Guaranteed Rate Research Articles

Evaluating the flexibility supply and demand reliability of hydro–wind–PV–battery complementary systems under different consumption modes

Evaluating the flexibility supply and demand reliability offers a new approach for configuring the capacity of hydro–wind–PV (HWP) or hydro–wind–PV–battery (HWPB) complementary systems. However, traditional flexibility evaluations ignore the operational characteristics of the complementary systems, and tend to underestimate the wind and PV flexibility demand in the case of cross-regional power delivery. To this end, we propose an approach for evaluating the flexibility supply and demand reliability in HWP/HWPB complementary systems under different consumption modes (i.e., external delivery consumption and local consumption). First, a novel flexibility demand optimization model is established by redefining the flexibility demand based on the day-ahead scheduling process of the system. The flexibility supply guaranteed rate of HWP/HWPB complementary systems is then calculated using a statistical model. Finally, the effectiveness of the proposed approach is verified under different wind and PV capacity configuration schemes, and the optimal capacity ratios of wind and PV are determined under different consumption modes. Results using a clean energy base in the upper Yellow River basin indicate that: (1) Stronger peak-shaving performance of the system produces higher wind and PV flexibility demand. On average, the flexibility demand under external delivery consumption is 33.7 % higher than that under local consumption. (2) Improving the system's flexibility can effectively enhance the flexibility supply guaranteed rate when the original guaranteed rate is less than 95 %. If the flexibility supply guaranteed rate is greater than 95 %, further increasing the flexibility of the system has little effect. (3) For the clean energy base in the upper Yellow River basin, appropriately increasing the proportion of wind power in the current system is beneficial for the operation of complementary systems. Generally, the proposed flexibility evaluation approach provides effective guidance for the planning and management of watershed-type HWP/HWPB complementary energy bases.

Isotonic subgroup selection

Abstract Given a sample of covariate–response pairs, we consider the subgroup selection problem of identifying a subset of the covariate domain where the regression function exceeds a predetermined threshold. We introduce a computationally feasible approach for subgroup selection in the context of multivariate isotonic regression based on martingale tests and multiple testing procedures for logically structured hypotheses. Our proposed procedure satisfies a non-asymptotic, uniform Type I error rate guarantee with power that attains the minimax optimal rate up to poly-logarithmic factors. Extensions cover classification, isotonic quantile regression, and heterogeneous treatment effect settings. Numerical studies on both simulated and real data confirm the practical effectiveness of our proposal, which is implemented in the R package ISS.

A projection-free method for solving convex bilevel optimization problems

AbstractWhen faced with multiple minima of an inner-level convex optimization problem, the convex bilevel optimization problem selects an optimal solution which also minimizes an auxiliary outer-level convex objective of interest. Bilevel optimization requires a different approach compared to single-level optimization problems since the set of minimizers for the inner-level objective is not given explicitly. In this paper, we propose a new projection-free conditional gradient method for convex bilevel optimization which requires only a linear optimization oracle over the base domain. We establish $$O(t^{-1/2})$$ O ( t - 1 / 2 ) convergence rate guarantees for our method in terms of both inner- and outer-level objectives, and demonstrate how additional assumptions such as quadratic growth and strong convexity result in accelerated rates of up to $$O(t^{-1})$$ O ( t - 1 ) and $$O(t^{-2/3})$$ O ( t - 2 / 3 ) for inner- and outer-levels respectively. Lastly, we conduct a numerical study to demonstrate the performance of our method.

Out-of-distribution Detection in Dependent Data for Cyber-physical Systems with Conformal Guarantees

