arXiv subjects

Rongxing Hu

Publications and source records attributed to Rongxing Hu.

13 recordsLinked to original sources

arXiv2026

HyMGP: A Customized MILP-Based Tool for Techno-Economic Planning of Islanded Microgrids

This paper presents a customized microgrid planning algorithm and tool, HyMGP, for remote sites in arid regions, which is formulated as a Mixed Integer Linear Programming (MILP) problem. HyMGP is compared with HOMER Pro to evaluate its performance in optimizing the sizing of microgrid components, including photovoltaic panels (PVs), vertical axis wind turbines (VAWTs), and battery energy storage systems (BESS), for remote and off-grid applications. The study focuses on a standalone microgrid in the Saudi Arabia, considering high solar irradiance, limited wind availability, and a constant load profile composed of continuous cathodic protection and daytime cooling. In the simulation environment, comparisons with HOMER solutions demonstrate the advantages of HyMGP, which provides optimal and more flexible solutions by allowing user-defined component specifications and strictly enforcing all constraints. Further analysis shows that incorporating wind turbines reduces the Net Present Cost (NPC) by decreasing the required PV and battery capacities. Increasing battery autonomy leads to a higher NPC in both PV-only and hybrid systems due to the need for larger storage. Finally, lithium iron phosphate (Li-ion LFP) batteries are found to be more cost effective than lead acid, offering lower NPCs due to their longer lifespan, deeper discharge capability, and fewer replacement cycles.

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arXiv2026

Stochastic Power-Water Coordination: Unlocking Flexibility in Hybrid RO Desalination Plants via Variable-Speed Pumps and Tank Mixing

Water desalination plants (DPs) are among the most critical infrastructures and largest electricity loads in water-scarce regions worldwide. Although reverse osmosis (RO) desalination is the most energy-efficient and dominant technology, it remains energy-intensive but can offer substantial flexibility potential for power systems. This paper proposes a coordinated operation framework for power systems and DPs that explicitly accounts for both systems' operational constraints and fully unlocks DP flexibility. To achieve this, a detailed DP model is developed, incorporating the characteristics of an actual high-pressure pump with variable-speed operation, on-off operation with flushing requirements, water quality constraints, and water dynamics and salt mixing in the storage tank. By proactively managing freshwater storage and tank salinity in a closed-loop coordinated scheduling framework, the operational flexibility of the DP is significantly enhanced. With appropriate simplification and linearization, the resulting coordinated scheduling problem is formulated as a tractable mixed-integer linear programming (MILP) model, and a two-step decomposed commitment-scheduling stochastic optimization (TDCSO) is proposed to efficiently address uncertainties. Case studies validate the proposed approach and demonstrate up to a 6% operating cost reduction.

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arXiv2024

Invisible Manipulation Deep Reinforcement Learning Enhanced Stealthy Attacks on Battery Energy Management Systems

This paper introduces "invisible manipulation," an innovative cyber-attack mechanism achieved through strategically timed stealthy false data injection attacks (SFDIAs). By stealthily manipulating measurements of a critical asset prior to the target time period, the attacker can subtly guide the engineering system toward a predetermined operational state without detection. Using the battery energy management system (BEMS) as a case study, we employ deep reinforcement learning (DRL) to generate synthetic measurements, such as battery voltage and current, that align closely with actual measurements. These synthetic measurements, falling within the acceptable error margin of residual-based bad data detection algorithm provided by state estimation, can evade detection and mislead Extended Kalman-filter-based State of Charge estimation. Subsequently, considering the deceptive data as valid inputs, the BEMS will operate the BESS towards the attacker desired operational states when the targeted time period come. The use of the DRL-based scheme allows us to covert an online optimization problem into an offline training process, thereby alleviating the computational burden for real-time implementation. Comprehensive testing on a high-fidelity microgrid real-time simulation testbed validates the effectiveness and adaptability of the proposed methods in achieving different attack objectives.

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arXiv2023

Multi-Feeder Restoration using Multi-Microgrid Formation and Management

This papers highlights the benefit of coordinating resources on mulitple active distribution feeders during severe long duration outages through multi-microgrid formation. A graph-theory based multi-microgrid formation algorithm is developed which is agnostic of the underlying energy management scheme of the microgrids and solved in a rolling horizon fashion. The algorithm is then enhanced to handle multiple feeders where formation of long laterals needs to be avoided due to potential voltage control issues in distribution systems. The algorithm is evaluated on a synthetic two feeder system derived from interconnecting two IEEE 123 node system. The results indicate increased service to loads in the system and better utilization of renewable resources.

