Congestion Management Methods Research Articles

Distributed Optimization-Based Dynamic Tariff for Congestion Management in Distribution Networks

This paper proposes a distributed optimization-based dynamic tariff (DDT) method for congestion management in distribution networks with high penetration of electric vehicles and heat pumps. The DDT method employs a decomposition-based optimization method to have aggregators explicitly participate in congestion management, which gives more certainty and transparency compared to the normal DT method. With the DDT method, aggregators reveal their final aggregated plan and respect the plan during operation. By establishing an equivalent overall optimization, it is proven that the DDT method is able to minimize the overall energy consumption cost and line loss cost, which is different from the previous decomposition-based methods such as multiagent system methods. In addition, a reconditioning method and an integral controller are introduced to improve convergence of the distributed optimization where challenges arise due to multiple congestion points, multiple types of flexible demands, and network constraints. The case studies demonstrate the efficacy of the DDT method for congestion management in distribution networks.

Adaptive real-time congestion management in smart power systems using a real-time hybrid optimization algorithm

In deregulated power systems, where a short period of service interruption causes extreme financial and social damages to customers and service providers, it is necessary to develop optimal intelligent algorithms in order to minimize unforeseen service interruptions due to unavoidable real-time contingencies. Nowadays, regarding the high implementation of communication infrastructure in smart power systems, as well as accurate sensors for a variety of purposes, it is possible to effectively collect and analyze real-time and synchronized data, run fast intelligent algorithms and send control commands to controllers. This paper proposes an adaptive real-time congestion management (RTCM) method which optimally employs adaptive thermal ratings of transmission lines to manage real-time congestions using all power system capabilities. This algorithm is considered as an essential ancillary service in a power market, where all generation companies and customers can participate. In this algorithm, a demand response program is modeled and also a real-time hybrid optimization algorithm is developed to solve the RTCM problem aimed at finding the optimal solution during a short time span. Incorporating an adaptive artificial neural network along with a modified particle swarm optimization (PSO) algorithm is proposed in this paper as a real-time hybrid optimization method. Advantages and effectiveness of this method are demonstrated by numerical results from analyzing the modified 39-bus New England system.

Congestion management in restructured power systems for smart cities in India

The development of smart cities in India will require a smart grid for power generation, transmission and distribution systems with a continuous power supply. The transmission line is one of the major elements to supply continuous power to the smart cities via a smart grid. Transmission line congestion is a major technical problem, which must be relieved in order to maintain the system is in a secure state. The present work proposes a combined FACTS device and a microgrid based congestion management (CM) method for relieving transmission line congestion. The optimal placements of the FACTS device and microgrid are obtained by using real power flow (RPF) and transmission line relief (TLR) sensitivity indices. The proposed method has been demonstrated on an IEEE 14 bus test system. The FACTS and MG based CM approached when combined were found to be superior to the performance when the methods are carried out individually.

Optimal Reconfiguration-Based Dynamic Tariff for Congestion Management and Line Loss Reduction in Distribution Networks

This paper presents an optimal reconfiguration-based dynamic tariff (DT) method for congestion management and line loss reduction in distribution networks with high penetration of electric vehicles. In the proposed DT concept, feeder reconfiguration (FR) is employed through mixed integer programming when calculating the DT, leading to minimized energy cost and reduced DT as compared with the DT concept without FR. This paper further demonstrates that the line losses can be taken into account during the calculation of DT. As a result, the line loss reduction can be realized in a decentralized manner through the DT framework. Three case studies were conducted to validate the optimal reconfiguration-based DT method for congestion management and line loss reduction in distribution networks.

Rescheduling of real power for congestion management with integration of pumped storage hydro unit using firefly algorithm

Post-deregulation era of power system operation produces more pressure on Independent System Operator (ISO) to ensure congestion free transmission network without destroying system security. In order to relieve transmission congestion, ISO initiates correct methods by maintaining system reliability and security. This paper considers a novel efficient technique for congestion management like generator real power rescheduling by integrating a pumped storage hydro unit (PSHU) in the system. This paper presents an optimization model for congestion management by incorporating the calculation of two factors like generator sensitivity factor (GSF) and bus sensitivity factor (BSF). Optimal location of PSHU is identified using the value of BSF’s and the numbers of participated generators for congestion management by rescheduling their outputs are determined using the value of GSF’s. The impact of PSHU has been investigated to manage transmission congestion which further reduces the congestion cost and improves security of the system. The proposed method for congestion management considering PSHU is tested on modified IEEE-39 bus New England test system and the validity is obtained by considering the same problem without the presence of PSHU. The result of proposed work with proper utilization of PSHU illustrates the impact of PSHU towards the congestion management and also exploration of firefly algorithm for minimizing the transmission congestion cost.

