## 1 Introduction
### Literature Review  
Numerous non-deterministic programming approaches have been used to handle uncertainties in EES. Table 1 lists some previous studies on non-deterministic programming problems. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Ref. No. **} & \multicolumn{3}{c}{**Non-Deterministic Programming**} & \multicolumn{3}{c}{**Research Area**} & \multicolumn{3}{c}{**Considering Traffic Sector**} \\ \cline{2-7}  & **TSP** & **RTSO** & **FPP** & **RFPP** & **Energy Systems** & **Others** & **Yes** & **No** \\ \hline
[5] & \(\surd\) & & & & \(\surd\) & & & \(\surd\) \\
[17] & \(\surd\) & & & & \(\surd\) & & & \(\surd\) \\
[18] & \(\surd\) & & & & \(\surd\) & & \(\surd\) \\
[19] &  
Among them, two-stage stochastic programming (TSP) has been widely used to tackle uncertainties expressed as a probability distribution [3; 16; 17; 18]. For instance, [PERSON] et al. [16] proposed a two-stage programming method to optimize electric power systems considering air pollutant emissions and CO\({}_{2}\) mitigation. [PERSON] et al. [19] developed a two-stage integer linear program model to optimize distributed energy systems, enable cost-optimized design decisions regarding technology selection and sizing before the determination of uncertain parameters. [PERSON] et al. [20] presented a two-stage stochastic method for managing the energy reserve of a microgrid system, with an emphasis on the different levels and sources of uncertainties. However, the TSP is unable to regard the variability of stochastic recourse values because it is based on the assumption that the manager adopts a risk-neutral attitude. Thus, TSP may become infeasible when managers are risk-averse under the conditions of high variability [21]. The robust two-stage stochastic optimization (RTSO) method is an attractive method for tackling the above shortcomings of TSP. It is specifically used to penalize the costs of the second-stage that are greater than the expected values and capture the associated risk of stochastic uncertainties [22; 23; 24]. In the last few decades, the RTSO method has been extensively employed in many research areas, such as supply chain systems, electric power systems, solid waste management, and water resource allocation [3; 25; 23]. For example, [PERSON] and [PERSON] [26] developed a stochastic robust programming method for improving retail supply chain planning through supply chain coordination, risk reduction, vendor selection, and sustainability assessment. [PERSON] et al. [24] proposed a robust TSP method for tackling water resource allocation problems, enabling the handling of uncertainties expressed as stochastic, and analysis of policy scenarios regarding economic penalties for the violation of predefined policies. However, in the real world, many economic parameters and energy demands often exhibit ambiguities, which can be shown as fuzzy sets [27; 28]. Fuzzy possibilistic programming (FPP) theory can effectively address the fuzzy uncertainties of goals and constraints [29]. For instance, [PERSON] et al. [30] employed an FPP method for closed-loop recycling collection networks, in which some uncertainty information (e.g., distance, capacity, demand, costs, as well as returned products quantity) were tackled by FPP. [PERSON] et al. [31] proposed an interval FPP method for managing China's energy systems with CO\({}_{2}\) emissions constraints, which could address the uncertainties presented in terms of fuzzy-boundary intervals in both the objective and constraints. However, an FPP algorithm is unable to ensure the minimization of objective function under all conditions because minimizing of the expected objective value is used as the objective function. This can result in significant deviations of the optimized decision schemes, even in the event of system optimization failure. Robust fuzzy possibilistic programming (RFPP) was developed by [PERSON] et al.