This report is intended to collect, present, and evaluate the various solutions applied in individual operational pilots for their (upscaling and transnational transfer) potential, in terms of opportunities and barriers, over the short and long(er)-term. This is done by identifying the main characteristics of the solutions and sites and the relevant influencing factors at different local (dimension) contexts.The analysis provides insights in barriers but also opportunities and conditions for success across four main dimensions that make up the local context landscape. We consider two main roll-out scenarios:1. Upscaling within the boundaries of the country where the operational pilot (OP) took place2. Transnational Transfer relates to the potential for transferring a (V4)ES solution to any of the other three (project) countriesThere are several aspects within the four main dimensions that are cross-cutting for all four countries, either because EU legislation lies at its roots, or because market conditions are fairly similar for certain influencing factors in those dimension.Ultimately, both Smart Charging and V2X market are still in their relevant infancies. The solutions applied in various SEEV4-City pilots are relatively straightforward and simple in ‘smartness’. This helps the potential for adoption but may not always be the optimal solution yet. The Peak shaving or load/demand shifting solutions are viable options to reduce costs for different stakeholders in the (electricity) supply chain. The market is likely to mature and become much smarter in coming 5 – 10 years. This also includes the evolvement (or spin-offs) of the solutions applied in SEEV4-_City as well. At least in the coming (approximately) 5 years Smart Charging appears to have the better financial business case and potential for large scale roll-out with less (impactful) bottlenecks, but looking at longer term V2X holds its potential to play a significant role in the energy transition.A common denominator as primary barriers relates to existing regulation, standards readiness and limited market availability of either hardware or service offerings.
Methods to design viable business networks (BNs) treat the concept of viability merely in terms of profitability. Further, the methods are restricted to mono-commodity (a single product or a service) BNs. However, business literature suggests that besides economic value (profit), non-economic values (e.g. lowering CO2 emission) play an important role in making BNs viable. Furthermore, BNs can also be multi-commodity (e.g. electricity, gas, heat). Hence, we aim to develop an method to determine a viable configuration of services for multi-commodity BNs. In addition, the term viability is used in an extended scope to include non-economic values.
The built environment requires energy-flexible buildings to reduce energy peak loads and to maximize the use of (decentralized) renewable energy sources. The challenge is to arrive at smart control strategies that respond to the increasing variations in both the energy demand as well as the variable energy supply. This enables grid integration in existing energy networks with limited capacity and maximises use of decentralized sustainable generation. Buildings can play a key role in the optimization of the grid capacity by applying demand-side management control. To adjust the grid energy demand profile of a building without compromising the user requirements, the building should acquire some energy flexibility capacity. The main ambition of the Brains for Buildings Work Package 2 is to develop smart control strategies that use the operational flexibility of non-residential buildings to minimize energy costs, reduce emissions and avoid spikes in power network load, without compromising comfort levels. To realise this ambition the following key components will be developed within the B4B WP2: (A) Development of open-source HVAC and electric services models, (B) development of energy demand prediction models and (C) development of flexibility management control models. This report describes the developed first two key components, (A) and (B). This report presents different prediction models covering various building components. The models are from three different types: white box models, grey-box models, and black-box models. Each model developed is presented in a different chapter. The chapters start with the goal of the prediction model, followed by the description of the model and the results obtained when applied to a case study. The models developed are two approaches based on white box models (1) White box models based on Modelica libraries for energy prediction of a building and its components and (2) Hybrid predictive digital twin based on white box building models to predict the dynamic energy response of the building and its components. (3) Using CO₂ monitoring data to derive either ventilation flow rate or occupancy. (4) Prediction of the heating demand of