A Contextual Bandit Approach for Value-oriented Prediction Interval Forecasting

Prediction interval (PI) is an effective tool to quantify uncertainty and usually serves as an input to downstream robust optimization. Traditional approaches focus on improving the quality of PI in the view of statistical scores and assume the improvement in quality will lead to a higher value in the power systems operation. However, such an assumption cannot always hold in practice. In this paper, we propose a value-oriented PI forecasting approach, which aims at reducing operational costs in downstream operations. For that, it is required to issue PIs with the guidance of operational costs in robust optimization, which is addressed within the contextual bandit framework here. Concretely, the agent is used to select the optimal quantile proportion, while the environment reveals the costs in operations as rewards to the agent. As such, the agent can learn the policy of quantile proportion selection for minimizing the operational cost. The numerical study regarding a two-timescale operation of a virtual power plant verifies the superiority of the proposed approach in terms of operational value. And it is especially evident in the context of extensive penetration of wind power.Comment: submitted to IEEE Transactions on Smart Gri

Value-oriented Renewable Energy Forecasting for Coordinated Energy Dispatch Problems at Two Stages

Energy forecasting is deemed an essential task in power system operations. Operators usually issue forecasts and leverage them to schedule energy dispatch ahead of time (referred to as the 'predict, then optimize' paradigm). However, forecast models are often developed via optimizing statistical scores while overlooking the value of the forecasts in operation. In this paper, we design a value-oriented point forecasting approach for energy dispatch problems with renewable energy sources (RESs). At the training phase, this approach incorporates forecasting with day-ahead/real-time operations for power systems, thereby achieving reduced operation costs of the two stages. To this end, we formulate the forecast model parameter estimation as a bilevel program at the training phase, where the lower level solves the day-ahead and real-time energy dispatch problems, with the forecasts as parameters; the optimal solutions of the lower level are then returned to the upper level, which optimizes the model parameters given the contextual information and minimizes the expected operation cost of the two stages. Under mild assumptions, we propose a novel iterative solution strategy for this bilevel program. Under such an iterative scheme, we show that the upper level objective is locally linear regarding the forecast model output, and can act as the loss function. Numerical experiments demonstrate that, compared to commonly used point forecasting methods, the forecasts obtained by the proposed approach result in lower operation costs in the subsequent energy dispatch problems. Meanwhile, the proposed approach is more computationally efficient than traditional two-stage stochastic program.Comment: submitted to European Journal of Operational Researc

Targeted Demand Response: Formulation, LMP Implications, and Fast Algorithms

Demand response (DR) is regarded as a solution to the issue of high electricity prices in the wholesale market, as the flexibility of the demand can be harnessed to lower the demand level for price reductions. As an across-the-board DR in a system is impractical due to the enrollment budget for instance, it is necessary to select a small group of nodes for DR implementing. Current studies resort to intuitive yet naive approaches for DR targeting, as price is implicitly associated with demand, though optimality cannot be ensured. In this paper, we derive such a relationship in the security-constrained economic dispatch via the multi-parametric programming theory, based on which the DR targeting problem is rigorously formulated as a mixed-integer quadratic programming problem aiming at reducing the averaged price to a reference level by efficiently reducing targeted nodes' demand. A solution strategy is proposed to accelerate the computation. Numerical studies demonstrate compared with the benchmarking strategy, the proposed approach can reduce the price to the reference point with less efforts in demand reduction. Besides, we empirically show that the proposed approach is immune to inaccurate system parameters, and can be generalized to variants of DR targeting tasks.Comment: submitted to IEEE Transactions on Power System

Continuous and Distribution-free Probabilistic Wind Power Forecasting: A Conditional Normalizing Flow Approach

We present a data-driven approach for probabilistic wind power forecasting based on conditional normalizing flow (CNF). In contrast with the existing, this approach is distribution-free (as for non-parametric and quantile-based approaches) and can directly yield continuous probability densities, hence avoiding quantile crossing. It relies on a base distribution and a set of bijective mappings. Both the shape parameters of the base distribution and the bijective mappings are approximated with neural networks. Spline-based conditional normalizing flow is considered owing to its non-affine characteristics. Over the training phase, the model sequentially maps input examples onto samples of base distribution, given the conditional contexts, where parameters are estimated through maximum likelihood. To issue probabilistic forecasts, one eventually maps samples of the base distribution into samples of a desired distribution. Case studies based on open datasets validate the effectiveness of the proposed model, and allows us to discuss its advantages and caveats with respect to the state of the art.Comment: The second revision to IEEE Transactions on Sustainable Energ

