Congestion Management Methods Research Articles

A Real-Time Zbus-based Method for Peer-to-Peer Energy Transactions in The Energy Internet

The Energy Internet (EI) has emerged as a promising field within smart grids, addressing capacity limitations and discrepancies in energy resources. It facilitates a peer-to-peer (P2P) energy trading environment for EI prosumers and consumers, reducing reliance on the main grid. To ensure an efficient P2P trading process, it is essential to route energy through the path with minimal power loss optimally. The Energy Router (ER) plays a pivotal role in governing power flow through EI. This paper presents the development of a novel real-time Zbus-based method for directing P2P energy transactions within the EI considering congestion management. The proposed method depends on bus impedance matrix in forming the routing matrix. Firstly, the ER’s structure and function are outlined in relation to the network, and then to design the EI’s topology using an adjacency matrix representation. Secondly, the proposed method methodology is presented in steps and explained on simple 7-bus microgrid. Additionally, a novel congestion management method is integrated into the routing algorithm, considering transmission line loading and available capacity during route selection. Furthermore, the paper applies a power flow technique to the selected routes to optimize power flow performance. The proposed method demonstrates enhanced directness and accuracy compared to existing approaches. Its efficiency is validated by testing a modified IEEE 14-bus system and the standard IEEE 30-bus system. Importantly, the method successfully identifies the minimum loss path, despite the unnecessity of power loss calculations in the routing algorithm. The key contribution of this research lies in providing a comprehensive and practical solution for P2P energy transactions in the EI, showcasing substantial improvements in accuracy, efficiency, and congestion management compared to previous methods.

The Devil in the details: The factors determining the selection of intrazonal congestion management methods across Europe

The European electricity market is becoming increasingly interconnected, raising questions about how intrazonal congestion management, typically governed by national regulations, interacts with the coupled cross-zonal markets. A major concern is the lack of information regarding intrazonal congestion management methods. This paper addresses this issue by examining different ways in which European transmission system operators (TSOs) use third-party resources to manage congestion in short-term electricity markets. We create a decision-tree-based classification to represent all identified congestion management methods and select three cases for comprehensive evaluation using predefined assessment criteria. While doing so, we identify a trade-off between efficiency and ease of implementation. The balance between these two factors is determined by the severity of the congestion. In a severely congested grid, locational signals are critical, requiring a better alignment between the network representation in the market clearing and the physical network constraints. When the congestion is less severe, TSOs can choose other congestion management methods based on factors such as the predictability of congestion and resource availability. These findings shed light on the complexities of congestion management in an integrated European market and can inform future policy decisions.

Assessing Improved Price Zones in Europe: Flow-Based Market Coupling in Central Western Europe in Focus

Theoretical papers have identified several sources of inefficiencies of flow-based market coupling (FBMC), the implicit congestion management method used to couple the Central Western European (CWE) electricity markets. These inefficiencies ultimately lead to welfare losses. In this paper, a large-scale model framework is introduced for FBMC assessments, focusing on modeling the capacity allocation and market clearing processes. The present paper completes this framework by presenting a newly developed redispatch model. Furthermore, we provide a case study assessing improved price zone configurations (PZCs) for the CWE electricity system, motivated by the debate on the currently-existing PZC. Our results show that improved PZCs—even while maintaining the number of price zones—can significantly reduce redispatch quantities and overall system costs. Moreover, making use of the insights of (Felten et al., 2021), we explain why increasing the number of price zones may not always increase welfare when using FBMC.

Intelligent TCP Congestion Control Policy Optimization

Network congestion control is an important means to improve network throughput and reduce data transmission delay. To further optimize the network data transmission capability, this research suggests a proximal policy optimization-based intelligent TCP congestion management method, creates a proxy that can communicate with the real-time network environment, and abstracts the TCP congestion control mechanism into a partially observable Markov decision process. Changes in the real-time state of the network are fed back to the agent, and the agent makes action commands to control the size of the congestion window, which will produce a new network state, and the agent will immediately receive a feedback reward value. To guarantee that the actions taken are optimum, the agent’s goal is to obtain the highest feedback reward value. The state space of network characteristics should be designed so that agents can observe enough information to make appropriate decisions. The reward function is designed through a weighted algorithm that enables the agent to balance and optimize throughput and latency. The model parameters of the agent are updated by the proximal policy optimization algorithm, and the truncation function keeps the parameters within a certain range, reducing the possibility of oscillation during gradient descent and ensuring that the training process can converge quickly. Compared to the traditional CUBIC control method, the results show that the TCP-PPO2 policy reduces latency by 11.7–87.5%.

