Based Modeling and Simulation of PV Adoption Decision

_{t}) represents the perceived benefits of saving electricity bills due to the use of PV, which is influenced by the export tariff. The performance expectance at time = 0 (PE

_{0}), which varied among the household agents, was acquired from the survey. According to the current regulation, the current export tariff, which was used as a basis, is 65%, indicating the reduction percentage of an electricity bill due to electricity export to the PLN grid. Every 1 kWh of PV electricity exported to the PLN grid, will be valued at 0.65 kWh. The PE

_{t}is updated when the change of export tariff exists, otherwise, it does not change. When the change of export tariff occurs, the PE

_{t}is updated following Equation (2), in which the performance expectance is set to its maximum value of 10 when the export tariff is larger or at least similar to the expected export tariff (ExpTariff), otherwise, the performance expectance is gradually increased based on the gap between actual and expected export tariff.

_{t}) is defined as the perceived investment cost of PV at time t, in which the value changes with the change of PV investment cost (PVcostdecrease

_{t}), which is influenced by the economies of scale—the more PV is produced, the less the investment cost for PV. The perceived investment cost of PV at the start of the simulation (PVal

_{0}) and the expected cost decrease (ExpPVcostdecrease), which are heterogeneous among the household agents, were obtained from the empirical survey. The PVal

_{t}value of 10 indicates that high perceived investment cost, whereas the PVal

_{t}value of 1 indicates a low perceived investment cost, achieved when the PVcostdecrease

_{t}is equal to or larger than ExpPVcostdecrease. The perceived investment cost for PV changes over time due to the cost decrease of PV, which is evaluated using Equation (3) as the following,

_{t}) corresponds to the perceived facilitating condition at time t, which varies over time depending on the availability of PV facilities, i.e., DCs with their role to provide information, services, procurement, installation, and maintenance of PV. According to Palm [62], getting a trusted, professional, and competent installation company is currently a demand for PV adoption. At every time step, the household agent evaluates the presence of the nearest DCs, which are obtained from the optimization model. The FC

_{t}becomes a maximum value of 10 when the household agent can find the nearest DCs within its threshold distance (DCthres). Otherwise, the FC

_{t}is gradually decreased based on the actual and preferred DC location. Assuming that FC

_{0}, which was acquired from the empirical survey, is the perceived facilitating condition at the start of the simulation, and DCnearest

_{0}

_{,}based on existing DCs, is the nearest DCs at the start of the simulation, the FC

_{t}is then evaluated using Equation (4) as follow,

_{t}), changes every time step. Given that each time step in the simulation represents one month, the age is updated by adding the factor of 1/12.

_{t}), which exceeds its intention threshold (Intthres), the household agent will evaluate both financial assessment and facility assessment. The procedures for financial assessment involve the calculation of electricity generated by PV (E

_{PVt}), annual cash flow at time t (R

_{t}), investment cost for PV (I

_{0}), and eventually the NPV. The amount of electricity generated by the PV (E

_{PVt}) is a function of the capacity factor (CF), which was set to 16% based on IRENA [38]; therefore, in one year the PV production becomes:

_{PVt}= PV

_{capt}× CF × 24 × 365

_{t}consists of two components Rsave

_{t}and Rmain

_{t}. Rsave

_{t}includes the benefits derived from PV installation, such as the decrease in electricity bills due to the direct use of the electricity generated by the PV and the income from selling the extra ones generated at the national electricity grid. Rmain

_{t}is the estimated annual maintenance cost of the PV, approximated as a certain percentage m of I

_{0}, which was set to 1%. Estimated savings are a function of the amount of electricity generated by the PV (E

_{PVt}), its percentage output directly used by households (Du), the price of electricity from the utility (P

_{El-import}), and the selling price of PV electricity to the power company according to applicable policies (P

_{El_export}).

_{t}= Rsave

_{t}− Rmain

_{t}

_{t}= E

_{PVt}(Du × P

_{El-import}) + ((1 − Du) × P

_{El_export}

_{t}= m × I

_{0}

_{0}) are the product of the PV capacity (PV

_{cap}) and the price installed per kWp of the PV system (PV

_{P}) following Equation (9).

