Controlling micro-CHP generators as a virtual power plant

C A G MacRae, R Weiskircher, S Dunstall, A T Ernst, N Kontoleon

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

Abstract

Deploying gas generators as a source of combined heat and power (CHP) in residential complexes offers the potential for substantial reductions in overall energy use and greenhouse-gas emissions. Here we use mathematical modelling to predict the operational behaviour of such generators in a hypothetical network of 100 residential apartment buildings.

In our models the generators produce electricity for local needs and for participation in the national energy market (NEM), while delivering space heating and hot water to the residences. The management strategies used optimise economic performance while satisfying the heat and power demands of the residential customers.The heat and power demand data has been synthesised using multiple information sources and is intended to represent a plausible demand pattern for residential apartment buildings in Victoria, Australia.

We present a linear programming based optimisation strategy for controlling micro-CHP generators that provide heating, hot water and electricity for apartment blocks. We assume that the operator of the generators can also participate in the NEM by providing a block of power once a day. Our system assumes that we have perfect prior knowledge about the power and heating demands in the apartment blocks as well as about the prices on the NEM.

By running the optimisation for each day of the year 2012, we show that an optimal strategy would provide about 67% of the annual electrical generation capacity of all generators for local use as well as for export to the NEM. We also show that the variable profit obtained from running the generators is extremely high on days with high NEM prices compared to days with average prices. Where it is unprofitable to operate the generators electricity is purchased from the grid at market rates.

An operator running CHP-generators according to the business model we describe in this paper must make sure that he or she can profit from the days with extreme NEM prices. A business model where the operator can only sell electricity to the occupants of the apartment blocks for the retail price has a much smaller earning potential. Therefore, allowing distributed generation operators with significant capacity to participate in the NEM should greatly encourage the installation of these devices and help reduce price peaks and congestion at the same time.

Our results also show that the savings in CO2 emissions from using CHP-generators in apartment buildings can be substantial with up to a 52% reduction in annual emissions compared to satisfying heat and electrical demand by conventional means. The CO2 savings are also strongly influenced by the wholesale prices in the NEM and being able to make use of high prices leads to significant additional savings which is demonstrated by the the amount saved on summer peak days.
In our future work, we want to examine control strategies that do not assume perfect knowledge about prices and demand on the next day but instead use available data like weather forecasts and the price forecasts provided by Australian Energy Market Operator (AEMO), combined with reacting to observed data. We could then use the system presented in this paper as a benchmark for measuring the predictive and adaptive strategies.
Original languageEnglish
Title of host publicationMODSIM2013, 20th International Congress on Modelling and Simulation
EditorsJ Piantadosi, R S Anderssen, J Boland
Place of PublicationCanberra ACT Australia
PublisherModelling and Simulation Society of Australia and New Zealand
Pages1523-1529
Number of pages7
ISBN (Electronic)9780987214331
Publication statusPublished - 2013
Externally publishedYes
EventInternational Congress on Modelling and Simulation 2013: Adapting to Change: the multiple roles of modelling - Adelaide Convention Centre, Adelaide, Australia
Duration: 1 Dec 20136 Dec 2013
Conference number: 20
https://www.mssanz.org.au/modsim2013/

Conference

ConferenceInternational Congress on Modelling and Simulation 2013
Abbreviated titleMODSIM2013
CountryAustralia
CityAdelaide
Period1/12/136/12/13
Internet address

