Xingpeng Li

Fan Jiang, Xingpeng Li

As grid-forming (GFM) battery energy storage systems (BESS) are increasingly deployed to enhance power system inertial response and frequency stability, incorporating their frequency support capabilities into day-ahead energy scheduling (DAES) is essential for achieving both frequency security and operational efficiency. However, accurately determining frequency metrics in grids with coexisting GFM inverters and synchronous generators requires electromagnetic transient (EMT) simulations, which are computationally prohibitive for direct embedding in grid operational optimization models. To bridge the gap between modeling accuracy and computational efficiency, a learning-assisted DAES (LA-DAES) framework is proposed in this work. By leveraging a surrogate model to represent the frequency support dynamics of GFM BESS, the proposed framework ensures frequency security with a reasonable solve time. Comparative results demonstrate that, relative to analytical frequency-constrained DAES, the proposed LA-DAES framework more accurately captures grid frequency metrics and improves the utilization of GFM BESS.

Bharat Bohara, Raymond I. Fernandez, Vysali Gollapudi et al.

Higher penetration of renewable and smart home technologies at the residential level challenges grid stability as utility-customer interactions add complexity to power system operations. In response, short-term residential load forecasting has become an increasing area of focus. However, forecasting at the residential level is challenging due to the higher uncertainties involved. Recently deep neural networks have been leveraged to address this issue. This paper investigates the capabilities of a bidirectional long short-term memory (BiLSTM) and a convolutional neural network-based BiLSTM (CNN-BiLSTM) to provide a day ahead (24 hr.) forecasting at an hourly resolution while minimizing the root mean squared error (RMSE) between the actual and predicted load demand. Using a publicly available dataset consisting of 38 homes, the BiLSTM and CNN-BiLSTM models are trained to forecast the aggregated active power demand for each hour within a 24 hr. span, given the previous 24 hr. load data. The BiLSTM model achieved the lowest RMSE of 1.4842 for the overall daily forecast. In addition, standard LSTM and CNN-LSTM models are trained and compared with the BiLSTM architecture. The RMSE of BiLSTM is 5.60%, 2.85% and 2.60% lower than the LSTM, CNN-LSTM and CNN-BiLSTM models respectively. The source code of this work is available at https://github.com/Varat7v2/STLF-BiLSTM-CNNBiLSTM.git.

Linhan Fang, Xingpeng Li

With the increasing demand for edge computing and AI-driven workloads, integrating small and medium-sized edge data centers into distribution networks has become increasingly important. This paper investigates the hosting capacity of distribution networks for data center integration and identifies the key physical mechanisms that limit the maximum allowable data center load. The baseline analysis shows that data center hosting capacity varies significantly across candidate buses due to network topology and electrical distance. Three dominant limiting mechanisms are identified: current-constrained locations, voltage-constrained locations, and mixed-constrained locations where both current loading and voltage deviation jointly affect hosting capacity. To increase the hosting capacity, this study evaluates multiple flexible resources, including battery energy storage systems (BESS), dispatchable distributed generators (DDG), and static synchronous compensators (STATCOM). Numerical results demonstrate that these resources provide complementary benefits through active power support, sustained local generation, and reactive power compensation, effectively expanding data center hosting capacity in distribution systems.

Xingpeng Li, Pranavamoorthy Balasubramanian, Mostafa Sahraei-Ardakani et al.

Transmission switching (TS) has gained significant attention recently. However, barriers still remain and must be overcome before the technology can be adopted by the industry. The state of the art challenges include AC feasibility and performance, computational complexity, the ability to handle large-scale real power systems, and dynamic stability. This two-part paper investigates these challenges by developing an AC TS-based real-time contingency analysis (RTCA) tool that can handle large-scale systems within a reasonable time. The tool proposes multiple corrective switching actions, after detection of a contingency with potential violations. To reduce the computational complexity, three heuristic algorithms are proposed to generate a small set of candidates for switching. Parallel computing is implemented to further speed up the solution time. Furthermore, stability analysis is performed to check for dynamic stability of proposed TS solutions. Part I of the paper presents a comprehensive literature review and the methodology. The promising results, tested on the Tennessee Valley Authority (TVA) system and actual energy management system (EMS) snapshots from Pennsylvania New Jersey Maryland (PJM) and the Electric Reliability Council of Texas (ERCOT), are presented in Part II. It is concluded that RTCA with corrective TS significantly reduces potential post-contingency violations and is ripe for industry adoption.

