grid integration of renewable energy - trends, challenges ...demand side management i goal: exploit...
TRANSCRIPT
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Grid Integration of Renewable Energy - Trends,Challenges, and Opportunities
Pramod P. Khargonekar
Department of Electrical Engineering and Computer ScienceUniversity of California, Irvine
4th JST-NSF-DFG-RCN Workshop12 June 2016
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Outline
Drivers for Renewable Electric Energy
Toward 100% Renewable Future
Key Collaborative Research DirectionsDistributed Control and Price of AnarchyStochastic Optimization for Smart Homes and Neighborhoods
Conclusions
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Global CO2 EmissionsS
ource: CD
IAC
; Le Quéré et al 2016; G
lobal Carbon B
udget 2016
Fossil Fuel and Industry
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Cumulative Net CO2 IncreaseS
ource: CD
IAC
; NO
AA
-ES
RL; H
oughton et al 2012; Giglio et al 2013; Joos et al 2013; K
hatiwala et al 2013;
Le Quéré et al 2016; G
lobal Carbon B
udget 2016
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CO2 Emissions and Temperature ChangeS
ource: Fuss et al 2014; CD
IAC
; IIAS
A A
R5 S
cenario Database; G
lobal Carbon B
udget 2016
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Remaining CO2 Quota for 66% Chance to Keep Below 20 C
Source: Peters et al 2015; Global Carbon Budget 2016
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Global Energy Consumption
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Electric Energy Sector
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Major Energy Transitions are Slow
I Coal: 5% to 50% in 60 years starting in 1840
I Oil: 5% to 40% in 60 years starting in 1915
I Natural gas: 5% to 25% in 60 years starting in 1930
I Modern renewables ≈ 5%
1.2 billion people lack access to electricity2.8 billion people rely on biomass for cooking and heating
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Toward 100% Renewable Future
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Investment in Renewable Power
Source: Bloomberg New Energy Finance
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PV and Wind Get Cheaper by the Year
Average US Power Purchase Agreements Prices
Souce; B
loomberg N
ew E
nergy Finance
Source: Tracking the Sun, LBNL
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Solar PV Growth
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Solar PV Deployment
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Wind Growth
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Net Result: Record Low Prices
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Storage
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Li Ion Battery Prices
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Grid Connected Battery Storage
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Examples of Deep Penetration of Renewable Generation
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California on May 13, 2017
Source: CAISO
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California on May 13, 2017
Source: CAISO
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California on May 13, 2017
Source: CAISO
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California on May 13, 2017
Source: CAISO
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Negative Prices in California
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Negative Prices in California
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Projected Solar Curtailment
Source: Emerging Issues and Challenges in Integrating High Levels of Solar into the Electrical Generation and Transmission System, NREL
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Impact of Curtailment on Cost of PV
Source: Emerging Issues and Challenges in Integrating High Levels of Solar into the Electrical Generation and Transmission System, NREL
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Capacity Credit Declines with Increasing Penetration
Source: Emerging Issues and Challenges in Integrating High Levels of Solar into the Electrical Generation and Transmission System, NREL
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Options for Flexibility
Source: Chochran et al, 2014
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Key Enablers to Deep Renewable Integration
I New transmission infrastructure
I Larger geographic balancing areas
I Greater flexibility in all aspects of power system operations
I Cost-effective energy storage
I Grid management and control
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Key Collaborative Research Directions
I Renewable producers in electricity markets
I Strip Packing for Peal Load Minimization
I Causation based Cost Allocation Principles and Algorithms
I Cybersecurity and smart grid
I Distributed control for integration of renewable sources
I Stochastic optimization for residential energy management
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Renewable Generators in Electricity Markets
I Scenario: One or more wind or solar producers operating in awholesale electricity market
I What is the optimal bid by a renewable generator in atwo-settlement market?
I Is there a benefit from several renewable generators combining theirproduction?
I What are the strategies to keep the coalition stable?
I What is the optimal operating policy for a renewable generator withlocal energy storage?
Collaboration with Baeynes, Bitar, Poolla, and Varaiya
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Stochastic Optimization for Residential EnergyManagement
I Scenario: one more more homes in a residential setting with localrenewable generation, storage, and elastic and inelastic loads
I What are stable policies for servicing the loads while optimizing thetotal cost of operation?
I Approach: put the loads into a queue and use Lyapunov basedstochastic optimization techniques that guarantee queue stability,storage limits, upper bounds on delays in serving the elastic loads,and bound on deviation from optimal performance
I Similar approach for data center optimization with local renewablegeneration and storage, virtual power plants, etc.
Collaboration with Guo, Fang, Pan, Gong and Geng
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Strip Packing for Peak Load Minimization
I Scenario: constant interruptible and non-interruptible power flexibleloads with start and end times
I How can these loads be scheduled so that the resulting peak load isas small as possible?
I NP hard problems
I Approach: strip packing algorithms from computer science literature
I Results: guaranteed bounds on deviation from optimality
Collaboration with Ranjan and Sahni
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Causation based Cost Allocation Principles and Algorithms
I Variability of renewable generation imposes costs on the system
I How should these costs be allocated as tarrifs?
