Flexigy Smart-grid Architecture
Tiago Fonseca
, Luis Lino Ferreira
, Lurian Klein
, Jorge Landeck
and Paulo Sousa
School of Engineering of the Polytechnical Institute of Porto, Porto, Portugal
Cleanwatts, MIT Portugal Programme, Energy for Sustainability Initiative, Coimbra, Portugal
Univ. Coimbra, LIBPhys, Department of Physics, Coimbra, Portugal
Keywords: Internet of Things, System Architecture, Energy, Demand-side Flexibility, Renewable Energy Community.
Abstract: The electricity field is facing major challenges in the implementation of Renewable Energy Sources (RES) at
a large scale. End users are taking on the role of electricity producers and consumers simultaneously
(prosumers), acting like Distributed Energy Resources (DER), injecting their excess electricity into the grid.
This challenges the management of grid load balance, increases running costs, and is later reflected in the
tariffs paid by consumers, thus threatening the widespread of RES. The Flexigy project explores a solution to
this topic by proposing a smart-grid architecture for day-ahead flexibility scheduling of individual and
Renewable Energy Community (REC) resources. Our solution is prepared to allow Transmission System
Operators (TSO) to request Demand Response (DR) services in emergency situations. This paper overviews
the grid balance problematic, introduces the main concepts of energy flexibility and DR, and focuses its
content on explaining the Flexigy architecture.
The adoption of Renewable Energy Sources (RES)
like wind and solar is growing at a significant rate,
with the annual installed capacity growing almost
45% in 2020 (International Energy Agency, 2021).
The prices for installing solar photovoltaic (PV)
panels keep dropping, household systems are now
capable of injecting their self-production surplus into
the grid (SEIA, 2021), hence owners become
producers and consumers – (prosumers).
The high penetration of RES into power grids
results in difficulties maintaining the necessary grid
balance. As a result, over the course of the day, the
grid energy demand generates a duck-shaped energy
consumption curve which highlights the increasingly
problematic grid unbalance phenomenon happening
with the increase of PV installations (CAISO, 2013).
Figure 1 illustrates the duck-shaped energy
consumption curve in California on the 31
of March
throughout several years; it represents the total energy
consumption minus the energy input from solar
generation. The imbalance between peak demand (at
21:00) and its minimum (at 14:00) is due to the peak
production from PV panels. This is particularly
problematic since conventional power plants require
long periods to start or stop producing energy.
Figure 1: Energy consumption curve (CAISO, 2013).
Fonseca, T., Ferreira, L., Klein, L., Landeck, J. and Sousa, P.
Flexigy Smart-grid Architecture.
DOI: 10.5220/0010918400003118
In Proceedings of the 11th International Conference on Sensor Networks (SENSORNETS 2022), pages 176-183
ISBN: 978-989-758-551-7; ISSN: 2184-4380
2022 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
In addition, at certain times of the year, there is
also the danger of overgeneration or under
generation, which can lead to permanent damage of
devices connected to the grid, so grid operators are
forced to curtail RES, activating costly
Interruptability Contracts or increasing the
consumption of energy by activating Regulation
One of the problems that we address with the
architecture proposed in this paper is to extend this
interruptability contract and regulation reserves up to
the prosumer, whose consumption/production or
storage capability can be aggregated into large loads.
For a healthy grid operation, it is also important
to balance consumption throughout the day and
avoid, as much as possible, consumption peaks.
Consumers and producers are now capable of
organizing themselves into Renewable Energy
Communities (RECs) which alongside peer-to-peer
(P2P) energy sharing and the aggregation of small-
scale demand-side flexibility present a new energy-
as-a-service business model as a solution to grid
balance. Consequently, the architecture being
proposed in this paper tackles these major challenges
by using a mix of tactics to smooth and match
consumption and productions curves. It does so by
assuming that prosumers are associated in RECs, as
described in Section 2.3, where a significant
percentage of them is capable of producing, storing,
as well as, consuming energy. The main idea is to
collect information about consumption flexibility, in
time and power, from multiple home appliances at
prosumer houses, by applying the Flex Offer (FO)
concept (Boehm et al., 2012), revised in Section 2.2.
