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The microgrid has become significant and commonly used; it has localized electricity sources and loads connected to a centralized electrical power network system when the need arises and disconnects to island mode. A microgrid can effectively be integrated with various distribution generators, which can improve the voltage level on the transmission line by reducing the real power losses. In this work, new technologies will permit power grids to be better prepared for future requirements. The numbers and diversity of such decentralized power plants require a new type of management in the operation of power grids and intelligent networks or “smart grid.” A SCADA system will improve coordination between power demand and generation and use of modern information technology such as the internet, sensors, controllers, and wireless transmission equipment and use smart metering. The Accelerated Particle Swarm Optimization technique will be used to select the optimum location of a wind turbine to install in the power grid considering minimum power losses with optimal operation consideration of the number of iterations, the execution time of the program, and the memory capacity. The analysis and the study are carried out in MATLAB and the SCADA system.

A microgrid is an isolated power system that can manage intelligent distribution power resources, interconnected loads and work in parallel with or independently of the network. In the same mood, electric power can be provided economically while enhancing energy quality and reliability by integrating and improving energy sources. However, the validity of a microgrid network depends on the combination of power sources, size or capacity allocated, and the transmission strategy [

To minimize the loss, a microgrid site has a key role. The losses are reduced to a minimum amount by increasing the installed generator’s size [

The main features affecting the cost of energy transfer losses are transmission line losses and the generating price. In this situation from the specified energy flow, all generations prices are the same in the traditional power grid, so, the minimum loss of transmission line means almost The minimum cost of transmission losses, but the cost of generating D.G. is much higher than traditional generations, location D.G. measurement may only consider the loss of transmission to increase the transmission cost in the power grid layout, the calculations are optimized with the cost of transmission loss goal function is necessary [

In this work, we proposed a schematic for the loss reduction system; the 4-step loss reduction plan, as described below, includes divided into sections, including analysis of the load flow, acquisition of the voltage profile, control and reduction of losses, and optimization using an approach based on two algorithms are obtained for the placement of D.G., the PSO, and APSO, and monitor the data profile [

The optimization of the distribution network is mainly from an economic point of view. However, single-objective optimization will ignore the interests of other aspects, and different factors need to be considered in D.G. investment subjects’ decisions. The power region or D.G. investors are more eager to get the maximum benefits. They pay more attention to the investment cost of D.G. As users, they hope to obtain safe and stable electricity, mainly focusing on the quality of power. It can be seen that under the various requirements of power network operation quality and economic benefits, the future direction of reactive power optimization is to comprehensively consider the economy, safety, and power supply reliability of power system operation [

The control system in standalone micro-grid is under severe compulsion to 1) achieve demand-supply equilibrium in all conditions; 2) control and restore the system frequency and bus voltages to their nominal values whenever a disturbance has happened [

Article movement factors consist of two main components: an inevitable element and a random component. Each particle is attracted to the best current position G and the best X i * Site in history, while at the same time tends to transfer arbitrarily. For example, let be the vector location x_{i} and the particles velocity v_{i} of i, respectively, so the following formula calculates the new speed transducer [

When required, the diversity in quality solutions increase, improving the standard PSO uses the best position individual X i * , and the global best g * [

v i t + 1 = v i t + α ϵ n + β [ g * − X i t ] (1)

where: ϵ n is outgoing N(0, 1) to replace the next term. To increase the convergence more, we can also write the location update in one step:

x i t + 1 = ( 1 − β ) x i t + α ϵ n + β g * (2)

Usually, α = 0.1K to 0.5K, where K is the measure of each variable and β selects from 0.1 to 0.7 for suitable in most applications. There is no requirement to accord with the vectors of velocity formation, so be noted that speed does not appear in Equation (5); therefore, APSO is much simpler, and the mechanism is easy to understand. Compared with many standard PSO variables, the APSO uses two exclusive parameters; another improvement in the accelerated PSO system is to reduce randomization while duplicates persist. This means that we can use a monotonous decrease function like:

