The world’s rising energy needs, and the depletion of fossil resources demand a shift from fossil-based feedstocks to organic waste to develop a competitive, resource-efficient, and low-carbon sustainable economy in the long run. It is well known that the production of fuels and chemicals via chemical routes is advantageous because it is a well-established technology with low production costs. However, the use of toxic/environmentally harmful and expensive catalysts generates toxic intermediates, making the process unsustainable. Alternatively, utilization of renewable resources for bioprocessing with a multi-product approach that aligns novel integration improves resource utilization and contributes to the “green economy”. The present review discusses organic waste bioprocessing through the anaerobic fermentation (AF) process to produce biohydrogen (H2), biomethane (CH4), volatile fatty acids (VFAs) and medium chain fatty acids (MCFA). Furthermore, the roles of photosynthetic bacteria... [more]

In recent years, time series forecasting has become an essential tool for stock market analysts to make informed decisions regarding stock prices. The present research makes use of various exponential smoothing forecasting methods. These include exponential smoothing with multiplicative errors and additive trend (MAN), exponential smoothing with multiplicative errors (MNN), and simple exponential smoothing with additive errors (ANN) for the forecasting of the stock prices of six different companies in the petroleum, electricity, and gas industries that are listed in the IBEX35 index. The database employed for this research contained the IBEX35 index values and stock closing prices from 3 January 2000 to 30 December 2022. The models trained with this data were employed in order to forecast the index value and the closing prices of the stocks under study from 2 January 2023 to 24 March 2023. The results obtained confirmed that although none of the proposed models outperformed the rest fo... [more]

Monitoring, protection, and control processes are becoming more complex as distributed energy resources (DERs) penetrate distribution networks (DNs). This is due to the inherent nature of power DNs and the bi-directional flow of current from various sources to the loads. To improve the system’s situational awareness, the grid dynamics of the entire DER integration processes must be carefully monitored using synchronized high-resolution real-time measurement data from physical devices installed in the DN. μPMUs have been introduced into the DN to help with this. In comparison to traditional measurement devices, μPMUs can measure voltage, current, and their phasors, in addition to frequency and rate of frequency change (ROCOF). In this study, an approach to generating realistic event data for a real utility DN utilizing strategically installed μPMUs is proposed. The method employs an IEEE 34 test feeder with 12 μPMUs installed in strategic locations to generate real-time events-based rea... [more]

The planning of offshore wind energy projects requires wind observations over long periods for the establishment of wind speed distributions. In the marine environment, high-quality in situ observations are sparse and restricted to point locations. Numerical modeling is typically used to determine the spatial variability of the wind resource. Synthetic Aperture Radar (SAR) observations from satellites can be used for retrieval of wind fields over the ocean at a high spatial resolution. The recent launch of constellations of small SAR satellites by private companies will improve the sampling of SAR scenes significantly over the coming years compared with the current sampling rates offered by multi-purpose SAR missions operated by public space agencies. For the first time, wind fields are retrieved from a series of StriX SAR scenes delivered by Synspective (Japan) and also from Sentinel-1 scenes delivered by the European Space Agency. The satellite winds are compared with wind speed obse... [more]

The share of residential building energy consumption in global energy consumption has rapidly increased after the COVID-19 crisis. The accurate prediction of energy consumption under different indoor and outdoor conditions is an essential step towards improving energy efficiency and reducing carbon footprints in the residential building sector. In this paper, a PSO-optimized random forest classification algorithm is proposed to identify the most important factors contributing to residential heating energy consumption. A self-organizing map (SOM) approach is applied for feature dimensionality reduction, and an ensemble classification model based on the stacking method is trained on the dimensionality-reduced data. The results show that the stacking model outperforms the other models with an accuracy of 95.4% in energy consumption prediction. Finally, a causal inference method is introduced in addition to Shapley Additive Explanation (SHAP) to explore and analyze the factors influencing... [more]

Promoting energy efficiency is a key element of the strategic commitment of the European Community. Prominent among the binding measures established by the 2012 Energy Efficiency Directive to further this vision is the requirement for large companies to conduct energy audits every four years. After receiving the second cycle of energy audit reports in December 2019, a new description of the energy situation of Italian companies was made available. This presented the previously inaccessible possibility of comparing the two situations reported in 2015 and 2019 to assess the development of energy efficiency practices in organizations subject to the legislative obligation of energy audits in the country. To this end, in collaboration with the Italian National Agency for New Technologies, Energy and Sustainable Economic Development (ENEA), a project was initiated with the aim of developing the tools and methodologies necessary to assess in more detail the evolution that has occurred in the... [more]

