Congestion Management Framework Research Articles

A long-term congestion management framework through market zone configuration considering collusive bidding in joint spot markets

A long-term congestion management framework through market zone configuration considering collusive bidding in joint spot markets

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.

Distributed Transactive Framework for Congestion Management of Multiple-Microgrid Distribution Systems

The privatization of distribution systems has resulted in the development of multiple-microgrid (multiple-MG) systems where each microgrid independently operates its local resources. Moreover, the high integration of independent distributed energy sources could lead to operational issues such as grid congestion in future distribution systems. Therefore, this paper provides a transactive-based energy management framework to operate multiple-MG distribution systems; while, alleviating grid congestion in a decentralized manner. In this respect, alternating direction method of multipliers (ADMM) is considered to develop an operational framework that copes with distributed nature of multiple-MG systems. In this context, a novel procedure in the context of ADMM is proposed to distributedly determine transactive coordinator signals which address energy prices as well as power losses and grid congestions. Furthermore, each MG takes into account stochastic programming and the conditional value-at-risk index to handle the uncertainty of its operational scheduling. At last, the proposed framework is applied on IEEE 37-bus and 123-bus test grids to investigate its efficacy in distributed energy management of multiple-MG systems.

A Market Framework for Decentralized Congestion Management in Smart Distribution Grids Considering Collaboration Among Electric Vehicle Aggregators

This paper proposes a day-ahead market framework for congestion management in smart distribution networks. The presented scheme provides a platform for collaboration between distribution-level market operator (DMO) and data traffic operator (DTO) to alleviate congested feeders such that data transmission traffic between market participants is effectively managed in a smart grid. In addition, a decentralized mechanism is developed for collaboration of electric vehicle (EV) aggregators with common clients to take advantage of EVs not only as flexible loads but also as mobile distributed storage (MDS) for congestion management. Moreover, the proposed framework outlines an administrative action for distribution system operator (DSO) to support the market when the decentralized competitions among distributed generation (DG) aggregators and EV aggregators do not fully relieve a serious congestion. The proposed day-ahead congestion management scheme is validated on an unbalanced 136-bus distribution system massively integrated with wind turbine DGs (WTDGs), photovoltaic DGs (PVDGs), diesel-engine DGs (DEDGs), and EVs.

Coordinating Cross-Country Congestion Management: Evidence from Central Europe

We employ a detailed two-stage model to simulate the operation of the Central Eastern European electricity market and network. Implementing different cases of coordination in congestion management between national transmission system operators, numerical results show the beneficial impact of closer cooperation. Specific steps comprise the sharing of network and dispatch information, cross-border counter-trading, and multilateral redispatch in a flow-based congestion management framework. Efficiency gains are accompanied by distributional effects. Closer economic cooperation becomes especially relevant against the background of changing spatial generation patterns, deeper international integration of national systems, and spillovers of national developments to adjacent systems.

Reliability constrained congestion management with uncertain negawatt demand response firms considering repairable advanced metering infrastructures

This paper presents a new framework for congestion management utilizing a reliability model of demand response resources under the smart grid environment. Demand response resources modeling is highly appertaining to uncertainty of customer's behavior. Interval data which is a core deliverable of advanced metering infrastructure, is essential for customers to participate in demand response events. Hence, a systematic method based upon frequency and duration approaches is utilized to present the multi-state model of multiple demand response resources considering repairable advanced metering infrastructures, the so-called demand response firm. Moreover, a new two-step congestion management structure with proposed demand response firm model is introduced for relieving congestion besides diminishing the risk of supplying loads. Firstly, system operator clears the electricity market based on economic and/or reliability-driven issues without considering transmission network limits. Afterwards, he/she will alleviate the existing transmission congestion considering a trade-off between demand response firms and load shedding besides generation rescheduling. In this regard, the impact of several important factors such as demand response firms' maximum achievable potential and forced outage rate of advanced metering infrastructures on the proposed framework are assessed. The simulation results discuss economic- and reliability-driven measures of the proposed demand response model in congestion management.

Coordinating Cross-Country Congestion Management

We employ a detailed two-stage model to simulate the operation of the Central Eastern European electricity market and network. Implementing different cases of coordination in congestion management between national transmission system operators, numerical results show the beneficial impact of closer cooperation. Specific steps comprise the sharing of network and dispatch information, cross-border counter-trading, and multilateral redispatch in a flow-based congestion management framework. Efficiency gains are accompanied by distributional effects. Closer economic cooperation becomes especially relevant against the background of changing spatial generation patterns, deeper international integration of national systems, and spillovers of national developments to adjacent systems.

Dynamic voltage stability constrained congestion management framework for deregulated electricity markets

Congestion management is an important part of power system operation in today deregulated electricity markets. However, congestion management is traditionally performed based on static analysis tools, while these tools may not correctly capture dynamic voltage stability limits of a power system. In this paper, a new congestion management framework considering dynamic voltage stability boundary of power system is proposed. For this purpose, precise dynamic modeling of power system equipment, including generators and loads, is incorporated into the proposed congestion management framework. The proposed method alleviates congestion with a lower congestion management cost and more dynamic voltage stability margin, resulting in a more robust power system, compared with the previous congestion management methods. The validity of proposed congestion management framework is studied based on the New England 39-bus power system. The obtained results confirm the validity of the developed approach.

