Abstract—The purpose of a power grid is to transfer electrical energy from the production to the consumption, while maintaining an acceptable reliability and voltage quality for all customers. This research paper present the integration of generation based on Biogas power renewable energy source to the Distribution network and how it stabilizes the network by normalizing the fluctuating voltage at the distribution end of power system. A Genetic Algorithm model was performed and evaluation of the impact of the DG by stimulating the developed model in the system. A mathematical formulation and optimization algorithm was performed using the MATLAB/Simulink program. The results obtained were correction of the faulty buses voltages and stable power supply which is 25% better than the conventional one. It was concluded that the implementation of the optimisation technique has improved the energy efficiency of the distribution network.
Index Terms—Distributed Generation; MATLAB- SIMULINK; Voltage Profile; Genetic Algorithm.
I. INTRODUCTION
The power distribution network is always faced with increasing load demand which is resulting into increased burden and reduced voltage [12]. There is need for improving the total efficiency of power delivery which has forced the power supplies to reduce the losses at distribution level. The losses can be reduced by arrangement of network reconfiguration, distributed generator placement, shunt capacitor placement shunt capacitor placement etc. [11]- [13]. The active power supply in distributed generators, thereby reducing the MVA and current in lines. The distributed generators installation network will aid in reducing energy losses, improvement in the networks voltage profile, peak demand losses, networks stability and power factor of the networks [13],[14], [9]-[10]. Distributed generation (DG) is a technology which forms the backbone of Electric distribution network [10]. The technologies are divided into two major categories: (i) renewable energy sources (RES) examples are: Photovoltaic, wind turbines, biomass, hydro, geothermal etc. (ii) Fossil fuel DGs which includes: combustion turbines, internal combustion engines and fuel cells [3], [6], [7]. Environmental, technical and economic factors have the most important role in development of DG technologies. Hence, it is very difficult to examine the technical impacts in power networks of DG.
Published on January 24, 2019.
S. Oodo, F. S. Owolabi are with the Department of Electrical and Electronics Engineering, University of Abuja, Nigeria. (e-mail:
Therefore, the generators are easier to be connected in distributed systems in other to avoid reliability and degradation of power quality. The DG technical evaluation impacts in power networks is very difficult and stressful.
Lack of proper allocation of DG in terms of its capacity and location can lead to voltage variations, increase in fault currents, voltage control interferes processes, increase operating costs etc. [4].
Moreover, installing DG units is not straight forward, and thus the placement and sizing of DG units should be carefully addressed [3], [4].
The DG always located in the primary distribution system which helps in reducing the losses in the system [2]. When the losses are maxima, the size and placement of DG is considered on peak single instantaneous demand [1].
II. DISTRIBUTED GENERATION
Distributed generation is an electric power source which is connected directly to the distribution network or on the customer site of the meter" [1].
This definition of distributed generation does not define the rating of the generation source, as the maximum rating depends on the local distribution network conditions, e.g.
voltage level. Also, the definition of distributed generation does not define the technologies, as the technologies that can be used vary widely. Ackermann's definition is the most generic one, because there is no limit on the DG size and capacity and his definition covers the location of the DG. [8]
Defines DG as "the generation of electricity by facilities that are sufficiently smaller than central generating plants so as to allow interconnection at nearly any point in a power system." IEEE compared the size of the DG to that of a conventional generating plant in their definition.
A more precise definition is provided by the International Council on Large Electric Systems [5] and The International Conference on Electricity Distribution (CIRED), which defines DG based on size, location, and type. CIGRE defines distributed generation as "all generation units with a maximum capacity of 50 MW to 100 MW, that are usually connected to the distribution network and that are neither centrally planned nor dispatched." CIRED defines DG to be
"all generation units with a maximum capacity of 50 MW to 100MW that are usually connected to the distribution network.