α = α 0 ⋅ exp [ − γ t ] (3)

or,

α = α 0 ⋅ γ t , ( 0 < γ < 1 ) (4)

where: α_{0}, approximately from 0.5 to 1, is the initial value of the random parameter. Here t is the number of duplicates or time steps, and γ is a control parameter [

α = 0.75 ⋅ t (5)

where: t ∈ [0 and t_{max}] and t_{max} is the maximum iteration [

In the power systems, the dispatch of reactive electricity power may have different goals, such as obtaining the best voltage quality and reducing real power losses. Considered the voltage magnitude is |V_{i}| and |V_{j}| at buses i and j, respectively so, the power loss as an objective function and described by the formula [

f : P l o s s = ∑ K = 1 M g i j ( | V i | 2 + | V j | 2 − 2 | V i | | V j | ⋅ cos θ i j ) (6)

M is the number of branches, while the angle θ_{ij} is the difference in the phase of voltage angle at bus i and bus j, respectively, and g_{ij} represents the branch conductance of impedance between the buses i andj [

There are two constraints dispatch issue of reactive power, equality, and inequality constraints as follows:

1) Equality Constraint: If we suppose the real and imaginary power supplying to bus i are P_{gi} and Q_{gj} respectively, and the real and imaginary demand load power at bus i are P_{di} and Q_{di} respectively, so The real and imagery power is given by the following formulas [

y 1 : P g i − P d i − V i ∑ V j ( G i j cos θ i j + B i j sin θ i j ) = 0 (7)

y 2 : Q g i − Q d i − V i ∑ V j ( G i j sin θ i j − B i j cos θ i j ) = 0 (8)

2) Inequality Constraint: It’s the voltage magnitude range value, tap changing transformer positions value, and injection reactive power to the system. In this paper, and based upon the IEEE 30-bus system’s data, the voltage magnitude values are selected from 0.950 p.u to 1.10 p.u, and the tap changing transformer range is usually from 0.975 to 1.025. The compensator’s injection of reactive power to the system from 0 MVAR to 20 MVAR. The Inequality Constraint is given by the following relations [

g 1 : V i min < V i < V i max (9)

g 2 : t j min < t j < t j max (10)

g 3 : Q g i min < Q g i < Q g i max (11)

So, the objective function given in equation nine can be expressed by follows:

F = P l o s s + ∑ r g i ⋅ ( V i − V i lim ) 2 + ∑ r t i ⋅ ( T i − T i lim ) + ∑ r Q i ⋅ ( Q i − Q i lim ) 2 (12)

And the values of voltage magnitude, tap changing transformer, and injection reactive power described as follows [

V i lim = { V i max ; V i > V i max V i min ; V i < V i min (13)

T i lim = { T i max ; T i > T i max T i min ; T i < T i min (14)

Q i lim = { Q i max ; Q i > Q i max Q i min ; Q i < Q i min (15)

This work examines the possibility of using wind power energy for electricity generation at optimum conditions and a simulation network for IEEE 3 buses. A MATLAB program was used to analyze the system; they allow comparison with different design options.

The proposed technique is performed and verified on the IEEE busbar network for three buses system. The power system network construction is indicated in

The Wind Power depends on [

• amount of air (volume);

• speed of air (velocity);

• mass of air (density) A.

flowing through the area of interest (flux).

The Kinetic Energy definition as:

Kinetic Energy KE = 1 2 ⋅ m ⋅ v 2 (16)

The Power is K.E. per unit time [

P = 1 2 ⋅ m ˙ ⋅ v 2 (17)

where m ˙ is the mass flux and given by:

m ˙ = d m d t (18)

Fluid mechanics gives the mass flow rate,

d m d t = ρ ⋅ A ⋅ v (19)

Thus,

P = 1 2 ⋅ ρ ⋅ A ⋅ v 3 (20)

where ρ is the air density.

A = π ⋅ r 2 (21)

• Therefore, the power proportional to air density, rotor swept area and the velocity cube.

