Download Systems and Control Opportunities in the Integration of Renewable

Systems and Control Opportunities in the
Integration of Renewable Energy into the
Smart Grid ?
E. Bitar ∗
P. P. Khargonekar
∗∗
K. Poolla ∗∗∗
∗
E. Bitar is with the Department of Mechanical Engineering, U.C.
Berkeley [email protected] (corresponding author)
∗∗
P. P. Khargonekar is with the Department of Electrical and
Computer Engineering, University of Florida [email protected]
∗∗∗
K. Poolla is with the Department of Electrical Engineering and
Computer Science, U.C. Berkeley [email protected]
Abstract: The Smart Grid is among the most important and ambitious endeavors of our time.
Deep integration of renewable energy sources is one component of the Smart Grid vision. A
fundamental difficulty here is that renewable energy sources are highly variable – they are not
dispatchable, are intermittent, and uncertain. The electricity grid must absorb this variability
through a portfolio of solutions. These include aggregation of variable generation, curtailment,
operating reserves, storage technologies, local generation, and distributed demand response.
The various elements in this portfolio must be dynamically coordinated based on available
information within the framework of electricity grid operations. This, in turn, will require critical
technologies and methods drawn from optimization, modeling, and control, which are the core
competencies of Systems and Control. This paper catalogues some of these systems and control
research opportunities that arise in the deep integration of renewable energy sources.
Keywords: Smart Grid, Renewable Energy Integration, Uncertainty, Systems, Control,
Modeling
1. THE OPPORTUNITY
What is the Smart Grid?
The Smart Grid is a vision of the future of the electric
energy system. There are many visions of this future,
but perhaps these are best summarized by US Energy
Secretary Steven Chu who writes that “the Smart Grid
is the key enabler for: integration of renewable energy
sources into the grid, management and deployment of
energy storage, load management, system transparency,
and cyber and physical security of the electric energy
system”. Concerns over global climate change and carbon
emissions have motivated development of integration of
renewable energy generation from sources such as wind
and solar to meet our electric energy needs. Another
particularly compelling motivation behind the Smart Grid
initiative is that the use of advanced communication, information, and control technologies may reduce the need
for expensive investments in the physical infrastructure –
generation, transmission, and distribution. Realizing these
visions of the Smart Grid and its benefits for combating
global climate change will require a portfolio of solutions
such as deep integration of renewable resources, grid scale
storage technologies, incorporation of micro-generation,
demand response and consumer participation, new market
? Supported in part by OOF991-KAUST US LIMITED under award
number 025478, the UC Discovery Grant ele07-10283 under the
IMPACT program, and NSF under Grant EECS-0925337, and the
Florida Energy Systems Consortium.
instruments, advances in materials, sensors, and powerelectronics, dedicated communication networks with secure protocols, and targeted infrastructure investments.
Renewable Energy Integration
The scope of Smart Grid objectives and technologies delineated above is indeed daunting. In this paper we will
confine ourselves to one of the many components of the
Smart Grid vision: deep integration of renewable energy
into the electric grid. Renewable energy sources such as
wind and solar are fundamentally different from conventional generation such as coal, nuclear, natural gas. The
energy production from these renewable sources is not dispatchable [cannot be controlled on demand], intermittent
[exhibits large fluctuations], and uncertain [random or not
known in advance]. We will use the term variability to
encompass these three characteristics of renewable generation [33]. Variability is the most important obstacle in
integrating renewable generation into the electric energy
system at deep penetration levels.
The Variable Generation Problem
For solar power, there is the natural diurnal cycle of
variability in insolation [incident solar radiation]. Beyond
this, cloud-cover can cause significant ramps in solar
insolation and electric power output. Solar insolation can
change by more than 80% of the peak insolation in
a matter of seconds [31]. The corresponding ramp in
the solar power plant output is also very large but it
also depends on panel size and geographical spread. In
addition, inverter tripping adds to the variability in solar
plant output.