I Principle: allocate costs to those who “cause” them
I Approach: tools from cooperative game theory
I Results: algorithms for cost allocation
Collaboration with Chakraborty and Baeynes
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Cybersecurity for Smart Grid
I Scenario: Adversary attacks data in energy management system
I How can false data injection attacks be detected?
I How can sensors help mitigate such attacks?
I Results: algorithms for detection and mitigation
Collaborations with Gianni, Poolla, Bitar, Garcia, McQueen, Bretas, Baeynes, Carvalho
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System Scenario
Demand Demand
Central Control
Authority
Supply Schedule
Thermal
Power plant
Wind Plant
Demand
Schedule
Price
Demand
Schedule
PriceDemand
SchedulePrice
Flexible Consumer 3Flexible Consumer 2Flexible Consumer 1
Figure: System Model
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Demand Side Management
I Goal: exploit the inherent flexibility of electric loads
I Two approaches: incentive based and price based
I Centralized control of loads — ex: direct load control
I Distributed controlI The central authority sends the control signal, e.g., price, to the
consumers.I The consumers optimize their consumption schedules accordingly.
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Price-Anticipating Consumers
I Game theoretic modeling to capture the price anticipating behaviorunder distributed control
I Key Question: What is the loss of efficiency in terms of socialobjective by distributed control as compared with centralizedcontrol?
I Price of Anarchy (PoA) : Worst-case ratio of the objective functionvalue of an equilibrium solution of the game to that of a centralizedoptimal solution.
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Nash Equilibrium
I The Nash equilibrium for the distributed control problem with priceanticipators is the set of expenditures {kGi : i ∈ N} such that
Ui (qi (kGi , k
G−i ))− 1>kGi ≥ Ui (qi (ki , k
G−i ))− 1>ki ,
ki ∈ Spai (kG−1), i ∈ N . (1)
Theorem (Existence of Nash equilibrium)The non-cooperative game has a Nash equilibrium if the search space isnonempty.
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Price of Anarchy is Less Than 25%
TheoremLet {qCi : i ∈ N} be a solution of the centralized problem (??) and{qGi : i ∈ N} a Nash equilibrium for the distributed problem with priceanticipating consumers. Let PoA be defined by:
PoA :=
∑i∈N Ui (qGi )∑i∈N Ui (qCi )
.
then PoA ≥ 0.75.
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Special Cases
CorollaryIf all the consumers have same utility function, i.e., Ui = U, there is noefficiency loss at Nash equilibrium solution, i.e. PoA is 1.
CorollarySuppose qi = 0 for all i ∈ N belongs to the set of load operationalconstraints, then the PoA approaches 1 as the number N of flexibleconsumers goes to infinity.
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Smart Neighborhood1
HANSmart Meter
Home EMS
ElectricUtility Grid
FAN
Power Flow
Information Flow
HANSmart Meter
Home EMS
HAN
Smart Meter
Home EMS
HAN
Smart Meter
Home EMS
1Y. Guo, M. Pan, Y. Fang, and P. P. Khargonekar, “Decentralized Coordination ofEnergy Utilization for Residential Households in the Smart Grid,” IEEE Transactionson Smart Grid, Vol. 4, No. 3, pp. 1341-1350, September 2013.
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System Model
I Load serving entity (LSE)I Supply cost function Φ: quadratic in aggregate demand in the
neighborhood
I Household i ∈ [1,N]I Energy storage dynamics: Ei (t + 1) = Ei (t) + η+
i r+i (t) − r−i (t)/η−
I Inelastic loads: di,1(t)I Elastic loads: Qi (t + 1) = [Qi (t) − yi (t) + di,2(t)]+
I Local renewable generation: si (t)I Net grid energy demand: gi (t)I Virtual queue to ensure worst-case delay for elastic loads:
Zi (t + 1) = [Zi (t) − yi (t) + εi1Qi (t)>0]+
I Goal: LSE coordinates the energy usage of households to minimizethe total cost of supplying electricity to the neighborhood
lim supT→∞
1
T
T−1∑t=0
E{
Φ( N∑
i=1
gi (t))
︸ ︷︷ ︸power supply cost
+N∑i=1
βi (r+i (t) + r−i (t))︸ ︷︷ ︸
storage operation cost
}
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Analytical Performance Results
Theorem
I Storage energy level bound: Emini ≤ Ei (t) ≤ Emax
i ,∀t
I Worst-case elastic load delay guarantee: Delayi ≤⌈
2VΦ′+dmax2,i +εi
εi
⌉I If random factors are i.i.d. over slots, and if εi ≤ E{di,2(t)}, then
C∗ ≤ Cost of Our Approach ≤ C∗ + B/V ,
where C∗ is the optimal average cost.
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Numerical Results
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Future Opportunities
I Joint control of storage, renewables, demand and grid
I Wide area stability and control under deep renewable penetrationscenarios
I Information and control architectures for future grid
I Negative carbon technologies
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Conclusions
I Grid integration of renewable energy will be an increasinglyimportant and difficult challenge
I Many opportunities for the systems and control field
I Energy systems present a unique mix of science, engineering,economics and social policy
I Decarbonization of the energy system remains a true grandchallenge for humanity