pwc-archive/papers-with-abstracts huggingface dataset:
Small-Signal Stability and SCR Enhancement of Offshore WPPs with Synchronous Condensers
Authors: Sulav Ghimire, Kanakesh V. Kkuni, Emerson D. Guest, Kim H. Jensen, Guangya Yang
Date: 2023-10-10 00:00:00

Synchronous condensers (SCs) have been reported to improve the overall stability and short-circuit power of a power system. SCs are also being integrated into offshore wind power plants (WPPs) for the same reason. This paper, investigates the effect of synchronous condensers on an offshore wind power plant with grid-following (GFL) and grid-forming (GFM) converter controls. Primarily, the effect of synchronous condensers can be two-fold: (1) overall stability enhancement of the WPP by providing reactive power support, (2) contribution to the effective short circuit ratio (SCR) of the WPP by fault current support. Therefore, this paper focuses on studies concerning these effects on an aggregated model of a WPP connected to the grid. To that end, a state-space model of the test system is developed for small-signal stability assessment and the synchronous condenser's effect on its stability. In addition, a mathematical explanation of SCR enhancement with synchronous condenser is provided and is verified with time-domain electromagnetic transient simulations.
The DeMaDs Open Source Modeling Framework for Power System Malfunction Detection
Authors: David Fellner, Thomas I Strasser, Wolfgang Kastner
Date: 2023-10-10 00:00:00

Modeling and simulation of electrical power systems are becoming increasingly important approaches for the development and operation of novel smart grid functionalities -- especially with regard to data-driven applications as data of certain operational states or misconfigurations can be next to impossible to obtain. The DeMaDs framework allows for the simulation and modeling of electric power grids and malfunctions therein. Furthermore, it serves as a testbed to assess the applicability of various data-driven malfunction detection methods. These include data mining techniques, traditional machine learning approaches as well as deep learning methods. The framework's capabilities and functionality are laid out here, as well as explained by the means of an illustrative example.
6G Wireless Communications in 7-24 GHz Band: Opportunities, Techniques, and Challenges
Authors: Zhuangzhuang Cui, Peize Zhang, Sofie Pollin
Date: 2023-10-10 00:00:00
Tasks: ISAC

The sixth generation (6G) wireless communication nowadays is seeking a new spectrum to inherit the pros and discard the cons of sub-6 GHz, millimeter-wave (mmWave), and sub-terahertz (THz) bands. To this end, an upper mid-band with a Frequency Range (FR) spanning from 7 GHz to 24 GHz, also known as FR3, has emerged as a focal point in 6G communications. Thus, as an inevitable prerequisite, a comprehensive investigation encompassing spectrum utilization and channel characteristics is the first step to exploiting potential applications and prospects of using FR3 in the 6G ecosystem. In this article, we provide FR3 synergies with emerging technologies including non-terrestrial network (NTN), massive multi-input multi-output (mMIMO), reconfigurable intelligent surface (RIS), and integrated sensing and communications (ISAC). Furthermore, leveraging ray-tracing simulations, our investigation unveils the similarity of channel characteristics in FR3 with other FRs. The analysis of RIS-aided communication shows the insight of higher spectral efficiency achieved in FR3 compared to other FRs when using the same RIS size. Finally, challenges and promising directions are discussed for wireless systems in FR3.
On the Peak AoI of UAV-assisted IoT Networks: A Stochastic Geometry Approach
Authors: Yujie Qin, Mustafa A. Kishk, Mohamed-Slim Alouini
Date: 2023-10-10 00:00:00

