arXiv subjects

Xingpeng Li

Publications and source records attributed to Xingpeng Li.

At least 91 records · Page 5Linked to original sources

arXiv2022

Reduced Optimal Power Flow Using Graph Neural Network

OPF problems are formulated and solved for power system operations, especially for determining generation dispatch points in real-time. For large and complex power system networks with large numbers of variables and constraints, finding the optimal solution for real-time OPF in a timely manner requires a massive amount of computing power. This paper presents a new method to reduce the number of constraints in the original OPF problem using a graph neural network (GNN). GNN is an innovative machine learning model that utilizes features from nodes, edges, and network topology to maximize its performance. In this paper, we proposed a GNN model to predict which lines would be heavily loaded or congested with given load profiles and generation capacities. Only these critical lines will be monitored in an OPF problem, creating a reduced OPF (ROPF) problem. Significant saving in computing time is expected from the proposed ROPF model. A comprehensive analysis of predictions from the GNN model was also made. It is concluded that the application of GNN for ROPF is able to reduce computing time while retaining solution quality.

eess.SY↗

arXiv2022

Resilient Operational Planning for Microgrids Against Extreme Events

This paper proposes a novel resilience index, a microgrid survivability rate (SR) under extreme events, and then proposes a novel Resilient Operational Planning (ROP) algorithm to maximize the proposed resilience index SR. The proposed ROP algorithm can incorporate predetermined inverter failure probabilities and generate multiple scenarios accordingly to optimize resilient operations during an extreme event. The implemented ROP algorithm consists of two main steps: (i) optimization of resilient operational planning, and (ii) preventive resilience enhancement if minimum SR is not met per the analysis in step 1. A typical microgrid (MG) is studied to compare the proposed ROP algorithm against a traditional microgrid energy management (MEM) model. Results indicate that an enhanced resilience operation is achieved by the ROP algorithm, which is demonstrated by the quantification of resilience via the SR. Moreover, the proposed ROP algorithm is able to obtain a greater SR overall compared to that achieved by the traditional MEM, and this benefit of using the proposed ROP increases as the inverter failure probabilities increase.

eess.SY↗

arXiv2022

Security-Constrained Unit Commitment Con-sidering Locational Frequency Stability in Low-Inertia Power Grids

With increasing installation of wind and solar generation, conventional synchronous generators in power systems are gradually displaced resulting in a significant reduction in system inertia. Maintaining system frequency within acceptable ranges becomes more critical for the stability of a power system. In this paper, we first study the impact of inter-area oscillations on the system rate-of-change-of-frequency (RoCoF) security; then, the limitations on locational RoCoFs accounting for G-1 contingency stability are derived. By enforcing these frequency related constraints, a location based RoCoF constrained security constrained unit commitment (LRC-SCUC) model is proposed. Furthermore, an effective piecewise linearization (PWL) technique is employed to formulate a RoCoF linearization problem and linearize the nonlinear function representing the location based RoCoF constraints in SCUC. Simulation results reveal that the inclusion of inertia-related constraints can substantially improve the system stability at the cost of higher operation cost. The results also show that deploying virtual inertia techniques not only reduces the total cost, but also improves the system market efficiency.

eess.SY↗

arXiv2022

Generalized Dynamic Junction Theory to Resolve the Mechanism of Direct Current Generation in Liquid-Solid Interfaces

Despite the unsettled mechanism of electricity generation from the continuous flow of liquids on a surface, the charge-discharge theory has been widely accepted for alternating current (AC) generation from a moving droplet. It has been recently extended to rationalize direct current (DC) generation across a droplet moving between two different materials. By designing a reconfigurable contact between a metal wire and a water droplet moving on graphene, we show that the charge-discharge theory cannot explain the reversal of current when water-metal interfaces switch from dynamic to static. All experiments can be described after we distinguish a dynamic from a static interface and generalize the photovoltaic-like effect to all dynamic junctions: excited electrons and holes in a moving interface will be separated and swept under the built-in electrical field, leading to a DC response. This generalized theory will lead to an understanding and the design of efficient electricity generation based on interfacial charge transfer.

cond-mat.mes-hall↗

arXiv2022

Neural Network-based Power Flow Model

Power flow analysis is used to evaluate the flow of electricity in the power system network. Power flow calculation is used to determine the steady-state variables of the system, such as the voltage magnitude/phase angle of each bus and the active/reactive power flow on each branch. The DC power flow model is a popular linear power flow model that is widely used in the power industry. Although it is fast and robust, it may lead to inaccurate line flow results for some transmission lines. Since renewable energy sources such as solar farms or offshore wind farms are usually located far away from the main grid, accurate line flow results on these critical lines are essential for power flow analysis due to the unpredictable nature of renewable energy. Data-driven methods can be used to partially address these inaccuracies by taking advantage of historical grid profiles. In this paper, a neural network (NN) model is trained to predict power flow results using historical power system data. Although the training process may take time, once trained, it is very fast to estimate line flows. A comprehensive performance analysis between the proposed NN-based power flow model and the traditional DC power flow model is conducted. It can be concluded that the proposed NN-based power flow model can find solutions quickly and more accurately than DC power flow model.