Multi-objective congestion management by modified augmented ε-constraint method

Congestion management is a vital part of power system operations in recent deregulated electricity markets. However, after relieving congestion, power systems may be operated with a reduced voltage or transient stability margin because of hitting security limits or increasing the contribution of risky participants. Therefore, power system stability margins should be considered within the congestion management framework. The multi-objective congestion management provides not only more security but also more flexibility than single-objective methods. In this paper, a multi-objective congestion management framework is presented while simultaneously optimizing the competing objective functions of congestion management cost, voltage security, and dynamic security. The proposed multi-objective framework, called modified augmented ε-constraint method, is based on the augmented ε-constraint technique hybridized by the weighting method. The proposed framework generates candidate solutions for the multi-objective problem including only efficient Pareto surface enhancing the competitiveness and economic effectiveness of the power market. Besides, the relative importance of the objective functions is explicitly modeled in the proposed framework. Results of testing the proposed multi-objective congestion management method on the New-England test system are presented and compared with those of the previous single objective and multi-objective techniques in detail. These comparisons confirm the efficiency of the developed method.

Stochastic congestion management in power markets using efficient scenario approaches

Congestion management in electricity markets is traditionally performed using deterministic values of system parameters assuming a fixed network configuration. In this paper, a stochastic programming framework is proposed for congestion management considering the power system uncertainties comprising outage of generating units and transmission branches. The Forced Outage Rate of equipment is employed in the stochastic programming. Using the Monte Carlo simulation, possible scenarios of power system operating states are generated and a probability is assigned to each scenario. The performance of the ordinary as well as Lattice rank-1 and rank-2 Monte Carlo simulations is evaluated in the proposed congestion management framework. As a tradeoff between computation time and accuracy, scenario reduction based on the standard deviation of accepted scenarios is adopted. The stochastic congestion management solution is obtained by aggregating individual solutions of accepted scenarios. Congestion management using the proposed stochastic framework provides a more realistic solution compared with traditional deterministic solutions. Results of testing the proposed stochastic congestion management on the 24-bus reliability test system indicate the efficiency of the proposed framework.

Multi-objective congestion management incorporating voltage and transient stabilities

Congestion in a power system is turned up due to operating limits. To relieve congestion in a deregulated power market, the system operator pays to market participants considering their bids to alter their active powers. After relieving congestion, the network may be operated with a reduced voltage or transient stability margin because of hitting security limits or increasing the contribution of risky participants. The proposed multi-objective framework for congestion management in this paper simultaneously optimizes competing objective functions of congestion management cost, voltage security, and dynamic security. The voltage stability margin and corrected transient energy margin are employed as indices to be incorporated into the multi-objective congestion management. A fuzzy decision maker is proposed to derive the most efficient solution among Pareto-optimal solutions of multi-objective mathematical programming problem. Results of testing the proposed method on the New-England test system elaborate the efficiency of the proposed method.

A novel congestion control mechanism for multicast real-time connections

Time framing strategies such as Stop-and-Go (S&G) [Golestani S.J., Congestion-free transmission of real-time traffic of real-time traffic in packet networks, IEEE INFOCOM, 1990, pp.527-536; Golestani S.J., A framing strategy for congestion management, IEEE JSAC, 9(7), 1991, pp.1064-1077; Golestani S.J., Congestion-free communication in high-speed packet networks, IEEE Trans. on Communications, 39(12), 1991, pp.1802-1812; Golestani S.J., A stop-and-go queueing framework for congestion management, ACM SIGCOMM, 1992, pp.8-18] and Continuous Framing (CF) [Jau-Hsiung Huang, Biau-Jwo Tsaur, Continuous framing mechanism for congestion control in broadband networks, Computer Communications, 18(10), 1995, pp.718-724] are designed to support the end-to-end delay bound and jitter bound for unicast connections. Considering the features of real-time multicast connections, a new time framing mechanism named Multicast Continuous Framing (MCF) is proposed in this article. S&G and CF allow only one time frame length per connection, but MCF allows changing of the time frame length of a connection at intermediate nodes so that the statistical multiplexing gain within a connection is increased, i.e., the bandwidth requirement can be reduced. We also present the multicast connection setup scheme for MCF in the article. Simulation results show that MCF has a much better performance than that of CF, and the tighter jitter bound a connection requests, the more performance improvement MCF can obtain.

A stop-and-go queueing framework for congestion management

A framework for congestion management in integrated services packet networks based on a particular service discipline, called stop-and-go queueing, is proposed. In this framework, loss-free and bounded-delay transmission is provided to the class of traffic with stringent delay and loss requirements, e.g., real-time traffic, while the bursty traffic without such requirements is treated on a different basis to achieve high transmission efficiency. Loss-free and bounded-delay transmission is accomplished by means of an admission policy which ensures smoothness of the traffic at the network edge, and the stop-and-go queueing which maintains the traffic smoothness throughout the network. Both the admission policy and the stop-and-go queueing are based on a time framing concept, addressed in a previous paper. This concept is further developed here to incorporate several frame sizes into the strategy, thereby providing the necessary flexibility in accommodating throughput and end-to-end delay requirements of different connections on an as-needed basis.