Arun Venkatesh Ramesh, Xingpeng Li

Security-constrained unit commitment (SCUC) is a computationally complex process utilized in power system day-ahead scheduling and market clearing. SCUC is run daily and requires state-of-the-art algorithms to speed up the process. The constraints and data associated with SCUC are both geographically and temporally correlated to ensure the reliability of the solution, which further increases the complexity. In this paper, an advanced machine learning (ML) model is used to study the patterns in power system historical data, which inherently considers both spatial and temporal (ST) correlations in constraints. The ST-correlated ML model is trained to understand spatial correlation by considering graph neural networks (GNN) whereas temporal sequences are studied using long short-term memory (LSTM) networks. The proposed approach is validated on several test systems namely, IEEE 24-Bus system, IEEE-73 Bus system, IEEE 118-Bus system, and synthetic South-Carolina (SC) 500-Bus system. Moreover, B-θ and power transfer distribution factor (PTDF) based SCUC formulations were considered in this research. Simulation results demonstrate that the ST approach can effectively predict generator commitment schedule and classify critical and non-critical lines in the system which are utilized for model reduction of SCUC to obtain computational enhancement without loss in solution quality

Thuan Pham, Xingpeng Li

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.

Xingpeng Li, Kory Hedman

Full AC power flow model is an accurate mathematical model for representing the physical power systems. In practice, however, the utilization of this model is limited due to the computational complexity associated with its nonlinear and nonconvexity characteristics. An alternative linearized DC power flow model is widely used in the power system operation and planning tools. However, when reactive power and voltage magnitude are of concern, DC power flow model will be useless. Therefore, a linearized AC (LAC) power flow model is needed to address this issue. This paper first introduces a regular LAC model. Subsequently, with the advance in regression analysis technique, a data driven linearized AC (DLAC) model is proposed to improve the regular LAC model. Numerical simulations conducted on the Tennessee Valley Authority (TVA) system demonstrate the performance and effectiveness of the proposed DLAC model.

Arun Venkatesh Ramesh, Xingpeng Li

Day-ahead operations involves a complex and computationally intensive optimization process to determine the generator commitment schedule and dispatch. The optimization process is a mixed-integer linear program (MILP) also known as security-constrained unit commitment (SCUC). Independent system operators (ISOs) run SCUC daily and require state-of-the-art algorithms to speed up the process. Existing patterns in historical information can be leveraged for model reduction of SCUC, which can provide significant time savings. In this paper, machine learning (ML) based classification approaches, namely logistic regression, neural networks, random forest and K-nearest neighbor, were studied for model reduction of SCUC. The ML was then aided with a feasibility layer (FL) and post-process technique to ensure high-quality solutions. The proposed approach is validated on several test systems namely, IEEE 24-Bus system, IEEE-73 Bus system, IEEE 118-Bus system, 500-Bus system, and Polish 2383-Bus system. Moreover, model reduction of a stochastic SCUC (SSCUC) was demonstrated utilizing a modified IEEE 24-Bus system with renewable generation. Simulation results demonstrate a high training accuracy to identify commitment schedule while FL and post-process ensure ML predictions do not lead to infeasible solutions with minimal loss in solution quality.

Mingjian Tuo, Xingpeng Li, Tianxia Zhao

Power flow analysis plays a crucial role in examining the electricity flow within a power system network. By performing power flow calculations, the system's steady-state variables, including voltage magnitude, phase angle at each bus, active/reactive power flow across branches, can be determined. While the widely used DC power flow model offers speed and robustness, it may yield inaccurate line flow results for certain transmission lines. This issue becomes more critical when dealing with renewable energy sources such as wind farms, which are often located far from the main grid. Obtaining precise line flow results for these critical lines is vital for next operations. To address these challenges, data-driven approaches leverage historical grid profiles. In this paper, a graph neural network (GNN) model is trained using historical power system data to predict power flow outcomes. The GNN model enables rapid estimation of line flows. A comprehensive performance analysis is conducted, comparing the proposed GNN-based power flow model with the traditional DC power flow model, as well as deep neural network (DNN) and convolutional neural network (CNN). The results on test systems demonstrate that the proposed GNN-based power flow model provides more accurate solutions with high efficiency comparing to benchmark models.

