# Review of Intelligent Methods and Their Potential Use towards a Smart Grid Negotiation Framework
## 3 Discussion
### Comprehensive Review of the Selected Studies
The experimental findings illustrate the effectiveness of the proposed ANS regarding the number of exchanged offers, the achieved joint utility, and the Pareto efficiency of negotiated contracts. However, this study's automated negotiations did not account for the quantities demanded by buyers or provided by suppliers. In practice, sellers supply products to multiple buyers, while buyers may distribute their demand across various sellers. The quantity aspect complicates the negotiation decision-making process as it interacts with factors like price. Although the proposed approach enables agents to negotiate with multiple opponents simultaneously, each negotiation is treated independently. It could be more advantageous if agents could leverage information from one negotiation into another. The utility function employed in this approach was relatively simplistic and rigid, influencing the outcomes of automated negotiations. To prevent unfavorable contracts, a minimum acceptable value for each issue is established. In a real Business-to-Business environment, a more sophisticated utility function should be adopted to enhance the utility of the proposed approach. A study on a topic relevant to SG and energy market concepts is presented in [19]. An AABS is devised and assessed concerning the energy exchanges between an integrated smart building and a utility grid system. The building adopts RES and ESS technologies and acts as a seller when the renewable energy has a surplus after supplying all demands. The agent bridges the smart building and the utility power grid. A Fuzzy controller is employed to estimate the necessary power under dynamic circumstances. The proposed model is not based on AI but on mathematical models expressed by equations. The strategy is adopted by a negotiation agent which explores various attitudes in bid determination. An important factor of the negotiation model is the \"eagerness\" of the involved sides to reach an agreement, behaving as human traders. For example, if the eagerness factor has a large value, the agent aims to accomplish the transaction within the set deadline at any cost, provided it aligns with the reservation price. Conversely, a low eagerness value suggests that the agent is willing to risk not making the sale by the deadline, opting instead to wait for a higher selling price. Comfort level and the battery's State Of Charge (SOC) are dynamic factors affecting the outcome of the negotiation. The agents adapt their behavior according to real-time situations and should be efficient computationally, relying on the hypothesis of bounded instead of perfect rationality. The proposed method offers customers more logical trade-off solutions by taking into account various dynamic factors across the entire system and emulating human behavior. The efficiency of the model proposed in making sound negotiation decisions is evident in its ability to lower customers' energy costs or enhance their economic profit. An enhancement of [19] is discussed in [21], where an AABS has been formulated and assessed for potential deployment within a cohesive system combining smart building technology and utility grid integration; the negotiation agent utilizes an adaptive strategy to explore various approaches in determining bids. However, a significant drawback of the AABS-based negotiation agent is its heavy reliance on assumptions regarding the negotiation environment, thus complicating generalization. A PSO-AABS negotiation agent is developed to eliminate the aforementioned limitation. The proposed agent approaches the tendency of the traders to maximize their own payoffs while ensuring the achievement of the agreement. The models demonstrate efficacy in generating rational negotiation choices while simultaneously diminishing customers' energy expenses or enhancing their economic gains. Intelligent technologies such as GA and NN can be utilized in the advancement of the negotiation agent. By comparing options, the most appropriate intelligent technology for negotiation can be identified.