## 6 Congestion Management
Another comparison [233] of three meta-heuristic techniques, simulated annealing, the tabu search method, and the genetic algorithm, made with five FACTS devices (SVC, TCSC, TCVR, TCPST, and UPFC) in the IEEE-118 bus network system concluded that the simulated annealing algorithm is less beneficial compared to the other compared algorithms. In the literature [234], the whale optimization algorithm is compared with grey wolf optimization, differential evolution, the quasi-opposition-based grey wolf optimization algorithm, and quasi-opposition-based differential evolution to minimize active power loss and operating costs while maintaining voltage profiles within permissible limits in IEEE-30 and 57 bus systems. It is observed that the whale optimization algorithm is superior and more effective than other algorithms under consideration because it provides reliable guidance for optimal coordination of FACTS devices (SVC and TCSC). Moreover, in [235], an imperialistic competitive algorithm approach was compared with pattern search, the gravitational search algorithm, non-linear programming, evolutionary programming, bat swarm optimization, asexual reproduction optimization, and the backtracking search algorithm to allocate FACTS devices (TCPST and TCSC) for relieving the power congestion (overloads and voltage deviations) in IEEE-39 bus systems. Results clearly show the usefulness and better performance of the imperialistic competitive algorithm than other meta-heuristic algorithms under study. Furthermore,another meta-heuristic algorithm named the improved harmony search algorithm [236] shows its superior performance compared to the harmonic search algorithm, improved genetic algorithm, tabu search, improved evolutionary programming, gradient method, genetic algorithm, modified differential evolution algorithm, refined genetic algorithm, and evolutionary programming techniques [236]. A detailed review of meta-heuristics optimization techniques to optimally place and size multiple FACTS devices in power systems is presented in [171]. In general, optimization techniques have their own importance for congestion relief. However, the selection of a particular optimization technique for a particular power network is debatable and out of the scope of this paper. But meta-heuristic techniques are preferred by researchers nowadays. Useful conclusions about congestion management and the role of FACTS devices in deregulated power systems are discussed below. 1. FACTS devices are one of the dominant approaches used in practical systems to provide congestion management. However, FACTS devices provide congestion management in three different ways, i.e., optimal placement of FACTS devices, price- or cost-based analysis, and sensitivity index [237]. Figure 3: Classification of meta-heuristic techniques. 2. Congestion management has two methods, i.e., CFM and NCFM. However, CFM is more popular and easier to implement compared to NCFM because the marginal cost (and not capital cost) is minimal. 3. The UPFC is the most effective FACTS controller used for congestion management. In fact, it provides maximum improvement in system load-ability, but the cost of installation is high, which is a major drawback of UPFCs economically. 4. The UPFC provides static system load-ability more effectively than the TCPST, TCVR, SVC, TCSC, SSSC, and STATCOM. 5. The TCSC is the most popular FACTS device in practical applications because it performs better with minimum installation cost. 6. Congestion can be reduced by optimally placed FACTS devices. Therefore, the best place for SVCs is those weak buses that have higher voltage deviation in response to increases in load-ability. Similarly, for TCSCs the best place is those transmission lines that have a higher value of the line stability index. And UPFCs should be placed at those transmission lines having higher active power. 7. FACTS devices reduced more congestion in combination with another FACTS devices or other conventional devices as compared to their individual usage. 8.