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Congestion Management in Power System- A Review

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  Congestion Management in Power System: A Review   Nurul Idayu Yusoff,  Abdullah Asuhaimi Mohd Zin,  Azhar Bin Khairuddin Faculty of Electrical Engineering, Universiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia  nurulidayuyusoff@gmail.com,  abdullah@fke.utm   my,  azhar@fke.utm.my  Abstract   — The development of deregulated power systems has resulted in overloading transmission networks or network congestion. Congestion has serious effects on power systems, including severe system damage. Congestion occurs when transmission networks fail to transfer power based on the load demand. These problems are managed using congestion management methods, which play an important role in current deregulated power systems. Several methods have been proposed to manage congestion. This paper reviews some congestion management methods, including Generators Rescheduling (GR), load shedding, optimal location of Distributed Generation (DG), Nodal Pricing, cost free methods, Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Mixed Integer Nonlinear Programming (MINLP), Shuffled Frog Leaping Algorithm (SFLA), Fuzzy-Logic System approaches and the additional renewable energy sources. The work of various publications is used to review the significance of each proposed technique in relieving congestion and minimizing system operating costs.  Keywords—power system; congestion management; congestion; optimization; methods. I.   I  NTRODUCTION Currently, the power industry in many countries is undergoing restructuring and deregulation with the replacement of previous monolithic regulated public utilities with competitive power markets [1]. This is to meet increasing demands for electricity around the world at affordable prices. This caused major problems of congestion in the transmission lines of deregulated electric power systems., endangering transmission system security [2]. Congestion management needs to be immediate performed so that congested systems are relieved or avoided. There are many reasons mentioned in technical literatures for power system congestion. In competitive market, congestion occurs when transmission networks are unable to accommodate all desired transactions due to violations of the system operating limits [3]. Congestion refers to an overloading transmission lines when the thermal  bounds and line capacities are violated [4]. Congestion also occurs when power flows in the transmission line are higher than the flow allowed by operating reliability limits [5]. The physical and system limitations of transmission network are mentioned as one of the factors and phenomena cause congestion on transmission lines [6]. Physical limitations such as thermal limitation of a transmission line or a transformer can be effective in the occurrence of congestion. Voltage limitation in a node, transient stability, dynamic stability, reliability and similar cases are some of the examples of system limitations of transmission network which contributed to congestion of the network. Congestion occurs due to the absence of matching generation and transmission services. Congestion is also caused by unexpected eventualities such as generation outages, unexpected escalation of load demand, and equipment failure [7]. Congestion in the power systems should be rectified immediately to ensure system security and to avoid further block-outs. The occurrence of congestion in power systems leads to system disturbances that cause further outages in an interconnected system. Congestion is also caused by grave damage to power system components if system outages frequently occur. It is not only equipment which is harmed  by congestion, but also the power quality [8]. To prevent  power system equipment from being damaged and to enhance power quality, congestion systems need to function immediately. The importance of congestion management is discussed in reference [6], which identify the congestion management as one the key issues to maintain security and reliability of transmission networks. Congestion management balances the system and solves financial issues arising from the congestion. Lack of attention to congestion in the system may lead to widespread blackouts associated with negative social and economic consequences. Many studies have been carried out to determine the best congestion management approaches for preventing congested transmission lines despite increased electricity demand. Some of the methods used are Generators Rescheduling (GR), load shedding, Distributed Generation (DG), Optimal Power Flow (OPF), Flexible Alternating Current Transformer System (FACTS) devices, Genetic Algorithms (GA), and Particle Swarm Optimization (PSO). These techniques have their own advantages and disadvantages. II.C ONGESTION M ANAGEMENT T ECHNIQUES Congestion management is described using technical methods and non-technical methods as in Fig. 1 [9], [10]. 