Tools

The IoT technologies are evolving fast, but old ones still exist, therefore an IoT architecture must support both old and new ones . Some examples include traditional cellular connectivity technologies like EDGE (2G), HSPA (3G), LTE (4G) and Low Power Wide Area (LPWA) technologies for IoT . The LPWA are low-power and low-cost technologies that are capable of covering a wide area network . The most widely used LPWA are LoRa and Sigfox. Furthermore, multiple connectivity technologies exist for shorter area cover. Short-area connectivity technologies are implemented mostly for HEMS and Building Energy Management Systems (BEMSs) to cover areas up to 100 meters. The most widely used short-area technologies are Bluetooth and Wireless-Fidelity (Wi-Fi) . Moreover, nowadays other technologies like Thread, WirelessHART , Z-Wave, Mod-Bus and Zigbee are mostly used in HEMS and BEMS.

As a result, a low-cost IoT architecture supporting multiple connectivity technologies is of key importance. CERTH deployed a method for deploying a low-cost machine-to-machine (M2M) centric architecture that supports multiple protocols and interoperability.

The suggested Machine-to-Machine (M2M) system utilizes a modular and layered architecture to increase the system’s overall robustness. Each gateway utilizes its own MQTT broker . The broker is used by all the sub-modules’ services to redirect the BMS server. The use of a local broker for each gateway solves many networking problems that may arise. The gateway can be connected to the internet without the use of static IP while its IP is not stored or used by the system. Additionally, by using the MQTT protocol, different modules of the system can be coded in various programming languages as long as they are compatible with MQTT and post their data to the predefined topics. Each protocol’s software is linked to a Linux service which monitors its network state, manages the sensor
data streams, and publishes the data to the gateway’s MQTT broker under predefined and mapped topics.
The ”Mqtt2Bms” service subscribes to all the topics in the broker using the same predefined mappings, gathers all available data, and periodically posts them to the BMS server’s API. This type of architecture allows the future addition of more protocols to the system without obstructing the existing modules’ functionality.

The Demand Response Flexibility Forecasting and Optimization (DRFFO) is a module developed in collaboration between ETRA I+D and CERTH. It constitutes a powerful dynamic and integrated tool for real-time building automated monitoring and control, allowing the forecasting of a building’s energy flexibility, based on extracted profiles and current contextual conditions, while further being able to coordinate operation of building’s assets in the optimal comfort and energy efficient manner.

The module’s operation is divided in seven submodules:

• Forecasting calculator: Responsible for the forecasting of the building energy consumption and production.
• Building simulator: Able to perform simulations for every operational state of the building.
• KPI’s calculator: Evaluates all operational states based on specific KPIs.
• Optimal state selection: Based on the KPIs evaluation, the optimal operational state of the building is selected and applied.
• Flexibility calculator: The flexibility (both lower and upper) of the building will be estimated.
• District simulation: Based on the buildings’ simulation, the overall district behaviour simulation is going to be performed by this module.
• Demand response strategies: Manages and coordinates the different demand response strategies among the buildings in the district.

Building simulation is the replication of possible device states using Computational Intelligence. The objective of  building simulation is to produce possible combinations of device states while simulating the aggregate consumption these devices consume. The combinations are stochastically produced based only on the fact that states that consume less energy from the current state are needed. This is implemented by assuming that devices that are on open mode can be turned off and not vice versa.

Distributed energy resources (DERs) consist of end use appliances and electric devices located at the end users’ premises. DERs may be classified into various categories according to some similarities.

End users’ appliances main classification

Overall, for the building simulation, DER’s are classified into two main categories:

• Modifiable. Devices and appliances their operation mode can be modified from on to off (e.g. HVAC, lights (during the day), heater, etc.).
• Non modifiable. Devices and appliances their operation mode cannot be modified (computers, fridge, washing machine, etc.).

Initially, all the DER’s are characterized as modifiable or non-modifiable. Consequently, from the current state all the devices that are Non- Modifiable are removed. The modifiable devices that are selected from the current state stochastically produce all the potential combinations by assuming they can be turned off. Finally, all the possible produced states are added to the current state. Consequently, for every produced state the consumption it consumes is simulated. Particularly, given the current state and the aggregated predicted consumption a lightweight energy simulation is performed (building simulation) that estimates the consumption in case the system transits into any of the produced states (states generator). Finally, the KPIs are calculated for each produced state.

