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Phasor Based Control is a novel approach to controlling Distributed Energy Resources that aims at relieving various constraints that arise in the distribution grid. It isa two-layer control system with a supervisory control that coordinates distributed controllers to reach voltage phasor targets. The distributed controllers use local synchrophasor measurements and operate as feedback controllers. This control method is currently under development with several algorithms being under consideration for both the central and ditributed components. In this paper, we present the experimental setup that was prepared to prototype a hardwareimplementation and validate the control method in Hardware-In-the-Loop.

The Oakland EcoBlock project (EcoBlock) seeks to develop a microgrid within an existing city block in Oakland California as a demonstration project on the viability of retrofitting an existing block of residential and commercial buildings into a microgrid capable of islanding from the grid. EcoBlock has considered a variety of different potential regulatory frameworks that might accommodate the objectives of the project. Based on an analysis of current law, the working model has been to rely upon existing provisions within the Public Utilities Code that recognize the right of individuals to install and operate electric equipment outside of Commission regulation if the generation, storage and distribution of power is limited to the owners’ “own use.” This white paper examines the “own use” exemption in section 218 of the Public Utilities Code and explores its potential as well as its limitations in the context of microgrids and recent Commission action. In so doing, it touches upon the recently issued Microgrid Proceeding Proposed Decision (Microgrid PD), the Commission Staff paper upon which the PD is premised and issues that arise out of both. This white paper, however, is not intended to be a critique of either and, rather, is intended to prompt questions and reflections on how the policy decisions made in these documents will foster or retard the development of projects like EcoBlock.

This project demonstrated that solar PV can be recruited to stabilize the grid, smooth out disturbances, manage power flows, and assist circuit switching operations. It developed a radically new, layered control framework for Distributed Energy Resources (DER) to act in response to real-time, measured conditions on their local distribution circuit, rather than waiting for a price signal to indicate preferred behavior. By enabling resources to act as good citizens on the electric grid, Phasor-Based Control (PBC) facilitates arbitrarily high solar penetration levels.

This interdisciplinary eXtensible Building Operating System–Vehicles project focuses on controlling plug-in electric vehicle charging at residential and small commercial settings using a novel and flexible open-source, open-architecture charge communication and control platform. The platform provides smart charging functionalities and benefits to the utility, homes, and businesses.This project investigates four important areas of vehicle-grid integration research, integrating technical as well as social and behavioral dimensions: smart charging user needs assessment, advanced load control platform development and testing, smart charging impacts, benefits to the power grid, and smart charging ratepayer benefits.

Widespread deployment of EcoBlocks has the potential to transform today's electricity system into one that is more resilient, flexible, efficient and sustainable. In this vision, the system will consist of self- su cient, renewable-powered, block-scale entities that can deliberately adjust their net power exchange and can optimize performance, maintain stability, support each other, or disconnect entirely from the grid as needed. This report is intended as an independent analysis of the potential relationships, both constructive and adverse, between EcoBlocks and the grid.

With the unprecedented growth of renewable resources, electric vehicles, and controllable loads, power system has been incorporating increasing amount of unconventional generations and loads. As a consequence, significant dynamic and stochastic power flow are introduced into distribution network, requiring high resolution monitoring technology and agile decision support techniques for system diagnosis and control. In this paper, we discuss the application of micro-synchrophasor measurement unit (µPMU) for power distribution network monitoring, and we propose a novel data-driven method, namely Ensembles of Bundle Classifier (EBC), for event detection. The main idea is: multiple classifiers are learned each with a short slot of µPMU measurement generated by a single event. Then their decisions are combined with a “winner-takes-all” scheme. This framework naturally resolves the challenging issue of heterogeneity in the high resolution µPMU data, and significantly outperforms classic data-driven event detection methods. In this paper, the proposed framework is tested on an actual distribution network with µPMUs, and is compared to other state-of-the-art methods. The result justifies the effectiveness of EBC as a promising tool to improve the security and reliability of distribution network.

Optimization of distributed power assets is a powerful tool that has the potential to assist utility efforts to ensure customer voltages are within pre-defined tolerances and to improve distribution system operations. While convex relaxations of Optimal Power Flow (OPF) problems have been proposed for both balanced and unbalanced networks, these approaches do not provide universal convexity guarantees and scale inefficiently as network size and the number of constraints increase. In balanced networks, a linearized model of power flow, the LinDistFlow model, has been successfully employed to solve approximate OPF problems quickly and with high degrees of accuracy. In this work, an extension of the LinDistFlow model is proposed for unbalanced distribution systems, and is subsequently used to formulate an approximate unbalanced OPF problem that uses VAR assets for voltage balancing and regulation. Simulation results on the IEEE 13 node test feeder demonstrate the ability of the unbalanced LinDistFlow model to perform voltage regulation and balance system voltages.