Wind speed and direction experience significant variations
at multiple time scales: minutes, hours, daily, seasonally,
and annually. In addition to the slow and gradual variations, wind plant output can ramp up or down in minutes
[23]. For example, on December 29-30, 2008, wind power
output experienced 95% drop in a two hour period in the
BPA balancing authority [35]. Also, severe weather events
can cause wind speed to become dangerously high making
operation of wind turbines unsafe, forcing shutdown. Aggregation over large areas tends to reduce the variability in
wind power production. These difficulties are compounded
by the fact that it is difficult to obtain reliable and accurate
forecasts of wind power production. Forecasting difficulty
increases with forecast horizon.
Dealing with Variable Generation
As we consider the variable generation characteristics of
renewable energy sources, it should be noted that electric
power system operations are designed to accommodate
the natural (and very large) variability in load demand as
well as planned and unplanned contingencies. This is done
at different time-scales through load-frequency control,
operational reserves, scheduling and unit-commitment, demand response, and load shedding. At deep penetration
levels, renewable generation add significantly to the extent
of the overall variability that must be handled. It has been
suggested that existing mechanisms to handle demand
fluctuations are adequate for renewable generation variability up to 20% penetration levels [15, 50]. Reconfiguring
these existing mechanisms to handle the increased variability comes with significant cost issues and operational
challenges [12].
Systems and Control Opportunities
At deep penetration levels, dealing with variable generation requires a portfolio of technologies which, in turn,
introduce operational challenges for the electricity grid.
Improved forecasting of wind and solar energy at multiple time-scales reduces uncertainty and permits efficient
scheduling of these variable resources into the generation mix. Dynamic optimization through optimal resource
scheduling and control can reduce the need for various
types of operational reserves thereby reducing integration cost and increasing the carbon reduction benefits
of renewable generation. Optimal operation of grid scale
storage can reduce the curtailment of wind energy when
the production of wind energy is too high. Smart charging
of plug-in electric vehicles can make greater use of wind
energy at night when the industrial and residential demand
is low. Demand response through real-time pricing or
appropriate contracting mechanisms can reduce the need
for peak generation and improve matching of renewable
generation with load. Managing increased variability in the
electricity grid will need advanced large scale distributed
control and optimization algorithms. Each of these avenues
to address the fundamental challenge of renewable resource
variability requires systems and control technologies. We
submit that system identification, modeling, estimation,
distributed optimization, and optimal control will play
central roles here.
What is in this paper?
In this paper we will explore those aspects of variable generation integration where systems and control technologies
will play a critical role in obtaining effective solutions. Our
objective is to catalogue an incomplete list of such problems and analyze the underlying issues. The opportunities
abound for our community of researchers, and the time is
ripe to seize them.
2. SYSTEMS AND CONTROL OPPORTUNITIES
We now discuss a number of specific problems which
need to be solved in order to achieve deep penetration
of renewable energy sources where systems and control
technologies can make critically important contributions.
A. Forecasting
Challenges in electric grid operations with deep renewable
penetration become much more tractable if accurate forecasts of wind and solar energy production are available in
advance. Accurate forecasts can alleviate negative impacts
on the required spinning reserves for reliable operation
of the grid. They reduce the total cost of integration of
renewable energy into the grid. Wind power forecasting is
a very active area of research [see [8] for a recent survey
and references to the literature]. Wind power is a nonstationary stochastic process. There are two main approaches to this problem: (a) physical approach where numerical weather predictions are coupled with a collection
of wind turbine physical models and data to predict the
wind power output of the wind farm, and (b) techniques
that combine empirical time-series models such as ARMA,
statistical forecasting, pattern matching, neural networks,
fuzzy systems, etc. with numerical weather predictions to
forecast the wind farm power output. Commercial wind
power forecasting tools include combinations of these approaches as well. Spatial diversity can decrease the total
uncertainty in the output of a wind farm. Moreover, the
forecasting error generally decreases with forecast horizon.