In this paper, we analyze the peak age of information (PAoI) in UAV-assisted internet of thing (IoT) networks, in which the locations of IoT devices are modeled by a Mat\'{e}rn cluster process (MCP) and UAVs are deployed at the cluster centers to collect the status updates from the devices. Specifically, we consider that IoT devices can either monitor the same physical process or different physical processes and UAVs split their resources, time or bandwidth, to serve the devices to avoid inter-cluster interference. Using tools from stochastic geometry, we are able to compute the mean activity probability of IoT devices and the conditional success probability of an individual device. We then use tools from queuing theory to compute the PAoI under two load models and two scenarios for devices, respectively. Our numerical results show interesting system insights. We first show that for a low data arrival rate, increasing the number of correlated devices can improve the PAoI for both load models. Next, we show that even though the time-splitting technique causes higher interference, it has a limited impact on the mean PAoI, and the mean PAoI benefits more from the time-splitting technique. This is because of the nature of UAV communication, especially at places where devices (users) are spatially-clustered: shorter transmission distances and better communication channels, comparing the links established by the cluster UAV and serving devices (users) to links established by interferers.
HoloFed: Environment-Adaptive Positioning via Multi-band Reconfigurable Holographic Surfaces and Federated Learning
Authors: Jingzhi Hu, Zhe Chen, Tianyue Zheng, Robert Schober, Jun Luo
Date: 2023-10-10 00:00:00
Tasks: Federated Learning, Position, Scheduling, Transfer Learning

Positioning is an essential service for various applications and is expected to be integrated with existing communication infrastructures in 5G and 6G. Though current Wi-Fi and cellular base stations (BSs) can be used to support this integration, the resulting precision is unsatisfactory due to the lack of precise control of the wireless signals. Recently, BSs adopting reconfigurable holographic surfaces (RHSs) have been advocated for positioning as RHSs' large number of antenna elements enable generation of arbitrary and highly-focused signal beam patterns. However, existing designs face two major challenges: i) RHSs only have limited operating bandwidth, and ii) the positioning methods cannot adapt to the diverse environments encountered in practice. To overcome these challenges, we present HoloFed, a system providing high-precision environment-adaptive user positioning services by exploiting multi-band(MB)-RHS and federated learning (FL). For improving the positioning performance, a lower bound on the error variance is obtained and utilized for guiding MB-RHS's digital and analog beamforming design. For better adaptability while preserving privacy, an FL framework is proposed for users to collaboratively train a position estimator, where we exploit the transfer learning technique to handle the lack of position labels of the users. Moreover, a scheduling algorithm for the BS to select which users train the position estimator is designed, jointly considering the convergence and efficiency of FL. Our simulation results confirm that HoloFed achieves a 57% lower positioning error variance compared to a beam-scanning baseline and can effectively adapt to diverse environments.
Reducing Detailed Vehicle Energy Dynamics to Physics-Like Models
Authors: Nour Khoudari, Sulaiman Almatrudi, Rabie Ramadan, Joy Carpio, Mengsha Yao, Kenneth Butts, Alexandre M. Bayen, Jonathan W. Lee, Benjamin Seibold
Date: 2023-10-10 00:00:00

The energy demand of vehicles, particularly in unsteady drive cycles, is affected by complex dynamics internal to the engine and other powertrain components. Yet, in many applications, particularly macroscopic traffic flow modeling and optimization, structurally simple approximations to the complex vehicle dynamics are needed that nevertheless reproduce the correct effective energy behavior. This work presents a systematic model reduction pipeline that starts from complex vehicle models based on the Autonomie software and derives a hierarchy of simplified models that are fast to evaluate, easy to disseminate in open-source frameworks, and compatible with optimization frameworks. The pipeline, based on a virtual chassis dynamometer and subsequent approximation strategies, is reproducible and is applied to six different vehicle classes to produce concrete explicit energy models that represent an average vehicle in each class and leverage the accuracy and validation work of the Autonomie software.
Stability of FFLS-based diffusion adaptive filter under a cooperative excitation condition
Authors: Die Gan, Siyu Xie, Zhixin Liu, Jinhu Lv
Date: 2023-10-10 00:00:00

In this paper, we consider the distributed filtering problem over sensor networks such that all sensors cooperatively track unknown time-varying parameters by using local information. A distributed forgetting factor least squares (FFLS) algorithm is proposed by minimizing a local cost function formulated as a linear combination of accumulative estimation error. Stability analysis of the algorithm is provided under a cooperative excitation condition which contains spatial union information to reflect the cooperative effect of all sensors. Furthermore, we generalize theoretical results to the case of Markovian switching directed graphs. The main difficulties of theoretical analysis lie in how to analyze properties of the product of non-independent and non-stationary random matrices. Some techniques such as stability theory, algebraic graph theory and Markov chain theory are employed to deal with the above issue. Our theoretical results are obtained without relying on the independency or stationarity assumptions of regression vectors which are commonly used in existing literature.
Distributed Multi-Time Slot Power Balancing Control of Power Systems with Energy Storage Devices
Authors: Luwei Yang, Tao Liu, David J. Hill
Date: 2023-10-10 00:00:00