Consequently, a regression model is used for training. After the training process the algorithm is tested for its results and if the model is accurate it is saved. This pre-trained model is used to simulate the building’s consumption assuming that from the current state the building transits to a state produced by the state generator. The overall procedure is described in Figure 29.

In the framework of Plug-n-Harvest activities, CERTH/CPERI developed a Model Predictive Control (MPC)-based strategy for the energy management of a district, whose buildings are equipped with adaptable/dynamic envelopes including Building Integrated Photovoltaic (BIPV) systems and Battery Energy Storage Systems (BESS). The main objective of the proposed MPC strategy lies on the maximization of the district’s autonomy that is achieved through the synergy of the interactive buildings. The proposed control scheme consists of three main components, i.e. the forecaster, the optimizer and the district, which interact periodically with each other.

The forecaster aims at forecasting the values of the stochastic parameters of the model, i.e., the photovoltaic production and the load of each building. The optimizer takes into consideration the real values of the stochastic parameters for the current time step, and the prediction provided by the forecaster for the next time steps, in order to optimally schedule the energy management of the district for the planning horizon. The proposed MPC-based control strategy is applied on a hypothetical 5-node distribution network located in Greece for four representative days of the year, followed by an extensive sensitivity analysis, to demonstrate the effect of seasons and parameters’ modifications on the system behavior.

Furthermore, CERTH/CPERI co-formulated and validated a numerical model representing the dynamic operation of the Plug-n-Harvest Adaptive Dynamic Building Envelope (ADBE) façade, including its individual components, in Modelica. The aim of this model is to energetically investigate four demo sites in different European countries, and estimate their energy demands before and after the installation of the Plug-n-Harvest façade. In order to compare different designs of the façade from an energetic point of view, the building simulations have been conducted by using Modelica. Modelica is an object-oriented language for component-oriented modeling of complex systems and offers the possibility of implementing in-house mathematical functions. This feature substantially enhances the range of applications.

In this project, the standard Modelica library is extended by the AixLib library developed by RWTH Aachen University, which follows the Modelica guidelines, but it can be more applicable to the energy modeling of a building. In AixLib library there are modular blocks that can be connected in parallel or hierarchically to perform a top-level modeling of the entire building, taking into account its structure and the appropriate boundary conditions, such as weather, heating/cooling systems, occupancies and control strategies. For the Plug-n-Harvest project, this ADBE modular toolkit in Modelica includes configurable technical components and passive elements based on real product databases or additional findings from CERTH/CPERI and RWTH Aachen University research efforts. The scope of the simulations is to qualitatively and quantitatively estimate the behavior of the system from an energetic point of view and compare the energetic performance of the buildings under investigation before/after their refurbishment with different hypothetical configurations of the Plug-n-Harvest façade. More specifically, this simulation is expected to provide valuable comparison between the two states of each building, i.e. before and after the implementation of the Plug-n-Harvest solution, in terms of the room temperature level, the room energy demands and the occupants’ comfort.

The Optimal Energy Management System (OEMS) is a module developed by ETRA I+D, focused on the monitoring and optimization of energy in a building as well as a set of buildings (district or neighbourhood). The OEMS receives information from sensors in the buildings and other modules in the PnH ecosystem, as well as information from external, and generates the optimal strategies for the grid.

The OEMS is responsible for the communication and energy exchange: 1) among all the Plug-N-Harvest buildings, 2) between the buildings and the energy networks and the communication between the buildings and 3) external actors (such as ESCOs or aggregators). The OEMS monitors the energy related to the assets (HVAC, lighting, electric heaters…) and the energy related to components of the ADBE (PV panels and batteries) in order to calculate the overall energy consumption, production and flexibility of each building in the district and generate the next set of control decisions for the considered appliances and assets.

