Transmission Congestion Management Research Articles

Quantitative impact assessment of transmission congestion and demand side management on electricity producers’ market power

Abstract This paper presents the impact of transmission congestion and demand side management on Electricity Producers’ market power using the quantitative investigation of pool-based market clearing mechanism. The mathematical problem formulation for pool-based market clearing consists of maximization of social welfare objective function, which incorporates maximization of demand benefit obtained from flexible demand bids and minimization of Electricity Producers’ real and reactive power costs obtained from actual and strategic supply bids. In this problem formulation, the nonlinear modeling of power system is used for real and reactive power flow equality constraints, transmission line capability inequality constraints (for incorporating the impact of transmission congestion), and generator capability curve equality constraints. The impacts of demand side management have been incorporated in the problem by considering single-period and multi-period demand flexibilities, and distributed generation placement at appropriate locations. The formulated optimization problem has been solved using nonlinear programming, which provides the nodal prices as byproducts of the solution. The electricity producers’ capability to exercise market power has been analyzed from their revenue and surplus indices, which have been obtained from nodal prices. Various case studies have been simulated on IEEE 30-bus system for quantitative determination of electricity producers’ market power. The results obtained are very interesting and demonstrate demand side strategies to tackle the electricity producers’ market power with actual and strategic bidding under normal and congested system conditions.

A novel day-ahead and real-time model of transmission congestion management using uncertainties prioritizing

A novel day-ahead and real-time model of transmission congestion management using uncertainties prioritizing

Optimization of position and rating of shunt and series connected FACTS devices for transmission congestion management in deregulated power networks

<abstract><p>Transmission congestions are caused by electricity trading between generators and distribution companies in a deregulated environment. Power system operation and security in liberalized scenarios are maintained by removing branch overloads. A flexible alternating current transmission system (FACTS) controller is installed in a suitable location to redistribute the power flow among the transmission lines so that the power flows are brought within the capacity of the lines. In this work, series-connected thyristor-controlled switched compensators (TCSCs) and shunt-connected Volt-Ampere reactive (VAR) static compensators (SVCs) are installed in appropriate locations to alter the power flow patterns and to remove overloads. It is proposed to reduce the overload of transmission lines by locating series and shunt connected FACTS devices at proper locations. The size and location of TCSC and SVC devices greatly affect their ability to meet a congestion management goal. An optimization process optimizes the location and size of these devices to maximize the congestion mitigation benefits of the TCSC and SVC controllers. In this work, the whale optimization algorithm (WOA) is used to optimize the value of the objective function by appropriately choosing the location and size of the FACTS controllers. This algorithm has a few parameters that are tuned to give the best overall results. A WOA-based method is proposed to optimize the size and location of the FACTS devices and is implemented on the IEEE-30 bus test case. The results were compared and found to be improved with those of other algorithms such as the particle swarm optimization algorithm (PSO) and the firefly algorithm (FFA).</p></abstract>

Research on Bidding Model of Electricity Selling Company Considering Green Certificate Trading and Electricity Selling Blocking

This article considers the optimal bidding model of traditional thermal power generation companies’ electricity sales companies, taking into account transmission congestion, under the influence of new policies such as the renewable portfolio standard (RPS) and the renewable exchange certificate (REC). Firstly, the transaction cost of green certificates is included in the cost of electricity sales, and an optimization model is established with the goal of maximizing the profit of the electricity sales company; Secondly, the node electricity price method is used to manage transmission congestion, and an optimization model is established with the goal of minimizing the cost of transmission congestion; Finally, multi agent technology and dynamic game theory are used to solve the optimal bidding strategy of the electricity selling company.

