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The purpose of this paper is to propose a new system for the distribution of electric power by using the concept of Active Demand procedures to obtain a low cost and ensure the continuity of energy. This paper consists of two parts: the first includes the method of optimal distribution of electricity in the presence of Active Demand based on the price of energy for the specific time in the system; this feature will help the consumer to determine the process of consumption through the advertised price; all this will appear through the energy box. The second part involves the introduction of the genetic algorithm to obtain the best solutions for costs and also the best choice for the distribution of electrical power, which makes this method optimize by introducing the genetic algorithm with electrical energy. The main drivers of this work are to provide continuous electrical energy at the lowest cost and also to simulate active demand, in addition to the rapid processing of energy failures by the aggregator and raising awareness of the process of energy conservation for the consumer.

Electricity consumption in general has become increasingly important. Electricity consumption without energy conservation contributes to the increase in interruption and leads to higher bills; the importance of rationalizing electrical energy is that it is one of the main pillars of the optimal exploitation of fossil energy sources such as petroleum and its derivatives used in power plants, which helps to preserve these sources for future generations [

Power distribution reliability is a unique topic in the electric power industry due to its high impact on the cost of electricity and its high correlation with customer satisfaction [

Since distribution systems account for up to 90% of all customer reliability problems, improving distribution system reliability is the key to improving customer reliability. To make effective improvements, a basic understanding of distribution system (functions, subsystems, equipment, and operation) is required [

From the customer viewpoint the power system reliability means uninterrupted power supply starting from generation to the consumers. The reliability of a power system is affected by the frequency (number of interruptions during an analysis period), duration (the time of the interruption), and extent of the interruption (how many customer loads are interrupted). From the Scientists point of view, reliability assessment depends on determining mathematically the frequency and duration of customer interruptions [

The electricity sector bears a large burden of electricity production to cover the peak loads, which only take a limited number of hours during the year. Therefore, it is necessary to inform citizens of the importance of rationalization and its benefits for the migration of unnecessary loads outside the peak time. The most important challenge to solve the problem is how to make the simulation for the consumer understandable so that the consumer can respond to the request submitted in the event of any increase in demand or failure in the processing of power in a particular area (active demand).

The work presented in this research is arranged in five departments containing the present department as follows:

Department Two: Proposed Methodology: Main Steps

1) Power distribution system in the presence of Active Demand

2) Genetic Algorithm

3) Experimental Results

Department Three: Conclusions

Department Four: References

The proposed methodology for two methods of identification of the energy distribution system (AD) is discussed and includes the following sections, a brief discussion of the distribution method of the electrical energy of the proposed system and how to reduce the cost.

A new concept in smart networks was first introduced within the Europe project (2008) [

Aggregator is a device that simulates the active demand in a particular area and within a certain time, the aim is to try to reach the best distribution of electric power and try to avoid the interruption of electricity by rationalizing the process of consumption and try to give up unnecessary electricity, and for the mechanism of work of the Aggregator will be addressed later [

The network is made up of active demand from the distribution system operator, which contains many aggregators (see

In

Through this method and using the program Matlab and make some important changes, including the introduction of the aggregator to the program enabled us to obtain three possibilities for the demand for electricity, including:

1) If the demand for energy is greater than the supply.

2) If the demand for energy is equal to the supply.

3) If the demand for energy is less than the supply.

In these three cases, the first case is important because the required amount of energy is greater than the quantity offered (see

Where the requested energy of the consumer is determined by a least square exponential method (see

By implementing the Matlab simulation program and adding the aggregator to it, have been able to get three types of energy demand and what concerns us is when the demand for energy is more than supply as shown

Years | 1980 | 1981 | 1982 | 1983 | 1984 | 1985 | 1986 | 1987 | 1988 |
---|---|---|---|---|---|---|---|---|---|

Peak | 2994 | 3038 | 2614 | 3667 | 3927 | 3691 | 4479 | 4336 | 5041 |

Years | 1989 | 1990 | 1991 | ||||||

Peak | 3992 | 5602 | 4245 |

In this method used the equation

y = a b x (1)

where y is the forecasted load, x is the number of years forecasted. A represents the initial load and b represents the load growth rate.