At short time-scales [minutes to hours], time-series models are among the best performers. State-of-the-art tools
provide error rates of 4-6% for short term forecasts. For
longer forecast horizons [24-48 hours], the prediction errors
are large and can exceed 20-30%. A special issue in wind
forecasting is the occurrence of wind ramps. In addition
to point forecasts, newer approaches to dealing with wind
uncertainty require forecasts of probability distribution
functions. Similar comments apply to forecasting methods
for solar energy production. Satellite and ground-based sky
imaging tools can be used to monitor and forecast cloud
movements.
Renewable generation forecasting is clearly an area where
advanced techniques from linear and nonlinear estimation
and prediction can have a major impact.
B. Electricity Markets
Electricity markets are recognized to be fundamentally
different than commodity markets. Electricity has physical constraints on transmission, generation, scheduling,
and storage, economic constraints on generation costs and
system infrastructure, and quality of service constraints
demanded by consumers. As a result, traditional com-
modity market structures are not suitable for electricity.
A certain level of centralized coordination and control is
necessary to ensure safe, economic, and reliable operation
of the electric power system. While there is increasing
competition and decentralization in generation and retail
distribution, transmission and system operation remain
centralized. In its most deregulated form, the system is
managed by an independent system operator [ISO] or a
transmission system operator [TSO]. A common market
structure [42, 30, 32, 25] consists of two successive ex-ante
markets: a day-ahead (DA) forward market and a realtime (RT) spot market. The DA market permits qualified
generators to bid and schedule energy transactions for the
following day. Depending on the region, the DA market
closes for bids and schedules by 10 A.M. and clears by 1
P.M. on the day prior to the operating day. The schedules
cleared in the DA market are financially binding and are
subject to deviation penalties. As the schedules submitted
to the DA market are cleared well in advance of the
operating day, a RT spot market is employed to ensure
the balance of supply and demand in real-time by allowing
market participants to adjust their DA schedules based on
more accurate wind and load forecasts. The RT market
is cleared 5 to 15 minutes before the operating interval,
which is on the order of 5 minutes.
In the context of deep penetration of renewable energy,
many new questions arise regarding these market mechanisms and structures [21]. Renewable energy producers are
often given favorable treatment via public policy support
through subsidies, price guarantees or other extra-market
mechanisms. A major unresolved issue in this context is
the pricing of carbon emissions by certain types of energy
producers. Also, costs of integrating variable generation
are often socialized and distributed to other participants
in the energy markets. However, as the penetration level
increases, such mechanisms are likely to be untenable and
the renewable energy producers will have to operate in the
market along with traditional energy producers.
What new market mechanisms are necessary to enable
deep penetration of renewable energy generators? Since
renewable generation is inherently variable and uncertain,
it might become necessary or advantageous to devise novel
contract structures [ex: interruptible contacts [49]] that
explicitly deal with this variability. Also, more frequent
intra-day markets where the wind and solar energy producers can enter bidding closer to the delivery time will
be better matched to the uncertainty in their production.
These recourse markets may also help in reducing the
cost of integrating variable generation by separating load
following reserves from regulation reserves and/or reduce
the need for more expensive regulation reserves. Fast intraday markets may be regarded as a form of closed loop
control to deal with variabile generation.
Designing and optimizing new market mechanisms that
explicitly account for variable generation is an important
research direction that will involve diverse methods from
control theory and stochastic dynamic programming.
C. Joint Optimization of Wind and Storage
At deep penetration levels, energy storage is expected to
be a key enabling technology for the Smart Grid [13].