The devices’ loads can then be shifted according
to electricity prices or other user preferences,
balancing the grid and incentivizing user
participation. As an example, assume an electric car
that arrives home at 16:00 and only has to leave on
the following day at 8:00. Consequently, the charging
of the car can be made anytime during this period,
fulfilling the objectives of the (i) car owner, e.g. by
using only green energy or the energy produced in its
house; and the (ii) grid, e.g. by scheduling energy
consumption in times of lower electricity cost,
meaning that the load is shifted to periods of
forecasted higher energy availability which
ultimately helps with its balancing.
For this purpose, our architecture is composed of
a set of IoT devices capable of measuring energy
consumption and controlling the home appliances,
whose data is aggregated by an in-house smart hub.
The data is analyzed in real-time at edge or cloud
level, scheduling and optimizing production and
consumption at 3 tiers: on the house (or office
building), at the REC level, and, if the request cannot
be fulfilled at these levels, at the grid.
This paper first overviews the main concepts on
energy flexibility in Section 2. Section 3 presents the
main architectural components and their rationale.
Finally, Section 4 presents the pilot results of the
Flexigy project, demonstrating the feasibility of the
This section explores some solutions to the grid
balancing problematic such as Demand Response
(DR) and energy flexibility through the concept of
Flex Offer (FO).
2.1 Demand Response
DR services are a set of methods used by grid
Transmission System Operators (TSO) to achieve
grid balance between energy supply and demand by
shifting and managing consumers' loads. Among the
benefits of this solution are the incentive payments
and cost savings for participants, increased reliability,
reduced volatility, and reduced infrastructure costs
for TSOs (Albadi & El-Saadany, 2008).
As an example, in Portugal, only two forms of DR
services are legislated: (i) interruptibility contracts;
and (ii) regulation reserve services, which are subject
to many restrictions as discussed in the next sections.
But legislation is evolving all over Europe and is also
expected to change in a way that will allow to
accommodate our proposal (Government, 2021).
Interruptibility contracts are a method used by
TSOs to request the reduction of the electricity
consumption of large industrial consumers to
maintain grid balance, in exchange for financial
As an example, in Portugal, this service is not an
effective solution for the flexible management of the
energy grid as the minimum interruptible power for a
consumer to establish an Interruptibility Service
Access Agreement contract is 4 MW, and aggregation
of loads is not allowed, excluding the participation of
small-scale consumers (e.g., domestic end-users) in
this process (ERSE, 2020a).
Regulation Reserve Services (RRSs) are an active
power reserve that ensures the safe operation of the
energy system in case of imbalances between energy
supply and demand, after the reserves of primary and
Flexigy Smart-grid Architecture
secondary regulations have been exhausted (ERSE,
2020a). These services are provided by certified
producers who indicate the maximum active power
available that can be increased or reduced to maintain
the grid stability. Once again, in Portugal, the
provision of RRSs is limited as it imposes a minimum
load mobilization capacity of 1 MW per consumer
and authorizes only the participation of consumers
connected to the medium- or high-voltage network
(ERSE, 2020a). Despite these limitations that exclude
small-scale end-users from taking part in the
provision of RRSs some pilots have been conducted
to further extend and stimulate the market with
aggregation (ERSE, 2020b).
It is expected that all over Europe and the world
RRSs and Interruptibility Contracts will evolve
allowing the participation of smaller loads, the
aggregation of loads, and the participation of end-
users (Government, 2021).
2.2 Demand-side Flexibility
Demand-side flexibility can be used as a key
contribution to complement a renewable energy-
based supply. This state-of-the-art concept is at the
basis of this work as it is used to increase grid balance
by managing, shifting, and optimizing energy
resources based on their schedule and power
flexibility. Energy flexibility can be characterized in
different ways and formally defined through the Flex
Offer concept.
2.2.1 Characterization of Device’s Flexibility
In terms of flexibility, devices can be categorized
according to two factors while maintaining the user
comfort levels unchanged: (i) instantaneous energy
consumption and (ii) usage time flexibility. More
specifically, three different kinds of devices have
been identified with interest to this project:
Fixed Devices: Devices whose energy
consumption and the moment of that consumption
cannot be modified (e.g., TV, lights).
