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Trihardani, L.;  Wang, C.;  Hsieh, Y. Optimal Decisions for Deploying Renewable Energy at B&Bs. Encyclopedia. Available online: https://encyclopedia.pub/entry/24605 (accessed on 21 July 2024).
Trihardani L,  Wang C,  Hsieh Y. Optimal Decisions for Deploying Renewable Energy at B&Bs. Encyclopedia. Available at: https://encyclopedia.pub/entry/24605. Accessed July 21, 2024.
Trihardani, Luki, Chi-Tai Wang, Ying-Jiun Hsieh. "Optimal Decisions for Deploying Renewable Energy at B&Bs" Encyclopedia, https://encyclopedia.pub/entry/24605 (accessed July 21, 2024).
Trihardani, L.,  Wang, C., & Hsieh, Y. (2022, June 29). Optimal Decisions for Deploying Renewable Energy at B&Bs. In Encyclopedia. https://encyclopedia.pub/entry/24605
Trihardani, Luki, et al. "Optimal Decisions for Deploying Renewable Energy at B&Bs." Encyclopedia. Web. 29 June, 2022.
Optimal Decisions for Deploying Renewable Energy at B&Bs
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This entry is adapted from the peer-reviewed paper 10.3390/app12126087

The adoption of renewable energy (RE) is a promising business strategy for bed and breakfasts (B&Bs) to mitigate climate change while maintaining a competitive edge. Bed and breakfast (B&B) has experienced rapid growth through sharing-economy platforms. The shift to RE has the potential to maintain the B&B’s competitive advantage by offering new service innovations continuously.

