Renewable Energy Integration: Technological and Market Design

Challenges Shmuel Oren, UC Berkeley

with: Anthony Papavasiliou, Duncan Callaway,

Johanna Mathieu, UC Berkeley Timothy Mount, Robert Thomas, Max Zhang,

Cornell University Alejandro Dominguez-Garcia, George Gross

University of Illinois, Urbana/Champaign

Future Grid Initiative PSERC IAB Meeting December 5-7, 2012

Uncertainty

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Negative Correlation with Load

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win

d po

wer

out

put

(MW

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24 48 72 96 120 144 168 3000

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(MW

)

hour

wind power

load

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All Rights Reserved to Shmuel Oren

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Conventional Solution

Source: CAISO

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I Need a Brain

Source: GE 6

• Accounting explicitly for uncertainty in operation and planning

• Stochastic unit commitment (with endogenous reserves determination) to support renewable penetration and demand response

• Probabilistic planning and simulation models (accounting for renewables, storage and demand response

• Mobilizing demand response (DR) and a paradigm shift to “load following available supply” provides an economically viable and sustainable path to a renewable low carbon future.

• Price responsive load • Energy efficiency • Deferrable loads:

• EV/PHEV • HVAC • Water heaters • Electric space heaters • Refrigeration • Agricultural pumping

7

TCLs

Making The Grid Smarter

Task 1: Stochastic unit commitment for high wind

penetration

Shmuel Oren Athony Papavasiliou

UC Berkeley 8

Motivation

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Model Structure

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Scenario Selection

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Parallelization

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Computational Efficiency Study

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Unit Commitment Summer Weekday

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Results

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Task 2: Mitigating renewables intermittency through

nondisruptive load control

Duncan Callaway Johanna Mathieu

UC Berkeley 16

Research Goals

• Development of new TCL population models. • Development of effective control strategies that

preserve end-use function while delivering systemic benefits.

• Analysis of the ability of TCLs to provide power systems services as a function of the information available for system identification, state estimation, and control.

• Analysis of the TCL resource potential, costs, and revenue potential.

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Modeling Aggregated TCLs: ‘State bin transition model’

A Markov Transition Matrix describes the movement of TCLs around the dead-band.

ON

OFF

normalized temperature

1 2 3 4

Nbin-1 Nbin-2 …

…

Nbin-3 Nbin Nbin 2 +4

Nbin 2 +3

Nbin 2 +2

Nbin 2

+1

Nbin 2

-3 Nbin 2

-2 Nbin 2

-1 Nbin 2

stat

e

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Controlling TCLs to track a 5-minute market signal

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How BIG is the Resource Potential?

Estimates for most of California (5 largest utilities) based on RECs and CEC data.

2012 Resource Duration Curve

2020 Resource Duration Curve, assuming

increased efficiency and 30% of water/space heaters converted to

electric

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Potential revenues for regulation and load following

(per TCL per year)

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Task 3: Planning and market design for using dispatchable loads to meet renewable portfolio standards and

emissions reduction targets

Max Zhang

Tim Mount

Bob Thomas

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“Intelligent” Charging of PEV I (20% penetration in NPCC region)

70/30 Level I/II 50/50 Level I/II 30/70 Level I/II

80% of PEV load is assigned to valley hours to take advantage of low steady-state cost The remaining 20% is assigned to shoulder and peak hours to reduce ramping cost.

Charging Flexibility Constraint (CFC) restricts PEV charging during morning commuting hours

Valentine, Temple and Zhang (2011) J. Power Sources 23

“Intelligent” Charging of PEV II (Wholesale Energy Costs are Reduced)

Intelligent Charging

Valley-fill Charging

• Intelligent charging results in significant systems cost reductions compared to unregulated and valley-fill charging

• Higher system cost reductions with higher PEV penetrations

Valentine, Temple and Zhang (2011) J. Power Sources 24

“Intelligent” Charging of PEV III (Less Wind Generation is Curtailed)

Higher PEV penetrations decrease the average percentage of wind curtailed for cases with both the high and low wind uncertainty.

A better wind forecast (low uncertainty) not only curtails less wind by itself (0% PEV penetration) but improves the PEV dispatch pattern to be more closely

aligned with real system conditions so that much less wind is curtailed with high PEV penetrations.

Valentine et al. Energy Policy, under review 25

Price Responsive Ice Storage Systems I (Total System Costs for NYISO are Reduced)

We are evaluating the benefits of aggregating Ice Storage Systems in large commercial and industrial buildings in New York State to reduce the system costs of the NYISO system. Heuristic methods were used to reduce system costs for a two-settlement wholesale market that accounts for both the steady-state and ramping costs of generating units. The optimal management of storage significantly reduces both the peak load and total

system costs, and flattens out the daily load profile.