Keywords

  • Distributed Generation
  • Electricity Markets
  • Micro-CHP

Cite this

MacRae, C. A. G., Weiskircher, R., Dunstall, S., Ernst, A. T., & Kontoleon, N. (2013). Controlling micro-CHP generators as a virtual power plant. In J. Piantadosi, R. S. Anderssen, & J. Boland (Eds.), MODSIM2013, 20th International Congress on Modelling and Simulation (pp. 1523-1529). Canberra ACT Australia: Modelling and Simulation Society of Australia and New Zealand.
MacRae, C A G ; Weiskircher, R ; Dunstall, S ; Ernst, A T ; Kontoleon, N. / Controlling micro-CHP generators as a virtual power plant. MODSIM2013, 20th International Congress on Modelling and Simulation. editor / J Piantadosi ; R S Anderssen ; J Boland. Canberra ACT Australia : Modelling and Simulation Society of Australia and New Zealand, 2013. pp. 1523-1529
@inproceedings{786e2e3febea4c1f9fbd22ac3510bf78,
title = "Controlling micro-CHP generators as a virtual power plant",
abstract = "Deploying gas generators as a source of combined heat and power (CHP) in residential complexes offers the potential for substantial reductions in overall energy use and greenhouse-gas emissions. Here we use mathematical modelling to predict the operational behaviour of such generators in a hypothetical network of 100 residential apartment buildings.In our models the generators produce electricity for local needs and for participation in the national energy market (NEM), while delivering space heating and hot water to the residences. The management strategies used optimise economic performance while satisfying the heat and power demands of the residential customers.The heat and power demand data has been synthesised using multiple information sources and is intended to represent a plausible demand pattern for residential apartment buildings in Victoria, Australia.We present a linear programming based optimisation strategy for controlling micro-CHP generators that provide heating, hot water and electricity for apartment blocks. We assume that the operator of the generators can also participate in the NEM by providing a block of power once a day. Our system assumes that we have perfect prior knowledge about the power and heating demands in the apartment blocks as well as about the prices on the NEM.By running the optimisation for each day of the year 2012, we show that an optimal strategy would provide about 67{\%} of the annual electrical generation capacity of all generators for local use as well as for export to the NEM. We also show that the variable profit obtained from running the generators is extremely high on days with high NEM prices compared to days with average prices. Where it is unprofitable to operate the generators electricity is purchased from the grid at market rates.An operator running CHP-generators according to the business model we describe in this paper must make sure that he or she can profit from the days with extreme NEM prices. A business model where the operator can only sell electricity to the occupants of the apartment blocks for the retail price has a much smaller earning potential. Therefore, allowing distributed generation operators with significant capacity to participate in the NEM should greatly encourage the installation of these devices and help reduce price peaks and congestion at the same time.Our results also show that the savings in CO2 emissions from using CHP-generators in apartment buildings can be substantial with up to a 52{\%} reduction in annual emissions compared to satisfying heat and electrical demand by conventional means. The CO2 savings are also strongly influenced by the wholesale prices in the NEM and being able to make use of high prices leads to significant additional savings which is demonstrated by the the amount saved on summer peak days.In our future work, we want to examine control strategies that do not assume perfect knowledge about prices and demand on the next day but instead use available data like weather forecasts and the price forecasts provided by Australian Energy Market Operator (AEMO), combined with reacting to observed data. We could then use the system presented in this paper as a benchmark for measuring the predictive and adaptive strategies.",
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year = "2013",
language = "English",
pages = "1523--1529",
editor = "J Piantadosi and Anderssen, {R S} and J Boland",
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MacRae, CAG, Weiskircher, R, Dunstall, S, Ernst, AT & Kontoleon, N 2013, Controlling micro-CHP generators as a virtual power plant. in J Piantadosi, RS Anderssen & J Boland (eds), MODSIM2013, 20th International Congress on Modelling and Simulation. Modelling and Simulation Society of Australia and New Zealand, Canberra ACT Australia, pp. 1523-1529, International Congress on Modelling and Simulation 2013, Adelaide, Australia, 1/12/13.

Controlling micro-CHP generators as a virtual power plant. / MacRae, C A G; Weiskircher, R; Dunstall, S; Ernst, A T; Kontoleon, N.

MODSIM2013, 20th International Congress on Modelling and Simulation. ed. / J Piantadosi; R S Anderssen; J Boland. Canberra ACT Australia : Modelling and Simulation Society of Australia and New Zealand, 2013. p. 1523-1529.