eess.SY↗

arXiv2021

Wholesale Electricity Price Forecasting using Integrated Long-term Recurrent Convolutional Network Model

Electricity price is a key factor affecting the decision-making for all market participants. Accurate forecasting of electricity prices is very important and is also very challenging since electricity price is highly volatile due to various factors. This paper proposes an integrated long-term recurrent convolutional network (ILRCN) model to predict electricity prices considering the majority contributing attributes to the market price as input. The proposed ILRCN model combines the functionalities of convolutional neural network and long short-term memory (LSTM) algorithm along with the proposed novel conditional error correction term. The combined ILRCN model can identify the linear and non-linear behavior within the input data. We have used ERCOT wholesale market price data along with load profile, temperature, and other factors for the Houston region to illustrate the proposed model. The performance of the proposed ILRCN electricity price forecasting model is verified using performance/evaluation metrics like mean absolute error and accuracy. Case studies reveal that the proposed ILRCN model is accurate and efficient in electricity price forecasting as compared to the support vector machine (SVM) model, fully-connected neural network model, LSTM model and the LRCN model without the conditional error correction stage.

cs.LG↗

arXiv2021

An Accelerated-Decomposition Approach for Security-Constrained Unit Commitment with Corrective Network Reconfiguration

Security-constrained unit commitment (SCUC) model is used for power system day-ahead scheduling. However, current SCUC model uses a static network to deliver power and meet demand optimally. A dynamic network can provide a lower optimal cost and alleviate network congestion. However, due to the computational complexity and the lack of effective algorithms, network reconfiguration has not been included in the SCUC model yet. This paper presents a novel approach to handle the computational complexity in security-constrained unit commitment (SCUC) with corrective network reconfiguration (CNR) while considering the scalability through accelerated-decomposition approach with fast screening non-critical sub-problems of SCUC-CNR. The proposed approach provides substantial computational benefits and is also applicable to SCUC. Simulation results on the IEEE 24-bus system show that the proposed methods are substantially faster without the loss in solution quality while the scalability benefits are demonstrated using larger cases: the IEEE 73-bus system, IEEE 118-bus system and Polish system.

math.OC↗

arXiv2021

Long-Term Recurrent Convolutional Network-based Inertia Estimation using Ambient Measurements

Conventional synchronous machines are gradually replaced by converter-based renewable resources. As a result, synchronous inertia, an important time-varying quantity, has substantially more impact on modern power systems stability. The increasing integration of renewable energy resources imports different dynamics into traditional power systems; therefore, the estimation of system inertia using mathematical model becomes more difficult. In this paper, we propose a novel learning-assisted inertia estimation model based on long-term recurrent convolutional network (LRCN) that uses system wide frequency and phase voltage measurements. The proposed approach uses a non-intrusive probing signal to perturb the system and collects ambient measurements with phasor measurement units (PMU) to train the proposed LRCN model. Case studies are conducted on the IEEE 24-bus system. Under a signal-to-noise ratio (SNR) of 60dB condition, the proposed LRCN based inertia estimation model achieves an accuracy of 97.56% with a mean squared error (MSE) of 0.0552. Furthermore, with a low SNR of 45dB, the proposed learning-assisted inertia estimation model is still able to achieve a high accuracy of 93.07%.

eess.SY↗

arXiv2021

Optimal Allocation of Virtual Inertia Devices for Enhancing Frequency Stability in Low-Inertia PowerSystems

As renewable resources gradually replace conventional generation based synchronous machines, the dynamics of the modern grid changes significantly and the system synchronous inertia decreases substantially. This transformation poses severe challenges for power system stability; for instance, it may lead to larger initial rate of change of frequency and increase frequency excursions. However, new opportunities also arise as novelconverter control techniques, so-called grid-forming strategies, show higher efficiency and faster response than conventional synchronous generators. They mainly involve virtual inertia (VI) emulation to mimic the behavior of synchronous machines. In this study, a state-space model for the power system network is developed with VI as a frequency regulation method. A reduced model based-norm algorithm (RMHA) considering the Fiedler mode impact is proposed in this paper to optimize the allocation of VI devices and improve power system frequency stability. Finally, case studies conducted on the IEEE 24-bus system demonstrate the efficacy of the proposed RMHA approach.