Implications of MDCK cell heterogeneity in cell-based influenza vaccine production

Influenza is a global public health issue that causes serious illness with high mortality rate. Currently, Madin-Darby canine kidney (MDCK) cell culture-based influenza vaccine production moving up to the front as an inexorable trend for the substitution of egg-based vaccine production, owing to its high degree of flexibility and scalability. However, MDCK cells are a continuous cell line and comprise a heterogeneous pool of non-clonal cells that differ in morphological as well as functional features in influenza virus production. The impurity of cell population may lead to fugacious tendency in virus production, and long-term culture may bring potential risk of unstable viral production or vaccine quality as cells in MDCK subclonal population may encounter unexpected manifestation of chromosomal rearrangement, loss of the virus susceptibility, or reduction of the virus partials packaging capability during the culture. Although many details of the influenza virus life cycle have already been unraveled, little is known about the ability of subclones in virus infection, intracellular replication, and virus release during viral vaccine production process. With the widely utilizing of omics-based approaches and progressively accumulating of omics database, transcriptome profile analysis will be a powerful strategy to explore the mechanism of cell heterogeneity, providing great significance for the development of robust virus producing cell line and robust virus production process. This work aims to explore a deeper understanding on the MDCK cell heterogeneity used in influenza virus production. For this purpose, a MDCK cell line that has been extensively used in industrial production was subcloned and examined for the influenza virus productivity. The virus productivity spread over a wide range of more than 300-fold among different clones, which revealed large variations in their ability to produce progeny viruses. The high and low producer as well as parent cell population were expanded to explore the intracellular virus propagation process, and the expression levels of all the annotated genes were quantified across the different subclones using RNA-seq. The RT-qPCR results showed that the influenza virus RNA synthesis and virus release differed dramatically among subclones during a synchronized single-cycle infection. Pathway analysis performed on the genes indicated that most of the genes are not differentially expressed, but a few key cellular metabolic pathways are differentially expressed among the subclones, especially the genes related to the virus infection, replication and release. These results spurs further hypothesis to improve our mechanistic understanding of cell line stability and virus propagation process, which will have significant impact on rationalizing cell line development of viral vaccine producing mammalian cells

Late-Quaternary paleoearthquakes along the Liulengshan Fault on the northern Shanxi Rift system

The Liulengshan Fault (LLSF), which lies on the northeastern edge of the Ordos Plateau, is a controlling boundary fault in the northern part of the Shanxi Rift system (SRS). The displaced landforms show that the fault has undergone strong and frequent late-Quaternary seismic activities. In 1989 and 1991, two moderate–strong earthquake swarms (Ms=6.1 and Ms=5.8) successively occurred in the LLSF, and GPS velocity shows that the areas are extending at around 1–2 mm/a. However, there is no surface-rupturing earthquake reported on the LLSF in historical records. Thus, the study of paleoseismic history and rupture behavior of paleoearthquakes in late-Quaternary on the LLSF is of fundamental importance for understanding the future seismic risk of this fault. To solve these problems, we conducted paleoseismological trench excavations at two sites on the LLSF to establish its paleoearthquake history. On the basis of the field geological survey and interpretation of high-precision topographic data, we carried out large-scale fault mapping and excavated two trenches in Xujiabao and Luofengwa across the LLSF. Then, four events in the Xujiabao trench and three events in the Luofengwa trench are identified. Finally, combined with radiocarbon dating (C14), optically stimulated luminescence (OSL) and OxCal modeling, we constrained the ages of these events. Together with the previous results of paleoseismology in Yin et al. (1997), we consider that different segments of the LLSF may rupture together at the same time. Therefore, a total of six paleoearthquake events since late-Quaternary have been finally confirmed at 44,151–30881a, 40,163-28045a, 28,233-19215a, 16,742-12915a, 12,788-8252a, and 8203–2300a BP. According to the empirical relationships between moment magnitude and rupture length, the best estimated magnitude is inferred to be in the range between Mw 6.9 and Mw 7.7. Considering the strong late-Quaternary activity and a long earthquake elapsed time, we propose that the LLSF might have a high seismic hazard potential in the near future

Corrigendum to: The TianQin project: current progress on science and technology

In the originally published version, this manuscript included an error related to indicating the corresponding author within the author list. This has now been corrected online to reflect the fact that author Jun Luo is the corresponding author of the article