Adaptive Area-Based Traffic Congestion Control and Management Scheme Based on Fog Computing

How to globally construct the effective traffic congestion control and management method on large-scale urban road network is an important research challenge. Since an urban region can be divided into many areas to be managed, grid management has great potential to improve traffic management on large-scale urban road network. In this paper, we propose an adaptive area-based traffic congestion control and management scheme, which is based on fog computing-based internet of vehicles. In our proposed scheme, an urban region is divided to many traffic management areas. Based on the real-time dynamic traffic tightness degrees and the static correlation degrees between these divided management areas, we can obtain the complete real-time regional traffic correlations of these divided management areas. Further, when a traffic congestion occurs, the fog servers use the constructed measures to control the signal lights in real time and effectively guide the traffic flow through the cooperation between these divided management areas. The experimental results show our proposed scheme can effectively implement the road guidance and traffic light control to alleviate or eliminate traffic congestion when a traffic congestion occurs.

A novel congestion management method through power system partitioning

• Presenting a novel real time partition based CM method through power tracing and sensitivity analysis. • Introducing a new congestion index (CI) that simultaneously considers the contributions of each element in the congested line(s) and the effectiveness of the elements in alleviating the congestion. • Developing a new zonal CM based rescheduling and load shedding procedure. Congestion management (CM) is inevitable in today's competitive power markets. A CM method should be fast, fair, effective, and motivational. Moreover, in critical congestions, the system simplification and congestion clearing time are also of considerable importance. All the mentioned features can be found in an intelligent zonal CM. In this paper, using sensitivity analysis and power tracing techniques, a new CM model is developed based on power system partitioning. By the proposed model, a congestion index (CI) is introduced by which a candidate zone(s) is specified including some elements (generators and loads) with the highest CIs which means that they have high participation in congestion creation and simultaneously high effectiveness in congestion alleviation. Using the proposed method, power system operators can put their focus on the candidate zone(s) and alleviate the congestion effectively by some remedial measures such as generation rescheduling and load shedding. The proposed methodology is applied on IEEE 39-bus New England test system including 46 transmission lines. Results show the effectiveness and practicality of the proposed model, so that the congestion at the critical lines is alleviated well and their available transfer capabilities (ATCs) are increased to the reliable amounts.

A Traffic Density-Based Congestion Control Method for VANETs

This research presents a vehicle ID-based congestion aware message (CAM) for beacon signals on the vehicle environment. At the MAC protocol of the vehicle environment, enhanced vehicle ID-based analysis model is given first. With the automobile ID embedded in their separate CAMs, the model weights the randomized back-off numbers chosen by cars engaging in the back-off procedure. This leads to identifying a car ID-based randomized back-off code, which reduces the likelihood of a collision due to the identical back-off number. A traffic density based-congestion control algorithm (TDCCA) is suggested in this research. The revised mathematical approach surpasses previous work’s overall packet latency because just one-fourth of the congestion window is employed during the experiment. The research includes a congestion management method that adjusts the rate of CAM transmitted over the host controller to improve the efficiency of the model parameters. The method considers various circumstances, from nonsaturated to substantially saturated networks (in terms of congestion probability) and sparsely dispersed and teemed networks (in the form of vehicular intensity). The technique is run across various automobile ID-based back-off values for high-standard results analysis. The simulation outcomes in terms of packet delivery ratio, energy consumption, delay, success rate, and collision ensure the effectiveness of the TDCCA method. Even at high traffic densities, the automobile ID-based CAM following information method outperforms the typical fixed CAM frequency IEEE 802.11p, according to simulation findings for all back-off figures.

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.

