Bi-Level Optimization for Available Transfer Capability Evaluation in Deregulated Electricity Market

Available transfer capability (ATC) is the transfer capability remaining in the physical transmission network for further commercial activity over and above already committed uses which needs to be posted in the electricity market to facilitate competition. ATC evaluation is a complicated task including the determination of total transfer capability (TTC) and existing transfer capability (ETC). In the deregulated electricity market, ETC is decided by the independent system operator’s (ISO’s) economic dispatch (ED). TTC can then be obtained by a continuation power flow (CPF) method or by an optimal power flow (OPF) method, based on the given ED solutions as well as the ETC. In this paper, a bi-level optimization framework for the ATC evaluation is proposed in which ATC results can be obtained simultaneously with the ED and ETC results in the deregulated electricity market. In this bi-level optimization model, ATC evaluation is formulated as the upper level problem and the ISO’s ED is the lower level problem. The bi-level model is first converted to a mathematic program with equilibrium constraints (MPEC) by recasting the lower level problem as its Karush-Kuhn-Tucher (KKT) optimality condition. Then, the MPEC is transformed into a mixed-integer linear programming (MILP) problem, which can be solved with the help of available optimization software. In addition, case studies on PJM 5-bus, IEEE 30-bus, and IEEE 118-bus systems are presented to demonstrate the proposed methodology

Research on short-term load forecasting of new-type power system based on GCN-LSTM considering multiple influencing factors

With the construction of new-type power system under the ”double carbon” target and the increasing diversification of the energy demand of the user side, the short-term load forecasting of power system is facing new challenges. In order to fully exploit the massive information contained in big data, this paper proposed a new short-term load forecasting method for new-type power system considering multiple factors, which based on Graph Convolutional Network (GCN) and Long Short-Term Memory network (LSTM). Spearman rank correlation coefficient was used to analyze the correlation between load and meteorological factors, and a quantitative model including meteorological factors, date factors and regional factors was established. Thus, GCN and LSTM were jointly used to extract the spatial and temporal characteristics of massive data respectively, and finally the short-term power load forecasting was achieved. The public data sets were used for performance verification compared with three comparison models, LSTM, CNN-LSTM and TCN-LSTM. The results show that the proposed method can make full use of the influence of multi-dimensional data, meanwhile improve the load prediction accuracy and training efficiency effectively

Exfoliation of amorphous phthalocyanine conjugated polymers into ultrathin nanosheets for highly efficient oxygen reduction

It is a significant challenge to develop a high-efficiency synthetic methodology to access fully conjugated 2D conjugated polymer (CP)/covalent organic framework (COF) nanosheets (NSs) that have great application potential for electronics and energy. Herein, we report the exfoliation of a series of amorphous ethynyl-linked phthalocyanine (Pc) CPs (MPc-CPs, M = Fe, Co, Fe0.5Co0.5) into ultrathin MPc-CP NSs. Random coupling between the four regioisomers (with D4h, D2h, C2v and Cs symmetry) of the two tetra-β-substituted phthalocyanine precursors endows the resulting phthalocyanine conjugated polymers MPc-CPs with intrinsic structural defects and a disordered framework on individual layers. This in turn induces a diminished interlayer overlapping and a weakened interlayer π–π stacking interaction, facilitating the possible exfoliation of MPc-CPs into ultrathin 2D NSs with a yield of over 50%. The direction observation by transmission electron microscopy (TEM) and atomic force microscopy (AFM) demonstrates that the ultrathin MPc-CP NSs possess a smooth surface with a uniform thickness of 1–3 nm and a lateral size of hundreds of nanometers. The as-prepared bimetallic Fe0.5Co0.5Pc-CP NSs were further used to fabricate a heterostructure Fe0.5Co0.5Pc-CP NS@G with graphene NSs as an oxygen reduction reaction (ORR) catalyst, which exhibits an onset potential of 1006 mV and a half-wave potential of 927 mV in 0.1 M KOH, representing one of the best values in an alkaline medium. Moreover, the excellent ORR activity of the exfoliated tetrapyrrole-based conjugated NSs hybridized with graphene has also been demonstrated by a Zn–air battery device, showing an open circuit voltage of 1.34 V and a peak power density of ca. 180 mW cm−2

Real-Time Wide-area Loading Margin Sensitivity (WALMS) in Power Systems

Abstract-Loading margin sensitivity (LMS) has been widely used in applications in the realm of voltage stability assessment. Typically, LMS is derived based on system equilibrium equations near bifurcation and therefore requires full detailed system model and significant computation effort. Availability of phasor measurement units (PMUs) due to the recent development of wide-area monitoring system (WAMS) provides an alternative computation-friendly approach for calculating LMS. With such motivation, this work proposes measurementbased wide-area loading margin sensitivity (WALMS) in bulk power systems. The proposed sensitivity, with its simplicity, has great potential to be embedded in real-time applications. Moreover, the calculation of the WALMS is not limited to low voltage near bifurcation point. A case study on IEEE 39-bus system verifies the proposed sensitivity. Finally, a voltage control scenario demonstrates the potential application of the WALMS

Real-time wide-area loading margin sensitivity (WALMS) in power systems

Loading margin sensitivity (LMS) has been widely used in applications in the realm of voltage stability assessment and control. Typically, LMS is derived based on system equilibrium equations near bifurcation and therefore requires full detailed system model and significant computation effort. Availability of phasor measurement units (PMUs) due to the recent development of wide-area monitoring system (WAMS) provides an alternative computation-friendly approach for calculating LMS. With such motivation, this work proposes measurement-based wide-area loading margin sensitivity (WALMS) in bulk power systems. The proposed sensitivity, with its simplicity, has great potential to be embedded in real-time applications. Moreover, the calculation of the WALMS is not limited to low voltage near bifurcation point. A case study on IEEE 39-bus system verifies the proposed sensitivity. Finally, a voltage control scenario demonstrates the potential application of the WALMS