_{t}) of the PV system, given the initial investment costs (I

_{0}) and the interest rate (i) following the equation below,

_{t}) is defined as the year (t) when Net Present Value (NPV) turns from negative to positive. If PP

_{t}≤ expected payback period (ExpPP), the value of financial assessment is set to 1, indicating worthy investment, and 0 otherwise, following Equation (11).

#### 3.2. Embedded Optimization Model of PV Supply Chain

- Sets:
- K: set of all household agents with intention (Int
_{t}) larger than the threshold (Intthres) indexed with k - J: set of all DC candidates indexed with j
- C: set of the capacity of DCs indexed with c
- I: set of manufacturers indexed with i
- T: set of the period (year) indexed with t
- Parameters:
- F
_{jc}: Fixed cost for opening DC j with capacity c - C
_{ij}: Transportation cost from the manufacturer i to facility/DC j - C
_{jk}: Transportation cost from DC j to household k - W
_{jc}: Capacity of DC jc - Si: Capacity of manufacturer i
- dkt: Demand of household agent k in period t
- Decision variables:
- Y
_{jc}: A binary variable that is equal to 1 if DC j with capacity c is open, otherwise zero. - X
_{ijt}: The number of products shipped from manufacturer I to facility/DC_{j}in period t - Z
_{jkt}: The number of products shipped from DC j to household k in period t.

- Objective function

- Subject to

#### 3.3. Performance Indicators

_{t}) equal to adopter. The financial performance was measured by supply chain unit cost corresponding to the supply chain cost for every 1 kWp of the installed PV system, which was calculated using Equation (22).

_{cap}

_{t}represents the accumulated installed PV system.

_{2}-eq/Wh and Widiyanto et al. [66] who estimated that PV generated GHG emissions of 0.053 kg CO

_{2}-eq/Wh. E

_{PVt}corresponding to the electricity generated by the PV system was calculated based on Equation (5).

#### 3.4. Verification and Validation

^{2}= 7835; df = 5; p = 11.070). Thus, the hybrid model has been able to reproduce the pattern of historical data of PV users both at the macro and micro levels.

#### 3.5. Scenario Development

#### 3.5.1. Export Tariff Regulation (S1)

#### 3.5.2. Incentives for PV Investment (S2)

#### 3.5.3. Environmental Campaign (S3)

#### 3.5.4. Combined Interventions

## 4. Results and Discussion

## 5. Methodological Reflections and Model Limitations

## 6. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 3.**Flowchart of the implemented simulation model (Note: the definition of the variables is described in Table 1).

**Figure 4.**A framework of household adoption decision-making, modified from UTAUT2 [44].

**Figure 6.**Percentage of intender for a different region in the second quarter of 2021 based on the most frequent single run (micro validation).

**Figure 7.**Spatial visualization of the simulation model (Note: red houses represent PV manufacturers, yellow houses represent DCs, red lines represent manufacturers—DCs network, black lines represent DCs—PV adopters’ network, green dots represent adopters, yellow dots represent intenders, red dots represent non-adopters. The figure is the screenshot of the simulation model which was developed in NetLogo using GIS data [63].

**Figure 9.**Spatial results for PV adopters in the combined intervention of export tariff, incentives, and campaign (S4c) in 2040. The figure is the screenshot of the simulation results developed in NetLogo using GIS data [63].

Symbol | Household Agent Variables | Possible Values | Type | Source |
---|---|---|---|---|

ID | Household ID number | 0–436 (437 household agents) | Static | Survey |

A_{t} | Age of respondents | 21–64 years | Dynamic * | Survey |

Inc | Income level | Household income | Static | Survey |

Ed | Education level | 1 = Elementary school 2 = High school 3 = University 4 = Postgraduate | Static | Survey |

R | Region | 1 = Java and surrounding islands 2 = Sumatra and surrounding islands 3 = Kalimantan and surrounding islands 4 = Sulawesi and surrounding islands 5 = Bali, Nusa Tenggara, and surrounding islands 6 = Maluku, Papua, and surrounding islands | Static | Survey |

