## 4 Blockchain Application in SG 2.0
### Prosumer Energy Management Algorithms
#### 4.6.1 Prosumer Genetic Algorithm  
Genetic algorithms are considered an attractive research issue due to their ability to solve the DSM and complicated economics. Furthermore, PGA is applied to increase energy transfer efficiently at a calculated level. GA has been involved in many areas of the energy system to solve optimization problems. The goal of the schedule should be to meet all plant and system constraints while meeting the demand for load at the lowest operating cost. Genetic engineering and development focus on PGA improvement strategies. In [129], the authors presented the PGA as a potential solution due to its ability to overcome the complex problem of improvement and is efficiently employed in various fields and sectors. The authors stated that the diversity of problem formulation is one of the essential advantages of PGA compared to other optimization techniques such as linear optimization or dynamic programming. This means that the PGA can deal with different types of restrictions. First of all, the strength of each Decentralized Generator (DG) must be kept within its range and handle various types of prosumer energy management concerns. In [133], the authors proposed PGA-controlled and supervised in real-time Decentralized Generators (DGs) and load transmissions based on models and time constraints associated with start time rather than optimum efficiency. A GA-based approach was proposed to control energy demand. The load usage is governed by taking into account the set point: the load that the consumer  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Prosumer-Optimization** & **Domain and Desired Objective** & **Model** & **Findings** & **Refs.** \\ \hline \multirow{4}{*}{Prosumer Genetic Algorithm (PGA)} & Domain: Residential energy management system. & \multirow{4}{*}{A hybrid-renewable generation and ES load were controlled and handled using a Genetic Algorithm (GA).} & \multirow{4}{*}{We are dividing devices into clusters.} & \multirow{4}{*}{[128; 129]} \\  & It is concerned with the cost of electricity and reducing the peak-to-average ratio. & & & \\ \hline \multirow{4}{*}{Prosumer Mixed Integer  Linear Programming (PMILP)} & Domain: Energy-management System and Grid connection & The energy management approach for residential based on renewable energy. & Peak-to-average ratio cost & \multirow{4}{*}{[130; 131]} \\  & aimed to reduce the peak-to-average ratio cost using renewable energy. & & & \\ \hline \multirow{4}{*}{Prosumer Particle Swarm Optimization (PPSO)} & Domain: Appliance scheduling. & Produce electricity and handle energy demand. & A reasonable deal was made regarding benefits and user comfort. & \multirow{4}{*}{[132; 133; 134; 135]} \\  & Aimed to reduce energy costs and improve consumer satisfaction. & energy demand. & & \\ \hline \multirow{4}{*}{Prosumer Linear programming (PLP)} & Domain: Residential energy management system. & Energy use scheduling to avoid maximum load times based on ESS. & Tasks are to use ESS off-grid and then modify them during peak hours. & \multirow{4}{*}{[136; 137; 138; 139; 140; 141]} \\  & Tends to reduce the electricity bills and peak-to-average ratio. & & & \\ \hline \multirow{4}{*}{Prosumer Integer Programming (PLP)} & Domain: Residential energy management system. & A scheduling approach to controlling home and neighborhoods energy demand. & The integration and management of electricity usage trends are achieving a significant reduction in peak periods. & \multirow{4}{*}{[142; 143; 144]} \\  & Appliance scheduling. & & demand. & in peak periods. & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of optimization PEM techniques.  
wants to operate within the cover of the set point. This proposed approach aims to control the use of the pack by observing a specific issue, that is, the prosumer payload is willing to work within the maximum specified point.