There are usually three operators in a typical genetic algorithm [6]: the first is the production operator (elitism) which makes one or more copies of any individual that possess a high fitness value; otherwise, the individual is
Application of a Genetic Algorithm for Improving Voltage Profile with Distributed Generation: A Case Study of
33/0.415 kV Abuja Airport Injection Substation
Stephen Oodo, and Felix S. Owolabi
Vol. 4, No. 1, January 2019 eliminated from the solution pool; the second operator is the
recombination (also known as the 'crossover’) operator.
III. GENETIC ALGORITHM
Genetic Algorithm is a technique which is based on general principles that inspired from the evolution and genetic mechanisms in living beings populations and natural systems. These principle involves the solution of population maintenance to a problem (genotype) that evolve in the individual information time [6].
In genetic algorithm there are basic three operators which include (i) the production operator, which produces one or more duplicate of any individual that possess high fitness value. (ii) The recombination operator also called crossover, which selects two individuals within the crossover site and the generation and moves a swapping operation of the strings bits to the crossover site of both individuals right hand [6].
Recombination operations synthesize bits of gained from both parents exhibiting better than average performance.
Hence, increase the probability of the offspring more productive. (iii) Mutation operator always acts as a background operator which can be used to explore some of invested point in space by flipping randomly a ‘bit’ in a population strings.
A. Proposed Algorithm
The Algorithm used in this paper for analyzing the system is presented in the power flow technique and the optimization method which was programmed in MATLAB/Simulink.
In order to study the system, the power flow method chosen has been the Newton Raphson. The Newton Raphson method is very effectiveness to achieve visual iterative solutions to the power flow analyses which always depends on the selection of suitable initial values for state variables involved in the study. The power flow solution is always started with voltage magnitudes of 1pu at all PQ buses. The slack and PV and PVT buses are given their specified values, which remain constant throughout the iterative solution if no generator reactive power limits are violated.
The initial voltage phase angles are selected to be 0 at all buses.
B. Objective functions
The basic objective of reactive power and voltage control is to identify the optimal values of reactive power control variables which minimize the objective function.
In this approach the following objectives are considered.
1) Minimization of system power losses:
The objective is to minimize the total real power losses in the system. This can be calculated as follows:
𝑃1= 𝑃𝑙𝑜𝑠𝑠 = ∑𝑛𝑏𝑘=1𝑝𝑙𝑜𝑠𝑠𝑖 (1) 𝑃𝑙𝑜𝑠𝑠= ∑𝑛𝑏 𝐺𝑘
𝑘=1 [𝑉𝑘2+ 𝑉𝑚2− 2𝑉𝑘𝑉𝑚𝑐𝑜𝑠(𝜃𝑘− 𝜃𝑚)] (2) Where
𝑛𝑏:the number of branches 𝑃𝑙𝑜𝑠𝑠𝑖:the power loss in branch 𝑖 𝐺𝑘:the conductance of the 𝑘line
𝑉𝑘& 𝑉𝑚: the voltage magnitude at the end buses 𝑘 & 𝑚 𝜃𝑘&𝜃𝑚: the voltage phase angle at the end buses 𝑘 & 𝑚
2) Minimization of voltage deviation:
Bus voltage is one of the important service quality and security indices. the load bus voltage deviation should be minimized in order to improve the voltage profile. This can be calculated as follows:
𝑷2= ∑𝒏𝒉ℎ=1[𝑽̅̅̅̅ − 𝑽ℎ 𝑟𝑒𝑓] (3) where:
𝑛ℎ:the number of 10-minutes period 𝑽ℎ
̅̅̅̅:average value of voltage magnitude of the system for time ℎ
𝑉𝑟𝑒𝑓: voltage reference generally valued as1
C. Problem Constraints 1) Equality Constraints:
The equality constraints are the real and reactive power balance equations at all the bus bars. The equality constraints can be formulated as:
𝑃𝐺𝑘− 𝑃𝐿𝑘= ∑𝑛𝑘=1|𝑉𝑘||𝑉𝑚||𝑌𝑘𝑚|𝑐𝑜𝑠(𝜃𝑘− 𝜃𝑚− 𝜃𝑘𝑚) (4) 𝑄𝐺𝑘− 𝑄𝐿𝑘= ∑𝑛𝑘=1|𝑉𝑘||𝑉𝑚||𝑌𝑘𝑚|𝑠𝑖𝑛(𝜃𝑘− 𝜃𝑚− 𝜃𝑘𝑚) (5) where
𝑛:the number of buses
𝑌𝑘𝑚:the mutual admittance between node 𝑘 and 𝑚qQ 𝜃𝑘, 𝜃𝑚:the bus voltage angle of bus 𝑘 and 𝑚 respectively.
