pwc-archive/papers-with-abstracts huggingface dataset:
Machine Learning-Assisted Distribution System Network Reconfiguration Problem
Authors: Richard Asiamah, Yuqi Zhou, Ahmed S. Zamzam
Date: 2024-11-18 00:00:00

High penetration from volatile renewable energy resources in the grid and the varying nature of loads raise the need for frequent line switching to ensure the efficient operation of electrical distribution networks. Operators must ensure maximum load delivery, reduced losses, and the operation between voltage limits. However, computations to decide the optimal feeder configuration are often computationally expensive and intractable, making it unfavorable for real-time operations. This is mainly due to the existence of binary variables in the network reconfiguration optimization problem. To tackle this issue, we have devised an approach that leverages machine learning techniques to reshape distribution networks featuring multiple substations. This involves predicting the substation responsible for serving each part of the network. Hence, it leaves simple and more tractable Optimal Power Flow problems to be solved. This method can produce accurate results in a significantly faster time, as demonstrated using the IEEE 37-bus distribution feeder. Compared to the traditional optimization-based approaches, a feasible solution is achieved approximately ten times faster for all the tested scenarios.
Design And Optimization Of Multi-rendezvous Manoeuvres Based On Reinforcement Learning And Convex Optimization
Authors: Antonio López Rivera, Lucrezia Marcovaldi, Jesús Ramírez, Alex Cuenca, David Bermejo
Date: 2024-11-18 00:00:00
Tasks: Combinatorial Optimization, reinforcement-learning, Reinforcement Learning

Optimizing space vehicle routing is crucial for critical applications such as on-orbit servicing, constellation deployment, and space debris de-orbiting. Multi-target Rendezvous presents a significant challenge in this domain. This problem involves determining the optimal sequence in which to visit a set of targets, and the corresponding optimal trajectories: this results in a demanding NP-hard problem. We introduce a framework for the design and refinement of multi-rendezvous trajectories based on heuristic combinatorial optimization and Sequential Convex Programming. Our framework is both highly modular and capable of leveraging candidate solutions obtained with advanced approaches and handcrafted heuristics. We demonstrate this flexibility by integrating an Attention-based routing policy trained with Reinforcement Learning to improve the performance of the combinatorial optimization process. We show that Reinforcement Learning approaches for combinatorial optimization can be effectively applied to spacecraft routing problems. We apply the proposed framework to the UARX Space OSSIE mission: we are able to thoroughly explore the mission design space, finding optimal tours and trajectories for a wide variety of mission scenarios.
Coevolution of Opinion Dynamics and Recommendation System: Modeling Analysis and Reinforcement Learning Based Manipulation
Authors: Yuhong Chen, Xiaobing Dai, Martin Buss, Fangzhou Liu
Date: 2024-11-18 00:00:00
Tasks: Collaborative Filtering, Recommendation Systems

In this work, we develop an analytical framework that integrates opinion dynamics with a recommendation system. By incorporating elements such as collaborative filtering, we provide a precise characterization of how recommendation systems shape interpersonal interactions and influence opinion formation. Moreover, the property of the coevolution of both opinion dynamics and recommendation systems is also shown. Specifically, the convergence of this coevolutionary system is theoretically proved, and the mechanisms behind filter bubble formation are elucidated. Our analysis of the maximum number of opinion clusters shows how recommendation system parameters affect opinion grouping and polarization. Additionally, we incorporate the influence of propagators into our model and propose a reinforcement learning-based solution. The analysis and the propagation solution are demonstrated in simulation using the Yelp data set.
On the Incorporation of Stability Constraints into Sequential Operational Scheduling
Authors: Wangkun Xu, Zhongda Chu, Florin Capitanescu, Fei Teng
Date: 2024-11-18 00:00:00
Tasks: Decision Making, Scheduling

