Decision-making for integrated energy systems 10th DS&OR Forum, - - PowerPoint PPT Presentation

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Decision-making for integrated energy systems 10th DS&OR Forum, - - PowerPoint PPT Presentation

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Decision-making for integrated energy systems 10th DS&OR Forum, Paderborn, 01/07/2017 Daniela Guericke ( dngk@dtu.dk ) Ignacio Blanco Henrik Madsen Technical University of Denmark Department of Applied Mathematics and Computer Science The

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Decision-making for integrated energy systems

10th DS&OR Forum, Paderborn, 01/07/2017 Daniela Guericke (dngk@dtu.dk) Ignacio Blanco Henrik Madsen Technical University of Denmark Department of Applied Mathematics and Computer Science

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The CITIES Project

funded by Det Strategiske Forskningsr˚ ad through the CITIES research center (no.1035-00027B)

CITIES = Center for IT-Intelligent Energy Systems in Cities and many partners from Danish Industry as well as international companies and research institutions www.smart-cities-centre.org

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The CITIES Project

The central hypothesis of CITIES is that by intelligently integrating currently distinct energy flows (heat, power, gas and biomass) in urban environments, we can enable large shares of renewables, and consequently obtain substantial reductions in CO2 emissions. Intelligent integration will enable lossless virtual storage.

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The CITIES Project

Appliance Building District

City

Country Continent

Geographical Scale Complexity

Data

Communication Optimisation

Models Power

Integration based on data and IT solutions leading to models and methods for planning and operation of future flexible energy systems

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The CITIES Project

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The CITIES Project

  Project’s Methodology and Results

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CITIES WP7 - Decision-making and Support Methods

Objectives:

  • 1. Development of decision-making models for the optimal market participation of

energy companies

  • 2. Using the decision-making models to perform cost/benefit analyses for smart cities

Methods:

  • Decision-making under uncertainty (stochastic programming, robust optimization)
  • Large-scale optimization (decomposition, heuristics)

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CITIES WP7 - Decision-making and Support Methods

Application areas:

  • Planning problems of energy companies
  • Evaluation of energy systems

with special focus on:

  • Energy systems integration (mostly power and heat)
  • Flexibility and controllability of those sources
  • Portfolios of energy sources
  • Different sources of uncertainty

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Work in progress

Decision support methods for

  • Biomass supply planning
  • Optimal heat production in district heating systems

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Work in progress

Decision support methods for

  • Biomass supply planning
  • Optimal heat production in district heating systems

Common background

  • Combined Heat and Power (CHP) plants (Heizkraftwerk)
  • District heating (Fernw¨

arme)

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Combined heat and power plants

  • produce electricity and warm water at the same time
  • are connected the electricity grid and the district heating network
  • in our case fueled by biomass

Biomass Boiler

~

Power Generation Extraction Turbine

Pmax Pmin Qmax

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Biomass supply planning

For a large scale producer the biomass is delivered based on supplier contracts which often have a runtime of one year. Our goal is to determine the optimal portfolio of biomass contracts minimizing the costs and taking uncertainty into account.

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Biomass supply planning

Biomass Boiler Biomass Storage

~

Power Generation Heat Exchanger Extraction Turbine Pump Gas Boiler Heat Tank Storage Cold Water from DH Hot Water to DH Natural Gas from Pipeline

CHP plant

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Biomass supply planning

Biomass Storage

Biomass contracts

  • The biomass can be delivered by a number of suppliers
  • Each supplier offers one or more contracts defining the

minimum and maximum amount of biomass per delivery, the total number of deliveries and frequency

Biomass storage

  • The biomass storage has a capacity and the maximum in-

and outflow is restricted per period

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Biomass supply planning

Extraction-condensing CHP unit

Biomass Boiler

~

Power Generation Extraction Turbine

Pmax Pmin Qmax

  • Technical characteristics and limitations (e.g. min. up and down time, ramping

constraints, capacity)

