Kory W. Hedman

Co-Optimization of Generation Unit Commitment and Transmission Switching With N-1 Reliability

Currently, there is a national push for a smarter electric grid, one that is more controllable and flexible. The full control of transmission assets are not currently built into electric network optimization models. Optimal transmission switching is a straightforward way to leverage grid controllability: to make better use of the existing system and meet growing demand with existing infrastructure. Previous papers have shown that optimizing the network topology improves the dispatch of electrical networks. Such optimal topology dispatch can be categorized as a smart grid application where there is a co-optimization of both generators and transmission topology. In this paper we present a co-optimization formulation of the generation unit commitment and transmission switching problem while ensuring N-1 reliability. We show that the optimal topology of the network can vary from hour to hour. We also show that optimizing the topology can change the optimal unit commitment schedule. This problem is large and computationally complex even for medium sized systems. We present decomposition and computational approaches to solving this problem. Results are presented for the IEEE RTS 96 test case.

Optimal Transmission Switching With Contingency Analysis

In this paper, we analyze the N-1 reliable DC optimal dispatch with transmission switching. The model is a mixed integer program (MIP) with binary variables representing the state of the transmission element (line or transformer) and the model can be used for planning and/or operations. We then attempt to find solutions to this problem using the IEEE 118-bus and the RTS 96 system test cases. The IEEE 118-bus test case is analyzed at varying load levels. Using simple heuristics, we demonstrate that these networks can be operated to satisfy N-1 standards while cutting costs by incorporating transmission switching into the dispatch. In some cases, the percent savings from transmission switching was higher with an N-1 DCOPF formulation than with a DCOPF formulation.

Optimal Transmission Switching—Sensitivity Analysis and Extensions

In this paper, we continue to analyze optimal dispatch of generation and transmission topology to meet load as a mixed integer program (MIP) with binary variables representing the state of the transmission element (line or transformer). Previous research showed a 25% savings by dispatching the IEEE 118-bus test case. This paper is an extension of that work. It presents how changing the topology affects nodal prices, load payment, generation revenues, cost, and rents, congestion rents, and flowgate prices. Results indicate that changing the topology to cut costs typically results in lower load payments and higher generation rents for this network. Computational issues are also discussed.

Comparing Hedging Methods for Wind Power: Using Pumped Storage Hydro Units vs. Options Purchasing

The main setback for wind energy is the uncertainty and uncontrollability of the energy source. Researchers are trying to create ways to handle this uncertainty by means of energy storage through pumped storage hydro facilities, compressed air facilities, etc. This paper will analyze the option of using a pumped storage hydro plant to negate this uncertainty from a financial viewpoint. In addition, this paper analyzes whether or not such an option is truly best by comparing it to the case where the wind farm can purchase call/put options to protect against the uncertainty of the wind. To determine the proper price of these options, the Black-Scholes options pricing model is used to ensure there is no ability to arbitrage, i.e. there is no free lunch. This second option will also consider the pumped storage plants financial gain when working independent as well in order to make a fair comparison. The objective of this paper is to analyze the effectiveness of these hedging methods and the financial implications

Robust Corrective Topology Control for System Reliability

Corrective transmission switching schemes are an essential part of grid operations and are used to improve the reliability of the grid as well as the operational efficiency. Today, the transmission switching schemes are established based on the operators past knowledge of the system as well as other ad-hoc methods. In this paper, three topology control (corrective transmission switching) methodologies are presented along with the detailed formulation of robust corrective switching. By incorporating robust optimization into the corrective switching framework, the switching solution is guaranteed to be feasible for a range of system operating states. The robust model can be solved offline to suggest switching actions that can be used in a dynamic security assessment tool in real-time. The proposed robust topology control algorithm can also generate multiple corrective switching actions for a particular contingency. The robust topology control formulation is tested on an IEEE 118-bus test case with different uncertainty sets.

