**1. Introduction**

30 Electrical Generation and Distribution Systems and Power Quality Disturbances

breaks down, the renewable generator converters can go on feeding active power into the DC bus, but the active power balance is no longer maintained on the DC bus. The generation unit can go on supplying into the grid the active power available from the renewable sources only if the interface converter control changes its operation. If the battery converter doesn't work anymore, the interface converter must maintain the energy balance on the DC bus, by regulating the output active power in order to maintain the DC bus voltage to its nominal value. It's not very reasonable to contemplate this working condition, as the system is not providing grid services and needs to be repaired. Thus, it can be stated that the DC bus generation units can't inject any power into the grid when the battery

Bollen, M.H.J. (2003). What is power quality. *Electric Power System Research,* Vol.66, No.1,

De Brabandere K. (2007). Control of Microgrids, *Proceedings of Power Engineering Society* 

Diaf S. et. Others (2007). A methodology for optimal sizing of autonomus hybrid PV/wind system. *Energy Policy,* Vol.35, No.11, (November 2007), pp. 5708-5718, Elster S. (2006). Re Hybrid systems: Coupling of Renewable energy sources on AC and DC

Kolhe, M. (2009). Techno-Economic Optimum Sizing of Stand Alone Solar Photovoltaic

side of the inverter. *ReFOCUS Journal,* Vol.7, No.5, (September-October 2006), pp.

System. *IEEE Transaction on Energy Conversion,* Vol.24, No.2, (June 2009), pp. 511-

*General Meeting*, pp. 1-7, Tampa, Florida, USA, June 24-27, 2007

converter breaks down, even if the interface converter is still working.

**7. References** 

46-48

519

(July 2003), pp. 5-14

Modern power system becomes more complex and difficult to control with the wide integration of renewable energy and flexible ac transmission systems (FACTS). In recent years many types of renewable source (Wind, solar,) and FACTS devices (SVC, STATCOM, TCSC, UPFC) integrated widely in the electricity market. Wind power industry has been developing rapidly, and high penetration of wind power into grid is taking place, (Bent, 2006), (Mahdad.b et al., 2011). According to the Global Wind Energy Council, GWEC, 15.197 MW wind turbine has been installed in 2006 (Chen et al. 2008), in terms of economic value, the wind energy sector has now become one of the important players in the energy markets, with the total value of new generating equipment installed in 2006 reaching US 23 billion.

FACTS philosophy was first introduced by (Hingorani, N.G., 1990), (Hingorani, N.G., 1999) from the Electric power research institute (EPRI) in the USA, although the power electronic controlled devices had been used in the transmission network for many years before that. The objective of FACTS devices is to bring a system under control and to transmit power as ordered by the control centers, it also allows increasing the usable transmission capacity to its thermal limits. With FACTS devices we can control the phase angle, the voltage magnitude at chosen buses and/or line impedances.

In practical installation and integration of renewable energy in power system with consideration of FACTS devices, there are five common requirements as follows (Mahdad. b et al., 2011):


Optimal placement and sizing of different type of renewable energy in coordination with FACTS devices is a well researched subject which in recent years interests many expert

Optimal Location and Control of

*Hybrid Methods* <sup>2011</sup>

*BBO* 

*PSO-DE* 

*ACO* 

*HS* 

*SA, TS* 

*GA* 

2008

2004

2006

1990

1995

1975

1985

1965 *EP, ES* 

has grown rapidly.

Multi Hybrid Model Based Wind-Shunt FACTS to Enhance Power Quality 33

general formulation, the optimal power flow (OPF) is a nonlinear, non-convex, large-scale, static optimization problem with both continuous and discrete control variables. It becomes even more complex when various types of practical generators constraints are taken in consideration, and with the growth integration of new technologies known as Renewable source and FACTS Controllers. Fig. 2 sumuarizes the basic optimization categories used by

In first category many conventional optimization techniques have been applied to solve the OPF problem, this category includes, linear programming (LP) (Sttot et al., 1979), nonlinear programming (NLP) (Wood et al., 1984), quadratic programming (Huneault et al., 1991),

*GA EP ACO*

*SA TS PSO*

*DE*

*HS* **BBO** 

Fig. 2. Presentation of optimization methods: *Global, Conventional*, and *hybrids* methods

All these techniques rely on convexity to find the global optimum; the methods based on these assumptions do not guarantee to find the global optimum when taking in consideration the practical generators constraints (Prohibited zones, Valve point effect), (Huneault et al., 1991) present a review of the major contributions in this area. During the last two decades, the interest in applying global optimization methods in power system field

