4.5.1. Prosumer Genetic Algorithm
Genetic algorithms are considered an attractive research issue due to its ability to solve the DSM and Complicated Economic. 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 [126], 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 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 Generators (DG) must be kept within its range. Handle various types of prosumer energy management concerns. The authors proved in [127] that PGA controlled and supervised in real-time Decentralized Generators (DGs) and load transmission 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 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.
Use of pregnancy depending on the distributed obstetrician, a specific point or limit is determined for pregnancy consumption. Different situations assess the effectiveness of the device.
4.5.2. Prosumer Mixed Integer Linear Programming
For the design of high-dimensional and non-linear systems, the PMILP was proposed. The classic principle of operation is applied to accelerate the cycle towards the device’s expandability. Indeed, PMILP is a methodology used to automate storing electricity in an intelligent system. PMILP differs from other dimensional methods of programming, which contain both actual and incorrect variables. The PMILP was introduced for designing high-dimensional and non-linear systems. The classic operating concept is applied to accelerate the process toward the expandability of the device. PMILP is a method used to optimize the handling of energy in a smart network. PMILP differs from other dimensional programming methods, which deploy actual and incorrect variables [128].
Many innovations usually generate just one ideal solution, while others may create many solutions. In [129], energy efficiency algorithms focused on the recommended PMILP was proposed to reduce costs and conserve electricity. Indeed, the authors proposed the PMILP-based energy efficiency algorithms to reduce costs and save electricity.
The results showed that the total cost of the optimization problem related to energy consumption was reduced through optimization techniques. PMILP Algorithm was used to encourage average users or residents to change purchasing costs to keep costs down. The authors explored and measured the concept of time limits.
4.5.3. Prosumer Particle Swarm Optimization
PPSO has seen growing numbers of applications for SG domains; the three largest application areas are scheduling, active control and network layout schedule. Indeed, SGs are used in PSO variants, including genetic SPO, unexpected PSO, and quantitative PSO. PPSO is often used in the smart grid to control electricity. A Particle Swarm Optimization (PSO) algorithm was proposed in [130] to minimize energy expenses for economic transmission issues related to demand exchange and for a random process that begins to create a series of alternatives. Indeed, PSO was also presented in to reduce operating costs and energy efficiency in conjunction with natural gas networks. They deployed PSO to implement a natural gas generator for the small grid to address problems in clean energy supplies and reduce the load and congestion of the gas. To prevent pollution and balance payments, the efficient distribution of all digital networks must be synchronized with the electricity grid. A transition is made to use PSO to address the related network issue.
In [131], PSO has been shown to outperform some standard methods used based on Information Engine Services (IES) and based on operating expenses appropriate for IES. The authors included challenging PSO improvements that converge faster and require less computational time. In [132], the authors discussed the PSO improvements, such as the fast convergence at less computational time. In [133], PSO was applied to obtain the optimum energy flow for renewable energy wires.
4.5.4. Prosumer Linear Programming
The PLP optimization algorithm was chosen as an attractive design method for storing electricity for the smart grid. PLP was used as a linear function of decision variables to find the correct approach to objective function problems and constraints. Many scientists have used PLP in various energy storage systems. Linear programming (LP) is used to increase daily consumption from peak demand. For example, in [134]-[135], the authors deployed the LP as a consumption scheduling for shaving peak load at home. The authors also suggested a network in which the grid, home, power plants, and integrated power management system are interconnected. LP was proposed in [136]-[137] to maximize energy requirements using green energy supplies in different regions. The authors also attempted to highlight the difference between energy production and end-use by utilizing the LP and the power grid. The planned power grid model enables sustainable utilization and urban solid waste use [138]-[139].
4.5. 5. Prosumer Integer Linear Programming
The PILP is another smart grid practice for improving electrical electricity systems. PILP differs from LP, because only numerical and binary values can be used. Similar to LP, ILP can be used to express other questions. Each vector is continually limited to one continuous period, which is the functional area of the LP model [140]. If the variables are bound to valid values, then the structure is PILP. Since the region is realistic, the ILP model is fundamentally different from the LP model. It is important to remember that these models can be explained by various LP sub-processes and are very comprehensive in practice as a vital LP programming implementation field.