Modern society relies heavily upon complex and widespread electric grids. In recent years, advanced sensors, intelligent automation, communication networks, and information technologies (IT) have been integrated into the electric grid to enhance its performance and efficiency. Integrating these new technologies has resulted in more interconnections and interdependencies between the physical and cyber components of the grid. Natural disasters and man-made perturbations have begun to threaten grid integrity more often. Urban infrastructure networks are highly reliant on the electric grid and consequently, the vulnerability of infrastructure networks to electric grid outages is becoming a major global concern. In order to minimize the economic, social, and political impacts of large-scale power system outages, the grid must be resilient in addition of being robust and reliable. The concept of a power system’s cyber-physical resilience centers around maintaining critical functionality of the system backbone in the presence of unexpected extreme disturbances. Resilience is a multidimensional property of the electric grid; it requires managing disturbances originating from physical component failures, cyber component malfunctions, and human attacks. In the electric grid community, there is not a clear and universally accepted definition of cyber-physical resilience. This paper focuses on the definition of resilience for the electric grid and reviews key concepts related to system resilience. This paper aims to advance the field not only by adding cyber-physical resilience concepts to power systems vocabulary, but also by proposing a new way of thinking about grid operation with unexpected extreme disturbances and hazards and leveraging distributed energy resources. The concepts of service availability and quality are not new, but many recognize the need of resilience in maintaining essential services to critical loads, for example to allow home refrigerators to operate for food conservation in the aftermath of a hurricane landfall. By providing a comprehensive definition of power system resilience, this paper paves the way for creating appropriate and effective resilience standards and metrics.

Power flow analysis is one of the tools that is required in most of the distribution system studies. An important characteristic of distribution systems is the load unbalance in the phases and a three-phase power flow analysis is needed. In this paper, a three-phase linear power flow (3LPF) formulation is derived based on the fact that in a typical distribution system, voltage angles and magnitudes vary within relatively narrow boundaries. The accuracy of the proposed 3LPF is verified using several test cases. Potential applications of the proposed method are in distribution systems state estimation and volt-VAR optimization.

The introduction and deployment of cheap, high precision, high-sample-rate next-generation synchrophasors en-masse in both the transmission and distribution tier – while invaluable for event diagnosis, situational awareness and capacity planning – poses a problem for existing methods of phasor data analysis and storage.Addressing this, we present the design andimplementation of a novel architecture for synchrophasor data analysis on distributed commodity hardware. At the core is a newfeature-rich timeseries store, BTrDB.Capable of sustained writes and reads in excess of 16 million points per second per cluster node, advanced query functionality and highly efficient storage, this database enables novel analysis and visualization techniques.Leveraging this, a distillate framework has been developed that enables agile development of scalable analysis pipelines with strict guarantees on result integrity despite asynchronous changes in data or out of order arrival. Finally, the system is evaluated in a pilot deployment, archiving more than 216 billion raw datapoints and 515 billion derived datapoints from 13 devices in just 3.9TB.We show that the system is capable of scaling to handle complex analytics and storage for tens of thousands of next-generation synchrophasors on off-the-shelf servers.

This paper proposes a data-driven approach to detect the switching actions and topology transitions in distribution networks. It is based on the real time analysis of time-series voltages measurements. The analysis approach draws on data from high-precision phasor measurement units (μPMUs or synchrophasors) for distribution networks. The key fact is that time-series measurement data taken from the distribution network has specific patterns representing state transitions such as topology changes. The proposed algorithm is based on comparison of actual voltage measurements with a library of signatures derived from the possible topologies simulation. The IEEE 33-bus model is used for the algorithm validation.

The distribution grid is changing to become an active resource with complex modeling needs. The new active distribution grid will, within the next ten years, contain a complex mix of load, generation, storage and automated resources all operating with different objectives on different time scales from each other and requiring detailed analysis. Electrical analysis tools that are used to perform capacity and stability studies have been used for transmission system planning for many years. In these tools, the distribution grid was considered a load and its details and physical components were not modeled. The increase in measured data sources can be utilized for better modeling, but also control of distributed energy resources (DER). The utilization of these sources and advanced modeling tools will require data management, and knowledgeable users. Each of these measurement and modeling devices have accuracy constraints, which will ultimately define their future ability to be planned and controlled. This paper discusses the importance of measured data accuracy for inverter control, interconnection and planning tools and proposes ranges of control accuracy needed to satisfy all concerns based on the present grid infrastructure.

This paper proposes a novel phase identification method for distribution networks where phases can be severely unbalanced and insufficiently labeled. The analysis approach draws on data from high-precision phasor measurement units (micro-synchrophasors or uPMUs) for distribution systems. A key fact is that time-series voltage phasors taken from a distribution network show specific patterns regarding connected phases at measurement points. The algorithm is based on analyzing cross-correlations over voltage magnitudes along with phase angle differences on two candidate phases to be matched. If two measurement points are on the same phase, large positive voltage magnitude correlations and small voltage angle differences should be observed. The algorithm is initially validated using the IEEE 13-bus model, and subsequently with actual uPMU measurements on a 12-kV feeder.

