Bi-level Mixed-Integer Nonlinear Optimization for Pelagic Island Microgrid Group Energy Management Considering Uncertainty

To realize the safe, economical and low-carbon operation of the pelagic island microgrid group, this paper develops a bi-level energy management framework in a joint energy-reserve market where the microgrid group (MG) operator and renewable and storage aggregators (RSA) are independent stakeholders with their own interests. In the upper level, MG operator determines the optimal transaction prices with aggregators to minimize MG operation cost while ensuring all safety constraints are satisfied under uncertainty. In the lower level, aggregators utilize vessels for batteries swapping and transmission among islands in addition to energy arbitrage by participating in energy and reserve market to maximize their own revenue. An upper bound tightening iterative algorithm is proposed for the formulated problem with nonlinear terms and integer variables in the lower level to improve the efficiency and reduce the gap between upper bound and lower bound compared with existing reformulation and decomposition algorithm. Case studies validate the effectiveness of the proposed approach and demonstrate its advantage of the proposed approach in terms of optimality and computation efficiency, compared with other methods.Comment: Accepted by CSEE Journal of Power and Energy System

Encoding Carbon Emission Flow in Energy Management: A Compact Constraint Learning Approach

Decarbonizing the energy supply is essential and urgent to mitigate the increasingly visible climate change. Its basis is identifying emission responsibility during power allocation by the carbon emission flow (CEF) model. However, the main challenge of CEF application is the intractable nonlinear relationship between carbon emission and power allocation. So this paper leverages the high approximation capability and the mixed-integer linear programming (MILP) representability of the deep neural networks to tackle the complex CEF model in carbon-electricity coordinated optimization. The compact constraint learning approach is proposed to learn the mapping from power injection to bus emission with sparse neural networks (SNNs). Then the trained SNNs are transformed equivalently as MILP constraints in the downstream optimization. In light of the ``high emission with high price'' principle, the blocked carbon price mechanism is designed to price emissions from the demand side. Based on the constraint learning and mechanism design, this paper proposes the carbon-aware energy management model in the tractable MILP form to unlock the carbon reduction potential from the demand side. The case study verifies the approximation accuracy and sparsity of SNN with fewer parameters for accelerating optimization solution and reduction effectiveness of demand-side capability for mitigating emission

Safety-aware Semi-end-to-end Coordinated Decision Model for Voltage Regulation in Active Distribution Network

Prediction plays a vital role in the active distribution network voltage regulation under the high penetration of photovoltaics. Current prediction models aim at minimizing individual prediction errors but overlook their collective impacts on downstream decision-making. Hence, this paper proposes a safety-aware semi-end-to-end coordinated decision model to bridge the gap from the downstream voltage regulation to the upstream multiple prediction models in a coordinated differential way. The semi-end-to-end model maps the input features to the optimal var decisions via prediction, decision-making, and decision-evaluating layers. It leverages the neural network and the second-order cone program (SOCP) to formulate the stochastic PV/load predictions and the var decision-making/evaluating separately. Then the var decision quality is evaluated via the weighted sum of the power loss for economy and the voltage violation penalty for safety, denoted by regulation loss. Based on the regulation loss and prediction errors, this paper proposes the hybrid loss and hybrid stochastic gradient descent algorithm to back-propagate the gradients of the hybrid loss with respect to multiple predictions for enhancing decision quality. Case studies verify the effectiveness of the proposed model with lower power loss for economy and lower voltage violation rate for safety awareness

Attribute-based concurrent signatures

This paper introduces the notion of attribute-based concurrent signatures. This primitive can be considered as an interesting extension of concurrent signatures in the attribute-based setting. It allows two parties fairly exchange their signatures only if each of them has convinced the opposite party that he/she possesses certain attributes satisfying a given signing policy. Due to this new feature, this primitive can find useful applications in online contract signing, electronic transactions and so on. We formalize this notion and present a con-struction which is secure in the random oracle model under the Strong Dif-fie-Hellman assumption and the eXternal Diffie-Hellman assumption

Conservative Sparse Neural Network Embedded Frequency-Constrained Unit Commitment With Distributed Energy Resources

The increasing penetration of distributed energy resources (DERs) will decrease the rotational inertia of the power system and further degrade the system frequency stability. To address the above issues, this paper leverages the advanced neural network (NN) to learn the frequency dynamics and incorporates NN to facilitate system reliable operation. This paper proposes the conservative sparse neural network (CSNN) embedded frequency-constrained unit commitment (FCUC) with converter-based DERs, including the learning and optimization stages. In the learning stage, it samples the inertia parameters, calculates the corresponding frequency, and characterizes the stability region of the sampled parameters using the convex hulls to ensure stability and avoid extrapolation. For conservativeness, the positive prediction error penalty is added to the loss function to prevent possible frequency requirement violation. For the sparsity, the NN topology pruning is employed to eliminate unnecessary connections for solving acceleration. In the optimization stage, the trained CSNN is transformed into mixed-integer linear constraints using the big-M method and then incorporated to establish the data-enhanced model. The case study verifies 1) the effectiveness of the proposed model in terms of high accuracy, fewer parameters, and significant solving acceleration; 2) the stable system operation against frequency violation under contingency

A state-of-the-art review on topologies and control techniques of solid-state transformers for electric vehicle extreme fast charging

Electrical vehicle (EV) technology has gained popularity due to its higher efficiency, less maintenance, and lower dependence on fossil fuels. However, a longer charging time is a significant barrier to its complete adaptation. Solid state transformer (SST) based extreme fast charging schemes have emerged as an appealing idea with an ability to provide a refuelling capability analogous to that of gasoline vehicles. Therefore, this paper reviews the EV charger requirements, specifications, and design criteria for high power applications. At first, the key barriers of using a traditional low frequency transformer (LFT) are discussed, and potential solutions are suggested by replacing the conventional LFT with high frequency SST at extreme fast-charging (XFC) stations. Then, various SST-based converter topologies and their control for EV fast-charging stations are described. The reviewed control strategies are compared while considering several factors such as harmonics, voltage drop under varying loading conditions, dc offset load unbalances, overloads, and protection against system disturbances. Furthermore, the realization of SST for EV charging is comprehensively discussed, which facilitates understanding the current challenges, based on which potential solutions are also suggested.This publication was made possible by UREP grant # [27-021-2-010] from the Qatar National Research Fund (a member of the Qatar Foundation). The statements made herein are solely the responsibility of the authors. The Article Processing Fee of this article is paid by the Qatar National Library.Scopu