This paper studies a crucial problem in power system balancing control, i.e., the multi-time slot economic dispatch (MTSED) problem, for power grids with substantial renewables, synchronous generators (SGs), and energy storage devices (ESDs). The target of MTSED is to optimally coordinate active/reactive power outputs of all controllable units to meet a forecast net demand profile over multiple time slots within a receding finite time horizon. Firstly, the MTSED is formulated as an optimization problem with operational constraints, including the limits on the output of each controllable unit, ramping rates of SGss, energy levels of ESDs, and bus voltages. Then, a novel projection-based algorithm is developed to solve the problem in a distributed way. In particular, the distributed algorithm is not limited to solving the MTSED problem but also applies to more general optimization problems with both generic convex objective functions and hard feasibility constraints. Finally, case studies verify the effectiveness of the proposed method.
Transmission Investment Coordination using MILP Lagrange Dual Decomposition and Auxiliary Problem Principle
Authors: Sambuddha Chakrabarti, Hosna Khajeh, Thomas R Nudell, Mohammad Reza Hesamzadeh, Ross Baldick
Date: 2023-10-10 00:00:00
Tasks: Distributed Optimization

This paper considers the investment coordination problem for the long term transmission capacity expansion in a situation where there are multiple regional Transmission Planners (TPs), each acting in order to maximize the utility in only its own region. In such a setting, any particular TP does not normally have any incentive to cooperate with the neighboring TP(s), although the optimal investment decision of each TP is contingent upon those of the neighboring TPs. A game-theoretic interaction among the TPs does not necessarily lead to this overall social optimum. We, therefore, introduce a social planner and call it the Transmission Planning Coordinator (TPC) whose goal is to attain the optimal possible social welfare for the bigger geographical region. In order to achieve this goal, this paper introduces a new incentive mechanism, based on distributed optimization theory. This incentive mechanism can be viewed as a set of rules of the transmission expansion investment coordination game, set by the social planner TPC, such that, even if the individual TPs act selfishly, it will still lead to the TPC's goal of attaining overall social optimum. Finally, the effectiveness of our approach is demonstrated through several simulation studies.
A Natural Indirect Adaptive Controller for a Satellite-Mounted Manipulator
Authors: Jacopo Giordano, Angelo Cenedese, Andrea Serrani
Date: 2023-10-09 00:00:00

The work considers the design of an indirect adaptive controller for a satellite equipped with a robotic arm manipulating an object. Uncertainty on the manipulated object can considerably impact the overall behavior of the system. In addition, the dynamics of the actuators of the base satellite are non-linear and can be affected by malfunctioning. Neglecting these two phenomena may lead to excessive control effort or degrade performance. An indirect adaptive control approach is pursued, which allows consideration of relevant features of the actuators dynamics, such as loss of effectiveness. Furthermore, an adaptive law that preserves the physical consistency of the inertial parameters of the various rigid bodies comprising the system is employed. The performance and robustness of the controller are first analyzed and then validated in simulation.
Toward More Accurate and Robust Optimal Power Flow for Distribution Systems
Authors: Dakota Hamilton, Loraine Navarro, Dionysios Aliprantis
Date: 2023-10-09 00:00:00

The objective of this paper is to improve the accuracy and robustness of optimal power flow (OPF) formulations for distribution systems modeled down to the low-voltage point of connection of individual buildings. An approach for addressing the uncertain switching behavior of building loads(e.g., air conditioners, water heaters, or pool pumps) and variable renewable generation (e.g., rooftop solar) in the OPF is proposed. Rather than using time-averaged forecasts to determine voltage magnitude constraints, we leverage worst-case minimum and maximum forecasts of loads and distributed energy resource generation. Sensitivities of the power flow equations are used to predict how these deviations in load and renewable generation will impact system voltages, and the voltage constraints in the OPF are dynamically adjusted to mitigate voltage violations due to this uncertainty. A methodology for incorporating models of split-phase components and transformer core losses in the OPF formulation is also proposed. The proposed approach is validated through numerical case studies on a realistic distribution feeder using GridLAB-D, a distribution system simulation software.
Energy Management in a Cooperative Energy Harvesting Wireless Sensor Network
Authors: Arghyadeep Barat, Prabuchandran. K. J, Shalabh Bhatnagar
Date: 2023-10-09 00:00:00
Tasks: energy management, Management

