AIMS Energy最新文献

The purpose of this article is to build a mathematical model for analyzing the sustainability of the development of an enterprise in the fuel and energy complex, integrated into an information management system. It is noted that one of the strategic dominants in achieving the national goal of accelerating the technological development of any country is to ensure the effective functioning of enterprises in the fuel and energy complex. It is substantiated that these enterprises represent the basis of the material life of society, thus, ensuring their sustainable development is a significant factor for the formation of the structure of sectoral and inter-sectoral industrial complexes. In order to analyze the sustainable development of enterprises, an integral indicator is proposed, the components of which are the vectors of production, organizational, economic, environmental and social characteristics. Due to the weak structure of some characteristics, to solve the problem of their synthesis with quantitatively defined indicators, it is proposed to use the mathematical apparatus of fuzzy logic. Weakly structured indicators are formally described by linguistic variables. To establish the dependence of the integral indicator of sustainable development on production, organizational, economic, environmental and social indicators, a fuzzy-logical model has been built, which makes it possible to use the knowledge of experts by constructing rules of fuzzy inference. The fuzzy logic model is implemented using MATLAB tools. On the constructed model, experiments were carried out to assess the impact of each of the local indicators of sustainable development of an enterprise on the integral indicator. The advantage of the constructed model is its adaptability to changes in the operating conditions of enterprises.

Energy consumption increases daily across the world. Electricity is the best means that humankind has found for transmitting energy. This can be said regardless of its origin. Energy transmission is crucial for ensuring the efficient and reliable distribution of electricity from power generation sources to end-users. It forms the backbone of modern societies, supporting various sectors such as residential, commercial, and industrial activities. Energy transmission is a fundamental enabler of well-functioning and competitive electricity markets, supporting reliable supply, market integration, price stability, and the integration of renewable energy sources. Electric energy sourced from various regions worldwide is routinely traded within these electricity markets on a daily basis. This paper presents a review of forecasting techniques for intraday electricity markets prices, volumes, and price volatility. Electricity markets operate in a sequential manner, encompassing distinct components such as the day-ahead, intraday, and balancing markets. The intraday market is closely linked to the timely delivery of electricity, as it facilitates the trading and adjustment of electricity supply and demand on the same day of delivery to ensure a balanced and reliable power grid. Accurate forecasts are essential for traders to maximize profits within intraday markets, making forecasting a critical concern in electricity market management. In this review, statistical and econometric approaches, involving various machine learning and ensemble/hybrid techniques, are presented. Overall, the literature highlights the superiority of machine learning and ensemble/hybrid models over statistical models.

Energy consumption in the tertial sector is largely attributed to cooling/heating energy consumption. Thus, forecasting the building's energy consumption has become a key factor in long-term decision-making, reducing the huge energy demand and future planning. This manuscript outlines to use of the variance analysis method (ANOVA) to study the building's passive parameters' effect, such as the orientation, insulation, and its thickness plus the glazing on energy savings through the forecasting of the heating/cooling energy consumption by applying the Seasonal Auto-Regressive Integrated Moving Average (SARIMA) and the Long Short-Term Memory (LSTM) models. The presented methodology compares the predicted consumed energy of a baseline building with another efficient building which includes all the passive parameters selected by the ANOVA approach. The results show that the improvement of passive parameters leads to a reduction of heating energy consumption by 1,739,640 kWh from 2021 to 2029, which is equivalent to a monthly energy consumption of 181.2 kWh for an administrative building with an area of 415 m². While the cooling energy consumption is diminished by 893,246 kWh from 2021 to 2029, which leads to save a monthly value of 93.05 kWh. Consequently, the passive parameters optimization efficiently reduces the consumed energy and minimizes its costs. This positively impacts our environment due to the reduction of gas emissions, air and soil pollution.

Battery needs are increasing due to the exponential growth in demand for electric vehicles and renewable energy generation. These factors lead to the growing waste management of lithium-ion batteries (LIBs). Thus, recycling or finding a second life for LIBs is a growing industry due to its environmental and economic benefits. This work compares the benefits, economic advantages and disadvantages of battery recycling, including second-life battery applications. Different reports and case studies are analyzed to define the materials that may be recovered and the efficiency of the recycling process. To understand the economics of using recycled, second use, or new LIBs, this work evaluates three distinct projects, namely residential, commercial, and solar farm storage. The investigation aims to calculate and compare the net present value (NPV) for the residential storage project and the equivalent annual cost (EAC) for each project to determine the most viable industrial process within those parameters. The data analysis demonstrated that the second-life battery project has the lowest EAC, making it the most viable industrial process. However, although the second-life battery project presents the highest NPV for the project's first 10 years, the recycled battery project shows the highest NPV for the remainder of a typical 20-year project.