Uncertainty in the predictions of learning-enabled components hinders their deployment in safety-critical cyber-physical systems (CPS). A shift from the training distribution of a learning-enabled component (LEC) is one source of uncertainty in the LEC’s predictions. Detection of this shift or out-of-distribution (OOD) detection on individual datapoints has therefore gained attention recently. But in many applications, inputs to CPS form a temporal sequence. Existing techniques for OOD detection in time-series data for CPS either do not exploit temporal relationships in the sequence or do not provide any guarantees on detection. We propose using deviation from the in-distribution temporal equivariance as the non-conformity measure in conformal anomaly detection framework for OOD detection in time-series data for CPS. Computing independent predictions from multiple conformal detectors based on the proposed measure and combining these predictions by Fisher’s method leads to the proposed detector CODiT with bounded false alarms. CODiT performs OOD detection on fixed-length windows of consecutive time-series datapoints by using Fisher value of the input window. We further propose performing OOD detection on real-time time-series traces of variable lengths with bounded false alarms. This can be done by using CODiT to compute Fisher values of the sliding windows in the input trace and combining these values by a merging function. Merging functions such as Harmonic Mean, Arithmetic Mean, Geometric Mean, Bonferroni Method, and so on, can be used to combine Fisher values of the sliding windows in the input trace, and the combined value can be used for OOD detection on the trace with bounded false alarm rate guarantees. We illustrate the efficacy of CODiT by achieving state-of-the-art results in two case studies for OOD detection on fixed-length windows. The first one is on an autonomous driving system with perception (or vision) LEC. The second case study is on a medical CPS for walking pattern or GAIT analysis where physiological (non-vision) data is collected with force-sensitive resistors attached to the subject’s body. For OOD detection on variable length traces, we consider the same case studies on the autonomous driving system and medical CPS for GAIT analysis. We report our results with four merging functions on the Fisher values computed by CODiT on the sliding windows of the input trace. We also compare the false alarm rate guarantees by these four merging functions in the autonomous driving system case study. Code, data, and trained models are available at https://github.com/kaustubhsridhar/time-series-OOD .

Partial recovery and weak consistency in the non-uniform hypergraph stochastic block model

Abstract We consider the community detection problem in sparse random hypergraphs under the non-uniform hypergraph stochastic block model (HSBM), a general model of random networks with community structure and higher-order interactions. When the random hypergraph has bounded expected degrees, we provide a spectral algorithm that outputs a partition with at least a $\gamma$ fraction of the vertices classified correctly, where $\gamma \in (0.5,1)$ depends on the signal-to-noise ratio (SNR) of the model. When the SNR grows slowly as the number of vertices goes to infinity, our algorithm achieves weak consistency, which improves the previous results in Ghoshdastidar and Dukkipati ((2017) Ann. Stat.45(1) 289–315.) for non-uniform HSBMs. Our spectral algorithm consists of three major steps: (1) Hyperedge selection: select hyperedges of certain sizes to provide the maximal signal-to-noise ratio for the induced sub-hypergraph; (2) Spectral partition: construct a regularised adjacency matrix and obtain an approximate partition based on singular vectors; (3) Correction and merging: incorporate the hyperedge information from adjacency tensors to upgrade the error rate guarantee. The theoretical analysis of our algorithm relies on the concentration and regularisation of the adjacency matrix for sparse non-uniform random hypergraphs, which can be of independent interest.

Second-order group knockoffs with applications to genome-wide association studies.

Conditional testing via the knockoff framework allows one to identify-among a large number of possible explanatory variables-those that carry unique information about an outcome of interest and also provides a false discovery rate guarantee on the selection. This approach is particularly well suited to the analysis of genome-wide association studies (GWAS), which have the goal of identifying genetic variants that influence traits of medical relevance. While conditional testing can be both more powerful and precise than traditional GWAS analysis methods, its vanilla implementation encounters a difficulty common to all multivariate analysis methods: it is challenging to distinguish among multiple, highly correlated regressors. This impasse can be overcome by shifting the object of inference from single variables to groups of correlated variables. To achieve this, it is necessary to construct "group knockoffs." While successful examples are already documented in the literature, this paper substantially expands the set of algorithms and software for group knockoffs. We focus in particular on second-order knockoffs, for which we describe correlation matrix approximations that are appropriate for GWAS data and that result in considerable computational savings. We illustrate the effectiveness of the proposed methods with simulations and with the analysis of albuminuria data from the UK Biobank. The described algorithms are implemented in an open-source Julia package Knockoffs.jl. R and Python wrappers are available as knockoffsr and knockoffspy packages.

BioMOF@PAN Mixed Matrix Membranes as Fast and Efficient Adsorbing Materials for Multiple Heavy Metals' Removal.

