Research Line 1 – ENERGYSE – Energy system integration: multi-sector/multi-vector/multi-level
Definition of energy and non-energy (comfort, wellbeing, healthy environment) use cases and characterization of the applicable regulatory framework: The first step towards defining local actions for carbon-neutral cities is the holistic and comprehensive analysis of the local energy system and associated emissions, in this case as applied to the context of urban areas in Northern Portugal. Within DECARBONYZE, RL1 will develop a framework that conceptualizes cities as a set of flexible functional units defined at dwelling, building, district and at the limit, the whole city level, representing the integration of a variety of actors, vectors, systems and sectors. The functional units are understood to be representative and transferable to any city and will be the basis for the definition and characterization of existing (and future) use cases at the local level according to sectors involved, main actors and stakeholders, energy (e.g., flexibility) and non-energy services (e.g., focused on comfort, health, productivity) and context specificities (e.g., regulations, policies). In order to fully explore synergies between different energy use sectors and local potential for renewable generation and carbon capture (non-energy abatement), RL1 will identify the main interactions between use cases and look for context-specific effective technical solutions towards carbon neutrality based on the initial characterization of the urban energy system and associated emissions. As the feasibility of the main solutions is dependent on the regulatory and policy framework, these will be comprehensively characterized in order to identify the main limitations to the implementation of the measures and the eventual changes needed to promote the energy transition at different levels.
Identification of local technical solutions (passive and active) and actions (integration of green areas in the built environment, urban and land use planning) for carbon-neutral and healthy cities – Based on the framework for analysis of carbon neutrality developed and the defined use cases, ENERGYSE will propose a set of feasible technical solutions and actions, individually and in combination, that can contribute to the achievement of carbon neutrality at the local level. The proposed solutions can be applied to the different districts/urban areas and will be easily replicable in a variety of urban contexts, with special emphasis on the specificities of the Northern region of Portugal. In order to assess the effectiveness of the solutions, energy and non-energy indicators will be defined and used in the classification and ranking of feasible solutions within the boundaries of functional units and/or use cases. The most promising technical solutions will be collated into a Handbook of actions towards carbon neutral and healthy cities to be made available to the public at large, clearly identifying the role of the different actors, from citizen to communities, businesses and authorities in the transition.
Building energy modelling and flexibility management strategies – Energy use in buildings is a major contribution to the overall energy consumption in cities. Therefore, good-quality building energy models allowing the identification of building usage patterns and their intrinsic power flexibility are essential for improved management of buildings and energy networks [2]. In fact, identifying and modelling energy usage patterns and energy flexibility in buildings are problems that depend on numerous conditions and variables, some of them being difficult to predict. Conventionally, the thermal behaviour and energy consumption in buildings can be modelled in different ways considering imperfect, approximated and grey approaches [3]. Within this, data-driven models are becoming the trend for better fitting the building energy modelling and respective usage patterns for determining not only the energy consumption but also the inherent energy flexibility of the buildings [4]. The downside of data-driven modelling is the limited sources from which data can be collected appropriately to be used as representative for the models in question (e.g., type of building, space, system). There are several EU initiatives focused on mitigating this limitation such as “big data for buildings” and “building stock data 4.0” that show promising availability of open data on building use.
Once the building energy flexibility is known, it can be used to improve the efficiency of the energy systems, as well as aid system operators in the managing and operation of power and energy systems. Particularly, building management systems can use energy flexibility to raise levels of self-sufficiency or provide remunerated flexibility services to energy communities or to system operators [5]. In this scope, flexibility management strategies are essential for building managers in order to reduce energy expenses, enhancing self-sufficiency or to obtain extra revenue from the available flexibility.
Integration of energy resources in energy communities – Citizens are starting to realise the advantages of living in a community, and the benefits it can produce to the local economy. The sharing economy has become particularly relevant, as citizens can generate locally and consume the energy they produce (the so-called prosumers), and even share energy surplus with their neighbours. To turn this int a reality, the development of local energy communities through peer-to-peer (P2P) methodologies is essential to support and encourage conventional consumers and new prosumers to adopt renewable energy sources (RES) and more efficient energy actions towards sustainable use of energy in cities. Thus, energy communities may play an important role in the proliferation of renewable energy sources (RES) in urban areas, encouraging citizens to be more self-sufficient, allowing them to exchange energy and other services within the energy community [6].
