The paper presents the problem of forecasting the operation condition of LM6000-PF gas turbine with a heat recovery hot water boiler in off-design mode. The presented unit is part of a real cogeneration heat and power plant. The maximum electrical power output for nominal conditions of the case study unit is 50 MWe. The proposed method is based on the thermodynamic modeling supplied with a data-driven model. The prediction model usesdetermined correction factors based on the real operating data. Correctionfactors for electricity production, heat rate, flue gas mass flow rate, and overall heat transfer coefficient in the boiler are proposed to increase the accuracy of the prediction results. The outcome from themodel can be used for short-term planning of energy production in the case studywithcombined heat and power plant. Here, fundamental production parameters such as electric power, heat production in the recovery boiler, and fuel consumption must be forecasted for the upcoming day in an hourly resolution. The results in the form of hourly parameter changes are presented and compared with real ones from two years of the system operation. The model's mean percentage error was reduced by using corrections factors to approximately 0.8% and 1.7% for electric power and recovered heat, respectively.
Greenhouse gas (GHG) emissions, particularly carbon dioxide, are the driving force for global climate change. Fossil fuel-fired power plants are one of the largest contributors to CO2 pollution. When carbon dioxide is released into the atmosphere, capturing and removing it is very difficult. Carbon capture systems (CCSs) can prevent the release of CO2 into the air. Recently, the use of microalgae photobioreactors as CCS has gained momentum. The produced microalgae can then be harvested for many applications including biofuels and other products. Closed photobioreactors are especially adept at this but at an increased price and energy input. Open photobioreactors are able to grow large amounts of algae at low prices and energies but with less control over the growth parameters. This paper briefly reviews the existing research literature in this field. Various systems, configurations, inefficiencies, and types of algae are reviewed and parameters impacting algae growth are presented.
This article presents a detailed study of the dynamic stability of microgrid-connected captive power plants linked to the agricultural sector located in western India. A refined modeling approach is developed to derive a reduced-order model using the Park-Gorev model along with Concordia and De Mello assumptions. This model contains six parameters, Kl to K6, that are described as the postulated constants associated with the power plant's initial operating conditions. Furthermore, the excitation regulator is modeled as a first order lag which improves the original IEEE Is model, providing the framework with detail. A detailed systematic dynamic stability analysis is performed for different operating conditions and identifies the stability and instability regions. Their consequences are noted for operational staff and focus on important factors that need to be considered prior to generator loading and commitment. This research is a detailed study of the dynamic behavior of captive power plants which is very useful to make operational decisions. Besides its primary purpose, this work will be useful for creating procedures and instructions on operational standards. The techniques used are crucial in improving the understanding of dynamic stability of power plants systems, which in turn modifies the existing knowledge and technological understanding in this area of concern.
Accurate power consumption (PN) forecasting under climate-sensitive operating conditions remains challenging due to the nonlinear, non-stationary, and dynamically coupled nature of demand and environmental factors. Existing studies predominantly rely on empirically selected auxiliary variables or static correlation analysis, which may fail to capture the temporal interaction mechanisms underlying PN dynamics. In this study, a recurrence-based dynamic characteristic analysis framework is proposed to systematically investigate both the intrinsictemporal behavior of PN and its variable-specific coupling with meteorological factors. Recurrence plots and cross recurrence plots are employed to visualize dynamic patterns and shared state evolution, while cross recurrence quantification analysis metrics are used to quantify coupling strength and temporal organization. To integrate heterogeneous dynamical indicators, a geometric-mean-based measure is introduced to rank the dynamic relevance of influencing variables in a unified manner. The recurrence-guided variable selection strategy is further validated through an echo state network (ESN) forecasting model, in which dynamically significant meteorological variables are incorporated as auxiliary inputs. Experimental results across three zones under high-load summer conditions of Tetouan city demonstrate that the proposed recurrence-guided ESN consistently outperforms the empirically configured multivariate ESN, achieving notable reductions in prediction error while maintaining a lightweight training structure. The proposed framework provides an interpretable and data-driven approach for identifying dynamically relevant influencing factors and improving PN forecasting under complex operating regimes.
The integration of advanced computational techniques, including artificial intelligence (AI), machine learning (ML), and genetic algorithms, is profoundly impacting the analysis and understanding of dynamic energy markets. This review article provides a comprehensive overview of how these methods are being applied to various economic and financial aspects within the energy sector. Drawing from a focused review of recent research, we explore their utility in areas such as pricing and cost, determination for commodities like electricity and crude oil, enhancing financial markets and investment strategies, and improving risk assessment capabilities. We synthesize key findings on the effectiveness and challenges associated with deploying these sophisticated computational tools, shedding light on emerging trends and highlighting promising avenues for future research. This synthesis aims to deepen the understanding of how AI, ML, and genetic algorithms are shaping strategic decision-making and fostering innovation within the evolving global energy landscape.