eess.SY↗

arXiv2021

Quantitative Analysis of Demand Response Using Thermostatically Controlled Loads

The flexible power consumption feature of thermostatically controlled loads (TCLs) such as heating, ventilation, and air-conditioning (HVAC) systems makes them attractive targets for demand response (DR). TCLs possess a brief period where their power utilization can be altered without any significant impact on customer comfort level. This indicates TCLs are hidden potentials for providing ancillary services. This paper proposes a novel metric of demand response support time (DRST) for HVAC enabled demand response and a novel algorithm for the quantification of such HVAC-DR. The consumers' comfort will not be compromised with the proposed DRST-based HVAC-DR. Case studies demonstrate its benefits in terms of cost saving in microgrid day-ahead scheduling and reduction of forced load shedding during a grid-microgrid tie-line outage event. This illustrates the reserve potential benefits and the increase of microgrid reliability when DRST-based HVAC-DR is considered.

eess.SY↗

arXiv2021

Optimal Skeleton Network Reconfiguration considering Topological Characteristics and Transmission Path

Power system restoration can be divided into three stages: black-start, network reconfiguration, and load restoration. A skeleton-network should be restored in the second stage to prepare for the subsequent large-scale system-wide load pickup. This paper proposes a novel integrated skeleton-network reconfiguration (ISNR) model which considers both the topological characteristics of the network and the transmission path energization constraints. A novel topological characteristics-based skeleton-network quality index (TCSNQI), an index based on the network importance and distance, is proposed to evaluate the quality of the skeleton-network. The proposed ISNR model can attain both the target network and the associated restoration sequence for that network. The attained target network reaches a certain quality level, and it requires the least restoration time based on the attained sequence of ordered switching actions. The proposed ISNR model is formulated as a mixed-integer linear programming (MILP) problem. Numerical simulations on the New England 39-bus test system demonstrate the performance of the proposed ISNR model.

eess.SY↗

arXiv2021

A Novel Real-Time Energy Management Strategy for Grid-Supporting Microgrid: Enabling Flexible Trading Power

In recent years, there has been significant growth of distributed energy resources (DERs) penetration in the power grid. The stochastic and intermittent features of variable DERs such as roof top photovoltaic (PV) bring substantial uncertainties to the grid on the consumer end and weaken the grid reliability. In addition, the fact that numerous DERs are widespread in the grid makes it hard to monitor and manage DERs. To address this challenge, this paper proposes a novel real-time grid-supporting energy management (GSEM) strategy for grid-supporting microgrid (MG). This strategy can not only properly manage DERs in a MG but also enable DERs to provide grid services, which enables a MG to be grid-supporting via flexible trading power. The proposed GSEM strategy is based on a 2-step optimization which includes a routine economic dispatch (ED) step and an acceptable trading power range determination step. Numerical simulations demonstrate the performance of the proposed GSEM strategy which enables the grid operator to have a dispatch choice of trading power with MG and enhance the reliability and resilience of the main grid.

eess.SY↗

arXiv2021

A Verifiable Framework for Cyber-Physical Attacks and Countermeasures in a Resilient Electric Power Grid

In this paper, we investigate the feasibility and physical consequences of cyber attacks against energy management systems (EMS). Within this framework, we have designed a complete simulation platform to emulate realistic EMS operations: it includes state estimation (SE), real-time contingency analysis (RTCA), and security constrained economic dispatch (SCED). This software platform allowed us to achieve two main objectives: 1) to study the cyber vulnerabilities of an EMS and understand their consequences on the system, and 2) to formulate and implement countermeasures against cyber-attacks exploiting these vulnerabilities. Our results show that the false data injection attacks against state estimation described in the literature do not easily cause base-case overflows because of the conservatism introduced by RTCA. For a successful attack, a more sophisticated model that includes all of the EMS blocks is needed; even in this scenario, only post-contingency violations can be achieved. Nonetheless, we propose several countermeasures that can detect changes due to cyber-attacks and limit their impact on the system.

eess.SY↗

arXiv2021

Network Reconfiguration Impact on Renewable Energy System and Energy Storage System in Day-Ahead Scheduling

Renewable energy sources (RES) has gained significant interest in recent years. However, due to favourable weather conditions, the RES is installed in remote locations with limited transmission capacity. As a result, it can lead to major curtailments of the free resource when the network is congested. Therefore, energy storage system (ESS) is considered as a viable solution to store energy and address the intermittent nature of RES though ESS is often distributed and may not be geographically close to RES. Therefore, ESS may also suffer from limited transmission capacity due to network congestion. Currently, grid operators overlook network flexibility as a congestion management tool in day-ahead scheduling. This paper addresses these issues and studies the benefits of introducing network reconfiguration (NR) as a preventive and corrective action for transmission flexibility in day-ahead stochastic security-constrained unit-commitment (SSCUC-PC) while considering a multi-scenario RES output. Simulation results demonstrate that NR can lower total system cost, reduce RES curtailments and utilize ESS for better impact by alleviating network congestion in both base-case and post-contingency networks.

