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Abstract A variety of novel, recyclable and reusable, construction materials has already been studied within literature during the past years, aiming at improving the overall energy efficiency ranking of the building envelope. However, several studies show that a delicate control of indoor climating elements can lead to a significant performance improvement by exploiting the building’s savings potential via smart adaptive HVAC regulation to exogenous uncertain disturbances (e.g. weather, occupancy). Building Optimization and Control (BOC) systems can be categorized into two different groups: centralized (requiring high data transmission rates at a central node from every corner of the overall system) and decentralized 1 (assuming an intercommunication among neighboring constituent systems). Moreover, both approaches can be further divided into two subcategories, respectively: model-assisted (usually introducing modeling oversimplifications) and model-free (typically presenting poor stability and very slow convergence rates). This paper presents the application of a novel, decentralized, agent-based , model-free BOC methodology (abbreviated as L4GPCAO) to a modern non-residential building (E.ON. Energy Research Center’s main building), equipped with controllable HVAC systems and renewable energy sources by utilizing the existing Building Management System (BES). The building testbed is located inside the RWTH Aachen University campus in Aachen, Germany. A combined rule criterion composed of the non-renewable energy consumption (NREC) and the thermal comfort index – aligned to international comfort standards – was adopted in all cases presented herein. Besides the limited availability of the specified building testbed, real-life experiments demonstrated operational effectiveness of the proposed approach in BOC applications with complex, emerging dynamics arising from the building’s occupancy and thermal characteristics. L4GPCAO outperformed the control strategy that was designed by the planers and system provider, in a conventional manner, requiring no more than five test days.

The unpredictable nature of renewable energies is drawing attention to lithium-ion batteries. In order to make full utilization of these batteries, some research works are focused on the management of existing systems, while others propose sizing techniques based on business models. However, in order to optimise the global system, a comprehensive methodology that considers both battery sizing and management at the same time is needed. This paper proposes a new optimisation algorithm based on a combination of dynamic programming and a region elimination technique that makes it possible to address both problems at the same time. This is of great interest, since the optimal size of the storage system depends on the management strategy and, in turn, the design of this strategy needs to take account of the battery size. The method is applied to a real installation consisting of a 100 kWp rooftop photovoltaic plant and a Li-ion battery system connected to a grid with variable electricity price. Results show that, unlike conventional optimisation methods, the proposed algorithm reaches an optimised energy dispatch plan that leads to a higher net present value. Finally, the tool is used to provide a sensitivity analysis that identifies key informative variables for decision makers The authors would like to acknowledge the support of the Spanish State Research Agency and FEDER-UE under grants DPI2016-80641-R and DPI2016-80642-R; of Government of Navarra through research project PI038 INTEGRA-RENOVABLES; and the FPU Program of the Spanish Ministry of Education, Culture and Sport (FPU13/00542).

Abstract With hydrogen becoming more and more important as energy carrier, there is a need for high capacity storage technologies preferably operating at low pressures. Chemical storage in metal hydrides is promising for that purpose, but they require thermal management for hydrogen release and storage, respectively. To overcome this challenge, it is beneficial to store the heat needed for hydrogen release during hydrogen storage in the storage system keeping the additional effort to provide that heat low. In this work, the experimental proof of concept of an adiabatic storage reactor is presented. Magnesium hydride and magnesium hydroxide have been used for hydrogen storage and thermochemical heat storage, respectively. A prototype reactor has been developed and experimentally investigated. It was found that the operating temperature of the materials can be adjusted with the gas pressure in a way to establish a temperature gradient from the MgH2 to the Mg(OH)2 and vice versa. Hydrogen storage and release is enhanced by the thermochemical heating/cooling. A pressure of 9 bar is sufficient to store hydrogen with a capacity of 20.8 gH2 L-1 based on the two materials only, without the steel vessel or insulation. In the heat storage compartment, 300 °C have been reached at 9.75 bar during heat release which is high enough to drive the MgH2 dehydrogenation.

Abstract The global share of photovoltaic plants in desert locations increases continuously due to inexpensive land and higher yield due to higher irradiation levels. However, PV modules suffer from harsh environmental conditions that influence their lifetime and, consequently, the levelized cost of electricity. Environmental factors such as high temperature differences between nights and days, high ultraviolet doses, high ambient temperatures, and high airborne dust lead to durability and performance issues such as delamination, discoloration, fatigue of interconnection, breakage of solar cells, hot-spots, and power loss due to the soiling. In this work, different bills of materials and module designs are evaluated, targeting optimum PV output power while increasing the service life and performance of the PV modules in desert climates. A stepwise optimization of module components (solar cells, glass coating and polymers/encapsulation) and module design (full vs. half cells, tab widths) are performed by simulation and experimental approaches. Simulations results analyzes the loss mechanisms and electricity production of PV modules by considering the impact of module material and design Experimentally, ultraviolet stress tests and thermal cycling tests are performed for polymer durability and interconnection fatigue analysis. The soiling reduction potential of a newly developed glass coating is investigated by outdoor exposure tests in Saudi-Arabia. It is shown by proper choice of materials and optimized interconnection design, the efficiency of the module is increased by 9.58%rel. relative to the reference module. Furthermore, the choice of encapsulant and module design strongly affect the expected service-life, and soiling losses could be reduced up to 35%.

