Wind power integration presents both economic and operational challenges for modern grids, particularly when managing curtailment and uncertainty in renewable generation. A new computational framework combines deep learning forecasting with integrated cybersecurity defenses to address these dual concerns simultaneously.
The proposed solution employs a bidirectional Long Short-Term Memory (BiLSTM) neural network to predict wind power output with high accuracy, enabling operators to schedule generation and storage more effectively. Complementing this forecasting capability, a ConvGAN-based generator produces realistic stochastic scenarios representing multiple weather and operational conditions. This foundation enables a multi-stage optimization process that determines optimal battery energy storage dispatch, reducing overall system costs while accommodating wind variability.
The innovation extends beyond traditional cost optimization. A Dynamic Defense Mechanism strategically modifies network reactance values across the system, effectively concealing the grid's operational characteristics from potential cyber adversaries. This defensive layer operates transparently to legitimate grid operations while degrading the utility of any information malicious actors might extract through grid probing or attack simulation.
Validation using the IEEE 200-bus representation of the Illinois power system demonstrated practical viability. The BiLSTM model achieved 98% accuracy in short-term wind forecasting, while the integrated optimization reduced operational expenses by more than 3.8% compared to baseline approaches. Notably, the system maintained these economic benefits while simultaneously improving defensive posture.
The research addresses a critical gap in power system planning: previous approaches typically treated cybersecurity and economic optimization as separate concerns. By embedding defensive mechanisms within the core scheduling algorithm, this framework demonstrates that security and efficiency can be complementary objectives rather than trade-offs. As wind penetration increases and grid complexity grows, such integrated approaches become increasingly essential for utilities seeking robust, cost-effective operations.