EA | Electricity access | 1 = No access to electricity 2 = less than 1300 W 3 = 1300 W 4 = 2200 W 5 = 3500–5500 W 6 = more than 5500 W | Static | Survey |

SI | Social influence (percentage of peers who installs PV | 1 = 0%; 2 = 1–5%; 3 = 6–10%; 4 = 11–20%, 5 = more than 20% | Dynamic ** | Survey |

Peers | Number of peers with who communicated about energy or PV | 1–30 peers | Static | Survey |

Intthres | Intention threshold | 0 (no intention)–1 (high intention) | Static | Survey |

DCthres | DCs/facilities’ location threshold | 1 = within the similar province with the household agent 2 = within the similar region with the household agent 3 = within the country | Static | Survey |

PN | Personal norms | 1 (low)–10 (high) | Static | Survey |

PE_{t} | Performance expectance at time t | 1 (low)–10 (high) | Dynamic ** | Equation (2) |

EE | Effort expectance | 1 (low)–10 (high) | Static | Survey |

HM | Hedonic motivation | 1 (low)–10 (high) | Static | Survey |

HB | Habit | 1 (low)–10 (high) | Static | Survey |

PVal_{t} | Perceived investment cost for PV at time t | 1 (very cheap)–10 (very expensive) | Dynamic ** | Equation (3) |

FC_{t} | Perceived facilitating condition at time t | 1 (inaccessible)–10 (highly accessible) | Dynamic ** | Equation (4) |

ExpPP | Expected payback period | 7–20 years | Static | Survey |

ExpPVcostdecrease | Expected PV cost decrease | 0–100% | Static | Survey |

ExpTariff | Expected export tariff | 0–100% | Static | Survey |

DCnearest_{t} | Nearest DCs (facilities) at time t | 1 = Nearest DCs/facilities are located in similar province 2 = Nearest DCs/facilities are located in similar regions 3 = Nearest DCs/facilities are located in the country | Dynamic ** | Optimization model |

Int_{t} | Intention toward PV at time t | 0 (no intention)–1 (high intention) | Dynamic ** | Equation (1) |

Adopt_{t} | PV adoption state at time t | adopter, intender, non-adopter | Dynamic ** | Equation (13) |

Construct | Description |
---|---|

Performance expectance (PE) | Households’ perceived usefulness of a PV to lower their electricity bills and decrease the use of natural resources |

Effort expectance (EE) | Households’ comfort level in using the system and ease of adoption |

Hedonic motivation (HM) | The influence of hedonic (emotive) parameters such as keeping up with new technology on the household purchase decision |

Price value (PVal) | The perceived investment cost for PV |

Habit (HB) | The degree to which households tend to perform behaviors automatically from learning accustoming due to familiarity with technical knowledge |

Facilitating condition (FC) | The degree to which a household believes that the technical and organizational infrastructure such as after-sales service for maintenance is in place to support the use of the technology |

Social influence (SI) | The influence of others’ opinions about the acceptance and usage of new technology |

Personal norms (PN) | The degree of feeling responsible for protecting the environment, moral obligation to reduce fossil fuel energy |

Predictors | Coefficient Symbol | Coefficient | Wald χ^{2} | df | p-Value | Odds Ratio |
---|---|---|---|---|---|---|

Constant | ${\beta}_{0}$ | −9.179 | 32.666 | 1 | 0.000 *** | 0.000 |

Performance expectance (PE) | ${\beta}_{1}$ | 0.358 | 10.929 | 1 | 0.000 *** | 1.430 |

Effort expectance (EE) | ${\beta}_{2}$ | 0.155 | 2.103 | 1 | 0.147 ^{ns} | 1.168 |

Personal norms (PN) | ${\beta}_{3}$ | 0.286 | 6.926 | 1 | 0.008 ** | 1.331 |

Hedonic motivation (HM) | ${\beta}_{4}$ | 0.027 | 0.127 | 1 | 0.722 ^{ns} | 1.027 |

