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The distributed energy system (DES) represents an innovative approach to energy generation and distribution that promotes decentralization and diversification of energy sources. DESs can offer numerous benefits, including increased resiliency, reduced transmission losses, improved efficiency, and lower carbon emissions. The optimal design of a DES requires careful consideration of various factors such as geographical location, climate conditions, and energy demand patterns. This paper utilizes a multi-objective genetic algorithm to optimize the combination of technologies and their corresponding sizes in a distributed energy system for three types of commercial buildings—hospitals, large offices, and large hotels across eight different climate zones in the U.S. A range of technologies are considered for integration into the DES. These technologies include photovoltaic systems, wind turbines, combined heat and power systems, solar thermal collectors, and electrical and thermal energy storage. The two objectives considered are maximizing the reduction in carbon dioxide emissions and minimizing the life cycle costs for the DES. The purpose of this study is to optimize and evaluate the multi-objective design of distributed energy systems aimed at decentralizing and diversifying energy sources. The analysis of optimized DES designs across all 24 case scenarios shows that a balance between cost saving and emission reduction has been achieved. Although this study primarily focuses on specific buildings and climate zones, the methods and findings can be adapted for a wider variety of building types across different geographical locations, thus paving the way for more widespread adoption of optimized distributed energy systems. [ABSTRACT FROM AUTHOR]

The short- and long-term volatility of oil and gas prices has a wide-ranging impact on both parties of petroleum contractual agreements, thus affecting the profitability of the project at any stage. Therefore, the government (first party) and the international oil company (second party) set the parameters of their contracts in a way that reduces the uncertainty. The effect of price fluctuations on economic indicators is investigated in this paper. The Taguchi method is used for the first time to find the best-agreement parameters, which are the "A" and "B" factors, in the standard Libyan agreement. There are four "A" components from "A1" to "A4", and four "B" components from "B1" to "B4". The purpose is to reduce the variability in the response variables, which are the company take (the percent of net cash flow for the international company) and average value of the second-party percent share of production (ASPS). The noise factors considered in this paper are oil, liquefied hydrocarbon byproduct (LHP), and gas prices. The method was applied to a case study of oil field development in Libya. The results showed that "A3" and "A4" were the most important control factors that affect the ASPS, while "B2" and "B3" are the most important factors affecting the company take. To obtain robust results, the most important factors to reduce variability were also determined. The effect of control parameters on the average NPV may be worth more than USD 22 MM in the 1-billion-barrel oilfield case study. The results showed that, for a given combination of "A" and "B" factors with a certain company take, the mean absolute deviation (MAD) of the NPV of the second party was reduced by 18% if the optimal combinations of the levels were used. [ABSTRACT FROM AUTHOR]

On 11 March 2020, the WHO declared the COVID-19 epidemic to be a global pandemic. This was a consequence of the rapid increase in the number of people with positive test results, the increase in deaths due to COVID-19, and the lack of pharmaceutical drugs. Governments introduced national lockdowns, which have impacted both energy consumption and economies. The purpose of this paper is to answer the following question: do COVID-19 lockdowns affect the business cycle? We used the cycle clock approach to assess the magnitude of decrease in electricity consumption in the three waves of the epidemic, namely, April 2020, November 2021, and April 2021. Additionally, we checked the relation between energy consumption and GDP by means of spectral analysis. Results for selected 28 European countries confirm an impact of the introduced non-pharmaceutical interventions on both energy consumption and business cycle. The reduction of restrictions in subsequent pandemic waves increased electricity consumption, which suggests movement out of the economic recession. [ABSTRACT FROM AUTHOR]

The European Union (EU), which has led international discussions on global warming, officially announced its plan for the Carbon Border Adjustment Mechanism (CBAM) in July 2021. Many existing studies have indicated the CBAM will curtail greenhouse gases, and will subsequently be positive in terms of reducing global warming. However, serious legal issues and trade disputes are expected in terms of the compatibility of the CBAM with the trade rules of the General Agreement on Tariffs and Trade (GATT). Contrary to the EU's explanation, the international community has a strong view of CBAM as a new trade barrier under the guise of preventing global warming. Above all, this is because it is an arbitrary measure by the EU and not the one that has been internationally agreed upon. Therefore, this paper tries to identify the pitfalls and estimate the global cost of CBAM, arguing that the mechanism is not in line with international trade rules, and that many countries will not sit back and suffer from it. The world economy will inevitably face a vicious cycle of trade retaliation. The CBAM will drive up trade costs and cause another trade distortion. While the goal of preventing climate change is good, the CBAM scheme is too costly for the world economy. [ABSTRACT FROM AUTHOR]

This paper presents the synchronisation of economic cycles of GDP and crude oil and oil products cargo volumes in major Polish seaports. On the one hand, this issue fits into the concept of sustainable development including decoupling; on the other hand, the synchronisation may be an early warning tool. Crude oil and oil products cargo volumes are a specific barometer that predicts the next economic cycle, especially as they are primary sources of energy production. The research study applies a number of TRAMO/SEATS methods, the Hodrick–Prescott filter, spectral analysis, correlation and cross-correlation function. Noteworthy is the modern approach of using synchronisation of economic cycles as a tool, which was described in the paper. According to the study results, the cyclical components of the cargo traffic and GDP were affected by the leakage of other short-term cycles. However, based on the cross-correlation, it was proved that changes in crude oil and oil products cargo volumes preceded changes in GDP by 1–3 quarters, which may be valuable information for decision-makers and economic development planners. [ABSTRACT FROM AUTHOR]