Mingjian Tuo, Fan Jiang, Xingpeng Li et al.

Day-ahead generation scheduling is typically conducted by solv-ing security-constrained unit commitment (SCUC) problem. However, with fast-growing of inverter-based resources, grid inertia has been dramatically reduced, compromising the dy-namic stability system. Traditional SCUC (T-SCUC), without any inertia requirements, may no longer be effective for renewa-bles-dominated grids. To address this, we propose the active linearized sparse neural network-embedded SCUC (ALSNN-SCUC) model, utilizing machine learning (ML) to incorporate system dynamic performance. A multi-output deep neural net-work (DNN) model is trained offline on strategically-selected data samples to accurately predict frequency stability metrics: locational RoCoF and frequency nadir. Structured sparsity and active ReLU linearization are implemented to prune redundant DNN neurons, significantly reducing its size while ensuring pre-diction accuracy even at high sparsity levels. By embedding this ML-based frequency stability predictor into SCUC as con-straints, the proposed ALSNN-SCUC model minimizes its com-putational complexity while ensuring frequency stability follow-ing G-1 contingency. Case studies show that the proposed ALSNN-SCUC can enforce pre-specified frequency requirements without being overly conservative, outperforming five bench-mark models including T-SCUC, two physics-based SCUC, and two ML-based SCUC. The proposed sparsification and active linearization strategies can reduce the DNN-SCUC computing time by over 95% for both IEEE 24-bus and 118-bus systems, demonstrating the effectiveness and scalability of the proposed ALSNN-SCUC model.

Jonathan Yang, Mingjian Tuo, Jin Lu et al.

Accurate load forecasting is critical for efficient and reliable operations of the electric power system. A large part of electricity consumption is affected by weather conditions, making weather information an important determinant of electricity usage. Personal appliances and industry equipment also contribute significantly to electricity demand with temporal patterns, making time a useful factor to consider in load forecasting. This work develops several machine learning (ML) models that take various time and weather information as part of the input features to predict the short-term system-wide total load. Ablation studies were also performed to investigate and compare the impacts of different weather factors on the prediction accuracy. Actual load and historical weather data for the same region were processed and then used to train the ML models. It is interesting to observe that using all available features, each of which may be correlated to the load, is unlikely to achieve the best forecasting performance; features with redundancy may even decrease the inference capabilities of ML models. This indicates the importance of feature selection for ML models. Overall, case studies demonstrated the effectiveness of ML models trained with different weather and time input features for ERCOT load forecasting.

Cunzhi Zhao, Fan Jiang, Xingpeng Li

Recently, neural networks have been widely applied in the power system area. They can be used for better predicting input information and modeling system performance with increased accuracy. In some applications such as battery degradation neural network-based microgrid day-ahead energy scheduling, the input features of the trained learning model are variables to be solved in optimization models that enforce limits on the output of the same learning model. This will create a neural network-embedded optimization problem; the use of nonlinear activation functions in the neural network will make such problems extremely hard to solve if not unsolvable. To address this emerging challenge, this paper investigated different methods for linearizing the nonlinear activation functions with a particular focus on the widely used rectified linear unit (ReLU) function. Four linearization methods tailored for the ReLU activation function are developed, analyzed and compared in this paper. Each method employs a set of linear constraints to replace the ReLU function, effectively linearizing the optimization problem, which can overcome the computational challenges associated with the nonlinearity of the neural network model. These proposed linearization methods provide valuable tools for effectively solving optimization problems that integrate neural network models with ReLU activation functions

Haoxiang Wan, Xingpeng Li

Data center electricity consumption reached 4.4% of U.S. total in 2023 and is projected to grow to 6.7--12% by 2028, imposing increasing stress on transmission networks while representing a largely untapped source of controllable demand-side flexibility. This paper proposes a modular security-constrained unit commitment (SCUC) framework that coordinates flexible data center workloads with system-level scheduling to reduce renewable curtailment, alleviate congestion, and lower operating costs. Three mixed-integer linear programming (MILP) models are formulated: the Data Center Spatial model (DC-S), enabling instantaneous workload redistribution across geographically distributed sites; the Data Center Temporal model (DC-T), permitting each site to shift its deferrable load across time while preserving the daily energy balance; and the Data Center Spatio-Temporal model (DC-ST), jointly activating both mechanisms and spanning the largest feasible operating region. Case studies on a modified IEEE 24-bus reliability test system show that DC-ST eliminates all base-case and post-contingency transmission violations at a flexibility ratio of 40%, and reduces renewable curtailment by up to 84.4% at 30% relative to the inflexible baseline. Sensitivity analysis further reveals that moderate flexibility levels of 20%--30% already capture most of the achievable benefits, supporting practical deployment with limited operational burden on data center operators.