Power Coefficient, C_{p}, is the ratio of power extracted by the turbine to the total contained in the wind resource [

C p = P T P W (22)

• Turbine power output,

P T = 1 2 ⋅ ρ ⋅ A ⋅ v 3 ⋅ C p (23)

The Betz Limit is the maximal possible C_{p} = 16/27. Thus, 59% efficiency is the BEST a conventional wind turbine can do in extracting power from the wind [

Capacity Factor (C.F.): The fraction of the year the turbine generator is operating at rated (peak) power [

CapacityFactor ( CF ) = AverageOutput PeakOutput ≈ 30 % (24)

C.F. is based on both the characteristics of the turbine and the site characteristics (typically 0.3 or above for a good site) ENERCON wind energy converters run with a particular storm control feature. An E-82 ENERCON wind energy converters as D.G. with 2.05 MW rated power.

speed characteristics for 2.05 MW/E-82 ENERCON wind energy converters, while

In this work, the particle selected in each technique as the voltage magnitude at slack bus and each P|V| generation bus also taps changing transformer ratio and the injected reactive power to the system. There are three particles: (|V1|, |V2|, and |V3|) no tap changing transformer ratio and no injected reactive power at buses. When the optimization development process begins, each particle position will continuously update until they reach the stop criteria. The minimum inertia weight is selected as 0.45, and the maximum weight limitations are chosen as 0.95. With continuous repetition, the weight value will drop from 0.95 to 0.45.

This section imparts a general practical knowledge of smart grids with the SCADA system applied in this paper. Providing shifts in household consumption away from peak load periods and running flexible applications such as air conditioning directly by power supply companies outside peak load periods. The main equipment used in this work as:

1) Fundamentals of electrical engineering;

2) Complex A.C. technology;

3) Three-phase technology;

4) Measurement technology;

5) Power transmission;

6) Line protection;

7) Power distribution;

8) Power management.

A three-phase power with conductor currents of up to 3 × 5 A can be conveyed, measured, and switched via the double busbar model. Also, the board is a

branch/connector, which can be turned on and off via isolators. The subsequent circuit breaker also has a three-phase design and switches the outgoing and incoming power feeders on and off and change busbars.

The isolators and circuit breakers are controlled manually using pushbuttons or using a remote switching device (e.g., PLC, protective relay) via a control input. In addition, the isolators and circuit breakers are equipped with an auxiliary contact via which indicator lights and other signaling devices can be switched. LEDs indicate the switching status. Overcurrent is also indicated by an LED and opens the switches.

Certain switching combinations are interlocked more on this later. Incorrect operations of this kind are signaled visually and acoustically by the models.

Switching can be performed in three different ways:

1) Manually on the device using on/off buttons.

2) Via digital control voltage inputs (max. 30 V DC).

3) Via Ethernet using SCADA system Designer/Viewer.

The modules can be interconnected via the RS485 port and operated via SCADA system Viewer/Designer.

Before using the SCADA system for the first time, we should make a one-time hardware connection for mutual communication. In some cases, the various devices are connected via different interfaces to the P.C. The configuration for each type of connection is described in the corresponding section of this topic.

The following installation instructions explain how to connect devices to the SCADA system via Ethernet. This is important when using a SCADA control computer with network access, the double busbars CO3301-5R/CO3301-5S, and other supported equipment such as the power quality meter CO5127-1S the human-machine interface (HMI) CO3301-5L.

To establish a structured hierarchy of I.P. addresses assigned to each Tableor set of equipment. This makes subsequent setting up much easier.

For the I.P. address of the P.C. could use 192.168.168.1, for example, as in

The SCADA Viewer/Designer now searches the double busbars using all the ports on the computer. The computer and the device to be connected do not have to be configured for a common sub-network, as shown in

Busbar 1;

Busbar 2;

Control panel for isolator Q1;

Control panel for isolator Q2;

Control panel for circuit breaker Q3;

Ethernet connections for measurement and control;

0 V control voltage connection (not a neutral conductor!);

400 V incoming/outgoing busbar feeder.

This work setup permits any required combination of energy sources and consumers to study smart grids’ basic relationships. The voltages and currents are evaluated using the SCADA for Power Lab software converter.