Shiftable Devices: Devices that allow only to
shift the moment of energy consumption in time
without modifying the load profile (e.g., washing
machine or a dishwasher). These devices offer a
possible solution to optimize grid load management.
Elastic Devices: These devices offer the most
flexibility, being fully adjustable in terms of usage
time and instantaneous power consumption (e.g.,
HVAC, electric vehicles). Like shiftable devices,
these devices provide extended grid load
management capabilities, but with higher complexity.
Some studies have been conducted on how to use
Elastic Devices, like HVACs as a demand-side
flexibility solution (Kohlhepp et al., 2019). In these
devices, flexibility can be introduced by changing the
temperature of a given space or building while
minimizing the impact on user comfort. In
(Maasoumy et al., 2014) the authors study how to use
the consumption flexibility of buildings' HVAC
systems to establish contracts that bring financial
rewards to the owners and increase energy flexibility
to the utility operator. The algorithm considers
forecasted weather conditions, occupancy rates, and
other constraints to decide its flexibility for the next
contractual period.
Similarly, studies had also been conducted on
how to take advantage of Shiftable Devices and how
these loads can be aggregated and submitted to a
flexibility market. (self-reference)
2.2.2 Flex Offer Concept
The Flex Offer (FO) concept was first proposed by
the EU MIRABEL project (Boehm et al., 2012),
which defined a standardized model for representing
flexible electric loads of both consumption (like the
charging electric vehicles, heat pumps, home
appliances) and production (like the discharging of
batteries and PV panels) devices. The early
applications of the concept revolve around energy
commercialization in a large flexibility energy market
for the overall grid load balancing, distinct from the
solution being proposed in this paper where FOs are
applied for flexibility management in RECs.
In their simplest form, FOs are generic
abstractions expressing an amount of energy, a
duration, a price, the earliest start time, and the latest
start time.
Figure 2: Flex Offer Example.
Figure 2 displays a visual representation of a FO
energy profile with the earliest start time (ES) and the
latest start time (LS), i.e. the time flexibility for the
FO. The energy requirements are expressed in
SENSORNETS 2022 - 11th International Conference on Sensor Networks
intervals of fixed length (slices). The striped area
expresses the flexibility between the maximum and
minimum amounts required.
Initially, a FO is an "option" that a prosumer
introduces to a flexibility platform, which can be
scheduled to optimize energy consumption
considering the prosumer preferences, environmental
concerns, and the financial motivations of the
numerous players involved. In the end, the scheduling
is carried out as specified, and the devices are
activated according to it.
2.2.3 Flexibility Aggregation
(Boehm et al., 2012) studied the aggregation of
energy flexibility, FOs, expressed by market players
as the key to balancing energy supply and demand.
After their creation and acceptance, the FOs are
aggregated into larger loads and submitted to
flexibility markets, since these markets do not handle
small loads. A response to the bid is returned (for the
aggregated FO) and its constituent FOs are
disaggregated and returned to the prosumer. Once the
execution is carried out, billing is conducted, and
incentives are distributed among prosumers.
Similarly, by aggregating loads of building
clusters with flexible demand, (Yin et al., 2016)
implement an optimization model for the
participation of a Distributed Energy Resources
(DER) aggregators in the day-ahead market.
2.3 Renewable Energy Communities
The need to encourage the use of new energy
technologies and the participation of prosumers in
energy market solutions is pivotal to achieve greater
levels of RES production, grid resilience, and
reliability at lower financial costs.
These prosumers can participate in RECs to
obtain environmental and financial benefits. RECs
involve groups of geographically close citizens,
entrepreneurs, public authorities, and community
organizations voluntarily participating by
cooperatively investing in, producing, storing,
sharing, and selling renewable energy. Moreover,
RECs must be autonomous from their members but
effectively controlled by them, contingent that: i) the
renewable projects are held and developed by the
REC; ii) the main objective of the REC is to provide
environmental, economic, and social benefits
(Hunkin & Krell, 2018).
RECs are also fully responsible for imbalances
caused to the energy grid, settling such imbalances,
or delegating them to a market participant or its
designated representative. In this paper, RECs are
described as a group of prosumers buildings/houses
under a local transformer capable of transforming
from high voltage to 230 V.
