Congestion Management In Power System Research Articles

Market integration and TSO-DSO coordination for viable Market-based congestion management in power systems

The article presents the findings on market-based congestion management (CM) in power systems. The main idea is to unlock flexibility from both small and large-scale resources by creating a platform so that flexibility can enter the markets through the platform and be used by system operators (DSOs and TSOs) for CM. The article recognizes two pressing issues in market-based CM: low liquidity and adverse impacts of flexibility activation. The article proposes leveraging market integration and TSO-DSO coordination to address the pressing problems and incorporate them into the platform. Bids from the intraday market at Nord Pool as well as the balancing market bids, were used for CM to show the possibility of addressing the low liquidity issue by receiving bids from well-established markets. In TSO-DSO coordination, an algorithm-agnostic process is proposed and implemented to involve SOs' network limitations before flexibility is traded to mitigate the adverse impacts of flexibility activation. As the market integration and TSO-DSO coordination functionalities rely on flexibility-related data that are often in huge quantities, a metadata register is also implemented to gather, process, and store data to be smoothly accessed by different stakeholders depending on their needs and access rights.

Congestion Management of Power Systems by Optimal Allocation of FACTS devices using Hybrid Techniques

For system operators, Congestion management is a difficult task as the market’s security and reliability are protected by this methodology. As the magnitude of an electric transmission system is extremely dynamic, limits must be estimated much beforehand, in order to manage the congestion issues at the right time. Flexible AC transmission systems (FACTS) are used to control voltage fluctuation by adjusting the system's real and reactive power. A combination of Improved Remora Optimization (IRO) and Improved Radial Basis Function (IRBF) is used to allocate positions and sizes of the FACTS devices. In this study, Static Synchronous Compensator (STATCOM), Interlink Power Flow Controllers (IPFC) and Unified Power Flow Controllers (UPFC) are among the FACTS devices used. In the proposed hybrid IRO-IRBF technique, following are the functional aims calculated: build-on-expenditure, Line Loading (LL), Total Voltage Deviation (TVD) and real power loss. Additionally, the hybrid IRO-IRBF technique is used to confirm the proper location using the IEEE 30 bus structure. TVD, power loss, installation costs, and line loading are the measurements used to assess the implementation performance of the hybrid IRO-IRBF approach. From the result analysis, the hybrid IRO-IRBF achieved a real power loss of 0.1591 p.u., and TVD of 0.02 p.u., which is lesser than the existing Whale Optimization Algorithm and Mayfly Optimization Algorithm.

A Comprehensive Review of Congestion Management in Power System

In recent decades, restructuring has cut across all probable domains, involving the power supply industry. The restructuring has brought about considerable changes whereby electricity is now a commodity and has become a deregulated one. These competitive markets have paved the way for countless entrants. This has caused overload and congestion on transmission lines. In addition, the open access transmission network has created a more intensified congestion issue. Therefore, congestion management on power systems is relevant and central significance to the power industry. This manuscript reviews few congestion management techniques, consists of Reprogramming Generation (GR), Load Shedding, Optimal Distributed Generation (DG) Location, Nodal Pricing, Free Methods, Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Fuzzy Logic System Method, as well as Additional Renewable Energy Sources. In this manuscript a review work is performed to unite the entire publications on congestion management.

Congestion Management in Power System Using Locational Marginal Price in Balancing Power Market

Congestion Management in Power System Using Locational Marginal Price in Balancing Power Market

Emergency congestion management of power systems by static synchronous series compensator

<span>From a transmission system point of view, any overload on the grid lines during operation in situations such as peak load or emergency conditions include line outage or generator outage, is refers to congestion. Generally, the congestion can be managed by controlling power flow. On the other hand, series compensation has a significant role in control power flow; therefore, series compensation equipment like fixed series capacitor (FSC), thyristor-controlled series capacitor (TCSC), and static synchronous series compensator (SSSC) can be used for congestion management. In this paper, an SSSC is used in a transmission line to manage congestion in emergency conditions, line outage and generator outage. The congestion rent contribution method has been used to determine the location of the SSSC in the IEEE 14-bus test system. This technique finds the transmission line 1-2 (from bus 1 to bus 2) is the best location of the SSSC to reduce congestion. After installing an SSSC in the specified line, simulation results show that the power flow has been controlled, leading to reducing the congestion. In other words, the effectiveness of the SSSC can be seen in reducing the total congestion rent, the total generation cost, and network losses.</span>

Congestion management of power systems by optimizing grid topology and using dynamic thermal rating

