Transmission Line Congestion Research Articles

GIANT TREVALLY OPTIMIZED CONGESTION MANAGEMENT USING FACTS CONTROLLER ALLOCATION IN DEREGULATED ELECTRICITY MARKETS

One of the technical issues arising from deregulation is transmission line congestion. The size and location of FACTS controllers like thyristor-controlled series compensators (TCSCs) and static VAR compensator (SVC) devices significantly affect their efficiency in congestion management problems. As a nonlinear problem, locating and sizing these devices in a power network is difficult. To solve this issue, this paper presents the technique for optimal FACTS placement using the giant trevally optimizer (GTO) algorithm for congestion management (CM). Three objective functions are considered to reduce the congestion, including voltage stability and eliminating real power loss overall cost. To determine the best location for FACTS devices in the MATLAB R2020b tool, the suggested GTO approach is implemented and discussed for the IEEE 14-bus and IEEE 30-bus systems. It is also compared with existing GSA, BBO, and ICSA approaches under three loading situations. From the simulation results, GTO minimized total costs and real power losses better than existing algorithm algorithms, and GTO provides high net saving costs.

A Novel Hybrid Harris Hawk Optimization–Sine Cosine Algorithm for Congestion Control in Power Transmission Network

In a deregulated power system, managing congestion is crucial for effective operation and control. The goal of congestion management is to alleviate transmission line congestion while adhering to system constraints at minimal cost. This research proposes a hybrid Harris Hawk Optimization–Sine Cosine Algorithm (hHHO-SCA) for an efficient generation rescheduling approach to achieve the lowest possible congestion cost. The hybridization has been performed by introducing the features of SCA in the HHO to boost the exploration and exploitation steps of HHO, providing an efficient global solution and effectively optimizing rescheduled power output. The effectiveness of this methodology is evaluated using IEEE 30 and IEEE 118-bus test systems, taking into account system parameters. The potency of the proposed method is analyzed by comparing the results of the hHHO-SCA with those from other recent optimization techniques. The findings show that the hHHO-SCA outperforms other methods by avoiding local optima and demonstrating promising convergence characteristics.

Comparative Analysis of Congestion Management Methods with Integrated Renewable Energy Generation

Inclusion of renewable energy in power grid at micro and macro level has imposed numerous challenges in the recent years. Occurrence and managing congestion in the power transmission line due to unpredictable and stochastic nature of Renewable Energy Source (RES) integration has become a challenging task to the grid operators. Transmission lines operate at bottlenecks during a congestion episode adding to the extra congestion cost and risk in grid stability which becomes burden to the generation as well as end users. Different methodologies are applied to detect and manage the congestion to eliminate the congestion cost factor and maintain grid stability. The presented analysis compares conventional methodology Linear Sensitivity Factors (LSF) and Heuristic methodology Particle Swarm Optimization (PSO) and Artificial Neural Network (ANN) methods for congestion management in power transmission lines with integrated RES (wind and solar) system. For comparative analysis, a modified standard IEEE 30 Bus system is chosen which is integrated with real time RES generation. Optimal locations of RES generation with minimized congestion cost and percentage of RES curtailment are chosen as objective function for comparison of the methodologies.

Optimal Reactive Power Dispatch and Demand Response in Electricity Market Using Multi-Objective Grasshopper Optimization Algorithm

Optimal Reactive Power Dispatch (ORPD) is a power system optimization tool that modifies system control variables such as bus voltage and transformer tap settings, and it compensates devices’ Volt Ampere Reactive (VAR) output. It is used to decrease real power loss, enhance the voltage profile, and promote stability. Furthermore, several issues have been faced in electricity markets, such as price volatility, transmission line congestion, and an increase in the cost of electricity during peak hours. Programs such as demand response (DR) provide system operators with more control over how small customers participate in lowering peak-hour energy prices and demand. This paper presents an extensive study on ORPD methodologies and DR programs for lowering voltage deviation, limiting cost, and minimizing power losses to create effective and economical operations systems. The main objectives of this work are to minimize costs and losses in the system and reduce voltage variation. The Grasshopper Optimization Algorithm (GOA) and Dragonfly Algorithm (DA) have been implemented successfully to solve this problem. The proposed technique has been evaluated by using the IEEE-30 bus system. The results obtained by the implementation of demand response systems show a considerable reduction in costs and load demands that benefit consumers through DR considerations. The results obtained from the GOA and DA are compared with those generated by other researchers and published in the literature to ascertain the algorithm’s efficiency.