Nevertheless, the rising of RES in cities also brings challenges to the system operation. Current grid operators are concerned with the proliferation of RES through P2P markets, as the city grids have been designed to support and supply the consumption and not to produce energy. Therefore, grid congestion and voltage problems can be a potential threat if not properly handled by system operators. In this case, P2P frameworks can also be part of the solution, presenting a new opportunity to rethink the use of common grid infrastructures and services, because P2P structures may allow the mapping of the energy exchanges [7]. In fact, P2P methodologies can encourage and mobilize customer’s flexibility and resilience through increased awareness and involvement in the system, through its energy community.
Research Line 2 – MULTIGOV – Multi-level governance and active citizenship
The definition and implementation of energy and climate actions at the local level have always been a challenge, given a large number of interactions between actors and sectors within and across the city boundaries [1]. With the new energy paradigm, additional challenges and opportunities are posed to local authorities, with the creation of new roles for local actors and the need for full integration of the local energy system. In this context, a multi-level governance approach with the coordination between legislative and non-legislative actions at different levels is required to achieve a just transition towards a sustainable low-carbon economy [2]. Moreover, the active participation of citizens is also key to the implementation of this new paradigm. Here, the definition of tailored solutions is key, as individual characteristics of actors were found to play a crucial role in the consumption patterns [3] and in their reaction to different policies and actions [4].
This research line focuses on the promotion of multi-level governance and active citizenship, based on co-creation approaches and the development of specific tools that can lever the involvement of different local actors in the energy transition. This RL is aligned with the higher-level goal of adopting a citizen-centred approach, through the design of policies and governance models with the citizens’ participation, to address existing gaps and support multi-level climate-neutral actions. Here, two main issues need to be considered: the governance models and policies in place, that should enable the multi-level energy system integration and the transition towards carbon neutrality at the local level; and the empowerment of citizens and communities, also taking into account the most vulnerable population (and respective specificities). These concerns are translated into the following objectives:
- Set multi-level policies and governance models for communities and cities towards local carbon neutrality and quality of life of citizens – Establish the necessary enabling framework for the development of the technical solutions that are considered/developed in RL1, taking into consideration the different actors, interests and regulatory frameworks in place.
- Define the energy actions and policies for the empowerment and behaviour change of citizens and communities – This refers to the conception, development, and implementation of energy and climatic actions and policies that encourage citizens to change their behaviour, in line with the technical solutions analysed and developed in RL1 and supported by the advances on digitalisation and data management from RL3.
- Develop dedicated solutions to vulnerable population – Identification, characterization and development of dedicated solutions for the vulnerable population (mitigation and adaptation in climate change scenarios), in order to ensure a just transition. Here, the goal is to consider the specificities of the vulnerable population and to develop tailored solutions that ensure that the impact of climate change for these individuals is minimized and that all citizens have the opportunity to actively participate and benefit from the energy transition.
Research Line 3 – DIGITISE – Digitalisation and IoT: digital platforms and data markets
This research line has been designed to support the implementation of the different models and approaches designed in previous RLs through digitalization and IoT means. More precisely, tools for the simulation of the models and use cases defined in previous research lines will be developed in this RL. The research line will mainly focus on the development of data-driven models, IoT platforms and Digital Twins applications directed to the developments of RL1 and RL2. This will include the design and creation of data-driven approaches targeted to support all the model characteristics defined in RL1. All the data available from sensors and smart meters will be collected in this RL, developing digital tools for its collection and simulation, accounting for the specificities defined in RL1. Thus, the main scientific objectives to be fulfilled in this research line are:
- Data-driven models for smart sustainable cities;
- Promote and support data services related to energy use and management considering data privacy issues;
- Creation of Digital Twins.