The degradation of solar photovoltaic (PV) modules is caused by a number of factors that have an impact on their effectiveness, performance, and lifetime. One of the reasons contributing to the decline in solar PV performance is the aging issue. This study comprehensively examines the effects and difficulties associated with aging and degradation in solar PV applications. In light of this, this article examines and analyzes many aging factors, including temperature, humidity, dust, discoloration, cracks, and delamination. Additionally, the effects of aging factors on solar PV performance, including the lifetime, efficiency, material degradation, overheating, and mismatching, are critically investigated. Furthermore, the main drawbacks, issues, and challenges associated with solar PV aging are addressed to identify any unfulfilled research needs. Finally, this paper provides new directions for future research, best practices, and recommendations to overcome aging issues and achieve the... [more]

As the climate crisis worsens, power grids are gradually transforming into a more sustainable state through renewable energy sources (RESs), energy storage systems (ESSs), and smart loads. Virtual power plants (VPP) are an emerging concept that can flexibly integrate distributed energy resources (DERs), managing manage the power output of each DER unit, as well as the power consumption of loads, to balance electricity supply and demand in real time. VPPs can participate in energy markets, enable self-scheduling of RESs, facilitate energy trading and sharing, and provide demand-side frequency control ancillary services (D-FCAS) to enhance the stability of the system frequency. As a result, studies considering VPPs have become the focus of recent energy research, with the purpose of reducing the uncertainty resulting from RESs distributed in the power grid and improving technology related to energy management system (EMS). However, comprehensive reviews of VPPs considering their formatio... [more]

The European electricity market model continues to evolve in the face of new challenges. This systematic literature review aims to assess the status of research and discussion on the current model and its market mechanisms. In addition, it aims to ascertain the kinds of modelling tools that have been used to model market mechanisms or formulate proposals for changes to current practice. The results show that the challenges of individual market mechanisms have been identified quite extensively in the research sample. However, the number of papers identified for inclusion in the systematic literature review was quite small, from which it can be concluded that there are surprisingly few publications focusing on this particular topic. Nevertheless, the trend indicates a probable increase in research in the subject area in the future. The general standpoint among researchers seems to be that the goals set by the EU are, as it were, a law of nature that cannot be deviated from. Consequently,... [more]

Accurate and efficient short-term forecasting of multiple loads is of great significance to the operation control and scheduling of integrated energy distribution systems. In order to improve the effect of load forecasting, a mogrifier-quantum weighted memory enhancement long short-term memory (Mogrifier-QWMELSTM) neural network forecasting model is proposed. Compared with the conventional LSTM neural network model, the model proposed in this paper has three improvements in model structure and model composition. First, the mogrifier is added to make the data fully interact with each other. This addition can help enhance the correlation between the front and rear data and improve generalization, which is the main disadvantage of LSTM neural network. Second, the memory enhancement mechanism is added on the forget gate to realize the extraction and recovery of forgotten information. The addition can help improve the gradient transmission ability in the learning process of the neural netwo... [more]

As a consequence of the COVID-19 pandemic, Korea’s economy has experienced significant setbacks. Thus, this article examines the implications of the COVID-19 pandemic on Korea’s key macroeconomic indicators via the transmission channels of oil prices and production technology. Using Bayesian estimation and impulse response functions for empirical investigation, the results suggest that the COVID-19 pandemic has intensified the reduction in firm production, consumption of oil-based goods, employment, and investment. Increasingly, households rely on non-oil goods rather than oil-based ones. Similarly, the results suggest that the drop in production technology levels brought on by the COVID-19 pandemic has a stronger impact on business output and investment but a lesser influence on household employment. The COVID-19 pandemic has led to a decline in household non-oil consumption as well as household and business consumption of oil-based goods. To sum up, the existing Korean literature on... [more]

The efficiency of electrical complexes depends directly on the level of power supply system reliability, which comprises extensive and branched distribution networks. A complex of single-phase ground fault (SPGF) relay protection and automation devices (RPA) is used to reduce the economic losses from the failure of the electrical receivers’ distribution networks. This paper presents a study of the protection sensitivity factor, taking into account the influence of the network capacity and the resistance during a fault. The results of this study determined the minimum permissible values of the sensitivity factor that ensures the stable operation of the protection device. This was achieved by taking into account the influence of the transient resistance at the point of short circuit. The practical significance of the study is as follows: the obtained characteristics will allow for the development of new functional logic circuits for SPGF protection. The practical implementation of the ob... [more]