A new bi-level model for the false data injection attack on real-time electricity market considering uncertainties

State Estimation (SE) plays a critical role in various applications of power systems including the electricity market. False Data Injection Attacks (FDIAs) are capable of manipulating the power system SE process aiming to gain financial profits by the players. In this paper, the potential economic influences of FDIA on SE and consequently the Real-Time (RT) electricity market are examined. Therefore, a new bi-level optimization framework is proposed to realistically model the FDIAs from the attacker perspective. At the upper-level, the attacker intends to intelligently choose the most critical congested transmission lines and manipulate their associated meter readings to maximize the expected financial profitability. At the lower-level, the RT market-clearing model is formulated under the influence of FDIA. Considering the incomplete attacker's prior knowledge of the network topology i.e., connection/disconnection of transmission lines, the angles of the phase-shifting transformers, and the real-time load uncertainty, the proposed formulation is extended to a new bi-level scenario-driven stochastic model. The developed attack model, which must remain undetectable by the system operator in all scenarios, is solved using the Karush–Kuhn–Tucker (KKT) approach. The correctness and effectiveness of the proposed optimization scheme have been validated in GAMS software using CONOPT and OQNLP solvers based on the IEEE 14-bus test system and also comparative results are presented to demonstrate the merits of the proposed formulation.

Robust co-planning of transmission expansion and merchant energy storage investments considering long- and short-term uncertainties

The decreasing cost of energy storage technologies and the increasing utilization of renewable energy sources have made distributed transmission-scale energy storage economically viable. Merchant energy storage investors can collect significant profits through spatiotemporal energy arbitrage from congested transmission lines. This paper formulates a three-stage, four-level min–min–max–min problem from the viewpoint of power system operators and planners such that they will build new transmission lines and identify potential locations for profitable merchant energy storage investments. The first level determines transmission expansion decisions from the system operator’s perspective. The second level plans potential investments in merchant energy storage to ensure profitability at a desired rate of return. The third level realizes the worst-case situations of long-term uncertainties related to the expansion of wind farms and peak load growth in each regional zone, while the fourth level simulates market clearing for representative days, modeling short-term uncertainties. A novel iterative nested column and constraint generation (C&CG) technique is adopted to cope with numerical issues caused by binary variables of transmission lines and storage blocks and the enormous size of the problem. The case study of a modified IEEE 24-bus test system was used to assess the effectiveness of the proposed algorithm.

Electrifying the Road to Net-Zero: Implications of Electric Vehicles and Carbon Emission Coefficient Factors in European Power Systems

The global trend is shifting towards adopting low-carbon transportation solutions, with electrification emerging as a prominent approach. The effectiveness of this transition in mitigating climate change hinges significantly on the source of electricity used for charging electric vehicles. This study focuses on four European Union countries: Switzerland, Italy, Germany, and Poland, each characterized by varying levels of carbon emissions from their power systems. Assumptions are made for the short-term (10%), medium-term (30%), and long-term (60%) penetration of electric vehicles, aligning with the 2050 net zero emissions targets. The study investigates the impact of these penetration levels on energy demand, exploring scenarios ranging from 100% renewable source-generated electricity to 100% coal-generated electricity for EV charging. Finally, utilizing PSS®E 35.5 simulation software, the study assesses the implications of the electric vehicles’ load on medium-voltage transmission lines. The findings highlight the substantial influence of electrifying the transport sector on both environmental sustainability and the power system infrastructure, underscoring the critical role of regional energy mixes and the power system carbon emissions coefficient factor. Regions with lower carbon emission coefficient factors witness significant benefits even with a modest transition to electric vehicles, whereas regions with high carbon emission coefficient factors experience minimal impact despite large-scale EV adoption. Additionally, densely populated urban areas may encounter challenges related to transmission line congestion to meet the growing demand for electric vehicle charging.

