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

-Norm Regularization in Volumetric Imaging of Cardiac Current Sources

Advances in computer vision have substantially improved our ability to analyze the structure and mechanics of the heart. In comparison, our ability to observe and analyze cardiac electrical activities is much limited. The progress to computationally reconstruct cardiac current sources from noninvasive voltage data sensed on the body surface has been hindered by the ill-posedness and the lack of a unique solution of the reconstruction problem. Common L2- and L1-norm regularizations tend to produce a solution that is either too diffused or too scattered to reflect the complex spatial structure of current source distribution in the heart. In this work, we propose a general regularization with Lp-norm () constraint to bridge the gap and balance between an overly smeared and overly focal solution in cardiac source reconstruction. In a set of phantom experiments, we demonstrate the superiority of the proposed Lp-norm method over its L1 and L2 counterparts in imaging cardiac current sources with increasing extents. Through computer-simulated and real-data experiments, we further demonstrate the feasibility of the proposed method in imaging the complex structure of excitation wavefront, as well as current sources distributed along the postinfarction scar border. This ability to preserve the spatial structure of source distribution is important for revealing the potential disruption to the normal heart excitation

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

Networked Multiagent Safe Reinforcement Learning for Low-carbon Demand Management in Distribution Network

This paper proposes a multiagent based bi-level operation framework for the low-carbon demand management in distribution networks considering the carbon emission allowance on the demand side. In the upper level, the aggregate load agents optimize the control signals for various types of loads to maximize the profits; in the lower level, the distribution network operator makes optimal dispatching decisions to minimize the operational costs and calculates the distribution locational marginal price and carbon intensity. The distributed flexible load agent has only incomplete information of the distribution network and cooperates with other agents using networked communication. Finally, the problem is formulated into a networked multi-agent constrained Markov decision process, which is solved using a safe reinforcement learning algorithm called consensus multi-agent constrained policy optimization considering the carbon emission allowance for each agent. Case studies with the IEEE 33-bus and 123-bus distribution network systems demonstrate the effectiveness of the proposed approach, in terms of satisfying the carbon emission constraint on demand side, ensuring the safe operation of the distribution network and preserving privacy of both sides.Comment: Submitted to IEEE Transactions on Sustainable Energ

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

V2HDM-Mono: A Framework of Building a Marking-Level HD Map with One or More Monocular Cameras

Marking-level high-definition maps (HD maps) are of great significance for autonomous vehicles, especially in large-scale, appearance-changing scenarios where autonomous vehicles rely on markings for localization and lanes for safe driving. In this paper, we propose a highly feasible framework for automatically building a marking-level HD map using a simple sensor setup (one or more monocular cameras). We optimize the position of the marking corners to fit the result of marking segmentation and simultaneously optimize the inverse perspective mapping (IPM) matrix of the corresponding camera to obtain an accurate transformation from the front view image to the bird's-eye view (BEV). In the quantitative evaluation, the built HD map almost attains centimeter-level accuracy. The accuracy of the optimized IPM matrix is similar to that of the manual calibration. The method can also be generalized to build HD maps in a broader sense by increasing the types of recognizable markings

The casting process and high temperature oxidation resistance of high chromium cast iron grate bar

The solidification process of a high chromium cast iron grate bar used for sinter machine was simulated, and high temperature oxidation resistance was also investigated. The simulation result shows that sequence solidification can be achieved and no shrinkage cavity and porosity were observed. Based on the analysis of the microstructure, it could be known that the grate bar was well protected by the Fe3O4、Fe2O3 and Cr2O3 oxide films at temperatures lower than 800°C

Neuropathologic damage induced by radiofrequency ablation at different temperatures

Objective: To explore the molecular mechanism of neuropathologic damage induced by radiofrequency ablation at different temperatures. Methods: This is basic research, and 36 SD rats were used to construct the neuropathological injury model. The rats were subjected to radiofrequency stimulation at different temperatures and were divided into 6 groups according to the temperature injury: 42°, 47°, 52°, 57°, 62°, and 67°C groups. Conduction time, conduction distance, and nerve conduction velocity were recorded after temperature injury. HE-staining was used to observe the histopathological morphology of the sciatic nerve. The expression of SCN9A, SCN3B, and NFASC protein in sciatic nerve tissue were detected by western blot. Results: With the increase in temperature, nerve conduction velocity gradually decreased, and neurons were damaged when the temperature was 67°C. HE-staining showed that the degrees of degeneration of neurons in rats at 47°, 52°, 57°, 62°, and 67°C were gradually increased. The expression of SCN9A, SCN3B protein in 57°, 62°, 67°C groups were much higher than that of NC, 42°, 47°, 52°C groups. However, the expression of NFASC protein in 57°, 62°, 67°C groups was much lower than that of the NC, 42°, 47°, 52°C groups. Conclusion: There was a positive correlation between temperature caused by the radiofrequency stimulation to neuropathological damage. The mechanism is closely related to the expression of SCN9A, SCN3B, and NFASC protein in nerve tissue caused by heat transfer injury

The Effect of Decomposed PbI2 on Microscopic Mechanisms of Scattering in CH3NH3PbI3 Films

Hybrid organic-inorganic perovskites (HOIPs) exhibit long electronic carrier diffusion length, high optical absorption coefficient, and impressive photovoltaic device performance. At the core of any optoelectronic device lie the charge transport properties, especially the microscopic mechanism of scattering, which must efficiently affect the device function. In this work, CH3NH3PbI3 (MAPbI(3)) films were fabricated by a vapor solution reaction method. Temperature-dependent Hall measurements were introduced to investigate the scattering mechanism in MAPbI(3) films. Two kinds of temperature-mobility behaviors were identified in different thermal treatment MAPbI(3) films, indicating different scattering mechanisms during the charge transport process in films. We found that the scattering mechanisms in MAPbI(3) films were mainly influenced by the decomposed PbI2 components, which could be easily generated at the perovskite grain boundaries (GBs) by releasing the organic species after annealing at a proper temperature. The passivation effects of PbI2 in MAPbI(3) films were investigated and further discussed with emphasis on the scattering mechanism in the charge transport process