• Energy Research
• Open Access close
• other engineering and technologies close
• GB close
• EU close

Editorial Complex Optimization and Simulation in Power Systems

This study focuses on formulating the most sustainable concrete by incorporating recycled concrete aggregates and other products retrieved from construction and demolition (C&D) activities. Both recycled coarse aggregates (RCA) and recycled fine aggregates (RFA) are firstly used to fully replace the natural coarse and fine aggregates in the concrete mix design. Later, the cement rich ultrafine particles, recycled glass powder and mineral fibres recovered from construction and demolition wastes (CDW) are further incorporated at a smaller rate either as cement substituent or as supplementary additives. Remarkable properties are noticed when the RCA (4–12 mm) and RFA (0.25–4 mm) are fully used to replace the natural aggregates in a new concrete mix. The addition of recycled cement rich ultrafines (RCU), Recycled glass ultrafines (RGU) and recycled mineral fibres (RMF) into recycled concrete improves the modulus of elasticity. The final concrete, which comprises more than 75% (wt.) of recycled components/materials, is believed to be the most sustainable and green concrete mix. Mechanical properties and durability of this concrete have been studied and found to be within acceptable limits, indicating the potential of recycled aggregates and other CDW components in shaping sustainable and circular construction practices. The authors wish to acknowledge the financial support from EU Horizon 2020 Project VEEP ‘‘Cost-Effective Recycling of C&DW in High Added Value Energy Efficient Prefabricated Concrete Compo-nents for Massive Retrofitting of our Built Environment” (No.723582).

AbstractShipbuilding is an energy-intensive industrial sector that produces a significant amount of waste, pollution and air emissions. However, the International Maritime Organization concentrates only on reducing emissions during the operational phase. In order to completely phase out emissions from the shipping industry, a life-cycle approach must be taken. The study implemented the proposed transdisciplinary energy management framework in a Bangladeshi shipyard. The framework aims to support shipyard decision makers in making rational and optimized decisions to make shipyards sustainable, while maintaining good product quality and reducing relative cost. This is achieved by applying the Fuzzy Analytical Hierarchy Process and Fuzzy Order of Preference by Similarity to Ideal Solution methods to identify optimal solutions. In addition to making shipyards more sustainable, the framework can enhance both the business and socio-economic prospects of the shipyard and promote the reputation of the shipyard and improve its competitiveness and, in line with this, lead to the promotion of nationally determined contributions under the Paris Agreement for States. The implementation of the framework shows that the political and legal discipline, the social criteria and the implementation of ISO 14001 and cyber security were the most important criteria and options for the yard's decision makers.

Effective access control techniques are in demand, as electronically assisted healthcare services require the patient’s sensitive health records. In emergency situations, where the patient’s well-being is jeopardized, different healthcare actors associated with emergency cases should be granted permission to access Electronic Health Records (EHRs) of patients. The research objective of our study is to develop machine learning techniques based on patients’ time sequential health metrics and integrate them with an Attribute Based Access Control (ABAC) mechanism. We propose an ABAC mechanism that can yield access to sensitive EHRs systems by applying prognostic context handlers where contextual information, is used to identify emergency conditions and permit access to medical records. Specifically, we use patients’ recent health history to predict the health metrics for the next two hours by leveraging Long Short Term Memory (LSTM) Neural Networks (NNs). These predicted health metrics values are evaluated by our personalized fuzzy context handlers, to predict the criticality of patients’ status. The developed access control method provides secure access for emergency clinicians to sensitive information and simultaneously safeguards the patient’s well-being. Integrating this predictive mechanism with personalized context handlers proved to be a robust tool to enhance the performance of the access control mechanism to modern EHRs System.

The hydraulic balance of heating network is considered as a pre-condition for the implementation of low temperature district heating (LTDH). Its imbalance result into high energy consumption and heat-losses in the network. In this study, a novel hydraulic model is presented which investigates hydraulic imbalance in the LTDH network, using real weather and hourly monitored operational heating data from an existing boiler based building. Analysis of delta t in space-heating system shows that the delta t is maximum when the outside air temperature is lowest and it decreases with increase in outside air temperature. Furthermore, the hydraulic imbalance is analysed for four different control scenarios with the aim to find an optimum scenario with minimum pumping power, energy consumption and heat-losses in the LTDH network. Results show that the hydraulic imbalance is due to the absence of flow-limiters and balancing valves on the return pipe and thermostatic radiator valves (TRVs) alone are unable to maintain hydraulic balance in the space-heating system of buildings. Moreover, the control scenario with variable flow-rate and fixed supply water temperature from the sub-station is found to be optimum. Compared to the constant flow-rate scenario, the pumping power, energy consumption and heat-losses in the LTDH network are reduced by approximately 2%, 63% and 14%, respectively.

