The selection of sustainable indicators is crucial in measuring and understanding the required targets within the theme of sustainability for an energy system. This is because sustainability, as a term, is used in several fields and covers a variety of indicators based on the problem’s context and identity. Each researcher looks at sustainability from their own perspective and selects the indicators which align best with their objectives and their understanding of the topic. This paper aims to implement a systematic approach to choosing the sustainable indicators for Bahrain’s electrical production with renewables. The proposed framework analyses the frequency of indicators in a sample of 73 studies and screens them in accordance with the selection principles and experts’ views. The results reveal 15 indicators with strong relevance to sustainable growth for the power sector with renewables. These indicators are classified as either qualitative or quantitative, depending on our case study’s context and the appropriate practice according to the literature. Finally, each of the selected indicators was defined to reflect its intended purpose in our study, since the common practice within the present literature is to provide such indicators without explaining their actual purpose.

Bahrain, like other GCC countries, has strong potential for solar energy, but the lack of available land makes it difficult to build large-scale solar farms. Another major challenge is attracting investors and encouraging private-sector involvement in renewable energy projects. To address these issues, Bahrain needs a clear and reliable business framework with transparent tariff policies that make investment more attractive, while also supporting the goals of Bahrain’s Economic Vision 2030 and its pledge to reach net-zero emissions by 2060. In this regard, rooftop-based Virtual Power Plants (VPPs) offer a practical solution, as they enable distributed generation that increases solar adoption while reducing both space and investment challenges. This paper proposes a multi-stage framework for establishing a VPP model for off-site solar deployment. The first VPP model is suggested to be in AlJuffair, Capital Governorate one of the highest population densities in Bahrain. A feasibility study was performed on three different power capacities 0.5MW, 1MW, and 1.5MW using PVsyst software and economic metrics such as Net Present Value (NPV), Discounted Payback Period (DPBP), and annual saving. The results of the feasibility study are then reflected on two VPP structuring options: Net metering and Time-of-Use Feed-in-Tariff (TOU FIT). Selecting the appropriate option was based on economic attractiveness and the sides that will benefit from adopting this pricing option: investors, regulator, and consumers. The results showed that TOU FIT with off-peak tariff is more attractive for investors with NPV between BD 140K and 543K, DPBP between about 10 to 8 years depending on the VPP size. Finally, a consumer with 20MWh can save roughly BD 41K-69K annually utilizing from the dynamic electricity prices.

Liquefied natural gas (LNG) procurement has become increasingly complex due to long maritime lead times, volatile spot markets, and structural shifts in global energy prices. For countries exploring LNG imports, such as Bahrain, long-term procurement decisions must balance cost efficiency with supply reliability under significant operational and market uncertainty. Traditional inventory and rule-based procurement approaches are limited in this setting because they typically assume relatively stable price dynamics and cannot adapt effectively to major market regime changes. To address this challenge, this study proposes a regime-aware reinforcement learning framework for long-horizon LNG procurement, employing the maximum-entropy Soft Actor–Critic (SAC) algorithm within a realistic simulation environment capturing more than five decades of heterogeneous price dynamics while modeling Bahrain’s LNG import system. To improve learning stability across historically distinct price regimes, the study introduces a relative-value reward design with strictly past-dependent rolling normalization. Two SAC policies are evaluated to examine the trade-off between procurement cost and operational reliability: a balanced policy emphasizing supply security and a cost-focused policy emphasizing stronger cost reduction. Zero-shot multi-regime evaluation across fifteen rollouts shows strong out-of-sample performance. The balanced policy achieves an average procurement cost of approximately $2.1B ± $0.37B while maintaining perfect service reliability and zero shortages, corresponding to roughly 80–85% lower cost than a traditional (s, S) policy and 95–97% lower cost than a myopic price-trigger strategy. The cost-focused variant further reduces procurement cost to approximately $1.7B ± $0.36B, with a modest decline in service reliability to about 0.96. Overall, the results show that regime-aware reinforcement learning can lower LNG procurement costs while maintaining reliable operations in changing market conditions.

Sustainable energy planning for power generation with renewables requires a multidimensional approach since it directly impacts several sectors such as the national grid, the environment, and the economy. This paper suggests an intelligent decision based on a combination of the analytical hierarchy process (AHP) and artificial neural networks (ANN) to assist policymakers in evaluating future renewable energy investments for power generation. A case study of Bahrain's power system is used to illustrate the usefulness of the proposed planning approach and also to discuss its efficiency. The AHP model outcomes revealed that wind turbines are the most appropriate technology for Bahrain with 32.5% priority, followed by PV and CSP with 32.2% and 16.3%, respectively. Then, the ANN model was structured based on the generated scenarios from the AHP model, and it reached its best performance after 384 cycles. The integrated approach overcomes some of the AHP's limitations and provides an intelligent tool for advanced assessment of a sustainable power system.