The integration of high-level renewable energy, increased load demand, and the aging of the transmission network have all exacerbated the congestion of the transmission system. This requires transmission service providers to make full use of the existing transmission infrastructure by using cost-effective new transmission technologies, thereby taking full advantage of the inherent flexibility of the system. Dynamic thermal rating (DTR) is one of several technologies that can help address challenges with transmission operation, especially congestion management. Optimal transmission switching (OTS) and DTR technologies together provide flexible methods to enhance the performance of the power system and maximize the use of existing transmission system. In this study, a probabilistic multi-objective based congestion management procedure is proposed using OTS strategies considering the maximization of system reliability and minimization of the total generation cost. Additionally, the prevention of islanding is considered to ensure the feasibility of transmission switching status. The uncertainties associated with load demand and wind power generation are handled in this study using the points estimation method (PEM). The formulated optimization problem is solved using the multi-objective teacher learning based optimization (MOTLBO) algorithm. As the studied problem is a nonlinear constrained multi-objective optimization problem with conflicting objectives, the Pareto-optimality is used to find the set of optimal solutions. The best compromise solution among the Pareto set is chosen by a fuzzy decision-making approach. Several case studies are simulated on the modified IEEE RTS-96 system. The simulation results show that transmission system reconfiguration while considering the DTR leads to the enhancement of the system performance as compared to static line rating (SLR). For example, the generation cost has dropped by 6.78% when utilizing the DTR as compared to the SLR.

Effect of SVC installation on loss and voltage in power system congestion management

<span>In this paper, a new hybrid optimization technique is proposed namely Adaptive Embedded Clonal Evolutionary Programming (AECEP). This idea comes from the combination part of the clone in an Artificial Immune System (AIS) and then combined with Evolutionary Programming (EP). This technique was implemented to determine the optimal sizing of Flexible AC Transmission Systems (FACTS) devices. This study focused on the ability of Static Var Compensator (SVC) is used for the optimal operation of the power system as well as in reducing congestion in power system. In order to determine the location of SVC, the previous study has been done using pre-developed voltage stability index, Fast Voltage Stability Index (FVSI). Congested lines or buses will be identified based on the highest FVSI value for the purpose of SVC placement. The optimizations were conducted for the SVC sizing under single contingency, where SVC was modeled in steady state analysis. The objective function of this study is to minimize the power loss and improve the voltage profile along with the reduction of congestion with the SVC installation in the system. Validation on the IEEE 30 Bus RTS and IEEE 118 Bus RTS revealed that the proposed technique managed to reduce congestion in power system.</span>

Chance-constrained stochastic congestion management of power systems considering uncertainty of wind power and demand side response

This study proposes a new stochastic model based on chance constraints for network congestion management in the day-ahead power market. By jointly considering the uncertainty of wind power and demand side response, the proposed optimization model can help determine the optimal daily dispatch of generators and demand responsive loads to minimize the risk of transmission congestion. To this end, transmission line congestion probability (TLCP) is constructed in chance constraints to ensure system congestion less than a certain level. The indexes of load loss probability (LLP) and wind curtailment probability (WCP) are proposed and incorporated as chance constraints to achieve a high reliability of power supply and high utilization of wind generation, respectively. In the optimization process, the proposed stochastic optimization model is transformed to an equivalent deterministic model by using the probability distribution of random variables, and the influence of transmission system loss on transmission congestion management is considered. To calculate the satisfaction degree of chance constraints under the determined dispatch of generators and demand responsive loads, the probabilistic power flow based on the cumulant method is used, and the risk of transmission congestion level is quantified by the index, congestion risk value (CRV). Simulation results of a modified PJM 5-bus system and an IEEE 118-bus test system demonstrate the effectiveness of the proposed approach for congestion management in the day-ahead power market.

Applications of FACTS/ESS in Power System Congestion Management with Large Renewable Energy Sources Using PSS/E and Experienced in National Grid of Pakistan

Network or transmission congestion is one of the technical challenges in context of power system operation. The congestion management have great impact in power system operations with the emerging technologies of large renewable sources. In Pakistan, the congestion issues are addressed in the area of National Grid of Pakistan. There are many techniques are proposed to relieve these issues. In this research paper, FACTS and ESS devices are proposed to curtail this issue in the PSS/E environment. It gives the reliable & encouraging results and cost-effective solution.

Applications of FACTS/ESS in Power System Congestion Management with Large Renewable Energy Sources Using PSS/E and Experienced in National Grid of Pakistan

Network or transmission congestion is one of the technical challenges in context of power system operation. The congestion management have great impact in power system operations with the emerging technologies of large renewable sources. In Pakistan, the congestion issues are addressed in the area of National Grid of Pakistan. There are many techniques are proposed to relieve these issues. In this research paper, FACTS and ESS devices are proposed to curtail this issue in the PSS/E environment. It gives the reliable & encouraging results and cost-effective solution.

