## 1 Introduction
### Problem Statement  
Shifting towards green energy is vital for meeting sustainability and climate objectives. However, it faces obstacles related to financial and operational issues. The energy sector involves uncertainties linked to policy changes and market fluctuations that influence investment portfolio management. This research explores strategies for optimizing portfolios in energy amidst changing policy and market conditions. The inconsistency of government policies can challenge investors in the energy sector. Projects in this field often depend on incentives like subsidies and regulatory backing that may change due to political shifts or market regulations [13; 14]. Sudden policy alterations, like feed-in tariffs or tax credits, can disrupt the profitability of endeavors and expose them to significant financial risks [15]. Similarly, how carbon pricing mechanisms are structured to promote investments can affect the returns on renewable portfolios [16]. The unpredictability of energy prices can complicate investments in energy sources even though they typically offer stability compared to traditional forms of energy production like fossil fuels [17]. Factors such as spot pricing and fluctuating peak demand in electricity markets play a role in this scenario, where wind power's intermittent nature adds to investors' financial uncertainty [18]. The challenges of forecasting energy expenses come from the difficulty in maximizing investment returns in this changing landscape. The rapid advancement of technology challenges the costs linked to energy infrastructure [19]. Progress in panels or battery storage could quickly make current investments obsolete. This tech-related uncertainty makes it tricky to figure out where to invest money, as investors must balance investing in technologies with maintaining existing infrastructure [20]. Investors' individual risk preferences add another layer of complexity to these challenges. Different investors have varying risk tolerance levels; this requires portfolio managers to assess the balance between risk and potential returns [21]. Traditional approaches, like Modern Portfolio Theory (MPT), often fail to address the intricacies associated with energy investments exposed to policy-related risks [22]. In addition, models that handle uncertainty with a mix of chance and resilience might bring about some challenges and need substantial data. Given these concerns identified earlier in the introduction, there is a need to create a model for investing in energy that considers factors such as regulatory uncertainties, market fluctuations, technological advancements, and investor references. As reported by [PERSON], [PERSON] [23], conventional methods for optimizing renewable energy  
Figure 1: Maximizing profits and reducing risk in investment portfolios. portfolios use deterministic models based on the assumption of fixed policy conditions and market prices. Even though these models offer key insights, they cannot account for the dynamic and uncertain real-life cases [24]. To account for this shortcoming, [PERSON], [PERSON] [25] proposed using stochastic optimization that accounts for probabilistic scenarios to accommodate these uncertainties. Few studies have used risk metrics such as Value at Risk (VaR) to quantify potential losses [26]. The proposed research uses optimization techniques such as stochastic programming and risk measures like VaR and Conditional Value at Risk (CVaR) to balance returns with risks under unpredictable scenarios. Data from known sources such as IRENA and Bloomberg will be used to build and confirm the model's accuracy. This research aims to develop a financial optimization model using real-world data to address uncertainties in the energy sector effectively. This study employs a quantitative research design to create a financial optimization model deployed with real-world data to identify and quantify uncertainties in the energy sector.