Centralized and decentralized optimal decision support for congestion management

Singh, Brijesh; Mahanty, R.; Singh, Shashi Kant

doi:10.1016/j.ijepes.2014.07.026

Cited by 15 publications

(7 citation statements)

References 30 publications

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“…In this stage, we solve (7), which has all the constraints from (5) except (5j), (5k) and (5l), which relaxes the voltage and line thermal limits. The additional constraints (7d) is added so that while voltage constraints are relaxed, deviation from unity voltage is penalized by a small value in the objective function (7a).…”

Section: Stagementioning

confidence: 99%

“…Because of the difficulty of solving the ACOPF problem, the majority of the grid operators use some form of a direct current optimal power flow (DCOPF) approximation as described by [5]. The DCOPF linearization shown in a market problem by [6,7] is based on a few assumptions that convert the nonlinear ACOPF problem to a linear formulation. Although the implementations enable solving optimal power flow within the practical time constraints, rough approximations of voltage and reactive power flow are applied, requiring the application of conservative transmission limits.…”

Section: Introductionmentioning

confidence: 99%

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Tuning successive linear programming to solve AC optimal power flow problem for large networks

Sadat

Sahraei-Ardakani

2022

International Journal of Electrical Power & Energy Systems

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Section: Stagementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Tuning successive linear programming to solve AC optimal power flow problem for large networks

Sadat

Sahraei-Ardakani

2022

International Journal of Electrical Power & Energy Systems

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“…In Reference 7, a genetic‐based optimization algorithm was used to optimize the rescheduling of the active power of GENCOs in order to mitigate the congestion of transmission lines and maximize the social welfare system by decreasing the difference of LMP between buses. A decentralized decision‐making method was proposed in Reference 8 for CMP of multi‐utility transmission systems. This article presented an interior point based optimization approach to find the best global solution aimed at maximizing the social welfare of the system.…”

Section: Introductionmentioning

confidence: 99%

InteractiveFACTSand demand response program as an incremental welfare consensus for maximizing wind power penetration

Gazijahani

Ghaemi

Farrokhifar

et al. 2020

Int Trans Electr Energ Syst

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This article deals with congestion management of transmission networks to maximize the penetration of wind power by proposing an incremental welfare consensus algorithm based on flexible alternating current transmission systems (FACTS) and demand response (DR) program. The proposed model takes the locational marginal prices as an input to manage both FACTS device and DR resources. To this end, an innovative two-stage market-clearing procedure is developed. Where in the first stage, the players bid to the market aimed at maximizing their profit, and the independent system operator clears the market based on social welfare maximization. In the second stage, a generation redispatch problem is executed in which FACTS device controllers and incentivebased DR are optimally coordinated to minimize the rescheduling costs of generation companies. Two distinct types of FACTS devices consisting of thyristor controlled series capacitor and static synchronous compensator are utilized here. Finally, a case study is accomplished on the modified IEEE one-area 24-bus reliability test system. The results achieved from simulations confirm that the proposed interactive DR and FACTS model not only alleviates the congestion of the system, but also increases the flexibility of the system to harvest the wind power as much as possible.

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“…Congestion management problems can be considered from different perspectives such as pricing methods, centralize/decentralized controlling methods, and special objective functions . In the first group, the autors focus on different methods to find the marginal prices in injection and consumption nodes.…”

Section: Introductionmentioning

confidence: 99%

“…In this manner, for a detailed orientation, a variety of regional boundaries and pricing methods including optimal zonal pricing and simplified zonal pricing as well as their advantages and disadvantages are listed . In classifying the controlling methods, general principles of centralized and decentralized planning have been examined by Singh et al In previous studies, the link between distributed generations (DGs) and LMP are studied. A new index is created by a combination of operational costs and congestion rents to determine the capacity of distributed generators .…”

Section: Introductionmentioning

confidence: 99%

A new restructuring of centralized congestion management focusing on flow-gate and locational price impacts

Nematbakhsh

Hooshmand

Hemmati

2017

Int Trans Electr Energ Syst

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Summary In the operation of a deregulated power system, congestion and power loss are the two major factors causing price fluctuation in different parts of the network. In this paper, a new method is proposed for calculating and investing the congestion surplus. In this method, congestion surplus, which is calculated on the basis of flow‐gate marginal prices, is used as an economic signal to control the network. In this regard, different strategies such as the definition of sensitive buses and priority list of the congested lines are proposed from the perspective of flow‐gate marginal price concept. In order to verify the novel reformation, distributed generation planning is presented as an illustrative example. In this approach, proper investment is applied to increase the social welfare under new formulation in the presence of distributed generation resources. The proposed algorithm is implemented on IEEE 30‐bus and IEEE 57‐bus networks. The results emphasize the accuracy and efficiency of the proposed algorithm for calculating the congestion surplus and increasing social welfare with the least amount of investment.

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Centralized and decentralized optimal decision support for congestion management

Cited by 15 publications

References 30 publications

Tuning successive linear programming to solve AC optimal power flow problem for large networks

Tuning successive linear programming to solve AC optimal power flow problem for large networks

InteractiveFACTSand demand response program as an incremental welfare consensus for maximizing wind power penetration

A new restructuring of centralized congestion management focusing on flow-gate and locational price impacts

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