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

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AU - Dunstall, S

AU - Ernst, A T

AU - Kontoleon, N

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N2 - Deploying gas generators as a source of combined heat and power (CHP) in residential complexes offers the potential for substantial reductions in overall energy use and greenhouse-gas emissions. Here we use mathematical modelling to predict the operational behaviour of such generators in a hypothetical network of 100 residential apartment buildings.In our models the generators produce electricity for local needs and for participation in the national energy market (NEM), while delivering space heating and hot water to the residences. The management strategies used optimise economic performance while satisfying the heat and power demands of the residential customers.The heat and power demand data has been synthesised using multiple information sources and is intended to represent a plausible demand pattern for residential apartment buildings in Victoria, Australia.We present a linear programming based optimisation strategy for controlling micro-CHP generators that provide heating, hot water and electricity for apartment blocks. We assume that the operator of the generators can also participate in the NEM by providing a block of power once a day. Our system assumes that we have perfect prior knowledge about the power and heating demands in the apartment blocks as well as about the prices on the NEM.By running the optimisation for each day of the year 2012, we show that an optimal strategy would provide about 67% of the annual electrical generation capacity of all generators for local use as well as for export to the NEM. We also show that the variable profit obtained from running the generators is extremely high on days with high NEM prices compared to days with average prices. Where it is unprofitable to operate the generators electricity is purchased from the grid at market rates.An operator running CHP-generators according to the business model we describe in this paper must make sure that he or she can profit from the days with extreme NEM prices. A business model where the operator can only sell electricity to the occupants of the apartment blocks for the retail price has a much smaller earning potential. Therefore, allowing distributed generation operators with significant capacity to participate in the NEM should greatly encourage the installation of these devices and help reduce price peaks and congestion at the same time.Our results also show that the savings in CO2 emissions from using CHP-generators in apartment buildings can be substantial with up to a 52% reduction in annual emissions compared to satisfying heat and electrical demand by conventional means. The CO2 savings are also strongly influenced by the wholesale prices in the NEM and being able to make use of high prices leads to significant additional savings which is demonstrated by the the amount saved on summer peak days.In our future work, we want to examine control strategies that do not assume perfect knowledge about prices and demand on the next day but instead use available data like weather forecasts and the price forecasts provided by Australian Energy Market Operator (AEMO), combined with reacting to observed data. We could then use the system presented in this paper as a benchmark for measuring the predictive and adaptive strategies.

AB - Deploying gas generators as a source of combined heat and power (CHP) in residential complexes offers the potential for substantial reductions in overall energy use and greenhouse-gas emissions. Here we use mathematical modelling to predict the operational behaviour of such generators in a hypothetical network of 100 residential apartment buildings.In our models the generators produce electricity for local needs and for participation in the national energy market (NEM), while delivering space heating and hot water to the residences. The management strategies used optimise economic performance while satisfying the heat and power demands of the residential customers.The heat and power demand data has been synthesised using multiple information sources and is intended to represent a plausible demand pattern for residential apartment buildings in Victoria, Australia.We present a linear programming based optimisation strategy for controlling micro-CHP generators that provide heating, hot water and electricity for apartment blocks. We assume that the operator of the generators can also participate in the NEM by providing a block of power once a day. Our system assumes that we have perfect prior knowledge about the power and heating demands in the apartment blocks as well as about the prices on the NEM.By running the optimisation for each day of the year 2012, we show that an optimal strategy would provide about 67% of the annual electrical generation capacity of all generators for local use as well as for export to the NEM. We also show that the variable profit obtained from running the generators is extremely high on days with high NEM prices compared to days with average prices. Where it is unprofitable to operate the generators electricity is purchased from the grid at market rates.An operator running CHP-generators according to the business model we describe in this paper must make sure that he or she can profit from the days with extreme NEM prices. A business model where the operator can only sell electricity to the occupants of the apartment blocks for the retail price has a much smaller earning potential. Therefore, allowing distributed generation operators with significant capacity to participate in the NEM should greatly encourage the installation of these devices and help reduce price peaks and congestion at the same time.Our results also show that the savings in CO2 emissions from using CHP-generators in apartment buildings can be substantial with up to a 52% reduction in annual emissions compared to satisfying heat and electrical demand by conventional means. The CO2 savings are also strongly influenced by the wholesale prices in the NEM and being able to make use of high prices leads to significant additional savings which is demonstrated by the the amount saved on summer peak days.In our future work, we want to examine control strategies that do not assume perfect knowledge about prices and demand on the next day but instead use available data like weather forecasts and the price forecasts provided by Australian Energy Market Operator (AEMO), combined with reacting to observed data. We could then use the system presented in this paper as a benchmark for measuring the predictive and adaptive strategies.

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MacRae CAG, Weiskircher R, Dunstall S, Ernst AT, Kontoleon N. Controlling micro-CHP generators as a virtual power plant. In Piantadosi J, Anderssen RS, Boland J, editors, MODSIM2013, 20th International Congress on Modelling and Simulation. Canberra ACT Australia: Modelling and Simulation Society of Australia and New Zealand. 2013. p. 1523-1529