Calculation of Available Transfer Capability Using Hybrid Chaotic Selfish Herd Optimizer and 24 Hours RES-thermal Scheduling

As fossil fuel stocks are being depleted,alternative sources of energy must be explored.Consequently,traditional thermal power plants must coexist with renewable resources,such as wind,solar,and hydro units,and all-day planning and operation techniques are necessary to safeguard nature while meeting the current demand.The fundamental components of contemporary power systems are the simultaneous decrease in generation costs and increase in the available transfer capacity(ATC)of current systems.Thermal units are linked to sources of renewable energy such as hydro,wind,and solar power,and are set up to run for 24 h.By contrast,new research reports that various chaotic maps are merged with various existing optimization methodologies to obtain better results than those without the inclusion of chaos.Chaos seems to increase the performance and convergence properties of existing optimization approaches.In this study,selfish animal tendencies,mathematically represented as selfish herd optimizers,were hybridized with chaotic phenomena and used to improve ATC and/or reduce generation costs,creating a multi-objective optimization problem.To evaluate the performance of the proposed hybridized optimization technique,an optimal power flow-based ATC was enforced under various hydro-thermal-solar-wind conditions,that is,the renewable energy source-thermal scheduling concept,on IEEE 9-bus,IEEE 39-bus,and Indian Northern Region Power Grid 246-bus test systems.The findings show that the proposed technique outperforms existing well-established optimization strategies.

A Scenario-classification Hybrid-based Banding Method for Power Transfer Limits of Critical Inter-corridors

To secure power system operations,practical dispatches in industries place a steady power transfer limit on critical inter-corridors,rather than high-dimensional and strong nonlinear stability constraints.However,computational complexities lead to over-conservative pre-settings of transfer limit,which further induce undesirable and non-technical congestion of power transfer.To conquer this barrier,a scenario-classification hybrid-based banding method is proposed.A cluster technique is adopted to separate similarities from historical and generated operating condition dataset.With a practical rule,transfer limits are approximated for each operating cluster.Then,toward an interpretable online transfer limit decision,costsensitive learning is applied to identify cluster affiliation to assign a transfer limit for a given operation.In this stage,critical variables that affect the transfer limit are also picked out via mean impact value.This enables us to construct low-complexity and dispatcher-friendly rules for fast determination of transfer limit.The numerical case studies on the IEEE 39-bus system and a real-world regional power system in China illustrate the effectiveness and conservativeness of the proposed method.

Effect of coating modification of cordierite carrier on catalytic performance of supported NiMnO_3 catalysts for VOCs combustion

NiMnO3 perovskite catalysts supported on cordierite modified by CexZr（1-x）O2 coatings were prepared using impregnation and sol-gel methods for catalytic combustion of single/double component VOCs at different concentrations and GHSV of 15,000 h^（-1）, which were characterized by BET, XRD, SEM, FT-IR, H2-TPR and O2-TPD. After coating modification, the specific surface area of catalysts is improved obviously.Among the catalysts, the Ce（0.75）Zr（0.25）O2 coating modified NiMnO3 catalyst exhibits the best catalytic activity for VOCs combustion with 95.6% conversion at 275 ℃ and has stable activity when catalyst is embalmed at 800 ℃. In addition, the catalyst also presents the excellent water-resistant and conversion stability over time-on-stream condition. The reason is that Ce（0.75）Zr（0.25）O2 coating can promote more lattice distortion and defects and smaller crystal size, which improve oxygen transfer capability and dispersion of active component.

An HPC Based Real-time Path Rating Calculation Tool for Congestion Management with High Penetration of Renewable Energy

Every year, transmission congestion costs billions ofdollars for electricity customers. This clearly identifies the criticalneed for more transmission capacity and also poses big challengesfor power grid reliability in stressed conditions due to heavyloading and in uncertain situations due to variable renewableresources and responsive smart loads. However, it becomesincreasingly difficult to build new transmission lines, whichtypically involve both economic and environmental constraints.In this paper, advanced computing techniques are developedto enable a non-wire solution that realizes unused transfercapabilities of existing transmission facilities. An integratedsoftware prototype powered by high-performance computing(HPC) is developed to calculate ratings of key transmission pathsin real time for relieving transmission congestion and facilitatingrenewable integration, while complying with the North AmericanElectric Reliability Corporation (NERC) standards on assessingtotal transfer capabilities. The innovative algorithms include: (1)massive contingency analysis enabled by dynamic load balancing,(2) parallel transient simulation to speed up single dynamicsimulation, (3) a non-iterative method for calculating voltagesecurity boundary and (4) an integrated package consideringall NERC required limits. This tool has been tested on realisticpower system models in the Western Interconnection of NorthAmerica and demonstrates satisfactory computational speedusing parallel computers. Various benefits of real-time path ratingare investigated at Bonneville Power Administration using realtime EMS snapshots, demonstrating a significant increase in pathlimits. These technologies would change the traditional goals ofpath rating studies, fundamentally transforming how the grid isoperated, and maximizing the utilization of national transmissionassets, as well as facilitating integration of renewable energy andsmart loads.