Power System Simulation for Policymaking and Making Policymakers by Michael Ari Cohen A ...

91 Pages·2016·3.21 MB·English

Power System Simulation for Policymaking and Making Policymakers
by
Michael Ari Cohen
A dissertation submitted in partial satisfaction of the
requirements for the degree of
Doctor of Philosophy
in
Energy and Resources
in the
Graduate Division
of the
University of California, Berkeley
Committee in charge:
Assistant Professor Duncan Callaway, Chair
Adjunct Professor Alexandra von Meier
Associate Professor Greg Niemeyer
Professor Marcia Linn
Spring 2016
Power System Simulation for Policymaking and Making Policymakers
Copyright 2016
by
Michael Ari Cohen
1
Abstract
Power System Simulation for Policymaking and Making Policymakers
by
Michael Ari Cohen
Doctor of Philosophy in Energy and Resources
University of California, Berkeley
Assistant Professor Duncan Callaway, Chair
Power system simulation is a vital tool for anticipating, planning for and ultimately address-
ing future conditions on the power grid, especially in light of contemporary shifts in power
generation, transmission and use that are being driven by a desire to utilize more environ-
mentally responsible energy sources. This dissertation leverages power system simulation
and engineering-economic analysis to provide initial answers to one open question about
future power systems: how will high penetrations of distributed (rooftop) solar power affect
the physical and economic operation of distribution feeders? We find that the overall im-
pacts of distributed solar power (both positive and negative) on the feeders we modeled are
minor compared to the overall cost of energy, but that there is on average a small net benefit
provided by distributed generation. We then describe an effort to make similar analyses
more accessible to a non-engineering (high school) audience by developing an educational
video game called “Griddle” that is based on the same power system simulation techniques
used in the first study. We describe the design and evaluation of Griddle and find that it
demonstrates potential to provide students with insights about key power system learning
objectives.
i
For Flint Cohmanesh and Laura Mehrmanesh; reason enough to want to save the world.
ii
Contents
Contents ii
List of Figures iv
List of Tables v
Introduction 1
1 Physical Effects of Distributed PV Generation on California’s Distribu-
tion System 3
1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
1.4 Concluding remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
1.A Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
2 Economic Effects of Distributed PV Generation on California’s Distri-
bution System 25
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
2.2 Simulation and utility data inputs . . . . . . . . . . . . . . . . . . . . . . . . 28
2.3 Economic Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
2.4 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
2.A Summary of Inputs to Physical Simulations . . . . . . . . . . . . . . . . . . 42
2.B Summary of Simulation Engineering Results . . . . . . . . . . . . . . . . . . 43
2.C Economic calculation details . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
3 Griddle: Video Gaming for Power System Education 47
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
3.2 Design Process and Game Description . . . . . . . . . . . . . . . . . . . . . 49
3.3 Alignment With National Standards . . . . . . . . . . . . . . . . . . . . . . 57
3.4 Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
3.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
3.A Griddle Learning Evaluation Materials . . . . . . . . . . . . . . . . . . . . . 64
iii
Conclusion 76
Bibliography 77
iv
List of Figures
1.1 System losses. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
1.2 Effect of PV on peak loads. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
1.3 Date and time of peak loads. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
1.4 Normalized hourly load and PV generation profiles for August 13, 2012. . . . . . 11
1.5 Line voltage regulator activity. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
1.6 Voltage control and minimum load (reverse power flow). . . . . . . . . . . . . . 14
1.7 PV energy penetration as a function of penetration by capacity. . . . . . . . . . 22
2.1 Representative realizations of our deployment ramp up function. . . . . . . . . . 30
2.2 Schematic of capacity investment deferral value calculation. . . . . . . . . . . . 32
2.3 PG&E system-wide capacity benefit. . . . . . . . . . . . . . . . . . . . . . . . . 35
2.4 Energy-levelized capacity benefit. . . . . . . . . . . . . . . . . . . . . . . . . . . 36
2.5 Average annualized capacity benefit. . . . . . . . . . . . . . . . . . . . . . . . . 37
2.6 Capacity benefit percentiles on deferred feeders. . . . . . . . . . . . . . . . . . . 38
2.7 Sensitivity of capacity benefit to discount rate. . . . . . . . . . . . . . . . . . . 46
3.1 Schematic representation of Griddle’s three key learning objectives. . . . . . . . 51
3.2 Flow chart outlining Griddle’s variable-frequency power flow solver. . . . . . . . 53
3.3 Detail from the first design iteration of Griddle . . . . . . . . . . . . . . . . . . 54
3.4 The Griddle prototype as it appeared during the spring 2015 field trial. . . . . . 55
3.5 Sequence of activities in the Griddle lesson plan. . . . . . . . . . . . . . . . . . . 57
3.6 Mockup of a new power flow visualization for Griddle. . . . . . . . . . . . . . . 64
v
List of Tables
1.1 Summary of Simulated Feeder Characteristics and Figure Legend . . . . . . . . 6
1.2 Power Factors by Load Type . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
1.3 Location Characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
2.1 Assumed frequency of R1 and R3 feeders. . . . . . . . . . . . . . . . . . . . . . 43
3.1 Summary of Griddle’s Alignment with the Next Generation Science Standards. . 58
vi
Acknowledgments
As noted in the acknowledgments for individual chapters, an enormous number of people
have supported the work described in this dissertation, for which I am very thankful. I would
like particularly to acknowledge my article co-authors Duncan Callaway, Paul Kauzman and
Greg Niemeyer and my other faculty advisors Alexandra (Sascha) von Meier, Marcia Linn
and Severin Borenstein. Thanks also to Laura Mehrmanesh for returning the favor, and to
Flint Cohmanesh for tolerating dada’s work time and letting him sleep...most nights.
1
Introduction
All models are wrong but some
are useful.
George Box
Box’s pithy observation about the nature of modeling is oft heard early in a student’s
education in the Energy and Resources Group (ERG) at Berkeley. ERG professors and
students are wary of an overreliance on models, especially complex, opaque or “black box”
models that dazzle with their sophistication but may provide no more useful insight than a
much simpler rendition of the system in question. And yet, some important phenomena in
the world – and the in the realm of energy and resources in particular – are truly complex,
and na¨ıve attempts to simplify them may render them so wrong as to no longer be useful.
This dissertation investigates the potential of a specific kind of modeling – ac power flow
simulation – to make predictions about the future of the electric grid, and to help educate
future engineers, business people and policymakers to manage that future grid. Power grid
modeling is irreducibly complex in two senses. First, on a physical level, the operational
conditions and behavior of any individual component in a networked power grid (e.g., loads
such as appliances, power plants, and grid control and protection equipment) is to at least
some extent dependent on the state of every connected component. That is, each network
topology is unique, and requires simulation at a fairly high level of detail to obtain a useful
result. Second, power grids are not simply physical systems, they are embedded in economic,
environmental,social,andpoliticalsystemsaswell. Thus,whenweconsiderevolvingthegrid
with certain goals in mind, such as greater environmental sustainability, we must consider
these intertwined systems carefully. They may both impact the physicality of the grid by
influencing what we build (or do not build) and also be influenced in important ways by
what is physically or technologically achievable, and at what cost.
Chapter 1 describes the physical modeling of the effect that increased deployment of
rooftop photovoltaics (“PV”; that is, solar panels) are likely to have on the local electrical
distribution network in California. Chapter 2 layers an additional economic model on top
of the physical modeling from Chapter 1 to estimate the overall economic value (or cost) of
these physical effects over the next several years within the service territory of Pacific Gas
and Electric (PG&E).