The second category includes many heuristique and stochastic optimization methods known as Global Optimization Techniques. (Bansal, 2005) represents the major

*Conventional Methods* 

*First Category*

 *Optimization Methods* 

*Hybrid Methods* 

*Third Category*

*Second C t*

*Fuzzy ANN*

researchers to analysis and enhance the optimal power flow solution.

and interior point methods (Momoh et al., 1999).

engineers. Optimal placement and sizing of renewable source in practical networks can result in minimizing operational costs, environmental protection, improved voltage regulation, power factor correction, and power loss reduction (Munteau et al., 2008). In recent years many researches developed to exploit efficiently the advantages of these two technologies in power system operation and control (Adamczyk et al., 2010), (Munteau et al., 2008).

Fig. 1. Optimal power flow control strategy based hybrid model: wind and Shunt FACTS

In this study a combined flexible model based wind source and shunt FACTS devices proposed to adjust dynamically the active power delivered from wind source and the reactive power exchanged between the shunt FACTS and the network to enhance the power quality. Wind model has been considered as not having the capability to control voltages. Dynamic shunt compensators (STATCOM) modelled as a PV node used to control the voltage by a flexible adjustment of reactive power exchanged with the network.

## **2. Review of optimization methods**

The optimal power flow (OPF) problem is one of the important problems in operation and control of large modern power systems. The main objective of a practical OPF strategy is to determine the optimal operating state of a power system by optimizing a particular objective while satisfying certain specified physical and security constraints. In its most 32 Electrical Generation and Distribution Systems and Power Quality Disturbances

engineers. Optimal placement and sizing of renewable source in practical networks can result in minimizing operational costs, environmental protection, improved voltage regulation, power factor correction, and power loss reduction (Munteau et al., 2008). In recent years many researches developed to exploit efficiently the advantages of these two technologies in power system operation and control (Adamczyk et al., 2010), (Munteau et

Max Max

*min min* 

*Coordinated Model based: FACTS-Wind Energy* 

Fig. 1. Optimal power flow control strategy based hybrid model: wind and Shunt FACTS

voltage by a flexible adjustment of reactive power exchanged with the network.

In this study a combined flexible model based wind source and shunt FACTS devices proposed to adjust dynamically the active power delivered from wind source and the reactive power exchanged between the shunt FACTS and the network to enhance the power quality. Wind model has been considered as not having the capability to control voltages. Dynamic shunt compensators (STATCOM) modelled as a PV node used to control the

The optimal power flow (OPF) problem is one of the important problems in operation and control of large modern power systems. The main objective of a practical OPF strategy is to determine the optimal operating state of a power system by optimizing a particular objective while satisfying certain specified physical and security constraints. In its most

**Active Power planning** 

**G1 G2 G3 G4 Gi** 

*min* 

Max Max

*min min* 

*PD* 

*Practical Network* 

**Reactive Power planning** 

al., 2008).

*Power QualityIndices* 

**2. Review of optimization methods** 

*-Voltage deviation -Power loss -Voltage Stability -Pollution Control -Service contunuity*  general formulation, the optimal power flow (OPF) is a nonlinear, non-convex, large-scale, static optimization problem with both continuous and discrete control variables. It becomes even more complex when various types of practical generators constraints are taken in consideration, and with the growth integration of new technologies known as Renewable source and FACTS Controllers. Fig. 2 sumuarizes the basic optimization categories used by researchers to analysis and enhance the optimal power flow solution.

In first category many conventional optimization techniques have been applied to solve the OPF problem, this category includes, linear programming (LP) (Sttot et al., 1979), nonlinear programming (NLP) (Wood et al., 1984), quadratic programming (Huneault et al., 1991), and interior point methods (Momoh et al., 1999).

Fig. 2. Presentation of optimization methods: *Global, Conventional*, and *hybrids* methods

All these techniques rely on convexity to find the global optimum; the methods based on these assumptions do not guarantee to find the global optimum when taking in consideration the practical generators constraints (Prohibited zones, Valve point effect), (Huneault et al., 1991) present a review of the major contributions in this area. During the last two decades, the interest in applying global optimization methods in power system field has grown rapidly.

The second category includes many heuristique and stochastic optimization methods known as Global Optimization Techniques. (Bansal, 2005) represents the major

Optimal Location and Control of

respectively.