𝑃𝐺𝑘, 𝑄𝐺𝑘:the real and reactive power generation at bus 𝑘 𝜃𝑘𝑚:the admittance angle of line between buses 𝑘 and 𝑚 𝑃𝐿𝑘, 𝑄𝐿𝑘:the real and reactive power demand at bus 𝑘
2) Inequality Constraints a) Transformer constraints
𝑻𝒌𝑚𝑖𝑛≤ 𝑻𝑘≤ 𝑻𝒌𝒎𝒂𝒙 (6)
Where:
𝑇𝒌𝑚𝑖𝑛and𝑇𝒌𝒎𝒂𝒙 are the minimum and maximum range of ratio of tap changing transformer at bus 𝑘.
b) Switchable var constraints
𝑄𝐶𝑘𝒎𝒊𝒏≤ 𝑄𝑪𝑘≤ 𝑄𝐶𝑘𝑚𝑎𝑥 (7) where:
𝑄𝐶𝑘𝒎𝒊𝒏and 𝑄𝐶𝑘𝑚𝑎𝑥are the minimum and maximum allowable output of reactive power compensation equipment at bus 𝑘.
TABLEI:THE PARAMETERS FROM ABUJA AIRPORT (DISTRIBUTION NETWORK)
33KV 11KV
TIME KV1 KV2 KV3 I I2 I3 PH
R-G HZ MW KV1 KV2 KV3
7am 32 32 32 25 25 25 G 50 1.2 11 10.6 11
8am
9am 30 30 30 27 27 27 G 50 1.4 10.6 10.6 10.6
10am
11am 30 30 30 27 27 27 G 50 1.4 11 11 11
12noon
1pm 30 30 30 28 28 28 G 50 1.4 11 110.5 11
2pm
3pm 33 33 33 27 27 27 G 50 1.4 10.5 10.5 10.5
4pm
5pm 33 33 33 25 25 25 G 50 1.3 11 11 11
6pm 7pm 8pm 9pm 10pm 11pm 12M.N 1am 2am
3am 33 33 33 20 20 20 G 50 1.1 11 11 11
4am 5am 6am
The empirical data in Table I was used to run the load flow of Newton Raphson as shown in Fig. 1. The reason of doing that is to determine the faulty buses that did not fall within the range of 0.95 to 1.05 per unit volts.
The distributed generation rating for overall efficiency of
the Biogas power is between 20-40%, and its power rating varies between 0.3 to 7 MW to avoid waste in the system.
The advantages of Biogas energy are that it is a clean power source, and the cheapest technology compared to other types of renewable energies.
Fig. 1. Simulated results when DG is added to the network optimized genetic algorithm technology TABLEII:COMPARING NINE FAULTY BUSES IN THE LOAD FLOW
BUS1 BUS9 BUS10 BUS11 BUS12 BUS13 BUS14 BUS15 BUS16
0 0 0 0 0 0 0 0 0
1,5 1.5 1.4 1.6 1.3 1.42 1.42 1.5 1.35
1 0.8 0.9 1.2 1 1 0.95 1 0.8
1.0 0.9 0.8 1.1 0.8 0.91 1 0.8 1
1.02 1 1 1.03 1 1.01 1.05 1 0.9
1.060 1.060 1.056 1.082 1.064 1.071 1.058 1.055 1.057 1.060 1.060 1.056 1.082 1.064 1.071 1.058 1.055 1.057 1.060 1.060 1.056 1.082 1.064 1.071 1.058 1.055 1.057 1.060 1.060 1.056 1.082 1.064 1.071 1.058 1.055 1.057 1.060 1.060 1.056 1.082 1.064 1.071 1.058 1.055 1.057 1.060 1.060 1.056 1.082 1.064 1.071 1.08 1.055 1.057
Vol. 4, No. 1, January 2019
Fig. 2. Comparing nine faulty buses in the load flow
Fig. 3 Corrected Voltage buses when a model of distributed generation was integrated in the distribution network.