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arXiv2023

Design Considerations of a Coordinative Demand Charge Mitigation Strategy

This paper presents a coordinative demand charge mitigation (DCM) strategy for reducing electricity consumption during system peak periods. Available DCM resources include batteries, diesel generators, controllable loads, and conservation voltage reduction. All resources are directly controlled by load serving entities. A mixed integer linear programming based energy management algorithm is developed to optimally coordinate of DCM resources considering the load payback effect. To better capture system peak periods, two different kinds of load forecast are used: the day-ahead load forecast and the peak-hour probability forecast. Five DCM strategies are compared for reconciling the discrepancy between the two forecasting results. The DCM strategies are tested using actual utility data. Simulation results show that the proposed algorithm can effectively mitigate the demand charge while preventing the system peak from being shifted to the payback hours. We also identify the diminishing return effect, which can help load serving entities optimize the size of their DCM resources.

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arXiv2023

A Novel Feeder-level Microgrid Unit Commitment Algorithm Considering Cold-load Pickup, Phase Balancing, and Reconfiguration

This paper presents a novel 2-stage microgrid unit commitment (Microgrid-UC) algorithm considering cold-load pickup (CLPU) effects, three-phase load balancing requirements, and feasible reconfiguration options. Microgrid-UC schedules the operation of switches, generators, battery energy storage systems, and demand response resources to supply 3-phase unbalanced loads in an islanded microgrid for multiple days. A performance-based CLPU model is developed to estimate additional energy needs of CLPU so that CLPU can be formulated into the traditional 2-stage UC scheduling process. A per-phase demand response budget term is added to the 1st stage UC objective function to meet 3-phase load unbalance limits. To reduce computational complexity in the 1st stage UC, we replace the spanning tree method with a feasible reconfiguration topology list method. The proposed algorithm is developed on a modified IEEE 123-bus system and tested on the real-time simulation testbed using actual load and PV data. Simulation results show that Microgrid-UC successfully accounts for CLPU, phase imbalance, and feeder reconfiguration requirements.

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arXiv2022

An ICA-Based HVAC Load Disaggregation Method Using Smart Meter Data

This paper presents an independent component analysis (ICA) based unsupervised-learning method for heat, ventilation, and air-conditioning (HVAC) load disaggregation using low-resolution (e.g., 15 minutes) smart meter data. We first demonstrate that electricity consumption profiles on mild-temperature days can be used to estimate the non-HVAC base load on hot days. A residual load profile can then be calculated by subtracting the mild-day load profile from the hot-day load profile. The residual load profiles are processed using ICA for HVAC load extraction. An optimization-based algorithm is proposed for post-adjustment of the ICA results, considering two bounding factors for enhancing the robustness of the ICA algorithm. First, we use the hourly HVAC energy bounds computed based on the relationship between HVAC load and temperature to remove unrealistic HVAC load spikes. Second, we exploit the dependency between the daily nocturnal and diurnal loads extracted from historical meter data to smooth the base load profile. Pecan Street data with sub-metered HVAC data were used to test and validate the proposed methods.Simulation results demonstrated that the proposed method is computationally efficient and robust across multiple customers.

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arXiv2022

Feeder Microgrid Management on an Active Distribution System during a Severe Outage

Forming a microgrid on a distribution system with large scale outage after a severe weather event is emerging as a viable solution to improve resiliency at the distribution level. This option becomes more attractive when the distribution system has high levels of distributed PV. The management of such feeder-level microgrid has however many challenges, such as limited resources that can be deployed on the feeder quickly, and the limited real-time monitoring and control on the distribution system. Effective use of the distributed PV is also challenging as they are not monitored and controlled. To handle these challenges, the paper proposes a 2-stage hierarchical energy management scheme to securely operate these feeder level micorgrids. The first stage of the scheme solves a sequential rolling optimization problem to optimally schedule the main resources (such as a mobile diesel generator and battery storage unit). The second stage adopts a dispatching scheme for the main resources to adjust the stage-1 set-points closer to real- time. The proposed scheme has unique features to assure that the scheme is robust under highly varying operating conditions with limited system observability: (i) an innovative PV forecast error adjustment and a dynamic reserve adjustment scheme to handle the extreme uncertainty on PV power output, and (ii) an intelligent fuel management scheme to assure that the resources are utilized optimally over the multiple days of the restoration period. The proposed algorithm is tested on sample system with real-time data. The results show that the proposed scheme performs well in maximizing service to loads by effective use of all the resources and by properly taking into account the challenging operating conditions.