The wind power forecasting error restricts the benefit of the wind farm in the electricity market. Considering the cooperation of wind power bidding and energy storage system (ESS) operation with uncertainty, this paper proposes a coordinated bidding/operation model for the wind farm to improve its benefits in the electricity market. The maximum entropy based deep reinforcement learning (RL) algorithm, Soft Actor-Critic (SAC) is used to construct the model. The maximum entropy framework enables the designed agent to explore various optimal possibilities, which means the learned coordinated bidding/operation strategy is more stable considering the forecasting error. Particularly, penalty terms are introduced into the benefit function to relax the constraints and improve the convergency. The case study illustrates that the learned policy can effectively improve the wind farm benefit while ensuring robustness.

Abstract The consumption of takeaway food is increasing worldwide. Single-use containers used for takeaway food represent a significant source of waste and environmental impacts due to their low recyclability. Consequently, it is important to identify the best available alternatives and improvement opportunities to reduce the environmental impacts of fast-food containers. For these purposes, this study estimates and compares for the first time the life cycle impacts of three most widely-used types of takeaway container: aluminium, polypropylene and extruded polystyrene. These are also compared to reusable polypropylene containers. The findings suggest that single-use polypropylene containers are the worst option for seven out of 12 impacts considered, including global warming potential. They are followed by the aluminium alternative with five highest impacts, including depletion of ozone layer and human toxicity. Overall, extruded polystyrene containers have the lowest impacts due to the lower material and electricity requirements in their manufacture. They are also the best option when compared to reused takeaway polypropylene containers, unless the latter are reused 3–39 times. The number of uses needed for the reusable “Tupperware” polypropylene food savers is even higher, ranging from 16 to 208 times, with terrestrial ecotoxicity being always higher than for extruded polystyrene, regardless of the number of uses. However, extruded polystyrene containers are currently not recycled and cannot be considered a sustainable option. If they were recycled in accordance with the European Union 2025 policy on waste packaging, most of their impacts would be reduced by >18%, while also reducing littering and negative effects on marine organisms. Most of the impacts of the other two types of container would also be reduced (>20%) through increased recycling. Implementing the European Union 2025 policy on recycling of waste packaging would reduce all the impacts by 2%–60%, including a 33% reduction in global warming potential. Based on 2025 million takeaway containers used annually in the European Union, the latter would save 61,700 t CO2 eq./yr, equivalent to the emissions of 55,000 light-duty vehicles. The outcomes of this study will be of interest to packaging manufacturers, food outlets, policy makers and consumers.

The successful electricity grid integration of solar energy into day-ahead markets requires at least hourly resolved 48 h forecasts. Technologies as photovoltaics and non-concentrating solar thermal technologies make use of global horizontal irradiance (GHI) forecasts, while all concentrating technologies both from the photovoltaic and the thermal sector require direct normal irradiances (DNI). The European Centre for Medium-Range Weather Forecasts (ECMWF) has recently changed towards providing direct as well as global irradiances. Additionally, the MACC (Monitoring Atmospheric Composition & Climate) near-real time services provide daily analysis and forecasts of aerosol properties in preparation of the upcoming European Copernicus programme. The operational ECMWF/IFS (Integrated Forecast System) forecast system will in the medium term profit from the Copernicus service aerosol forecasts. Therefore, within the MACC‑II project specific experiment runs were performed allowing for the assessment of the performance gain of these potential future capabilities. Also the potential impact of providing forecasts with hourly output resolution compared to three-hourly resolved forecasts is investigated. The inclusion of the new aerosol climatology in October 2003 improved both the GHI and DNI forecasts remarkably, while the change towards a new radiation scheme in 2007 only had minor and partly even unfavourable impacts on the performance indicators. For GHI, larger RMSE (root mean square error) values are found for broken/overcast conditions than for scattered cloud fields. For DNI, the findings are opposite with larger RMSE values for scattered clouds compared to overcast/broken cloud situations. The introduction of direct irradiances as an output parameter in the operational IFS version has not resulted in a general performance improvement with respect to biases and RMSE compared to the widely used Skartveit et al. (1998) global to direct irradiance conversion scheme. Cloudy situations and especially thin ice cloud cases are forecasted much better with respect to biases and RMSE, but large biases are introduced in clear sky cases. When applying the MACC aerosol scheme to include aerosol direct effects, an improvement especially in DNI biases is found for cloud free cases as expected. However, a performance decrease is found for water cloud cases. It is assumed that this is caused by the lack of an explicit modelling of cloud-aerosol interactions, while other meteorological forcings for cloud processes like the temperature field are modified by the aerosols.