Bi-level Programming Based Optimal Strategy to LSEs with Demand Response Bids

With the increasing demand-side participation in electricity market, as a profit-seeking market participant, load-serving entities (LSEs) have been trying to apply demand response (DR) programs to induce the demand elasticity to further their profit. However, due to the different preference of DRs, it is difficult for LSEs to generate the optimal strategic bidding strategy considering DR in the ISO/RTO’s market. Therefore, this paper proposed a bi-level optimization model with the consideration of demand response bidding to maximize the total profit of LSEs: 1) conceptually, different from previous related works, the consumers participate DR through setting their bidding prices to LSEs with respect to their own preference and LSEs should determine the optimal reward value of DR as well as the amount of demanded electricity; and 2) technically, an original method has been implemented to solve the bi-level optimization model. The closed form of shadow price function with respect to the total load demand is derived to reduce the complexity of the proposed bi-level model. Hence, the proposed model is converted to a mixed integer second order cone programming and able to achieve the global optimality. It needs to be note that the closed form of shadow price introduced in this paper can also be applied to other bi-level programming models. Moreover, case studies have been performed to demonstrate the validity of the proposed method: 1) the proposed method to obtain the closed form of real-time price is verified on a 9-bus system; 2) 118-bus test system with three demand response participants is tested to show that by the proposed method, LSE can benefit from the DRs under various circumstance

AutoPCF: Efficient Product Carbon Footprint Accounting with Large Language Models

The product carbon footprint (PCF) is crucial for decarbonizing the supply chain, as it measures the direct and indirect greenhouse gas emissions caused by all activities during the product's life cycle. However, PCF accounting often requires expert knowledge and significant time to construct life cycle models. In this study, we test and compare the emergent ability of five large language models (LLMs) in modeling the 'cradle-to-gate' life cycles of products and generating the inventory data of inputs and outputs, revealing their limitations as a generalized PCF knowledge database. By utilizing LLMs, we propose an automatic AI-driven PCF accounting framework, called AutoPCF, which also applies deep learning algorithms to automatically match calculation parameters, and ultimately calculate the PCF. The results of estimating the carbon footprint for three case products using the AutoPCF framework demonstrate its potential in achieving automatic modeling and estimation of PCF with a large reduction in modeling time from days to minutes

The Structural Characterization and Antigenicity of the S Protein of SARS-CoV

The corona-like spikes or peplomers on the surface of the virion under electronic microscope are the most striking features of coronaviruses. The S (spike) protein is the largest structural protein, with 1,255 amino acids, in the viral genome. Its structure can be divided into three regions: a long N-terminal region in the exterior, a characteristic transmembrane (TM) region, and a short C-terminus in the interior of a virion. We detected fifteen substitutions of nucleotides by comparisons with the seventeen published SARS-CoV genome sequences, eight (53.3%) of which are non-synonymous mutations leading to amino acid alternations with predicted physiochemical changes. The possible antigenic determinants of the S protein are predicted, and the result is confirmed by ELISA (enzyme-linked immunosorbent assay) with synthesized peptides. Another profound finding is that three disulfide bonds are defined at the C-terminus with the N-terminus of the E (envelope) protein, based on the typical sequence and positions, thus establishing the structural connection with these two important structural proteins, if confirmed. Phylogenetic analysis reveals several conserved regions that might be potent drug targets

Economic dispatch constrained by central multi-period security for Global Energy Interconnection and its application in the Northeast Asia

AbstractIn recent years, the global energy interconnection (GEI) has more and more profound influence around the world, which is a highly practical way for humans to handle the energy crisis. In the studies of GEI, the economic dispatch (ED) is a basic and important content. In this paper, a model of dynamic economic dispatch (DED) of GEI is presented, which include the renewable energy generation. The objective function of this model is composed of the operating costs and the renewable energy curtailment. A series of case studies for the transnational energy interconnection in Northeast Asia are given to verify the superiority of GEI and for further analysis. Keywords: Global energy interconnection, Dynamic economic dispatch, Renewable energy resource

XA4C: eXplainable representation learning via Autoencoders revealing Critical genes.

Machine Learning models have been frequently used in transcriptome analyses. Particularly, Representation Learning (RL), e.g., autoencoders, are effective in learning critical representations in noisy data. However, learned representations, e.g., the "latent variables" in an autoencoder, are difficult to interpret, not to mention prioritizing essential genes for functional follow-up. In contrast, in traditional analyses, one may identify important genes such as Differentially Expressed (DiffEx), Differentially Co-Expressed (DiffCoEx), and Hub genes. Intuitively, the complex gene-gene interactions may be beyond the capture of marginal effects (DiffEx) or correlations (DiffCoEx and Hub), indicating the need of powerful RL models. However, the lack of interpretability and individual target genes is an obstacle for RL's broad use in practice. To facilitate interpretable analysis and gene-identification using RL, we propose "Critical genes", defined as genes that contribute highly to learned representations (e.g., latent variables in an autoencoder). As a proof-of-concept, supported by eXplainable Artificial Intelligence (XAI), we implemented eXplainable Autoencoder for Critical genes (XA4C) that quantifies each gene's contribution to latent variables, based on which Critical genes are prioritized. Applying XA4C to gene expression data in six cancers showed that Critical genes capture essential pathways underlying cancers. Remarkably, Critical genes has little overlap with Hub or DiffEx genes, however, has a higher enrichment in a comprehensive disease gene database (DisGeNET) and a cancer-specific database (COSMIC), evidencing its potential to disclose massive unknown biology. As an example, we discovered five Critical genes sitting in the center of Lysine degradation (hsa00310) pathway, displaying distinct interaction patterns in tumor and normal tissues. In conclusion, XA4C facilitates explainable analysis using RL and Critical genes discovered by explainable RL empowers the study of complex interactions