With the increasing penetration of Inverter-Based Resources (IBRs), power system stability constraints must be incorporated into the operational framework, transforming it into stability-constrained optimization. Currently, there exist parallel research efforts on developing the stability constraints within DC power flow-based unit commitment (UC) and AC Optimal Power Flow (OPF). However, few studies discuss how including such constraints can interact with each other and eventually impact grid stability. In this context, this work simulates a realistic power system decision making framework and provides a thorough analysis on the necessity of incorporating frequency nadir and small signal stability constraints into these sequentially connected two operation stages. The simulation results demonstrate that including both stability constraints in the UC is essential to maintain power system stability, while the inclusion in AC OPF can further improve the stability index.
Approximate predictive control barrier function for discrete-time systems
Authors: Alexandre Didier, Melanie N. Zeilinger
Date: 2024-11-18 00:00:00

We propose integrating an explicit approximation of a predictive control barrier function (PCBF) in a safety filter framework. The approximated PCBF is implicitly defined through an optimal control problem and allows guaranteeing invariance of an implicitly defined safe set as well as stability of this safe set within a larger domain of attraction. By extending existing theoretical analysis of the PCBF, we establish inherent robustness of the original algorithm and translate the guarantees to input-to-state stability of the proposed algorithm with respect to possible approximation errors, recovering the same guarantees in the absence of approximation errors. The proposed algorithm allows certifying inputs with respect to state constraint satisfaction through a single function evaluation and filtering unsafe inputs through a control barrier function based safety filter, which is independent of the time horizon of the original predictive optimisation problem, resulting in significant online computational benefits. We demonstrate the stability properties of the proposed algorithm on a linear system example as well as its use a fast safety filter for miniature race cars in simulation.
Integrating and Comparing Radiality Constraints for Optimized Distribution System Reconfiguration
Authors: Pablo Cortes, Alejandra Tabares, Fredy Franco
Date: 2024-11-18 00:00:00
Tasks: Computational Efficiency

The reconfiguration of electrical power distribution systems is a crucial optimization problem aimed at minimizing power losses by altering the system topology through the operation of interconnection switches. This problem, typically modelled as a mixed integer nonlinear program demands high computational resources for large scale networks and requires specialized radiality constraints for maintaining the tree like structure of distribution networks. This paper presents a comprehensive analysis that integrates and compares the computational burden associated with different radiality constraint formulations proposed in the specialized literature for the reconfiguration of distribution systems. By using consistent hardware and software setups, we evaluate the performance of these constraints across several well known test cases. Our findings reveal significant differences in computational efficiency depending on the chosen set of radiality constraints, providing valuable insights for optimizing reconfiguration strategies in practical distribution networks.
A Linear Differential Inclusion for Contraction Analysis to Known Trajectories
Authors: Akash Harapanahalli, Samuel Coogan
Date: 2024-11-18 00:00:00

Infinitesimal contraction analysis provides exponential convergence rates between arbitrary pairs of trajectories of a system by studying the system's linearization. An essentially equivalent viewpoint arises through stability analysis of a linear differential inclusion (LDI) encompassing the incremental behavior of the system. In this note, we study contraction of a system to a particular known trajectory, deriving a new LDI characterizing the error between arbitrary trajectories and this known trajectory. As with classical contraction analysis, this new inclusion is constructed via first partial derivatives of the system's vector field, and contraction rates are obtained with familiar tools: uniform bounding of the logarithmic norm and LMI-based Lyapunov conditions. Our LDI is guaranteed to outperform a usual contraction analysis in two special circumstances: i) when the bound on the logarithmic norm arises from an interval overapproximation of the Jacobian matrix, and ii) when the norm considered is the $\ell_1$ norm. Finally, we demonstrate how the proposed approach strictly improves an existing framework for ellipsoidal reachable set computation.
Reduced Network Cumulative Constraint Violation for Distributed Bandit Convex Optimization under Slater Condition
Authors: Kunpeng Zhang, Xinlei Yi, Jinliang Ding, Ming Cao, Karl H. Johansson, Tao Yang
Date: 2024-11-18 00:00:00