An iterative quota calculation method for congestion management and optimal grid utilization

A quota-based approach can help grid operators deal with congestions in distribution grids in a non-discriminatory way for customers. The quota limits the maximum feed-in or consumption of a group of flexibility providing units (FPU) within a grid cluster. The novel iterative quota calculation (IQC) method is introduced in this paper. In this approach, the maximal active power limits of the FPU are constantly adapted, and the feasible operating region (FOR) of the grid is computed. The quota is computed from the resulting FORs after ensuring that the FPU operation cannot cause grid instability. The proposed method is compared to a previously developed static approach in a real LV grid. Results show the benefits of adding reactive power to the quota calculation, especially in distribution grids with inadequate reactive power compensation.

Congestion management under peer-to-peer energy trading scheme among microgrids through cooperative game

The peer-to-peer (P2P) energy trading among microgrids (MGs) can promote the renewable energy accommodation of network while maximizing the benefits of their own. However, unregulated trade behavior may cause network congestion which affect the safe and stable operation of the system. To this end, this paper proposes an effective congestion management method under P2P energy trading of MGs based on cooperative game strategy. For a congestion caused by several MGs, the Shapley value is firstly used to allocate the congestion marginal contribution. Then, a congestion price coefficient is optimally determined by minimizing the overall congestion cost while maximizing the overall revenue of all MGs, the MGs who are cost-sensitive are advised to make a change on their trading scheme. Taking a system composed of 4 MGs as an example, simulation study is finally conducted to validate the effectiveness and applicability of the proposed methods.

A survey on recent advances in transmission congestion management

AbstractFor the last few decades, the power sector has been restructuring throughout the world, and because of this, congestion is bound to take place in the network. Congestion can lead to market failure, violate transmission capability limits and high electricity prices, and end up threatening the power systems’ reliability and security. Increased congestion may also lead to unexpected price differences in power markets leading to market power. In a deregulated power market (DPM), the independent system operator (ISO)’s fundamental challenge is to preserve the power market’s reliability and safety by improving market efficiency when the network is congested. Therefore, congestion management (CM) is essential in DPM and is the key to the power system. This paper carries out a congestion management methods survey to bring together all recent publications in the DPM. It aims to help readers summarize progressive CM methods, along with traditional CM methods that have been discussed so far. In this paper, we have carried out a comparative survey of the various well-known CM methods.

Coordination of dynamic tariff and scheduled reprofiling product for day-ahead congestion management of distribution networks

The increasing penetration of distributed energy resources in distribution networks poses new challenges to the secure and efficient operation of distribution networks. One major challenge is the network congestion caused by the non-coordinated operation of flexible demands, such as electrical vehicles and heat pumps. In general, there are two categories of market-based day-ahead congestion management methods for distribution networks: price-based method and incentive-based method. In order to use the synergy of the two types of methods and to resolve potential conflicts against regulations when one type of method is implemented solely, a coordination scheme of the two types of methods is proposed for efficient day-ahead congestion management. In the proposed coordination scheme, the dynamic tariff (DT) as a price signal is used to partly resolve congestion firstly, and the scheduled reprofiling product (SRP) as an incentive-based flexibility service product is used to deal with the remaining congestion. By employing the coordination scheme, the distribution system operator (DSO) holds the profit neutral position in terms of congestion management, denoting that the DSO does not have the congestion management cost or revenue. Based on the DT model and SRP-based model, the coordination problem is formulated as a two-level non-convex mixed-integer non-linear programming (MINLP) model that is transformed into a one-level MINLP model with linear constraints and is solved by a proposed particle swarm optimization-based solution strategy. The Roy Billinton Test System was used to conduct case studies to validate the effectiveness of the proposed coordination scheme for day-ahead congestion management in distribution networks. The case study results demonstrate that the proposed coordination scheme can efficiently resolve congestion by the coordination of the DTs and SRPs while ensuring that the DSO is in a profit neutral position.