22 978-1-5090-5353-7/17/$31.00 ©2017 IEEE  Technical methods are cost-free methods which take into consideration outages in congested lines and do not cause economic effect. Some methods are the use of FACTS and the operation of transformer taps or phase shifters. Non-technical methods or non-cost free methods take into consideration security-constrained generation dispatches, network security factors methods, congestion pricing, and market-based methods. A few common methods that are Generators Rescheduling (GR), load shedding, Distributed Generations (DG), Demand Response (DR), and nodal  pricing schemes [11]. This paper reviews some of the methods and techniques used for relieving congestion in power systems by dividing these methods and techniques into several conservative sections. Congestion management methods are based on  power market, conventional optimization methods, and congestion management techniques are based on artificial intelligence. III.C ONGESTION M ANAGEMENT B ASED ON P OWER M ARKET Different models for power market have been developedall over the world to manage congested systems. Methods that are well-known in power markets for congestion management are the GR, load shedding, market splitting, nodal pricing, and DG. GR involves the selections the appropriate generators to  be rescheduled to meet load demands. The literatures [12], [13] have presented GR techniques to manage congestion in  power systems. Both studies focused on minimizing generation and reducing load operational costs. From reference [14], GR approach is a common method in congestion management, but it is slow and ineffective. An optimization in rescheduling generators methods was  performed with the use of artificial intelligence algorithms such as GA [15-17] and PSO [18]. Fig. 1. Summary of Congestion Management Methods [9] GR also performed based on the real and reactive power rescheduling with the virtual power flow through the overloaded line [19]. The concept of virtual power flows is implemented based on the principle of superposition and DC load flow, which contributed to a simple and faster rescheduling method. The literature [20] proposed the optimal rescheduling of active power generators based on Generator Sensitivity. The re-dispatch generators are selected based on large magnitude of generator sensitivity, which required a proper iteration of mathematical analysis to avoid errors, while maintaining the efficiency of GR method.  Nodal pricing methods were introduced for the efficient use of transmission grids and generation resources with the  provision of appropriate economic signals. The marginal cost of providing the next increment of power at a bus is known as a nodal pricing. Each participant involved in this congestion management was charged based on the nodal  price available in the system. The literatures [21], [22] have  presented nodal pricing method for congestion management and has proved that the technique is a market based pricing schemes, in which the costs of the system change based on the selected nodal price. The literature [22] proposed a congestion management technique called DG based on Locational Marginal Price (LMP) schemes. This method places DG in the system to relieve congestion and minimizes generation costs. DG is a method for congestion management. LMP calculation has  become a popular method in restructured power markets to address the congestion price, which is proposed in the literature [23]. LMP’s at all buses are calculated considering the concentrated loss model and a distributed loss model to remove the high mismatch at the slack bus. The distributed loss model considering linear bids shows reduced generation fuel cost compared to concentrated loss model, which both indicate the performance of the LMP calculation method in managing congestion. The literature [24] has conducted a comprehensive study for optimal placement of DG considering minimization of  power or energy losses, enhancement of voltage stability, and the improvement of voltage profile. The paper proposed an attempt of summarized the existing approaches on the DG methods in solving the congestion system. The optimum  placement of DG is necessary to achieve the maximum  benefits with less investment cost, hence the Optimal Allocation of Distributed Generation (OADG) is introduced to resolve the issues related to DG’s sizing and suitable  placement with multi-objective considerations. DR is a method for congestion management market  based on power markets. DR provides opportunities to customers to participate in the energy market by enabling them to change their consumption based incentive payments  provided [25], [26]. Studies [27], [28] discuss DR programs implemented in power systems to relieve congestion. These  papers claim that the DR approach plays a major rule in the competitive electricity market that helps both generation and load remain stable [27], [28]. DR is proposed with a better performance with the combination of FACTS devices and DR program for relieving congestion in system [7]. The effectiveness of the method is illustrated by a representative market clearing study in which various options of using FACTS devices and/or DR are compared, which showed the combination 23  method is better than others. The literature [3] also proposed the combination of FACTS and DR program used to minimize the total congestion costs and maximize the social welfare. FACTS and DR combination method provides the most reliable and efficient solution to relieve congestion in the system. IV.C ONVETIONAL O PTIMIZATION M ETHODS Conventional optimization methods are used to manage congested power systems. The methods are advanced methods that used FACTS devices as an alternative to efficiently minimize the power flows in the system especially during the heavy demands [29]. The use of FACTS devices helps improve power capability, lower system losses, and increase system stability by controlling power flows. Optimization methods are needed whenever FACTS devices are used in congestion management so that optimal  performance is achieved [30]. Optimization methods include the Mixed Integer Nonlinear