  • Commitment status

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Biomass supply planning

Gas Boiler Natural Gas from Pipeline

Heat Tank Storage

Gas Boiler

  • Handles heat demand peaks
  • Gas is bought instantaneously from the gas grid at

the natural gas spot price

  • Technical characteristics and limitations of the gas

boiler (e.g. capacity)

  • Cheaper heat production than the CHP

Heat storage

  • Stores heat that can be supplied to the DH

network

  • Provides some flexibility to the system
  • Is limited in capacity and flow

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Biomass supply management

Objective Cost minimization

  • biomass supply
  • operation

... and always satisfying the heat demand Uncertainty

  • Heat demand
  • Electricity prices
  • Natural gas prices

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Biomass supply mass

Heat demand

5000 10000 15000 20000 25000 5 10 15 20 25 30 Time [hours] Heat Demand [MWht]

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Biomass supply planning

Stochastic programming with scenarios for heat demand, electricity prices and gas prices Hybrid model combining stochastic programming and robust

  • ptimization
  • Heat demand scenarios
  • Confidence intervals for gas and electricity prices, due to variability.

50 100 150 20 40 60 80 100 Power prices for 1 week in June Time [hours] Power Price [Euro−MWhe] Estimate Price 95% Confidence Interval Forecasts from ARIMA(2,0,1)(1,0,1)[7] 500 1000 1500 2000 2500 10 15 20 25 30 35 40 45 Price [Euro−MWh] Time [hours] 5000 10000 15000 20000 25000 5 10 15 20 25 30 Time [hours] Heat Demand [MWht]

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Biomass supply planning

Decision stages

Selection of biomass contracts and delivery weeks Operation of CHP plant and gas boiler in scenario 2 Operation of CHP plant and gas boiler in scenario 1 Operation of CHP plant and gas boiler in scenario Ω

. . .

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Biomass supply planning

Integrated model:

  • Time scale: Hourly for one year
  • Long computation times due to complexity
  • Apply decomposition techniques to reduce computation time

Two-phase approach:

  • Divide in yearly planning and weekly planning
  • Forecasts can be updated every week
  • Loss of optimization potential, but closer to planning in practice

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Biomass supply planning

Two-phase approach

Long-term planning Selection of biomass contracts based on heat demand scenarios

  • Time scale: 1 year with weekly

resolution

  • Without most of the technical

requirements

Operational planning Weekly production scheduling

  • Time scale: 1 week with hourly

resolution

  • Higher level of detail
  • Uncertainty of heat demand,

electricity price and gas price

Resembles the planning process in practice

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Optimal heat production in district heating systems

Demo project with EMD International A/S and ENFOR A/S

CHP CHP TS TS SC SC FB FB EB EB HP HP

Heating Grid Power Grid

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Optimal heat production in district heating systems

1-stage decisions: Unit commitment for slow dispatchable units Participation in electricity market 2-stage decisions: Unit commitment of fast dis- patchable units Based on forecasts regarding:

  • Heat demand
  • Solar thermal production
  • Electricity price

Reactive decisions based on realizations of uncertainty

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Optimal heat production in district heating systems

Requirements Heat demand fulfillment Technical requirements of the producing units Thermal storage characteristics Costs Network

Operational planning Operational planning

CHP CHP TS TS SC SC FB FB EB EB HP HP Heating Grid Power Grid

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Optimal heat production in district heating systems

Methodology Two-stage stochastic program Depending on the complexity and runtime experiments → Decomposition techniques or heuristic solution approaches

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Summary

CITIES aims at IT-intelligent solutions for integrated energy systems CITIES WP 7 addresses decision making and planning problems for in companies in smart cities / integrated energy systems with special focus on uncertainties We are currently addressing two planning problems

  • Biomass supply planning for CHP plants
  • Optimal operation of production in district

heating networks

Contact: Daniela Guericke dngk@dtu.dk (+45) 45253428 Department of Applied Mathematics and Computer Science Technical University of Denmark www.smart-cities-centre.org www.citiesinnovation.org www.compute.dtu.dk

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