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.

1 INTRODUCTION

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

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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).

176

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

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

Reserves.

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

solution.

2 ENERGY FLEXIBILITY AND

RELATED WORKS

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

compensation.

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

177

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

178

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.

3 SYSTEM 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

179

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

production.

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

Module.

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

profiles:

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

consumption.

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

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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,

2021).

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

Approach.

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

RECs.

Figure 5: DR services implementation.

Flexigy Smart-grid Architecture

181

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.

4 PILOT RESULTS

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

4.2.

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

Time

Flexibility

Load

Profile

Washing

machine

Shiftable

ES: 09:30

LS: 13:45

Depicted

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.

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5 CONCLUSIONS

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.

ACKNOWLEDGMENTS

This work was supported by project FLEXIGY, nº

034067 (AAC nº 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

Reginal Development (FEDER).

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