Mingjian Tuo, Xingpeng Li, Pascal Van Hentenryck

TThe rapid expansion of inverter-based resources, such as wind and solar power plants, will significantly diminish the presence of conventional synchronous generators in fu-ture power grids with rich renewable energy sources. This transition introduces in-creased complexity and reduces dynamic stability in system operation and control, with low inertia being a widely recognized challenge. However, the literature has not thoroughly explored grid dynamic performance associated with energy scheduling so-lutions that traditionally only consider grid steady-state constraints. This paper will bridge the gap by enforcing grid dynamic constraints when conducting optimal energy scheduling; particularly, this paper explores locational post-contingency rate of change of frequency (RoCoF) requirements to accommodate substantial inertia reductions. This paper introduces a machine learning-assisted RoCoF-constrained unit commit-ment (ML-RCUC) model designed to ensure RoCoF stability after the most severe generator outage while maintaining operational efficiency. A graph-informed NN (GINN)-based RoCoF predictor is first trained on a high-fidelity simulation dataset to track the highest locational RoCoF, which is then reformulated as mixed-integer linear programming constraints that are integrated into the unit commitment model. Case studies, by solving the optimization problem ML-RCUC and validating its solutions with time-domain simulations, demonstrate that the proposed method can ensure loca-tional RoCoF stability with minimum conservativeness.

Hassan Zahid Butt, Xingpeng Li

The transition to renewable energy has positioned photovoltaic (PV) systems and battery energy storage systems (BESS) as essential assets in microgrids, particularly for remote installations. However, traditional planning models often neglect dynamic degradation costs or rely on complex or non-linear approaches, limiting their scalability and practical applicability. This paper introduces a microgrid planning model that integrates adaptive degradation cost modeling to enable accurate, efficient, and scalable long-term resource allocation. The proposed model employs the iterative post-optimization correction (IPOC) framework, solving a sequence of mixed-integer linear programming problems. Each iteration refines BESS degradation costs based on observed depth-of-discharge profiles and incorporates PV degradation costs to ensure realistic asset performance assessments. Sensitivity analysis of PV and BESS capital costs further underscores the model's robustness under varying economic conditions, with the IPOC framework achieving up to ~1% additional cost savings for the given test system compared to static approaches. The results demonstrate that by iteratively adjusting degradation penalties based on actual usage, the methodology optimizes BESS performance, ensures precise resource allocation, resolves issues of under- or overutilization, enhances system reliability, and facilitates scalable, sustainable microgrid planning.

Rida Fatima, Hassan Zahid Butt, Xingpeng Li

Conservation voltage reduction (CVR) and network topology reconfiguration (NTR) are widely employed to improve distribution system performance; however, existing approaches largely treat them independently, overlooking their coupled impact on load demand, voltage profiles, and power flow distribution, thereby limiting their overall effectiveness. This paper proposes a coordinated optimization framework for day-ahead operational planning of distribution networks, integrating CVR and NTR to enhance overall network efficiency and reduce active power losses in radial distribution networks. The problem is formulated as a mixed-integer conic programming model incorporating AC power flow constraints, voltage-dependent load representation, and radiality constraints. CVR is implemented to achieve load reduction through coordinated voltage control, while NTR redistributes line loading via optimal switching of controllable branches. The proposed framework is validated on the IEEE 33 and 123-bus distribution systems under varying load conditions. Results demonstrate that the coordinated approach consistently outperforms independent strategies, achieving up to 20.6% reduction in active power losses while maintaining voltage compliance and improving branch loading uniformity. These findings confirm that coordinated optimization provides an effective and scalable solution for enhancing efficiency in modern distribution networks.