Energy can be stored using pumped hydro, compressed
air, batteries, flywheels, molten salt, etc. Today, pumped
hydro is the most widely used storage modality but it is
geographically limited. In the future, electric vehicles may
provide distributed energy storage capacity which could
be harnessed with Smart Grid technologies. Depending on
the capacity of the storage device, it can be used on the
generation and transmission side or on the distribution
side. From the perspective of renewable integration, the
key consideration is – when should energy storage be
distributed across the grid versus co-located at generation
sites. These are dramatically different visions of large
scale storage deployment. If storage devices are co-located
with the renewable generation plant, it opens up the
possibility of real-time control of storage device to reduce
the variability of the renewable energy output. This can
be posed as a stochastic optimal control problem to match
a given profile of power output by suitably extracting
from and injecting into the storage device. An important
question then is the minimum storage size needed to
achieve a given level of probability of meeting the output
power profile. The ultimate objective would be to make the
wind plant dispatchable. This will require high precision
control of the power output from the renewable plant. On
the other hand, since storage is expensive [≈ $ 1.5M per
MW-hr], it is quite possible that the best economic value
can be derived by optimizing its operation at the grid level.
On the other hand, in the scenarios involving distributed
renewable energy generation, community scale distributed
storage may be the optimal choice.
Optimal sizing and operations of distributed storage modalities with different dynamic characteristics will require
methods from distributed optimization and stochastic decentralized optimal control.
D. Reserves and Local Generation
Wind farm energy production variability can be compensated by the use of local generation sources such as
natural gas or diesel generators. Local generation adds to
capacity costs as well as operational costs of the system.
Also, as natural gas plants produce carbon emissions, this
approach attenuates the clean energy benefit from wind
power production. One can pose a joint stochastic optimal
control problem involving wind and local generation to
deliver a certain amount of total power in the presence of
stochastic wind variability.
Deep penetration of renewables can have a very large
impact on various different types of reserves needed to
operate the grid and meet the relevant reliability criteria.
Generally speaking, renewable generation has no significant impact on the contingency reserves as the largest
contingencies arise from base-load generating units. By
contrast, the impact on operating reserves and regulation
reserves is substantial. There are many studies to assess
the additional reserve requirements [15, 50]. Increased need
for operating and regulation reserves is one of the pressing
issues in integrating large amounts of renewable energy [9].
They add to the cost of integration and thereby reduce the
beneficial impact of renewables. Since reserve generators
often use hydrocarbon fuels, they detract from the positive impact of renewables on global warming. However,
a critical assumption made in many of the current studies on required reserve requirements is that they assume
no change in the current operating procedure. Hence, it
may be possible to apply techniques from systems and
control to mitigate the impact of variability in renewables
on reserve requirements. For example, dynamic stochastic
optimization of reserves that can meet the required loss
of load probability by taking advantage of reduced uncertainty with shorter forecast horizons.
Progress in both these research areas will use tools from
stochastic optimization.
E. Power System Stability
Experience in island power systems indicates that severe
system level problems arise when the penetration of wind
power exceeds 15%. In Denmark, where the wind penetration exceeds 20%, this problem has been avoided by
the use of interconnections to power systems in Sweden,
Norway, Germany, etc. Spain has almost 100% reserve
capacity. Analysis of the impact of high penetration of
variable generation sources on the stability and performance [voltage, frequency, etc.] of the power system is a
difficult problem. Transient frequency response, regulation
response, automatic generation control, load following,
and unit commitment processes, operating at different
time scales [seconds to hours], together determine the
overall impact on the voltage and frequency of fluctuations
in the renewable power production. Also, wind turbines
are variable speed generators as opposed to synchronous
generators for traditional power generation. These variable speed generators [ex: doubly fed induction generator]
may adversely impact the transient stability of the power
system [3] as they reduce the inertia of the system by
displacing synchronous generators. Analysis and design of
suitable control systems to enhance power system stability
at high penetration levels is a key problem where systems
and control techniques will prove invaluable.
Power system stability is a networked control problem,
the scale of which will require the creative development of
computational tools for stability analysis.
F. Voltage Support
Maintaining tight voltage control across the distribution
system is an important part of delivering power quality.