Heavy metal ions are a common source of water pollution. In this study, two novel membranes with biobased metal-organic frameworks (BioMOFs) embedded in a polyacrylonitrile matrix with tailored porosity were prepared via nonsolvent induced phase separation methods and designed to efficiently adsorb heavy metal ions from oligomineral water. Under optimized preparation conditions, stable membranes with high MOF loading up to 50 wt % and a cocontinuous sponge-like morphology and a high water permeability of 50-60 L m-2 h-1 bar-1 were obtained. The tortuous flow path in combination with a low water flow rate guarantees maximum contact time between the fluid and the MOFs, and thus a high heavy metal capture efficiency in a single pass. The performances of these BioMOF@PAN membranes were investigated in the dynamic regime for the simultaneous removal of Pb2+, Cd2+, and Hg2+ heavy metals from aqueous environments in the presence of common interfering ions. The new composite adsorbing membranes are capable of reducing the concentration of heavy metal pollutants in a single pass and at much higher efficiency than previously reported membranes. The enhanced performance of the mixed matrix membranes is attributed to the presence of multiple recognition sites which densely decorate the BioMOF channels: (i) the thioether groups, deriving from the S-methyl-l-cysteine and (S)-methionine amino acid residues, able to recognize and capture Pb2+ and Hg2+ ions and (ii) the oxygen atoms of the oxamate moieties, which preferentially interact with Cd2+ ions, as revealed by single crystal X-ray diffraction. The flexibility of the pore environments allows these sites to work synergically for the simultaneous capture of different metal ions. The stability of the membranes for a potential regeneration process, a key-factor for the effective feasibility of the process in real life applications, was also evaluated and confirmed less than 1% capacity loss in each cycle.

Influence Analysis of Material Parameter Uncertainties on the Stability Safety Factor of Concrete Gravity Dams: A Probabilistic Method

Anti-sliding stability safety is a critical issue that must be given sufficient and widespread attention during the entire lifecycle of gravity dams. The calculated anti-sliding stability safety factor (ASS-SF) is usually compared with the allowable value required by the standards in the traditional method, which ignores the influence of material parameter uncertainties and leads to unreasonable safety evaluation results. Therefore, the nonlinear functional relationship between the stability safety factor (SF) and the random variable parameters is constructed based on the response surface equations, and the distribution types of SF sequences calculated by the Monte Carlo sampling are determined, then a probabilistic stability evaluation method for concrete gravity dams is proposed. Engineering application shows that the calculated SF obeys the normal distribution; the minimum guaranteed rate of different sliding paths in a gravity dam is 86.66%, and the guaranteed rate for the overload safety factor (OSF) is 36.00%. The results imply that a guaranteed rate for the allowable value of the ASS-SF should be provided when making the stability safety evaluation of the dams, especially the OSF. The outcome of this research will advance the understanding of stability evaluation of concrete dams, and reduce the potential risk of sliding instability of concrete dams.

Improved Message Mechanism-Based Cross-Domain Security Control Model in Mobile Terminals

Dual-domain terminal with two built-in independent operating systems - Life Domain and Work Domain, provides convenience for daily use and mobile office. However, the security isolation between the two domains also causes that message reminders cannot be delivered and viewed across domains, which restricts the improvement of work efficiency and the expansion of mobile services. This paper conducts an in-depth study on this pain point and proposes the concept and implementation method of a cross-domain instant messaging reminder service system for mobile office, focusing on solving the problems of: cross-domain isolated boundary exchange of message reminders, timeliness and delivery rate guarantee of message reminders, and security check filtering of message contents. Technically, on the side of mobile office platform, based on AMQP technical framework and protocol, the cross-domain isolated border message queue push and synchronization services are built, which are real-time, reliable and high-throughput.

Mbtransfer: Microbiome intervention analysis using transfer functions and mirror statistics.

Time series studies of microbiome interventions provide valuable data about microbial ecosystem structure. Unfortunately, existing models of microbial community dynamics have limited temporal memory and expressivity, relying on Markov or linearity assumptions. To address this, we introduce a new class of models based on transfer functions. These models learn impulse responses, capturing the potentially delayed effects of environmental changes on the microbial community. This allows us to simulate trajectories under hypothetical interventions and select significantly perturbed taxa with False Discovery Rate guarantees. Through simulations, we show that our approach effectively reduces forecasting errors compared to strong baselines and accurately pinpoints taxa of interest. Our case studies highlight the interpretability of the resulting differential response trajectories. An R package, mbtransfer, and notebooks to replicate the simulation and case studies are provided.