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arXiv2022

SA-HMTS: A Secure and Adaptive Hierarchical Multi-timescale Framework for Resilient Load Restoration Using A Community Microgrid

Distribution system integrated community microgrids (CMGs) can partake in restoring loads during extended duration outages. At such times, the CMG is challenged with limited resource availability, absence of robust grid support, and heightened demand-supply uncertainty. This paper proposes a secure and adaptive three-stage hierarchical multi-timescale framework for scheduling and real-time (RT) dispatch of CMGs with hybrid PV systems to address these challenges. The framework enables the CMG to dynamically expand its boundary to support the neighboring grid sections and is adaptive to the changing forecast error impacts. The first stage solves a stochastic extended duration scheduling (EDS) problem to obtain referral plans for optimal resource rationing. The intermediate near-real-time (NRT) scheduling stage updates the EDS schedule closer to the dispatch time using newly obtained forecasts, followed by the RT dispatch stage. To make the dispatch decisions more secure and robust against forecast errors, a novel concept called delayed recourse is proposed. The methodology is evaluated via numerical simulations on a modified IEEE 123-bus system and validated using OpenDSS/hardware-in-loop simulations. The results show superior performance in maximizing load supply and continuous secure CMG operation under numerous operating scenarios.

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arXiv2021

Reinforcement Learning for Volt-Var Control: A Novel Two-stage Progressive Training Strategy

This paper develops a reinforcement learning (RL)approach to solve a cooperative, multi-agent Volt-Var Control (VVC) problem for high solar penetration distribution systems. The ingenuity of our RL method lies in a novel two-stage progressive training strategy that can effectively improve training speed and convergence of the machine learning algorithm. In Stage 1(individual training), while holding all the other agents inactive, we separately train each agent to obtain its own optimal VVC actions in the action space: {consume, generate, do-nothing}. In Stage 2 (cooperative training), all agents are trained again coordinatively to share VVC responsibility. Rewards and costs in our RL scheme include (i) a system-level reward (for taking an action), (ii) an agent-level reward (for doing-nothing), and(iii) an agent-level action cost function. This new framework allows rewards to be dynamically allocated to each agent based on their contribution while accounting for the trade-off between control effectiveness and action cost. The proposed methodology is tested and validated in a modified IEEE 123-bus system using realistic PV and load profiles. Simulation results confirm that the proposed approach is robust and computationally efficient; and it achieves desirable volt-var control performance under a wide range of operation conditions.

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arXiv2021

A Meta-learning based Distribution System Load Forecasting Model Selection Framework

This paper presents a meta-learning based, automatic distribution system load forecasting model selection framework. The framework includes the following processes: feature extraction, candidate model labeling, offline training, and online model recommendation. Using user load forecasting needs as input features, multiple meta-learners are used to rank the available load forecast models based on their forecasting accuracy. Then, a scoring-voting mechanism weights recommendations from each meta-leaner to make the final recommendations. Heterogeneous load forecasting tasks with different temporal and technical requirements at different load aggregation levels are set up to train, validate, and test the performance of the proposed framework. Simulation results demonstrate that the performance of the meta-learning based approach is satisfactory in both seen and unseen forecasting tasks.

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arXiv2021

Hierarchical Multi-timescale Framework For Operation of Dynamic Community Microgrid

Distribution system integrated community microgrids (CMGs) can restore loads during extended outages. The CMG is challenged with limited resource availability, absence of a robust grid-support, and demand-supply uncertainty. To address these challenges, this paper proposes a three-stage hierarchical multi-timescale framework for scheduling and real-time (RT) dispatch of CMGs. The CMG's ability to dynamically expand its boundary to support the neighboring grid sections is also considered. The first stage solves a stochastic day-ahead (DA) scheduling problem to obtain referral plans for optimal resource rationing. The intermediate near real-time scheduling stage updates the DA schedule closer to the dispatch time, followed by the RT dispatch stage. The proposed methodology is validated via numerical simulations on a modified IEEE 123-bus system, which shows superior performance in terms of RT load supplied under different forecast error cases, outage duration scenarios, and against the traditionally used two-stage approach.

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arXiv2020

A Load Switching Group based Feeder-level Microgrid Energy Management Algorithm for Service Restoration in Power Distribution System

This paper presents a load switching group based energy management system (LSG-EMS) for operating microgrids on a distribution feeder powered by one or multiple grid-forming distributed energy resources. Loads on a distribution feeder are divided into load switching groups that can be remotely switched on and off. The LSG-EMS algorithm, formulated as a mixed-integer linear programming (MILP) problem, has an objective function of maximizing the served loads while minimizing the total number of switching actions. A new set of topology constraints are developed for allowing multiple microgrids to be formed on the feeder and selecting the optimal supply path. Customer comfort is accounted for by maximizing the supply duration in the customer preferred service period and enforcing a minimum service duration. The proposed method is demonstrated on a modified IEEE 33-bus system using actual customer data. Simulation results show that the LSG-EMS successfully coordinates multiple grid-forming sources by selecting an optimal supply topology that maximizes the supply period of both the critical and noncritical loads while minimizing customer service interruptions in the service restoration process.

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