Tight voltage control is necessary for efficiency in many
end-use applications. Distribution system voltage levels
are traditionally controlled by adjusting load tap changes
in substation transformers, and through capacitor banks to
provide reactive power support locally. Integration of large
amounts of renewable power in the distribution system
[ex: rooftop solar] will create new sources of variation in
the voltage level in the distribution system. The random
locations of these generation devices can also lead to
increased imbalance operation in the 3-phase distribution
network. These variations and imbalances change along
the length of the feeder circuit.
There is active discussion of modifications to the standards
(IEEE 1547-1547.8) which regulate connection of renewable generators to the distribution system. There are also
considerable variations in local rules that also constrain
the connection of renewable generators to the distribution
system. Among other issues, there is concern that unless
these regulations are properly conceived, they may lead to
increased occurrences of disconnection from the grid due
to voltage fluctuations [22].
Modern commercially available solid state inverters are
capable of supplying (capacitive as well as inductive) reactive power. At the same time, fast communications of
voltage measurements across the distribution network may
allow for coordinated control actions. Thus, it is possible
to conceive of control system architectures and algorithms
that leverage these actuation capabilities to minimize
voltage fluctuations [34, 39]. In addition, DSTATCOMs
(Distributed Static Compensators) are controllable reactive power resources that can be employed to deal with
voltage quality issues. What is the optimal placement of
these devices? How should they be controlled to optimize
metric of voltage quality? These are some of the emerging
problems that are beginning to attract attention [41].
Mitigating undesirable voltage fluctuations will require the
use of techniques from large scale distributed control and
optimization.
G. Matching Variable Generation to Adjustable Demand
Demand side actions offer another strategy to absorb
injected variability from renewable generation. There is
a considerable effort in developing demand response as
a Smart Grid technology for this (and other) objective.
See for example [7, 29, 44]. Profitable business models for
demand response with commercial customers (ex: refrigerated warehouses) have been realized by companies such as
EnerNOC Corporation [14]. Much of the existing research
studys implementation aspects (ex: communication infrastructure, appliance control, price signalling) of demand
response. The operational aspects of matching variable
generation with adjustable demand in transmission constrained power systems remain largely unexplored.
There are a diversity of consumers with different electricity needs. Some of these are power consumers: they
require power on demand and offer little opportunity for
demand shaping [ex: continuous manufacturing facilities
that cannot defer their demand for electricity]. Others are
energy consumers: they can accept variable power within
certain constraints with advance notice and coordination.
Examples here include warehouses which can chill their refrigeration space on variable cooling schedules, and electric
vehicle charging stations which can use variable charging
schedules.
What are generic models for the electricity needs of energy
consumers? What is the set of power profiles P that
they can accept for the period [0, T ]? Once we have
models of acceptable power profiles from certain classes
of energy consumers, we can formulate various flavors of
demand matching problems. Consider a variable generator
and an energy consumer [EC] who can absorb arbitrary
power variability and assume we have no transmission
constraints. In this happy situation, all variable generation
can be sold to the EC, through bilateral contracts or
other arrangements. However, if the transmission network
is constrained, or if the energy consumer can accept only
certain power profiles, then some variable generation must
be curtailed and/or absorbed by operating reserves. How
much variable generation can be absorbed by adjustable
loads? This problem is compounded by the fact that the
wind w(t) is random.
The ideas above suggest introduction of the concept of
Quality-of-Service [QoS] for power. On the generation side,
QoS is intended to capture reliability in supply, while
on the demand side, QoS is meant to capture variability
in acceptable power profiles. Pricing mechanisms can be
introduced to reflect QoS in both generation and demand,
and may prove to be the natural mechanism to treat
variability in competitive markets [46].
