Learning from past bids to participate strategically in day-ahead electricity markets

We consider the process of bidding by electricity suppliers in a day-ahead market context, where each supplier bids a linear non-decreasing function of her generating capacity with the goal of maximizing her individual profit given other competing suppliers' bids. Based on the submitted bids, the market operator schedules suppliers to meet demand during each hour and determines hourly market clearing prices. Eventually, this game-theoretic process reaches a Nash equilibrium when no supplier is motivated to modify her bid. However, solving the individual profit maximization problem requires information of rivals' bids, which are typically not available. To address this issue, we develop an inverse optimization approach for estimating rivals' production cost functions given historical market clearing prices and production levels. We then use these functions to bid strategically and compute Nash equilibrium bids. We present numerical experiments illustrating our methodology, showing good agreement between bids based on the estimated production cost functions with the bids based on the true cost functions. We discuss an extension of our approach that takes into account network congestion resulting in location-dependent pricesFirst author draf

Network anomaly detection: a survey and comparative analysis of stochastic and deterministic methods

7 pages. 1 more figure than final CDC 2013 versionWe present five methods to the problem of network anomaly detection. These methods cover most of the common techniques in the anomaly detection field, including Statistical Hypothesis Tests (SHT), Support Vector Machines (SVM) and clustering analysis. We evaluate all methods in a simulated network that consists of nominal data, three flow-level anomalies and one packet-level attack. Through analyzing the results, we point out the advantages and disadvantages of each method and conclude that combining the results of the individual methods can yield improved anomaly detection results

Learning from Past Bids to Participate Strategically in Day-Ahead Electricity Markets

We consider the process of bidding by electricity suppliers in a day-ahead market context where each supplier bids a linear non-decreasing function of her generating capacity with the goal of maximizing her individual profit given other competing suppliers' bids. Based on the submitted bids, the market operator schedules suppliers to meet demand during each hour and determines hourly market clearing prices. Eventually, this game-theoretic process reaches a Nash equilibrium when no supplier is motivated to modify her bid. However, solving the individual profit maximization problem requires information of rivals' bids, which are typically not available. To address this issue, we develop an inverse optimization approach for estimating rivals' production cost functions given historical market clearing prices and production levels. We then use these functions to bid strategically and compute Nash equilibrium bids. We present numerical experiments illustrating our methodology, showing good agreement between bids based on the estimated production cost functions with the bids based on the true cost functions. We discuss an extension of our approach that takes into account network congestion resulting in location-dependent prices

Personalized hypertension treatment recommendations by a data-driven model

BACKGROUND: Hypertension is a prevalent cardiovascular disease with severe longer-term implications. Conventional management based on clinical guidelines does not facilitate personalized treatment that accounts for a richer set of patient characteristics. METHODS: Records from 1/1/2012 to 1/1/2020 at the Boston Medical Center were used, selecting patients with either a hypertension diagnosis or meeting diagnostic criteria (≥ 130 mmHg systolic or ≥ 90 mmHg diastolic, n = 42,752). Models were developed to recommend a class of antihypertensive medications for each patient based on their characteristics. Regression immunized against outliers was combined with a nearest neighbor approach to associate with each patient an affinity group of other patients. This group was then used to make predictions of future Systolic Blood Pressure (SBP) under each prescription type. For each patient, we leveraged these predictions to select the class of medication that minimized their future predicted SBP. RESULTS: The proposed model, built with a distributionally robust learning procedure, leads to a reduction of 14.28 mmHg in SBP, on average. This reduction is 70.30% larger than the reduction achieved by the standard-of-care and 7.08% better than the corresponding reduction achieved by the 2nd best model which uses ordinary least squares regression. All derived models outperform following the previous prescription or the current ground truth prescription in the record. We randomly sampled and manually reviewed 350 patient records; 87.71% of these model-generated prescription recommendations passed a sanity check by clinicians. CONCLUSION: Our data-driven approach for personalized hypertension treatment yielded significant improvement compared to the standard-of-care. The model implied potential benefits of computationally deprescribing and can support situations with clinical equipoise.GM135930 - National Institute of General Medical Sciences; UL54 TR004130 - National Center for Advancing Translational Sciences; IIS-1914792 - National Science Foundation; DMS-1664644 - National Science Foundation; CCF-2200052 - National Science FoundationPublished versio

The impact of payer status on hospital admissions: evidence from an academic medical center