A novel nature-inspired nutcracker optimizer algorithm for congestion control in power system transmission lines

In the restructured power system, where uncertainties are common, managing congestion becomes a crucial aspect of power system operation and control. Congestion management aims to alleviate the power system transmission line congestion while meeting the system constraints at minimal cost. This research introduces a generation rescheduling method for congestion management in the electricity market, leveraging an innovative nutcracker optimizer algorithm. The nutcracker optimizer algorithm, inspired by nutcrackers’ food accumulation mechanisms, is a recently developed nature-inspired algorithm. The efficacy of this proposed approach is assessed across modified IEEE 30-bus, and IEEE 118-bus test systems, considering the system parameters. The effectiveness of the proposed congestion management with the nutcracker optimizer algorithm is analyzed by comparing its results with those generated by other recent optimization techniques. Results demonstrated that the nutcracker optimizer algorithm surpasses other comparative methods, requiring fewer fitness function evaluations, avoiding local optima, and displaying encouraging convergence traits. Implementing this approach can assist the system operators in swiftly addressing contingencies, ensuring secure and reliable power system operation within a deregulated environment.

Multi-objective optimization of solar resource allocation in radial distribution systems using a refined slime mold algorithm

The integration of distributed generation resources in power systems offers various advantages, such as peak load management and reduced transmission line congestion. However, it also introduces challenges related to voltage stability. This paper presents a novel multi-objective model for optimizing the allocation of solar resources in radial distribution systems. The model aims to achieve an optimal voltage profile, minimize losses, and maximize penetration levels. To address the conflicting nature of these objectives, a refined multi-objective slime mold algorithm (MOSMA) is proposed. This algorithm demonstrates exceptional capabilities in finding Pareto fronts, avoiding local optima, and effectively solving multi-objective problems compared to other optimization methods. Additionally, the corrected social hierarchy method is integrated to enhance performance. The proposed method is evaluated using a standard system under various operational conditions, showing superior results in terms of maintaining an acceptable voltage profile and significantly reducing losses. The study reveals that while losses decrease for penetration levels ranging from low to medium, they start to increase for levels exceeding 100 %. Notably, the proposed method achieves approximately 12 % system efficiency improvement, as measured by the voltage profile, at a penetration level of 300 %. These findings highlight the effectiveness of the proposed method, even at high penetration levels, surpassing other optimization approaches based on the inverse generation distance parameter.

A novel solution for the power transmission congestion of deregulated power system using TCSC and TLBO algorithm

The expansion of the electrical power sector has introduced heightened competition in electric markets, compelling utilities to enhance the efficiency of their power system equipment to provide customers with cost-effective and premium-quality power. Deregulation has, however, led to challenges such as congestion in transmission lines, adversely affecting power quality, voltage profiles, and system security. The congestion can be alleviated either by technical or non-technical methods. To address this issue, the manuscript proposes the implementation of Thyristor Control Series Capacitors (TCSC) as a technical solution for congestion mitigation. TCSC is used as a variable reactance device. The study employs line utilization factors (LUF) and disparity line utilization factors (DLUF) to obtain the optimal locations for TCSC installation. Additionally, the Teaching-Learning-Based Optimization (TLBO) algorithm is employed for the optimization of TCSC reactance. The manuscript also suggests another approach by simultaneously optimizing the location and parameters of multiple TCSCs using the TLBO algorithm without using the sensitivity factors. Comparative analyses with other optimization algorithms like gray Wolf Optimization (GWO) and Particle Swarm Optimization (PSO) are conducted to determine the superior method for ensuring stable system operation. By application on the standard IEEE-30 bus system, the TLBO algorithm is validated, demonstrating its effectiveness in mitigating congestion by minimizing power loss, and voltage deviation, and enhancing security margins. The findings contribute valuable insights for practitioners seeking robust solutions in managing congestion within deregulated electric markets.

Evaluating Streamflow Forecasts in Hydro‐Dominated Power Systems—When and Why They Matter

AbstractThe value of seasonal streamflow forecasts for the hydropower industry has long been assessed by considering metrics related to hydropower availability. However, this approach overlooks the role played by hydropower dams within the power grid, therefore providing a myopic view of how forecasts could improve the operations of large‐scale power systems. With the aim of understanding how the value of streamflow forecasts penetrates through the power grid, we developed a coupled‐water energy model that is subject to reservoir inflow forecasts with different levels of predictive performance. We implement the modeling framework on a real‐world case study based on the Cambodian grid, which relies on hydropower, coal, oil, and imports from neighboring countries. In particular, we evaluate the performance in terms of metrics selected from both the reservoir and power systems, including available and dispatched hydropower, power production costs, CO2 emissions, and transmission line congestion. Through this framework, we demonstrate that streamflow forecasts can positively impact the operations of hydro‐dominated power systems, especially during the transition from wet to dry seasons. Moreover, we show that the value largely varies with the specific metric of performance at hand as well as the level of operational integration between water and power systems.

