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.