Transmission congestion management by optimal placement of FACTS devices

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University, 13/09/2010.This thesis describes the implementation of the Flexible AC Transmission Systems (FACTS) devices to develop a market-based approach to the problem of transmission congestion management in a Balancing Market. The causes, remedies and pricing methods of transmission congestion are briefly reviewed. Balancing Market exists in markets in which most of the trading is done via decentralized bilateral contracts. In these markets only final adjustments necessary to ensure secure system operation is carried out at a centralized Balancing Market. Each market player can participate in the Balancing Market by submitting offers and bids to increase and decrease its initially submitted active generation output. In this research a method is proposed to reduce costs associated with congestion re-dispatch in a Balancing Market by optimal placement of FACTS devices, and in particular Thyristor Controlled Phase Shifter Transformers (TCPST). The proposed technique is applicable to both Mixed Integer Linear Programming (MILP) and Mixed Integer Non-Linear Programming (MINLP). In the MILP a power system network is represented by a simplified DC power flow under a MILP structure and the Market participants' offers and bids are also represented by linear models. Results show that applications of FACTS devices can significantly reduce costs of congestion re-dispatch. The application of the method based on the MINLP creates a nonlinear and non-convex AC OPF problem that might be trapped in local sub-optima solutions. The reliability of the solution that determines the optimal placement of FACTS devices is an important issue and is carried out by investigation of alternative solvers. The behavior of the MINLP solvers is presented and finally the best solvers for this particular optimization problem are introduced. The application of DC OPF is very common in industry. The accuracy of the DC OPF results is investigated and a comparison between the DC and AC OPF is presented

Transmission congestion management by optimal placement of FACTS devices

This thesis describes the implementation of the Flexible AC Transmission Systems (FACTS) devices to develop a market-based approach to the problem of transmission congestion management in a Balancing Market. The causes, remedies and pricing methods of transmission congestion are briefly reviewed. Balancing Market exists in markets in which most of the trading is done via decentralized bilateral contracts. In these markets only final adjustments necessary to ensure secure system operation is carried out at a centralized Balancing Market. Each market player can participate in the Balancing Market by submitting offers and bids to increase and decrease its initially submitted active generation output. In this research a method is proposed to reduce costs associated with congestion re-dispatch in a Balancing Market by optimal placement of FACTS devices, and in particular Thyristor Controlled Phase Shifter Transformers (TCPST). The proposed technique is applicable to both Mixed Integer Linear Programming (MILP) and Mixed Integer Non-Linear Programming (MINLP). In the MILP a power system network is represented by a simplified DC power flow under a MILP structure and the Market participants' offers and bids are also represented by linear models. Results show that applications of FACTS devices can significantly reduce costs of congestion re-dispatch. The application of the method based on the MINLP creates a nonlinear and non-convex AC OPF problem that might be trapped in local sub-optima solutions. The reliability of the solution that determines the optimal placement of FACTS devices is an important issue and is carried out by investigation of alternative solvers. The behavior of the MINLP solvers is presented and finally the best solvers for this particular optimization problem are introduced. The application of DC OPF is very common in industry. The accuracy of the DC OPF results is investigated and a comparison between the DC and AC OPF is presented.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

An agent-based approach to modeling electricity spot markets

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2003.Includes bibliographical references (p. 323-327).(cont.) The model could also be used to analyze market factors (such as new market rules) and their effects on market price dynamics and market participants' behaviors, as well as to identify the "best" response action of one participant against the opponents' actions.Current approaches used for modeling electricity spot markets are static oligopoly models that provide top-down analyses without considering dynamic interactions among market participants. This thesis presents an alternative model, an agent-based model, and uses it to analyze the markets under various conditions. These markets, in which the participants engage in sealed-bid auctions to sell and/or buy electricity regularly, are viewed as multiagent systems, or as repeated games, played by participants with incomplete information. To represent these market characteristics, the agent-based model is selected, consisting of several power-producing agents with non-uniform portfolios of generating units. These agents employ learning algorithms, including Auer et al. 's, softmax action selection, or Visudhiphan and IliC's model-based algorithms, in determining bid-supply functions from available information. The simulated outcomes from the agent-based model depend on the choice of non-uniform portfolios and on the learning algorithms that the agents employ. Model verifications against the actual markets are suggested; however, due to a lack of certain confidential information, numerical examples cannot be presented. Nevertheless, the model is used to analyze the effects of market structures and the effect of load-serving entities on the power-producer bidding behavior and market outcomes. This model could provide one of the main tools for regulators, system planners, and market participants to use scenario simulations to investigate market conditions that could lead to high electricity prices.by Poonsaeng Visudhiphan.Ph.D