For both the electrically transported equipment and the displacement over time, the authors suggest a demand management system using ILP to increase the load for end-users in smart grids [141]. The authors propose a demand management program using ILP to increase the burden on smart grids for consumers for both the electrically transported equipment and the movement over time [142]. Three major components of the planned network are smart meters, appliance interfaces, and home appliances. To capture customer data from devices and usage plans through an interface, smart meters play an essential role in the proposed scheme. The smart meter was made using the data optimization algorithm obtained [143]. The planned network has three major components: smart meters, appliance interfaces and home appliances. Smart meters play a significant role in the proposed system to collect consumer data from devices and consumption plans via an application. The smart meter was developed using the algorithm achieved for data optimization [144].
The order scheduling method is described as a linear maximization feature of the mathematical formula to reduce the daily load.
5. Open Issues and Future Directions
Demand response and market management for unexplored areas is still under study, and applications based on machine learning for energy efficiency and cost analysis may include peer-to-peer energy trade. For example, a real-time billing system can optimize energy pricing based on current and potential energy prices (forecast) and charge the consumer accordingly. The blockchain is viewed as black box in most blockchain solutions. For example, many strategies [146] [145] use smart contracts as the blockchain protocol to grow the architecture. This limits the leverage over the overall architecture and performance of optimizations that cannot be made to the blockchain used in smart contracts. In the future, instead of using the blockchain as black boxes, blockchain could adopt a problem specific approach to energy trade.
There is a need for a network in which all prototypes can operate as a common framework and adapt their behavior to consumers’ needs. For example, consumers must be able to sell electricity domestically and globally for large-scale energy storage systems.
The traditional architecture of the energy supply smart meter and every other revolutionary system are not used in blockchain. Many prosumers/consumers are eager to adopt this architecture. Given that most of the energy sharing frameworks presented to us presume that prosumers and consumers have intelligent devices. This new architecture blends conventional design with a cryptocurrency may be implemented. Consolidated energy trade by a group of consumers is outperformed by the inefficiency and robustness of operating individual consumers as autonomous firms in terms of renewable energy supplies. In addition, the power source for individual consumers may be insufficient to handle conventional power generators and may be unpredictable due to climatic conditions.
6. Conclusion and Future Works
This paper focuses on Prosumers SG and the main features examined with regard to monitoring functions and communication capabilities. Current and homogeneous technologies for Prosumer SGs require an additional attempt to achieve an independent and decentralized concept of intelligence level. To improve connectivity through the continuous creation of Prosumer SG, IoT edge computing was detailed and discussed. Indeed, several open issues and technological challenges related to energy management in the future were identified. In this context, the new challenges will provide the possibility to develop potential research in the industrial and professional fields. The concept behind this research is that the deep knowledge of SGs Prosumer and its interactions will allow consumers to properly evaluate issues/solutions and implement SG innovations such as blockchain structure and IoT edge computing. Based on our review, we have highlighted several studies, which include smart, public markets, household energy demand from clients and stakeholders, and energy demand for the service provider. Furthermore, we have presented the concept and the techniques used in the literature to manage the energy based on ProSG. The most important techniques deployed and evaluated by the authors are detailed in this survey paper such as PGA, PMILP, PPSO, PLP, and PILP. Moreover, the P2P Energy trading was detailed in both cases prosumer and consumer. On the other hand, we introduced the edge computing systems in IoT where Edge Computing IoT architecture, information processing in ProSG, edge computing smart home model, and future energy management systems are described and detailed. In potential improvements, it is highly suggested that stakeholders and the market be combined with the blockchain to ensure consumer efficacy and to improve the multidisciplinary electrical home appliances. In ongoing studies, SG security requirements will be strengthened. Blockchain and Edge Computing technology may be effectively combined to achieve secure remote management. The long-term perspective will enable potential consumers of a better environment with reliable and smart mandates and maintain low-cost consumption.
Author Contributions: Conceptualization, S.S.B. and S.B.S.; methodology, S.B.S.; validation, S.S.B.; investigation, S.B.S.; resources, S.S.B.; writing—original draft preparation, S.B.S.; writing—review and editing, S.S.B.; visualization, S.B.S.; project administration, S.S.B.; funding acquisition, S.S.B. All authors have read and agreed to the published version of the manuscript.
Acknowledgments: The authors acknowledge with thanks the Deanship of Scientific Research (DSR) at King Abdulaziz University (KAU), Jeddah, Saudi Arabia, for the financial support.
Conflicts of Interest: The authors declare no conflict of interest.