Uncertainty Management of Dynamic Tariff Method for Congestion Management in Distribution Networks

The dynamic tariff (DT) method is designed for the distribution system operator (DSO) to alleviate congestions that might occur in a distribution network with high penetration of distributed energy resources (DERs). Uncertainty management is required for the decentralized DT method because the DT is determined based on optimal day-ahead energy planning with forecasted parameters such as day-ahead energy prices and energy needs which might be different from the parameters used by aggregators. The uncertainty management is to quantify and mitigate the risk of the congestion when employing the DT method, which is achieved by firstly formulating the problem as a chance constrained two-level optimization and then solving the problem through an iterative procedure. Two case studies were conducted to demonstrate the efficacy of the uncertainty management of DT method.

A new multi-objective solution approach to solve transmission congestion management problem of energy markets

Transmission congestion management plays a key role in deregulated energy markets. To correctly model and solve this problem, power system voltage and transient stability limits should be considered to avoid obtaining a vulnerable power system with low stability margins. Congestion management is modeled as a multi-objective optimization problem in this paper. The proposed scheme includes the cost of congestion management, voltage stability margin and transient stability margin as its multiple competing objectives. Moreover, a new effective Multi-objective Mathematical Programming (MMP) solution approach based on normalized normal constraint (NNC) method is presented to solve the multi-objective optimization problem of the congestion management, which can generate a well-distributed and efficient Pareto frontier. The proposed congestion management model and MMP solution approach are implemented on the New-England’s test system and the obtained results are compared with the results of several other congestion management methods. These comparisons verify the superiority of the proposed approach.

Determining Optimal Buses for Implementing Demand Response as an Effective Congestion Management Method

Demand response (DR) can be counted as an effective congestion management method in power systems. However, appointing the optimal buses for the demand response programs (DRPs) implementation is one of the main challenges for the power system operators. In this paper, a new procedure has been developed by which the optimal locations and times of DRPs implementation are determined. Optimal buses are identified based on the power transfer distribution factors (PTDFs), available transfer capability, and dynamic dc optimal power flow problem. Applying the developed method results in reducing lines' congestion, increasing customers, and independent system operator's benefits, improving load curve's characteristics, preventing line outages and black outs, and consequently increasing the network reliability. The proposed method has been applied on the IEEE 39-bus New England test system. Results indicate the effectiveness and practical benefits of the proposed method.

Congestion Management Considering Wind Energy Sources Using Evolutionary Algorithm

It is necessary to maintain power system security within a reasonable limit without harming the electric power supply in an emerging electricity market. Power systems are said to be congested when power transmission reaches beyond its limits. Therefore, in a deregulated power system environment, the system operator must look after the power transactions made by market parties. In this article, a novel congestion management method considering wind energy sources is introduced. The proposed congestion management technique is constructed considering the calculation of bus sensitivity factor and generator sensitivity factor. The bus sensitivity factor is used for finding the optimal location of a wind farm. The most sensitive generators are identified to reschedule their output by using the generator sensitivity factor. The artificial bee colony algorithm and particle swarm optimization techniques are applied to compare the generator rescheduling with earlier literature for congestion management. The proposed method has been tested on the 39-bus New-England test system to investigate the effectiveness of the proposed approach of wind farm integration for congestion management.

