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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.

Wind turbine gearboxes are required to operate under adverse operational conditions over a long service lifetime. Unfortunately, gearbox designers are yet to achieve the reliability anticipated by wind turbine manufacturers and operators. The poor understanding of variable loading conditions has resulted in the majority of wind turbine gearboxes being unable to reach their expected service lifetime of 20-25 years. This has led to an increasing need to investigate the fundamental issues associated with the degradation of wind turbine gearbox materials during operation in order to improve existing designs and optimise future ones. This paper investigates the various challenges that need to be addressed in order to achieve a noteworthy increase in the operational service lifetime of large-scale industrial wind turbine gearboxes.

AbstractWe devise a methodology to predict failures in wind turbine drive‐train components and quantify its utility. The methodology consists of two main steps. The first step is the set up of a predictive model for shutdown events, which is able to raise an alarm in advance of the fault‐induced shutdown. The model is trained on data for shutdown events retrieved from the alarm log of an offshore wind farm. Here, it is assumed that the timely prediction of low‐severity events, typically caused by abnormal component operation, allows for an intervention that can prevent premature component failures. The prediction models are based on statistical classification using only supervisory control and data acquisition (SCADA) data. In the second step, the shutdown prediction model is combined with a cost model to provide an estimate of the benefits associated with implementing the predictive maintenance system. This is achieved by computing the maximum net utility attainable as a function of the model performance and efficiency of intervention carried out by the user. Results show that the system can be expected to be cost‐effective under specific conditions. A discussion about potential improvements of the approach is provided, along with suggestions for further research in this area.

Short-term generation scheduling is an important function in daily operational planning of power systems. It is defined as optimal scheduling of power generators over a scheduling period while respecting various generator constraints and system constraints. Objective of the problem includes costs associated with energy production, start-up cost and shut-down cost along with profits. The resulting problem is a large scale nonlinear mixed-integer optimization problem for which there is no exact solution technique available. The solution to the problem can be obtained only by complete enumeration, often at the cost of a prohibitively computation time requirement for realistic power systems. This paper presents a hybrid algorithm which combines Lagrangian Relaxation (LR) together with Evolutionary Algorithm (EA) to solve the problem in cooperative and competitive energy environments. Simulation studies were carried out on different systems containing various numbers of units. The outcomes from different algorithms are compared with that from the proposed hybrid algorithm and the advantages of the proposed algorithm are briefly discussed.

As a relatively mature technology, biomass molded fuel (BMF) is widely used in distributed and centralized heating in China and has received considerable government attention. Although many BFM incentive policies have been developed, decreased domestic traditional fuel prices in China have caused BMF to lose its economic viability and new policy recommendations are needed to stimulate this industry. The present study built a regionalized net present value (NPV) model based on real production process simulation to test the impacts of each policy factor. The calculations showed that BMF production costs vary remarkably between regions, with the cost of agricultural briquette fuel (ABF) ranging from 86 US dollar per metric ton (USD/t) to 110 (USD/t), while that of woody pellet fuel (WPF) varies from 122 USD/t to 154 USD/t. The largest part of BMF’s cost composition is feedstock, which accounts for up 50%–60% of the total; accordingly a feedstock subsidy is the most effective policy factor, but in consideration of policy implementation, it would be better to use a production subsidy. For ABF, the optimal product subsidy varies from 26 USD/t to 57 USD/t among different regions of China, while for WPF, the range is 36 USD/t to 75 USD/t. Based on the data, a regional BMF development strategy is also proposed in this study.

Purpose This study aims to consider environmental sustainability, a global challenge under the preview of sustainable development goals, highlighting the significance of knowledge economy in attaining sustainable aggregate demand behavior globally. For this purpose, 155 countries that have data available from 1995 to 2021 were selected. The purpose of selecting these countries is to test the global responsibility of the knowledge economy to attain environmental sustainability. Design/methodology/approach Results are estimated with the help of panel quantile regression. The empirical existence of aggregate demand-based environmental Kuznets curve (EKC) was tested using non-linear tests. Moreover, principal component analysis has been incorporated to construct the knowledge economy index. Findings U-shaped aggregate demand-based EKC at global level is validated. However, environmental deterioration increases with an additional escalation after US$497.945m in aggregate demand. As a determinant, the knowledge economy is reducing CO2 emissions. The knowledge economy has played a significant role in global responsibility, shifting the EKC downward and extending the CO2 reduction phase for every selected country. Further, urbanization, energy intensity, financial development and trade openness significantly deteriorate the environmental quality. Originality/value This study contains the empirical existence of aggregate demand-based EKC. The role of the knowledge economy is examined through an index which is calculated by using four pillars of the knowledge economy (technology, innovations, education and institutions). This study is based on a combined panel of all the countries for which the data was available.

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.

Given the global energy and environmental situation, the European Union has been issuing directives with increasingly demanding requirements in term of the energy efficiency in buildings. The international competition of sustainable houses, Solar Decathlon Europe (SDE), is aligned with these European objectives. SDE houses are low energy solar buildings that must reach the near to zero energy houses’ goal. In the 2012 edition, in order to emphasize its significance, the Energy Efficiency Contest was added. SDE houses’ interior comfort, functioning and energy performance is monitored. The monitoring data can give an idea about the efficiency of the houses. However, a jury comprised by international experts is responsible for carrying out the houses energy efficiency evaluation. Passive strategies and houses services are analyzed. Additionally, the jury's assessment has been compared with the behavior of the houses during the monitoring period. Comparative studies make emphasis on the energy aspects, houses functioning and their interior comfort. Conclusions include thoughts related with the evaluation process, the results of the comparative studies and suggestions for the next competitions.

With the 2015 Paris Agreement pursuing efforts to limit global temperature increase to below 2°C above pre-industrial levels and the “energy trilemma” goals of energy security, energy equity and environmental sustainability, decarbonisation remains a priority across all of the United Kingdom (United Kingdom) energy system, not just electricity. Electricity and thermal energy storage technologies can offer a host of benefits across the energy value chain through the abilityS to capture, store and then release electricity or thermal energy over a period of time. These benefits include helping capture the full potential of renewable generation and providing services such as frequency response and reserve to Great Britain’s (GB) electricity system. In addition, with the aforementioned climate targets in mind, energy storage can also play a role in facilitating the decarbonisation of other activities and sectors. Here we delve deeper into how energy storage technologies can contribute to both energy sector transformation and more broadly, decarbonisation. Furthermore, we discuss the importance of ensuring a technology-agnostic approach to the development of policy and regulation with relevance to energy storage. This ensures that storage technologies with significant potential to contribute to the ‘energy trilemma’ goals are not precluded from entering the market due to unfavourable policy and regulatory frameworks.

AbstractThis work reports the synthesis, characterization, photophysical, and photovoltaic properties of five new thieno[3,2‐b][1]benzothiophene isoindigo (TBTI)‐containing low bandgap donor–acceptor conjugated polymers with a series of comonomers and different side chains. When TBTI is combined with different electron‐rich moieties, even small structural variations can have significant impact on thin film morphology of the polymer:phenyl C70 butyric acid methyl ester (PCBM) blends. More importantly, high‐resolution electron energy loss spectroscopy is used to investigate the phase‐separated bulk heterojunction domains, which can be accurately and precisely resolved, enabling an enhanced correlation between polymer chemical structure, photovoltaic device performance, and morphology.