Matching variable generation to distributed demand response resources will require techniques drawn from model
predictive control, and stochastic dynamic programming.
phase measurement units [synchro-phasors], control of
asynchronous wind turbines, power electronics for optimal
operation of large PV arrays, reactive power compensation
issues in wind farms, micro-grid control, distributed energy
generation, etc. Even in the confines of our narrow focus,
it is evident that our community of researchers in systems
and control will be major contributors to realizing the
vision of the Smart Grid.
REFERENCES
[1]
H. Optimal Coordination
Today, wind power is commonly treated as a negative load
and the utility (or system operator) must accept all wind
power produced at a fixed price (feed-in tariff). This forces
the utility to use reserves to compensate for the injected
variability in the wind power w(t).
Reserve generation imposes both a capacity (reservation)
and energy (use) cost. There are many different types
of reserves, each with its own start, stop and ramping
characteristics. Reserve capacity is traditionally procured
well in advance (∼ 1 hr) of the delivery time. Greater
forecast uncertainty therefore increases reserve capacity
costs. This can be mitigated through: (a) improved forecasting, (b) reserve capacity purchases over short horizons,
and (c) matching adjustable demand to variable generation [see subsection G above]. These strategies require
coordination: optimal reserve power purchases and load
adjustment strategies are dynamically coupled through
forecast information.
How can we dynamically aggregate variable generation
with various types of reserves so that the portfolio collectively behaves as reliably as dispatchable thermal generation. In this dynamic portfolio problem we must select the
most economical mix based on current generation forecasts
and current cost information of available firming resources.
Implementation of dynamic portfolios will leverage the
Smart Grid infrastructure – sensors and communications
to provide the necessary real-time information.
The problem of optimally computing resource acquisition and dispatch decisions is a challenging constrained
stochastic optimal control problem.
[2]
[3]
[4]
[5]
[6]
[7]
[8]
[9]
[10]
[11]
[12]
3. CONCLUSIONS
The deep integration of renewable energy resources into
the electricity grid is a compelling and challenging endeavor. It offers a rich set of research problems, many of
which will critically require systems and control methods
in their solution. We have collated a partial list of specific problems driven by the variability and uncertainty of
renewable resources.
Our focus in this paper has been on the issue of integration
of variable generation. We have not discussed many other
research opportunities for the systems and control research
community in the broad topic of Smart Grid. These
include cyber-physical security, optimal deployment of
[13]
[14]
[15]
[16]
[17]
J. L. Angarita, J. Usaolab, J. Martinez-Crespob,
“Combined hydro-wind generation bids in a poolbased electricity market,” Electric Power Systems
Research, vol. 79, pp. 10381046, 2009.
G. N. Bathurst, J.Weatherill, and G. Strbac, “Trading
wind generation in short term energy markets,” IEEE
Trans. Power Syst., vol. 17, pp. 782-789, 2002.
A.R. Bergen and V. Vittal, “Power Systems Analysis,” Prentice-Hall, Englewood Cliffs, NJ, 2000.
http://www.transmission.bpa.gov/business/ operations/Wind/default.aspx
E. Bitar et al., “Bringing Wind Energy to Market”,
Submitted to IEEE Transactions on Power Systems,
2011.
E. Bitar et al., “Optimal Contracts for Wind Power
Producers in Electricity Markets,” Proc. of the Conf.
on Decision and Control, pp: 1919-1926, Atlanta,
2010.
S. Borenstein, M. Jaske and A. Rosenfeld, “Dynamic
Pricing, Advanced Metering and Demand Response
in Electricity Markets,” Center for the Study of Energy Markets Working Paper, University of California
at Berkeley, October, 2002.
C. Monteiro, R. Bessa, V. Miranda, A. Botterud, J.
Wang, and G. Conzelmann, “Wind Power Forecasting: State-of-the-Art 2009,” Argonne National Laboratory, ANL/DIS-10-1, November 2009.
California Independent System Operator, “Integration of Renewable Resources, August 2010.”
