Cesar A. Silva-Monroy

Toward scalable, parallel progressive hedging for stochastic unit commitment

Given increasing penetration of variable generation units, there is significant interest in the power systems research community concerning the development of solution techniques that directly address the stochasticity of these sources in the unit commitment problem. Unfortunately, despite significant attention from the research community, stochastic unit commitment solvers have not made their way into practice, due in large part to the computational difficulty of the problem. In this paper, we address this issue, and focus on the development of a decomposition scheme based on the progressive hedging algorithm of Rockafellar and Wets. Our focus is on achieving solve times that are consistent with the requirements of ISO and utilities, on modest-scale instances, using reasonable numbers of scenarios. Further, we make use of modest-scale parallel computing, representing capabilities either presently deployed, or easily deployed in the near future. We demonstrate our progress to date on a test instance representing a simplified version of the US western interconnect (WECC-240).

Ensuring Profitability of Energy Storage

Energy storage (ES) is a pivotal technology for dealing with the challenges caused by the integration of renewable energy sources. It is expected that a decrease in the capital cost of storage will eventually spur the deployment of large amounts of ES. These devices will provide transmission services, such as spatiotemporal energy arbitrage, i.e., storing surplus energy from intermittent renewable sources for later use by loads while reducing the congestion in the transmission network. This paper proposes a bilevel program that determines the optimal location and size of storage devices to perform this spatiotemporal energy arbitrage. This method aims to simultaneously reduce the system-wide operating cost and the cost of investments in ES while ensuring that merchant storage devices collect sufficient profits to fully recover their investment cost. The usefulness of the proposed method is illustrated using a representative case study of the ISO New England system with a prospective wind generation portfolio.

Conceptual Framework for Developing Resilience Metrics for the Electricity, Oil, and Gas Sectors in the United States