**G1** 

**Gn** 

constraints).

can be written in the following form:

**2.1 Standard Optimal Power Flow formulation** 

**Reactive Power** 

Security Contraints **G1** 

*Max* 

*Min* 

two coordinated sub problemes: **a. Active Power Planning** 

*Min* 

*Max* 

*Generation* 

In general, the state vector includes bus voltage angles

Fig. 3. Optimal power flow (OPF) strategy

Multi Hybrid Model Based Wind-Shunt FACTS to Enhance Power Quality 35

The OPF problem is considered as a general minimization problem with constraints, and

Min *f xu* (,) (1)

Subject to: *gxu* (,) 0 = (2)

*u uu* min ≤ ≤ max (5)

Where; *f* (,) *x u* is the objective function, *gxu* (,) and *hxu* (,) are respectively the set of equality and inequality constraints. The vector of state and control variables are denoted by x and u

*ng*

*i*

*Voltage* 

*<sup>i</sup>* =∑∑ <sup>+</sup> = 1 = 1

**Active Power** *Active Power* 

*PG PD Ploss n i*

*i*

*Frequency* 

*Indices of Power Quality* 

slack bus real power generation *Pg*, *slack* and generator reactive power *Qg* . Fig. 3 shows the optimal power flow strategy. The problem of optimal power flow can be decomposed in

The main role of economic dispatch is to minimize the total generation cost of the power system but still satisfying specified constraints (generators constraints and security

δ

**Fn** 

*Reactive Power* 

, load bus voltage magnitudes*VL* ,

*Demand* 

**U Umax** 

**Umin** 

**Fmax** 

**Fmin** 

*hxu* (,) 0 ≤ (3)

min max *x xx* ≤ ≤ (4)

contributions in this area. (Chiang, 2005) presents an improved genetic algorithm for power economic dispatch of units with valve-point effects and multiple fuels. (Chien, 2008) present a novel string structure for solving the economic dispatch through genetic algorithm (GA). To accelerate the search process (Pothiya et al., 2008) proposed a multiple tabu search algorithm (MTS) to solve the dynamic economic dispatch (ED) problem with generator constraints, simulation results prove that this approach is able to reduce the computational time compared to the conventional approaches. (Gaing, 2003) present an efficient particle swarm optimization to solving the economic dispatch with consideration of practical generator constraints, the proposed algorithm applied with success to many standard networks. Based on experience and simulation results, these classes of methods do not always guarantee global best solutions. Differential evolution (DE) is one of the most prominent new generation EAs, proposed by Storn and Price (Storn et al., 1995), to exhibit consistent and reliable performance in nonlinear and multimodal environment (price et al., 2005) and proven effective for constrained optimization problems. The main advantages of DE are: simple to program, few control parameters, high convergence characteristics. In power system field DE has received great attention in solving economic power dispatch (EPD) problems with consideration of discontinuous fuel cost functions.

The third category includes, a variety of combined methods based conventional (mathematical methods) and global optimization techniques like (GA-QP), artificial techniques with metaheuristic mehtods, like 'Fuzzy-GA', 'ANN-GA', 'Fuzzy-PSO'. Many modified DE have been proposed to enhance the optimal solution, (Coelho et al., 2009) present a hybrid method which combines the differential evolution (DE) and Evolutionary algorithms (EAs), with cultural algorithm (CA) to solve the economic dispatch problems associated with the valve-point effect. Very recently, a new optimization concept, based on Biogeography, has been proposed by Dan Simon (Simon, D., 2008), Biogeography describes how species migrate from one island to another, how new species arise, and how species become extinct.

To overcome the drawbacks of the conventional methods related to the form of the cost function, and to reduce the computational time related to the large space search required by many methaheuristic methods, like GA, (Mahdad, B. et al., 2010) proposed an efficient decomposed GA for the solution of large-scale OPF with consideration of shunt FACTS devices under severe loading conditions, (Mahdad, B. et al., 2009) present a parallel PSO based decomposed network to solve the ED with consideration of practical generators constraints.

This chapter presents a hybrid controller model based wind source and dynamic shunt FACTS devices (STATCOM Controller) to improve the power system operation and control. Choosing the type of FACTS devices and deciding the installation location and control of multi shunt FACTS coordinated with multi wind source is a vital research area. A simple algorithm based differential evolution (DE) proposed to find the optimal reactive power exchanged between shunt FACTS devices and the network in the presence of multi wind source. The minimum fuel cost, system loadability and loss minimization are considered as a measure of power system quality. The proposed methodology is verified on many practical electrical network at normal and at critical situations (sever loading conditions, contingency). Simulation results show that the optimal coordination operating points of shunt FACTS (STATCOM) devices and wind source enhance the power system security.