Programming (MINLP), Unified Power Flow Controllers (UPFC), and the promotion of renewable energy. Papers [31-33] have presented an improved method for congestion management using the optimal placement of Thyristor Controlled Series Compensators (TCSC) in transmission networks. The MINLP was formulated to determine the location of TCSCs in the system [32]. The optimization method was introduced to minimize the reactive  power procurement cost of the method. TCSC is used to remove congestion with minimum cost of installation by optimization approach in locating the devices [34]. The congestion is solved by finding the optimal location of TCSC using the sensitivity analysis technique, and claimed that the method proposed has successfully relieved the congestion. FACTS devices can control the  power flow in a fast manner, hence improve the power transfer capability limit of the line. Some benefits of using FACTS devices are summarized in the literature [35], such as the capability of reducing cost of production and increase the load ability and others. UPFC is FACTS device and the most effective device for congestion management [36]. The optimization of UPFC is introduced to minimize generation costs and relieve congestion systems. An optimal model for managing congestion was proposed with concern for the promotion of Renewable Energy Sources (RES) in the power system network [36]. The literature [31] presented the wind power system with additions of FACTS devices in the system, and showed improvement in transfer capabilities and congestion relief. A stochastic self-scheduling of RES considering Compressed Air Energy Storage (CAES) is proposed with the presence of DR program [37]. RESs in this paper is included the used of wind turbine (WT) and Photovoltaic (PV) system simultaneously. Mixed Integer Linear Programming (MILP) is formulated in the model and being solved optimally by using GAMS-based optimization method. The effects of DR program and CAES on self-scheduling problem are assessed with utilization of four case studies, which indicate the validity of the proposed stochastic  program based on the remarkable results obtained. V.   A RTIFICIAL I  NTELLIGENCE A PPROACHES  Artificial intelligence approaches are methods for developing numerical algorithms using computer based technologies to solve congestion in power system networks. Several techniques are reviewed in this section, such as the PSO, GA, Fuzzy-Logic systems, Simulated Annealing (SA) algorithm, General Algebraic Modelling Systems (GAMS)  based optimization, Artificial Bee Colony (ABC) algorithm, Fish School Optimization (FSO) algorithm, Flower Pollination Algorithm (FLA), Strength Pareto Evolutionary Algorithm (SPEA), Multiobjective Evolutionary Algorithm (MOEA) and SFLA. The literatures [38-40] presented an algorithm of PSO as a method used to manage congestion in power markets to minimize generator rescheduling costs and relieve congestion. IEEE 30-bus system is implemented and analyzed with the use of PSO and at the end proved that PSO is a superior method for congestion management [19]. Security constraints from both load bus voltage and line loading are efficiently handled with the PSO technique [38]. The literature [39] used PSO algorithms for managing congestion in deregulated power systems. The proposed approach efficiently relieves line overloads and has been compared with the DR approach. The main concern in managing congestion is the lines limits transfer capability and available generation capacity. Study [16], [17], [40] proposed a GA to solve the numerical formulated problem for system congestion. GA method are used with FACTS devices or GR method which functioned as a combination of congestion management techniques. The proposed methods are useful optimization tools able to relieve congestion and minimize system costs [41]. Optimization by using a multi objective approach, Non-dominated Sorting Genetic Algorithm II (NSGA-II) is used to solve the optimization problem in a GR congestion method [42]. The paper proposed congestion management method with two objectives; optimizing the transmission line overload, and the congestion costs in the system, hence introduced the NSGA-II optimization method to be used. The proposed methods effectively relieve the congestion economically with the minimum shifts in GR method, which indicates that NSGA-II method is an efficient method required when dealing with more than one objective of congestion management. Fuzzy Logic optimization approach is proposed by literature [43] with the purposed of control active power flow for congestion management by using FACTS devices. The effectiveness of the algorithm has been tested successfully and the congestion is relieved without the rescheduling  process in the system. The literature [44] has proposed a Fuzzy Based technique in determining the optimal location of TCSC to control the active power flow and reduction of congestion in a transmission line. The proposed method helped to form a priority list of optimal locations for TCSC 24  and avoid excessive computation for congestion, which indicates the effectiveness of the fuzzy optimization method. As deregulated power markets suffer from congestion, FACTS devices are used to minimize power flows in loaded lines to increase stability and reduce power losses [43], [45]. The literatures [44, 46, 47] presented a fuzzy technique to select optimal locations for FACTS devices to control active  power flows and relieve congestion in the transmission line. Study [46] proposes optimization approaches for the optimal choice, location, and