competitive energy generation scheduling in microgrids
DESCRIPTION
Competitive Energy Generation Scheduling in Microgrids. Lian Lu*, Jinlong Tu *, Minghua Chen. Chi-Kin Chau. Xiaojun Lin. TexPoint fonts used in EMF. Read the TexPoint manual before you delete this box.: A A A A A A A A A A A. Power Grid Landscape. generation. transmission. - PowerPoint PPT PresentationTRANSCRIPT
Competitive Energy Generation Scheduling in Microgrids
Lian Lu*, Jinlong Tu*,Minghua Chen
Chi-Kin Chau Xiaojun Lin
2
Power Grid Landscape
Transcos
Transmission: enerator to
substation
generation
transmission
distribution
Transmission: from generator to substation, long distance, high voltageDistribution: from substation to customer, shorter distance, lower voltage
generation
substation
3
Power Grid Abstraction and Challenges
Households
Electricity Grid
Electricity
Power Generator
Wind Energy
Electricity
Electr
icity
Grid ReliabilityEconomic loss due to outage: 150 billion USD / year
Renewable IntegrationIntermittent generation makes it difficult to balance supply and demand
Generation Efficiency62% energy loses in conversion
Microgrid opens up new design space here!
Source: U.S. Energy Information Administration, Annual Energy Review 2011, 2012. DoE Intro to SG, 2008
4
Microgrid: A New Paradigm
□ Microgrid is a distributed power system that satisfies heat and electricity demand with two sources of supply:– External electricity and heat supply– Local generation (renewable, combined heat and power(CHP))
Households
Wind Energy
Combined Heat and PowerLocal Heating
Electricity Grid
Natural Gas
Microgrid
Heating
Elec-tricity
Elec-tricity
Heating
Electricity
Gas
Households
Electricity Grid
Electricity
5
Potential Benefits of Microgrids
Today’s Challenge The Microgrid Solution
Reliability Local generation provides heterogeneous power quality and reliability
Renewable Integration
Absorb the renewable uncertainty locally, reducing its negative impact on grid stability
Generation Efficiency
CHP-generation improves generation efficiency by 2-3x
• Worldwide deployment of pilot microgrids in the US, Japan, Europe, and more.
6
Worldwide Deployment of Pilot Microgrids
□ Lawrence Berkeley Lab project of a San Francisco hotel – Cost reduced by 12.4% & carbon footprint by 13.7%
NTT’s microgrid, Japan
Bornholm Microgrid, Denmark
Scheneider microgrid, Berlin, Germany
UCSD microgrid, San Diego, US
7
A Key Problem in Microgrid Operation
□ Real-time balancing supply and demand with minimum cost– Electricity cannot be stored cheaply, yet
Picture source: Lawrence Berkeley National Laboratory, 2007.
Energy Generation
Decision Module
Electricity demand
Heat demand
Grid electricity and natural gas tariffs
Wind/solar generation and CHP generation
8
Prior Approaches: Predict-and-Optimize
□ Assumption: Demand is predictable□ Applications: Unit commitment and economic
dispatching
Time
Electricity Demand
Electricity Supply
Supply Capacity
9
Microgrid Demands Are Difficult to Predict: Prior Approaches Fail
□ Prior assumption:– Demand is predictable
□ Local (net) demands are difficult to predict– Net electricity demand
inherits uncertainty from wind and solar
– Electricity and heat demands express different uncertainty pattern
10
Conventional Wisdom and Our Perspective
□ Model-centric perspective:
Model the future, then optimize accordingly.
□ Optimal but (usually) rely on accurate modeling/prediction
□ Solution-centric perspective:
□ Do not rely on modeling/prediction
□ Characterize the value of modeling/prediction
□ Can explore prediction to further improve performance
11
Our Contributions
Prior Art:Offline
Our Competitive Approach:Online – CHASE
Predict and optimize
No performance guarantee in the
presence of uncertainty
When future input is completely unknown: Competitive Ratio (CR)of CHASEfast-resp. < 3
That is, for arbitrary input,
CR improves with look-ahead
CHASE – Competitive Heuristic Algorithms for Scheduling Energy-generation
12
Problem Formulation
□ Challenges:– Generator characteristics makes decisions across slots coupled: on/off
and output-level decisions depend on future input– Future demand and renewable generation are highly volatile and
unpredictable
Microgrid operational cost in [0,T]
generator operating constraints
supply-demand constraints
capacity constraints
13
“Make It Simple, But Not Simpler.”