Transmission Congestion Management in Restructured Power System: A Review

Transmission Congestion Management in Restructured Power System: A Review

Calculation and optimization of inter-regional available transfer capability considering wind power uncertainty and system improvement measures

The intermittency and uncertainty of wind energy make transmission congestion management increasingly complex. In this paper, we investigate the impact of wind energy on available transmission capacity (ATC) and then explore ways to mitigate transmission congestion from the grid and load side, respectively. The impact of a stochastic variable is considered by applying Latin Hypercube Sampling (LHS) and backward curtailment techniques to generate typical scenarios. ATC is improved by the optimal allocation of Thyristor Controlled Series Compensation (TCSC) on the grid side and the use of Demand Response (DR) on the load side. The source-grid-load cooperative optimisation model for ATC is solved by an improved particle swarm optimisation (PSO) algorithm. Based on the IEEE-30 bus system, an experimental scheme is designed and analytical calculations are performed. The results show that the joint application of TCSC and DR can help to improve ATC in a comprehensive way. The work done in this paper can achieve the purpose of promoting renewable energy consumption by improving the utilisation efficiency of the transmission network without changing the network structure.

A Review of Optimization Methods for Transmission Congestion Management Systems

A Review of Optimization Methods for Transmission Congestion Management Systems

Transmission Congestion Management in Deregulated Power System Using Adaptive Restarting Genetic Algorithm

Power systems in a deregulated environment have more intense and recurrent transmission line congestion than conventionally regulated power systems. With the help of generation rescheduling, this article shows how to effectively manage congestion in the day-ahead energy market by taking corrective measures to reduce congestion. The research employs an adaptive restarting genetic algorithm (ARGA) to provide an effective congestion management strategy in a deregulated power market (DPM). The study makes two significant contributions. First, the generator sensitivity factors (GSF) are calculated to choose re-dispatched generators. Second, the least congestion cost is calculated using the adaptive restarting genetic algorithm. Several different line outage contingency cases on IEEE 30 bus systems are used to examine the suggested algorithm’s implementation efficacy. The simulation results demonstrate a significant reduction in net congestion costs, resulting in a more reliable and secure power system operation. The proposed algorithm was tested in a python environment, and power flow analysis was done using the PANDAPOWER tool. The acquired results are contrasted using several contemporary optimization approaches to validate the suggested technique’s validity. The ARGA technique gives a lower congestion cost solution than the particle swarm optimization (PSO), real coded genetic algorithm (RCGA), and differential evolution (DE) algorithm.

Multiobjective Salp Swarm Algorithm Approach for Transmission Congestion Management

In the newly emerged electric supply industry, the profit maximizing tendency of market participants has developed the problem of transmission congestion as the most crucial issue. This paper proposes a multiobjective salp swarm algorithm (MOSSA) approach for transmission congestion management (CM), implementing demand side management activities. For this, demand response (DR) and distributed generation (DG) have been employed. For willingly reducing the demand, demand response has been called by providing appropriate financial incentives that supports in releasing the congestion over critical lines. Distributed generation implementing wind plant as renewable independent power producer (RIPP) has also been included in order to reduce the load curtailment of responsive customers to manage transmission congestion. The proposed incentive-based demand response and distributed generation approach of CM, has been framed with various strategies employing different thermal limits over transmission lines and has resulted into significant reduction in congestion and in-turn improvement of transmission reliability margin. Diversity has been obtained in multiobjective optimization by taking two and three objective functions, respectively (minimization of overall operational cost, CO2 emission, and line loading). The by-products of the proposed algorithm for multiobjective optimization are minimized demand reduction, optimum size, and location of DG. To examine the proposed approach, it has been implemented on IEEE 30-bus system and a bigger power system IEEE 118-bus system; as well as the proposed technique of MOSSA has been compared and found better than reported methods and two other meta heuristic algorithms (multiobjective modified sperm swarm optimization and multiobjective adoptive rat swarm optimization).