TABLEIII:CORRECTED NINE BUSES USING OPTIMIZED GENETIC ALGORITHM
BUS1 BUS9 BUS10 BUS11 BUS12 BUS13 BUS14 BUS15 BUS16 TIME(s)
0 0 0 0 0 0 0 0 0 0
1.25 1.23 1.25 1.45 1.2 1.3 1.2 1 1 1
0.94 0.91 0.92 0.97 0.93 0.95 0.92 0.8 0.9 2
1.02 1,01 0.9 1.04 1.0 1.02 1.0 0.9 0.8 3
0.85 0.84 0.6 1 0.8 0.9 0.9 0.7 0.6 4
0.9636 0.9636 0.96 0.9836 0.9673 0.9736 0.9618 0.95931 0.9609 5 0.9636 0.9636 0.96 0.9836 0.9673 0.9736 0.9618 0.95931 0.9609 6 0.9636 0.9636 0.96 0.9836 0.9673 0.9736 0.9618 0.95931 0.9609 7 0.9636 0.9636 0.96 0.9836 0.9673 0.9736 0.9618 0.95931 0.9609 8 0.9636 0.9636 0.96 0.9836 0.9673 0.9736 0.9618 0.95931 0.9609 9 0.9636 0.9636 0.96 0.9836 0.9673 0.9736 0.9618 0.95931 0.9609 10
IV. DISCUSSION OF RESULT
The modeling of the components discussed in this section is based on the assumption that the three phase system is balanced under steady state conditions. Using this assumption, per phase analysis can be done. With the result obtained after the load flow, it shows that the faulty buses that did not fall within the range of 0.95 to 1.05 per unit volts are buses 1 = 1.06 per unit volt, bus 9 = 1.06 per unit volt, bus 10 = 1.056 per unit volt, bus11 =1.082 per unit volt., bus 13 = 1.071 per unit volt, bus14 = 1.058 per unit volts, bus15 = 1.055 per unit volts and bus 16 = 1.057 per unit volts.
Fig. 2 shows the analysis of the four faulty buses in the load flow. The faulty buses are buses 1, 9, 10, 11, 12, 13, 14, 15 and 16 as shown in Fig. 2 and 3; having abnormal voltages. These abnormal voltages observed in these buses causes low power factor, over current and power losses thereby resulting in constant power system instability. This lead to poor quality power supply to the consumers.
Fig. 3 shows that the power is stable since it has been corrected. Therefore, high quality power supply is distributed to the consumers with minimal load loss.
V. CONCLUSION
This paper deals with the genetic algorithmic integration of renewable energy generation on the distribution network and how it affects the operation of the voltage regulation and reactive power distribution network.
The integration of distributed generation into the power systems has been analysed, In order to model a generation based on renewable energy, firstly energy sources have been studied; secondly, the technologies have been revised and
finally the integration of distributed generation into the grid it has been carried out. Power flow analysis has been programmed, by formulating main equations of electrical circuits in MATLAB enabling the study test system to be modelled.
In this paper, a standard system has been modelled based on load flow analysis in different days which shows the impact of distributed generation on Abuja AirPort injection Substation.
Various optimisation algorithms have been implemented based on the principle of natural selection to solve issues such as the location, the level of generation or control of the power factor of the connected generators.
All optimization algorithms and mathematical formulation have been performed using the MATLAB/Simulink program. It can be concluded that the implementation of the optimisation technique has improved the energy efficiency of the distribution network.
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