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tomers participation and the effects of regulatory fra-
meworks in DR/DSM events. Consequently, Energy
Aggregators can evaluate their impact on the market-
place. This scenario is a long-term planning activity.
Thus, simulations needs monthly or yearly time reso-
lution. This scenario exploits the following modules:
i) Agent-Based Model for Market Impacts, ii) Hou-
sehold Electricity Behaviour , iii) Smart Metering In-
frastructure, iv) Photovoltaic Energy, v) Smart Grid
Simulator and vi) Thermal Building Simulator.
City managers can use MESsi to evaluate the so-
lar potential and its impact on the distribution net-
work considering also load profiles and network con-
strains. This scenario involves the following modules:
i) Photovoltaic Energy to estimate the generation pro-
file for each PV system installed in building rooftops;
ii) Smart Metering Infrastructure to retrieve real load
profiles or Household Electricity Behaviour to gene-
rate realistic energy consumption patterns, iii) Solar
Thermal Collector to simulate the behaviours of so-
lar thermal panels in heating water for domestic use
or for heat pumps. If the simulation scenario inclu-
des building heating systems supplied by solar ther-
mal panels, the Thermal Building Simulator is needed
to evaluate the impact on indoors temperature beha-
viours. The scenarios needs monthly or yearly time
resolution.
MESsi can also be used for testing or validating
already existing algorithms, such as Non-Intrusive
Load Monitoring (NILM). NILM is a signal proces-
sing technique, which discerns the energy consump-
tion of the appliances from the aggregated data acqui-
red from a single point of measurement, i.e. the Smart
Meter (Zoha et al., 2012). In this case, the input nee-
ded by the NILM algorithm are historical households
load profiles retrieved from Smart Metering Infra-
structure. As an alternative, the NILM service can ex-
ploit the Household Electricity Behaviour module to
create realistic electrical consumption patterns. Time
resolution for this scenario ranges from microseconds
to 1 second.
It is worth noting that, thanks to the microservice
design pattern, MESsi is opened to build and run new
simulation scenarios to meet latest requirements from
the end-users.
5 CONCLUSION
In this paper, we presented MESsi, which is a no-
vel distributed infrastructure for modelling and co-
simulating Multi-Energy-Systems in cities. First, we
discussed the motivations and challenges we addres-
sed to design such infrastructure. Then, we intro-
duced our proposed framework that is suitable for
general purpose energy simulations with different
spatio-temporal resolutions. MESsi combines dif-
ferent technologies and correlates heterogeneous in-
formation, also sent in (near-) real-time, to simu-
late multi-energy-flows and to evaluate the impact
of novel policies in cities and distribution networks.
Finally, we discussed possible simulation scenarios
i) for analysing the operational status of energy dis-
tribution systems, ii) for planning and refurbishment
activities, and iii) for testing or validating already ex-
isting algorithms.
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