Price value (PVal) | ${\beta}_{5}$ | −0.259 | 9.921 | 1 | 0.002 ** | 0.772 |

Habit (HB) | ${\beta}_{6}$ | 0.013 | 0.013 | 1 | 0.909 ^{ns} | 1.013 |

Social influence (SI) | ${\beta}_{7}$ | 0.409 | 3.184 | 1 | 0.074 ^{ms} | 1.505 |

Facilitating condition (FC) | ${\beta}_{8}$ | 0.162 | 5.744 | 1 | 0.017 * | 1.176 |

Age (A) | ${\beta}_{9}$ | 0.047 | 7.089 | 1 | 0.008 ** | 1.048 |

Income (Inc) | ${\beta}_{10}$ | 0.008 | 0.382 | 1 | 0.537 ^{ns} | 1.008 |

Education (Ed) | ${\beta}_{11}$ | 0.111 | 0.274 | 1 | 0.601 ^{ns} | 1.118 |

Electricity access (EA) | ${\beta}_{12}$ | 0.170 | 1.016 | 1 | 0.313 ^{ns} | 1.186 |

^{ms}marginal significant,

^{ns}not significant.

Scenario | Baseline | Export Tariff | Incentives | Campaign | Combined Interventions | |||
---|---|---|---|---|---|---|---|---|

Parameters | S0 | S1 | S2 | S3 | S4a Export Tariff and Campaign | S4b Export Tariff and Incentives | S4c Export Tariff, Incentives, and Campaign | |

Export Tariff (%) | 65 | 100 | 65 | 65 | - | 65 | 100 | |

PV cost decrease (thousand IDR/kWp) | - | - | 1450 * | - | - | 1450 * | 1450 * | |

Personal Norms | Based on survey | Based on survey | Based on survey | 10 | 10 | Based on survey | 10 |

Scenario | Number of DCs | Financial Performance | Environmental Performance | |||
---|---|---|---|---|---|---|

Total Supply Chain Cost (Million IDR) | PV Capacity Installed (MWp) | Supply Chain Unit Cost (Thousand IDR/kWp) | PV Electricity Generated (MWh) | GHG Reduction (ton CO _{2}-eq) | ||

S0: Baseline | 2 | 14,698 | 15.54 | 946 | 462,353 | 493,330 |

S1: Export tariff | 7 | 34,193 | 36.33 | 941 | 990,931 | 1,057,324 |

S2: Incentive | 6 | 30,845 | 30.88 | 999 | 852,961 | 910,110 |

S3: Campaign | 6 | 30,253 | 26.88 | 1125 | 741,052 | 790,703 |

S4: Export tariff and campaign | 7 | 36,008 | 37.38 | 963 | 1,028,336 | 1,097,235 |

S4b: Export tariff and incentive | 7 | 35,538 | 37.17 | 956 | 1,056,544 | 1,127,332 |

S4c: Export tariff, incentive, and campaign | 7 | 41,901 | 38.22 | 1096 | 1,101,920 | 1,175,749 |

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**MDPI and ACS Style**

Nurwidiana, N.; Sopha, B.M.; Widyaparaga, A.
Simulating Socio-Technical Transitions of Photovoltaics Using Empirically Based Hybrid Simulation-Optimization Approach. *Sustainability* **2022**, *14*, 5411.
https://doi.org/10.3390/su14095411

**AMA Style**

Nurwidiana N, Sopha BM, Widyaparaga A.
Simulating Socio-Technical Transitions of Photovoltaics Using Empirically Based Hybrid Simulation-Optimization Approach. *Sustainability*. 2022; 14(9):5411.
https://doi.org/10.3390/su14095411

**Chicago/Turabian Style**

Nurwidiana, Nurwidiana, Bertha Maya Sopha, and Adhika Widyaparaga.
2022. "Simulating Socio-Technical Transitions of Photovoltaics Using Empirically Based Hybrid Simulation-Optimization Approach" *Sustainability* 14, no. 9: 5411.
https://doi.org/10.3390/su14095411