Research on the impact of financial transmission rights on transmission expansion: A system dynamics model

With the development of the power market, the traditional transmission congestion management method of using a power generation plan is no longer applicable, and congestion management using financial transmission rights (FTRs) is becoming a trend. However, after the introduction of FTRs, the profit from transmission lines changes, and those changes affect the expansion of and investment in transmission lines. Therefore, this paper constructs a system dynamics analysis model and presents specific calculation methods and a case analysis. Load growth, construction time, congestion, the step nature of the bid from generating units, and relative changes in the locational marginal prices (LMPs) at the sink and source nodes under the expansion and non-expansion scenarios are considered for a simulation calculation of net present value of transmission expansion project. Sensitivity analyses of factors such as generator bid prices, transmission expansion costs, the proportion of expansion capacity to transmission congestion, and the market maturity of FTRs are analysed. The results show that decision-making on the basis of transmission expansion investment in combination with FTRs is feasible and such decision-making can reflect the value of transmission expansion via the market, which conforms to market rules. That decision-making method is very important for the marketisation of power transmission.

Congestion Management of Microgrids With Renewable Energy Resources and Energy Storage Systems

With the increasing share of renewable energy resources in the microgrid, the microgrid faces more and more challenges in its reliable operation. One major challenge is the potential congestion caused by the uncoordinated operation of flexible demands such as heat pumps and the high penetration of renewable energy resources such as photovoltaics. Therefore, it is important to conduct microgrid energy management to ensure its reliable operation. The energy storage system (ESS) scheduling as an efficient means to alleviate congestion has been widely used. However, in the existing literature, the ESSs are usually scheduled by the microgrid system operator (MSO) in a direct control manner, which is impractical in the case where customers own ESSs and are willing to schedule ESSs by themselves. To resolve this issue, this study proposes a network reconfiguration integrated dynamic tariff–subsidy (DTS) congestion management method to utilize ESSs and network reconfiguration to alleviate congestion in microgrids caused by renewable energy resources and flexible demands. In the proposed method, the MSO controls sectionalization switches while customers or aggregators schedule ESSs in response to DTS to alleviate congestion. The DTS calculation model is formulated as a mixed-integer linear programming model, considering heat pumps (HPs), ESSs, and reconfigurable microgrid topology. The numerical results demonstrate that the proposed method can effectively use ESSs and network topology to alleviate congestion and the MSO does not need to take over the scheduling of the ESS.

Robust dynamic tariff method for day-ahead congestion management of distribution networks

With the large-scale deployment of distributed energy resources (DERs) in distribution networks, network congestion could occur due to the non-coordinated operation of DERs. The dynamic tariff (DT) method as a decentralized day-ahead congestion management method has been widely studied. In the DT method, it is assumed that the distribution system operator (DSO) and aggregators use the same energy requirement parameters, which is impractical due to the DSO’s forecast error. The discrepancy between the DSO’s forecast parameters and aggregator’s accurate parameters leads to a certain level of uncertainty that needs to be handled when employing the DT method. Therefore, a robust DT method is proposed for day-ahead congestion management while dealing with the uncertainty in the DT framework. A three-level robust DT model is formulated to obtain a robust DT solution, based on which the network constraints are respected even in the worst-case scenario. Moreover, due to the nonconvexity of the three-level robust DT model, the robust DT model is reformulated as a two-level optimization model, and a heuristic solution method is developed to obtain the robust DT solution with an iterative procedure. The Roy Billinton Test System (RBTS) was used to conduct case studies to validate the effectiveness of the proposed robust DT method for day-ahead congestion management in distribution networks. The case study results demonstrate that the deterministic DT method may be ineffective due to the DSO’s forecast errors whereas the proposed robust DT method can resolve congestion efficiently under the uncertain condition.

Modelling Demand Response to Alleviate Congestion in Active Power Market

A new method of congestion management in deregulated and competitive power system based on a combination of Demand Response (DR) program and generation re-dispatch is proposed in this research. One of DR program called Emergency Demand Response Program (EDRP) is carried out through customer's willingness to participate in this program in order to reduce their consumption during congestion. EDRP is modeled based on demand elasticity of the load and considering incentives. Different level of demand elasticity values is introduced to the customers to observe their contribution in congestion relief. The proposed method is examined on IEEE 30 bus system by using the Optimal Power Flow tool and it indicates that by integrating the customer’s elasticity for EDRP can decrease the cost to relieve the congestion and lead more benefit for all participants. The obtained results are the cost to manage congestion problem and optimal re-dispatch of generators by involving the participation of customers in EDRP.