To efficiently implement, manage and control
RECs new energy projects, models which take
advantage of smart home metering systems, sensors,
and Internet of Things (IoT) infrastructure are
required. For example, the authors in (Oprea & Bâra,
2021) envision an adaptive day-ahead load
optimization and control solution for residential
homes with an edge and fog IoT architecture.
This section overviews the physical system
architecture and its main components. Moreover, it
enumerates a set of consumer and producer profiles,
focusing on the reasoning and constraints behind the
proposed three-tier flexibility scheduling approach.
Finally, the design to respond to DR service requests
is explained in detail.
3.1 Main Components
The Flexigy architecture (Figure 3) is mainly
composed by components distributed among two
distinct locations: the End-User Premises and the
Cloud Servers. The End-User Premises is where the
home appliances, like the washing machine and the
fridge, as well as, the smart meters and the smart hub,
are located.
Figure 3: Physical System Architecture.
Smart (energy) meters are devices capable of
acquiring energy consumptions and turning on and
off home appliances, this is an important requirement
in order to execute the scheduled FO. These are
connected to the smart hub device through the 802.15
(Zigbee) protocol, but the newer version can also
connect to the house Wi-Fi network. The smart hub
Flexigy Smart-grid Architecture
receives information from all the devices and
manages the communication with the Cloud Servers.
The Cloud Servers are where the platform’s
middleware broker, named Middleware API, and the
Flexigy Platform are installed. The middleware API
handles the communication with the prosumer’s
smart hubs.
The External Services include all external systems
and platforms responsible for providing data to the
Flexigy Platform. The components of this layer are
managed and maintained by third parties and are
strictly not part of the Flexigy platform, although they
are essential to maintain the expected system
operation. Some examples of data sources are the
Weather Platform and Energy Market Platform.
The Weather Platform refers to an external service
that provides real-time and historical weather data
and weather forecasts for specific locations.
The Energy Market Platform refers to a system
that provides information about prices traded on the
wholesale energy market, in our case the OMIE. The
OMIE is the Nominated Electricity Market Operator
(NEMO) for managing the Iberian Peninsula’s day-
ahead and intraday electricity markets and prices
(About Us | OMIE, 2021).
Inside the Flexigy Platform, the most relevant
modules are:
User Interface: It is responsible for presenting
the data by providing a dashboard where the user can,
for example, pick profiles, add devices, specify FOs,
and check schedules (Rocha et al., 2018).
Energy Forecasting Module: provides energy
consumption and production forecasts. It relies on the
Middleware API to fetch devices' historical data to
forecast the consumption devices and on the external
Weather Platform API to predict the day-ahead PV
DR Module: is used to handle interruptibility and
regulating reserve requests. It provides a DR API
through where TSO's can make these requests. It uses
the Middleware API to fetch instantaneous device
consumptions and uses the system DB to obtain FO
scheduling information.
Flex Offer Scheduler Module: provides
optimized Day-Ahead FOs schedules, using the
external Energy Market Platform to fetch energy
prices and request energy consumption and
production values from the Energy Forecasting
The architecture and implementation of this last
module are subject to several constraints, which is
one of the main topics of this paper and detailed next.
3.2 Energy Scheduling Approach
This section addresses the design concerns and the
proposed architecture for the Flex Offer Scheduler
Module, assuming the prosumer as part of a REC.
Scheduling FOs plays a crucial role in the
management of flexibility. The scheduling is
supported by a set of our own proposed heuristics
(VPS, ISEP, Ionseed, PH Energia, 2021) that
optimize prosumer needs and system constraints. To
better incorporate the prosumer requirements, we
defined some possible user profiles, considering both
the roles of producer and consumer. With these, each
prosumer can customize their experience according to
what best fits their goals and beliefs:
Bold Profile: the FO scheduling maximizes
renewable energy consumption regardless of the
electricity price. This profile is designed to answer
consumers that prioritize environmentally friendly
energy sources.
Cautious Profile: FO of consumers with this
profile are always scheduled at the lowest total cost
possible. This profile aims to meet the financial needs
of consumers.
