Optimal configuration of distributed power flow controller to enhance system loadability via mixed integer linear programming

Abstract

Increasing energy consumption has caused power systems to operate close to the limit of their capacity. The distributed power flow controller (DPFC), as a new member of distributed flexible AC transmission systems, is introduced to remove this barrier. This paper proposes an optimal DPFC configuration method to enhance system loadability considering economic performance based on mixed integer linear programming. The conflicting behavior of system loadability and DPFC investment is analyzed and optimal solutions are calculated. Thereafter, the fuzzy decision-making method is implemented for determining the most preferred solution. In the most preferred solution obtained, the investment of DPFCs is minimized to find the optimal number, locations and set points. Simulation results on the IEEE-RTS79 system demonstrate that the proposed method is effective and reasonable.

Series Compensation of Transmission Systems: A Literature Survey

“…In Reference [227], the model minimizes the generation and investment costs, while guaranteeing that the robust solution is customized to cover the wind uncertainty interval. In Reference [228], an AC power flow model is proposed and applied to a real power system problem, and the resulting MINLP is solved using MATLAB routines; in Reference [229], an MILP is applied initially to find the optimal number, locations and set points of multiple DSSCs, and then a fuzzy algorithm is used to select the most preferred solution.…”

Distributed Stochastic Scheduling of Massive Backup Batteries in Cellular Networks for Operational Reserve and Frequency Support Ancillary Services

“…Distributed optimization offers a solution to address both the computational burden brought by massive BSBBs and the need to protect the privacy information of BS. Existing research works have applied distributed optimization to the scheduling of various entities, including microgrids [26], energy communities [27], [28], and electric vehicles [29], aiming to address the computational burden associated with centralized solutions. Besides, some studies have employed distributed optimization to address privacy concerns.…”

Optimal Coordinated Operation of Distributed Static Series Compensators for Wide-area Network Congestion Relief

“…The planning tries to determine the optimal DSSC deployment (e.g., number, rating, and location) given a limited budget and a series of future planning scenarios. This usually leads to a least-cost problem, where the resulting DSSC investment is compared with alternative reinforcement techniques [15] - [17], or a compromise solution between investment and system loadability enhancement is targeted [18]. Conversely, the operational problems are devoted to determining the real-time settings of a set of already installed DSSCs so that the network operating state is somehow optimized.…”