bed and breakfast renewable energy tourism accommodation emission optimization

1. Introduction

Climate change has brought various threats to humanity. Among them, global warming is arguably the most serious. According to the World Meteorological Organization, 2011–2020 was the warmest decade since the 1800s [1]. Global warming comes from human-induced emissions, mainly from burning fossil fuels [2]. As a significant contributor to the world economy, international tourism’s revenue was USD 1481 billion in 2019, a 4.8% year-on-year increase [3]. However, tourism emissions increased from 3.9 to 4.5 GtCO2e between 2009 and 2013, representing 8% of global emissions [4]. Tourism can only be sustainable in the long term if it transforms into a climate-resilient industry [5]. Therefore, there is an urgent need for the tourism industry to embrace a low-carbon pathway to mitigate global warming and ultimately achieve sustainable tourism. However, sustainable tourism is a continuous process that requires seamless collaboration among tourism stakeholders, including the business, local community, government, tourists, and international community [6]. Instead of focusing on only one aspect (economic growth), sustainable tourism considers a balance between economic, environmental, and social–cultural aspects to ensure the long-term sustainability of tourism development [7]. These considerations lead the tourism industry to achieve productivity while preserving the natural environment and maintaining the quality of life. However, under a business-as-usual scenario, tourism emissions are projected to increase by 169% between 2010 and 2050 [8]. Since reducing emissions is a challenge for sustainable development, this finding recognizes that sustainability practices in the tourism industry are still insufficient [5].
Sustainable tourism should optimize environmental resources, a vital element of tourism development [9]. However, accommodation is a resource-intensive tourism subsector [10][11]. As more than 70% of energy demand is consumed for electricity [10], accommodation is the second-highest emitter after transportation, accounting for 21% of tourism emissions [12]. This entry focuses on a particular type of accommodation, bed and breakfast (B&B), which has experienced rapid growth through sharing-economy platforms [13]. As a small tourism business owned and operated by a family, a B&B is an innovative sideline business built and developed from a family’s underutilized assets [14]. Unfortunately, many B&Bs have recently experienced a significant decline in guest numbers. For example, in Taiwan [15] and Northern Ireland [16], the occupancy rate was below 25% from 2013 to 2018. Oversupply and homogeneity are the reasons for this severe challenge [17][18]. Therefore, B&Bs need to build a competitive advantage in a saturated market by differentiating themselves from the competition with novel features. Recent literature has investigated whether environmentally friendly practices in hotels (e.g., waste management, energy efficiency) can increase customer satisfaction, a critical factor in hotel selection [19]. In fact, the adoption of renewable energy (RE) as a primary energy source is the least adopted by accommodations [20]. However, B&Bs can improve energy security by changing their energy sources while reducing energy costs with a short payback period, which has been implemented in real-world applications [21]. Using low-carbon energy in tourism destinations also contributes to community economies by generating revenue, creating jobs, and diversifying income [22]. Therefore, energy transition would not only reshape a B&B’s image as an eco-friendly accommodation business [23] but also facilitate the sustainable development of local communities.
In addition, the shift to RE has the potential to maintain the B&B’s competitive advantage by offering new service innovations continuously. Considering that visitors are more likely to stay at B&Bs than traditional hotels due to their authenticity [24][25] and social interaction [26], B&Bs can collaborate with the entire community to offer novel experiences that leverage and integrate local resources [27]. Another interesting innovation is the increasing adoption of electric vehicles (EVs), which is expected to increase six-fold between 2019 and 2030 [28]. As pioneered by several hotel chains [29][30], B&Bs can offer EV charging services at their facilities to proactively respond to market changes, generate new revenue streams, and develop more innovations. In terms of the internal conflict due to energy transition, B&Bs can take the opportunity of transgenerational ownership (succession from the old to the young generation). When the new management reevaluate their business strategy during this period [14][31], this would be an ideal time to conduct an energy transition. A significant reduction in RE costs, even below 3.0 US cents/kWh in some countries [32], means it is also an ideal time for initiating the transition. Unfortunately, despite these opportunities, RE is still underutilized in the accommodation industry. For example, only 17.3% of hotels in the Asia-Pacific region have installed renewable energy generation devices (REGDs) [33]. The most common reason for not deploying RE at the accommodations is the personal belief of (high) upfront investment [34]. Compared to other accommodations, the lack of capital reserves [35] and heavy dependence on the power grid in most B&Bs [36] pose a challenge to a successful energy transition. Furthermore, there is a lack of know-how in initiating energy transition in accommodation businesses [34], mainly due to several complexities. B&Bs have to balance fluctuations in energy production and consumption due to the intermittent nature of most RE energy sources and the occurrence of peak demand at certain times.
The hybrid RE system that combines two or more energy sources (e.g., solar and wind) can adequately manage these fluctuations [37]. However, a hybrid RE system, often equipped with batteries, increases the complexity of the problem compared to a single-source RE system. Further complexity arises from the variety of REGDs on the market, including conventional REGDs (e.g., solar panels and wind turbines) and unconventional REGDs (e.g., the Wind Tree). Both offer different tradeoffs that B&Bs must evaluate when transitioning to RE. B&Bs also need to manage the site availability since an installed RE system requires more land than a fossil power plant [38]. There is an urgent need for a decision-making framework to manage this complexity to ensure a resilient energy supply at an affordable cost. The decision should include the type and size of RE technologies for each suitable location on the B&B. The optimization approach provides good energy planning for ensuring sustainability [39]. Therefore, this entry proposes a practical approach to address the critical need for all B&B businesses to make optimal decisions on deploying hybrid RE technologies in their locations. This entry provides a model that integrates system components, including hybrid RE systems, batteries, the power grid, different ratios of self-sufficiency, and different RE technologies, to ensure a smooth energy transition. This integration provides B&Bs the flexibility to choose their preferred self-sufficiency as a target to be achieved by allocating a specific share of the energy demand to be met by RE and the rest by the power grid. In addition to bridging the gap between the high cost of RE technologies and the affordability of B&Bs, the model can also evaluate the tradeoff between conflicting goals, costs, and emissions when designing the system. This integration also assists the B&B in evaluating the tradeoff between conventional and/or unconventional REGDs and investigating the need to determine a specific size of batteries to regulate energy flow. The mixed-integer programming (MIP) model is developed as a proposed method to solve the problem. The model then demonstrates its capabilities in a case study on a simulated B&B facility.

