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As power distribution systems evolve in complexity and scale, the coordination and control of distributed energy resources (DERs), intelligent devices, and agents become increasingly challenging. Distribution utilities invest in advanced distribution management systems (ADMSs) and distributed energy resource management systems (DERMSs) to enhance distribution systems’ reliability, resiliency, and efficiency. Standardizing the Application Programming Interface (API) for ADMS applications is crucial to accelerate the integration of advanced distribution technologies. This paper introduces a distributed application architecture within the GridAPPS-D platform, aiming to address the limitations of centralized architectures in terms of scalability, maintainability, and flexibility. The proposed architecture draws inspiration from the Laminar Coordination Framework and is validated through extensive stakeholder engagement. Emphasizing extensibility, boundary deference, structural scalability, and securability, the layered framework is well-suited for large-scale distribution networks with diverse grid-edge devices and ownership structures. The contributions of this paper include a distributed layered architecture with defined distributed areas, a Common Information Model (CIM)-based standardized API for developing and deploying distributed applications (Distributed App API), and the design process and reference implementation of distributed services and applications. Based on laminar coordination, the software architecture combines centralized, distributed, and edge-control paradigms for effective distributed operations. The paper concludes with extending the centralized API in GridAPPS-D to distributed APIs for standards-based message exchange, emphasizing the need for scalable communication to coordinate diverse distributed agents. This work provides a foundation for advancing the field of distributed control in power distribution systems, supporting both centralized and distributed analytics and control arrangements.

With the rapid proliferation of distributed energy resources and advancement in sensing and control, deploying advanced applications has become crucial for power distribution operations to achieve resilience, economy, decarbonization, and other system mandates. However, conflicts can arise when multiple applications attempt to control the same devices, potentially leading to oscillatory system behavior and suboptimal performance. This paper aims to characterize conflicts and measure the resulting system performance through relevant metrics when applying various deconfliction solution techniques. First, we introduce a conflict matrix that comprehensively registers the conflicts between different applications. Next, we propose a novel metric to quantify the severity of conflicts, facilitating their prioritization and resolution. Finally, we quantify application success and system volatility after different techniques have been applied to resolve conflict. Three competing applications, namely resilience, decarbonization, and conservation voltage reduction, are considered to showcase potential conflicts. Two deconfliction solution techniques, exclusivity and compromise, are implemented, and their impacts on the apps’ performance are evaluated using a modified IEEE 123-bus model as the demonstration test system. Applying the derived conflict metric and relevant volatility metrics provides a quantitative assessment of the system’s performance and the impact of conflicting setpoints.

Advanced distribution systems need to integrate and orchestrate intelligent subsystems and grid-edge devices that are increasing both in number and sophistication while also serving multiple system-level objectives such as resilience, decarbonization, equity, and profitability. A modular platform-based approach to distribution system operations technology enables operators to deploy a tailored set of best-of-breed algorithms and applications. Combined with the parallel deployment and control of intelligent grid-edge and internet of things devices, this creates a complex distributed-control environment with applications that span ownership boundaries. Conflicts can emerge between applications that want to control overlapping sets of device setpoints. We propose a formalized approach to resolving these conflicts that can be applied when integrating new algorithms or developing customized solutions. A Deconfliction Pipeline is inserted between the device-controlling applications and the device protocol converter, which transmits control setpoints from the operations platform to the devices. The Deconfliction Pipeline executes a process that sets up, solves, and acts on a formally defined deconfliction problem. The deconfliction problem can be solved using a combination of rules and heuristics, application engagement, and optimization. We demonstrate how a few of the most basic solution strategies can be used to orchestrate harmonious behavior between a pair of simple applications with conflicting greedy optimization objectives.

Ongoing decarbonization efforts and increasing penetrations of distributed energy resources (DERs) may soon disrupt current operational paradigms, requiring a shift from centralized control and optimization to hierarchical and distributed applications. To support this transition, a distributed application architecture for communication and control of distribution network assets is introduced. The architecture defines a structure of communication and coordination layers based on the Laminar Coordination Framework. The challenge of defining distributed control areas is resolved through definition of switch-delimited topological areas based on the physical topology of the feeder instead of communications network layers. Implementation of the architecture in standards-based platforms is enabled by a robust graph-based topology processor leveraging the class structure of the Common Information Model (CIM), which differs significantly from traditional bus-branch representations by explicitly modeling the nodes and terminals of every type of power system equipment. The topology processor creates a set of LinkNet linked list structures for indexing the nodes and terminals of all CIM class object instances and mapping of the feeder topology in real-time. The data structures and algorithm are computationally lightweight, with performance tests presented for several IEEE distribution test systems.

