key: cord-1000742-fb8urfrp authors: Ali, Tausif; Aghaloo, Kamaleddin; Chiu, Yie-Ru; Ahmad, Munir title: Lessons learned from the COVID-19 pandemic in planning the future energy systems of developing countries using an integrated MCDM approach in the off-grid areas of Bangladesh date: 2022-03-04 journal: Renew Energy DOI: 10.1016/j.renene.2022.02.099 sha: 4ada246d9defc9dd9291096dff5535bc35b77162 doc_id: 1000742 cord_uid: fb8urfrp The COVID-19 pandemic is hindering the progress of energy development in developing countries, and further worsening the dilemmas of energy planning in off-grid areas. To address such complicated decision-making issues and consider scenarios during this long-lasting pandemic, this study proposes a novel integrated MCDM (Multi-Criteria Decision Making) using Delphi based FO-BWM (Fuzzy Optimistic Best-Worst Method), IDOCRIW (Integrated Determination of Objective Criteria Weights) and the Aggregated Weighting Method integrated with the CoCoSo method under different normalization methods based on a case study of the off-grid areas in Bangladesh. The results of Delphi analysis showed that a total of five criteria were agreed upon by the expert panel. After integrating five normalization methods with CoCoSo and using three weighting methods separately, a total of 15 models were sorted out. The final result from 8 models demonstrates that Solar Home System (SHS) and Mini-Grid systems need to be prioritized, and the criterion Opportunity of Local Funding (OLF) is essential for choosing between SHS and Mini-Grid systems. Sensitivity analysis showed that the proposed method is effective for easing the dilemmas of energy planning in off-grid areas and provides useful insight to address the impacts of future pandemics on energy planning. Keywords: COVID-19, off-grid, Delphi, BWM, fuzzy, CoCoSo . 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 1 Introduction 46 The global COVID-19 outbreak has slowed the demand and development of renewable energy 47 technologies, and thereby reduced investment in renewable energy, leading to the loss of many 48 jobs [1] [2]. In this context, developing countries are exposed to more sustainable development 49 challenges and economic implications than developed countries [3] . Moreover, in the off-grid 50 areas of developing countries, the dilemma of whether to extend the existing grid or adopt off-grid 51 energy systems has been further worsened due to the impact of the pandemic. Renewable energy, 52 both short-term and long-term, can bring economic, social, and environmental benefits for all 53 countries, including developing countries [4] . However, the fundamental steps in legislation, and connected renewable, off-grid and conventional energy systems in off-grid areas; and the lessons 67 learned from this process will be crucial for the progress of SDG7 in developing countries. In the case of Bangladesh, the total current power generation installed capacity is 25,187 MW, 70 of which 96.96% (gas 44.98%, oil 29.17%, coal 7.02%, imported 4.61% and captive 11.12%) of 71 electricity is generated from fossil fuel and renewables contribute only 3.1% [11] . However, 72 Bangladesh ranks the fourth highest in the rate of off grid electricity access, showing that the 73 country is dominating in the field of off-grid energy system [12] . For example, the Infrastructure the rural areas of Bangladesh, which covers 12% of the population [13] . Although electricity 76 production from Mini-Grids is relatively undeveloped in Bangladesh, Mini-Grids may be more 77 economical, consistent, reliable, and could solve real-life economic dispatch problems [14, 15] . To 78 promote a clean environment, the GoB (Government of Bangladesh) is also promoting large scale 79 solar park and wind energy systems which will be integrated in the national grid. It has been 80 reported that by 2023 the gas reserve will be depleted unless new gas fields are discovered. Thus, 81 GoB discourages gas based power generation and prioritizes coal power generation for the future, 82 as it is cheaper than other forms of fossil fuel based power generation [16] . As for the off-grid 83 areas, most of them are located in the southern part of the country. Yet, the north eastern part of 84 J o u r n a l P r e -p r o o f the country also has off-grid areas that are mainly known as 'Haor' areas. Most of these areas are 85 using off-grid energy systems, but due to grid extension a number of off-grid areas have now 86 become on-grid areas. However, it is still necessary to choose between off-grid and on-grid energy 87 systems in the off-grid areas of the country, particularly under the current pandemic situation. 