Electricity is currently one of the most popular sources of energy. Considering such widespread use of electric energy, we may ask, what is the economic cost of producing and supplying it? The climate crisis and the social pressure associated with it have triggered the necessity to make further investments in renewable and low-emission energy sources, while the COVID-19 pandemic has abruptly limited electricity consumption in industry. All these factors can have an impact on disruptions or loss in the liquidity of companies responsible for supplying electricity to end users. Guaranteeing cash flow for energy sector entities is a prerequisite for energy supply continuity. In this context, the selection and application of reliable sources of information are vital for the management of the financial liquidity of energy sector entities. The aim of this article is to prove the value of the financial information of individual (IFR) and consolidated financial statements (CFR) essential for th... [more]

As a result of the dissemination of the sustainability concept, social disclosures have become an important area of non-financial reporting, and the energy sector is no exception. The purpose of our article is a multi-faceted evaluation of sustainability reports published by companies operating in the Polish energy sector, from the perspective of social disclosures. The study involved the Polish listed companies that made up the WIG-Energia index. The time scope of the study covers the 2017−2021 period. In total, 54 non-financial reports were analyzed. In the first place, a comparative analysis was carried out to assess the social disclosures made by the WIG-Energia companies against the background of the biggest and the most liquid (blue chip) WIG20 companies. All the applied tools: ESG rating, NFR_S index, and multidimensional data visualization, have confirmed that the energy companies year by year have been presenting larger and larger extents of social disclosures. At the same tim... [more]

Investigating the deposition evolution and stratal stacking patterns in continental rift basins is critical not only to better understand the mechanism of basin fills but also to reveal the enrichment regularity of hydrocarbon reservoirs. The Pearl River Mouth Basin (PRMB) is a petroliferous continental rift basin located in the northern continental shelf of the South China Sea. In this study, the depositional evolution process and stacking pattern of the Zhu III Depression, western PRMB were studied through the integration of 3D seismic data, core data, and well logs. Five types of depositional systems formed from the Eocene to the Miocene, including the fan delta, meandering river delta, tidal flat, lacustrine system, and neritic shelf system. The representative depositional systems changed from the proximal fan delta and lacustrine system in the Eocene−early Oligocene, to the tidal flat and fan delta in the late Oligocene, and then the neritic shelf system in the Miocene. The statal... [more]

The increase in heterogeneous data in the building energy domain creates a difficult challenge for data integration. Schema matching, which maps the raw data from the building energy domain to a generic data model, is the necessary step in data integration and provides a unique representation. Only a small amount of labeled data for schema matching exists and it is time-consuming and labor-intensive to manually label data. This paper applies semantic-similarity methods to the automatic schema-mapping process by combining knowledge from natural language processing, which reduces the manual effort in heterogeneous data integration. The active-learning method is applied to solve the lack-of-labeled-data problem in schema matching. The results of the schema matching with building-energy-domain data show the pre-trained language model provides a massive improvement in the accuracy of schema matching and the active-learning method greatly reduces the amount of labeled data required.

It is critical to address energy issues as we move through the first half of the twenty-first century, as societies become firmly aware of the consequences of resource scarcity and the disastrous consequences of climate change from human activity (especially heavy industry) [...]

The Special Issue on “Green Energy Economies” was open for submission on 30 March 2021 and closed on 30 March 2022 [...]

It is generally known that violent oil price volatility will cause market panic; however, the extent to which is worthy of empirical test. Firstly, this paper employs the TVP-VAR model to analyze the time-varying impacts of oil price volatility on the panic index using monthly data from January 1990 to November 2021. Then, after using the SVAR model to decompose the oil price volatility, this paper uses the PDL model to analyze the heterogeneous impacts of oil price volatility from different sources. Finally, based on the results of oil decomposition, this paper uses the TARCH model to analyze the asymmetric impacts of oil price volatility in different directions. The results show that: (1) oil price volatility can indeed cause market panic, and these impacts exhibit time-varying characteristics; (2) oil price volatility from different sources has different impacts on the panic index, and the order from high to low is oil-specific demand shocks, supply shocks, and aggregate demand shoc... [more]

