# Review of Intelligent Methods and Their Potential Use towards a Smart Grid Negotiation Framework
## 3 Discussion
### Comprehensive Review of the Selected Studies
The findings indicate that, in three out of six cases, the proposed algorithm achieves the lowest costs for purchasing electrical energy and maximizes agent profits from electrical energy sales in the market. In one case, both algorithms act similarly. The suggested methodology holds the potential to facilitate autonomous energy transactions and foster the development of an intelligent energy market. Subsequent endeavors should focus on analyzing and substantiating the optimality and convergence of the proposed algorithm. In [37], q-ROF set information is used on the prioritization of AO which is based on the AATN and AATCN. q-ROF sets cover wide information, therefore they are more flexible and suitable. The study proposes two new AOs based on q-ROF, AATN, and AATCN. q-Rung Orthopair Fuzzy Aczel-Alsina Averaging (q-ROFPAAA) and q-Rung Orthopair Fuzzy Aczel-Alsina Geometric (q-ROFPAAG) operators are proposed to involve the information's priority weights. An MCGDM algorithm was developed based on the proposed operators since the prioritization of attributes and experts is of crucial importance in decision-making problems. MCGDM algorithm was applied to a real-life problem concerning the selection of energy resources and the proposed concepts compared with various q-ROF-based AO. The rival AO solved the example but failed to utilize prioritization, showcasing the contribution of the proposed approach. Additionally, AO relying on Pythagorean and Intuitionistic Fuzzy Sets, failed to solve the given problem highlighting the diverse nature of q-ROF sets and the proposed operators. In conclusion, q-ROFPAA operators exhibit superior performance compared to existing AOs due to their consideration of prioritization relationships during information aggregation. Additionally, they have the capability to encompass a broader spectrum of information, unlike other operators that neglect certain data. Moreover, the proposed operators represent a generalization of the previous AO. Lastly, in [38], a review of the application of PSO-based algorithms in DSM fields discusses the challenges of previous work. The variants of PSO are used to address the high complexity and uncertainties of the DSM modeling within the acceptable timeframe while considering user comfort. New prospects are discerned, paving the way for the advancement of PSO methods in forthcoming research initiatives. In a systematic classification according to the application of PSO variants in DR programs, the Binary Particle Swarm Optimization (BPSO), MOPSO, and Enhanced Leader Particle Swarm Optimization (ELPSO) variants of PSO algorithm are proposed as suitable in RTP strategies. Specifically, BPSO qualifies in P2P trading between prosumers and consumers. Although the study is limited in DSM, the study establishes the significance of MA as a prominent component of AI in addressing optimization challenges. These methodologies demonstrate a level of precision suitable for addressing a wide array of optimization problems, yielding near-optimal solutions with minimal computational burden. Their efficiency surpasses that of classical approaches due to their capacity to traverse the search space, approximating a globally optimal solution. MA are applicable to problems featuring numerous decision variables and can be seamlessly adapted to scenarios with multiple constraints, including real-world engineering design problems.