• Energy Research
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Electrical energy is one of the most important daily needs. Shortage of energy can be very dangerous for any society. This can affect the standard of living and quality of life of the people and even endanger the lives of those in hospitals, and so forth. Developed countries do not face such risks in general because they have well organized electrical systems and high energy security. The developing countries are faced daily with electric system collapses, especially in the case of wars, where many parts of the electrical grid in the country can be damaged and fuel transmission lines for generators cut off. Urban areas in developing countries should have a strategic plan to deal with any unexpected occurrence of energy shortages using any available renewable energy sources. City of Latakia is located in the region which has been suffering from the consequences of war for more than six years. The fact that a high number of migrants from other cities have come to Latakia along with a lack of fuel makes the energy shortage in the city worse. An emergency system could use the cheapest available renewable energy sources in addition to few big portable generators to provide an acceptable energy supply for the most needed requirements of daily life.

Large buildings cause more than 20% of the global energy consumption in advanced countries. In buildings such as hospitals, cooling loads represent an important percentage of the overall energy demand (up to 44%) due to the intensive use of heating, ventilation and air conditioning (HVAC) systems among other key factors, so their study should be considered. In this paper, we propose a data-driven analysis for improving the efficiency in multiple-chiller plants. Coefficient of performance (COP) is used as energy efficiency indicator. Data analysis, based on aggregation operations, filtering and data projection, allows us to obtain knowledge from chillers and the whole plant, in order to define and tune management rules. The plant manager software (PMS) that implements those rules establishes when a chiller should be staged up/down and which chiller should be started/stopped according different efficiency criteria. This approach has been applied on the chiller plant at the Hospital of León.

The separation of resistances during their measurement is important because it helps to identify contributors in polymer electrolyte membrane (PEM) fuel cell performance. The major methodologies for separating the resistances are electrochemical impedance spectroscopy (EIS) and polarization curves. In addition, an equivalent circuit was selected for EIS analysis. Although the equivalent circuit of PEM fuel cells has been extensively studied, less attention has been paid to the separation of resistances, including protonic resistance in the cathode catalyst layer (CCL). In this study, polarization curve and EIS analyses were conducted to separate resistances considering the charge transfer resistance, mass transport resistance, high frequency resistance, and protonic resistance in the CCL. A general solution was mathematically derived using the recursion formula. Consequently, resistances were separated and analyzed with respect to variations in relative humidity in the entire current density region. In the case of ohmic resistance, high frequency resistance was almost constant in the main operating load range (0.038–0.050 Ω cm2), while protonic resistance in the CCL exhibited sensitivity (0.025–0.082 Ω cm2) owing to oxygen diffusion and water content.

Portable power sources have attracted increasing interest and attention, with a focus on the reduction of the system volume. Thus, portable power sources often use polymer electrolyte fuel cell (PEFC) systems with dead-ended operation—which are simpler and more fuel-efficient than conventional PEFC systems. In these systems, the fuel may be supplied under nonhumidified conditions to minimize the balance of plant (BOP). In recent studies, metal foams have been used as flow fields to improve fuel diffusion and water management in the PEFC; the performance can be compared to that of a conventional channel. This study compared the performance and water management ability of channel and metal foam flow fields under nonhumidified conditions with dead-ended operation. The results demonstrate that the average output was similar for both flow fields. In terms of fuel efficiency, the PEFC with the metal foam could be operated for a significantly longer time without purging than that with the channel.

In light of the advancing energy transition and an increasing amount of intermittent renewable energy to be integrated, flexibility from distributed energy resources will be key. In this paper, the Italian UVAM (Unità Virtuali Abilitate Miste, i.e., virtually aggregated mixed units) project, one of the biggest pilots in Europe to serve this purpose, is critically reviewed and mapped after two years of operation. The pilot is analyzed on a global level as well as the individual participant level. Based on the extensive analysis of actual market data, different strategies of participating companies to obtain capacity in accordance with the pilot project’s design are identified. Furthermore, the specific bidding strategies of individual participating units on the balancing market are outlined. Alongside this, the overall pilot project’s market integration, in terms of offered and accepted bids, is depicted. The thorough data analysis, therefore, serves as an input and fundamental building block for future electricity market modeling. Comprehending specific data from the coronavirus disease 2019 (COVID-19) pandemic, provides insights for future high renewable-energy scenarios. Based on the analysis findings, valuable deliverables are devised for both policy-makers and decision-makers who aim to leverage the flexibility potential of distributed resources.

More accurate wind power prediction (WPP) is of great significance for the operation of electrical power systems, as offshore wind power penetration increases continuously. As the offshore wind turbines (OWT) are a key system in converting offshore wind power into electrical power, maintaining their condition plays a pivotal role in WPP. However, it is seldom considered in traditional WPP. This paper proposes an ultra-short term offshore WPP methodology based on the condition assessment (CA) of OWTs. Firstly, a modified fuzzy comprehensive evaluation (MFCE) based CA of the OWT is presented with a new defined deterioration of indicators calculated by the relative errors. Long short-term memory (LSTM) neural network is introduced to deal with the complicated interactions between the various monitoring data of an OWT and the dynamic marine environment. Then, with the classifications of the health conditions of the OWT, the historical operation data is classified accordingly. An OWT-condition based WPP with a backpropagation (BP) neural network is developed to deal with the non-linear mapping relations between the numerical weather prediction (NWP) information, health conditions of OWT, and the output power. The results of the case study show the influences of the OWT health conditions to its output power and verifies the effectiveness and higher accuracy of the proposed method.

The main objective of the study is to assess the similarity between the time series of energy commodity prices and the time series of daily COVID-19 cases. The COVID-19 pandemic affects all aspects of the global economy. Although this impact is multifaceted, we assess the connections between the number of COVID-19 cases and the energy commodities sector. We analyse these connections by using the Dynamic Time Warping (DTW) method. On this basis, we calculate the similarity measure—the DTW distance between the time series—and use it to group the energy commodities according to their price change. Our analysis also includes finding the time shifts between daily COVID-19 cases and commodity prices in subperiods according to the chronology of the COVID-19 pandemic. Our findings are that commodities such as ULSD, heating oil, crude oil, and gasoline are weakly associated with COVID-19. On the other hand, natural gas, palm oil, CO2 allowances, and ethanol are strongly associated with the development of the pandemic.

The most recent research confirms that airborne transmission may be the dominant mode of SARS-CoV-2 virus spread in the interior spaces of buildings. Consequently, based on some prescriptions that implemented natural ventilation during face-to-face lessons in a university centre, an experimental characterization of several complementary options aimed at reinforcing the prevention and safety of the occupants was carried out. The action protocol adopted was based on the combination of mandatory natural ventilation, a maximum contribution of outdoor air supply in the air conditioning system, and the use of filtering devices located inside the classroom. All the strategies were incorporated concomitantly with necessary compliance with the basic conditions of social distance, occupation, use of masks and guidelines for use and cleaning within educational buildings. The suitability of this protocol was further evaluated throughout the teaching day with students and teachers by measuring the CO2 concentration. The results showed that the measures implemented successfully removed the possible pollutants generated inside.