SmartGrid

We examine the history of government energy policy, the forecasting paradox, the geopolitics of energy policy and recent surprises. Next, we examine the failures and successes in the evolution of electricity markets, and potential improvements from a smarter grid. Finally, we ask the question have we gotten better at seeing the future.

SmartGrid website

When deciding on wind power investments, three major issues arise: the production variability and uncertainty of wind facilities, the eventual future decline in wind power investment costs, and the significant financial risk involved in such investment decisions. Recognizing the above important issues, this presentation proposes a risk-constrained multi-stage stochastic programming model to make optimal investment decisions on wind power facilities along a multistage horizon. The proposed model is illustrated using a clarifying example and a case study.

SmartGrid Seminar website

Our aging grid infrastructure faces increasing challenges from multiple sources including greater demand variability, stricter environmental regulations and growing cyber security concerns. Advanced smart grid technologies provide possible solutions to tackle these challenges. Meanwhile how to best utilize these new devices and technologies such as PMUs and electric vehicles remains a challenge by itself. In this talk, I will address various topics which span a multitude of areas including demand response, stochastic optimization for renewable integration, microgrids and cyber security. I will present the technical issues in implementing these technologies and corresponding potential solutions.

Reliable operation of the power grid relies on the real-time stabilization of an interconnected continentalscale network of dynamic components. These include everything from central-station thermal and hydro power plants to end-use loads. The natural uncontrolled responses to disturbances are oscillatory, and swings in voltage and power flows are evident in data from disturbances. Specific control systems are used to damp these natural oscillations that otherwise can result in large-scale blackouts. In order to design and tune controllers it is imperative to understand and anticipate the oscillations, and to develop high fidelity models that can represent this behavior.

In this seminar we focus on the modal analysis of power system data to study oscillations for three purposes:

1. Better the understanding of the oscillations actually present in the grid. (Off-line engineering analysis.)
2. Validation of models used to study the grid. (Operational Planning.)
3. Improve situational awareness by tracking system modes in real-time. (Operations.)

We will provide examples of oscillations observed in the grid in the context of these three objectives. We review the industry standard approaches for power system modal analysis and highlight their shortcomings in practice. They often require preprocessing the data, fine-tuning in model order, and give inconsistent results with related data sets. We suggest that the main attraction of these established methods is that they are computationally easy to use; the calculations require linear algebra. Based on our experience and dissatisfaction with these methods, we explore the use of nonlinear fitting techniques to estimate modes from data. With current computing power, computational barriers are low, and we show excellent results using a variable projection method.

Our method is currently in use by some power engineers to characterize observed oscillations in the West. We show results of using modal analysis to aid in power plant model validation. We discuss and present initial results for adapting our method to estimating modal behavior from ambient data in the grid, which is the focus of our on-going research in this area.

We focus on the key developments in the implementation of demand response resources or DRRs, with special attention on their economics and policy aspects. The Federal Energy Regulatory Commission (FERC) forecasts an achievable 2019 DRR penetration range of 4 – 14 % of system peak load in the various ISO/RTOs under its jurisdiction. We discuss the three key factors driving the rapid growth in the DRR implementation: the rollout of the smart grid, the emergence of curtailment service providers or aggregators, and the developments on the demand response policy front. The large-scale implementation of advanced metering solutions to replace the legacy metering infrastructure and the deployment of appropriate technologies, devices, and services to access and leverage energy usage information are direct outcomes of the smart grid advancements. The creation of an important new class of market participants – the load aggregators – makes possible the deeper penetration of DRRs as viable competitors to supply-side resources. Recent policies, starting with the Energy Policy Act of 2005 and followed by FERC Order Nos. 719 and 745, and the various state-level initiatives have been instrumental in the removal of barriers to DRR participation and in bringing about the persistent deepening of DRR penetrations. We highlight some of the unintended consequences of FERC Order No. 745 and the challenges that deepening DRR penetrations present. While DRR curtailments result in lower loads, which reduce prices and emissions at specific nodes in the system during the curtailment hours, some portion of the curtailed energy is recovered in subsequent hours, resulting in impacts on prices and emissions in those hours — the so-called DRR payback effects. The recovery severely affects the economic benefits and emission reductions. Such outcomes underline the importance of the formulation and implementation of effective DRR policies.

TBA

Control center operation is becoming more complex as new and often-conflicting reliability, economics, and public policy issues emerge. Computer simulations will be required to analyze larger and larger amounts of system data (of different types) and what-if-scenarios to derive succinct information for operators to make informed decisions. Existing control center applications are primarily based on the original digital computing infrastructure first designed in the 1970's. While some incremental improvements have been made over the past several years, control center applications do not take full advantage of computing power in their existing infrastructure or in the computing industry in general.

High Performance Computing (HPC) and advanced computer are used widely within the government and in selected industry applications to solve important problems of high complexity, providing a factor from hundreds to millions times improvements in time-to-solution over desktop computer solutions. This presentation will share and discuss some research work of applying high performance computing to solve power system problems.

With the help of a Smart Grid Investment Grant from DOE, SMUD completed over 40 smart grid initiatives valued at $308 million. SMUD also completed $43 million worth of R&D projects bringing total smart grid spending to over $350 million over a four-year period.

The eight broad smart grid categories included:
1. Advanced Metering Infrastructure
2. Distribution Automation
3. Consumer Behavior Study (Smart Pricing Options)
4. Customer Applications
5. Demand Response
6. Technology Infrastructure
7. Cyber Security and
8. R&D Projects

Many innovative projects were developed and implemented as part of this project and many lessons were learned while going through the process of designing initiatives, procuring and installing equipment, and implementing, operating and evaluating programs and pilots.

Jim Parks of SMUD will talk about the projects, highlight the lessons learned and discuss next steps.