The practical work is to select the optimum location of the distribution generator on the power system. A three-bus system tested using the Supervisory Control and Data Acquisition (SCADA) program to compare APSO results. The algorithm is done by using APSO by MATLAB.

A three-bus test system was applied to test the optimum power flow and compare the APSO and hand calculation results. The test power system shown in

Bus No. | Type | |V| (p.u) | δ (Degree) | V_{actual} (Volt) | V_{base} (Volt) | Generation | Load | ||
---|---|---|---|---|---|---|---|---|---|

P (W) | Q (VAR) | P (W) | Q (VAR) | ||||||

1 | Slack | 1.01667 | 0 | 244 | 240 | 100 | - | 0 | 0 |

2 | P|V| | 1.0 | 0 | 240 | 240 | 104.79 | - | 56 | 35 |

3 | P|V| | 1.0 | 0 | 240 | 240 | 155.73 | - | 106 | 0 |

From bus | To bus | R (Ω) | X (Ω) | B (Ω^{−1}) | R (p.u) | X (p.u) | B (p.u) |
---|---|---|---|---|---|---|---|

1 | 2 | 7.2 | 86.7 | 1.13 × 10^{−4} | 0.2922 | 3.52 | 2.78 × 10^{−3} |

1 | 3 | 0.75 | 7.07 | 2.26 × 10^{−3} | 0.0304 | 0.287 | 0.05571 |

2 | 3 | 7.2 | 86.7 | 1.13 × 10^{−4} | 0.2922 | 3.52 | 2.78*10^{−3} |

Load No. | Bus No. | R (Ω) | X (Ω) | R (p.u) | X (p.u) |
---|---|---|---|---|---|

1 | 2 | 500 | 452.4 | 20 | 18.36 |

2 | 3 | 750 | 0 | 23.14 | 0 |

based on 2338.26 VA and 249 V, so the base impedance and current are 24.633 Ω and 5.625 A, respectively.

The manual calculation used Fast Decoupled Load Flow to calculate the voltage at each bus, power flow, and power losses in each line. The bus admittance is given as:

Y b u s = [ 0.388 − j 3.698 − 0.02342 + j 0.2821 − 0.365 + j 3.445 − 0.02342 + j 0.2821 0.04684 − j 0.5615 − 0.02342 + j 0.2821 − 0.365 + j 3.445 − 0.02342 + j 0.2821 0.3919 − j 3.7178 ]

The current flow to bus 2 is 0.25A, and bus 3 is 0.46A, respectively.

The power mismatch,

Δ P 2 = P 2 s p e c . − P 2 c a l c . = ( 0.0448 − 0.024 ) − 0.044444 = − 0.0236 p .u

Δ P 3 = P 3 s p e c . − P 3 c a l c = ( 0.0666 − 0.0453 ) − 0.08177 = − 0.06047 p .u

and,

Δ Q 2 = Q 2 s p e c . − Q 2 c a l c = ( 0 − 0.015 ) − 0 = − 0.015 p .u

[ Δ P 2 | V 2 | Δ P 3 | V 3 | ] = − [ B 22 B 23 B 32 B 33 ] [ Δ δ 2 Δ δ 3 ]

[ − 0.0236 1.0 − 0.06047 1.0 ] = − [ − 0.5615 0.28121 0.28121 − 3.7178 ] [ Δ δ 2 Δ δ 3 ]

[ Δ δ 2 Δ δ 3 ] = [ 0.5615 − 0.28121 − 0.28121 3.7178 ] − 1 [ − 0.0236 − 0.06047 ]

[ Δ δ 2 Δ δ 3 ] = [ − 0.052184 − 0.02 ] rad = [ − 2.9899 − 1.1459 ] degree

[ Δ Q 2 | V 2 | ] = − [ B 22 ] [ Δ | V 2 | ]

[ − 0.015 1.0 ] = − [ − 0.5615 ] [ Δ | V 2 | ]