A. Cavallo, “High capacity factor wind energy systems,” J Sol Energy Eng, vol. 117, no. 5, 1995.
A. Cavallo, “Controllable and affordable utilityscale electricity from intermittent wind resources
and compressed air energy storage (CAES),” Energy,
2007;32(2):1207.
J. DeCesaro and K. Porter, “Wind energy and power
system operations: A review of wind integration studies to date,” The Electricity Journal, vol. 22, pp. 3443, 2009.
P. Denholm, E. Ela, B. Kirby, and M. Milligan, “The
Role of Energy Storage with Renewable Electricity Generation,” Technical Report, NREL/TP-6A247187, January 2010.
http://www.enernoc.com/
EnerNex Corp., Eastern Wind Integration and Transmission Study, National Renewable Energy Laboratory, Report NREL/SR-550-47078, January 2010.
W.A. Gardner, A. Napolitano, and L. Paura, “Cyclostationarity: Half a century of research,” Signal
Processing (Elsevier) (2006) 639–697.
Global Wind 2009 Report, Global Wind Energy
Council, Brussels, Belgium, 2010.
[18] T. Gomez, C. Marnay, A. Siddiqui, L. Liew, and M.
Khavkin, “Ancillary Services Markets in California,”
[19] J. B. Greenblatt, S. Succar, D. C. Denkenberger, R.
H. Williams, and R. H. Socolow, “Baseload wind energy: modeling the competition between gas turbines
and compressed air energy storage for supplemental
generation,” Energy Policy, vol. 35, pp. 1474-1492,
Mar 2007.
[20] G. Gautam, V. Vittal, and T. Harbour, “Impact of
Increased Penetration of DFIG-Based Wind Turbine
Generators on Transient and Small Signal Stability
of Power Systems,” IEEE Trans. on Power Systems,
Vol. 24, No. 3, August 2009, pp. 1426-1434.
[21] W. W. Hogan, “Electricity Wholesale Market Design
in a Low Carbon Future,” in Harnessing Renewable
Energy, Jorge Padilla and Richard Schmalensee, Editors.
[22] http://grouper.ieee.org/groups/scc21/dr shared/
[23] C. Kamath, “Understanding wind ramp events
through analysis of historical data,” IEEE PES
Transmission and Distribution Conference, New Orleans, April 2010.
[24] A. Kusiak and W. Y. Li, “Short-term Prediction of
Wind power with a Clustering Approach,” Renewable
Energy, vol. 35, pp. 2362-2369, 2010.
[25] M. Korpaas, A. T. Holen, and R. Hildrum, “Operation and sizing of energy storage for wind power
plants in a market system,” International Journal of
Electrical Power and Energy Systems, vol. 25, no. 8,
pp. 599-606, Oct. 2003.
[26] H. Lund and G. Salgi, “The role of compressed air
energy storage (CAES) in future sustainable energy
systems,” Energy Conversion and Management 50
(2009) 1172-1179.
[27] J. Matevosyan and L. Sajder, “Minimization of imbalance cost trading wind power on the short-term
power market,” IEEE Trans. Power Syst., vol. 21, pp.
1396-1404, 2006.
[28] D. L. Hawkins, J. Blatchford, and Y. V. Makarov,
“Wind integration issues and solutions in California,”
in Proc. IEEE PES General Meeting, Tampa, FL,
Jun. 24-28, 2007.
[29] Z. Ma, D.S. Callaway and I.A. Hiskens, “Decentralized Charging Control for Large Populations of Plugin Electric Vehicles,” Proceedings of the IEEE Conference on Decision and Control, Atlanta Georgia,
2010.
[30] Y. Makarov, C. Loutan, J. Ma, and P. de Mello,
“Operational impacts of wind generation on California power systems,” IEEE Transactions on Power
Systems 2009; 24: 1039-1050.
[31] J. Marcos, L. Marroyo, E. Lorenzo, D. Alvira and E.
