The scenario of congestion can arise from various factors, such as excess demand in power from the consumer side, sudden tripping of the generators, unforeseen power flows, and transmission line trips etc. The state of congestion can severely disturb the system reliability, instigate sharp increase in the electricity prices for power transactions, and can affect the continuity in the power transmission to the consumers. It is the system operator's key responsibility to monitor and address congestion to prevent these issues. Thus, the implementation of effective Congestion Management (CM) strategies is essential for reliable operations. Traditional CM methods that are adopted by the system operators are generator rescheduling, integrating reactive power support, managing consumer demand, implementing demand response programs, and building new transmission infrastructure. The challenge for power system researchers lies in applying these CM techniques at the lowest operational cost, offering considerable potential for improvement through the use of advanced mathematical methods.
The researchers in the field of power systems have made notable advancements in CM by implementing diverse strategies to prevent and mitigate issues caused by the demand for electricity transmission exceeding network capacity. Wang and colleagues explored CM through system frequency monitoring and the integration of distributed energy resources, which also serve as providers of ancillary services. Attar et al. explored CM by proposing a platform to unlock flexibility from various resources through market integration of TSO-DSO coordination, and facilitate data access via a metadata register for improved SO decision-making. Zakaryaseraji and Ghasemi-Marzbali combined the influence distribution generation and the effect of demand response to investigate CM approaches based on the consumer. Roustaei et al. proposed a voltage stability-based CM strategy aimed at improving power flow control while ensuring voltage security within the transmission network. Mishra et al. applied artificial intelligence techniques, including neural networks to address congestion. Dehnavi et al. introduced a novel CM framework based on adapting a segregation and zonal approach in the topology of power system network with restructured electricity market.
In the power system operations, the Generator Rescheduling (GR) serves as a vital strategy for CM by optimizing power generation schedules to mitigate congestion in transmission networks. This process enables better utilization of the already existing infrastructure of the transmission lines, while minimizing the urge for rapid transmission system expansion. By adjusting the output of generators, system operators can relieve congestion, thereby promoting stable operating conditions. Additionally, the combination of the GR and the renewable energy system also provide cost-effective generation dispatch. Subramaniyan and Gomathi used of soft computing methods, such as fuzzy logic and genetic algorithms, to enhance congestion management in transmission networks through GR. Their study highlighted the potential of these techniques to streamline the CM process and achieve efficient power system operation. Similarly, Agrawal et al. proposed a cascaded deep neural network-based framework to facilitate customer participation in CM. Their approach emphasized the role of renewable energy and distributed energy resources (DERs) in optimizing GR, which contributes to congestion relief in deregulated power markets. Chakravarthi et al. included the design of controllers to support efficient generator rescheduling, ensuring the timely mitigation of congestion in power systems. Thiruvel et al. managed congestion that incorporates demand response and accounts for the uncertainties of renewable energy sources. Their strategy also incorporated DG and generator rescheduling to enhance congestion management. Similarly, Saravanan and Anbalagan introduced an intelligent hybrid technique that combined genetic algorithms (GA) with PSO to achieve optimal GR, thereby mitigating congestion in deregulated power markets. Haq et al. introduced a game-theoretic method for CM, leveraging plug-in electric vehicles (PEVs) in conjunction with GR. Their approach involved adjusting the charging and discharging schedules of PEVs to support congestion relief. Verma and Mukherjee developed a CM strategy based on real power GR using an ant lion optimizer. Their approach focused on optimizing generator schedules to alleviate congestion and improve power system performance.
The influence of renewable energy on the power system network plays a significant role in managing the power flow in the transmission line. In, Mouassa et al. introduced a Dwarf Mongoose Optimization Algorithm to address the stochastic optimal power flow in power systems with renewable energy sources, achieving superior performance in minimizing generation costs, transmission losses, and environmental. In another research, Mohamed et al. considered Chaotic African Vultures Optimization Algorithm to maintain optimal power flow incorporating Weibull-based wind power predictions, penalty and reserve costs, and FACTS devices, achieving superior performance over AVOA in cost reduction, power loss minimization. Swirydowicz et al. proposed a GPU-native sparse direct solver for addressing the issue of power flow congestion and economic dispatch considering GPU hardware from both AMD and NVIDIA to accelerate sparse linear system computations while achieving significant performance improvements and highlighting opportunities for further optimization in heterogeneous computing environments. Gracia et al. studied the architecture of the bipolar DC grid to control the power flow and alleviate congestion with asymmetric loading, transforming a nonlinear programming (NLP) model. Naderi et al. proposed a hybrid wavelet mutation-based algorithm to solve the congestion with multi-fuel constraints, optimizing generation cost, emissions, power loss, and voltage deviation. Khani et al. proposed a bi-level stochastic model for integrated energy management in interconnected transmission and distribution networks, incorporating renewable energy to reduce the risk of overloading in the transmission network. Ullah et al. proposed a hybrid PSO-GSA algorithm for optimal energy trading in interconnected microgrids (MGs) for optimizing the power flow considering the influence uncertainties in the renewable energy utilization.