Smart Flexible Just-in-Time Transmission and Flowgate Bidding

There is currently a national push to create a smarter grid. Currently, the full control of transmission assets is not built in network optimization models. With more sophisticated modeling of transmission assets, it is possible to better utilize the current infrastructure to improve the social welfare. Co-optimizing the generation with the network topology has been shown to reduce the total dispatch cost. In this paper, we propose the concept of just-in-time transmission. This concept is predicated on the fact that transmission that is a detriment to network efficiency can be kept offline when not needed and, with the proper smart grid/advanced technology, can be switched back into service once there is a disturbance. We determine which lines to have offline based on the optimal transmission switching model previously proposed. A secondary topic of this paper focuses on flowgate bidding. Approved by the Federal Energy Regulatory Commission and implemented within the SPP and NYISO networks, flowgate bidding is defined as allowing a lines flow to exceed its rated capacity for a short period of time for a set penalty, i.e., price. We demonstrate the effectiveness of these models by testing them on large-scale ISO network models.

Analyzing valid inequalities of the generation unit commitment problem

The use of Mixed Integer Programming (MIP) within the electric industry is increasing. Many US ISOs are testing and planning to use MIP in the near future or they are already using MIP. There are various MIP formulations published for generation unit commitment with little consensus as to which formulation is preferred. In particular, various valid inequalities are used to model the minimum up and down time constraints for generation unit commitment. In this paper, we first discuss valid inequalities and facet defining valid inequalities. We then present and compare these previously published valid inequalities and we demonstrate why certain valid inequalities dominate other valid inequalities. We also present previously published facet defining valid inequalities.

Economic Assessment of Energy Storage in Systems With High Levels of Renewable Resources

High penetration levels of renewable resources impose increasing uncertainty and variability on power system operations. Traditionally, power systems rely on conventional generators (CGs) to balance the uncertainty in renewable generation. However, as renewable penetration levels increase, CGs may suffer from higher operating costs while receiving lower profits. In contrast, since bulk energy storage can store and shift clean energy and have fast ramping capability, they may become more competitive under high renewable penetration levels. In this paper, a stochastic unit commitment model with energy storage will be presented to evaluate the short-term profitability of CGs and energy storage under different levels of renewable penetrations. The short-term profitability of CGs and energy storage units will be compared to identify the impact of increasing renewable penetration on the attractiveness of bulk energy storage in comparison to CGs.

Dynamic Reserve Zones for Day-Ahead Unit Commitment With Renewable Resources

As more non-dispatchable renewable resources are integrated into the grid, it will become increasingly difficult to predict the transfer capabilities and the network congestion. At the same time, renewable resources require operators to acquire more operating reserves. With todays deterministic reserve requirements unable to ensure optimal reserve locations, improvements to reserve policies are needed to ensure reserve deliverability while maintaining a reliable system at least cost. This paper proposes a daily reserve zone determination procedure, which is able to reflect system operating conditions by utilizing probabilistic power flows. A statistical clustering algorithm is used to cluster buses together to produce the zones; the algorithm uses a centrality measurement, which is based on weighted power transfer distribution factors. The proposed method is validated by testing it on a modified IEEE 118-bus system for multiple days; the proposed method is compared against existing reserve zone partitioning procedures. While the proposed reserve zone determination method is a heuristic, it is shown to be effective and it is a computationally tractable method. The proposed method can be used on its own and can be used along with stochastic programming techniques that implicitly determine reserves.

Topology Control for Load Shed Recovery

This paper introduces load shed recovery actions for transmission networks by presenting the dc optimal load shed recovery with transmission switching model (DCOLSR-TS). The model seeks to reduce the amount of load shed, which may result due to transmission line and/or generator contingencies, by modifying the bulk power system topology. Since solving DCOLSR-TS is computationally difficult, the current work also develops a heuristic (MIP-H), which improves the system topology while specifying the required sequence of switching operations. Experimental results on a list of N-1 and N-2 critical contingencies of the IEEE 118-bus test case demonstrate the advantages of utilizing MIP-H for both online load shed recovery and recurring contingency-response analysis. This is reinforced by the introduction of a parallelized version of the heuristic (Par-MIP-H), which solves the list of critical contingencies close to 5x faster than MIP-H with 8 cores and up to 14x faster with increased computational resources. The current work also tests MIP-H on a real-life, large-scale network in order to measure the computational performance of this tool on a real-world implementation.