Hassan Zahid Butt, Xingpeng Li

The growing availability of second-life batteries (SLBs) from electric vehicles is reshaping future microgrid design, requiring planning frameworks that explicitly account for reduced capacity and efficiency over time. However, traditional microgrid planning models often neglect degradation effects or rely on highly simplified formulations, leading to unreliable sizing decisions and increased long-term costs. This paper proposes a degradation-aware iterative optimization framework for long-term microgrid planning that incorporates photovoltaic efficiency fading, battery capacity and efficiency degradation, and SLB characteristics. A cumulative multi-year optimization model is first solved to obtain an initial investment and operational strategy under simplified degradation assumptions, ensuring computational tractability. Subsequently, a yearly validation model evaluates degradation impacts on photovoltaic and battery assets, updating efficiencies and available capacity to assess reliability. An iterative refinement process then adjusts resource allocation to eliminate load shedding while minimizing total system cost. Sensitivity analyses on photovoltaic degradation rates, SLB capital costs, and grid tariffs are conducted to evaluate robustness under varying technical and economic conditions. Results demonstrate that neglecting degradation can compromise reliability and increase blackout risk, while SLBs offer meaningful cost-saving opportunities. The proposed framework provides a scalable and practical tool for planning future microgrids in degradation-constrained environments.

Hassan Zahid Butt, Xingpeng Li

Traditional long-term microgrid planning models assume constant power charging for battery energy storage systems (BESS), overlooking efficiency losses that occur toward the end of charge due to rising internal resistance. While this issue can be mitigated at the cell level using constant current-constant voltage (CCCV) charging, it is impractical at the pack level in large-scale systems. However, battery management systems and inverter controls can emulate this effect by tapering charging power at high state-of-charge (SOC) levels, trading off charging speed for improved efficiency and reduced thermal stress. Ignoring this behavior in planning models can lead to undersized batteries and potential reliability issues. This paper proposes a tractable and scalable approach to approximate CCCV behavior using SOC-dependent tapered charging power (TCP) constraints. A MATLAB-based proof of concept demonstrates the energy delivery and efficiency benefits of tapering. The method is integrated into a long-term planning framework and evaluated under a synthetic load and solar profile. Results show tapering significantly affects BESS sizing, cost, and reliability under dynamic operating conditions that demand fast charging. These findings highlight tapering as a critical modeling factor for accurately capturing BESS performance in long-term microgrid planning.

Ann Mary Toms, Xingpeng Li

The global transition towards renewable energy has accelerated the deployment of utility-scale wind farms, increasing the need for accurate performance and economic assessments. Although wind energy offers substantial potential for carbon emission reduction, investment decisions are highly sensitive to predicted annual energy production and economic profitability. Conventionally wind farm analyses often estimate turbine power output based solely on incoming wind conditions, neglecting wake interactions between turbines. These wake effects can significantly reduce downstream turbine performance, leading to overestimation of energy yield and financial returns. This study proposes WAKE-NET a wake-aware optimization framework that incorporates both turbine layout optimization and hub height diversification across turbines of varying capacities. Unlike traditional approaches that assume a uniform hub height or ignore wake dynamics, the proposed methodology accounts for wake-induced power losses in its framework. Results indicate that the benchmark model that neglects wake effects can overestimate annual profits, while the use of multiple hub heights reduces wake overlap and associated power losses. Overall, the findings demonstrate that wake-aware design and hub height diversity improve energy yield accuracy and economic viability, offering a valuable guidance for wind farm developers and investors seeking to invest in renewable energy systems.

Haoxiang Wan, Linhan Fang, Xingpeng Li

Data centers are facilities housing computing infrastructure for processing and storing digital information. The rapid expansion of artificial intelligence is driving unprecedented growth in data center capacity, with global electricity demand from data centers projected to double by 2026. This growth creates substantial challenges for power transmission networks, as large concentrated loads can cause congestion and threaten grid reliability. Meanwhile, the intermittent nature of solar and wind generation requires flexible resources to maintain grid reliability and minimize curtailment. This paper assesses whether data center spatial flexibility-the ability to migrate computational workloads geographically-can serve as a grid resource to address these challenges. An optimal power flow model is developed to co-optimize generation dispatch, security reserves, and flexible data center loads. Case studies on a modified IEEE 73-bus system show that inflexible data center placement can lead to severe transmission violations, with line overloads reaching 30.1%. Enabling spatial flexibility mitigates these violations in the studied scenarios and restores system feasibility. This flexibility also reduces solar curtailment by up to 61.0% by strategically reallocating load to solar-rich areas. The results suggest that spatial flexibility offers a viable approach to defer transmission upgrades and enhance renewable utilization.

