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Short-term generation scheduling is an important function in daily operational planning of power systems. It is defined as optimal scheduling of power generators over a scheduling period while respecting various generator constraints and system constraints. Objective of the problem includes costs associated with energy production, start-up cost and shut-down cost along with profits. The resulting problem is a large scale nonlinear mixed-integer optimization problem for which there is no exact solution technique available. The solution to the problem can be obtained only by complete enumeration, often at the cost of a prohibitively computation time requirement for realistic power systems. This paper presents a hybrid algorithm which combines Lagrangian Relaxation (LR) together with Evolutionary Algorithm (EA) to solve the problem in cooperative and competitive energy environments. Simulation studies were carried out on different systems containing various numbers of units. The outcomes from different algorithms are compared with that from the proposed hybrid algorithm and the advantages of the proposed algorithm are briefly discussed.

Purpose This study aims to consider environmental sustainability, a global challenge under the preview of sustainable development goals, highlighting the significance of knowledge economy in attaining sustainable aggregate demand behavior globally. For this purpose, 155 countries that have data available from 1995 to 2021 were selected. The purpose of selecting these countries is to test the global responsibility of the knowledge economy to attain environmental sustainability. Design/methodology/approach Results are estimated with the help of panel quantile regression. The empirical existence of aggregate demand-based environmental Kuznets curve (EKC) was tested using non-linear tests. Moreover, principal component analysis has been incorporated to construct the knowledge economy index. Findings U-shaped aggregate demand-based EKC at global level is validated. However, environmental deterioration increases with an additional escalation after US$497.945m in aggregate demand. As a determinant, the knowledge economy is reducing CO2 emissions. The knowledge economy has played a significant role in global responsibility, shifting the EKC downward and extending the CO2 reduction phase for every selected country. Further, urbanization, energy intensity, financial development and trade openness significantly deteriorate the environmental quality. Originality/value This study contains the empirical existence of aggregate demand-based EKC. The role of the knowledge economy is examined through an index which is calculated by using four pillars of the knowledge economy (technology, innovations, education and institutions). This study is based on a combined panel of all the countries for which the data was available.

The wind power forecasting error restricts the benefit of the wind farm in the electricity market. Considering the cooperation of wind power bidding and energy storage system (ESS) operation with uncertainty, this paper proposes a coordinated bidding/operation model for the wind farm to improve its benefits in the electricity market. The maximum entropy based deep reinforcement learning (RL) algorithm, Soft Actor-Critic (SAC) is used to construct the model. The maximum entropy framework enables the designed agent to explore various optimal possibilities, which means the learned coordinated bidding/operation strategy is more stable considering the forecasting error. Particularly, penalty terms are introduced into the benefit function to relax the constraints and improve the convergency. The case study illustrates that the learned policy can effectively improve the wind farm benefit while ensuring robustness.

The division of return stroke current among the arresters and groundings of two unenergized test distribution lines, one horizontally configured and the other vertically configured, was studied at the International Center for Lightning Research and Testing in Florida. The division of return stroke currents for the vertically configured line was initially similar to the division on the horizontally configured line: at the time the return stroke current reached peak value (after one microsecond, or so) the two closest arresters/grounds on both lines passed about 90% of the total current. However, the time during which the return stroke current flowed primarily through the closest arresters to the neutral conductor was significantly shorter on the vertically configured line. On that line, the arrester current was about equally divided among all four arresters after several tens of microseconds. The arrester current division as a function of time measured on the vertical line was successfully modeled using the published VI-characteristic, while the division on the horizontal line after some tens of microseconds was only successfully modeled if the residual voltage of the two arresters closest to the current injection point was reduced by 20%. Based on the triggered lightning current division observed on our line, the minimum energy absorbed in each of the two arresters closest to the strike point during a typical natural first stroke is estimated to be 40 kJ.

A new direction for the analysis of nonlinear models of nuclear systems is suggested to overcome fundamental limitations of sensitivity analysis and optimization methods currently prevalent in nucl...

Power System Stabilizers (PSS) have broad application throughout the world. PSS application requires careful tuning which is usually accomplished in the field with the generator and power system in an abnormal condition. Recently developed equipment which combines fast Fourier transform capability with digital computer technique provides a means of PSS tuning which is faster and more accurate than was previously obtainable. This paper describes a PSS tuning test conducted using the new technique. The advantages will be apparent to the reader. The described test is very poignant as evidenced by two unexpected occurrences of instability.

Abstract Molten salt reactors (MSRs) are a class of next-generation nuclear reactors that have received recent industrial and research interest. A generalized species transport solver was implemented in the Virtual Environment for Reactor Applications (VERA) computing suite to extend this tool to analyze liquid-fueled MSRs. This core simulator has been extended to model the transport of fission product gases into a collection of circulating gas bubbles with the purpose of removing the gases. This paper presents the governing species transport equation, along with various nuclear source terms. Development of the source term for phase migration is discussed, along with a simplified interfacial area tracking method. Finally, a case study on a simplified MSR loop is presented in which modeling parameters were varied to assess their impact on gas removal. The steady state results show that parameters such as bubble diameter, gas injection rate and mass transfer coefficient have a low to moderate effect on the fraction of xenon in the core region. Removal efficiency has the greatest effect on the fraction in the core region. After the pump bowl, bubble diameter has a minor effect on the fraction of xenon in the gas void. These results point out that increasing parameters such as mass transfer coefficient, gas injection rate, and removal efficiency drives the xenon into the circulating gas void, while decreasing bubble diameter also drives xenon into the gas void by increasing interfacial area.