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Smart-grid optimization increasingly integrates digital twins, reinforcement learning, and cyber-physical resilience analytics into a shared research space. This convergence reflects a shift from isolated grid-control studies toward data-intensive, adaptive, and resilience-oriented infrastructure analytics. This bibliometric review maps the research landscape at the intersection of these three domains. It quantifies publication trends, identifies thematic clusters, and interprets emerging fronts in smart-grid optimization research. A structured bibliometric protocol was applied to 28 peer-reviewed journal articles published between 2017 and 2025. Co-word analysis, citation analysis, thematic clustering, and citation burst interpretation were used to organize the evidence base. The analysis reveals rising publication activity after 2019 and three dominant thematic clusters. Digital twin research centers on simulation and cyber-physical representation, reinforcement learning emphasizes adaptive grid control, and cyber-resilience analytics focuses on vulnerability, disruption, and recovery. The field is moving toward integrated frameworks that combine digital twins and reinforcement learning for proactive, cyber-resilient grid optimization. These fronts suggest a growing need for real-time, explainable, and safety-aware analytics.

Smart-grid optimization increasingly uses artificial intelligence, automated control, and data-driven market coordination to manage low-carbon electricity systems. These developments create new opportunities for flexibility, but they also raise unresolved questions about equity, accountability, and justice. Existing reviews often examine energy justice, smart-grid optimization, algorithmic governance, and flexibility markets as separate domains. This fragmentation limits understanding of how optimization models and market rules may reproduce or reduce social inequalities. This article critically reviews the literature on justice-aware smart-grid optimization and develops a structured future research agenda. It focuses on how energy equity, algorithmic governance, and low-carbon flexibility markets can be integrated into future smart-grid research and policy. A targeted review of 26 peer-reviewed articles published between 2017 and 2025 integrates perspectives from energy justice, smart-grid studies, AI governance, and electricity market design. The review identifies conceptual tensions, methodological gaps, and governance challenges rather than presenting new empirical data. The review shows that justice is often operationalized narrowly in optimization, while broader concerns about recognition, participation, and accountability remain underdeveloped. It also finds that flexibility markets can distribute benefits unevenly unless market design explicitly addresses barriers to access and agency. A more interdisciplinary and justice-centered approach is needed to guide smart-grid optimization. The proposed agenda calls for stronger integration between technical modeling, energy justice theory, participatory governance, and regulatory innovation.