BACKGROUND: There are plenty of studies investigating the disparity of payer status in accessing to care. However, most studies are either disease-specific or cohort-specific. Quantifying the disparity from the level of facility through a large controlled study are rare. This study aims to examine how the payer status affects patient hospitalization from the perspective of a facility. METHODS: We extracted all patients with visiting record in a medical center between 5/1/2009-4/30/2014, and then linked the outpatient and inpatient records three year before target admission time to patients. We conduct a retrospective observational study using a conditional logistic regression methodology. To control the illness of patients with different diseases in training the model, we construct a three-dimension variable with data stratification technology. The model is validated on a dataset distinct from the one used for training. RESULTS: Patients covered by private insurance or uninsured are less likely to be hospitalized than patients insured by government. For uninsured patients, inequity in access to hospitalization is observed. The value of standardized coefficients indicates that government-sponsored insurance has the greatest impact on improving patients' hospitalization. CONCLUSION: Attention is needed on improving the access to care for uninsured patients. Also, basic preventive care services should be enhanced, especially for people insured by government. The findings can serve as a baseline from which to measure the anticipated effect of measures to reduce disparity of payer status in hospitalization.72033003 - National Natural Science Foundation of China; 71720107003 - National Natural Science Foundation of China; DMS-1664644 - National Science Foundation; IIS-1914792 - National Science Foundation; CNS-1645681 - National Science Foundation; R01 GM135930 - NIGMS NIH HHS; N00014-19-1-2571 - Office of Naval Research; R01-GM135930 - NIH HHSPublished versio

Smart building real time pricing for offering load-side regulation service reserves

Abstract-Provision of Regulation Service (RS) reserves to Power Markets by smart building demand response has attracted attention in recent literature. This paper develops tractable dynamic optimal pricing algorithms for distributed RS reserve provision. It shows monotonicity and convexity properties of the optimal pricing policies and the associated differential cost function. Then, it uses them to propose and implement a modified Least Squares Temporal Differences (LSTD) Actor-Critic algorithm with a bounded and continuous action space. This algorithm solves for the best policy within a pre-specified broad family. In addition, the paper develops a novel Approximate Policy Iteration (API) algorithm and uses it successfully to optimize the parameters of an analytic policy function. Numerical results are obtained to demonstrate and compare the Actor-Critic and Approximate Policy Iteration algorithms, demonstrating that the novel API algorithm outperforms the Bounded LSTD Actor-Critic algorithm in both computational effort and policy minimum cost

An optimal transmission line switching and bus splitting heuristic incorporating AC and N-1 contingency constraints

Optimal transmission line switching and/or bus splitting is shown to contribute in relieving congestion and reducing the operation cost by rerouting power flows throughout the network. Although bus splitting may be as powerful as line switching in congestion mitigation and is typically considered a smaller disturbance compared with line switching, it has received less attention in the literature in part due to the more complicated node-breaker modeling requirement. In this paper, an optimal transmission line switching and bus splitting heuristic is presented to minimize the operation cost while respecting AC and N-1 contingency constraints. We present a two-level solution method where switching decisions are made in the upper level problem formulated as a mixed integer second order cone programming master problem, while the resulting network topology is checked against AC and N-1 contingency constraints in lower level subproblems. Line switching and bus splitting are modeled as switching actions assuming double-bus double-breaker substation arrangements where all elements at a substation, including generators, loads, lines and shunt elements, are given switches to connect to either of the busbars if the respective substation is split. We also introduce additional constraints to model a breaker-and-a-half substation scheme. Furthermore, a pre-screening step is presented to limit the search space of the problem, thus accelerating the solution process. We demonstrate the application of the proposed method on IEEE standard test systems.Accepted manuscrip

Informative predictors of pregnancy after first IVF cycle using eIVF practice highway electronic health records

The aim of this study is to determine the most informative pre- and in-cycle variables for predicting success for a first autologous oocyte in-vitro fertilization (IVF) cycle. This is a retrospective study using 22,413 first autologous oocyte IVF cycles from 2001 to 2018. Models were developed to predict pregnancy following an IVF cycle with a fresh embryo transfer. The importance of each variable was determined by its coefficient in a logistic regression model and the prediction accuracy based on different variable sets was reported. The area under the receiver operating characteristic curve (AUC) on a validation patient cohort was the metric for prediction accuracy. Three factors were found to be of importance when predicting IVF success: age in three groups (38-40, 41-42, and above 42 years old), number of transferred embryos, and number of cryopreserved embryos. For predicting first-cycle IVF pregnancy using all available variables, the predictive model achieved an AUC of 68% + /- 0.01%. A parsimonious predictive model utilizing age (38-40, 41-42, and above 42 years old), number of transferred embryos, and number of cryopreserved embryos achieved an AUC of 65% + /- 0.01%. The proposed models accurately predict a single IVF cycle pregnancy outcome and identify important predictive variables associated with the outcome. These models are limited to predicting pregnancy immediately after the IVF cycle and not live birth. These models do not include indicators of multiple gestation and are not intended for clinical application.IIS-1914792 - National Science Foundation; N00014-19-1-2571 - Office of Naval Research Global; GM135930 - Office of Extramural Research, National Institutes of HealthPublished versio