# Review of Intelligent Methods and Their Potential Use towards a Smart Grid Negotiation Framework
## 3 Discussion
### Comprehensive Review of the Selected Studies
Negotiations occur sequentially between the seller and individual buyers. Simulations within this framework, coupled with agent modeling, enable prosumers to enhance their revenues while facilitating affordable electricity access for the community. Linear heuristic strategy proved to provide similar results with Boulware but in a shorter time. The penetration of DER and deferrable loads such as EV introduces new challenges in the operation and control of modern power systems. In [34], an event-driven autonomous distributed architecture with real-time communication between a Transactive Agent (TA) communicating with multiple HEMS agents in real time where the residential customers possess generation and storage assets as well as diverse loads is introduced. For this reason, a Transactive Demand Response (TDR) algorithm is deployed. HEMS agents negotiate with the TA to determine their best response to the DR request, using a Fuzzy inference engine to satisfy the users' comfort levels and preferences in responding to rewards offered by the TA. The DR scheme is meticulously designed to adhere fully to the IEEE2030.5 interoperability standard, enabling customers to choose their DR compliance level and offering incentives to optimize their engagement. Real-world smart meter data from Hydro Ottawa's service area was utilized to model customer behavior. Employing k-means clustering, customers were categorized, and this information was leveraged by the TA to initiate the transactive negotiation process with each HEMS agent. The TDR algorithm integrates a flexible reward function to accommodate diversity in both power consumption and TDR compliance, facilitating a more precise quantification of customer behavior. TA engaged in negotiations with customers' HEMS agents utilizing event-based optimization techniques and a reward mechanism. The agents' response to DR requests led to a reduction in consumption, aligned with compliance levels linked to their comfort preferences, as articulated through Fuzzy rule bases. The findings indicate the effectiveness of the proposed scheme, suggesting that full capability is a viable approach for vendors and solution providers to realize the SG concept. A negotiation algorithm is proposed in [35], where users and end-users are modeled as prosumers. A multi-agent scheme enables the modeling of each user as a generator, consumer, or both and manages energy trading within the local market. The multi-agents scheme consists of a Multi-Objective Optimization algorithm, a Pareto front, and an FDM algorithm. The Multi-Objective Optimization algorithm evaluates several scenarios of buying or selling energy to or from the main grid and other agents. Each electrical load profile and electrical producer profile is treated as a decision variable and assessed within an objective function, aimed at maximizing earnings from electrical energy sales while minimizing high-cost electrical energy purchases. The evaluation outcomes are represented in the Pareto front to determine the optimal participation strategy in a free market, facilitated by the FDM algorithm. Here, Fuzzy sets are incorporated through partial values of elements defined via membership functions. A Pareto front solution is conceptualized as a continuous, monotonically decreasing function, akin to a membership function, aiming to identify the optimal solution in the optimization process. Each membership function is characterized on a scale ranging from 0 to 1, denoting compatibility and incompatibility, respectively. Hence, the solutions identified through the Pareto front graph can be characterized as a membership function. The FDM determines the decisions regarding buying or selling electrical energy. The proposed algorithm, employing the Multi-Objective Bat Algorithm (MOBA), was compared with a Multi-Objective Particle Swarm Optimization (MOPSO) algorithm across six instances of buying and selling electrical energy.