A Novel Combined Objective Hybrid Framework for Ultra-Short-Term Ramp Event Forecasting in Photovoltaic and Wind Power Generation

Ultra-Short-Term Photovoltaic Power Forecasting Based on a Hybrid Decomposition Linear Long Short-Term Memory Model

The rapid expansion of photovoltaic (PV) systems poses significant challenges to grid stability. Hybrid Energy Systems (HES) are intended to alleviate this volatility, yet their coordinated dispatch often remains suboptimal due to communication delays and ramp-rate constraints. Accurate ultra-short-term PV power forecasting is therefore essential, as it enables preemptive control and timely dispatch adjustments that unlock the full potential of HES. In this study, we propose a novel AI hybrid forecasting framework that integrates a rule-based model with a Decomposition Linear (DLinear) Long Short-Term Memory (LSTM) deep learning core, representing, to the best of our knowledge, a novel integration of a decomposition-based linear model (DLinear) with LSTM networks for ultra-short-term PV power forecasting. The DLinear component decomposes the time series into trend and remainder sequences, which are then independently modeled by separate LSTM networks to capture distinct dynamics. Using data from a 300 kWp PV power station, the framework achieves an average daily prediction accuracy exceeding 93% for both 5-min and 15-min horizons. The model reliably tracks power variations under sunny and rainy conditions, while under volatile cloudy weather its accuracy decreases but still captures essential fluctuation patterns. These results demonstrate the potential of the proposed framework for improving the dispatch and operational reliability of hybrid energy systems. However, further validation across additional seasons and sites is needed to establish broader generalizability.

Hybrid transformer model for wind power generation forecasting

The Impact of PV Generation Forecast and Multi-Objective Control Policy on Optimal Operation of Grid Connected PV-BESS Microgrid

The variability of photovoltaic (PV) generation poses significant challenges to the reliable and efficient operation of grid-connected microgrids. Accurate PV output power forecasting and efficient energy scheduling strategies are essential not only for optimizing PV system operation but also for improving the overall performance and reliability of the system. This study proposes a long short-term memory (LSTM)-based PV power forecasting model integrated with a multi-objective scheduling framework for a grid-connected PV-battery energy storage system (BESS). The proposed approach enables detailed performance monitoring and assessment by quantifying how PV forecast accuracy influences key operational metrics, including PV self-consumption ratio, grid energy cost, grid injection, and battery utilization. Three forecasting scenarios (perfect forecast, persistence model, and LSTM-based forecast) are compared to evaluate their impact on system performance and operational reliability. Results show that the LSTM-based forecast reduces root mean squared error (RMSE) by 6% compared with the persistence model, increases the PV self-consumption ratio from 78.1% to 84.5%, and reduces grid injections by 82%. The analysis also highlights trade-offs, as higher battery throughput associated with improved performance may contribute to accelerated aging. These findings demonstrate the importance of accurate PV forecasting in improving system performance and ensuring reliable operation. Future work will focus on probabilistic forecasting to properly quantify uncertainties, incorporate load prediction, and develop smart control strategies that allow grid-support functionalities from the PV side.

Convolution Neural Network and Long Short-Term Memory Network to Mitigate High Power Ramp Rates in Utility Grid Integrated Wind–Solar System

On integration of the wind and solar based renewable energy systems to supply large loads of industries, it leads to high power ramp rates due to power grid stability issues raised by wind gust in deployed area of the system. Traditionally many control strategies has been designed for power converters using machine learning. In this paper, a new hybrid deep learning approach integrating Convolution Neural Network and Long Short-Term Memory Network is applied to power converter of Utility Grid Integrated Wind–Solar System as it is highly efficient in mitigating high power ramp rates. CNN Model extracts spatial features such as wind speed, solar irradiance etc. An extracted feature is employed to Long Short-Term Memory to identify complex relationships and long-term dependencies as it is highly efficient in processing nonlinear relationships. Finally, dependency map in the processed further to forecast the power generation the wind and solar system for efficient management of the load in the industries through other conventional energy backups as conventional generators to compensate the power fluctuations. Especially forecasting of the wind and solar based integrated energy system is performed to provide smooth overall power profile to industrial loads. Simulation results demonstrate that the proposed CNN–LSTM controller achieves an RMSE of 0.038, MAE of 0.026, and forecasting accuracy of 98.2%, thereby improving grid stability and mitigating high power ramp-rate fluctuations.

Short-term wind power forecasting with lag-based feature engineering: a turbine-level machine learning comparison

A Multi-Objective Short-Term Complementary Scheduling Model for Hydro-Wind-Solar Systems Considering Conditional Value-at-Risk

The large-scale integration of wind and solar power has significantly intensified peak-shaving pressure and operational risk in provincial power grids. Effectively leveraging the flexible regulation capability of hydropower to mitigate the uncertainty of wind and solar output is a promising approach to enhancing grid security and stability. To simultaneously improve the peak-shaving performance and risk resilience of hydro-wind-solar systems for a provincial power grid, this paper proposes a multi-objective short-term scheduling model that jointly minimizes the peak value of net load and the Conditional Value-at-Risk (CVaR) of flexibility shortage. Specifically, the residual peak load is used to quantify the system’s peak-shaving burden, while the average CVaR of upward/downward ramping deficits across all time periods characterizes the tail risk associated with insufficient flexibility. Historical wind and solar forecast error data are employed to generate representative uncertainty scenarios via Gaussian mixture model, and the Rockafellar–Uryasev formulation is adopted to accurately embed CVaR into a mixed-integer linear programming (MILP) framework. Furthermore, the normalized normal constraint (NNC) method is introduced to compute a well-distributed Pareto front. Numerical simulations based on a real-world hydro-wind-solar system in a provincial grid in Southwest China demonstrate that the proposed model can significantly reduce the peak load while effectively mitigating flexibility shortfall risk. The resulting Pareto front clearly reveals the trade-off between peak-shaving effectiveness and risk control, providing a scientific basis for day-ahead generation scheduling and coordinated dispatch of flexible resources.