Quantifying the effects of future environmental changes on water supply and hydropower generation benefits of cascade reservoirs in the Yellow River Basin within the framework of reservoir water supply and demand uncertainty

Inflow and water demand prediction plays an important role in reservoir operation rulemaking. Uncertainties in meteorological and hydrological modeling increase the complexity of inflow and water demand prediction, which are not sufficiently considered in conventional inflow and water demand prediction. Thus, this study took cascade reservoirs as the research object. These cascade reservoirs were located in the mainstream of the Yellow River Basin (YRB). A three-step framework was proposed: (1) developing a distributed Soil & Water Assessment Tool (SWAT) model to determine historical and future water availability in the reservoirs; (2) constructing the AquaCrop agricultural irrigation water demand model to predict future water demand of water users at 264 nodes (reservoirs and hydrological stations) in the YRB; (3) developing an optimal regulation model of cascade reservoirs in the YRB mainstream to quantitively determine the influence of future environmental changes on hydropower generation and water supply benefits of cascade reservoirs. The results indicate that the inflow of cascade reservoirs increased in the flood season and decreased significantly during the non-flood season compared with that in the historical period (1970–2016). However, there was a slight increase in the agricultural irrigation water demand, and the contradiction became more prominent between water resource supply and demand. During the future period (2020–2050), the guaranteed rate of hydropower generation and water supply in the YRB’s lower reaches will increase significantly. Uncertainties in climate models greatly impact hydropower generation benefits and reservoir water supply, especially in the flood season. These findings can facilitate the understanding of the complexity of hydropower management and production of cascade reservoirs under future environmental changes (inflow and demand) and encourage the use of regulation models for impact assessment studies and adaptive reservoir management to mitigate climate change.

A Foundation for Real-time Applications onFunction-as-a-Service

Serverless (or Function-as-a-Service) compute model enables new applications with dynamic scaling. However, all current Serverless systems are best-effort, and as we prove this means they cannot guarantee hard real-time deadlines, rendering them unsuitable for such real-time applications. We analyze a proposed extension of the Serverless model that adds a guaranteed invocation rate to the serverless model called Real-time Serverless. This approach aims to meet real-time deadlines with dynamically allocated function invocations. We first prove that the Serverless model does not support real-time guarantees. Next, we analyze Real-time Serverless, showing it can guarantee application real-time deadlines for rate-monotonic real-time workloads. Further, we derive bounds on the required invocation rate to meet any set of workload runtimes and periods. Subsequently, we explore an application technique, pre-invocation, and show that it can reduce the required guaranteed invocation rate. We derive bounds for the feasible rate guarantee reduction, and corresponding overhead in wasted compute resources. Finally, we apply the theoretical results to improve the experience quality of a distributed virtual reality/ augmented reality application as well as simplify the application design and resource management.

Data-Driven Compositional Optimization in Misspecified Regimes

With a manifold growth in the scale and intricacy of systems, the challenges of parametric misspecification become pronounced. These concerns are further exacerbated in compositional settings, which emerge in problems complicated by modeling risk and robustness. In “Data-Driven Compositional Optimization in Misspecified Regimes,” the authors consider the resolution of compositional stochastic optimization problems, plagued by parametric misspecification. In considering settings where such misspecification may be resolved via a parallel learning process, the authors develop schemes that can contend with diverse forms of risk, dynamics, and nonconvexity. They provide asymptotic and rate guarantees for unaccelerated and accelerated schemes for convex, strongly convex, and nonconvex problems in a two-level regime with extensions to the multilevel setting. Surprisingly, the nonasymptotic rate guarantees show no degradation from the rate statements obtained in a correctly specified regime and the schemes achieve optimal (or near-optimal) sample complexities for general T-level strongly convex and nonconvex compositional problems.

Adaptive novelty detection with false discovery rate guarantee

Adaptive novelty detection with false discovery rate guarantee

A Third-Order Majorization Algorithm for Logistic Regression With Convergence Rate Guarantees

A Third-Order Majorization Algorithm for Logistic Regression With Convergence Rate Guarantees

Long-term operation rules of a hydro–wind–photovoltaic hybrid system considering forecast information

The large-scale integration of wind and solar energy into cascade hydropower stations increases the complexity of hydraulic/electrical relationships and requires a modification of conventional scheduling mode for better energy system performance. This study focuses on the long-term operation rules for hydro–wind–photovoltaics (PV) hybrid energy systems (HESs). Based on the conventional operation chart, a cascade energy operation chart that couples wind, PV output, and runoff forecasts is proposed to obtain the total output of HES, which can promote the utilization of the joint regulation of cascade hydropower stations. A total output allocation method based on the cascade energy operation chart is established to coordinate the operation of each power station. Then an operation chart optimization model with objectives of maximizing power generation and output reliability is proposed to improve HES performance. The case study reveals that compared with the conventional reservoir operation chart, 1) the optimized cascade energy operation chart increased power generation of HES by 9.2 %; 2) the reservoir impounding timing was delayed, the drawdown timing was brought forward, and the drawdown depth was increased; 3) the reliability of HES power output was improved, and the output corresponding to the 95 % guaranteed rate requirement was increased by 18 %.