As the natural gas market is moving towards short-term planning, accurate and robust short-term forecasts of the demand and supply of natural gas is of fundamental importance for a stable energy supply, a natural gas control schedule, and transport operation on a daily basis. We propose a hybrid forecast model, Functional AutoRegressive and Convolutional Neural Network model, based on state-of-the-art statistical modeling and artificial neural networks. We conduct short-term forecasting of the hourly natural gas flows of 92 distribution nodes in the German high-pressure gas pipeline network, showing that the proposed model provides nice and stable accuracy for different types of nodes. It outperforms all the alternative models, with an improved relative accuracy up to twofold for plant nodes and up to fourfold for municipal nodes. For the border nodes with rather flat gas flows, it has an accuracy that is comparable to the best performing alternative model.

Access to decentralized energy solutions is critical for the development of rural communities in Sub-Saharan Africa (SSA). One form of decentralized energy technology, agrivoltaics (AV), has gained attention in the last decade as a means to tackle energy poverty in off-grid communities. The success of renewable energy (RE) projects of any kind, however, depends on existing challenges and risks that may be faced at different stages. Therefore, the aim of this paper is to identify and analyze the main risk factors when implementing agrivoltaics on a community level in SSA. Based on the PESTLE framework, the paper develops a first risk classification system for international agrivoltaic projects in rural farming communities in SSA. By making use of a mixed-methods approach, the research enables the prioritization of potential risks in terms of their occurrence and significance; furthermore, it discusses mitigation strategies. Results indicate that there are various multifaceted risks associated with agrivoltaics, primarily including financial challenges and regulatory barriers. In addition, the study emphasizes the importance of stakeholder involvement as well as a need for long-term community engagement and capacity building to ensure a successful implementation and sustainability of agrivoltaic projects.

Abstract Biofuels and the oleochemical industry are highly dependent on plant oils for the generation of renewable product lines. Consequently, production of plant lipids, such as palm and rapeseed oil, for industrial applications competes with agricultural activity and is associated with a negative environmental impact. Additionally, established chemical routes for upgrading bio-lipids to renewable products depend on metal-containing catalysts. Metal leaching during oil processing results in heavy metal contaminated process wastewater. This water is difficult to remediate and leads to the loss of precious metals. Therefore, the biofuels and chemical industry requires sustainable solutions for production and upgrading of bio-lipids. With regard to the former, a promising approach is the fermentative conversion of abundant, low-value biomass into microbial, particularly yeast-based lipids. This study describes the holistic, value-adding conversion of underexploited, macroalgae feedstocks into yeast oil, animal feed and biosorbents for metal-based detoxification of process wastewater. The initial step comprises a selective enzymatic liquefaction step that yields a supernatant containing 62.5% and 59.3% (w/dwbiomass) fermentable sugars from L. digitata and U. lactuca, respectively. By dispensing with chemical pretreatment constraints, we achieved a 95% (w/w) glucose recovery. Therefore, the supernatant was qualified as a cultivation media without any detoxification step or nutrition addition. Additionally, the hydrolysis step provided 27–33% (w/dwbiomass) of a solid residue, which was qualified as a metal biosorbent. Cultivation of the oleaginous yeast C. oleaginosus on the unprocessed hydrolysis supernatant provided 44.8 g L−1 yeast biomass containing 37.1% (w/dwbiomass) lipids. The remaining yeast biomass after lipid extraction is targeted as a performance animal feed additive. Selectivity and capacity of solid macroalgae residues as biosorbents were assessed for removal and recycling of rare and heavy metals, such as Ce+3, Pb+2, Cu+2 and Ni+2 from model wastewater. The biosorption capacity of the macroalgae residues (sorption capacity ∼ 0.7 mmol g−1) exceeds that of relevant commercially available adsorption resins and biosorbents. To facilitate the integration of our technology in existing chemical and biotechnological production environments, we have devised simple, rapid and cost-efficient methods for monitoring both lipogenesis and metal sorption processes. The application of the new optical monitoring tools is essential to determine yeast cell harvesting times and biosorption capacities respectively. For the first time we report on a waste-free bioprocess that combines sustainable, microbial lipid production from low value marine biomass with in-process precious metal recycling options. Our data allowed for a preliminary economic analysis, which indicated that each product could be cost competitive with current market equivalents. Hence, the synaptic nature of the technology platform provides for the economic and ecologic viability of the overall process chain.

Abstract In this paper, an open source tool is introduced to represent urban energy infrastructure in the City of Philadelphia, and different renewable energy scenarios are compared with respect to minimization of the standard deviation of the residual load. Renewable energy sources play a critical role in the world’s ongoing energy transition in response to climate change. Urban Energy Systems may be particularly sensitive to this transition due to the high energy demand density associated with urban environments. Open energy analysis and modeling tools can provide important information that can be used by urban energy planners, policy makers, and other stakeholders during this transition. In the present study, we apply FlexiGIS, an open energy modeling tool developed in a European context, to a case study in the City of Philadelphia. Due to the importance of open access to energy data, we pay particular attention to open energy data sources. Notably, OpenStreetMap was incomplete in its spatial coverage, but alternate open data resources were identified. This work conducts an optimization of the renewable energy mix to minimize the amount of balancing energy required for the residual load. We observe that Philadelphia has an optimal mix of renewables that favors a roughly even share of wind and solar, but that, compared to a previous case study in Oldenburg, Germany, requires more balancing energy at comparable levels of renewable penetration.