Mutual-benefit of district heating market and network operation for prosumers integration

ABSTRACT

Integration of prosumers in district heating networks brings new challenges to the market and the network operation since they can change the thermal flow and increase competition. Thus, it is mandatory to develop new market structures and network management mechanisms. In this scope, this work proposes the implementation of a coordination methodology based on a peer-to-peer market without a supervising entity. The goal is to achieve higher revenue by coping with the requirements of each agent. Furthermore, the model is validated through network nodal analysis inspired by the power sector. The results in a Nordic network point out that the coordination methodology can provide compromise solutions between market negotiation and network operation. This methodology succeeded in providing reliable network solutions, fixing 99.88% of network burdens just after one iteration, and encouraging prosumers’ integration. This increases market competition which lowers the energy costs for consumers while avoiding the network’s operating burdens.

KEYWORDS:

• District heating network
• energy exchange
• peer-to-peer market
• prosumer
• thermal flow

Nomenclature

Sets and indexes	=
tperiod index	=
nAgents index	=
mAgents index	=
ΩnSet of agents n	=
ΩmSet of agents m	=
${\mathrm{\Omega }}_c$Set of consumers	=
${\mathrm{\Omega }}_p$Set of producers	=
${\mathrm{\Omega }}_{pip}$Set of pipelines	=
Parameters	=
Ct,nAgent n bid in time frame t	=
cn,mPenalty between trade n,m	=
$\underset{t,n}{\underset{\hspace{0.17em}}{P}}$Heat power lower bound of agent n in time frame t	=
${\bar{P}}_{t,n}$Heat power upper bound of agent n in time frame t	=
$D_{n,m}$Geographical distance between agents n and m	=
$B_{i,j}$Element of matrix B representing the nodes i and j	=
$C_{\rho }$Specific heat capacity of water	=
$\rho$Density of water	=
$\mathrm{\Delta }T$Difference between supply and return temperatures	=
$V_{t,i,j}$Water velocity in pipeline i,j in time frame t	=
Variables	=
Pt,nAgent n heat power in time frame t	=
Pt,n,mHeat power trade between agents n and m in time frame t	=
${\lambda }_{n,m}^{k\ast }$Market clearing price for trade n,m	=
${ }_{t,i}$Angle of node i in time frame t	=
$H_{t,i,j}$Heat in the pipeline i,j in time frame t	=
$Q_{t,i,j}$Volumetric flow rate	=
$A_{t,i,j}$Required are for the pipeline i,j in time frame t	=
Acronyms	=
DH – District Heating	=
DHN – District Heating Network	=
P2P – Peer-to-Peer	=
CHP – Combined Heat and Power	=
DHS – District Heating Systems	=
KPI – Key Performance Indicators	=
ADG – Average Dispatched Generation	=
SPM – Successful Participation in the Market	=
QoS – Quality of Service	=
MiM – Min-Max Indicator	=
QoE – Quality of Experience	=

Acknowledgements

This work is partially supported by the European Union’s Horizon 2020 through the EU Framework Program for Research and Innovation, within the EMB3Rs project under agreement No. 847121. It is also supported by Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF), within the DECARBONIZE project under agreement NORTE-01-0145-FEDER-000065 and by the Scientific Employment Stimulus Programme from the Fundação para a Ciência e a Tecnologia (FCT) under the agreement 2021.01353.CEECIND.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the Horizon 2020 [847121].