This paper describes the Grid Optimization (GO) Competition Challenge 3, focusing on the problem motivation, formulation, solvers submitted by competition entrants, and analysis of the solutions produced. Funded by DOE/ARPA-E and led by a collaboration of national labs and academia members, the GO Competition addresses challenging problems in power systems planning and operations to drive research in advanced solution methods essential for a rapidly evolving electric power sector. Challenge 3 targets a multi-period unit commitment problem, incorporating AC power modeling and topology switching to reflect the dynamic grid management techniques required for future power systems. The competition results offer significant benefits to both researchers and industry practitioners. For researchers, it fosters innovation, encouraging the development of new algorithms to address the complexities of modern power systems. For industry practitioners, the competition drives the creation of more efficient and reliable computational tools, directly improving grid management practices. This collaboration bridges the gap between theory and practical implementation, advancing the field in meaningful ways. This paper documents the problem formulation, solver approaches, and the effectiveness of the solutions developed.

Intro to the ARPA-E Grid Optimization Competition In “Recent Developments in Security-Constrained AC Optimal Power Flow: Overview of Challenge 1 in the ARPA-E Grid Optimization Competition,” we review the state of the art in practical algorithms for scheduling power-systems operations in the short term and the results of the recent competition organized by the U.S. Advanced Research Projects Agency–Energy. We explain the mixed-integer nonlinear formulation used in the competition for nonspecialists in electrical engineering, the context and organization of the competition, and the performance of competitors. We find that the collective approaches and results of competitors provide support for efforts to move nonlinear optimization techniques into industrial applications, as they have proven to be a robust and efficient alternative to current linear approximation techniques.

This paper summarizes a grid optimization (GO) competition effort in the United States to find the best solution strategies for up to interconnect-scale power system networks with around 32,000 buses. The optimization problem is a mixedinteger, non-convex non-linear problem, (MINLP) and includes discrete variables such as unit commitment and line switching, control settings (transformer taps and phase shifters with impedance correction tables), and bus shunts. The case study includes six actual industry grids as well as 16 realistic synthetic grids created by three different dataset teams. The winners are selected and ranked based on scoring criteria, which consider the solution quality (such as objective functions) within time limits. Nine winner teams are selected from 26 competitor teams. The results achieved by different teams are described and the performance of different algorithms on synthetic grids and actual industry grids are compared and analyzed.

The Pacific Northwest Smart Grid Demonstration was an electricity grid modernization project conducted in the Northwest U.S. This paper presents the analysis of renewable generation at the Renewable Energy Park located in the City of Ellensburg, WA. The community energy park concept is an intriguing model for community investment in renewable resources,but the lessons in this paper should be considered.

Pacific Northwest National Laboratory (PNNL) and the National Rural Electric Cooperative Association (NRECA) created a Data Repository for Power system Open models With Evolving Resources (DR POWER) to establish, curate, and evolve open-access power grid models and scenarios, and measurements and observations, collectively referred to as datasets. Existing open-access datasets are inadequate (too small and not representative of the complexity of the modern grid, and scattered all over the world wide web) for the investigation and benchmarking of the advanced optimization methods needed for next-generation grids. Open-access datasets are static and inadequate in other aspects: they do not provide format transformation tools; there is missing information needed for advanced applications; hard to cite; no model evolution history; difficult to update current collections; and little to no community involvement and feedback. Overall, the current landscape is hindering model evolution and benchmarking standards needed for transformational approaches to be developed, tested, and accepted by the power modeling community.

The homolytic bond dissociation energies of a series of five- and six-coordinate mono- and dihydride complexes of the type HM(diphosphine)2 and [H2M(diphosphine)2]+ (where M = Co, Rh, and Ir) are calculated and compared with experimental values. This work probes the relationship between the homolytic bond dissociation energies (HMBDEs) of these complexes in these two different coordination environments and formal oxidation states. The results of these calculations and previous experimental observations suggest that for M = Rh the HMBDE of the five-coordinate HM(diphosphine)2 species are 0-2 kcal/mol larger than the HMBDE of the corresponding six-coordinate [H2M(diphosphine)2]+ species. For M = Ir the bond energies of the five- and six-coordinate complexes are nearly the same and for M = Co the six-coordinate species are 1-5 kcal/mol less than the corresponding five-coordinate species. Simplified models of large and complicated ligands seem to capture the essential trends and give very good estimates of these thermodynamic properties compared with experimentally available data that are difficult to obtain.

The power grid evolves towards a new mix of generation and consumption that introduces new dynamic and stochastic behaviors. These emerging grid behaviors would invalidate the steady-state assumption in today's state estimation - an essential function for real-time power grid operation. This paper examines this steady-state assumption and identifies the need for estimating dynamic states. Supporting technologies are presented as well as a proposed formulation for estimating dynamic states. Metrics for evaluating methods for solving the dynamic state estimation problem are proposed, with example results to illustrate the use of these metrics. The overall objective of this paper is to provide a basis that more research on this topic can follow.

