**2. Literature Review**

Due to economic growth, technological advances, and increasing demand, planning for energy is now a complex multi-variable, multi-objective problem. Accordingly, a variety of models are developed to solve the problem based on a different point of view worldwide [11]. While they have pros and cons, many of them cannot be considered as decision-making assistance tools. Also, some of them do not adequately reflect energy policies. For instance, they do not take into account the policies which the World Energy Council has proposed: e.g., by 2050, new technologies should generate about 37% of the total energy in the world [12].

Pollution and environmental problems caused by overuse of fossil fuels, especially for transportation, have exacerbated the need for alternative fuels. Romm [13] thoroughly investigated alternative fuels for transportation systems in the future. Arslan et al. [14] reviewed possible scenarios of supplying energy for cars rather than fossil fuels in Turkey. Also, Babtista et al. [15] studied short-term and long-term resources and road consumption scenarios in Portugal and found alternative fuels necessary for longer horizons. Sehatpour et al. [16] made a comprehensive research on fossil-fuel alternatives for light-duty vehicles and, based on a multi-criteria evaluation, concluded NG and biogas are superior options for the midterm in Iran. Santisirisomboon et al. [17] studied policies of carbon taxation to study the competitiveness of biomass energy with fossil fuels in Thailand.

Due to the importance of the problem, there are many decision support tools and simulation models available, such as the Vienna Automated System Planning Package, MESSAGE (Model for Energy Supply Strategy Alternatives and their General Environmental Impact) [18], the Long-range Energy Alternatives Planning system (LEAP), the MARKAL-EFOM Integrated System (TIMES), and the Energy PLAN [19]. These models allocate energy based on minimizing costs and priorities of demands. Environmental concerns, planning policies, and availability of energy resources can be defined as constraints [20].

A different application of these models is recorded in the literature. Strachan & Kannan [21] employed MARKAL-Macro (M-M) to study the long-term reduction of CO2 emissions in the UK energy sector. Liu et al. [22] applied the energy model of MESSAGE-China to study the trend of novel energy technologies and their contributions to GHG reduction in China. Ball et al. [23] employed the energy system model MOREHyS to plan spatially and temporally a set-up of a hydrogen-based transport infrastructure system in Germany for the horizon of 2030. Chiodi et al. [24] analyzed the competing demands for land-use, import dependency, availability of sustainable bioenergy, and economics under the framework of an Irish energy systems model of TIMES. Tavakoli et al. [25] and Valinejad et al. [26] found the energy model system as a convenient and user-friendly approach to analyze energy policies.

Goal programming (GP) is a current multi-objective optimization method, which can address multi-criteria decision analysis (MCDA) problems. Jayaraman et al. [27] used a GP model for efficient allocation of labor resources considering the criteria of economic, energy, and environment in the United Arab Emirates using the approach of prioritizing areas for strategic planning and resource allocation for the sustainability of the strategies. They presented mathematical and economic indicators in order to digitize criteria. Kumar et al. [28] developed an insight into the application of various multiple criteria decision-making (MCDM) methods in the renewable energy sector. Zografidou et al. [29] programmed a GP model with all possible weight combinations to analyze energy allocation and budgeting in Greece and provided a multi-dimensional decision-makers' framework to determine the optimal budgeting mix to attract investors and guarantee the success of the venture. Kumar et al. [30] optimized priorities among suppliers considering the three dimensions of economic, social, and environmental sustainability in India. They integrated fuzzy AHP and fuzzy multi-objective linear programming approaches. Other extensions to GP are also applied to energy suitability problems, e.g., stochastic goal programming [7], weighted goal programming [31], fuzzy goal programming [32], and fractional goal programming [33]. Flisberg et al. [34] kept a schedule of the harvest and chipping operations of forest fuels in Sweden and studied alternatives. They employed indicators for all operations and solved them by a decision support system. Mekonnen et al. [35] explored the commutation between domestic and other applications of biomass energy sources in Ethiopia by employing a no-separable-farm household model in which labor energy is also considered in the stages of collection and farming. They concluded that the application of dung as a domestic fuel source negatively affects the value of harvested crops, however, the application of on-farm fuelwood is compromised with an increase in the value of agricultural output. Chong et al. [36] analyzed factors which had an impact on the energy consumption growth in Guangdong Province by employing the logarithmic mean Divisia index I (LMDI) based on the physical processes of energy utilization from the source to end-users. They concluded GDP and population are the most influential factors in energy consumption.

Atabaki and Aryanpur [37] developed a multi-objective linear planning model to analyze Iran's long-term power sector development from economic, environmental, social,

and sustainable perspectives. Three objective functions in this study are included: minimizing the cost, carbon production, and maximizing the job market. To assess expert-based weights, analytical hierarchy process (AHP) methods are employed and, moreover, to support the decision-makers, the MESSAGE model as a planning tool is used to define different scenarios for developing clean technologies. The results show that a sustainable scenario leads to high technology diversification. Furthermore, the combined cycle would be the dominant option in Iran's long-term generation mix. In addition, power generation from non-hydro renewables, solar PV in particular, should grow faster than the total electricity demand. The findings indicate that the economic scenario fulfills Iran's commitment to 4% reduction of emissions compared to the current trend. However, the sustainable and environmental scenarios would achieve the superior 12% reduction goal. Multi-objective analysis shows that moving away from one's objective optimum value leads to significant improvements in other objective values. Adnan et al. [38] formulated a multi-objective scheduling problem to optimize the allocation of renewable energy resources and electric vehicle (EV) charging stations.