Izco, “Power output fluctuations in large scale PV
plants: one year observations with one second resolution and a derived analytic model,” Prog. Photovolt:
Res. Appl., DOI: 10.1002/pip.1016, (2010)
[32] J.M. Morales, A.J. Conejo, J. Perez-Ruiz, “Shortterm trading for a wind power producer,” IEEE Trans
Power Syst; in press.
[33] NERC Special Report, “Accommodation of High Levels of Variable Generation,” April 2009.
[34] M. Oshiro et al., “Optimal voltage control in distribution systems using PV generators,” International
[35]
[36]
[37]
[38]
[39]
[40]
[41]
[42]
[43]
[44]
[45]
[46]
[47]
[48]
[49]
[50]
Journal of Electrical Power & Energy Systems, vol.
33, no. 3, pp:485 - 492, 2011.
J. M. Pease, “Critical Short-term ForecastingNeeds
for Large and Unscheduled Wind Energy on the
BPA System,” 3rd Workshop on the Best Practice
in the Use of Short-term Forecasting of Wind Power,
October 2009.
P. Pinson, C. Chevallier, and G. N. Kariniotakis,
“Trading wind generation from short-term probabilistic forecasts of wind power,” IEEE Trans. Power
Syst., vol. 22, pp. 1148-1156, 2007.
P. Pinson, H. A. Nielsen,H. Madsen, and K. Kariniotakis, “Skill Forecasting from Ensemble Predictions
of Wind Power,” Applied Energy, vol. 86, pp. 13261334, 2009.
R. Rajagopal et. al., “Risk Limiting Dispatch,” in
preparation.
K. M. Rogers, R. Klump, H. Khurana, A. A. AquinoLugo, and T. J. Overbye, “An Authenticated Control
Framework for Distributed Voltage Support on the
Smart Grid,” IEEE Transactions on Smart Grid, Vol.
1, pp. 40 - 47, June 2010.
S. Sethi, H. Yan, J. Yan, and H. Zhang, “An Analysis
of Staged Purchases in Deregulated Time-Sequential
Electricity Markets,” Journal of Industrial and Management Optimization 2005, 1, 443463.
F. Shahnia, A. Ghosh, G. Ledwich, and F. Zare,
“Voltage unbalance reduction in low voltage distribution networks with rooftop PVs,” Proceedings of
Australian Universities Power Engineering Conference : Power Quality for the 21st Century, pp: 5-8,
Christchurch, New Zealand, December 2010.
F. P. Sioshansi (Ed.), “Competitive Electricity Markets - Design, Implementation, Performance,” Elsevier, 2008.
“Market Operator of the Electricity Market of the
Iberian Peninsula,” OMEL 2008. [Online]. Available:
http://www.omel.es.
K. Spees, L. Lave, “Impacts of Responsive Load
in PJM: Load Shifting and Real Time Pricing,”
Carnegie Mellon Electricity Industry Center Working
Paper CEIC-07-02; Carnegie Mellon University, 2007.
S. Succar and R. H. Williams, “Compressed air energy
storage: Theory, resources, and applications for wind
power,” Energy Systems Analysis Group, Princeton
University, 2008.
C.W. Tan, P.P. Varaiya, “Interruptible electric power
service contracts,” Journal of Economic Dynamics
and Control, Vol. 17, No. 3, pp. 495:517, 1993.
G.C. Thomann, M.J. Barfield, “The time variation of
wind speeds and windfarm output in kansas,” IEEE
Transactions on Energy Conversion 1988.
P.P. Varaiya, F. Wu, J.W. Bialek, “Smart Operation
of Smart Grid: Risk-limiting Dispatch,” submitted to
Proceedings of the IEEE, 2010.
C.W. Tan, and P.P. Varaiya, “Interruptible electric
power service contracts,” Journal of Economic Dynamics and Control 17 (May, 1993): 495-517.
GE Energy, Western Wind and Solar Integration
Study, National Renewable Energy Laboratory, Report NREL/SR-550-47434, May 2010.