Data-driven models for smart sustainable cities are becoming crucial as we are moving towards cities filled of instrumentation, datafication and computerization. More precisely, increasing monitoring and automatization of cities through digital sensors and equipment generate a huge amount of data that needs to be assessed, treated and understood in order to be useful for optimizing processes aiming to increase efficiency [1]. The data-driven models are especially important for analysing data from buildings, in what concerns to energy and non-energy characteristics. Buildings energy efficiency strongly depends on building usage, and therefore, data-driven approaches allow for more specific and detailed control of the energy consumption in the building, aiming to improve energy efficiency. Multiple works have been looking for the best data-driven techniques and methods to achieve high levels of efficiency, generating savings at both economic and environmental levels [2]. However, there is a lack of complete and efficient data-driven models to handle the issues enumerated in RL1. In this way, RL3 will support RL1 with state-of-the-art data-driven models (detailed in activities 3.2.1 to 3.2.3) to enable its application in modelling the physics of buildings, energy markets, etc.
There is no doubt that smart meters are an integral part of future smart and sustainable cities. The potential that smart meter data can deliver to private and public interest benefits is undeniable, as it can contribute to the development of numerous services [3]. For instance, energy companies can use such data to obtain a better understanding of the consumers and provide them individual services to improve their energy usage. In addition, public entities can use such data to promote services and energy actions for the welfare of a community, city, or even a district. Therefore, an upcoming concern is the use of such smart meter data taking into account the data privacy of each end-user, while ensuring energy and non-energy services based on such data. There are different methods for ensuring data privacy, but then the quality of the data for providing energy and non-energy services is affected [4]. Thus, in order to promote and support data services related to energy use and management considering data privacy issues, this RL will provide models and tools capable of providing energy services under the developments in RL1 and RL2. Data models will support a trade-off between the service benefit and data privacy for the end-user as detailed in WP3.
In the last years, Digital Twins have become widely spread in the industry, as it is a digital replica of physical systems that can be modelled and simulated in an offline and online mode [5]. In the power sector, digital twins are built based on the massive information of models, sensors and operational data allowing the creation of digital replicas of the physical system. That is, Digital Twins describe the mapping of physical assets to a digital platform. The digital twin is used to simulate the physical system behaviour under different conditions, supporting tests and validations of decisions and events that might occur. There are numerous potential applications of digital twins to improve energy networks operation, reduce unplanned outages and improve management under different market conditions [5].
Nevertheless, digital twins are only possible due to the development of several technologies related to IoT, big data, artificial intelligence, etc. In fact, the creation of Digital Twins implies the design, usage, and deployment of multiple technologies, so that the bridge between the Physical and the Digital Twin is accomplished, and the accurate representation of the Physical Twin is successfully achieved by the Digital Twin. This mainly includes the use of IoT and wireless communications to enable the flexible collection of data from the Physical Twin that feeds the creation of the corresponding Digital Twin. The selection of the most suitable IoT wireless technologies (e.g., Wi-Fi, 5G, NB-IoT) requires a good knowledge of the Physical Twin where the IoT devices will be deployed.
In order to assist this selection and the deployment of the IoT devices in the Physical Twin, besides assessing the IoT wireless technologies available in the state of the art for this purpose, in this RL we will focus on the creation of a Digital Twin of the IoT wireless network associated to the Physical Twin (e.g., building), going beyond the state of the art Offline Experimentation (OE) approach INESC TEC has developed [6-8]. This Digital Twin will enable the study of the different network configurations, different types of IoT devices generating data (from raw data to video) and different wireless technologies, used at different scales (number of devices, the density of devices) without the need of testing over the Physical Twin.
In addition to the challenge of real-time communications of data and latency discussed above, other challenges and enabling technologies for the creation of the Digital Twins should be addressed [5]. For instance, data management, data privacy, and security are a concern that enabling digital platforms and big data technologies can potentially solve. Data-driven, machine and reinforcement learning models, as well as artificial intelligence techniques, are also important to provide physical realism and future projections of the physical energy systems. Finally, computational infrastructure capable of large-scale simulation is essential to support the complex simulation of the Digital Twins, in which cloud and parallel computing can give a high contribution, as well as distributed optimization and artificial intelligence techniques.