A chance-constrained optimization framework for transmission congestion management and frequency regulation in the presence of wind farms and energy storage systems

• Providing a chance-constrained framework for congestion management and frequency regulation. • Evaluating the performance of the model in both normal and emergency conditions. • Investigating the effect of spinning reserve rate on congestion, LMP and frequency. • Investigating the effect of EES systems on congestion, LMP and frequency. This paper presents a chance-constrained optimization framework for transmission congestion management in the presence of wind turbines and electrical energy storage (EES) systems. In the proposed model, the uncertainties of the wind farms’ output power and electricity demand are considered, and in order to model these uncertainties, the chance-constrained method is used. In order to reduce the computational burden in this problem, a filtering algorithm is proposed that detects sensitive lines and applies security constraints only on them. The results illustrate that applying the proposed algorithm has reduced the solution time by about 83%. In addition, the proposed model is implemented on a modified IEEE 30-bus test system and solved under normal and emergency conditions (N-1). The simulation results demonstrate that considering the chance constraints, by increasing the spinning reserve, leads to a decrease in the amount of energy not supplied (ENS) and consequently operating costs during the contingent events. The numerical results illustrate that considering the spinning reserves, despite a 10% increase in operating costs under normal operating conditions, has reduced the amount of ENS by about 18% during the outage of the thermal unit. In addition, the results demonstrate that EES systems keep the system frequency within the allowable range under emergency conditions due to their fast ramp rate.

Transmission congestion management through sensitivity based rescheduling of generators using improved monarch butterfly optimization

The emergence of competitive power markets in recent decades has paved the way for the enormous increase in the number of transactions between producers and consumers to satisfy the exponentially increasing load demand. These transactions usually occur through direct bilateral, multi-lateral agreements or some common platform. However, this operation puts additional strain on the transmission network, which serves as a conduit between producers and consumers leading to congestion. As a result, satisfying all the committed power transactions under congested transmission lines economically becomes infeasible. Independent System Operator (ISO) is an entity that is responsible for ensuring congestion-free power transactions to preserve the system's security and reliability. The objective of this work is to mitigate the congestion in the network while satisfying the system constraints resulting in more stable operation with reduced line flows and low system losses. The generator sensitivity factor (GSF) is utilized to single out the generating units that need to be rescheduled to alleviate transmission lines congestion. Further, the proposed variants of Monarch Butterfly Optimization (MBO) namely Improved MBO with linear weighting factor (IMBO-LW) and chaos-embedded MBO (CHMBO) are used to reschedule the generators with an aim to minimize the cost of rescheduling and the total change in power after rescheduling. Additionally, standard benchmark functions are used to validate the effectiveness of the proposed MBO variants and the same variants have been scrutinized on modified IEEE 30-bus and New England 39-bus test systems to alleviate the problem of congestion for secure and reliable operation of power system.

Data-driven joint TEP-BESS co-planning scheme to relieve transmission lines congestion: A min-max regret method

Transmission lines congestion has recently been a vital challenge in power systems operation due to the intermittent outputs of renewable energy sources (RES). Therefore, an efficient transmission congestion management (TCM) method should be defined to deal with the congestion issue. This paper aims to propose a simultaneous linearized two-stage TEP-BESS co-planning optimization model to relieve transmission lines congestion. In doing so, a novel TCM structure is suggested in a pool-based deregulated data-driven optimal power flows (d-OPF) by the computationally effective min-max regret method to consider future scenarios of generating units and demanded loads. To improve the efficiency of the proposed algorithm, an accurate cost design for module-based BESS considering RES uncertainties is proposed; also, a regional classification is considered to simulate different weathers, specialized for the TCM measurement. The major goal is to efficiently trade-off between performing the TCM and minimizing system total cost as conflicting objectives. The output of the proposed model is to optimally find the best expansion plans by applying the computationally effective min-max regret method where performing the TCM and minimizing the system total cost. The effectiveness of the proposed model is examined on the wind-integrated regional RTS test system considering an uncertainty set for the intermittent outputs of RES. The results of this fast-converged model show the difficulty of selecting an expansion plan as the best options can be selected to relieve the transmission lines congestion while increasing the system total cost.

Multi-objective chance-constrained transmission congestion management through optimal allocation of energy storage systems and TCSC devices

Multi-objective chance-constrained transmission congestion management through optimal allocation of energy storage systems and TCSC devices

Congestion Management by Phase Shifting Transformer Using Fuzzy Logic Control.