We can calculate a and b by using the following equations [

Log a = 1 n ∑ i = 1 n log y i

Log b = ∑ i = 1 n x i log y i / ∑ i = 1 n x i 2 (2)

where n is the number of years of previous data.

By replacing the regression coefficient, a and b, in Equation (1) and replacing the value of x we can get the forecasted peak load.

Example 1:

The average rate of growth and the forecasted demands for the next (27) years are simulated in the matlab program. As shown in

Year | Actual | Forecast | Year | Actual | Forecast | Year | Actual | Forecast |
---|---|---|---|---|---|---|---|---|

1980 | 2994 | 2944 | 1993 | ------- | 5663 | 2006 | ------- | 10,894 |

1981 | 3038 | 3095 | 1994 | ------- | 5955 | 2007 | ------- | 11,457 |

1982 | 2614 | 3255 | 1995 | ------- | 6263 | 2008 | ------- | 12,048 |

1983 | 3667 | 3423 | 1996 | ------- | 6586 | 2009 | ------- | 12,670 |

1984 | 3927 | 3600 | 1997 | ------- | 6926 | 2010 | ------- | 13,324 |

1985 | 3691 | 3786 | 1998 | ------- | 7283 | 2011 | ------- | 14,012 |

1986 | 4479 | 3981 | 1999 | ------- | 7659 | 2012 | ------- | 14,735 |

1987 | 4336 | 4187 | 2000 | ------- | 8055 | 2013 | ------- | 15,495 |

1988 | 5041 | 4403 | 2001 | ------- | 8471 | 2014 | ------- | 16,295 |

1989 | 3992 | 4630 | 2002 | ------- | 8908 | 2015 | ------- | 17,136 |

1990 | 5602 | 4869 | 2003 | ------- | 9368 | 2016 | ------- | 18,021 |

1991 | 4245 | 5121 | 2004 | ------- | 9851 | 2017 | ------- | 18,951 |

1992 | ------- | 5385 | 2005 | ------- | 10,360 | 2018 | ------- | 19,929 |

for the number (3), which represents the agriculture sector, it also does not need a lot of energy and the amount of energy is also semi-stable. The number (4) represents the public sector, which needs different amounts of energy according to the size of its establishment. The number (5) represents the housing sector, which plays an important role in energy conservation.

In

Genetic algorithm is one of the most important tools of artificial intelligence as the program of the genetic algorithm is characterized by the characteristics of the smart program, namely (thinking, conclusion and learning) and this is what the smart program differs from other traditional programs. Genetic algorithms are an important technique in the search for the best option of a set of solutions available for a specific design. Genetic manipulation passes the optimal advantages through successive breeding processes and strengthens these traits. These traits have the greatest ability to enter reproduction, produce an optimal generation, by repeating the genetic cycle, the quality of the generation gradually improves (see

The mechanism of the genetic algorithm starts with the choice of the chromosome (Population) data set. Data is often represented in binary system. Also calculate the optimization function (fitness function) for each chromosome, which is the function of evaluating the intermediate and final results. The basic stages of the algorithm are as follows:

1) Selection: process of selecting the best individuals based on the optimization function.

2) Crossover: process of generating a new generation through the mating of the best individuals that have been chosen. This process is often carried out by the exchange of half-representation between parents.

3) Mutation: process of changing some of the characteristics of the generation resulting from the crossover process with the aim of improving it, and is often the change of the value of one or the change of location with another.

4) The algorithm ends with the evaluation of the new generation based on the optimization function and the decision to repeat the above basic processes or accept the results of the interim and to be satisfied and according to the requirements of the solution [

The proposed system was tested by its implementation in the Matlab program and the positive results were obtained. At the beginning of the system, the important division of the day was determined for the time and the prices of each time were also set. The total estimate of the energy produced by the generator was determined and distributed to the feeders. The demand was obtained from the aggregator where the demand for energy is greater than the supply. The system was run, power was distributed and distribution was obtained but not the best (see

Through the use of genetic algorithm (see

Through

1)

2)

The authors declare no conflicts of interest regarding the publication of this paper.

Ibrahim, A.A. and Dawood, A.L. (2019) Design & Implementation of an Optimization Loading System in Electric by Using Genetic Algorithm. Journal of Computer and Communications, 7, 135-146. https://doi.org/10.4236/jcc.2019.77013