IMLANNs for Congestion Management in Power System

In this paper, Integrated Multi-Layer Artificial Neural Networks (IMLANNs) model has been developed for congested line prediction in a power system. The master characteristic of an ANN is the superiority to achieve complicated input-output mappings through a learning procedure, without exhaustive programming efforts. The IMLANNs model was developed to predict the congested lines in a power system. Before the IMLANNs model is developed, a case study was selected to receive an early result in power system load current during normal condition and contingency based on heavily loaded term. In order to optimize the architecture of the neural network and minimize the computational effort, but those state variables with major impact on the power system are selected as inputs. A pre-developed index, namely Fast Voltage Stability Index (FVSI) is employed as a benchmark to identify the locations declared as congested lines. This indicator was produced which aims for an analytic thinking, sustainable power system when an excessive load was imposed on the power system network. In addition, voltage collapse can be identified when the index is approaching 1.000 or unity. The value of FVSI is chosen as the targeted output in the IMLANNs model. The strength of the proposed IMLANNs model has been validated on the IEEE 30- Bus RTS. Results obtained from the study demonstrated that the proposed IMLANNs is feasible for congested line prediction, which in turns beneficial to power system operators in the planning unit of a utility.

Renewable energy curtailment: A case study on today's and tomorrow's congestion management

This work aims at contributing to the development of congestion management in power systems facing a strong expansion of renewable electricity generation. We address a comparatively new but increasingly important challenge to electricity markets with a uniform price zone: Renewable curtailment due to grid constraints. Our goal is to provide a model framework suitable to investigate the impact of expanding the grid operator's flexibility under varying congestion management regimes. We aim to expose the limitations of the current regime and to present a favorable alternative. For our analysis, we develop an optimal power flow program to replicate the current congestion management regime for distribution grids with high penetration of distributed generation in Germany. Furthermore, we introduce district heating as additional flexibility option and investigate the impact under different approaches to congestion management. Our results suggest that introducing additional flexibility, while keeping the current congestion management regime, bears significant risk to increase the cost of congestion management. Adjusting the regime to take into account economic criteria, as outlined in this work, eliminates this risk and grants direct control on the trade-off between curtailment and cost.

Congestion management in power systems

In liberalized power systems, generation and transmission services are unbundled, but remain tightly interlinked. Congestion management in the transmission network is of crucial importance for the efficiency of these inter-linkages. Different regulatory designs have been suggested, analyzed and followed, such as uniform zonal pricing with redispatch or nodal pricing. However, the literature has either focused on the short-term efficiency of congestion management or specific issues of timing investments. In contrast, this paper presents a generalized and flexible economic modeling framework based on a decomposed inter-temporal equilibrium model including generation, transmission, as well as their inter-linkages. The model covers short-run operation and long-run investments and hence, allows to analyze short and long-term efficiency of different congestion management designs that vary with respect to the definition of market areas, the regulation and organization of TSOs, the way of managing congestion besides grid expansion, and the type of cross-border capacity allocation. We are able to identify and isolate implicit frictions and sources of inefficiencies in the different regulatory designs, and to provide a comparative analysis including a benchmark against a first-best welfare-optimal result. To demonstrate the applicability of our framework, we calibrate and numerically solve our model for a detailed representation of the Central Western European (CWE) region, consisting of 70 nodes and 174 power lines. Analyzing six different congestion management designs until 2030, we show that compared to the first-best benchmark, i.e., nodal pricing, inefficiencies of up to 4.6% arise. Inefficiencies are mainly driven by the approach of determining cross-border capacities as well as the coordination of transmission system operators’ activities.

Real Time Congestion Management in Power Systems Considering Quasi-Dynamic Thermal Rating and Congestion Clearing Time

In a smart power system, smart measurement infrastructure brings so many advantages in monitoring and control of the power system. This paper proposes an optimal real time transmission congestion management algorithm in a competitive electricity market based on real time measurements in a smart power system. Instead of static and fix thermal rates, quasi-dynamic thermal rates of transmission lines are calculated and used to solve the real time congestion management optimization problem. The optimization problem includes an optimal generation rescheduling and load shedding scheme to minimize the congestion management costs in a specific time interval. This time interval is introduced and defined as congestion clearing time in this paper. Furthermore, in the proposed algorithm, the congestion clearing time is divided into few subsequent subintervals aimed at considering loads and power system variations during rescheduling procedure. The proposed method could be used by system operators (SOs) to adapt the real time congestion management procedure with regard to the power system loads and generations, or system configuration variations during rescheduling and load shedding process. Numerical results obtained from running the algorithm on the modified IEEE 30-bus test system and the transmission network of Esfahan Regional Power Network (ERPN), as a real size power system, demonstrate convincingly the ability of the proposed algorithm in the congestion management cost and load shedding reduction, while it increases the power system security margin in intensive congestion cases significantly.