Palacio et al. In preparation 26

Price Responsive Ice Storage Systems II (Dispatch Patterns for a hot summer day in NPCC I)

Case 1: Base Case 2: Base + 32GW Wind

Results from simulations using the SuperOPF Case 1:Ramping for the daily load profile is provided by oil and natural gas generation Case 2:Wind displaces mainly oil and natural gas capacity and the remaining capacity

also provides additional ramping services to mitigate wind variability

Mount et al. HICSS 2013

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Price Responsive Ice Storage Systems III (Dispatch Patterns for a hot summer day in NPCC II)

Case 3: Base + 32GW Wind + 136GWh Deferrable Demand

Case 4: Base + 32GW Wind + 136GWh Collocated Storage

Case 3 v Case 2: More wind is dispatched, the daily pattern of Conventional Generation (CG) is flatter and the peak power delivered to customers is lower Case 4 v Case 2: Even more wind is dispatched and the daily pattern of CG is flatter than Case 3, but the peak power delivered to customers is still the same as Case 2

Mount et al. HICSS 2013

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Price Responsive Ice Storage Systems IV (Total System Costs for a hot summer day in NPCC)

Case 3 has DD, Deferrable Demand (Thermal Storage), at six load centers Case 4 has ESS, Energy Storage Systems, collocated at 16 wind sites Annual Capital Cost for a Peaking Unit is $88k/MW/year, allocated to 100 peak hours. Assuming there are 2 peak hours on this hot day, the capital cost is $1,760/MW

Adding wind capacity in Case 2 reduces the Total System Costs by 6.6% Adding deferrable demand in Case 3 reduces the Total System Cost by 16.7% The smaller decrease of 4.9% in Case 4 results from charging for storage capacity (using the same capacity price as a peaking unit) because customers will eventually have to pay for all system costs incurred by suppliers

Mount et al. HICSS 2013

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Task 4: Probabilistic Simulation Methodology for Evaluating the

Impact of Renewables Intermittency on Operation and

Planning

Alejandro Dominguez-Garcia, George Gross

University of Illinois, Urbana/Champaign

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THRUST OF THE SIMULATION APPROACH

• We develop a comprehensive, computationally efficient Monte Carlo simulation approach to emulate the behavior of the power system with integrated storage and renewable energy resources

• We model the system load and the resources by discrete-time stochastic processes

• We develop the storage scheduler to exploit arbitrage opportunities in the storage unit operations

• We emulate the transmission-constrained hourly day-ahead

markets (DAMs) to determine the power system operations in a competitive environment

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PROPOSED SIMULATION APPROACH: CONCEPTUAL STRUCTURE

“dri

ver”

stoc

hast

ic p

roce

sses

renewable power outputs

conventional generator available

capacities

loads

storage

schedule

market

clearing

procedure

(DCOPF)

congestion rents

CO 2

emissions

storage

operations

. .

.

“out

com

e” st

ocha

stic

pro

cess

es

LMPs

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KEY ELEMENTS OF THE APPROACH

• We construct appropriate c.d.f. approximations to evaluate the expected system variable effects

• Metrics we evaluate include:

• nodal electricity prices (LMPs)

• wholesale purchase payments

• generation by resource and revenues

• congestion rents

• CO 2 emissions

• LOLP and EUE system reliability indices 33

CASE STUDY: IMPACTS OF DEEPENING WIND PENETRATION

• We perform a wind penetration sensitivity analysis and quantify the impacts of wind integration on power system economics, reliability and CO 2 emissions over a one-year period

• We use a modified IEEE 118-bus system with 4 wind farms in Midwest integrated with total nameplate installed capacity in multiples of 680 MW

annual peak load: 8,090.3 MW

conventional generation resource mix: 9,714 MW

unit commitment uses a 15 % reserves margin provided by conventional units

wind power is assumed to be offered at 0 $/MWh 34

ANNUAL INDICES VS. TOTAL WIND NAMEPLATE CAPACITY

thou

sand

$

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.1 %

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%

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%

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%

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.6 %

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%

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%

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%

0.70

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0.95

1.00

1.05

thou

sand

met

ric

tons

power system variable effects

relia

bilit

y

CO2 emissions

total wholesale purchase payments

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x 10-4

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14 LOLP

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• The deeper penetration of wind resources consis-tently reduces the total wholesale purchase payments and the CO2 emissions, while improving system reliability

• However, the diminishing returns in the benefits of integrating deeper penetrations of wind resources is a key limitation

• Future research to include the incorporation of storage resources into the simulation approach and the assessment of storage resource impacts, in combination with the wind resources, on the power system variable effects

Results

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