This paper studies the distributed bandit convex optimization problem with time-varying inequality constraints, where the goal is to minimize network regret and cumulative constraint violation. To calculate network cumulative constraint violation, existing distributed bandit online algorithms solving this problem directly use the clipped constraint function to replace its original constraint function. However, the use of the clipping operation renders Slater condition (i.e, there exists a point that strictly satisfies the inequality constraints at all iterations) ineffective to achieve reduced network cumulative constraint violation. To tackle this challenge, we propose a new distributed bandit online primal-dual algorithm. If local loss functions are convex, we show that the proposed algorithm establishes sublinear network regret and cumulative constraint violation bounds. When Slater condition holds, the network cumulative constraint violation bound is reduced. In addition, if local loss functions are strongly convex, for the case where strongly convex parameters are unknown, the network regret bound is reduced. For the case where strongly convex parameters are known, the network regret and cumulative constraint violation bounds are further reduced. To the best of our knowledge, this paper is among the first to establish reduced (network) cumulative constraint violation bounds for (distributed) bandit convex optimization with time-varying constraints under Slater condition. Finally, a numerical example is provided to verify the theoretical results.
Distributed Learning with Partial Information Sharing
Authors: P Raghavendra Rao, Pooja Vyavahare
Date: 2024-11-18 00:00:00

This work studies the distributed learning process on a network of agents. Agents make partial observation about an unknown hypothesis and iteratively share their beliefs over a set of possible hypotheses with their neighbors to learn the true hypothesis. We present and analyze a distributed learning algorithm in which agents share belief on only one randomly chosen hypothesis at a time. Agents estimate the beliefs on missed hypotheses using previously shared beliefs. We show that agents learn the true hypothesis almost surely under standard network connectivity and observation model assumptions if belief on each hypothesis is shared with positive probability at every time. We also present a memory-efficient variant of the learning algorithm with partial belief sharing and present simulation results to compare rate of convergence of full and partial information sharing algorithms.
Towards Mitigating Sim2Real Gaps: A Formal Quantitative Approach
Authors: P Sangeerth, Abolfazl Lavaei, Pushpak Jagtap
Date: 2024-11-18 00:00:00

In this paper, we introduce the notion of simulation-gap functions to formally quantify the potential gap between an approximate nominal mathematical model and the high-fidelity simulator representation of a real system. Given a nominal mathematical model alongside a quantified simulation gap, the system can be conceptualized as one characterized by bounded states and input-dependent disturbances. This allows us to leverage the existing powerful model-based control algorithms effectively, ensuring the enforcement of desired specifications while guaranteeing a seamless transition from simulation to real-world application. To provide a formal guarantee for quantifying the simulation gap, we develop a data-driven approach. In particular, we collect data using high-fidelity simulators, leveraging recent advancements in Real-to-Sim transfer to ensure close alignment with reality. We demonstrate the effectiveness of the proposed method through experiments conducted on a nonlinear pendulum system and a nonlinear Turtlebot model in simulators.
Network-Security Informed Offer-Making of Aggregator with Utility-Owned Storage Lease Opportunity: Stochastic Stackelberg Game and Distributed Solution Methods
Authors: Congcong Liu, Zhengshuo Li
Date: 2024-11-18 00:00:00

