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This article develops an operational framework for hydrogen microgrids integrated with traffic and power networks to optimize decision-making strategies. It tackles challenges in traffic flow prediction exacerbated by the rise of electric and hydrogen vehicles, which significantly affect power systems and hydrogen microgrids. We employ a risk-averse information gap decision theory to ensure secure operations under uncertain traffic conditions. Our framework utilizes a hybrid deep-learning forecasting method, combining a 1-D convolutional neural network and bidirectional long short-term memory to accurately predict traffic flow for origin–destination pairs in Edmonton, Canada. Enhanced by a Bayesian algorithm for hyperparameter tuning, this method improves prediction accuracy and operational efficiency. The framework also integrates operational strategies with urban travel plans to optimize charging for electric and hydrogen vehicles, thereby enhancing energy efficiency and supporting thermal demands. Validated in Edmonton's power and traffic networks, our framework enhances optimal charging, routing, and operation conditions, surpassing traditional methods to maintain secure operations during outages and improve the overall system robustness.

This paper addresses the dynamic stability of a hybrid photovoltaic (PV) and wind turbine (WT) system tied to a weak grid by back-to-back voltage-source converters (VSCs). The overall system stability is assessed by developing a detailed time-domain nonlinear model (TDNLM) and a linearized state-space model (LSSM). The dynamic interactions at both the peak-power point (PPP) and off-PPP operation of the PV and WT power characteristics curves have been examined. The results reveal that the complete system is stable under PV-only and WT-only generation scenarios at the PPP and off-PPP operation regions. However, the PV-WT hybrid system experiences low- and high-frequency fluctuation instabilities in the current-limited region (CLR) of the PV characteristic due to the interaction between the high dynamic resistances of the PV-WT source and the weak grid. Conversely, the system remains stable at the PPP and voltage-limited region (VLR) of the PV characteristic and in all operational regions of the WT characteristic. Motivated by these challenges, a novel active damping method is proposed to mitigate the interaction dynamics, maintaining overall system stability by relocating unstable eigenmodes and reshaping the dc-link transfer function. The proposed damping method offers several benefits: 1) it is simple yet efficient and can be implemented and modeled using linear analysis tools; 2) it allows for dynamic and stable operation across various operating regions; 3) it does not interfere with steady-state performance in both stiff and weak grid operations; 4) it facilitates successful low-voltage-ride-through (LVRT) at different operating regions; and 5) it effectively addresses its shortcomings by eliminating the need for additional voltage or current sensors, thereby enhancing simplicity and reducing cost in comparison to other approaches. Detailed offline and real-time simulations validate the analytical findings and the effectiveness of the proposed stabilization method under different operational scenarios.

Grid-forming solid-state transformer (GFm-SST) is an emerging technology in modernized power grids that delivers the merits of SSTs and GFm control, thus, providing a compact, controllable, and grid-supporting means of renewable resources integration. This article presents a comprehensive and coordinated GFm controller for two-stage SST-interfaced solar photovoltaic (PV) systems. The controllers of both SST stages are coordinated, and a virtual governor is implemented within the coordinated reference generation stage so that the PV power directly interacts with grid-side frequency events while preserving minimal dc voltage variations. A bandgap ac voltage controller is proposed to ensure accurate reactive power sharing, even under parallel operation with line parameters mismatch, and reduced ${\bm{Q}}$–${\bm{P}}$ coupling. In addition, the proposed GFm-SST-PV system is equipped with a low-voltage ride-through controller to bypass faulty conditions safely. Furthermore, a linearized small-signal model of the whole system is developed, validated, and used to 1) investigate the system stability under broad variations of solar irradiance, power reserve, and control parameters and 2) provide guidelines for a coordinated design of all control parameters, considering all system dynamics and control loops interactions. Extensive time-domain simulations and hardware-in-the-loop real-time tests are conducted to verify all features and capabilities of the proposed configuration.

Accurate parameters of photovoltaic (PV) arrays are essential for the modeling, analysis, and control of PV systems. Due to the lack of complete datasheets from manufacturers, different techniques have been introduced to extract the unknown parameters of PV modules. A novel approach based on one of the most recent metaheuristic (MH) optimization algorithms, the Flow Direction Algorithm, is developed in this paper to estimate the PV-modules parameters accurately. The proposed approach extracts the parameters for single-, double-, and three-diode models under different operating conditions. Comparative studies with state-of-the-art MH algorithms showed that the proposed approach is more accurate and robust and reduces the computational burden. Furthermore, a general formula is derived to obtain the dynamic resistance of different PV models. It is shown in this paper that inaccurate PV model parameters might negatively impact the steady-state and dynamic performance assessment of grid-connected PV systems under different operating conditions. Detailed time-domain simulations are presented to validate the analytical results and show the effectiveness of the proposed approach.

Compressed air energy storage (CAES) is a scalable and clean energy storage technology with great potential in renewables accommodation. From the point of view of the facility owner participating in the energy market, the profit of a CAES-PV system’s coordinated operation is still at a notable risk. This paper addresses this problem by using a novel model-free deep reinforcement learning (DRL) method to optimize the CAES energy arbitrage in the presence of a sky images-based short-term solar irradiance forecasting model. To overcome the risk associated with the highly intermittent solar power productions, and thus efficient participation in an electricity market, a hybrid forecasting model based on 2-D convolutional neural networks (CNNs) and bidirectional long short-term memory (BLSTM) units is developed to capture high levels of abstractions in solar irradiance data, especially during cloudy days. Moreover, the thermodynamic characteristics of the CAES facility are considered to achieve more realistic real-time scheduling results. The comparative results based on a realistic-based case study demonstrate the effectiveness and applicability of the proposed framework compared to the state-of-the-art methods in the recent literature.