The production of biogas through anaerobic digestion is one of the technological solutions to convert biomass into a readily usable fuel. Biogas can replace natural gas, if the biogas is upgraded to green gas. To contribute to the EU-target to reduce Green House Gases emissions, the installed biogas production capacity and the amount of farm-based biomass, as a feedstock, has to be increased. A model was developed to describe a green gas production chain that consists of several digesters connected by a biogas grid to anupgrading and injection facility. The model calculates costs and energy use for 1 m3 of green gas. The number of digesters in the chain can be varied to find results for different configurations. Results are presented for a chain with decentralized production of biogas, i.e. a configuration with several digesters, and a centralized green gas production chain using a single digester. The model showed that no energy advantage per produced m3 green gas can be created using a biogas grid and decentralized digesters instead of one large-scale digester. Production costs using a centralized digester are lower, in the range of5 Vct to 13 Vct per m3, than in a configuration of decentralized digesters. The model calculations also showed the financial benefit for an operator of a small-scale digester wishing to produce green gas in the cooperation with nearby other producers. E.g. subsidies and legislation based on environmental arguments could encourage the use of decentralized digesters in a biogas grid.
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Author supplied: In a production environment where different products are being made in parallel, the path planning for every product can be different. The model proposed in this paper is based on a production environment where the production machines are placed in a grid. A software entity, called product agent, is responsible for the manufacturing of a single product. The product agent will plan a path along the production machines needed for that specific product. In this paper, an optimization is proposed that will reduce the amount of transport between the production machines. The effect of two factors that influence the possibilities for reductions is shown in a simulation, using the proposed optimization scheme. These two factors are the redundancy of production steps in the grid and the
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A transparent and comparable understanding of the energy efficiency, carbon footprint, and environmental impacts of renewable resources are required in the decision making and planning process towards a more sustainable energy system. Therefore, a new approach is proposed for measuring the environmental sustainability of anaerobic digestion green gas production pathways. The approach is based on the industrial metabolism concept, and is expanded with three known methods. First, the Material Flow Analysis method is used to simulate the decentralized energy system. Second, the Material and Energy Flow Analysis method is used to determine the direct energy and material requirements. Finally, Life Cycle Analysis is used to calculate the indirect material and energy requirements, including the embodied energy of the components and required maintenance. Complexity will be handled through a modular approach, which allows for the simplification of the green gas production pathway while also allowing for easy modification in order to determine the environmental impacts for specific conditions and scenarios. Temporal dynamics will be introduced in the approach through the use of hourly intervals and yearly scenarios. The environmental sustainability of green gas production is expressed in (Process) Energy Returned on Energy Invested, Carbon Footprint, and EcoPoints. The proposed approach within this article can be used for generating and identifying sustainable solutions. By demanding a clear and structured Material and Energy Flow Analysis of the production pathway and clear expression for energy efficiency and environmental sustainability the analysis or model can become more transparent and therefore easier to interpret and compare. Hence, a clear ruler and measuring technique can aid in the decision making and planning process towards a more sustainable energy system.
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This paper describes an agent-based software infrastructure for agile industrial production. This production is done on special devices called equiplets. A grid of these equiplets connected by a fast network is capable of producing a variety of different products in parallel. The multi-agent-based underlying systems uses two kinds of agents: an agent representing the product and an agent representing the equiplet.
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The European Union is striving for a high penetration of renewable energy production in the future energy grid. Currently, the EU energy directive is aiming for 20% renewable energy production in the year 2020. In future plans the EU strives for approximately 80% renewable energy production by the year 2050. However, high penetration of wind and solar PV energy production, both centrally and de-centrally, can possibly destabilize the electricity grid. The gas grid and the flexibility of gas, which can be transformed in both electricity and heat at different levels of scale, can help integrate and balance intermittent renewable production. One possible method of assisting the electricity grid in achieving and maintaining balance is by pre-balancing local decentralized energy grids. Adopting flexible gas based decentralized energy production can help integrate intermittent renewable electricity production, short lived by-products (e.g. heat) and at the same time minimize transport of energy carriers and fuel sources. Hence, decentralized energy grids can possibly improve the overall efficiency and sustainability of the energy distribution system. The flexibility aforementioned, can potentially give gas a pivotal role in future decentralized energy grids as load balancer. However, there are a lot of potentially variables which effect a successful integration of renewable intermittent production and load balancing within decentralized energy systems. The flexibility of gas in general opens up multiple fuel sources e.g., natural gas, biogas, syngas etc. and multiple possibilities of energy transformation pathways e.g. combined heat and power, fuel cells, high efficiency boilers etc. Intermittent renewable production is already increasing exponentially on the decentralized level where load balancing is still lacking.
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The energy transition requires the transformation of communities and neighbourhoods. It will have huge ramifications throughout society. Many cities, towns and villages have put together ambitious visions about how to achieve e.g. energy neutrality, zero-emission or zero-impact. What is happening at the local level towards realizing these ambitions? In a set of case study’s we investigate the following questions: How are self-organized local energy initiatives performing their self-set tasks? What obstacles are present in the current societal set-up that can hinder decentralized energy production? In our cases local leadership, vision, level of communication and type of organisation are important factors of the strength of the ‘local network’. (Inter)national energy policy and existing energy companies largely determine the ‘global’ or outside network. Stronger regional and national support structures, as well as an enabling environment for decentralized energy production, are needed to make decentralized sustainable energy production a success.
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Abstract written for an poster presentation at the EBA conference in Alkmaar. The flexibility of biogas makes it a very capable load balancer within decentralized smart energy systems. However, within this context the sustainability of biogas production is not fully understood. What is needed is a tool for analyzing the ustainability of biogas production pathways. The main goal, of this research is to design a transparent flexible planning tool capable determining the sustainability of decentralized biogas production chains. This insight will help in designing a tailor-made biogas production chain for a specific geographic location, increasing the effectiveness and sustainability of biogas as a renewable resource.
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Renewable energy is often suggested as a possible solution for reducing greenhouse gas emissions and decreasing dependency on fossil energy sources. The most readily available renewable energy sources in Europe, wind, solar and biomass are dispersed by nature, making them ideally suited for use within Decentralized Energy Systems. Decentralized energy grids can help integrate renewable production, short lived by-products e.g. heat, minimize transport of energy carriers and fuel sources and reduce the dependency on fossils, hence, possibly improving the overall efficiency and sustainability of the energy distribution system. Within these grids balance between local renewable production and local energy demand is an important subject. Currently, fluctuations between demand and production of energy are mainly balanced by input from conventional power stations, which operate on storable fossil energy sources e.g. coal, oil, natural gas and nuclear. Within the long term scope of transition towards a low carbon intensive energy system, sustainable systems must be found which can replace fossil energy sources as load balancer in our energy supply systems.
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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.
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Peer-to-peer (P2P) energy trading has been recognized as an important technology to increase the local self-consumption of photovoltaics in the local energy system. Different auction mechanisms and bidding strategies haven been investigated in previous studies. However, there has been no comparatively analysis on how different market structures influence the local energy system’s overall performance. This paper presents and compares two market structures, namely a centralized market and a decentralized market. Two pricing mechanisms in the centralized market and two bidding strategies in the decentralized market are developed. The results show that the centralized market leads to higher overall system self-consumption and profits. In the decentralized market, some electricity is directly sold to the grid due to unmatchable bids and asks. Bidding strategies based on the learning algorithm can achieve better performance compared to the random method.
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