88 Therefore, SHS, Mini-Grids, solar parks, wind and coal based power generation systems can be 89 considered for the off-grid areas of Bangladesh, which needs proper ranking and prioritization to 90 achieve SDG 7. Moreover, the long-lasting impact of COVID-19 pandemic should be furthered 91 considered; and an effective and resilient planning approach should be well developed. Acknowledging the importance of addressing such conflicting objectives and complicated criteria 93 in prioritizing the energy alternatives, researchers often adopt Multiple Criteria Decision Making 94 (MCDM) methods, which can consider the complexity of social, technical, economic, and 95 environmental aspects; and, therefore, this approach is adopted in this study. In recent years, off-grid and on-grid energy systems using MCDM methods have become 98 popular in the field of energy planning and development research. Ramezanzade between on-grid and off-grid energy systems in Iran. The findings of the study showed that 106 integration of solar, wind and biogas was the most inexpensive alternative. Although the inclusion 107 of a fuel cell to the system increased the cost of the system, it improved system efficiency. The 108 findings also revealed that on-grid energy systems are more cost-effective than off-grid energy 109 systems. Nsafon Determination of Objective Criteria Weights) proposed by Zavadskas and Podvezko [29] with OF-147 BWM increases the accuracy of the weighting method. This is also a new approach. The 148 IDOCRIW method is nonbiased in nature. In this method, the intense possible values gained by 149 the entropy method can be recouped by the CILOS (Criterion Impact LOSs) method, which is a 150 major advantage [30] . Additionally, to obtain the most precise weights of the criteria, objective 151 and subjective weighting methods must be combined [31] . On the other hand, the CoCoSo (Combined Compromise Solution) method has not been 154 applied to rank the energy systems in developing countries, particularly Bangladesh. Furthermore, To select the most important criteria, we applied the Delphi method. The process of this method 202 is explained below. Step 1: Establish a list of initial decision criteria = { 1 , 2 , 3 , … … . , }. Step 2: Send the list of criteria = { 1 , 2 , 3 , … … . , } to the expert panels. . where is the proportion of agreements on relevance and is the probability of chance 210 occurring. Step 5: Confirm the list of criteria = { 1 , 2 , 3 , … … . , }. To estimate the weights of the criteria, we applied FO-BWM. The following procedure was used 217 to calculate the weights. Step 1: Find the best (most desirable) and worst (least desirable) criterion [25]. Step Step 3: Set the parameters = = 1, ( = 1,2,3; = , , ). Step 9, we adopted the optimistic approach [28]. Step 4: Construct the linear programming model. (2) Step 5: Solve the linear model and calculate the optimal fuzzy weight vector ̃ * = 235 (̃1 * ,̃2 * ,̃3 * , … . . ,̃ * ) Step 6: Calculate fuzzy deviation * = ( * , * , * ) using the following equation. Where, Step 7: Calculate the fuzzy consistency ratio (FCR). where, ̃= ( , , ) is demonstrated in Table 3 and ̃ * is obtained from equation (3). 245 Step 8: Estimate R (FCR). When ( ) ≤ 0.10, judgments can be considered as reliable. Therefore, the defuzzification 248 process can be carried out. Step 9: Defuzzify the obtained weights from Step 10 and calculate the crisp weights using Graded This study implemented the IDOCRIW technique according to the following steps [29] . Step 1: Calculate the normalized values of the decision matrix. = ∑ =1 , = 1,2,3, … , J o u r n a l P r e -p r o o f where denotes the normalized value of the decision matrix for the th alternative in the th 256 criterion. And is the number of alternatives. Step 2: Calculate the numerals of the entropy method. Where ℎ is the entropy constant and is denoted as ℎ = 1 ln( ) and is defined as 0 if =0. Step 3: Calculate the entropy weight. where, denotes the degree of diversification and is expressed as = 1 − . Step 4: Construct the square matrix. After that, the decision matrix was normalized by equation (7). Lastly, a square matrix was made 264 using equation (11). Here, states the maximum number of the ℎ criteria, which were taken from the decision 267 matrix with rows to formulate the square matrix and = and = [38]. Step 5: Estimate the relative impact loss matrix with respect to the values obtained from the 270 previous step. where, is the relative impact loss of the ℎ criterion. Step 6: Develop the weight system matrix. J o u r n a l P r e -p r o o f Step 7: Calculate the criterion impact loss weight. 