Daniel Arnold

Model-Free Optimal Control of VAR Resources in Distribution Systems: An Extremum Seeking Approach

Distributed power electronic reactive power sources-such as smart PV inverters-may play an important role in regulating customer voltages and reducing system losses in future distribution systems. Though it is natural to consider model-based optimal control algorithms to coordinate these resources, those strategies typically require relatively large communications capabilities and accurate models. In this paper we present an alternative way to implement the optimization problem that circumvents these communications and modeling challenges. We present an Extremum Seeking (ES) control algorithm to modulate the reactive power output of VAR resources to minimize total real power delivery at the feeder substation subject to voltage magnitude constraints, without an explicit feeder model. We present results that guarantee a variety of distribution feeder objective functions are convex over a broad range of power flows and perform an analysis showing the convergence of the ES approach. Simulation results demonstrate that the method is equivalent to recently developed convex relaxations used to solve distribution optimal power flow problems.

Accuracy and validation of measured and modeled data for distributed PV interconnection and control

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.

An architecture for integrated commercial building demand response

Enthusiasm for commercial demand response (DR) has inspired research efforts to create new building electric load control frameworks. In this paper we introduce a software architecture for an integrated building control system, the Central Load Shed Coordinator (CLSC). The CLSC provides functional control over three building systems which are large power consumers: lighting, HVAC, and plug loads. The system acts upon external demand response signals to meet load-shed criteria, while minimizing occupant inconvenience. In this paper, we present the purpose and relevant characteristics of this architecture. In addition, we discuss its deployment in a UC Berkeley building and present results from preliminary testing.

Optimal dispatch of reactive power for voltage regulation and balancing in unbalanced distribution systems

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.

An Energy Information Gateway for use in residential and commercial environments

Growing demands for products which enable consumers to manage their energy use more efficiently has led to the development of Energy Information Gateways, which are just beginning to gain traction in the marketplace. Such devices are envisioned to provide a communications and control infrastructure for appliances within their domain of influence, as well as communication with metering equipment. All relevant energy consumption information is expected to be relayed to the occupant in a practical manner. This paper outlines a software architecture for an EIG which is applicable to both residential and commercial environments. The software package utilizes the highly modular Open Services Gateway Initiative (OSGI) JAVA software framework to allow customization of individual EIG functionality and facilitates interoperability for existing smart grid products. In addition, the software in question provides a connection to external demand response resources and hosts a dynamic web based user interface to facilitate occupant interaction.

Synchrophasor data analytics in distribution grids

The deployment of high-fidelity, high-resolution sensors in distribution systems will play a key role in enabling increased resiliency and reliability in the face of a changing generation landscape. In order to leverage the full potential of such a rich dataset, it is necessary to develop an analytics framework capable of both detecting and analyzing patterns within events of interest. This work details the foundation of such an infrastructure. Here, we present an algorithm for detecting events, in the form of edges in voltage magnitude time series data, and an approach for clustering sets of events to reveal unique features that distinguish different events from one another (e.g. capacitor bank switching from transformer tap changes). We test the proposed infrastructure on distribution synchrophasor data obtained from a utility in California over a one week period. Our results indicate that event detection and clustering of archived data reveals features unique to the operation of voltage regulation equipment. The chosen data set particularly highlights the value of the derivative of the localized voltage angle as a distinguishing feature.

Real-time distribution grid state estimation with limited sensors and load forecasting

High penetration levels of distributed generation (DG) and electric vehicles (EVs) diversify power flow and bring uncertainty to distribution networks, making planning and control more involved for distribution system operators (DSOs). The increased risk of constraint violation triggers the need to augment forecasts with real- time state estimation. This is economically and technically challenging since it requires investing in a large number of sensors and these have to communicate with often older and slower supervisory control and data acquisition (SCADA) systems. We address distribution grid state estimation via combining only a limited set of sensors with load forecast information. It revisits open problems in a recent paper that proposes a Bayesian estimation scheme. We derive the estimator for balanced power networks via rigorous modeling. An off-line analysis of load aggregation, forecast accuracy and number of sensors provides concrete engineering trade-offs to determine the optimal number of sensors for a desired accuracy. This estimation procedure can be used in real time as an observer for control problems or off-line for planning purposes to asses the effect of DG or EVs on specific network components.

The next generation energy information gateway for use in residential and commercial environments

Both the current state of technology as well as emerging trends in home/office automation and energy management necessitate the presence of a piece of software dedicated towards facilitating interoperability amongst heterogeneous components; therefore empowering occupants to manage these spaces more effectively. This paper outlines the software architecture of an Energy Information Gateway, which is the latest evolution of an open source reference design that utilizes a modular software architecture to create an environment where dissimilar communicable components can not only exchange energy related information, but participate in supervisory control efforts. This latest version of the software allows for the abstraction of physical devices into a common standard, thereby allowing unlike components to join the home or office energy network. In addition, the software allows for definitions to be made of the hierarchical relationship between devices in the physical world and for these relationships to be dynamically altered.

An architecture for enabling distributed plug load control for commercial building demand response

Traditionally, a commercial buildings responsiveness to a demand response event is limited to actuation of the building HVAC and/or lighting system. However, current technological innovations allow for a more “integrated” energy control system via the incorporation of plug-loads as actionable load. This paper outlines the software architecture of such a system to enable plug-loads as a demand response resource. Distributed local plug-load control is facilitated via the use of smart communicating power strips and Energy Information Gateways (EIGs), which bridge the gap between the central building controller an individual loads. Coordinated control of these distributed EIGs is accomplished via the central building controller, which receives and interprets external demand response signals and hosts a bidding process amongst the EIGs to meet the load shed criteria. The deployment of prototype systems based on this design architecture at UC Berkeley and the United States Air Force Academy will also be discussed.

Extremum Seeking control of smart inverters for VAR compensation

Reactive power compensation is used by utilities to ensure customer voltages are within pre-defined tolerances and reduce system resistive losses. While much attention has been paid to model-based control algorithms for reactive power support and Volt Var Optimization (VVO), these strategies typically require relatively large communications capabilities and accurate models. In this work, a non-model-based control strategy for smart inverters is considered for VAR compensation. An Extremum Seeking control algorithm is applied to modulate the reactive power output of inverters based on real power information from the feeder substation, without an explicit feeder model. Simulation results using utility demand information confirm the ability of the control algorithm to inject VARs to minimize feeder head real power consumption. In addition, we show that the algorithm is capable of improving feeder voltage profiles and reducing reactive power supplied by the distribution substation.