In this paper, we consider the problem of finding an optimal energy management policy for a network of sensor nodes capable of harvesting their own energy and sharing it with other nodes in the network. We formulate this problem in the discounted cost Markov decision process framework and obtain good energy-sharing policies using the Deep Deterministic Policy Gradient (DDPG) algorithm. Earlier works have attempted to obtain the optimal energy allocation policy for a single sensor and for multiple sensors arranged on a mote with a single centralized energy buffer. Our algorithms, on the other hand, provide optimal policies for a distributed network of sensors individually harvesting energy and capable of sharing energy amongst themselves. Through simulations, we illustrate that the policies obtained by our DDPG algorithm using this enhanced network model outperform algorithms that do not share energy or use a centralized energy buffer in the distributed multi-nodal case.
Hybrid AC/DC Transmission Expansion Planning Considering HVAC to HVDC Conversion Under Renewable Penetration
Authors: Mojtaba Moradi-Sepahvand, Turaj Amraee
Date: 2023-10-09 00:00:00

In this paper, a dynamic (i.e. multi-year) hybrid model is presented for Transmission Expansion Planning (TEP) utilizing the High Voltage Alternating Current (HVAC) and multiterminal Voltage Sourced Converter (VSC)-based High Voltage Direct Current (HVDC) alternatives. In addition to new HVAC and HVDC lines, the possibility of converting existing HVAC transmission lines to HVDC lines is considered in the proposed model. High shares of renewable resources are integrated into the proposed hybrid AC/DC TEP model. Due to the intermittency of renewable resources, the planning of large-scale Energy Storage (ES) devices is considered. In order to accurately estimate the total TEP costs and hence capturing the scenarios of load and renewable generation uncertainty, using a clustering approach, each year of the planning horizon is replaced with four representative days. The proposed model is formulated as a Mixed-Integer Linear Programming (MILP) problem. Using Benders Decomposition (BD) algorithm, the proposed model is decomposed into a Master investment problem to handle the decision variables, and Sub-problems to check the feasibility of master problem solution and optimize the operation and ES investment cost. Three test systems are used as case studies to demonstrate the effectiveness of the proposed hybrid AC/DC TEP model.
Deep Learning-Based Hurricane Resilient Co-planning of Transmission Lines, Battery Energy Storages and Wind Farms
Authors: Mojtaba Moradi-Sepahvand, Turaj Amraee, Saleh Sadeghi Gougheri
Date: 2023-10-09 00:00:00

In this paper, a multi-stage model for expansion co-planning of transmission lines, Battery Energy Storages (BESs), and Wind Farms (WFs) is presented considering resilience against extreme weather events. In addition to High Voltage Alternating Current (HVAC) lines, Multi-Terminal Voltage Source Converter (MTVSC) based High Voltage Direct Current (HVDC) lines are planned to reduce the impact of high-risk events. To evaluate the system resilience against hurricanes, probable hurricane speed (HS) scenarios are generated using Monte Carlo Simulation (MCS). The Fragility Curve (FC) concept is utilized for calculating the failure probability of lines due to extreme hurricanes. Based on each hurricane damage, the probable scenarios are incorporated in the proposed model. Renewable Portfolio Standard (RPS) policy is modeled to integrate high penetration of WFs. To deal with the wind power and load demand uncertainties, a Chronological Time-Period Clustering (CTPC) algorithm is introduced for extracting representative hours in each planning stage. A deep learning approach based on Bi-directional Long Short-Term Memory (B-LSTM) networks is presented to forecast the yearly peak loads. The Mixed-Integer Linear Programming (MILP) formulation of the proposed model is solved using a Benders Decomposition (BD) algorithm. A modified IEEE RTS test system is used to evaluate the proposed model effectiveness.
Integrated Expansion Planning of Electric Energy Generation, Transmission, and Storage for Handling High Shares of Wind and Solar Power Generation
Authors: Mojtaba Moradi-Sepahvand, Turaj Amraee
Date: 2023-10-09 00:00:00