The evolving nature of electric power distribution systems is motivating the development of advanced applications for utility planning, operation, management, and control. These model-based applications interact with multiple subsystems within software platforms referred to as advanced distribution management systems. Recently, the Common Information Model has been used to enable data model standardization across several entities. Model fidelity is the key to the development of leading-edge, scalable model-based algorithms and assessment of emerging technologies such as distributed energy resources for grid planning and operation. However, distribution system models are error-prone and model validation is challenging due to: (1) the need for considerable time and effort to maintain the network models, (2) update delays due to the evolving nature of the real-world systems that models are derived from, and (3) inevitable human errors in the process which are difficult to detect. In this paper, we extend a model validation application from our prior work referred to as the Model Validator to derive and validate a system admittance matrix using the Common Information Model. First, we introduce different modules for computing device-level primitive admittance matrices from Common Information Model parameters. These individual primitive admittance matrices are then used to validate corresponding entries in a full system admittance matrix. In addition, we validate the overall system admittance matrix by checking for gaps in coverage or extra elements that are not expected. The proposed approach checks the validity of each piece of network model equipment separately and identifies inconsistencies between Common Information Model parameters and the system admittance matrix at the device level. Furthermore, where devices can be specified using different combinations of Common Information Model parameters, each parameter combination is validated separately. This breakdown can help distribution engineers more efficiently apply root cause analysis for inconsistencies. The Model Validator application is implemented on top of GridAPPS-D, an open-source standards-based platform for advanced distribution management system application development. The effectiveness of the proposed application is demonstrated on six different test cases: four IEEE feeders, the Electric Power Research Institute J1 circuit, and a taxonomy feeder.

Thermostatically controlled residential appliances have built-in thermodynamic storage that, even within a narrow temperature band that does not degrade the comfort level of occupants, can be used to provide a variety of value streams to power system operators and customers. In this paper, residential air conditioners and electric water heaters are used to improve feeder power factor in a distribution system where photovoltaic systems cause the feeder power factor to dip daily, increasing losses. Improving distribution feeder power factor improves the efficiency of the transmission and bulk generation systems. A daily optimal dispatch regime is used to maximize the daily minimum feeder power factor. Following this regime, electric water heaters cool off in preparation for a low-feeder-power-factor event, turn on to improve the power factor during the event and return to a neutral condition after the event. Air conditioners, which have a power factor lower than the feeder overall, are optimally dispatched in the inverse pattern. Using a model of a real commercial and residential distribution feeder in the western United States, the optimal dispatch of virtual batteries is shown to be capable of improving the daily minimum power of that feeder by as much as 0.026. The power factor correction and optimal dispatch techniques are based on a robust virtual battery framework, making them portable to other applications, such as volt-var optimization and transactive energy systems.

To quantitatively determining the temporal ordering of abnormal age onsets (AAO) among various biomarkers for Alzheimer's disease (AD), we introduced a computational Monte-Carlo simulation (CMCS) to statistically examine such ordering of an AAO pair or over all AAOs. The CMCS 1) simulates longitudinal data, estimates AAO for each iteration, and finally assesses the type-I error of an AAO pair or all AAO ordering. Using hippocampus volume (VHC), cerebral glucose hypometabolic convergence index (HCI), plasma neurofilament light (NfL), mini-mental state exam (MMSE), the auditory verbal learning test-long term memory (AVLT-LTM), short term memory (AVLT-STM) and clinical-dementia rating sum of box scale (CDR-SOB) from 382 mild cognitive impairment converters and non-converters, the CMCS estimated type-I error for the earlier AAO of VHC, AVLT_STM and AVLT_LTM each than MMSE was significant (p<0.002). The type-I error for the overall AAO temporal ordering of VHC ≤ AVLT_STM ≤ AVLT_LTM < HCI ≤ MMSE ≤ CDR-SOB ≤ NfL was p = 0.012. These findings showed that our CMCS is capable of providing statistical inferences for quantifying AAO ordering which has important implications in advancing our understanding of AD. [ABSTRACT FROM AUTHOR]