This review proposes a novel integration of game-theoretical methods—specifically Evolutionary Game Theory (EGT), Stackelberg games, and Bayesian games—with deep reinforcement learning (DRL) to optimize electricity markets. Our approach uniquely addresses the dynamic interactions among power purchasing and generation enterprises, highlighting both theoretical underpinnings and practical applications. We demonstrate how this integrated framework enhances market resilience, informs evidence-based policy-making, and supports renewable energy expansion. By explicitly connecting our findings to regulatory strategies and real-world market scenarios, we underscore the political implications and applicability of our results in diverse global electricity systems. By integrating EGT with advanced methodologies such as DRL, this study develops a comprehensive framework that addresses both the dynamic nature of electricity markets and the strategic adaptability of market participants. This hybrid approach allows for the simulation of complex market scenarios, capturing the nuanced decision-making processes of enterprises under varying conditions of uncertainty and competition. The review systematically evaluates the effectiveness and cost-efficiency of various control policies implemented within electricity markets, including pricing mechanisms, capacity incentives, renewable integration incentives, and regulatory measures aimed at enhancing market competition and transparency. Our analysis underscores the potential of EGT to significantly enhance market resilience, enabling electricity markets to better withstand shocks such as sudden demand fluctuations, supply disruptions, and regulatory changes. Moreover, the integration of EGT with DRL facilitates the promotion of sustainable energy integration by modeling the strategic adoption of renewable energy technologies and optimizing resource allocation. This leads to improved overall market performance, characterized by increased efficiency, reduced costs, and greater sustainability. The findings contribute to the development of robust regulatory frameworks that support competitive and efficient electricity markets in an evolving energy landscape. By leveraging the dynamic and adaptive capabilities of EGT and DRL, policymakers can design regulations that not only address current market challenges but also anticipate and adapt to future developments. This proactive approach is essential for fostering a resilient energy infrastructure capable of accommodating rapid advancements in renewable technologies and shifting consumer demands. Additionally, the review identifies key areas for future research, including the exploration of multi-agent reinforcement learning techniques and the need for empirical studies to validate the theoretical models and simulations discussed. This study provides a comprehensive roadmap for optimizing electricity markets through strategic and policy-driven interventions, bridging the gap between theoretical game-theoretic models and practical market applications.

Inorganic tin–lead perovskites with low bandgap (1.2–1.4 eV) are desired absorber materials for solar cells owing to their ideal bandgap and compositional stability. However, such tin–lead perovskites are currently subject to inferior power conversion efficiency (PCE) and the origin remains unclear. Here, for the first time, we report the metal-cation-derived unsynchronized crystallization behavior of inorganic tin–lead perovskite, exemplifying by a representative composition CsPb0.7Sn0.3I3. A tin-perovskite-targeted crystallization modulation agent, 1-(4-fluorophenyl) piperazine (1-4FP), is introduced to synchronize the B-site alloying through its strong targeted bonding with SnI2, resulting in substantially enhanced film quality with better morphology and photoelectrical properties. Furthermore, first-principles molecular dynamics simulations reveal that the agent regulates the crystallization route toward the pure phase of CsPb0.7Sn0.3I3 by suppressing the preforming of tin perovskite. With our proposed approach, the best device attains PCE of 17.55%, which is record-high for inorganic tin–lead perovskite solar cells. In addition, treated devices show excellent stability with only 10% and negligible loss after being exposed to 1 sun intensity for 700 h and being stored in N2 after over 4000 h, respectively. Our findings open a new avenue of crystallization route design in inorganic tin–lead perovskites, so as to obtaining high-quality perovskite films and associated solar cells.

Since real‐world multiview data frequently contains numerous samples that are not observed from some viewpoints, the incomplete multiview clustering (IMC) issue has received a great deal of attention recently. However, most existing IMC methods choose to zero‐fill the missing instances, which leads to the failure to exploit information hidden in the missing instances, and high‐order interactions between various views. To tackle these problems, we proposed an effective IMC method using low‐rank tensor ring completion, which was demonstrated to be powerful in exploiting high‐order correlation. Specifically, we first stack the incomplete similarity graphs of all views into a 3^rd‐order incomplete tensor and then restore it via the tensor ring decomposition. Next, using an adaptive weighting technique, we apply multiview spectral clustering to all entire graphs in order to balance the contributions of different viewpoints and identify the consensus representation for grouping. Finally, we employ the alternating direction method of multipliers (ADMM) to optimize the suggested model. Numerous experimental findings on numerous different datasets show that the suggested approach is superior to other cutting‐edge approaches.

In recent years, the organic–inorganic hybrid perovskite community has been widely employed as the photo-active layer in optical-electronic devices. The black α-phase formamidinium lead iodide (FAPbI3) is the most popular perovskite for realizing high-efficiency solar cells due to its suitable bandgap. However, the issue of stability is also a concern in the research on FAPbI3 solar cells. In this study, different ammonium ions, such as butylamine (BA), guanidine (GA), and butylene diamine (BDA), which are commonly used to construct two-dimensional perovskites, including Ruddlesden–Popper, Dion–Jacobson, and alternating cations in the interlayer space, respectively, were introduced in the fabrication of FAPbI3 using a sequential deposition method. Several structures of PbI2 precursor films were formed by introducing the aforementioned ions, which exhibited different arrangements and connection modes in lead iodides. BA-PbI2 precursor films exhibited higher specific surface areas, which were beneficial to the diffusion, ion exchange, and sequential reaction of FA+. The BDA-PbI2 precursor film slowed down the sequential reaction of FAPbI3 because of reduced van der Waals bonds. The nucleation dynamics and degradation processes of perovskites were deeply investigated in this study. Solar cells based on BA-PbI2, GA-PbI2, and BDA-PbI2 were also fabricated.