Problem with multiple generators
Problem with multiple fast-responding generators
Problem with single fast-responding generator
Solve the simplified problem
Problem with single fast-responding generator
Find an optimal offline solution
CHASE the offline optimal in an online fashion
Characterize the Competitiveness
14
Problem with Single Fast-Responding Generator
CHP generator
External generation:on-demand but
expensive
Local CHP generation: cheap but has start-
up cost
Electricity Grid
Separate Heating
Net electricity demand
Heat demand
15
Dense Demand: Local GenerationSparse Demand: External Generation
CHP generator
External generation:on-demand but
expensive
Local CHP generation: cheap but has start-
up cost
Electricity Grid
Separate Heating
Net electricity demand
Heat demand
Net electricity demand
Heat demand
Sparse demandDense demand
16
OFAyOffline optimal solution
CHASEsyCHASE ‘s solution without look-head
CHASElk( )sy
CHASE’s solution with look-head
Solutions and IntuitionsArrivals of demand
(t) type-start type-1 type-2 type-1 type-end
-
0
𝑇 1𝑐 𝑇 2
𝑐 𝑇 3𝑐 𝑇 4
𝑐𝑇 5𝑐𝑇 0
𝑐~𝑇3
𝑐~𝑇2
𝑐~𝑇1
𝑐
𝜔 𝜔 𝜔𝜔
Net electricity demand
Heat demand
Function captures “sparseness/denseness” of
the demand
17
Extending CHASE to Multiple Generators
□ Theorem: Layered partition induces zero optimality loss.
Gen. #1
Gen. #2
Electricity Grid
Separate Heating
External generation
18
CHASE Is the Best Deterministic Algorithm
□ Theorem: For the problem with multiple fast-responding generators, CHASE achieves the best competitive ratio (CR) of all deterministic online algorithms:
CR(CHASE) ,
where captures maximum price discrepancy between local generation and external generation.
□ Price of uncertainty is at most 3
19
Worst Demand vs. Real-world Demand
𝑇 0𝑐 𝑇 1
𝑐 𝑇 2𝑐
OFAy
SCHASEy
0
(t)
OFAy
SCHASEy
a(t)
h(t)
Worst Case Demand Real-world Demand
20
Look-Ahead Improves Performance
□ Theorem: For the problem with multiple fast-responding generators and look-ahead window size ,
CR(CHASE) ,
where captures the benefit of look-ahead.
𝑡𝑡+𝜔
look-ahead window
21
Numerical Evaluation
□ Electricity/heat demand traces from a San Francisco college
□ Power output from a nearby wind station
□ Compare OFFLINE, CHASE, and an alternative RHC
(a) Summer week
(b) Winter week
22
Benefit of Combined Heat and Power (CHP)
□ CHP leads to additional 10% cost saving
□ CHASE performs close to the offline optimal
□ RHC performs badly and may give zero cost saving
(a) With CHP
(b) Without CHP
23
Benefit of Looking Ahead
□ CHASE saves 20% cost even without look-ahead□ CHASE is robust to prediction error
24
Conclusions
□ We propose CHASE: a paradigm-shift solution for energy generation scheduling in microgrids– Does not rely on demand prediction– Achieves the best competitive ratio without look-ahead– Performance improves with look-ahead– Leads to 20% cost reduction in case studies
Households
Wind Energy
Combined Heat and PowerLocal Heating
Electricity Grid
Natural Gas
Microgrid
Heating
Elec-tricity
Elec-tricity
Heating
Electricity
Gas
Minghua Chen ([email protected])http://www.ie.cuhk.edu.hk/~mhchen
Thank you!
CHASE – Competitive Heuristic Algorithms for Scheduling Energy-generation
CHASE – Track the offline optimal in an online fashion