Local Community Supporter Profile: the FO
scheduling of consumers with this profile maximizes
community consumption irrespective of its price.
This profile allows consumers to support local
producers by buying electricity from other
community members before grid sources are needed.
Also, an energy producer might choose different
Go-ahead Profile: The producer wants to sell all
is renewable electricity before maximizing self-
consumption. This profile is created specifically to
the case where the company implementing this
solution at a REC supplies the equipment, e.g., smart
meters, smart hub, PV panels, to the prosumer in
exchange for a contract that requires that prosumer to
sell all its production, before optimization, during a
finite period (e.g., 6 months).
Tactical Profile: the producer only wants to sell
its surplus of renewable generation after optimizing
self-consumption. This is the default profile, as it
prioritizes self-consumption, minimizing costs for the
prosumer, and maximizing RES and REC
Considering the several kinds of prosumer
profiles combined with a large number of prosumers
lead to a scheduling approach based on levels.
However, before describing such levels we present
some of our assumptions.
Self-consumption (i.e., the consumption of
energy produced in a house or office building) is
SENSORNETS 2022 - 11th International Conference on Sensor Networks
considered to have no cost to its owner, which means
it is always more beneficial (except for producers
with a Go-Ahead profile) to shift the flexible
consumptions to periods of peak self-production.
This also implies that for FOs with energy needs
greater than the self-production available in the FO
time interval it is necessary to check forecasted
electricity prices, and in a way, that minimizes the
total energy cost. For example, scheduling 4 kWh
with 3 kWh of self-production and buying 1 kWh for
0.18€ is more expensive than using the maximum 2
kWh of self-production and buying the remaining at
a total cost of 0.14€. A consumer with a Bold profile
may prefer the first solution, as it maximizes
renewable energy consumption, on the other hand, the
second solution fits better a consumer with a Cautious
profile as it minimizes the total solution cost.
Another constraint is that REC members should
be able to fulfil their FOs using the excess self-
production of other community members. This
requires that all Tactical profile prosumers with self-
production must be scheduled first so that their
energy surplus can be aggregated and sold to satisfy
other community FOs. The details of these algorithms
are described in (VPS, ISEP, Ionseed, PH Energia,
So, for scheduling the day-ahead flexibility of
prosumers appliances we envision a three-tier
approach. The three levels are as follows:
Level 1: Prosumer level, in this level the
scheduling is performed for each individual
prosumer, considering the minimization of energy
costs and maximization of individual renewable
energy self-consumption.
Level 2: REC level, the scheduler tries to
minimize overall energy costs and optimize the usage
of energy produced at the REC.
Level 3: Grid level, if FO is not fulfilled at Level
1 or Level 2 the scheduler schedules the FO taking
into account the market prices and the requirements
from different stakeholders. As an alternative, it
aggregates several FO into larger ones that can be
submitted to flexibility markets.
Figure 4 summarizes the system architecture from
a logical point of view. The Level 1 scheduling,
depicted in green, is executed for every prosumer
building with energy self-production, by collecting
each household device's flexibility, generating FOs,
and scheduling them according to the user profile.
Depicted in red in Figure 4 are 2 logical Level 2
partitions. Each of these is logically executed at the
REC level. The solution presented collects all the FOs
generated at the households of the community,
including the FOs partially or not totally scheduled at
Level 1, and then it schedules them according to each
user profile.
Finally, Level 3, represents the more traditional
electric grid containing flexibility markets and being
able to give different energy prices according to the
hour of the day and its source.
Figure 4: Three-Tier Architecture FO Scheduling
3.3 DR Module Implementation
The proposed logical architecture also enables the
system to respond to DR services. In this architecture
the Level 2 communities can aggregate a large set of
Level 1 households’ appliances, enabling the support
of such services, eventually in conjunction with other
Figure 5: DR services implementation.
Flexigy Smart-grid Architecture
Figure 5 shows a flowchart of the process. At its
basis, the implemented solution expresses an action,
consisting of the increase or decrease by an energy
amount, and a duration. If the DR service requested is
to reduce the load, the system tries to postpone FOs
due to start in the next few minutes while also turning
off devices according to user comfort preferences. In
the case of a power increase request, our solution tries
to anticipate already scheduled flex offers to start
consuming in the specified DR service period. In the
end, if the request is fulfilled, the corresponding FO
schedule and actuations are updated, and the request
is saved as fulfilled. Otherwise, the system informs
the TSO it cannot fulfil the request.