The OEMS is composed by two levels of abstraction. On one side, the OEMS Building is in charge of early detection of incidents and local optimization, whilst a module OEMS District is the responsible to generate the common strategies for all the district. The OEMS Building receives measures from sensors in the buildings and executes the first calculations per each building. On the other side, the OEMS District is in deal of the common strategy for the whole neighbourhood. Both levels can be watched in next pictures.

The OEMS is not only able to make typical EMS operations, but it can extract the consumption/production baselines, detect failures in any of the assets of the buildings, compose demand response strategies based on the tariffs, estimate a flexibility based on the reduction of use of devices, etc. All these features are oriented to the saving and sharing of energy in the district without sacrificing inhabitants’ comfort.

The Weather Module is an application which retrieves information from an external weather forecaster service and translates that information to be understandable by the rest of modules in PnH.

The Weather Module informs about the current weather as well as the predicted weather for the following hours in each configurated point, that is, each pilot site.

The Weather module is able to operate asynchronous or in a loop way, and it consider all locations indicated in its configuration settings. Besides, it included not only the typical features in weather services, like pressure or temperature, but information about the air quality or the solar radiation. Finally, it is able to inform about the needed following hours, being them another configured value.

In order to implement the principles of circularity in the design of the product, the Plug-N-Harvest Façade Modular Kit, Eco Intelligent Growth, a circular economy advisory and innovation company, has developed a tool: a circular economy design guideline based on the following five Circular Economy Design Requirements (CDR). The implementation of the CDRs across all Plug-N-Harvest Work Packages is the road to arrive to a circular business model:

• CDR 1: Use safe materials
• CDR 2: Think in System circularity
  • CDR 2.1: Define the right cycle
  • CDR 2.2: Make it easy to disassemble
  • CDR 2.3: Enhance materials productivity
  • CDR 2.4: Choose the inner cycle
• CDR 3: Preserve transparency and traceability
• CDR 4: Keep track of valuable materials
• CDR 5: Rethink business model / New Partnership models / implement business models that support a circular transition.

For each Circular Design Requirement defined the following characteristics are examined:

1. Methodology: used to assess and meet the intention defined in each CDR
2. Tools: available to implement the CDR
3. Key Objectives (KO), Key Performance Indicators (KPI) and (DPI) of Plug n Harvest project directly or indirectly related with this CDR
4. Strategies defined to implement each CDR
5. Relevant case studies. Examples and iconic case studies to exemplify the implementation of each CDR, and example of how the CDR has been implemented in Plug-N-Harvest project.

CDR 1: Use safe materials

Circular economy is based in the use of nontoxic materials to provide a healthy environment now and in the future. Design with materials that are safe for humans & the environment in the applications in which they are used:

• Safe during extraction and manufacturing,
• Use phase (intentional & likely unintentional),
• Cycling phase such as reuse, remanufacturing, recycling, composting, etcetera.
• Unintentional but probable end uses such as landfilling, incineration, etcetera.

The goal is for all materials of the Plug-N-Harvest system to be manufactured using only those materials that are optimal and do not contain any toxic or unknown materials.

CDR 2: Think in System circularity

In nature, there is no concept of waste. Everything is effectively food for another organism or system. Materials are reutilized in safe cycles. The Circular Economy aims to work on the same principles as Nature.
However, products and artefacts are not always compatible with the biological cycle and can’t be safely returned to nature. Many materials such as plastics, composites, minerals or even paper, once processed and mixed to make products, might become harmful both for humans and environment, in addition to not being biodegradable. Thus, they are converted into wastes and can potentially become a global problem.
Besides, many of these materials come from limited resources (virgin and non-renewable), leading to the depletion of available and valuable resources.
Both first and second dimensions as described in this CDR, have to be considered to enable the industrial development at the required pace, without compromising the future availability of materials and the unintended consequences for living beings. This means that biodegradable and non-biodegradable materials are to be circularity sourced and properly cycled.

