HyungSeon Oh

A method for transmission system expansion planning considering probabilistic reliability criteria

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.

Optimal Planning to Include Storage Devices in Power Systems

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.

Demand-Side Bidding Agents: Modeling and Simulation

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 New Network Reduction Methodology for Power System Planning Studies

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.

Identification of Market Power in Large-Scale Electric Energy Markets

Market power potential is a serious concern for efficient and competitive operation of centrally-dispatched electricity markets. Traditional measures for market power ignore underlying physical characteristics of the electric grid that may be exploited for local advantage. In our prior work we have proposed a revenue sensitivity-based approach for identifying market participants with market power potential, and we demonstrated detailed cases using a 30-bus system[1] [2] [3]. In this paper we address computational challenges for scaling our method to large systems, and we present practical extensions to a portion of our work that enables the evaluation of very large, RTO-scale electric power grids.

Aggregation of Buses for a Network Reduction

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.

Optimal Topology Control With Physical Power Flow Constraints and N-1 Contingency Criterion

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.

Estimation of a Sensitivity-Based Metric for Detecting Market Power

The abuse of market power is a potentially serious problem for market designers. Few indices, if any, exist to measure the potential for market power in real time. In this regard, Murillo-Sanchez et al. derived an expression for a dispatch-to-price sensitivity matrix, M . The expression requires information about network topology and parameters, as well as the rules used to operate the market. While computing the matrix is conceptually easy for those with all the market and system information, such as an independent system operator, the method is probably impractical for market participants due to the inaccessibility of much of the information. In this paper, a method for estimating the M matrix by using publically available data are suggested, indicating that any market participant who does the computation will know when conditions permit them to lower or raise prices through decreased/increased bids and/or offers.

Real and Reactive Power Prices and Market Power

The exercise of market power is a major concern in deregulating the electric energy business in the U.S. Exploitation of market power can destroy any market efficiencies and award unwarranted profits based solely on the lack of competition rather than on economic merit. There have been several attempts to quantify market power with much of the work focused separately on either real power or reactive power. This paper addresses a new possibility for a market participant to exploit an opportunity to increase revenue by gaming both real and reactive markets.

A method for classifying offer strategies observed in an electricity market

The idea that large-scale generating units will operate at marginal cost when given the ability to offer their power for sale in a uniform price auction is at best wishful thinking. In fact, both real and experimental data show that the more uncertainty a supplier faces (e.g., load uncertainty, uncertainty of other suppliers, etc.), the more they will hedge their profits through higher than marginal cost offers and through withholding units if permitted. This makes predicting unit commitment and dispatch ahead of time difficult. This paper explores characteristics of software agents that were designed based on the outcome of human subject experiments on a uniform price auction with stochastic load. The agent behavior is compared to the behavior of the subjects. Both subject and agent behavior is classified based on the data. Differences and similarities are noted and explained. Based on the result of the simulation, a model was suggested to explain an offer submitted in deregulated markets based on double layer diffusion.