The results presented in this section are supported by
data obtained from the OMIE electricity prices
(Subsection 4.1) and from data acquired by the smart
meters installed in five different households. After the
system collects the data and the prosumer specifies
the flexibility parameters to create FO, the scheduling
algorithms are executed as described in Subsection
4.1 Electricity Prices
To test and evaluate the scheduling algorithms
proposed, we obtained the day-ahead electricity
prices with 15 minutes granularity from the OMIE
market. The price returned by the OMIE API for the
day-ahead was used as the reference for RECs
electricity prices and the prices used for grid suppliers
were simulated. Figure 6 shows the prices from three
different energy suppliers. The red line shows the
price profile for REC electricity, the orange price
profile represents the prices for a renewable energy
grid supplier, and the blue price profile illustrates grid
suppliers with mixed sources of energy.
Figure 6: OMIE electricity prices.
4.1 Flex-Offer Scheduling
This section presents an example of the pilot results
obtained from the scheduling of a Shiftable FO
originated from a washing machine and created in the
system by a prosumer with a Cautious consumer
profile and a producer Tactical profile. The main
features of the FO are resumed in Table 1 and Fig. 7.
Table 1: Shiftable FO.
Device Type
ES: 09:30
LS: 13:45
in blue in
Figure 7
Given that the prosumer has self-production
(yellow in Fig. 7) and a Tactical supplier profile, it is
expected that the Level 1 algorithm should be
executed to maximize self-consumption on all its FOs
before selling to the community the energy surplus.
Figure 7: Level 1 scheduling of a Shiftable Flex Offer.
In this case, the scheduling obtained from Level 1
(green bars in Figure 7) is due to start at 13:00, as this
solution minimizes the estimated overall cost, the aim
of a Cautious consumer. Even though the
consumption could have been shifted to match the
peak self-production at 12:30, the algorithm
schedules it a little forward in time, as some energy
prices from 13:00 onwards are lower, thus
minimizing the FO total cost. Note that in this
instance, the Shiftable device FO is not fully
scheduled in Level 1, thus its energy profile is
updated for Level 2 scheduling.
In Level 2 scheduling, depicted by the red bars in
Figure 7, it is possible to see that the remaining
energy was scheduled using community energy as the
supplier (the cheapest during both time slices). In the
end, combining both the self-consumption from Level
1 and the energy scheduled during Level 2 the FO is
totally fulfilled.
SENSORNETS 2022 - 11th International Conference on Sensor Networks
This project presents a smart energy optimizing
platform architecture that tackles different players'
economic and social needs in the energy market value
chain. The energy consumers could benefit from the
platform's optimized device management based on
their energy flexibility. This feature helps lower
electricity prices according to each user preference
and even provides financial compensations through
the fulfilment of DR. If approved by legislation, TSO
can use an intelligent module ready to receive and
handle DR at the REC level, aiming to minimize the
energy imbalance problem and, consequently, the
costs of introducing RES in the grid.
Furthermore, the solution proposed in this paper
encourages the use of RES, since it helps producers
reduce the investment pay-out time by not only
maximizing the use of self-produced energy but also
by selling the energy surplus to other community
members at a profitable price.
Ultimately, society itself could benefit from the
solutions provided, as it reduces electricity prices to
end-users while promoting the widespread adoption
of RES. The objectives tackled in this project are very
complex and didn’t include a significant size pilot
that proves the scalability of the architecture proposed
in this paper and the fairness of the scheduling
algorithms. We also need to extend the testbed with
several RECs and include other kinds of devices, such
as cars.
This work was supported by project FLEXIGY,
034067 (AAC 03/SI/2017) POCI-01-0247-
FEDER-034067, financed through National Funds
through FCT/MCTES (Fundação para a Ciência e
Tecnologia) and co-financed by Programa
Operacional Regional do Norte (NORTE2020),
through Portugal2020 and the European Fund for
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