Cyber Security of Market-Based Congestion Management Methods in Power Distribution Systems

As the penetration rate of flexible loads and distributed energy resources in the distribution networks increases, congestion management techniques that utilize demand-side management (DSM) have been developed. These are indirect methods that rely on information exchange between the distribution network operator, aggregators, and consumers’ meters to encourage customers to change their demand to relieve congestion. Cyber attacks against aggregators can compromise the operation of DSM-based congestion management methods, and hence, affect the security and reliability of electrical networks. In this article, the vulnerability of indirect congestion management methods to load-altering attacks is studied. An optimization algorithm is developed to determine the aggregators a cyber attacker would compromise, via minimum alteration of their load profiles, to cause congestion problems. The impact of such attacks on congestion and consumers’ electricity bill is then studied. A mitigation scheme is formulated to determine the most critical aggregators in the network. The security of these aggregators is then reinforced to mitigate such cyber attacks.

Locating Multi Types of FACTS for Congestion Management in Deregulated Power Systems Considering Voltage Stability

Congestion management is a vital and important problem especially in deregulated power system and may lead to system collapse. So, system operator should be ready to relieve congestion as fast as possible to keep system reliability and stability. There are many methods for congestion management; using Flexible Alternating Current Transmission Systems (FACTS) devices is considered an effective solution for removing congestion in transmission lines employing Optimal Power Flow based methods. Due to congestion problem, the system may be deteriorated and lead to voltage instability and this appears in reducing voltage stability margin (VSM (which is used as an index for voltage stability. In this paper Multi-Types of FACTS devices are used to solve the congestion problem, and because of the high cost of FACTS devices, the optimal locations and sizes is determined by solving the objective function which consists of two parts, maximizing the social welfare (SW), and maximizing the VSM to ensure system stability. The objective function is solved using the Genetic Algorithm (GA). The proposed method is applied on IEEE 14-bus test system and the results confirm the efficiency of the proposed method.

Market-based congestion management in electric power systems with exploitation of aggregators

The paper addresses market-based congestion management (MBCM) in electric power systems utilizing aggregators of dispersed small-scale (residential, commercial and small industrial) electricity consumers, producers and prosumers at the distribution network. The proposed method is based on the market principle, where the system operator performs minimum-cost redispatching of generators, loads and aggregators as to relive congested lines. The power system is linearized and presented by the direct current (DC) model for power flow calculation. In this way, Power Transfer Distribution Factors (PTDFs) and Topological Generation and Load Distribution Factors (TGDFs and TLDFs) which describe the relation between generators/loads/aggregators and line-power flows are applied. The proposed solution applies two decoupled optimizations. The upper-level optimization includes bid-based redispatching for congestion relief, while low-level optimization provides the optimal bid of an aggregator for the MBCM procedure, where block offers are introduced for aggregators that have multiple connection points (MCPs) to the transmission network and inject power synchronously at different locations. Both optimization problems are solved by linear programming. Case studies show the applicability of the proposed congestion management method on a simple test model of a power system.

SDNOFS: Software Defined Networking with Openflow Switches & BCN-ECN with ALTQ for Congestion Avoidance

High-performance computing cluster in a cloud environment. High-performance computing (HPC) helps scientists and researchers to solve complex problems involving multiple computational capabilities. The main reason for using a message passing model is to promote application development, porting, and execution on the variety of parallel computers that can support the paradigm. Since congestion avoidance is critical for the efficient use of different applications, an efficient method for congestion management in software-defined networks based on Open Flow protocol has been presented. This paper proposed two methods; initially, to avoid the congestion problem used by Software Defined Networks (SDN) with open flow switches, this method was originally defined as a communication protocol in SDN environments which allows the SDN controller to interact directly with the forwarding plane of network devices such as switches and routers, both physical and virtual (hypervisor-based), so that it could better adapt to changing business requirements.. Second, to enhance the quality of service and avoid the congestion problem used BCN-ECN with ALTQ. While comparing the existing method, the SDN open flow switches and BCN-ECN with ALTQ provides 98 % accuracy. Usage of these proposed methods will enhance the parameters structures delay time, level of congestion quality time and execution time