size of TCSC and Static Var Compensators (SVC) to reduce congestion, reduce line losses, and improve voltage stability with the used of Differential Evolution (DE). Studies [48] presented an approach for selecting optimal locations and capacities for multiple TCSC to relieve congestion issues rise in the system. Optimal location of UPFC in relieving congestion is  proposed with the used of SA algorithm [49]. UPFC has both shunt and series compensation principles, which believed to increase the ability transfer capability and stabilizes system from congestion conditions [50]. The SA is an outstanding method used to find the optimal location of UPFC, thus claimed to be a powerful technique for determination optimization issues. It is found that SA algorithm is capable to improve the UPFC system, hence improving the system’s ability in reducing congestion. GAMS based optimization method is used in solving the nonlinear programming problem in optimizing the location of TCSC [51]. OPF and sensitivity based methods have been used to minimize the operational cost and obtaining the optimal location of TCSC in a complex network, respectively. Both methods are optimized by GAMS and the results are compared, hence it is observed that GAMS is an efficient optimization methods used for both OPF and sensitivity based methods. GAMS is stated as a high-level modelling system for mathematical programming and optimization used for complex, large scale modelling applications [52]. It is designed specifically to solve the problem involved with linear, nonlinear, and mixed integer optimization problems, which identified useful with large, and complex problems [52], [53]. GAMS-based optimization is found to be more accurate and reliable than the RMS-based solution in simultaneously solve the mathematical programming  problems with multiobjective optimization. Meanwhile, ABC algorithm is proposed as the congestion management method used, which optimally rescheduling the active power in system [54]. ABC algorithms are a new artificial intelligence method of congestion management, which is inspired from the intelligent foraging behavior of the honey bee swarms. The developed method was tested and the results show that the ABC algorithm is capable to relieve the congestion management problem in the system. The literature [55] proposed a technique to manage congestion considering both economical and voltage deviation aspects, by using ABC and FSO algorithms. These methods are implemented with the purposed of achieving the minimization of cost for managing congestion and ensuring minimum voltage deviation after load disturbance. The results obtained are compared with the PSO algorithm method, and confirmed that the cost of rescheduling using FSO is less in all cases compared to other methods considered. Hence, concluded that the ABC and FSO optimization methods are useful in congestion management with economic and technical considerations. Generator Sensitivity Factor (GSF) and FPA were used to decide the number of generators to be used in GR methods of congestion management [56], [57]. The authors concluded that FPA is the best approach used for relieving system congestion by speeding up decision making for GR methods [57]. GR-based approach to congestion management in electricity market using a novel Ant Lion Optimizer (ALO) algorithm is proposed effectively in literature [58]. The  proposed ALO method outcomes are compared with several well-known algorithms (PSO, SA, FPA, DE and RSM), and found that the cost of GR is reduced with the used of ALO- based algorithm compared to other methods. ALO algorithm is claimed as a new and effective approach to solve congestion management problem, which performed a fastest optimization approach for the considered test cases compared to various adopted algorithms implemented in the  paper. The transmission line congestion and the usage of FACTS devices are significantly linked due to their role in  power delivery system improvement. Study [59] shows the advancement of FACTS devices with the optimization approaches using the SPEA. The proposed algorithm optimizes the location and size of TCSC and SVC devices in a system. A multiobjective optimization approach is  proposed in literature [60], to minimize the energy usage cost with minimum delay in a smart grid system. It is stated that the major problem is unable to satisfy consumer energy demand during peak hours. Thus, MOEA is implemented to optimize the load balancing method used in the congestion management. A real-time DR algorithm is proposed with the optimization approach by Stackelberg Game-based DR (SGDR) algorithm [61]. These optimization algorithms are formulated for achieving the optimal load control of devices in response to real-time price changes with a trivial computation burden. The proposed SGDR was found able to induce less energy consumption, and achieved the efficiency in load management of the system. The literature [62]  proposed a Game-Theoretic (GT) approach to optimize the Time-of-Use (TOU) pricing schemes in a DR program. The  proposed method showed an effective result in levelling the user demand by setting optimal TOU prices, potentially decreasing costs for the utility companies, and increasing user benefits. Therefore, indicates that the GT-TOU optimization method is useful in optimally improving the DR congestion method. Meanwhile, an effective heuristic optimization framework for SFLA was proposed by [59], [63] with the objective of managing congestion problems in a system. The 

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