A Nodal Pricing Model for the Nordic Electricity Market

In the Nordic day-ahead electricity market zonal pricing or market splitting is used for relieving congestion between a predetermined set of bidding areas. This congestion management method represents an aggregation of individual connection points into bidding areas, and flows from the actual electricity network are only partly represented in the market clearing. Because of several strained situations in the power system during 2009 and 2010, changes in the congestion management method have been considered by the Norwegian regulator. In this paper we discuss nodal pricing in the Nordic power market, and compare it to optimal and simplified zonal pricing, the latter being used in today’s market. A model of the Nordic electricity market is presented together with a discussion of the calibration of actual market data for four hourly case studies with different load and import/exports to the Nordic area. The market clearing optimization model incorporates thermal and security flow constraints. We analyze the effects on prices and grid constraints and quantify the benefits and inefficiencies of the different methods. We find that the price changes with nodal pricing may not be dramatic, although in cases where intra-zonal constraints are badly represented by the aggregate transfer capacities in the simplified zonal model the nodal prices may be considerably higher on average and vary more than the simplified zonal prices. On the other hand nodal prices may vary less than the simplified zonal prices if aggregate transfer capacities are set too tightly. Allowing for more prices in the Nordic power market would make dealing with capacity limits easier and more transparent.

Research on the Congestion Management of Power System Based on Multi-Objective Functions and Primal-Dual Interior Point Method

Congestion management is a hot point for research in power system. Optimal power flow (OPF) model is often used to solve this problem. However, the results cannot satisfy with market needing. To solve this problem, this paper presents a novel method of congestion management based on risk constraint and fuzzy optimization in electricity power markets. The risk constraint is used for transmission line thermal security instead of the traditional deterministic current or power constraint. The multi-objective functions are used to coordinate the conflict between security and efficiency of transmission network. Primal-Dual Interior Point Method (PDIPM) is adopted to solve the RBOPF problem. The method can minimize re-dispatch cost and the system operating risk.

Nodal Pricing in a Coupled Electricity Market

This paper investigates a pricing model for an electricity market with a hybrid congestion management method, i.e. part of the system applies a nodal pricing scheme and the rest applies a zonal pricing scheme. The model clears the zonal and nodal pricing areas simultaneously. The nodal pricing area is affected by the changes in the zonal pricing area since it is directly connected to the zonal pricing area by commercial trading. The model is tested on a 13-node power system. Within the area that is applying nodal pricing, prices and surpluses given by the hybrid pricing model match well with those given by the full nodal pricing model. Part of the network is better utilized compared to the solutions given by the full zonal pricing model. However, the prices given by the hybrid system may send wrong economic signals which triggers unnecessary generation from existing capacities, exacerbates grid congestion, and induces higher re-dispatching costs.

Simulation of Congestion Management and Security Constraints in the Nordic Electricity Market

Presently in the Nordic day-ahead market, zonal pricing or market splitting is used for relieving congestion between a predetermined set of price areas. Constraints internal to the price areas are resolved by counter trading or redispatching in the regulation market. In a model of the Nordic electricity market we consider an hourly case from winter 2010 and present analyses of the effects of different congestion management methods on prices, quantities, surpluses and network utilization. We also study the effects of two different ways of taking into account security constraints.

A Novel Method for Generation Rescheduling to Alleviate Line Overloads

This paper presents a method for real-time transmission congestion management. Congestion can be alleviated by incorporating line capacity constraints in the dispatch and scheduling process. The objective of this paper is to alleviate the overload and minimize the cost of operation. Here, two objectives congestion and cost are simultaneously minimized. Generation rescheduling of participating generators is done to overcome the congestion. Particle Swarm Optimization (PSO) technique, aims in finding the global optimum of the real-valued function (fitness function) defined in the given space (search space). The technique has been tested on Western System Coordinating Council (WSCC) 3-Machine 9-Bus system and the results are discussed.

Transmission Line Congestion Management by Specifying Optimal Placement of FACTS Devices Using Artificial Bee Colony Optimization

An evolutionary based approach is proposed to congestion management of transmission lines in a restructured market environment by optimizing the Flexible Alternating Current Transmission System (FACTS) devices. The specification and readjustment of electricity markets has enhanced competition and electricity may be generated and used in amounts that would make the transmission system to act beyond transfer limits. Therefore, congestion management is a primary transmission management crisis. Considering to the worldwide exestuation of congestion management methods, different schemes can be described. The different international execution suggests that there is no specific congestion management system. In this study, we attempted to improve an OPF solution incorporating FACTS devices in a given market mode. FACTS devices facilitate the power grid owners to enhance existing transmission network capacity while preserving the operating margins necessary for grid stability. Consequently, extra power can achieve consumers with a minimum impact on the environment, after significantly shorter project implementation times and at lower assessment costs-all compared to the alternative of building new transmission lines or power generation facilities. FACTS devices are controlled in a mode so as to guarantee that the formal obligations are implemented as far as possible by minimizing line congestion. Different optimization approaches available in the literature have been used to solve OPF problem. For optimizing the FACTS devices placement in the market, Artificial Bee Colony (ABC) algorithm is utilized and compared with GA base optimizing. The proposed methods are tested on the standard IEEE9 bus reliability test systems.