This paper proposes a novel approach to detecting the topology of distribution networks based on the analysis of time series measurements. The analysis approach draws on data from high-precision phasor measurement units (PMUs or synchrophasors) for distribution systems. A key fact is that time-series data taken from a dynamic system show specific patterns regarding state transitions such as opening or closing switches, as a kind of signature from each topology change. The algorithm proposed here is based on the comparison of the actual signature of a recent state transition against a library of signatures derived from topology simulations. The IEEE 33-bus model is used for initial algorithm validation.

This white paper describes the circumstances in California around the turn of the 21st century that led the California Energy Commission (CEC) to direct additional Public Interest Energy Research funds to address critical electric grid issues, especially those arising from integrating high penetrations of variable renewable generation with the electric grid. It contains an assessment of the beneficial science and technology advances of the resultant portfolio of electric grid research projects administered under the direction of the CEC by a competitively selected contractor, the University of California’s California Institute for Energy and the Environment, from 2003-2014.

In this work we address the problem of static state estimation (SE) in distribution grids by leveraging historical meter data (pseudo-measurements) with real-time measurements from synchrophasors (PMU data). We present a Bayesian linear estimator based on a linear approximation of the power flow equations for distribution networks, which is computationally more efficient than standard nonlinear weighted least squares (WLS) estimators. We show via numerical simulations that the proposed strategy performs similarly to the standard WLS estimator on a small distribution network. A key advantage of the proposed approach is that it provides explicit off-line computation of the estimation error confidence intervals, which we use to explore the tradeoffs between number of PMUs, PMU placement and measurement uncertainty. Since the estimation error in distribution systems tends to be dominated by uncertainty in loads and scarcity of instrumented nodes, the linearized method along with the use of high-precision PMUs may be a suitable way to facilitate on-line state estimation where it was previously impractical.

In recent years, extreme weather events have severely affected the performance of the electric grid. Very large-scale events (VLSE) with potentially catastrophic impacts on the grid pose more than an inconvenience in today's electricity-driven lifestyle, and the frequency and severity of such events may continue to increase as a consequence of global climate change. This article summarizes the state of the art in leveraging distributed resources to improve resilience of the electric grid. It also highlights the technical questions that need to be addressed through additional research and development if the value of distributed resources is to be maximized.

This paper describes challenges for integrating high fidelity data with utility distribution operations. Two  research projects are described microsynchrophasors (μPMU) and OpenPMU and explore the applications of data for electric power distribution systems.

This paper describes a research project to develop a network of high-precision phasor measurement units, termed micro-synchrophasors or μPMUs, and explore the applications of μPMU data for electric power distribution systems.

Successfully integrating renewable resources into the electric grid at penetration levels to meet a 33 percent Renewables Portfolio Standard for California presents diverse technical and organizational challenges. This report characterizes these challenges by coordinating problems in time and space, balancing electric power on a range of scales from microseconds to decades and from individual homes to hundreds of miles. Crucial research needs were identified related to grid operation, standards and procedures, system design and analysis, and incentives, and public engagement in each scale of analysis. Performing this coordination on more refined scales of time and space independent of any particular technology, is defined as a “smart grid.” “Smart” coordination of the grid should mitigate technical difficulties associated with intermittent and distributed generation, support grid stability and reliability, and maximize benefits to California ratepayers by using the most economic technologies, design and operating approaches.

This report articulates and justifies the preliminary selection of diagnostic applications for data from micro-synchrophasors (µPMUs) in electric power distribution systems that will be further studied and developed within the scope of the three-year ARPA-e award titled Micro-synchrophasors for Distribution Systems.

For most of the 20th century, the electric grid had a relatively simple role: moving electricity from central power plants to the consumers. Its behavior was predictable, operation was largely deterministic, and an operator was in control. But with growing uncertainty, aging infrastructure and uncertainty in energy markets, we must begin to think about the future of the energy grid and restructuring to effectively rise to these challenges.

California is striving to achieve 33% renewable penetration by 2020 in accordance with the state’s Renewable Portfolio Standard (RPS). The behavior of renewable resources and electric vehicles in distribution systems is creating constraints on the penetration of these resources into the distribution system. One such constraint is the ability of present-­‐‑day voltage management methodologies to maintain proper distribution system voltage profiles in the face of higher penetrations of PV and electric vehicle technologies. This white paper describes the research gaps that have been identified in current Volt/VAR Optimization and Control (VVOC) technologies, the emerging technologies which are becoming available for use in VVOC, and the research gaps which exist and must be overcome in order to realize the full promise of these emerging technologies.

This paper explains conflict over technological process innovation in cultural terms, drawing primarily on a case study of electric power distribution and strategies to automate its operation.