275 We first solved the linear system of equations as follows: To obtain more reliable and consistent weight, we implemented the following aggregated formula, 284 which is a combination of the OF-BWM and IDOCRIW methods [39] . where Δ is the contribution factor and its suggested range is from 0-1. Here, = 0.5 was 286 considered. Here, is the number of off-grid and on-grid energy system alternatives, is the number of 290 assessment criteria and is the performance of the ℎ alternative with respect to the 291 ℎ criterion. Step 16: Normalize the decision matrix based on the following normalization methods [40] . Step 17: Calculate the values of for each normalization method, which are derived from the grey 303 relational generation technique. The values of are obtained from equation (6), (18) and (19). Step 18: Calculate the values of for each normalization method and each weighting method, which are obtained based on the WASPAS multiplicative attitude. Step 19: Determine the aggregated appraisal score for each normalization method. Step 20: Calculate the final appraisal scores for each energy system using each normalization After selecting the list of criteria, we implemented the Delphi process. BWM was based on expert ratings, which were subjective in nature. Therefore, to avoid biasness, 348 we implemented an objective weighting method called IDOCRIW. After calculating the weights 349 of the entropy and CILOS, we calculated the IDOCRIW weight of the criteria using equation (18). 350 Figure 2 shows that the weights of COE, PIT, OLF, PSR and EB are 33.6%, 11.9%, 15.9%, 27.3% 351 and 11.2%, respectively. The deviation of results between these two methods raised new concern. 352 Therefore, this study aggregated the OF-BWM and IDOCRIW methods to get more reliable and (3) weighting methods, the simulation generated a total of fifteen (15) models which rank the 374 energy systems. Figure 3 shows the following ranking of the first 5 models: SHS>Mini-Grid>Solar The majority of the models demonstrated that SHS is the best energy system. However, some 396 models prioritizing Mini-Grids as the ranking models work differently with different normalization 397 and weighting methods. It is also observable that all models prioritize clean energy systems, except 398 for one model, which also creates a dilemma regarding the final decision. Figure 6 shows the Table 7 . It should also be noted that the values 412 obtained for the energy systems from the Borda and Copeland methods are ranked from 413 descending to ascending order, which means the highest value is considered as the best energy 414 system. Figure 7 shows the final results of these three methods. The rankings obtained from the The previous SA proved the utility of M5, M10, and M15. To prove its overall effectiveness, this 442 study created a total of 16 scenarios (including the original weight) from each weighting method 443 and varied to 30%, 40% and 50%. Figures 9, 10 and 11 show that the rankings of the energy 444 systems are stable and SHS and Mini-Grid are the best for off-grid areas if the impact of the 445 pandemic lasts a long time. However, M5 and M10 demonstrate that OLF (C3) will be an important 446 criterion for Bangladesh that will strongly influence the decision making when choosing between 447 these two off-grid energy systems. On the other hand, M15 shows that all criteria will play almost 448 equal roles with respect to the energy system alternatives during the decision making process. Bangladesh, and it is expected that the country will need to depend on local funding in the future 470 rather than foreign investment. Also, urgent local funding is necessary to establish power supply 471 as soon as possible with stable system reliability and sustainability, so that emergency situations 472 can be handled in the future. this study also show that Mini-Grids also provide a safe option during lockdown periods, which 488 allow consumers to get reliable electricity from stakeholders without concern for the technical 489 difficulties. A study has demonstrated that SHSs are a better option than Mini-Grids based on a case study which is the lowest when compared with other fossil fuels. Meanwhile, renewable energy had the 516 highest positive growth at 0.79% [50] . This implies that in the future, if the COVID-19 pandemic 517 extends for a long time, the energy demand from fossil fuel based energy systems will decrease 518 and the energy demand from renewables will increase. Such a finding justifies the importance of 519 this study, as the results show that renewable energy will be prioritized in Bangladesh. It is 520 important to note that the reliability of the grid connected system is questionable since the power 521 failure of the system may cause a complete blackout. On the other hand, an off-grid energy system 522 can store electricity in batteries and can be used when necessary, particularly during a lockdown 523 period. Therefore, SHS and mini-grid based off-grid energy systems can be promoted in the future, 524 considering the long-term effects of the COVID-19 pandemic in Bangladesh. developers are unable to take advantage of this subsidy package, they will be forced to finance 532 their capital expenditures