This paper introduces Fuzzy logic to the control of phase shifting transformer. Phase shifting transformer is already installed FACTS device in many major countries. It is used for power transfer improvement, reducing cost of transmission system, congestion management, improves stability and many more. In this paper congestion management is discussed using MATLAB software. As load on power transformers is increasing and some transformer gets power congestion. This burden can be taken care by phase shifting transformer and by using Fuzzy logic the controlling of phase shifting transformers is optimized. Keywords— Power Controllers(PC), Fuzzy logic, Phase shifting transformer (PST)

A novel graph theory based two-stage minimum cost routing mechanism in energy internet

Energy internet is a multi-energy system with “source-network-load-storage” coordinated and optimized operation, and the research on the routing mechanism of its core equipment called electric energy router (EER) is a significant aspect of the energy optimization in the energy internet. In this paper, the existing EERs routing mechanisms are improved in terms of optimization objectives, congestion management, and heavy load power supply. Firstly, the structure and function of the EER are described from the perspective of the network, and the topology of the energy internet is designed based on graph theory. Secondly, in combination with the concept of virtual circuit transmission, open shortest path first routing algorithm, policy-based routing, and routing policy in information internet, a two-stage routing mechanism of day-ahead peer-to-peer transaction and intraday power balance is proposed. Furthermore, a minimum cost routing selection algorithm with the cost weight coefficients is also proposed to support the studied mechanism. Besides, an equal-cost incremental principle is proposed to optimize transmission congestion management and heavy load power supply, which can realize the lowest cost transmission through the least path. Finally, the routing mechanism is verified by case analyses which prove the relevance of the proposed method.

Optimization of Voltage Security with Placement of FACTS Device Using Modified Newton–Raphson Approach: A Case Study of Nigerian Transmission Network

Power flow reliability, voltage security and transmission congestion management are paramount operational issues in a power system. Flexible AC transmission system (FACTS) controllers are suitable technologies that can provide compensation and dynamic control of power system transmission parameters to enhance effective performance and reliability. The interline power flow controller (IPFC), if optimally placed, can regulate the impedance of multiple lines to improve active power transfer capacity and voltage profile. This study examines the performance of IPFCs for voltage enhancement by suppressing fluctuation. A modified Newton–Raphson load flow problem with an incorporated IPFC variable has been formulated with the objective to improve voltage stability and maintain active power flow. The effectiveness of the proposed method was tested on the Nigerian 41 bus transmission network. The obtained result of the system with an IPFC placed at the weakest bus of the network was compared with Newton–Raphson load flow analysis of the same network without an IPFC. The results of load flow analysis for Case 1 (the system without an IPFC) showed that the transmission network without an IPFC had a real power loss of 4.699488 p.u., and reactive power loss of 4.467413 p.u., whereas the integration of an IPFC to the power flow formation in Case 2 resulted in the reduction in the transmission network’s overall losses to 0.55297 p.u. and −38.3329 p.u. The modified method proves effective as the power system network with an IPFC returns a more stable voltage profile and improves active power flow. In addition, this method, similar to all other mathematical optimization approaches, returns a strong accurate result but may be a drawback in terms of longer computational time compared with metaheuristic methods which are preferred for a larger network system.

Hybrid Scenario-IGDT-Based Congestion Management Considering Uncertain Demand Response Firms and Wind Farms

Demand response resources (DRRs) have been recently introduced as one of the most economic tools of congestion alleviation in power systems. Nevertheless, the severe uncertainty of multiple DRRs constituting a demand response firm (DRF) is an indispensable issue to be considered for the resilient operation of future power systems. Consequently, the information gap decision theory (IGDT) technique is utilized for addressing the uncertainty of consumers’ participation in demand response programs. This article presents a novel hybrid scenario-IGDT-based framework, designated as SIGDT, for corrective transmission congestion management (CM) in the presence of large-scale uncertain wind farms and DRFs as well as the uncertainty of conventional generating units. A reliability network modeling of a repairable <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> -component wind turbine (WT) is presented, considering failure and repair rates of turbines’ components, and then the uncertainty of wind farms’ generation is handled using the scenario-based approach. The proposed framework is applied to the IEEE-reliability test system (RTS) system to demonstrate its accuracy and capability. The results discuss the impact of failure and repair rates of WTs components on the proposed SIGDT-based CM and emphasize the application of the proposed framework for decision-makers to ensure the optimal operation of power systems under uncertainties of DRFs, wind farms, and conventional units.