2. Deploying Hybrid Renewable Energy at Bed and Breakfasts

There is limited research investigating the feasibility of using RE in accommodation businesses [40]. Among those papers, design system optimization, that is, identifying a reliable and affordable system configuration, has been the main focus. Due to highly complicated dynamics between the major factors (costs, system performance, and energy demand/supply management), designing such an RE system is challenging. Therefore, a cost-effective energy solution requires an appropriate combination of RE systems [41]. Generally speaking, simulation-based optimization is arguably the most popular tool for solving this kind of problem [42]. Hybrid optimization of multiple energy resources (HOMER) [43] is one of the most well-known software developed for such purposes, with cost minimization as the primary objective. At a high level, HOMER works by simulating various system configurations based on user inputs [44]. Limited by its optimization technique, HOMER does not always produce an optimal solution. To be precise, when there are a large number of system configurations, HOMER often identifies only a suboptimal solution. Some researchers [45][46] also use mathematical optimization models, and the results are more cost-effective than HOMER.
Despite these issues, several tourism-related papers have used HOMER to evaluate the usage of RE at accommodation facilities. Dalton et al. [47] conducted a cost study and evaluated the allocation of various configurations, including solar or wind energy (i.e., stand-alone RE), diesel-only, and diesel–RE. Using HOMER, these simulations revealed the feasibility of using renewables to meet all energy needs in an Australian resort. Another finding was that diesel–RE would cost 30% less than the other configurations. Furthermore, Dalton et al. [48] extended the research to investigate the current and future feasibility of a stand-alone hybrid solar–wind system using three tourist hotels as case studies. Güler et al. [49] used HOMER to determine the optimal size of RE technologies, including solar panels, wind turbines, batteries, and converters, for an uninterrupted power supply for a Turkish hotel. Since the hotel had a surplus RE (that is, RE neither consumed by the hotel nor saved in the battery) in certain time periods, it would be able to open up new revenue streams for the hotel. Fazelpour et al. [50] investigated the environmental benefits of deploying an RE system at an Iranian hotel with HOMER. When emission costs were considered, the most effective configuration changed from a diesel-only to a diesel–wind system. Using HOMER, Shezan et al. [51] analyzed the performance of a stand-alone diesel–wind system to power an eco-lodge in the highlands of Malaysia. In addition to the applicability of the RE system for supplying remote areas, the study also proves that the optimal allocation of renewables requires accurate weather data. Khan et al. [52] explored the competitiveness of various hybrid systems with HOMER for supplying energy in an island resort. A diesel–solar–wind–hydro system had the highest RE penetration compared to others. However, this configuration is not feasible since only a certain amount of land is available for solar panel installation. Finally, Hossain et al. [53] demonstrated that a hybrid energy system consisting of two or more RE sources used together (e.g., diesel–solar–wind system) is more viable than energy systems with only one type of energy source. Another finding was that energy demand may vary greatly depending on the season due to occupancy rates, which was also studied by Borowski et al. [54].
Another simulation-based tool called transient system simulation program (TRNSYS) [55] has also been used to study energy problems for tourism facilities. This software offers flexibility by allowing users to modify their models [56] or adjust the component library when necessary [57]. Buonomano et al. [58] used TRNSYS to simulate a geothermal–solar energy system at an Italian resort. The study proves the importance of oil tanks used as energy storage to regulate the fluctuating solar energy supply. Beccali et al. [59] confirmed the critical role of a battery in balancing the intermittency of renewables. Using TRNSYS, their results highlight that the battery can reduce the purchased electricity from the power grid by up to 61%. In addition to simulation, heuristic algorithms are also a popular tool for solving RE problems in accommodation facilities. Soheyli et al. [60] developed a multi-objective particle swarm optimization (PSO) algorithm to minimize costs, land requirements, and CO2 emissions for an Iranian hotel. According to their results, an integrated RE system for cooling, heating, and power generation is less costly than a separate system. Meschede et al. [61] showed that occupancy fluctuations between weekends and weekdays can significantly affect energy demand. Therefore, generating synthetic time series data of energy demand based on simulated occupancy and weather data would be useful in providing a realistic setting. The levelized cost of electricity (LCOE) is also used to evaluate RE configurations from a financial perspective as it is able to capture long-term economic sustainability.