In this paper, an integrated multi-period model for long term expansion planning of electric energy transmission grid, power generation technologies, and energy storage devices is introduced. The proposed method gives the type, size and location of generation, transmission and storage devices to supply the electric load demand over the planning horizon. The sitting and sizing of Battery Energy Storage (BES) devices as flexible options is addressed to cover the intermittency of Renewable Energy Sources (RESs), mitigate lines congestion, and postpone the need for new transmission lines and power plants installation. For efficient handling of RESs uncertainties, and operational flexibility, the upward and downward Flexible Ramp Spinning Reserve (FRSR) are modeled. Besides, the Low-Carbon Policy (LCP) is considered in the objective function of the proposed Transmission, Generation, and Storage Expansion Planning (TGSEP) model. A hierarchical clustering method that can preserve the chronology of input time series throughout the planning horizon periods is developed to capture the short-term uncertainties of load demand and RESs. The short-term operational flexibility requirements make the joint long-term transmission and generation planning a high computational problem. Therefore, the Mixed-Integer Linear Programming (MILP) formulation of the model is solved using an accelerated Benders Dual Decomposition (BDD) method. The IEEE RTS test system is utilized to validate the effectiveness of the proposed joint expansion planning model.
Coordinated Expansion Planning of Transmission and Distribution Systems Integrated with Smart Grid Technologies
Authors: Mojtaba Moradi-Sepahvand, Turaj Amraee, Farrokh Aminifar, Amirhossein Akbari
Date: 2023-10-09 00:00:00

Integration of smart grid technologies in distribution systems, particularly behind-the-meter initiatives, has a direct impact on transmission network planning. This paper develops a coordinated expansion planning of transmission and active distribution systems via a stochastic multistage mathematical programming model. In the transmission level, in addition to lines, sitting and sizing of utility-scale battery energy storage systems and wind power plants under renewable portfolio standard policy are planned. Switchable feeders and distributed generations are decision variables in the distribution level while the impact of demand response programs as a sort of behind-the-meter technologies is accommodated as well. Expansion of electric vehicle taxi charging stations is included as a feasible option in both transmission and distribution levels. In order to deal with short-term uncertainty of load demand, renewable energy sources output power, and the charging pattern of electric vehicle taxis in each station, a chronological time-period clustering algorithm along with Monte Carlo simulation is utilized. The proposed model is tackled by means of Benders Dual Decomposition (BDD) method. The IEEE RTS test system (as the transmission system) along with four IEEE 33-node test feeders (as distribution test systems) are examined to validate effectiveness of the proposed model.
Two stage Robust Nash Bargaining based Energy Trading between Hydrogen-enriched Gas and Active Distribution Networks
Authors: Wenwen Zhang, Gao Qiu, Hongjun Gao, Tingjian Liu, Junyong Liu, Yaping Li, Shengchun Yang, Jiahao Yan, Wenbo Mao
Date: 2023-10-09 00:00:00
Tasks: energy trading

Integration of emerging hydrogen-enriched compressed natural gas (HCNG) distribution network with active distribution net-work (ADN) provides huge latent flexibility on consuming re-newable energies. However, paucity of energy trading mechanism risks the stable earnings of the flexibility for both entities, especially when rising highly-efficient solid oxide fuel cells (SOFCs) are pioneered to interface gas and electricity. To fill the gap, a two-stage robust Nash bargaining strategy is pro-posed. In the first stage, a privacy-preserved Nash Bargaining based on the ADMM is applied to clear energy trading between the two autonomous entities, i.e., ADN and gas distribution network (GDN). Via robust dispatch of configured energy storage in ADN, the next stage de-risks ADN profit collapse from transaction biases, caused by forecasting errors of distributed energy resources. C&CG is finally utilized to loop the two stages. The convergence of the entire energy trading strategy is theoretically proved. As such, sustain-able returns from the integration of ADN and GDN bridged by SOFC and HCNG are facilitated. Numerical studies indicate that, the proposed cooperative strategy reaps a stable social welfare of nearly 1.6% to total cost, and benefit-steady situations for both ADN and GDN, even in the worst case.