The operation of competitive energy markets has driven the need for advanced optimization algorithms that effectively address power system challenges, yielding accurate and improved results. Traditionally, power system researchers have relied on deterministic methods, including classical approaches, to solve the optimal power generation and dispatch problem. These issues have been tackled using various traditional techniques in convex optimization. However, the use of these deterministic methods is limited by constraints. In such cases, the solutions derived from these methods are highly dependent on the initial conditions, which may lead to local optima. To overcome these limitations, stochastic methods offer a more efficient solution by bypassing the issues inherent in deterministic approaches. This has led to the adoption of heuristic and metaheuristic techniques, which are better equipped to provide optimal solutions compared to deterministic methods. Many researchers have turned to these techniques to find effective solutions for optimal power generation, ensuring a reliable and sustainable power flow through transmission networks. The application of the heuristics techniques for the optimal operation of the power system can be found in the areas like system risk mitigation in competitive systems with artificial gorilla troops, IPD-(1 + I) controller for frequency control in a power system with sine-cosine algorithmic technique, particle swarm optimization for maximum power point tracking in inverters. A well-chosen selection and proper tuning of control parameters in meta-heuristic methods significantly influence the quality of the solutions obtained. These techniques offer a practical approach to finding optimal solutions while reducing computational effort. The primary objective of this study is to design and implement an effective optimization strategy aimed at addressing the CM problem, with a focus on achieving substantial cost reduction in the final outcome.
In this research, the Manta Ray Forge Optimization (MRFO) has been introduced with the hybridization of Sine Cosine Algorithm (SCA) for obtaining better output by avoiding the MRFO getting trapped in the local optima. The selection of MRFO and SCA for this research has been based on the "No Free Lunch" (NFL) theorems for optimization assert that no optimization algorithm can outperform all others across every possible problem. In essence, if an algorithm shows strong performance on a particular group of problems, it must, on average, perform less effectively on other problems. According to this Manta-Ray Forge Optimization (MRFO) has contributed appreciable outcome for the power system optimization problem like ELD, optimal sizing of renewable energy Load frequency control, DG placement, power transfer capabilitiesbut in many cases it has struggled to in its exploration and exploitation phases and have confined the results in the region of local optima. Moreover, in addition to this, it has been observed that the Sine Cosine Algorithm (SCA) has also delivered significant improved results in the field of power system like LFC, ELD, Parameter estimation solar PV reactive power dispatch and it has been also used a part of hybridization to improve the phases of exploration and exploitation in several recent optimization techniques that has delivered appreciable outcome like in the field of its SCA-Jaya optimization for OPF, SCA-Chimp optimization based feature selection, automatic generation control system based on hybrid Aquila Optimizer-SCA, reactive power optimization. From these literatures it can be considered that the inclusion of the features of SCA in the MRFO to form a hybrid MRFO-SCA that can also deliver significant improvement in the results for the considered CM problem with the application of the proposed MRFO-SCA.
This study introduces a hybrid Manta Ray Forge Optimization-Sine Cosine Algorithm (MRFO-SCA) to address the CM cost optimization challenge, aiming to efficiently manage power generation while integrating a WES to reduce congestion costs. The optimal location for WES integration is identified using Bus Sensitivity Factors (BSF). Incorporating WES not only mitigates power flow violations but also reduces real power losses and enhances system voltage.
The MRFO-SCA has been applied to the IEEE 30-bus system to evaluate its effectiveness in minimizing CM costs while reducing system losses and improving voltage levels. Furthermore, a statistical comparison of MRFO-SCA with other optimization techniques has been conducted to demonstrate its efficiency. For additional benchmarking, the CM problem has been solved using Firefly Algorithm (FFA), Particle Swarm Optimization (PSO), African Vulture Optimization Algorithm (AVOA), Sine-Cosine Algorithm(SCA), Aquila Optimizer (AO), and MRFO, showcasing the relative performance of the MRFO-SCA.