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Abstract AP1000® Generation III+ reactor bases its safety concept on passive systems, differently from the previous Generation II reactors. This fact has led the approximations and methodologies previously used for modeling active safety systems to be reviewed and adapted to simulate the physics of passive systems. Diverse studies about the AP1000 containment have demonstrated the difficulty to correctly model the occurring phenomenology. In this paper, an integral AP1000 3D containment GOTHIC model is presented, including the Passive Containment Cooling System (PCCS). The model includes the compartments inside and outside the metallic containment liner that influence the thermal–hydraulic behavior. The model is tested against a Large Break Loss of Coolant Accident (LBLOCA) to assess its thermal–hydraulic performance, assuming a PCS tank malfunction, what is a conservative hypothesis. The pressure and temperature evolution predicted by the 3D containment model is analyzed and compared with a single node Lumped Parameters model, allowing to evaluate some preliminary benefits of 3D modeling for containment safety analysis. The 3D containment model allows to predict the thermal evolution in each containment compartment capturing the heterogeneity of this phenomenon, with higher resolution than the lumped parameters models traditionally used in this kind of analyses. It allows to observe the thermohydraulic conditions locally at any time during the transient.

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.

Abstract In many visions and roadmaps, there is a broad agreement that fuel cells – both for stationary and mobile applications – are the key technology to allow the development of a hydrogen infrastructure. Furthermore, this development is generally thought to be based on a gradual, decentralised evolution. Nevertheless, in this paper it is argued that, taking into account the entire hydrogen chain (production, transport, storage, distribution and end-use), this decentralised fuel-cell based philosophy shows some serious flaws. Therefore, a new hydrogen-transition approach was pushed forward: mixing in of hydrogen into the natural-gas bulk. Using Flanders – the Northern part of Belgium – as a case study, the development of a transitory hydrogen infrastructure has been studied, taking into account the entire hydrogen chain and its dynamics, from production to end use. In a next step, this transition is being quantified. An optimisation model has been developed using Matlab and the commercial solvers GAMS and CPLEX. Following a mixed-integer linear-programming approach, this model is able to determine the economically optimal hydrogen-production mix and operational behaviour of each hydrogen-production plant separately. The model then allows gaining valuable insights in the importance of storage and the influence of fuel prices and carbon taxes with regard to the development of an early hydrogen economy.

Abstract In the new competitive electricity supply industry, there is a renewed interest in algorithms that can provide savings in operation costs. An optimal scheduling of generators can provide substantial annual savings in fuel costs, but this highly constrained non-linear mixed integer optimisation problem can only be full v solved by complete enumeration, a process which is not computationally feasible for realistic power systems. In the recent past, evolutionary computation techniques have been applied to the solution of the unit commitment problem, but when implemented as stand alone systems, they suff er from computational time limitations, especially when the systems are scaled up. This paper proposes a hybridgenetic algorithm incorporating a priority list unit ordering scheme to solve the generator scheduling problem. Test results on networks with up to 110 generators are presented and the results demonstrate the viability of the hybrid GA method for unit commitment in realistic power systems.

Previous work carried out for the last eight years resulted in the proposal of a complete system of dimensionless groups in order to represent the performance of different kinematic Stirling engine configurations. When looking for experimental support for the proposed model, some differences between the performances of several prototypes were observed. In this paper an equation is introduced to be applied to all known kinematic engines and to their whole range of performance. The coefficients appearing in this equation can be computed from temperature and geometrical dimensionless ratios and from experimental measurements at the maximum indicated power operating point. The meaning of those coefficients is interpreted and their usefulness to provide an engine performance overview is shown. The quasi-static simulation and the characteristic Mach number at the maximum indicated power operating point appear to be interesting criteria in order to evaluate the performance of prototypes.

This paper gives a introduction of a SMES unit using 2G HTS wires. A complete SMES system including both superconducting coils and control circuit has been designed to operate at 77 K. Three single-phase H-bridge converters have been used in the control circuit. A loop control signal is sent out by using 32 fixed point Digital Signal Processor (DSP). The complete circuit has been both modelled in simulation and built experimentally. The results validate that this SMES successfully compensates a voltage sag in a power system.