Aggregators of distributed energy resources are increasingly encouraged to participate in wholesale market bidding. However, the delivery of the power they are awarded can result in over-voltage or congestion issues within the distribution network (DN). The opportunity to lease energy storage from the utility that manages the DN provides the aggregator with a means to mitigate these issues, while also benefiting the utility in terms of additional lease revenue. Nevertheless, this leasing opportunity considerably complicates the aggregator's offer-making process, as it requires the consideration of market uncertainties, uncertain power injection at DN buses, and the strategic interactions between the aggregator and the utility. This paper presents a stochastic Stackelberg game model that effectively captures the interactions between the aggregator and the utility, ensuring DN security across all potential uncertainty scenarios. Furthermore, in light of the privacy concerns of both the aggregator and the utility, two distributed solution methods are proposed. The first method follows a traditional predict-then-optimize framework and has been validated to achieve the game equilibrium. The second method employs an end-to-end framework, which has been empirically shown to yield superior economic results. Case studies conducted on 69 and 533-bus DNs illustrate the efficacy of the proposed methods.
Leveraging Bitcoin Mining Machines in Demand-Response Mechanisms to Mitigate Ramping-Induced Transients
Authors: Elinor Ginzburg-Ganz, Ittay Eyal, Ram Machlev, Dmitry Baimel, Leena Santosh, Juri Belikov, Yoash Levron
Date: 2024-11-17 00:00:00
Tasks: Scheduling

We propose an extended demand response program, based on ancillary service for supplying flexible electricity demand. In our proposed scheme, we suggest a broader management model to control the scheduling and power consumption of Bitcoin mining machines. The main aspect that we focus on is suppressing the power ramping and related transient effects. We extend previous works on the subject, that study the impact of incorporating cryptocurrency mining machines into existing power grid, and explore the potential profit of exploiting this flexible load in the Israeli electricity market. We analyze a trend based on historical data, of increasing electricity prices and ramping costs due to the increasing penetration of renewable energy sources. We suggest an extension to the unit commitment problem from which we obtain the scheduling scheme of the Bitcoin mining machines. We use simulation and the real-world data acquired from the "Noga" grid operator to verify the proposed ancillary service and test its practical limits for reducing the ramping costs, under changing ratio of energy production from renewable sources. Out results suggests that the machine price and ratio of production from renewable sources plays a significant role in determining the profitability of the proposed demand-response program.
Iterative Learning Control for Ramp Metering on Service Station On-ramps
Authors: Hongxi Xiang, Carlo Cenedese, Efe C. Balta, John Lygeros
Date: 2024-11-17 00:00:00

Congestion on highways has become a significant social problem due to the increasing number of vehicles, leading to considerable waste of time and pollution. Regulating the outflow from the Service Station can help alleviate this congestion. Notably, traffic flows follow recurring patterns over days and weeks, allowing for the application of Iterative Learning Control (ILC). Building on these insights, we propose an ILC approach based on the Cell Transmission Model with service stations (CTM-s). It is shown that ILC can effectively compensate for potential inaccuracies in model parameter estimates by leveraging historical data.
Dynamic Dimensioning of Frequency Containment Reserves: The Case of the Nordic Grid
Authors: Jöbke Janssen, Alessandro Zocca, Bert Zwart, Jalal Kazempour
Date: 2024-11-17 00:00:00

One of the main responsibilities of a Transmission System Operator (TSO) operating an electric grid is to maintain a designated frequency (e.g., 50 Hz in Europe). To achieve this, TSOs have created several products called frequency-supporting ancillary services. The Frequency Containment Reserve (FCR) is one of these ancillary service products. This article focuses on the TSO problem of determining the volume procured for FCR. Specifically, we investigate the potential benefits and impact on grid security when transitioning from a traditionally \textit{static} procurement method to a \textit{dynamic} strategy for FCR volume. We take the Nordic synchronous area in Europe as a case study and use a diffusion model to capture its frequency development. We introduce a controlled mean reversal parameter to assess changes in FCR obligations, in particular for the Nordic FCR-N ancillary service product. We establish closed-form expressions for exceedance probabilities and use historical frequency data as input to calibrate the model. We show that a dynamic dimensioning approach for FCR has the potential to significantly reduce the exceedance probabilities (up to $37\%$) while maintaining the total yearly procured FCR volume equal to that of the current static approach. Alternatively, a dynamic dimensioning approach could significantly increase security at limited extra cost.
Immersion of General Nonlinear Systems Into State-Affine Ones for the Design of Generalized Parameter Estimation-Based Observers: A Simple Algebraic Procedure
Authors: Romeo Ortega, Alexey Bobtsov, Jose Guadalupe Romero, Leyan Fang
Date: 2024-11-17 00:00:00
Tasks: parameter estimation