Calculation of the available transfer capability of trading channels based on power network congestion forecasting

Against the backdrop of increasing renewable energy penetration, frequent power network congestion has become a key issue limiting the available transfer capability of trading channels. In order to avoid power network congestion while improving the overall utilization of trading channels, we propose a method for calculating the available transfer capability of a trading channel based on power network congestion forecasting. First, this study uses a histogram-based gradient tree boosting (HGTB) model to forecast the severity and probability of power network congestion, which enables power system operators to determine the specific circumstances of congestion occurrence. Second, we analyze the sensitivity of the power trading network and generating units to transmission lines, aiming to calculate the available transfer capability of the trading channel on the basis of taking into account the control of transmission line congestion and the balance of supply and demand in the power market. Finally, the validity of the validation methodology is verified based on a case study of the power system in central China.

Optimization of transmission lines congestion management in integrated electricity and natural gas multi‐carrier energy systems

AbstractThe increasing interactivity between energy carrier networks such as electrical, natural gas (NG), and heating can be appropriately used for improving the operation optimality along with overcoming some challenges of those systems. With such a view, this paper studies the practical issue of power transmission lines congestion management in integrated multicarrier energy systems (MCESs) and makes a comparison with the only electrical operation mode. The MCES consisting of electrical and NG network infrastructures with exchanged energy between them is modelled by considering the several local energy hubs and their electrical and heat loads. Rescheduling of energy sources (power plants and gas wells) and demand side management in both electrical and NG networks have been utilized as the two important tools in the developed congestion alleviation framework. The proposed method confirms that the provided flexibility in the integrated operation of the MCESs can lead to a remarkable reduction in the congestion management cost index. Implementing the proposed congestion management framework on an assumed test MCES with a combined 39‐bus New England power system and 20‐node Belgium NG system demonstrates its effectiveness and applicability for the system operators.

Line Congestion Management in Modern Power Systems: A Case Study of Pakistan

The surging electricity demand in Pakistan has led to frequent blackouts, prompting government initiatives to expand power plant capacities and improve the national grid. The government prioritizes integrating large‐scale renewable energy sources, such as wind and solar power, to reduce dependence on conventional power plants. However, the intermittency of renewables leads to forecasting errors, requiring extra power reserves from conventional units, thereby escalating operational costs and CO2 emissions. The country currently utilizes a manual mechanism for power balancing operations, overlooking critical grid constraints of the transmission line loadings. In such conditions, injecting large‐scale power from renewables can lead to significant fluctuations in line power flows, risking transmission line loadings and compromising the system’s secure operation. Hence, this paper has developed an automatic generation control (AGC) model for the highly wind‐integrated power system to alleviate line congestions in the network and enhance the economic operation of the system. The study utilizes the Pakistan power system as a case study to simulate the proposed model. The developed real‐time power dispatch strategy for the AGC system considers the constraints of the transmission line to avoid congestion. It integrates wind energy as operating reserves to enhance the economic operation of the system. When managing line congestion, it identifies overloaded bus lines and adjusts power regulation accordingly while compensating for shortfalls by augmenting transmitted power from regional grid stations. However, it maintains a constant dispatch ratio without line overloads, aligned with generation capacities. Additionally, the strategy integrates reserve power from the wind power plant and traditional generating units to further improve economic operations. Simulations have been conducted using PowerFactory software, employing the eight‐bus and five‐machine models to replicate the characteristics of the Pakistan power system. The results demonstrate the effectiveness of the proposed AGC design in mitigating transmission line congestion of power systems that are heavily integrated with wind energy sources while simultaneously ensuring the economic operation of generating units.