With the large-scale construction and commissioning of transmission lines over the past two decades, the grid is facing a large-scale centralized decommissioning of transmission lines. The transmission line's economic life is crucial to rationalizing its construction and reducing the grid's development costs. Based on the minimum economic life calculation principle, the static and dynamic transmission line economic life calculation model is established, considering the whole life cycle for transmission line cost. The improved gray GM (1,1) model is applied to forecast cost data during the economic life assessment of transmission lines with fewer samples. Considering the cost uncertainty in life-cycle costing, the interval cost model based on the coefficient of variation wave amplitude is proposed to determine the economic life intervals under different guarantees by using the normal distribution probability density function, which reduces the influence of cost fluctuations on the economic life calculation error. The economic life analysis of a 500 kV transmission line is used as a case study to verify the model's accuracy and effectiveness. The method shows the economic life intervals under different guarantee degrees based on the most probable economic life determination, which provides theoretical support for calculating the economic life elasticity of transmission lines. [ABSTRACT FROM AUTHOR]

The next group of four articles, although concerning different issues, focuses on indicating the possibility of taking actions aimed at achieving sustainable development by reducing energy consumption [[17]], increasing the energy efficiency of devices [[19]], or optimizing the energy system for organization facilities [[20]]. 10.3390/en14217037 20 Gorbach O.G., Thomsen J. Comparing the Energy System of a Facility with Uncertainty about Future Internal Carbon Prices and Energy Carrier Costs Using Deterministic Optimisation and Two-Stage Stochastic Programming. Biresselioglu and Demir [[16]] present a decision support tool for the energy policymakers and energy providers to facilitate an analytical decision-making framework where the key drivers, motivators, and barriers are taken into account. The work of Gorbach and Thomsen [[20]] assesses the benefits of using two-stage stochastic programming in the context of energy system optimization, with uncertain CO SB 2 sb prices and energy carrier cost as two important but uncertain input parameters. [Extracted from the article]

10.3390/en15010340 26 Zinecker M., Doubravsky K., Balcerzak A.P., Pietrzak M.B., Dohnal M. The COVID-19 disease and policy response to mitigate the economic impact in the EU: An exploratory study based on qualitative trend analysis. Of particular importance seems to be the analysis of changes in the prices of electricity and primary fuels and the relationship between these markets and the renewable energy market. Bus. Econ. 2020; 19: 114-135 7 Dmytrów K., Bieszk-Stolorz B. Comparison of changes in the labour markets of post-communist countries with other EU member states. Q. J. Econ. Econ. [Extracted from the article]

The identification of critical periods and business cycles contributes significantly to the analysis of financial markets and the macroeconomy. Financialization and cointegration place a premium on the accurate recognition of time-varying volatility in commodity markets, especially those for crude oil and refined fuels. This article seeks to identify critical periods in the trading of energy-related commodities as a step toward understanding the temporal dynamics of those markets. This article proposes a novel application of unsupervised machine learning. A suite of clustering methods, applied to conditional volatility forecasts by trading days and individual assets or asset classes, can identify critical periods in energy-related commodity markets. Unsupervised machine learning achieves this task without rules-based or subjective definitions of crises. Five clustering methods—affinity propagation, mean-shift, spectral, k-means, and hierarchical agglomerative clustering—can identify anomalous periods in commodities trading. These methods identified the financial crisis of 2008–2009 and the initial stages of the COVID-19 pandemic. Applied to four energy-related markets—Brent, West Texas intermediate, gasoil, and gasoline—the same methods identified additional periods connected to events such as the September 11 terrorist attacks and the 2003 Persian Gulf war. t-distributed stochastic neighbor embedding facilitates the visualization of trading regimes. Temporal clustering of conditional volatility forecasts reveals unusual financial properties that distinguish the trading of energy-related commodities during critical periods from trading during normal periods and from trade in other commodities in all periods. Whereas critical periods for all commodities appear to coincide with broader disruptions in demand for energy, critical periods unique to crude oil and refined fuels appear to arise from acute disruptions in supply. Extensions of these methods include the definition of bull and bear markets and the identification of recessions and recoveries in the real economy. [ABSTRACT FROM AUTHOR]

In response to the uncertainty of extreme climate change, energy consumption structure has been actively adjusted globally. Based on panel data of 101 countries or regions from 2006 to 2019, a panel data model with fixed effects is used to analyze the heterogeneous impacts of extreme climate risks on global consumption transition. The results show that extreme climate change has promoted the transition of the energy structure, reduced the consumption of fossil energy, and increased the consumption of renewable energy. Meanwhile, there are heterogeneous impacts of extreme climate change risks on the energy transition when different countries suffering from extreme weather conditions. Areas with high levels of economic development and coastal countries are more inclined to respond to climate change through energy transition. It is further confirmed that, under the impact of business cycle and oil price fluctuations, economic recession and falling oil prices will strengthen the correlation between climate risk and the global energy transition, and governments need to pay more attention to the impact of climate risks. [ABSTRACT FROM AUTHOR]