Improving Congestion Management: How to Facilitate the Integration of Renewable Generation in Germany

In this paper the German congestion management regime is analyzed and future congestion management costs are assessed given a higher share of intermittent renewable generation. In this context, cost-based re-dispatching of power plants and technical flexibility through topology optimization are considered as market-based and technical congestion management methods. To replicate the current market regime in Germany a two-step procedure is chosen consisting of a transactional spot market model and a congestion management model. This uniform pricing model is compared to a nodal pricing regime. The results show that currently congestion can mainly be managed by re-dispatching power plants and optimizing the network topology. However, congestion management costs tend to increase significantly in future years if the developments of transmission as well as generation infrastructure diverge. It is concluded that there is a need for improving the current congestion management regime to achieve an efficient longterm development of the German electricity system.

A Classification Management Method for the Traffic Congestion Based on RFID Application

At the present stage, the problem of cities traffic congestion has been attracted a lot of scholars’ attention. In order to enhance the traffic congestion management efficiency, in this paper, a new traffic congestion management method is proposed. This method contain two parts: firstly, based on the foundational traffic parameters, combined with the RFID technology, the proposed method considers the recognition and the classification of congestion strategies; secondly, this paper present improvement strategies and a framework of traffic congestion management system based on RFID.

Does One Design Fit All? On the Transferability of the PJM Market Design to the German Electricity Market

Germany’s nuclear phase out and an increasing share of fluctuating RES production amplifies the North-South congestion problem in the German electricity grid. But congestion management becomes a serious issue not only in the German but in the whole European electricity system as German wind production does not only affect the German grid. In theory it is well established that nodal pricing is the most efficient congestion management method. In literature the PJM well-established nodal market design often serves as a reference and is viewed as benchmark. To benefit from experiences made in the U.S. the transfer of the PJM market design to Germany could be advantageous. This article compares key elements of the generation mix, the network structure, the cross-border interconnection as well as the congestion situation of both electricity markets to assess potentials and impediments for an implementation of the PJM nodal market design in Germany. We show that both markets are less different in structure than expected but that large differences in performance respectively in congestion frequency lead probably to much lower welfare gains. Transfer of the PJM market design to Germany is possible in principle, but adjustments to RES would be ad-vantageous.

Evaluating congestion management in the Dutch electricity transmission grid

Due to the increase in electricity generation capacity in the Netherlands and a new connection policy, transmission system operator (TSO) TenneT expects a significant increase in congestion in the Dutch transmission grid. To manage this, the Dutch government implemented redispatching, a method which is argued in the literature to potentially impose large congestion costs upon the TSO. A quantitative model of the Dutch electricity system was developed in order to evaluate this method. The outcomes were compared to the performance of three alternative congestion management methods. Regardless of the method, congestion costs were found to be substantially lower than in previous studies. Because combined-cycle gas turbines are the marginal generation technology in almost all cases, the costs of up and down regulation do not differ much. Consequently, the redispatching costs for the TSO are expected to be relatively low, and the opportunities for abuse of market power appear to be limited. While all the evaluated methods are effective and economically efficient, they have significantly different welfare effects. Market splitting creates significantly larger welfare effects than the different varieties of redispatching.

Congestion management in hybrid power markets using modified Benders decomposition

Congestion management is one of critical tasks in electric power markets. Although congestion management using a centralized optimal power flow may give an efficient solution, it lacks enough transparency in a competitive power market for its participants about congestion related costs. On the other hand, clearing the market and mitigating congestion separately may not result in an efficient solution from cost viewpoint. In this paper, a two-stage method using a modified Benders decomposition technique is proposed for congestion management in hybrid electricity markets including pool and bilateral transactions. An innovative convergence criterion and a new type of the sub-problem are proposed in using Benders decomposition in congestion management. The proposed method not only gives the efficient solution, but also provides enough transparency about congestion cost. Results of testing the method on the New-England test system are presented and discussed in detail. The results confirm the efficiency of the proposed congestion management method.