Stability limits are considered in power system planning and operations to estimate the available stability margins and, if possible, to maximize the use of transmission facilities. These important tasks are influenced by configuration of the voltage stability boundary. This paper first propose a new method to explore static voltage stability conditions in Cartesian coordinates instead of polar coordinates. In this way, the formulated singularity problem can be reduced to solving a set of linear equations with respect to real and imaginary components of nodal voltages. Using the proposed method, several case studies were performed for 17939-bus U.S. Western Interconnection planning model. Significant peculiarities of the boundary configuration were identified, including its non-convexity, discontinuity, branching and internal singularities (“holes”) that were not known before and could not be found by traditional methods.

With the increasing complexity resulting from uncertainties and stochastic variations introduced by intermittent renewable energy sources, responsive loads, mobile consumption of plug-in vehicles, and new market designs, more and more dynamic behaviors are observed in everyday power system operation. To operate a power system efficiently and reliably, it is critical to adopt a dynamic paradigm so that effective control actions can be taken in time. The dynamic paradigm needs to include three fundamental components: dynamic state estimation; look-ahead dynamic simulation; and dynamic contingency analysis (Figure 1). These three components answer three basic questions: where the system is; where the system is going; and how secure the system is against accidents. The dynamic state estimation provides a solid cornerstone to support the other 2 components and is the focus of this study.

This report demonstrates promising capabilities and performance characteristics of the proposed method using several power systems models. The new method will help to develop a new generation of highly efficient tools suitable for real-time parallel implementation. The ultimate benefit obtained will be early detection of system instability and prevention of system blackouts in real time.

Financial Transmission Rights (FTRs) help power market participants reduce price risks associated with transmission congestion. FTRs are issued based on a process of solving a constrained optimization problem with the objective to maximize the FTR social welfare under power flow security constraints. Security constraints for different FTR categories (monthly, seasonal or annual) are usually coupled and the number of constraints increases exponentially with the number of categories. Commercial software for FTR calculation can only provide limited categories of FTRs due to the inherent computational challenges mentioned above. In this paper, a novel non-linear dynamical system (NDS) approach is proposed to solve the optimization problem. The new formulation and performance of the NDS solver is benchmarked against widely used linear programming (LP) solvers like CPLEX™ and tested on large-scale systems using data from the Western Electricity Coordinating Council (WECC). The NDS based solver can be easily parallelized while exploiting the data structure of the revised formalism to avoid backfill of coupled blocks, maintain numerical stability, and simultaneously reducing computational complexity. The NDS is demonstrated to outperform the widely used CPLEX algorithms by over two orders of magnitude while exhibiting superior scalability.

Financial Transmission Rights (FTRs) are financial insurance tools to help power market participants reduce price risks associated with transmission congestion. FTRs are issued based on a process of solving a constrained optimization problem with the objective to maximize the FTR social welfare under power flow security constraints. Security constraints for different FTR categories (monthly, seasonal or annual) are usually coupled and the number of constraints increases exponentially with the number of categories. Commercial software for FTR calculation can only provide limited categories of FTRs due to the inherent computational challenges mentioned above. In this paper, first an innovative mathematical reformulation of the FTR problem is presented, which dramatically improves the computational efficiency of optimization problem. After having re-formulated the problem, a novel non-linear dynamic system (NDS) approach is proposed to solve the optimization problem. The new formulation and performance of the NDS solver is benchmarked against widely used linear programming (LP) solvers like CPLEX™. Tests are performed on both standard IEEE test systems and large-scale systems using data from the Western Electricity Coordinating Council (WECC). The NDS is demonstrated to be comparable, and in many cases outperforms the widely used CPLEX algorithms. The proposed formulation and NDS based solver are easily parallelizable, enabling further computational improvement.

A novel analysis of the chemical bonding pattern in the valence isoelectronic series of triatomic molecules O-3, S-3, SO2, and OS2 is reported It is based on examining the bond order matrix elements between the oriented localized molecular orbitals (OLMOs) that are localized on the three individual atoms. left (L), center (C), and right (R) The analysis indicates that there is a (L-C) and (C-R) pi-bonding interaction and a (L-R) pi-antibonding interaction. It supports the earlier proposed "partial biradical" interpretation of these systems, which had recently been challenged The degree of biradical character is shown to increase from SO2 to S-3 to O-3 to OS2.

Through the development of new classes of software, algorithms, and hardware, data-intensive applications provide timely and meaningful analytical results in response to exponentially growing data complexity and associated analysis requirements.

The advancement in computing technology has enabled scientists to collect massive amounts of data, taking us a step closer to solving complex problems such as global climate change and uncovering the secrets hidden in genes. The exponential growth in the amount of data collected from experiments, measurements and observations, however, has created an urgent technical challenge. A talented group of computational scientists are leading the effort at PNNL to tackle the challenge through a major initiative on high-performance and data-intensive computing. PNNL’s data-intensive computing initiative will attempt to accelerate the creation of computational solutions to support the study of problems of national scope involving large amounts of data from very complex systems.