This report has been written for the Department of Energy’s Energy Policy and Systems Analysis Office to inform their writing of the Quadrennial Energy Review in the area of energy resilience. The topics of measuring and increasing energy resilience are addressed, including definitions, means of measuring, and analytic methodologies that can be used to make decisions for policy, infrastructure planning, and operations. A risk-based framework is presented which provides a standard definition of a resilience metric. Additionally, a process is identified which explains how the metrics can be applied. Research and development is articulated that will further accelerate the resilience of energy infrastructures. Conceptual Framework for Developing Resilience Metrics for US Electricity, Oil, and Gas Sectors September, 2014 4 SAND2014-18019 ACKNOWLEDGMENTS The authors of this paper wish to acknowledge the many experts that provided invaluable effort and information for this document. Dan Ton from the Department of Energy Office of Electricity Delivery and Energy Reliability and Rima Oueid and Kate Marks, from the Department of Energy Office of Energy Policy and Systems Analysis. Thanks is given for their tremendous leadership in formalizing the development of resilience concepts and bringing ideas to fruition. Acknowledgment is also given to Henry Willis from the Rand Corporation, who provided instrumental feedback and guidance. From the Sandia Corporation, thanks to Eric Vugrin for providing foundational help in establishing this document. Thanks to Lori Parrott and Steve Conrad for both having leveraged significant accomplishments via their long-standing contributions in NISAC. Further appreciation is given to Abe Ellis for his work in electric power resilience activities and Jason Stamp for providing leadership through the development of the Energy Surety Microgrid and design methodologies. And acknowledgement is also given to Bob Hwang for his continuous efforts and guidance. We appreciate all of your efforts and look forward to collaborating in the future. Conceptual Framework for Developing Resilience Metrics for US Electricity, Oil, and Gas Sectors SAND2014-18019 5 September, 2014 CONTENTS Executive Summary ...................................................................................................................... 11 Conclusions and Recommendations ...................................................................................... 15 1. Introduction .............................................................................................................................. 17 2. What is Resilience? .................................................................................................................. 19 2.1 Literature Review .......................................................................................................... 19 2.2 Defining Resilience ....................................................................................................... 19 3. Resilience Analysis Process ..................................................................................................... 23 3.1 Define Resilience Goals ................................................................................................ 24 3.2 Define System and Resilience Metrics ......................................................................... 24 3.3 Characterize Threats ..................................................................................................... 24 3.4 Determine Level of Disruption ..................................................................................... 24 3.5 Define and Apply System Models ................................................................................ 25 3.6 Calculate Consequence ................................................................................................. 25 3.7 Evaluate Resilience Improvements ............................................................................... 25 4. Resilience Metrics .................................................................................................................... 27 4.1 Metrics: Measures of Resilience ................................................................................... 27 4.1.1 The Metric Is in Terms of Threat ..................................................................... 27 4.1.2 The Metric Is Based on Performance ............................................................... 27 4.1.3 The Metric Measures Consequence ................................................................. 28 4.1.4 The Metric Accounts for Uncertainty .............................................................. 28 4.1.5 The Metric Effectively Captures Resilience .................................................... 28 4.1.6 The Metric Is Not a Value Judgment ............................................................... 29 4.1.7 Multiple Metrics Are Often Necessary ............................................................ 29 4.1.8 System-Level Models in Resilience Metric Computation ............................... 29 4.2 Resilience Analysis Use Cases ..................................................................................... 30 5. A Framework for Developing Resilience Metrics ................................................................... 33 6. Populating Metrics ................................................................................................................... 35 7. Simplified, Anecdotal Use Case .............................................................................................. 37 7.1 Define Resilience Goals ................................................................................................ 37 7.2 Define System and Resilience Metrics ......................................................................... 37 7.3 Characterize Threats and Determine Level of Disruption ............................................ 38 7.4 Define and Apply System Models ................................................................................ 38 7.5 Calculate Consequence ................................................................................................. 39 7.6 Evaluate Resilience Improvements ............................................................................... 39 7.7 Incorporating the Resilience Improvement Process ...................................................... 40 8. Advanced Use Case Summaries .............................................................................................. 41 8.1 Electricity ...................................................................................................................... 41 8.2 Petroleum ...................................................................................................................... 41 8.3 Natural Gas ................................................................................................................... 42 9. What can be simplified today .................................................................................................. 43 9.1 How we can prepare for a more resilient framework .................................................... 43 Conceptual Framework for Developing Resilience Metrics for US Electricity, Oil, and Gas Sectors September, 2014 6 SAND2014-18019 10. Research & Development ...................................................................................................... 45 10.1 Furthering Resilience ................................................................................................... 45 10.1.1 Characterize Threats ....................................................................................... 45 10.1.2 Determine Level of Disruption ....................................................................... 45 10.1.3 Define and Apply System Models .................................................................. 46 10.1.4 Calculate Consequence ................................................................................... 46 10.1.5 Evaluate Resilience Improvements ................................................................. 46 11. Moving Forward .................................................................................................................... 47 11.1 Identifying Specific Metrics that Could Be Generally Applied Across Many Different Systems ......................................................................................................................... 47 11.2 Stakeholder and Buy-In Process .................................................................................. 47 11.3 Development of Sector-Specific System Models ......................................................... 49 11.4 Consequence Quantification ........................................................................................ 49 11.5 Conclusions and Recommendations ............................................................................. 50 Appendix A: Reliability and Other Complementary Metrics ...................................................... 53 A.1 Resource Adequacy ...................................................................................................... 54 A.1.1 Loss of Load Probability (LOLP) .................................................................... 54 A.1.2 Loss of Load Expectation (LOLE) .................................................................. 55 A.1.3 Expected Unserved Energy (EUE) or Loss of Energy Expectation (LOEE) .. 55 A.1.4 Effective Load Carrying Capability (ELCC) .................................................. 55 A.2 Transmission Reliability Indices and Measures ..............................................

The Unit Commitment Problem With AC Optimal Power Flow Constraints

We propose a mathematical programming-based approach to optimize the unit commitment problem with alternating current optimal power flow (ACOPF) network constraints. This problem is a nonconvex mixed-integer nonlinear program (MINLP) that we solve through a solution technique based on the outer approximation method. Our solution technique cooptimizes real and reactive power scheduling and dispatch subject to both unit commitment constraints and ACOPF constraints. The proposed approach is a local solution method that leverages powerful linear and mixed-integer commercial solvers. We demonstrate the relative economic and operational impact of more accurate ACOPF constraint modeling on the unit commitment problem, when compared with copperplate and DCOPF constraint modeling approaches; we use a six-bus, the IEEE RTS-79, and the IEEE-118 test systems for this analysis. Our approach can be extended to solve larger scale power systems as well as include security constraints or uncertainty through decomposition techniques.