Statistical Evaluation of Sustainable Urban Planning: Integrating Renewable Energy Sources, Energy-Efficient Buildings, and Climate Resilience Measures

In the face of increasing climate-related challenges, sustainable urban energy networks must grapple with transmission congestion in deregulated power systems. This study introduces a novel statistical approach that synergizes two powerful methodologies for evaluating and managing urban energy networks: the integration of photovoltaic (PV) renewable energy sources and Gated Recurrent Unit (GRU)-based PV forecasting. By combining evolutionary optimization techniques with GRU forecasting, we aim to bolster the sustainability and reliability of urban energy networks while addressing climate change adaptation. In this study, we employ statistical methods, including generator sensitivity ratios and bus sensitivity ratios (BSR), to pinpoint optimal locations for PV power integration within urban networks. The Lion Optimization Algorithm (LOA), an evolutionary optimization method, is employed within a digital twin framework to optimize active power rescheduling values and congestion prices. The incorporation of GRU-based PV forecasting enhances the precision and resilience of our energy management approach. Through extensive simulations conducted on the New England 39-bus system, our findings reveal that the combination of the LOA method and GRU forecasting outperforms traditional optimization techniques such as the Ant Lion Optimizer (ALO) and Particle Swarm Optimization (PSO). This dual-optimization strategy leads to significantly reduced active power rescheduling values and congestion prices, underlining the potential of our statistical model to effectively manage congestion in transmission lines within sustainable urban energy networks. In the context of integrating renewable PV sources and adapting urban energy infrastructure to climate change impacts, the synergy of evolutionary optimization and GRU-based forecasting offers a robust pathway to enhance sustainability and reliability in urban energy networks.

Congestion cost estimation using adaptive red fox algorithm in restructured electricity markets

Congestion of the power system is the most common challenge an Independent System Operator (ISO) faces in restructured electricity markets. It affects the efficiency of the market when transmission lines are congested causing transmission costs to rise. To prevent transmission line congestion, ISO needs to take the necessary steps. To solve these issues, this paper introduces a new method namely the Adaptive Red Fox Optimization algorithm (ARFOA) to compute the congestion cost considering the power losses in the transmission line system. Initially, all the generators in the system are selected to reschedule real power outputs. Second, by establishing a proposed optimization issue, ARFOA is employed to control transmission line congestion. The implementation of the proposed method is evaluated on the IEEE 30 bus system. The algorithm’s adaptability is tested using several case studies involving the base case and line outages, also compared with the other existing techniques such as PSO, ASO, and GSO approaches. The simulation outcomes indicate that the proposed strategy outperforms existing techniques in terms of congestion cost, power loss, generation rescheduled power, and computational time.

The Operation Strategy of the MIDC Systems for Optimizing Renewable Energy Integration of Jeju Power System

The power system on Jeju Island currently faces challenges due to an imbalance between power supply and demand owing to increasing renewable energy (RE) generation. Consequently, the multi-infeed high-voltage direct-current (MIDC) systems are expected to play an important role in reducing RE curtailment and increasing RE penetration. However, despite the balance between power supply and demand, transmission line (TL) overloads on Jeju Island, which has a low permissible rating, may lead to RE curtailment. We propose an operational strategy for the MIDC systems to mitigate TL overloading and RE curtailment. This strategy was aimed at managing the possible congestion of TLs, maintaining a power supply–demand balance, and increasing the penetration level of RE. In this paper, we present a scheme for calculating the operating points of the MIDC systems and allocating RE curtailment using optimization with security constraints to mitigate TL overloading. The proposed optimization model simplifies solving the mathematical equations involved by employing a sensitivity factor called the power transfer distribution factor (PTDF), using which linear programming (LP) optimization can be performed. To verify the feasibility of the operational strategy, case studies are conducted using an actual Jeju Island power system with a power system analysis tool—Power System Simulator for Engineering (PSS/E). The optimization problem was solved using MATLAB.

