Active Linearized Sparse Neural Network-based Frequency-Constrained Unit Commitment

Conventional synchronous generators are gradually being re-placed by low-inertia inverter-based resources. Such transition introduces more complicated operation conditions, frequency deviation stability and rate-of-change-of-frequency (RoCoF) security are becoming great challenges. This paper presents an active linearized sparse neural network (ALSNN) based frequency-constrained unit commitment (ALSNN-FCUC) model to guarantee frequency stability following the worst generator outage case while ensuring operational efficiency. A generic data-driven predictor is first trained to predict maximal frequency deviation and the highest locational RoCoF simultaneously based on a high-fidelity simulation dataset, and then incorporated into ALSNN-FCUC model. Sparse computation is introduced to avoid dense matrix multiplications. An active data sampling method is proposed to maintain the bindingness of the frequency related constraints. Besides, an active ReLU linearization method is implemented to further improve the algorithm efficiency while retaining solution quality. The effectiveness of proposed ALSNN-FCUC model is demonstrated on the IEEE 24-bus system by conducting time domain simulations using PSS/E

Convolutional Neural Network-based RoCoF-Constrained Unit Commitment

The fast growth of inverter-based resources such as wind plants and solar farms will largely replace and reduce conventional synchronous generators in the future renewable energy-dominated power grid. Such transition will make the system operation and control much more complicated; and one key challenge is the low inertia issue that has been widely recognized. However, locational post-contingency rate of change of frequency (RoCoF) requirements to accommodate significant inertia reduction has not been fully investigated in the literature. This paper presents a convolutional neural network (CNN) based RoCoF-constrained unit commitment (CNN-RCUC) model to guarantee RoCoF stability following the worst generator outage event while ensuring operational efficiency. A generic CNN based predictor is first trained to track the highest locational RoCoF based on a high-fidelity simulation dataset. The RoCoF predictor is then formulated as MILP constraints into the unit commitment model. Case studies are carried out on the IEEE 24-bus system, and simulation results obtained with PSS/E indicate that the proposed method can ensure locational post-contingency RoCoF stability without conservativeness

Analysis of Weather and Time Features in Machine Learning-aided ERCOT Load Forecasting

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

Wholesale Electricity Price Forecasting Using Integrated Long-Term Recurrent Convolutional Network Model

Electricity price forecasts have become a fundamental factor affecting the decision-making of all market participants. Extreme price volatility has forced market participants to hedge against volume risks and price movements. Hence, getting an accurate price forecast from a few hours to a few days ahead is very important and very challenging due to various factors. This paper proposes an integrated long-term recurrent convolutional network (ILRCN) model to predict electricity prices considering the majority of contributing attributes to the market price as input. The proposed ILRCN model combines the functionalities of a 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 nonlinear behavior within the input data. ERCOT wholesale market price data along with load profile, temperature, and other factors for the Houston region have been used 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 shows the highest accuracy and efficiency in electricity price forecasting as compared to the support vector machine (SVM) model, fully connected neural network model, LSTM model, and the traditional LRCN model without the conditional error correction stage