Damping of inter-area oscillations using energy storage

Low frequency inter-area oscillations have been identified as a significant problem in utility systems due to the potential for system damage and the resulting restrictions on power transmission over select lines. Previous research has identified real power injection by energy storage based damping control nodes as a promising approach to mitigate inter-area oscillations. In this paper, a candidate energy storage system based on UltraCapacitor technology is evaluated for damping control applications in the Western Electric Coordinating Council (WECC), and an analytical method for ensuring proper stability margins is also presented for inclusion in a future supervisory control algorithm. Dynamic simulations of the WECC were performed to validate the expected system performance. Finally, the Nyquist stability criteria was employed to derive safe operating regions in the gain, time delay space for a simple two-area system to provide guaranteed margins of stability.

Integrating Energy Storage Devices Into Market Management Systems

Intuitively, the integration of energy storage technologies such as pumped hydro and batteries into vertically integrated utility and independent system operator/regional transmission operator (ISO/RTO)-scale systems should confer significant benefits to operations, ranging from mitigation of renewables generation variability to peak shaving. However, the realized benefits of such integration are highly dependent upon the environment in which the integration occurs. Further, integration of storage requires careful modeling extensions of existing market management systems (MMSs), which are currently responsible for market and reliability operations in the grid. In this paper, we outline the core issues that arise when integrating storage devices into an MMS system, ranging from high-level modeling of storage devices for purposes of unit comment and economic dispatch to the potential need for new mechanisms to more efficiently allow for storage to participate in market environments. We observe that the outcomes of cost-benefit analyses of storage integration are sensitive to system-specific details, e.g., wind penetration levels. Finally, we provide an illustrative case study showing significant positive impacts of storage integration.

A comparison of policies on the participation of storage in U.S. frequency regulation markets

Because energy storage systems have better ramping characteristics than traditional generators, their participation in frequency regulation should facilitate the balancing of load and generation. However, they cannot sustain their output indefinitely. System operators have therefore implemented new frequency regulation policies to take advantage of the fast ramps that energy storage systems can deliver while alleviating the problems associated with their limited energy capacity. This paper contrasts several U.S. policies that directly affect the participation of energy storage systems in frequency regulation and compares the revenues that the owners of such systems might achieve under each policy.

Potential revenue from electrical energy storage in the Electricity Reliability Council of Texas (ERCOT)

This paper outlines the calculations required to estimate the maximum potential revenue from participation in arbitrage and regulation in day-ahead markets using linear programming. Then, we use historical Electricity Reliability Council of Texas (ERCOT) data from 2011-2012 to evaluate the maximum potential revenue from a hypothetical 32 MWh, 8 MW system. We investigate the maximum potential revenue from two different scenarios: arbitrage only and arbitrage combined with regulation. Our analysis shows that, with perfect foresight, participation in the regulation market would have produced more than twice the revenue compared to arbitrage in the ERCOT market in 2011 and 2012. Three simple trading strategies that do not rely on perfect knowledge are then compared to the optimization results.

Wind generation controls for damping of inter-area oscillations

Inter-area oscillations are one of the factors that limit transmission capacity in large interconnected systems. In this paper we investigate the effects of increasing wind generation on inter-area modes and propose the use of additional control schemes for wind plants for mitigation of inter-area oscillations. Control schemes include droop control and inertial emulation, which are originally aimed at improving transient stability. The sensitivities of inter-area modes to droop control and inertial emulation gains are identified. Implementation of suggested controls schemes via collocated energy storage devices is also explored.

Estimating potential revenue from electrical energy storage in PJM

FERC order 755 and FERC order 784 provide pay-for-performance requirements and direct utilities and independent system operators to consider speed and accuracy when purchasing frequency regulation. Independent System Operators (ISOs) have differing implementations of pay-for-performance. This paper focuses on the PJM implementation. PJM is a regional transmission organization in the northeastern United States that serves 13 states and the District of Columbia. PJMs implementation employs a two part payment based on the Regulation Market Capability Clearing price (RMCCP) and the Regulation Market Performance Clearing Price (RMPCP). The performance credit includes a mileage ratio. Both the RMCCP and RMPCP employ an actual performance score. Using the PJM remuneration model, this paper outlines the calculations required to estimate the maximum potential revenue from participation in arbitrage and regulation in day-ahead markets using linear programming. Historical PJM data from 2014 and 2015 was then used to evaluate the maximum potential revenue from a 5 MWh, 20 MW system based on the Beacon Power Hazle Township flywheel plant. Finally, a heuristic trading algorithm that does not require perfect foresight was evaluated against the results of the optimization algorithm.