Thuan Pham, Xingpeng Li

Optimal power flow (OPF) is used to perform generation redispatch in power system real-time operations. N-1 OPF can ensure safe grid operations under diverse contingency scenarios. For large and intricate power networks with numerous variables and constraints, achieving an optimal solution for real-time N-1 OPF necessitates substantial computational resources. To mitigate this challenge, machine learning (ML) is introduced as an additional tool for predicting congested or heavily loaded lines dynamically. In this paper, an advanced ML model known as the augmented hierarchical graph neural network (AHGNN) was proposed to predict critical congested lines and create N-1 reduced OPF (N-1 ROPF). The proposed AHGNN-enabled N-1 ROPF can result in a remarkable reduction in computing time while retaining the solution quality. Several variations of GNN-based ML models are also implemented as benchmark to demonstrate effectiveness of the proposed AHGNN approach. Case studies prove the proposed AHGNN and the associated N-1 ROPF are highly effective in reducing computation time while preserving solution quality, highlighting the promising potential of ML, particularly GNN in enhancing power system operations.

Oluwafolajimi Samuel Bolusteve, Linhan Fang, Xingpeng Li

Renewable energy adoption has increased significantly over the past few years. However, with the increasing adoption of renewable energy, forecasting the net load has become a major challenge due to the inherent uncertainty associated with these renewable sources. To mitigate the impact of uncertainties, this study utilizes long short-term memory (LSTM) model and fully connected neural networks (FCNN) to predict net load based on two independent approaches: the direct method and indirect method. While the conventional direct method directly forecasts the target net load, the indirect approach derives it by separately predicting total load and renewable energy generation. Furthermore, this study innovatively incorporates renewable energy capacity as an input feature to train the forecasting model. The indirect method for FCNN provided a better estimate than the direct method, and the indirect method for LSTM model gave the best prediction. These findings suggest that recurrent architectures like LSTM are particularly well-suited for net load forecasting applications, while the choice between direct and indirect methods depends on the specific neural network architecture employed. By advancing reliable forecasting tools for renewable energy integration, this work enhances grid resilience and accelerates the transition toward renewable-dominant power systems.

Linhan Fang, Fan Jiang, Ann Mary Toms et al.

Accurate wind speed prediction is crucial for designing and selecting sites for offshore wind farms. This paper investigates the effectiveness of various machine learning models in predicting offshore wind power for a site near the Gulf of Mexico by analyzing meteorological data. After collecting and preprocessing meteorological data, nine different input feature combinations were designed to assess their impact on wind power predictions at multiple heights. The results show that using wind speed as the output feature improves prediction accuracy by approximately 10% compared to using wind power as the output. In addition, the improvement of multi-feature input compared with single-feature input is not obvious mainly due to the poor correlation among key features and limited generalization ability of models. These findings underscore the importance of selecting appropriate output features and highlight considerations for using machine learning in wind power forecasting, offering insights that could guide future wind power prediction models and conversion techniques.

Thuan Pham, Xingpeng Li

Power system networks are often modeled as homogeneous graphs, which limits the ability of graph neural network (GNN) to capture individual generator features at the same nodes. By introducing the proposed virtual node-splitting strategy, generator-level attributes like costs, limits, and ramp rates can be fully captured by GNN models, improving GNN's learning capacity and prediction accuracy. Optimal power flow (OPF) problem is used for real-time grid operations. Limited timeframe motivates studies to create size-reduced OPF (ROPF) models to relieve the computational complexity. In this paper, with virtual node-splitting, a novel two-stage adaptive hierarchical GNN is developed to (i) predict critical lines that would be congested, and then (ii) predict base generators that would operate at the maximum capacity. This will substantially reduce the constraints and variables needed for OPF, creating the proposed ROPFLG model with reduced monitor lines and reduced generator-specific variables and constraints. Two ROPF models, ROPFL and ROPFG, with just reduced lines or generators respectively, are also implemented as additional benchmark models. Case studies show that the proposed ROPFLG consistently outperforms the benchmark full OPF (FOPF) and the other two ROPF methods, achieving significant computational time savings while reliably finding optimal solutions.