cs.NI↗

arXiv2020

An Accelerated-Decomposition Approach for Security-Constrained Unit Commitment with Corrective Network Reconfiguration- Part II: Results and Discussion

This paper presents a novel approach to handle the computational complexity in security-constrained unit commitment (SCUC) with corrective network reconfiguration (CNR) to harness the flexibility in transmission networks. This is achieved with consideration of scalability through decomposing the SCUC/SCUC-CNR formulation and then fast screening non-critical sub-problems. This is compared against the extensive formulations of SCUC and SCUC-CNR to show the advantages of the proposed typical-decomposition and accelerated-decomposition approaches to SCUC and SCUC-CNR respectively. Simulation results on the IEEE 24-bus system show that the proposed methods are substantially faster without the loss in solution quality. The proposed accelerated-decomposition approaches can be implemented for large power systems as they have great performance in the scalability tests on the IEEE 73-bus system and the Polish system when compared against the respective extensive formulations and typical-decomposition approaches. Overall, a dynamic post-contingency network can substantially alleviate network congestion and lead to a lower optimal cost.

math.OC↗

arXiv2020

Enhancing System Flexibility through Corrective Demand Response in Security-Constrained Unit Commitment

Currently, system operators implement demand response by dispatching controllable loads for economic reasons in day-ahead scheduling. Particularly, demand shifting from peak hours when the cost of electricity is higher to non-peak hours to maintain system reliability by flattening the load profile. However, the system flexibility and economic benefits of such action in post-contingency scenarios are not explicitly considered in short-term operations. Hence, this paper highlights the benefits of demand response as a corrective action for potential post-contingency emergencies in day-ahead scheduling. A security-constrained unit commitment (SCUC) model which considers the flexibility offered through corrective demand response (CDR) to maintain system reliability when a line or generator outage occurs is proposed. The proposed model was tested on IEEE 24-bus system where simulation results point to significant total cost savings in daily operations. Moreover, the results point to better long-term reliability of generators along with the ability to use existing system flexibility and serve higher critical demands in base-case when CDR is implemented.

math.OC↗

arXiv2020

Dynamic Estimation of Power System Inertia Distribution Using Synchrophasor Measurements

Integration of intermittent renewable energy sources in modern power systems is increasing very fast. Replacement of synchronous generators with zero-to-low variable renewables substantially decreases the system inertia. In a large system, inertia distribution may vary significantly, areas of low inertia are more susceptible to frequency deviation, posing risks of load shedding and generation trip. Therefore, it is necessary for operators to evaluate and quantify the system inertia and its distribution in real time. This paper proposes a novel synchronized phasor measurement units (PMUs)-based dynamic system inertia estimation method. The proposed inertia estimation method is derived using electrical distance and clustering algorithm, which considers the impact of location of measurements relative to in-feed load and impact of oscillations. The center of inertia (COI) area and area of low inertia are also determined during the estimation. Numerical simulations are conducted on the IEEE 24-bus system with various load profiles using Transient Security Analysis Tools (TSAT), a core module of the DSATools, which demonstrate the efficacy of the proposed approach.

eess.SP↗

arXiv2020

A Novel Real-Time Energy Management Strategy for Gird-Friendly Microgrid: Harnessing Internal Fluctuation Internally

Typically, a large portion of microgrid generating capacity is from variable renewable resources that are greatly impacted by the environment and can be intermittent as well as stochastic. This would result in uncertainty of microgrid net-load, and negatively affect the grid reliability. A two-phase real-time energy management strategy for networked microgrid is proposed in this paper to address microgrid internal fluctuation internally, which enables a microgrid to become grid-friendly. The proposed strategy is based on coordination between the real-time dispatch (RTD) phase and the real-time control (RTC) phase. In the RTD phase, model predictive control (MPC) is used to optimally dispatch microgrid resources in the current time interval while considering near future situations. The RTC phase addresses microgrid internal net-load fluctuation with fast-acting batteries, which aims to maintain a constant tie-line power flow between the main grid and the microgrid for the current dispatch interval. Numerical simulations conducted on ten different net-load scenarios can demonstrate the performance of the proposed two-phase energy management strategy that will enable a microgrid to operate as a controllable asset with static electricity consumption or production in an economic dispatch interval from the perspective of the bulk grid operator.

eess.SY↗