To align the ADBE System design with Circular Economy business model, it is necessary to eliminate the concept of “waste”, and converting all the potential waste into nutrients.
To achieve this goal is necessary to consider the 3 dimensions of products and materials:

• Source for circularity: select recycled/ rapidly renewable materials
• Design for circularity: select reciclable materials
• Systems circularity: Plan materials recovery. Material Passports, take-back programs

The methodology followed to optimize the Material Reutilization and Product circularity of ADBE System is the Cradle to Cradle CertifiedTM Products Program version 4. The new version considers the following dimensions, shown in figure, and aligned with the Scope of CDR 4:

To promote and enable Circular business models, CDR 4 has been broken down in the following 4 design steps:

CDR 2.1: Define the right cycle

The Circular Economy, based on the Cradle to Cradle® principles, distinguishes to different cycles: the Biological Cycle and the Technical Cycle

All products present in a Circular System need to be identified as Biological Nutrient (BN) or as a Technical Nutrient (TN). Biological nutrients have been designed to flow though biological cycled and be safely reintroduced into nature. Otherwise, TN are mainly non-renewable materials designed to be cycled by industry.

CDR 2.2: Make it easy to disassemble

The aim is to design ADBE to enable the disassembly of the system into separate materials, to make each of them recyclable through the adequate path. In the design process always keep in mind deconstruction after use, which will influence in how to construct. Design ADBE to enable the disassembly of the system into separate materials, to make each of them recyclable through the adequate path.
According to Circular Economy principles, buildings are material banks for the future. Thus, design for deconstruction should enable to recover materials separately to return each to the corresponding material cycle, for its infinite reuse, recovering in this manner the value of each material, which in several cases will be higher over time (i.e. copper).
If materials are joined and mixed together the quality of the recycled output is in general lower than the input (downcycling). In this case, material’s residual value is drastically reduced and its future potential applications are limited – often to a sole new cycle, creating restrictions to the business model, with associated costs (i.e. for new waste management).
In contrast, designing from a System Circularity perspective, products are prepared to be disassembled: every component and subcomponent can be recovered separately to enable its upcycling.

CDR 2.3: Enhance materials productivity

Cascading keeps materials in circulation for longer. Thinks in life extension through maintenance, repair, parts and components recycling for other possible ways to enhance its productivity.

CDR 2.4: Choose the inner cycle:

The power of the inner circle refers to minimizing comparative materials use vis-à-vis the linear production system. The tighter the circle, i.e. the less a product has to be changed in reuse, refurbishment and remanufacturing and the faster it returns to use, the higher the potential savings on the shares of material, labour, energy and capital still embedded in the product, and the associated externalities (such as greenhouse gas (GHG) emissions, water and toxicity).

CDR 3: Preserve transparency and traceability

In general waste are “Materials without Identity” (Madaster, 2018). Correct identification and characterization of materials is a critical step to convert Wastes into Nutrients, which can be perpetually cycled in a safe way. Furthermore, this information needs to go through different actor along the supply chain. Transparency between different stakeholders as well as traceability along the materials flows are key elements, to effectively cycle the materials within an economy, and jump form a linear model into a circular model.

CDR 4: Keep track of valuable materials

Many resources are forecasted to run out within a relatively short period, other are subject to risk of supply disruption or serve for a purpose deemed important. Mineral criticality is a subjective concept that has evolved throughout history (Hayes et al, 2018). An abundance of literature has been published over the last decade, encompassing a variety of criteria and methodologies to define critical raw materials.
The approach of this Requirement to identify which materials are considered Critical is aligned with Critical Raw Materials (CRMs) defined by European Commission.

CDR 5: Rethink business model / New Partnership models / implement business models that support a circular transition.

The Circular Economy requires the development of new relationships between stakeholders in order to create and grow circular models.
The implementation of a new economy requires the development of new business models and to redefine relationships among all the stakeholders involved in building process.
Relation among users, building contractor, product manufacturer and materials suppliers change drastically, they go from a linear, static relationship (stakeholders are either suppliers, manufacturers of goods, or users/clients, etc.) towards an ‘ecosystemic’ scenario, in which stakeholders play different roles, and require new partners to create and grow circular models. In general, the relation is two or multiple-way, and all stakeholders become partners and co-responsible for product circularity.
This circular economy design guideline has been reflected in a tool that is openly available on the website. On this page there are the complete guideline and the details of each characteristic for each one of the five Circular Economy Design Requirements (CDR)