Generalized parameter estimation-based observers have proven very successful to deal with systems described in state-affine form. In this paper, we enlarge the domain of applicability of this method proposing an algebraic procedure to immerse} an $n$-dimensional general nonlinear system into and $n_z$-dimensional system in state affine form, with $n_z>n$. First, we recall the necessary and sufficient condition for the solution of the general problem, which requires the solution of a partial differential equation that, moreover, has to satisfy a restrictive injectivity condition. Given the complexity of this task we propose an alternative simple algebraic method to identify the required dynamic extension and coordinate transformation, a procedure that, as shown in the paper, is rather natural for physical systems. We illustrate the method with some academic benchmark examples from observer theory literature -- that, in spite of their apparent simplicity, are difficult to solve with the existing methods -- as well as several practically relevant physical examples.
Wildfire Risk Metric Impact on Public Safety Power Shut-off Cost Savings
Authors: Ryan Greenough, Kohei Murakami, Jan Kleissl, Adil Khurram
Date: 2024-11-17 00:00:00

Public Safety Power Shutoffs (PSPS) are a proactive strategy to mitigate fire hazards from power system infrastructure failures. System operators employ PSPS to deactivate portions of the electric grid with heightened wildfire risks to prevent wildfire ignition and redispatch generators to minimize load shedding. A measure of vegetation flammability, called the Wildland Fire Potential Index (WFPI), has been widely used to evaluate the risk of nearby wildfires to power system operation. However, the WFPI does not correlate as strongly to historically observed wildfire ignition probabilities (OWIP) as WFPI-based the Large Fire Probability (WLFP).Prior work chose not to incorporate wildfire-driven failure probabilities, such as the WLFP, because constraints with Bernoulli random variables to represent wildfire ignitions could require non-linear or non-convex constraints. This paper uses a deterministic equivalent of an otherwise complicating line de-energization constraint by quantifying the wildfire risk of operating transmission line as a sum of each energized line's wildfire ignition log probability (log(WIP)) rather than as a sum of each energized line's WFPI. A day-ahead unit commitment and line de-energization PSPS framework is used to assess the cost differences driven by the choice between the WFPI and WLFP risk metrics. Training the optimization on scenarios developed by mapping WLFP to log(WIP) rather than mapping the WFPI to log(WIP) leads to reductions in the total real-time costs. For the IEEE RTS 24-bus test system, mapping transmission line WLFP values to log(WIP) resulted in a 14.8 % (on average) decrease in expected real-time costs.
Adaptive Soft Actor-Critic Framework for RIS-Assisted and UAV-Aided Communication
Authors: Abuzar B. M. Adam, Elhadj Moustapha Diallo, Mohammed A. M. Elhassan
Date: 2024-11-16 00:00:00
Tasks: Fairness

In this work, we explore UAV-assisted reconfigurable intelligent surface (RIS) technology to enhance downlink communications in wireless networks. By integrating RIS on both UAVs and ground infrastructure, we aim to boost network coverage, fairness, and resilience against challenges such as UAV jitter. To maximize the minimum achievable user rate, we formulate a joint optimization problem involving beamforming, phase shifts, and UAV trajectory. To address this problem, we propose an adaptive soft actor-critic (ASAC) framework. In this approach, agents are built using adaptive sparse transformers with attentive feature refinement (ASTAFER), enabling dynamic feature processing that adapts to real-time network conditions. The ASAC model learns optimal solutions to the coupled subproblems in real time, delivering an end-to-end solution without relying on iterative or relaxation-based methods. Simulation results demonstrate that our ASAC-based approach achieves better performance compared to the conventional SAC. This makes it a robust, adaptable solution for real-time, fair, and efficient downlink communication in UAV-RIS networks.