Balancing equity and policyholder protection: Assessing insurer's interests in green lending under cap-and-trade regulations.

This paper presents a contingent claim model designed to assess an insurer's equity within the framework of carbon trading regulations imposed on borrowing firms while also considering the integration of green lending. The development of this model is particularly relevant for regions with established carbon trading markets, with a specific focus on the post-period following the 2015 Paris Agreement concerning climate change. We focus on shareholders and policyholders to optimize equity and ensure maximum protection. Strict caps in cap-and-trade harm interest margins, reducing guaranteed rates for equity maximization and compromising policyholder protection. Government intervention through sustainable production carbon trading hinders win-win outcomes. Green subsidies can improve insurer margins, but achieving win-win solutions remains challenging. A collective approach is needed to share sustainable production and finance benefits among diverse economic sectors.

Distributed exponential state estimation of linear systems over jointly connected switching networks

Recently, the distributed state estimation problem for continuous-time linear systems over jointly connected switching networks was solved. Due to the use of the generalized Barbalat’s Lemma, it can only show that the estimation errors asymptotically converge to the origin. By a completely different approach, this paper further studies the distributed state estimation problem with two new features. First, the asymptotic convergence is strengthened to the exponential convergence. This strengthened result not only offers a guaranteed convergence rate, but also renders the error system total stability and thus the capability to withstand small disturbances. Second, the coupling gains of our local observers can be different from each other and thus offers greater design flexibility, while the coupling gains in the existing result were required to be identical. These two new features are achieved by establishing exponential stability for two classes of linear time-varying systems, which may have other applications.

Evaluation of the effectiveness of government support for technology entrepreneurship

This article explores the role of state support in stimulating technological entrepreneurship and its impact on the innovation process. Through analyzing existing research and using empirical evidence, the article develops a theoretical framework that explains the mechanisms of the impact of government measures on technological entrepreneurship. The article provides an overview of the main measures and tools implemented in the Republic of Kazakhstan over the past 10 years. In particular, the effectiveness of measures of JSC "Entrepreneurship Development Fund "Damu", which realizes the functions of an agent for accounting and monitoring the use of funds from the state budget, is assessed. The draw a conclusion about the stable growth of the sector, despite the negative factors of the impact of non-stability of the external environment. A quantitative assessment of the macroeconomic impact of financial measures of state support on technological entrepreneurship using the methods of correlation and regression analysis is given. The results of the assessment showed that there are weak and strong influence of state support measures: interest rate subsidies do not show macroeconomic effect, loan guarantees have a weak effect on the opening of new business entities; conditional placement of funds in banks and interest rate guarantees show good influence. In conclusion, our article presents concrete practical implications and policy recommendations to enhance state support strategies for technology entrepreneurship, aimed at promoting innovation and reinforcing economic competitiveness. The research has revealed a noteworthy surge in innovation activity and growth in economic competitiveness. In particular, comparative assessments indicate that in 2021, the SME sector accounted for 33.3 % of the economy, signifying a substantial increase from the 32.8 % of GDP it constituted in 2020. These numbers underline the success of the current policies and provide a strong rationale for the continued enhancement of state support for technology entrepreneurship. The findings not only reaffirm the significance of continuing this policy but also establish the groundwork for more ambitious targets, such as achieving a 35 % share of SMEs in the economy by 2025.

The impact of war on insurer safety: a contingent claim model analysis

Determining the optimal guaranteed rate of life insurance policies can effectively promote sustainable societies and disaster-resilient communities in times of war. Nevertheless, such strategic coverage remains uncommon in many countries. This article presents a capped-down-and-out call option model to assess life insurers’ safety during conflicts. Wars may lead to reduced life insurance businesses due to lower guaranteed rates set by insurers, yet they can also improve insurer safety within an imperfectly competitive insurance market. By increasing the surrender rate of the policy associated with reducing the optimal guarantee rate, the insurer’s security is improved, thereby contributing to the stability of the overall insurance. Our findings suggest setting guaranteed rates is critical to asset-liability matching management, especially in wartime, to maintain insurance stability.