Multi-agent based modeling and learning approach for intelligent day-ahead bidding strategy in wholesale electricity market

This work considers a restructured electricity market where the agents are considered as entities, and each agent has a learning capability. Also, the Independent System Operator (ISO) adopted the nodal pricing technique for managing transmission line congestion. In this multi-agent platform, the objective of each Generating Company (GenCo) agent is maximizing its net earnings. GenCo exhibits economic capacity withholding to enhance its daily net income ports updated cost parameters to the ISO. The basic model free policy-based reinforcement algorithms fail to calculate the probability that the rewards obtained are negative or zero. To overcome this drawback, the co-learning agent-based modeling approach implements an interactive learning algorithm applied to GenCo’s for reward consideration in all respects and thereby applies the strategic approach in improvising marginal pricing, commitments, and net incomes. The co-learning effect of GenCo is accomplished with the optimum value of the learning parameters by deploying a novel interactive Variant Roth – Erev (VRE) Reinforcement learning technique for efficient electricity market operation. The introduction of various parameters in learning helps to improve the mutual interaction among the agents and provides better experimentation implementable by GenCo’s meeting the social welfare and smart bidding. The result presented in this paper suggests that the learning capability of the agents is having an impact on the marginal pricing, which supports the operator in managing the congestion in the transmission line. The analysis showcases the effectiveness of the co-learning approach adopted by the agents in the electricity market which is tested on a standard IEEE system and the performance of the technique is validated on a realistic Indian grid network.

Development and Validation of a Load Flow Based Scheme for Optimum Placing and Quantifying of Distributed Generation for Alleviation of Congestion in Interconnected Power Systems

The energy supply entities widely adopt distributed generators (DG) to meet the additional power requirement due to scheduled or unscheduled interruptions. The expansion of transmission and distribution systems via the inclusion of loads and generators and the occurrence of line interruptions are significant causes of congestion of transmission lines in interconnected systems. The management and alleviation of congested lines is a primary requirement for a power system network’s reliable and efficient operation. The researchers investigated the potential scope of distributed generation (DG) to alleviate the congested branches in interconnected transmission systems. The development of a reliable scheme to arrive at the best location and size of local generators for alleviating congestion deserves considerable importance. This paper attempted to develop a simple and reliable strategy for the optimum placement and sizing of DGs to be integrated with a transmission line system of DGs for congestion relief in transmission lines by analyzing power flow solutions. This research work considered the 14-bus system of IEEE for the preliminary analysis to identify the parameters employed for assessing the severity of line congestion and the best placement and sizing of DGs for congestion relief. This work analyzed power flows by load flow algorithms using ETAP software in the 14-bus IEEE system for different line outage cases. The analysis of power flow solutions of the 14-bus system of IEEE revealed that the percentage violation of the system can be regarded as an essential parameter to assess the extent of congestion in an interconnected system. A detailed power flow analysis of the system with various capacities of DG integration at several buses in the system revealed the application of two indices, namely the index of severity (SI) and sensitivity factor (SF), for optimum placement with the best capacity of DGs for congestion alleviation in the system. This work proposed a reliable algorithm for the best siting and sizing of DGs for congestion relief by using the identified indices. The proposed methodology is system indices allied load flow-based algorithm. This work produced a fast simulation solution without any mismatch through this developed scheme. The approximations linked with the algorithm were very minute, resulting in comprehensive bests instead of inexact limited bests with less simulation time and more convergence probability and availing the benefits of the mathematical approach. The work investigated the feasibility of the proposed methodology for optimum placing and quantifying DGs for congestion solutions for a practical interconnected bus system in the supply entity of the Kerala grid with many buses. Any transmission system operator can adopt this method in similar connected systems anywhere. The proposed algorithm determined the most severe cases of congestion and the optimum site and size of DGs for managing congested feeders in the grid system. The analysis of the losses in the system for different cases of DG penetration by load flow analysis validated the suitability of the obtained results.

Optimization of location and rating of unified power flow controllers for congestion management

Large number of contracted power exchanges among sellers and purchasers, in a deregulated power market causes transmission line congestion. Congestion of lines undermines the security and economy of power networks. Flexible AC transmission system (FACTS) devices are introduced at appropriate spots to change the power flow in order to keep the flow of the lines within their capability. In this work, the most commonly used unified power flow controller (UPFC) is proposed for achievement of changing the line flow and for easing congestion from the over loaded lines. The size and site of UPFC controllers significantly impact their performance in congestion management task. The location and size of the UPFC devices are to be optimized by using an optimization algorithm. Whale optimization algorithm (WOA) is exploited in this effort for realizing the aim of determination of site and size of the FACTS converters. The present method has lesser quantity of variables which are tuned to get best outcomes. The suggested WOA based technique is executed on the IEEE 57 bus system. The outcomes comparison is made with particle swarm optimization algorithm (PSO) and firefly algorithm (FFA) and the better improved outcomes are found.