a building. (5) Feedforward neural network model to predict the building energy usage and its uncertainty. (6) Prediction of PV solar production. The first model aims to predict the energy use and energy production pattern of different building configurations with open-source software, OpenModelica, and open-source libraries, IBPSA libraries. The white-box model simulation results are used to produce design and control advice for increasing the building energy flexibility. The use of the libraries for making a model has first been tested in a simple residential unit, and now is being tested in a non-residential unit, the Haagse Hogeschool building. The lessons learned show that it is possible to model a building by making use of a combination of libraries, however the development of the model is very time consuming. The test also highlighted the need for defining standard scenarios to test the energy flexibility and the need for a practical visualization if the simulation results are to be used to give advice about potential increase of the energy flexibility. The goal of the hybrid model, which is based on a white based model for the building and systems and a data driven model for user behaviour, is to predict the energy demand and energy supply of a building. The model's application focuses on the use case of the TNO building at Stieltjesweg in Delft during a summer period, with a specific emphasis on cooling demand. Preliminary analysis shows that the monitoring results of the building behaviour is in line with the simulation results. Currently, development is in progress to improve the model predictions by including the solar shading from surrounding buildings, models of automatic shading devices, and model calibration including the energy use of the chiller. The goal of the third model is to derive recent and current ventilation flow rate over time based on monitoring data on CO₂ concentration and occupancy, as well as deriving recent and current occupancy over time, based on monitoring data on CO₂ concentration and ventilation flow rate. The grey-box model used is based on the GEKKO python tool. The model was tested with the data of 6 Windesheim University of Applied Sciences office rooms. The model had low precision deriving the ventilation flow rate, especially at low CO2 concentration rates. The model had a good precision deriving occupancy from CO₂ concentration and ventilation flow rate. Further research is needed to determine if these findings apply in different situations, such as meeting spaces and classrooms. The goal of the fourth chapter is to compare the working of a simplified white box model and black-box model to predict the heating energy use of a building. The aim is to integrate these prediction models in the energy management system of SME buildings. The two models have been tested with data from a residential unit since at the time of the analysis the data of a SME building was not available. The prediction models developed have a low accuracy and in their current form cannot be integrated in an energy management system. In general, black-box model prediction obtained a higher accuracy than the white box model. The goal of the fifth model is to predict the energy use in a building using a black-box model and measure the uncertainty in the prediction. The black-box model is based on a feed-forward neural network. The model has been tested with the data of two buildings: educational and commercial buildings. The strength of the model is in the ensemble prediction and the realization that uncertainty is intrinsically present in the data as an absolute deviation. Using a rolling window technique, the model can predict energy use and uncertainty, incorporating possible building-use changes. The testing in two different cases demonstrates the applicability of the model for different types of buildings. The goal of the sixth and last model developed is to predict the energy production of PV panels in a building with the use of a black-box model. The choice for developing the model of the PV panels is based on the analysis of the main contributors of the peak energy demand and peak energy delivery in the case of the DWA office building. On a fault free test set, the model meets the requirements for a calibrated model according to the FEMP and ASHRAE criteria for the error metrics. According to the IPMVP criteria the model should be improved further. The results of the performance metrics agree in range with values as found in literature. For accurate peak prediction a year of training data is recommended in the given approach without lagged variables. This report presents the results and lessons learned from implementing white-box, grey-box and black-box models to predict energy use and energy production of buildings or of variables directly related to them. Each of the models has its advantages and disadvantages. Further research in this line is needed to develop the potential of this approach.