Table 1 presents the key features of tourism accommodation papers on the system optimization of RE systems. It also includes the cost of energy for comparing the average cost per kWh for energy generation. From this table, findings and proposed contributions can be summarized as follows.
Table 1. Key features of selective tourism accommodation papers.
Prior Study Accommodation Types (No. of Room) Optimization Technique/Software Used Cost of Energy (USD/kWh) System Component Emission 6 Optimization PDW 7
SP 1 WT 2 OT 3 BT 4 GR 5 Size Location  
Dalton et al. [47] Resort (378) HOMER n/a ✓ ✓ x ✓ x ✓ ✓ x x
Dalton et al. [48] Resort (34) HOMER n/a ✓ ✓ x ✓ x x ✓ x x
Güler et al. [49] Hotel (133) HOMER 1.02 ✓ ✓ ✓ ✓ ✓ x ✓ x x
Fazelpour et al. [50] Hotel (125) HOMER 0.34 ✓ ✓ x ✓ x ✓ ✓ x x
Shezan et al. [51] Lodge (100) HOMER 0.27 x ✓ x x x ✓ ✓ x x
Khan et al. [52] Resort (n/a) HOMER 0.14 ✓ ✓ x ✓ x ✓ ✓ x x
Hossain et al. [53] Resort (268) HOMER 0.28 ✓ ✓ x ✓ x ✓ ✓ x x
Buonomano et al. [58] Resort (17) TRNSYS 0.21 ✓ x GW 8 ✓ x x ✓ x x
Beccali et al. [59] Hotel (20) TRNSYS n/a ✓ x STC 9 ✓ x x ✓ x x
Soheyli et al. [60] Hotel (n/a) PSO n/a ✓ ✓ x ✓ x ✓ ✓ ✓ x
Meschede et al. [61] Hotel (n/a) PSO 0.29 ✓ ✓ ✓ ✓ ✓ x ✓ x ✓
1 SP = solar panels; 2 WT = wind turbines; 3 OT = other REGDs; 4 BAT = batteries; 5 GR = the power grid; 6 Emission = emissions consideration; 7 PDW = peak demand on weekend; 8 GW = geothermal wells; 9 STC = solar thermal collectors; n/a = not available in the entry.
  • Most prior studies do not determine the optimal location for deploying REGDs. Although Soheyli et al. [60] investigate the total area required, they do not investigate which locations are suitable for each RE technology. The study proposes the optimal location-sizing decisions on deploying a hybrid RE system at the B&B location to fill this gap. To the author’s knowledge, this is the first study in tourism accommodations that determines the optimal size of the RE system for each suitable location.
  • While simulation often provides suboptimal solutions, all of the prior studies use simulation-based optimization using software [47][48][49][50][51][52][53][58][59] and heuristic algorithms (PSO) [60][61]. Therefore, the MIP model is developed in this entry as a proposed method to solve the problem.
  • Since the cost of implementing 100% RE is relatively high for some B&Bs, integrating the RE system with the power grid would be an attempt to bridge the high costs of RE and the affordability for B&Bs. Based on the literature, only the research of [49][61] include the power grid in the RE system. This integration also offers more flexibility in evaluating the conflicting objectives of energy planning (costs vs. emissions). Interestingly, almost half of the prior studies do not consider the volume of emissions that can be reduced in the RE system [48][49][58][59][61]. This entry attempts to fill this gap by integrating the power grid into the RE system to eventually reach 100% RE. Therefore, the model would allocate a specific portion of energy demands that RE must meet and the power grid supplies the rest (e.g., 50% RE, 60% RE, …, 100% RE).
  • All prior studies use a specific type of solar panel and/or wind turbine for the RE system. They neglect the existence of various REGDs on the market, especially new-generation RE technologies, which may provide higher energy generation at higher costs. This entry evaluates various types of solar and wind REGDs, both conventional and unconventional, to assist B&Bs in investing in RE technologies.
  • With regards to energy demand, only Meschede et al. [61] consider that higher occupancy on weekends creates a higher energy demand than on weekdays. This entry uses this finding when establishing energy demand data for the B&B.
In summary, various energy components are integrated into a model to ensure smooth energy transition, including hybrid RE, batteries, the power grid, different self-sufficiency targets, and different RE technologies.

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Subjects: Energy & Fuels
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :
Luki Trihardani
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Chi-Tai Wang
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Ying-Jiun Hsieh
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Entry Collection: Environmental Sciences
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Update Date: 30 Jun 2022
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