Renewable energy is boosting the deployment of microgrids (MGs) with stochastic and low inertia nature. To improve the operational efficiency of MGs while guaranteeing frequency security, a three-stage stochastic unit commitment problem is proposed, where renewable energy can be deloaded. In the first stage, the diesel generators (DGs) are scheduled, responding to uncertainties of loads and photovoltaic generator output. In the second stage, the outputs of DGs are optimized to reduce the operational cost under uncertain disturbances. In the third stage, a novel PV deloading strategy is proposed to test the frequency security of MGs. The three-stage optimization problem is formulated as a multi-stage stochastic optimization problem with recourse. This problem is then solved using a novel nested Benders decomposition algorithm with both dual cuts and partial primal cuts. Simulations are performed on an AC MG under different PV penetration levels and disturbances. The results verify the effectiveness of the proposed model in balancing operational efficiency, renewable energy utilization, and frequency security.

Conversion loss modeling plays a crucial role in hybrid AC/DC microgrid (MG) energy management (EM). However, accurate calculation of the conversion losses is often very costly. Additionally, existing surrogate models typically rely on fixed-voltage DC buses, leading to excessive voltage magnitudes. To overcome these limitations, we propose surrogate models based on piecewise linear neural networks (NNs) that estimate conversion losses using converter power and variable-voltage DC buses. These NNs employs the Rectified Linear Unit (ReLU) activation function, allowing reformulation into linear constraints. During NN training, additional hard constraints are enforced via the augmented Lagrangian method to enhance feasibility. To limit the computational cost of the reformulated NN constraints, weights sparsity and ReLU stability regularizations are applied to the NN training process. The efficient energy management is implemented as a two-stage stochastic unit commitment (UC) problem with integer recourse. To solve this UC problem, we propose a finite iteration convergent Benders decomposition algorithm with several acceleration techniques, which reduce the computational burden significantly. Our case study demonstrates that: 1) the proposed surrogate model accurately approximates conversion losses while satisfying hard constraints, 2) optimizing the DC bus voltage leads to more efficient system operation, energy savings, and better overall performance, and 3) combining the NN weight optimization with Benders decomposition acceleration techniques significantly reduces the computational associated with the NN surrogates.

Nowadays, self-sustained highway transportation systems (HTSs) are calling for the inter-operation of road networks, distribution networks (DNs), and electric vehicles (EVs) towards the unexpected impacts inducted by extreme weather events (EWEs). A novel joint resilience assessment scheme is proposed to quantify the resilience of self-sustained HTSs under tropical cyclone (TC) events. The impacts of TCs on transmission lines and highways are formulated using a two-step ambiguity set. In the first step, a multitask learning (MTL) based model with a novel training strategy is proposed to simulate the future TC tracks; the impacts of TCs are then calculated in the second step based on the simulation results. Using the ambiguity set, the self-sustained HTSs resilience assessment problem is formulated based on the dynamic optimal traffic assignment problem, where EVs are integrated as third-party emergency resources before the advent of TCs. This scheme is then formulated as a two-stage distributionally robust optimization (DRO) problem and reformulated as a mixed-integer linear programming (MILP) problem. Case studies have been conducted on a self-sustained HTS consisting of a modified IEEE-14 bus test system with 2 wind farms (WFs) and a 14-node transportation network with 4 charging stations (CSs). Numerical results indicate that the proposed scheme can quantify the resilience of self-sustained highway transportation systems under the joint operation of EVs regarding the unmet travel demand and load-shedding costs. In comparison to the statistical TC forecasting models, the proposed MTL-based model is more accurate and can reduce the system cost in most cases.

The evolving microgrid (MG) technology integrates various converters for different energy sources and loads, leading to increased complexity in optimization problems due to additional decision variables introduced by power conversion processes in modern energy management systems (EMS).Existing conversion loss models are too complex for optimization. This paper presents a neural network-based linear surrogate model to accurately and efficiently approximate power conversion losses in EMS. In EMS, the black-box nature of neural networks (NNs) raises concerns about violating optimization constraints. To address this, the proposed NN model is trained using the augmented Lagrangian method (ALM), enforcing additional hard constraints on the network input/output variables. Moreover, the trained NN is reformulated as mixed-integer linear programming (MILP) constraints, allowing the model to be integrated into energy management (EM) problems solvable by MILP solvers. To optimize the model architecture, a novel neural architecture search (NAS) algorithm is introduced, striking a balance between accuracy and complexity. The case study results demonstrate that the proposed model effectively approximate conversion loss with small absolute errors while satisfying the additional hard constraints. Compared to the manually tuned models, the NAS generated model achieves superior performance with a compact architecture. Moreover, the reformulated MILP model can be solved efficiently using state-of-the-art MILP solvers, facilitating the integration of surrogate models into EM problems. Lastly, nn example optimal power flow (OPF) problem is presented in the case study, showcasing the practical usability of the proposed model in EM problems.

Tropical cyclone (TC) track forecasting is critical for wind risk assessment. This work proposes a novel probabilistic TC track forecasting model based on mixture density network (MDN) and multitask learning (MTL). The existing NN-based probabilistic TC track prediction models focus on directly modeling the distribution of the future TC positions. Multitask learning has been shown to boost the performance of single tasks when the tasks are relevant. This work divides the probabilistic track prediction task into two sub-tasks: a deterministic prediction of the future TC position and a probabilistic prediction of the residual between the deterministic prediction and the actual TC location. The MDN is employed to realize the probabilistic prediction task. Since the target values of the MDN in this work are the residuals, which depend on the prediction result of the deterministic task, a novel training method is developed to train the MTL model properly. The proposed model is tested against statistical and other learning-based models on historical TC data. The results show that the proposed model outperforms other models in making probabilistic predictions. This approach advances TC track forecasting by integrating MDN and MTL, showing promise in enhancing probabilistic predictions and improving disaster preparedness.