Cross-Re-Tour supports European tourism SME while implementing digital and circular economy innovations. The three year project promotes uptake and replication by tourism SMEs of tools and solutions developed in other sectors, to mainstream green and circular tourism business operations.At the start of the project existing knowledge-gaps of tourism SMEs will be researched through online dialogues. This will be followed by a market scan, an overview of existing state of the art solutions to digital and green constraints in other economic sectors, which may be applied to tourism SME business operations: water, energy, food, plastic, transport and furniture /equipment. The scan identifies best practices from other sectors related to nudging of clients towards sustainable behaviour and nudging of staff on how to best engage with new tourism market segments.The next stage of the project relates to two design processes: an online diagnostic tool that allows for measuring and assessing (160) SME’s potential to adapt existing solutions in digital and green challenges, developed in other economic sectors. Next to this, a knowledge hub, addresses knowledge constraints and proposes solutions, business advisory services, training activities to SMEs participating. The hub acts as a matchmaker, bringing together 160 tourism SMEs searching for solutions, with suppliers of existing solutions developed in other sectors. The next key activity is a cross-domain open innovation programme, that will provide 80 tourism SMEs with financial support (up to EUR 30K). Examples of partnerships could be: a hotel and a supplier of refurbished matrasses for hospitals; a restaurant and a supplier of food rejected by supermarkets, a dance event organiser and a supplier of refurbished water bottles operating in the cruise industry, etc.The 80 cross-domain partnerships will be supported through the knowledge hub and their business innovation advisors. The goal is to develop a variety of innovative partnerships to assure that examples in all operational levels of tourism SMEs.The innovation projects shall be presented during a show-and-share event, combined with an investors’ pitch. The diagnostic tool, market scan, knowledge hub, as well as the show and share offer excellent opportunities to communicate results and possible impact of open innovation processes to a wider international audience of destination stakeholders and non-tourism partners. Societal issueSupporting the implementation of digital and circular economy solutions in tourism SMEs is key for its transition towards sustainable low-impact industry and society. Benefit for societySolutions are already developed in other sectors but the cross-over towards tourism is not happening. The project bridges this gap.
In the road transportation sector, CO2 emission target is set to reduce by at least 45% by 2030 as per the European Green Deal. Heavy Duty Vehicles contribute almost quarter of greenhouse gas emissions from road transport in Europe and drive majorly on fossil fuels. New emission restrictions creates a need for transition towards reduced emission targets. Also, increasing number of emission free zones within Europe, give rise to the need of hybridization within the truck and trailer community. Currently, in majority of the cases the trailer units do not possess any kind of drivetrain to support the truck. Trailers carry high loads, such that while accelerating, high power is needed. On the other hand, while braking the kinetic energy is lost, which otherwise could be recaptured. Thus, having a trailer with electric powertrain can support the truck during traction and can charge the battery during braking, helping in reducing the emissions and fuel consumption. Using the King-pin, the amount of support required by trailer can be determined, making it an independent trailer, thus requiring no modification on the truck. Given the heavy-duty environment in which the King-pin operates, the measurement design around it should be robust, compact and measure forces within certain accuracy level. Moreover, modification done to the King-pin is not apricated. These are also the challenges faced by V-Tron, a leading company in the field of services in mobility domain. The goal of this project is to design a smart King-pin, which is robust, compact and provides force component measurement within certain accuracy, to the independent e-trailer, without taking input from truck, and investigate the energy management system of the independent e-trailer to explore the charging options. As a result, this can help reduce the emissions and fuel consumption.
The research, supported by our partners, sets out to understand the drivers and barriers to sustainable logistics in port operations using a case study of drone package delivery at Rotterdam Port. Beyond the technical challenges of drone technology as an upcoming technology, it needs to be clarified how drones can operate within a port ecosystem and how they could contribute to sustainable logistics. KRVE (boatmen association), supported by other stakeholders of Rotterdam port, approached our school to conduct exploratory research. Rotterdam Port is the busiest port in Europe in terms of container volume. Thirty thousand vessels enter the port yearly, all needing various services, including deliveries. Around 120 packages/day are delivered to ships/offices onshore using small boats, cars, or trucks. Deliveries can take hours, although the distance to the receiver is close via the air. Around 80% of the packages are up to 20kg, with a maximum of 50kg. Typical content includes documents, spare parts, and samples for chemical analysis. Delivery of packages using drones has advantages compared with traditional transport methods: 1. It can save time, which is critical to port operators and ship owners trying to reduce mooring costs. 2. It can increase logistic efficiency by streamlining operations. 3. It can reduce carbon emissions by limiting the use of diesel engines, boats, cars, and trucks. 4. It can reduce potential accidents involving people in dangerous environments. The research will highlight whether drones can create value (economic, environmental, social) for logistics in port operations. The research output links to key national logistic agenda topics such as a circular economy with the development of innovative logistic ecosystems, energy transition with the reduction of carbon emissions, societal earning potential where new technology can stimulate the economy, digitalization, key enabling technology for lean operations, and opportunities for innovative business models.