[ Δ | V 2 | ] = − 0.0267 p .u

| V 2 | = 1.0 − 0.0267 = 0.9733 p .u

So, the voltage after the first iteration:

V 2 ( 1 ) = 1.0 ∠ − 2.9899 p .u And

V 3 ( 1 ) = 0.9733 ∠ − 1.1459 p .u

After three iterations, the final results were the convergence of the value given in

Branch # | Before optimization | APSO By MATLAB (W) | Manual Calculations | SCADA Results |
---|---|---|---|---|

1 - 2 | 2.33826 | 1.3232 | 1.2023 | 2.33826 |

1 - 3 | 23.3826 | 20.1236 | 20.1233 | 23.3826 |

2 - 3 | 2.33826 | 1.3232 | 1.2021 | 2.33826 |

Total | 28.05912 | 22.770 | 22.5277 | 28.0591 |

Bus # | Without optimization | optimization APSO Without DG | Manual Calculations | SCADA Results |
---|---|---|---|---|

1 | 1.017 | 1.065 | 1.0167 | 1.017 |

2 | 0.991 | 0.992 | 1.0 | 0.99 |

3 | 0.981 | 0.987 | 0.987 | 0.981 |

The performance of the proposed method is verified on the three bus systems. In addition, the MATLAB code for the power dispatch without and with adding new D.G. has been run. The optimization process’s stopping criteria are set as the iteration number reaching 200 iterations to give the particles enough opportunities to reach the global minimum. The size of the swarm is 50. The initial weight inertia is set to 0.9, and the final weight inertia is set to 0.4. As the iterations go on, the weight value will drop from 0.9 to 0.4. When the optimization process begins, each particle’s position will be continuously updated until they reach the stopping criteria.

The results show that the minimum losses occurred when the D.G. was installed at bus no.3; it is given 15.944 MW when used the APSO technique. From this Table, we can learn that the real power loss can be reduced by 10.47%; after adding a new D.G. to the system and using the APSO algorithm to further adjust the control variables’ values, the real power loss can be reduced by as much as 14.42%. A comparison of the loss reduction when D.G. is installed, the results also show that the losses result in each line, and the total losses for the system are close for both techniques. The losses after adding D.G. are less than the losses before installed D.G., so the power losses are modified in each line and the system due to installing a wind generator at the optimum location of bus no. 3. The voltage magnitude and the angle are modified after installed the wind turbine at the optimum location.

The voltage profile was evaluated using The Fast-Decoupled Load Flow (FDLF) method and then calculated the system’s real power loss. After that, put wind generators at each bus to evaluate the total corresponding real power losses to obtain the D.G.’s optimal location when the total loss accord. The proposed approach is based on the APSO technique and aims to identify the load’s position and compare the results with the applied standard PSO technique.

Based on the analysis and results presented in this work, the following conclusions are drawn: The comparison between two techniques is used to identification of an optimum location by using standard particle swarm optimization Technique and accelerated particle swarm optimization Technique, and the obtained results explain the affiant the APSO method when taking the consideration of iteration number, fast of execution time, reducing power losses, improve the power losses on the transmission line and improve the power flow, and improve the magnitude and angle voltage profile. A better efficiency than standard low speed wind turbines is achieved using a wind turbine. There are no ends to the development of wind turbines: Global research initiatives are underway with wind turbines of diverse capacities. The energy mix of the future cannot be anticipated properly because of the diversity of energy sources available. However, regenerative energy sources are certain to rise in importance, especially wind, and will probably substitute for fossil fuels such as coal and gas in the long term. Nevertheless, in the future, long-term energy-mix policies, which incorporate not only the environment but also economic and reliable supply, will not be enough for regenerative energy alone.

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

Abood, S., Ali, W., Attia, J., Obiomon, P. and Fayyadh, M. (2021) Microgrid Optimum Identification Location Based on Accelerated Particle Swarm Optimization Techniques Using SCADA System. Journal of Power and Energy Engineering, 9, 10-28. https://doi.org/10.4236/jpee.2021.97002