Disparity Line Utilization Factor and Galaxy-based Search Algorithm for Advanced Congestion Management in Power Systems

In this paper a new approach has been used for finding the optimal location for placing the Interline Power Flow Controller (IPFC). The IPFC is used to reduce the system loss and power flow in the heavily loaded lines and improve stability of the system. Here a new method, the Disparity Line Utilization Factor (DLUF) is used for determining the optimal placement of the (IPFC) to control the congestion in transmission lines. The DLUF ranks the transmission lines in terms of line congestion. The IPFC is accordingly placed in between the most congested and the least congested line connected to the same bus. The Galaxy-Based Search Algorithm (GBSA) is used for the optimal sizing of the IPFC. The results obtained by the proposed algorithm have been compared with that of the Genetic Algorithm (GA) to ascertain the effectiveness of the proposed method on the power system performance. The proposed method is tested on an IEEE 30 bus test system. It has been shown that by using the GBSA, the tuning of the IPFC further reduces congestion in the system by about 15%.

Stochastic congestion management considering power system uncertainties: a chance-constrained programming approach

Considering system uncertainties in developing power systems, algorithms such as congestion management (CM) are vital in power system analysis and studies. This paper proposes a new model for power system CM by considering power system uncertainties based on chance-constrain programming (CCP). In the proposed approach, transmission constraints are taken into account by stochastic, instead of deterministic, models. The proposed approach considers network uncertainties with a specic level of probability in the optimization process, and then an analytical approach is used to solve the new model of stochastic congestion management. In this approach, the stochastic optimization problem is transformed into an equivalent deterministic problem. Moreover, an efficient numerical approach based on a real-coded genetic algorithm and Monte Carlo technique is proposed to solve the CCP-based congestion management problem in order to make a comparison to the analytical approach. The effectiveness of the proposed approach is evaluated by applying the method to the IEEE 30-bus test system. The results show that the proposed CCP model and the analytical solving approach outperform the existing models.

Congestion management in power systems – A review

Abstract In the past few decades, restructuring has overtaken all possible domains including the electric supply industry. Restructuring has brought about considerable changes by the virtue of which electricity is now a commodity and has converted into deregulated one. Such a competitive market has paved way for innumerable participants. This has led to overloading and congestion of transmission lines. Moreover, open access transmission network has ingenerated a more intensified problem of congestion. Thus, congestion management in power systems is germane and of central importance to the power industry. In this paper a review work is carried out to unite all the publications in congestion management.

An application of reference point‐based NSGA‐II for power system congestion management ensuring system stability

In this paper, we formulate the power transmission congestion problem as a multiobjective optimization problem and solve it using the reference point NSGA‐II (R‐NSGA‐II) technique. Restructuring of the electric power industry has led to intensified use of the transmission grid, thereby causing more frequent power transmission congestion. Congestion threatens the power system security and reliability and is therefore a crucial issue in the unbundled power system scenario, which is usually managed by rescheduling the generators and the demand. Stability considerations should also be incorporated within the congestion management (CM) methodology so as to ensure secure stability margins post CM. In this paper, we formulate the CM problem in a pool model as a true multiobjective optimization problem with the three conflicting objectives of minimizing CM cost and maximizing the voltage stability margin and the transient stability margin. The multiobjective CM problem is solved using the R‐NSGA‐II method, which is a modification of the well‐known non‐dominated sorting genetic algorithm II (NSGA‐II) optimization algorithm. R‐NSGA‐II makes use of decision maker (DM)‐supplied preference information to guide the search for better solutions corresponding to the DM's preferences. The R‐NSGA‐II‐based CM approach is tested and verified on the IEEE‐39 bus system model, and its performance is compared with those of NSGA‐II and other reported method. The results obtained demonstrate that the proposed method is capable of obtaining Pareto‐optimal solutions as per the user's choice with fewer iterations, and therefore could be effectively used for solving the multiobjective CM problem. © 2014 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Congestion Management Strategies of Real-Time Market

The high penetration of distributed energy resources (DERs) will significantly challenge the power system operation and control due to their stochastic, intermittent, and fluctuation characteristics. This enhances the difficulty of congestion management of power systems in cross-border electricity market among different regions. For handling this, the Real-Time Market is proposed for balancing capacity trading against congestions. Several strategies for Real-Time Market dealing with congestions are proposed. The strategy of two-stage crossborder markets in Day-ahead, Intra-day and Real Time Market are introduced with the congestion constraints complied. Pre-Contingency strategy is proposed as the advance preparation for the future congestion, and In-Day redispatch is used for regulation. Accordingly, the requirements on facilities considering telemetry and remote control in a fast manner are discussed at last.

Congestion Management in Power System by Optimal Location And Sizing of UPFC

Congestion Management in Power System by Optimal Location And Sizing of UPFC