Renewable energy (RE) is gaining great attention nowadays, as opposed to traditional energy, such as fossil fuels, which have various negative impacts and issues. RE faces multiple risks and challenges, so these risks need to be evaluated and mitigated such as technical risks, environmental risks, security risks, policy risks, and technological risks. This study proposed a multi-criteria decision-making (MCDM) method for assessing RE risks. The MCDM method is integrated with neutrosophic sets (NSs) to deal with inconsistent data in the evaluation process. Two MCDM methods are used in this study: Entropy and Ranking of Alternatives with Weights of Criterion (RAWEC). The neutrosophic entropy is used to compute the criteria weight, and the neutrosophic RAWEC method ranks the alternatives. This study applied the proposed method to two case studies. In the first case study, the RE risks are ranked. In the second case study, various strategies are proposed by blockchain, artificial intelligence (AI), the Internet of Things (IoT), big data, and zero-trust to reduce RE risks. There are six main factors; 31 sub-factors and 19 risks are used in the first case study, and 19 factors and 20 strategies are used in the second case study. The sensitivity analysis was conducted to show the stability of the rank. The proposed methodology was compared with MCDM methods such as neutrosophic TOPSIS, neutrosophic VIKOR, and fuzzy CoCoSo. The results show various proposed strategies can reduce RE risks.