Accurately forecasting carbon prices is key to managing associated risks in the financial market for carbon. To this end, the traditional strategy does not adequately decompose carbon prices, and the kernel extreme learning machine (KELM) with a single kernel function struggles to adapt to the nonlinearity, nonstationarity, and multiple frequencies of regional carbon prices in China. This study constructs a model, called the VMD-ICEEMDAN-RE-SSA-HKELM model, to forecast regional carbon prices in China based on the idea of ‘decomposition−reconstruction−integration’. The VMD is first used to decompose carbon prices and the ICEEMDAN is then used to decompose the residual term that contains complex information. To reduce the systematic error caused by increases in the mode components of carbon price, range entropy (RE) is used to reconstruct the results of its secondary decomposition. Following this, HKELM is optimized by the sparrow search algorithm and used to forecast each subseries of c... [more]

In order to make reasonable production-sales-stock decision-making for gasoline production enterprises, it is necessary to make an accurate prediction of the gasoline demand. However, gasoline demand is often affected by many factors, which makes it very difficult to predict. Therefore, this paper tries to construct a trend- and periodicity-trait-driven decomposition-ensemble forecasting model in terms of trend and periodicity characteristics of gasoline demand data. In order to verify the effectiveness of the proposed model, the demand data of a typical gasoline product-93# gasoline in China, is used. The empirical results show that the proposed trend- and periodicity-trait-driven decomposition-ensemble forecasting model can achieve better prediction results than the single models, indicating that the proposed methodology can be used as a feasible solution to predict the gasoline demand series with trend and periodicity traits.

This paper discusses the authors’ experience gained with the selection of Projects of Common Interest (PCIs) in the thematic area of smart grids deployment, in the context of the TEN-E Regulation. It presents the framework for assessing candidate electricity smart grids for inclusion in the European Union list of PCIs, in view of the TEN-E Regulation and the existing literature on assessment methodologies for energy infrastructure projects. It also provides an overview of smart grid projects included in the PCI lists, with the aim to shed light on the types of projects and their contribution to accelerating the development of European cross-border energy infrastructure projects to respond to EU energy and climate targets. The paper concludes with discussion of recent regulatory initiatives and their potential implications on the presented methodology.

Photovoltaic (PV) systems are becoming increasingly prevalent worldwide, particularly in power distribution networks. However, their intermittency and integration into distribution networks can have adverse effects. This study investigates the impact of large-scale solar integration into a typical Malaysian power grid network, focusing on voltage stability, short circuits, and power loss under peak and no-load conditions. Using Digsilent Power Factory software, static and dynamic power flow analyses were performed on a network consisting of two 132/11 kV transformers, an 11 kV busbar, and 112 loads served through eight feeders. Solar PV of 100 kW was integrated into each node, and the maximum allowable solar grid connection level was determined. The static results show that there were no violations in no-load conditions at 100 kW PV penetration. However, during peak load, there were violations at 0% PV penetration, but by increasing the level of solar grid connection to 60% (60 kW), th... [more]

We are witnessing the growth of microgrid technology and the development of electric vehicles (EVs) in the world. These microgrids seek demand response (DR) and energy storage for better management of their resources. In this research, microgrids, including wind turbines, photovoltaics, battery charging/discharging, and compressed air energy storage (CAES), are considered. We will consider two scenarios under uncertainty: (a) planning a microgrid and DR without considering CAES, and (b) planning a microgrid and DR considering CAES. The cost of charging the battery in the second study decreased by $0.66 compared to the first study. The battery is charged with a difference of $0.7 compared to the case of the first study. We will also pay for unsupplied energy and excess energy in this microgrid. Then, we test the scheduling of vehicles to the grid (V2G) in the IEEE 33-bus network. The first framework for increasing network flexibility is the use of EVs as active loads. The scheduling of... [more]

This paper presents the application of prediction in the analysis of market price volatility in Polish conditions of wood processing by-products in the form of biomass. The ARIMA model, which takes into account cyclical, seasonal, irregular fluctuations of historical data on the basis of which the forecast and long-term trends of selected wood products were made, was used in predicting prices. Comparisons were made between the ARIMA prediction method and the multiplicative Winters−Holt model. During the period studied (2017−2022), the changes in the market price of biomass were characterized by a wide spread of values. On average, the price of these products increased from 2017 to the end of 2022 by 125%. The price prediction analysis showed seasonal fluctuations in the case of wood chips. The uncertainty in price prediction is due to changes in supply resulting from the influence of global factors. The Diebold−Mariano test of matching accuracy confirms that the price prediction of the... [more]