Thuan Pham, Xingpeng Li

Optimal power flow (OPF) has been used for real-time grid operations. Prior efforts demonstrated that utilizing flexibility from dynamic topologies will improve grid efficiency. However, this will convert the linear OPF into a mixed-integer linear programming network-reconfigured OPF (NR-OPF) problem, substantially increasing the computing time. Thus, a machine learning (ML)-based approach, particularly utilizing graph neural network (GNN), is proposed to accelerate the solution process. The GNN model is trained offline to predict the best topology before entering the optimization stage. In addition, this paper proposes an offline pre-ML filter layer to reduce GNN model size and training time while improving its accuracy. A fast online post-ML selection layer is also proposed to analyze GNN predictions and then select a subset of predicted NR solutions with high confidence. Case studies have demonstrated superior performance of the proposed GNN-accelerated NR-OPF method augmented with the proposed pre-ML and post-ML layers.

Cunzhi Zhao, Xingpeng Li

Battery energy storage system (BESS) can effec-tively mitigate the uncertainty of variable renewable generation. Degradation is unpreventable and hard to model and predict for batteries such as the most popular Lithium-ion battery (LiB). In this paper, we propose a data driven method to predict the bat-tery degradation per a given scheduled battery operational pro-file. Particularly, a neural network based battery degradation (NNBD) model is proposed to quantify the battery degradation with inputs of major battery degradation factors. When incorpo-rating the proposed NNBD model into microgrid day-ahead scheduling (MDS), we can establish a battery degradation based MDS (BDMDS) model that can consider the equivalent battery degradation cost precisely with the proposed cycle based battery usage processing (CBUP) method for the NNBD model. Since the proposed NNBD model is highly non-linear and non-convex, BDMDS would be very hard to solve. To address this issue, a neural network and optimization decoupled heuristic (NNODH) algorithm is proposed in this paper to effectively solve this neural network embedded optimization problem. Simulation results demonstrate that the proposed NNODH algorithm is able to ob-tain the optimal solution with lowest total cost including normal operation cost and battery degradation cost.

Vasudharini Sridharan, Mingjian Tuo, Xingpeng Li

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.

Thuan Pham, Xingpeng Li

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.

Arun Venkatesh Ramesh, Xingpeng Li

Security-constrained unit commitment (SCUC) is solved for power system day-ahead generation scheduling, which is a large-scale mixed-integer linear programming problem and is very computationally intensive. Model reduction of SCUC may bring significant time savings. In this work, a novel approach is proposed to effectively utilize machine learning (ML) to reduce the problem size of SCUC. An ML model using logistic regression (LR) algorithm is proposed and trained with historical nodal demand profiles and the respective commitment schedules. The ML outputs are processed and analyzed to reduce variables and constraints in SCUC. The proposed approach is validated on several standard test systems including IEEE 24-bus system, IEEE 73-bus system, IEEE 118-bus system, synthetic South Carolina 500-bus system and Polish 2383-bus system. Simulation results demonstrate that the use of the prediction from the proposed LR model in SCUC model reduction can substantially reduce the computing time while maintaining solution quality.

Xingpeng Li, Kory W. Hedman

State estimation estimates the system condition in real-time and provides a base case for other energy management system (EMS) applications including real-time contingency analysis and security-constrained economic dispatch. Recent work in the literature shows malicious cyber-attack can inject false measurements that bypass traditional bad data detection in state estimation and cause actual overloads. Thus, it is very important to detect such cyber-attack. In this paper, multiple metrics are proposed to monitor abnormal load deviations and suspicious branch flow changes. A systematic two-stage approach is proposed to detect false data injection (FDI) cyber-attack. The first stage determines whether the system is under attack while the second stage identifies the target branch. Numerical simulations verify that FDI can cause severe system violations and demonstrate the effectiveness of the proposed two-stage FDI detection (FDID) method. It is concluded that the proposed FDID approach can efficiently detect FDI cyber-attack and identify the target branch, which will substantially improve operators situation awareness in real-time.