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Decision-making under uncertainty methods tosupport the planning of biomass supplyCITIES PhD/PostDoc Meeting, 15.03.2017
Ignacio Blanco ([email protected])Daniela Guericke ([email protected])Henrik Madsen ([email protected])
Agenda
1. Demo project proposal
2. Proposal for solution approach
3. Current status
2 Department of Applied Mathematics and Computer Science Decision support for biomass supply 14.3.2017
Demo project proposal
This demo project is carried out in the framework of the CITIESproject.
CITIES Work Package 7: Decision-making and Support Methods
PhD project: Decision-making for the management and planning ofintegrated energy systems• Decision-making models for the optimal market participation of energy companies in
smart cities.
• Cost/benefit analyses in smart cities (business cases and strategic investments).
3 Department of Applied Mathematics and Computer Science Decision support for biomass supply 14.3.2017
Demo project proposal
Decision support for the planning of biomass supply, especiallythe selection of biomass contracts
Heat demand is uncertain at timeof decision.
Decision-making underuncertainty methods
4 Department of Applied Mathematics and Computer Science Decision support for biomass supply 14.3.2017
Decision-making under uncertainty
Using mathematical optimization models with two stages
Here and now decisions x Wait and see decisions yω2
Wait and see decisions yω1
Wait and see decisions yω3
5 Department of Applied Mathematics and Computer Science Decision support for biomass supply 14.3.2017
Decision-making under uncertainty
• Includes uncertainty explicitly in the optimization models.• Allows to control the risk level of decisions.• Applied very successfully in energy and finance planning problems.
minimizex,yω
cTx+ π1dTω1yω1 + π2d
Tω2yω2 + π3d
Tω3yω3
s.t Ax ≤ b
Ex+ Fω1yω1 ≤ gω1
Ex+ Fω2yω2 ≤ gω2
Ex+ Fω3yω3 ≤ gω3
x, yω ∈ R
6 Department of Applied Mathematics and Computer Science Decision support for biomass supply 14.3.2017
Proposal for solution approach
Using decision-making under uncertainty methods to support theplanning of biomass supply
General assumptions:• Total planning horizon is up to 1 year.
• Portfolio of different suppliers.• Maximum and minimum amount of biomass to be delivered.• Frequency of delivery.• Flexible and fixed contracts.
7 Department of Applied Mathematics and Computer Science Decision support for biomass supply 14.3.2017
Flexibility in Contracts
We define the amount to be supplied. In addition, we pay for theopportunity to provide flexibility in the contract.
Each supplier must determine the willingness to provide reservescapacity and must be ready to do it for all the deliveries.
8 Department of Applied Mathematics and Computer Science Decision support for biomass supply 14.3.2017
Proposal for solution approach
Three-phase approach due to the complexity of the planning task
1. Generation of heat demand scenarios
2. Selection of biomass contracts
3. Study feasibility of the contracts
Scenarios
Contracts
9 Department of Applied Mathematics and Computer Science Decision support for biomass supply 14.3.2017
Generating Heat Demand Scenarios
0 2000 4000 6000 8000
02
46
810
Time
Hea
t Dem
and
[MW
h]
0 2000 4000 6000 80000
24
68
10
Time
Hea
t Dem
and
[MW
h]
10 Department of Applied Mathematics and Computer Science Decision support for biomass supply 14.3.2017
2. Selection of biomass contracts
Solve stochastic model to select biomass contracts
Time scale: weekly for one year
CONTRACTING MODEL
Fuel consumption scenariosPortfolio of suppliers
Storage characteristicsFuel contracts (supplier and amount)
Preliminary delivery schedule
Characteristics:• fuel demand satisfaction• contract restrictions (maximum
amount, delivery periods)• biomass storage restrictions
Decisions:• amount contracted per supplier• delivery weeks and amount
Objective:• fuel supply costs
11 Department of Applied Mathematics and Computer Science Decision support for biomass supply 14.3.2017
3. Weekly operation of CHP plant
To study the feasibility of the contracts, we solve stochastic model todetermine the optimal weekly operation of the CHP plant taking thecontracts into account
OPERATIONAL MODEL
Electricity price scenariosHeat demand scenarios
Technical characteristicsFuel contracts
Result of previous week
Unit commitmentHeat and power production
Probability of running out of fuel
Characteristics:• technical characteristics of the
units.• heat storage• heat demand satisfaction• biomass storage• contracted delivery amounts
Decisions:• unit commitment• power and heat production• revised weekly delivery schedule
Objective:• operational costs
12 Department of Applied Mathematics and Computer Science Decision support for biomass supply 14.3.2017
Current status
1. Generation of heat demand scenarios
2. Selection of biomass contracts
3. Weekly operation of CHP plant
Scenarios
Contracts
Time series models
First executable model with input to phase 3
Work-in-progress
Phase Status
13 Department of Applied Mathematics and Computer Science Decision support for biomass supply 14.3.2017
Thank you for your attention
Ignacio Blanco ([email protected])Daniela Guericke ([email protected])Henrik Madsen ([email protected])