Prosumer‐centric energy storage system and high voltage distribution network topology Co‐optimisation for urban grid congestion management

The surge of electricity load and the growing number of renewable energy sources (RESs) in urban power grid result in severe and frequent congestion for transmission networks. This causes the transmission system operators (TSOs) perform the high voltage distribution network (HVDN) reconfiguration frequently. The frequent reconfiguration of the HVDN will cause the fast degradation of high voltage circuit breakers, high maintenance and operational cost, and stability problems. To reduce the frequency of HVDN reconfiguration, this paper proposes a prosumer-centric energy storage system (ESS) and HVDN topology co-optimisation for transmission congestion management. Numerical results show that with the assist of ESS operational strategy, the reconfiguration frequency of HVDN can be significantly reduced while mitigating the transmission congestion.

Transmission Congestion Management Using Generator Sensitivity Factors for Active and Reactive Power Rescheduling Using Particle Swarm Optimization Algorithm

Independent System Operators have difficulty in fulfilling all contractual power transactions in a competitive energy market due to transmission network congestion. As a result, applications of generator rescheduling become one of the antidotes in alleviating this difficulty in the consequence of ever-increasing numerous power transactions. The goal of this research is to lower the cost of active and reactive power of the generators by reducing the deviation of rescheduled active and reactive power from scheduled values. The inclusion of reactive power rescheduling and voltage stability in this paper is innovative, as compare to other existing methodologies solely examine active power rescheduling. This paper made the following contributions: formulated a multi-objective function for congestion control in an electric transmission network. Furthermore, formulated the generator sensitivity factors to identify overloaded lines and which generators will be involved in congestion management. Developed a particle swarm optimization (PSO) algorithm to solve the multi-objective function of the transmission congestion management system. In addition, the developed PSO method for CM approach was validated on three IEEE standard test system networks (14, 30, and 118). The simulation results prove that reduces active and reactive power, lowering the cost of generator rescheduling, and demonstrating the usefulness of developed PSO method for transmission network congestion. Furthermore, voltage stability and voltage profile improvements demonstrate the performance effectiveness of the PSO algorithm used in this work.

Effective Transmission Congestion Management via Optimal DG Capacity Using Hybrid Swarm Optimization for Contemporary Power System Operations

Managing transmission congestion had been a major problem with growing competition in the power networks. Competitiveness emerges through the network&#x2019;s reconfiguration and the proliferation of secondary facilities. Congestion of transmission lines is a critical issue, and their regulation poses a technical challenge as the power system is deregulated. The present research illustrates a multi-objective strategy for reaching the optimal capabilities of distributed generators (DG) like wind power plants and geothermal power-producing plants to alleviate congestion throughout the transmission network. Goals such as congestion management during power delivery, power loss reduction, power flow improvement with the enhancement of voltage profile, and investment expenditure minimization are considered to boost the network&#x2019;s technological and economic reliability. The congestion management is achieved using the locational marginal price (LMP) and calculation of transmission congestion cost (TCC) for the optimal location of DG. After identification of congested lines, DG is optimally sized by particle swarm optimization (PSO) and a newly proposed technique that combines the features of modified IL-SHADE and PSO called hybrid swarm optimization (HSO) which employs linear population size reduction technique which improves its performance greatly by reducing the population size by elimination of least fit individuals at every generation giving far better results than those obtained with PSO. In addition, optimal rescheduling of generations from generators has been done to fulfill the load demand resulting in alleviation of congested lines thereby enhancing the performance of the network under investigation. The performance of the proposed methodology of HSO and PSO has been tested successfully on standard IEEE-30 &#x0026; IEEE-57 bus configurations in a MATLAB environment with the application of MATPOWER power system package.