This paper proposes a method for choosing the best transmission system expansion plan considering a probabilistic reliability criterion (/sub R/LOLE). The method minimizes the investment budget for constructing new transmission lines subject to probabilistic reliability criteria, which consider the uncertainties of transmission system elements. Two probabilistic reliability criteria are used as constraints. One is a transmission system reliability criterion (/sub R/LOLE/sub TS/) constraint, and the other is a bus/nodal reliability criterion (/sub R/LOLE/sub Bus/) constraint. The proposed method models the transmission system expansion problem as an integer programming problem. It solves for the optimal strategy using a probabilistic branch and bound method that utilizes a network flow approach and the maximum flow-minimum cut set theorem. Test results on an existing 21-bus system are included in the paper. They demonstrate the suitability of the proposed method for solving the transmission system expansion planning problem subject to practical future uncertainties.

It is difficult to forecast renewable energy resources due to their variability. High uncertainty of the resources increases the concern about the reliable operation of the electric power system. Storage devices have been considered a candidate to control variable energy resources, and most studies on storage devices are performed in the economic dispatch. Therefore, electric power adequacy may be an issue. In this paper, a new approach in the optimal power flow framework is proposed for deploying storage devices, and the feasibility and economic impact analyses are discussed.

System planning on a large-scale electric power system is computationally challenging. Network reduction into a small system can significantly reduce the computational expense. The Ward equivalent technique is widely used for the reduction; however, it may not yield the same flow pattern as the original network. In this paper, a new methodology for network reduction is proposed and the results are compared with those from other methodologies.

Problems such as price volatility have been observed in electric power markets. Demand-side participation is frequently offered as a potential solution by promising to increase market efficiency when hockey-stick-type offer curves are present. However, the individual end-consumer will surely value electricity differently, which makes demand-side participation difficult as a group and at a bus. In this paper demand is categorized into two groups: one that highly values reliability and one that does not. The two types are modeled separately and a new optimal bidding function is developed and tested based on this model.

A simple but precise model would improve the computation efficiency for planning a large-scale power system. Recent development of a new network reduction algorithm yields such a model with a given grouping of buses. While the flow over the reduced network is estimated precisely, a proper value of the flow limit still remains unresolved. In this study, a method is proposed to group buses based on the congestion profile in the original network and to assign the flow limits of a reduced network. The method is tested on modified IEEE 30-bus and 118-bus systems at various load profiles, and the simulation results are compared.

In this paper, a novel solution method is proposed for the optimal topology control problem in AC framework with N-1 reliability. The resultant new topology of the proposed method is guaranteed to yield a better objective function value than the global solution of the problem with the original topology. In order to prove our statement, semi-definite programming relaxation is formulated to find a lower bound to the objective function value of the problem. The proposed method is favorable for parallelization to increase the computational efficiency, and the parallel computing flowchart is presented. Computational time of the algorithm for IEEE test cases is reported. An in-house power market simulator is developed to simulate a market environment complete with optimal topology control mechanism. Simulations held by participating human subjects are analyzed under the subtitles of the total operating cost, real power losses, and locational marginal price (LMP) variance. A simple example is illustrated to show that the resultant network topology may increase the real power losses while still providing a lower operating system cost than that of the original topology.