with commercial or foreign loans. With current commercial loan interest 533 rates in Bangladesh and a 5-year loan term, this kind of project's net present value would be more 534 than four times higher than it would be without IDCOL subsidies. In this context, a transparent 535 and consistent subsidy scheme is required, which could be beneficial because most mini-grid 536 initiatives require initial funding. In order to promote greater social empowerment during this 537 pandemic, the GOB may also grant specific tariff subsidies for farming, livelihood enhancement, 538 and small cottage industries. As the global vaccination rate rises, developing countries will require more vaccines. However, it is critical to not only increase the amount of vaccines available, but also to ensure that Grids.  Sensitivity analysis concludes that the rankings obtained from the 8 models are stable and 580 valid when the CoCoSo model parameter is varied. Both sensitivity analyses also 581 demonstrate that CoCoSo is highly stable with logarithmic normalization in the decision 582 matrix used in this study. The major limitation of this study is the small dimension of the decision matrix. Therefore, 584 more criteria and alternatives can be considered in the future. Different off-grid and on-grid energy 585 systems can also be further designed using optimization tools and the MCDM method can then be 586 used to ease the dilemmas of energy planning in off-grid areas in developing countries. J o u r n a l P r e -p r o o f This study makes the following recommendations for the development of a country's clean 589 energy expansion during the COVID-19 pandemic, which will not only improve comprehension 590 of the selection process but also provide useful policy and practice insights.  In a long-term pandemic scenario, funding from foreign agencies can be unpredictable and Rasool, 612 Unprecedented environmental and energy impacts and challenges of COVID-19 pandemic Impacts of COVID-19 pandemic on the global energy system and the shift progress 616 to renewable energy: Opportunities, challenges, and policy implications The impact of COVID-19 pandemic on sustainable development goals -A 619 survey 621 Justice in solar energy development Sustainable Development Goals: pandemic reset COVID-19 pandemic facilitating energy transition opportunities Assessment Of An Off-Grid Power Supply System For Small Settlements Evaluation and optimization of off-grid and on-grid photovoltaic power system for 631 typical household electrification Techno-economic analysis of photovoltaic-633 biomass-based microgrid system for reliable rural electrification Grid Connected Hybrid Power System Design Using HOMER Determinants of off-grid electrification choice and expenditure: Evidence 639 from Bangladesh Default risks, moral hazard and market-based 641 solution: Evidence from renewable energy market in Bangladesh Hybrid renewable energy systems for off-grid electric 644 power: Review of substantial issues Technical and economic assessment of hybrid energy 647 systems in South-West Nigeria A multi-criteria analysis of coal-based power 649 generation in Bangladesh A new hybrid decision-making framework 651 to rank power supply systems for government organizations: A real case study Techno-economic analysis of 654 a hybrid power system based on the cost-effective hydrogen production method for rural 655 electrification, A case study in Iran Integrating multi-criteria 657 analysis with PDCA cycle for sustainable energy planning in Africa: Application to hybrid mini-grid 658 system in Cameroon Multi-Objective Decision-Making for Hybrid Renewable Energy 660 Systems for Cities: A Case Study of Xiongan New District in China Selection of a Hybrid Renewable Energy Systems for a 663 Low-Income Household The Selection of Multiattribute Decision Making Methods for Scholarship Student 665 Selection An Experimental Application of the DELPHI Method to the Use of Experts Best-worst multi-criteria decision-making method A review of selected weighing methods in MCDM with a case study Fuzzy best-worst multi-criteria decision-making method and its applications Fuzzy best-worst method based on triangular fuzzy numbers for 677 multi-criteria decision-making Integrated Determination of Objective Criteria Weights in MCDM Assessment of different metal screw joint parameters 681 by using multiple criteria analysis methods, Metals (Basel) The Recalculation of the Weights of Criteria Normalization Techniques for Multi Decision Making: Analytical Hierarchy Process Case Study, in: Dr Investigation of circular economy practices in the context of emerging 690 economies: a CoCoSo approach Stability and agreement criteria for the termination of Delphi 693 studies Is the CVI an acceptable indicator of content validity? 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