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The wide integration of inverter based renewable energy sources (RESs) in modern grids may cause severe voltage violation issues due to high stochastic fluctuations of RESs. Existing centralized approaches can achieve optimal results for voltage regulation, but they have high communication burdens; existing decentralized methods only require local information, but they cannot achieve optimal results. Deep reinforcement learning (DRL) based methods are effective to deal with uncertainties, but it is difficult to guarantee secure constraints in existing DRL training. To address the above challenges, this paper proposes a projection embedded multi-agent DRL algorithm to achieve decentralized optimal control of distribution grids with guaranteed 100% safety. The safety of the DRL training is guaranteed via an embedded safe policy projection, which could smoothly and effectively restrict the DRL agent action space, and avoid any violation of physical constraints in distribution grid operations. The multi-agent implementation of the proposed algorithm enables the optimal solution achieved in a decentralized manner that does not require real-time communication for practical deployment. The proposed method is tested in modified IEEE 33-bus distribution and compared with existing methods; the results validate the effectiveness of the proposed method in achieving decentralized optimal control with guaranteed 100% safety and without the requirement of real-time communications

Tianshu Liu, Mengfan Zhang, Jin Zhang, Naijin Kang, Linlin Zheng, Zhiying Ding School of Pharmaceutical Sciences, Jilin University, Changchun, 130021, People’s Republic of ChinaCorrespondence: Zhiying Ding, School of Pharmaceutical Sciences, Jilin University, Changchun, 130021, People’s Republic of China, Tel/Fax +86 13843180286, Email dzy@jlu.edu.cnPurpose: Ginsenoside Rg3 (Rg3) and Panax notoginseng saponins (PNS) can be used for ischemic stroke treatment, however, the lack of targeting to the ischemic region limits the therapeutic effect. To address this, we leveraged the affinity of macrophage membrane proteins for inflamed brain microvascular endothelial cells to develop a macrophage membrane-cloaked liposome loaded with Rg3 and PNS (MM-Lip-Rg3/PNS), which can precisely target brain lesion region through intranasal administration.Methods: MM-Lip-Rg3/PNS was prepared by co-extrusion method and was performed by characterization, stability, surface protein, and morphology. The cellular uptake, immune escape ability, and blood-brain barrier crossing ability of MM-Lip-Rg3/PNS were studied in vitro. The in vivo brain targeting, biodistribution and anti-ischemic efficacy of MM-Lip-Rg3/PNS were evaluated in MACO rats, and we determined the diversity of the nasal brain pathway through the olfactory nerve blockade model in rats. Finally, the pharmacokinetics and brain targeting index of MM-Lip-Rg3/PNS were investigated.Results: Our results indicated that MM-Lip-Rg3/PNS was spherical with a shell-core structure. MM-Lip-Rg3/PNS can avoid mononuclear phagocytosis, actively bind to inflammatory endothelial cells, and have the ability to cross the blood-brain barrier. Moreover, MM-Lip-Rg3/PNS could specifically target ischemic sites, even microglia, increase the cumulative number of drugs in the brain, improve the inflammatory environment of the brain, and reduce the infarct size. By comparing olfactory nerve-blocking rats with normal rats, it was found that there are dir

Deep reinforcement learning (DRL) is a promising solution for voltage control of distribution grids with high penetration of inverter-based renewable energy sources (RESs). Yet, when adopting the DRL-based control method, the safe and optimal operation of the system cannot be guaranteed at the same time, as the conventional DRL agent is not designed to solve the hard constraint problem. To address this challenge, this paper proposes a deep neural network (DNN) assisted projection based DRL method for safe control of distribution grids. First, a finite iteration projection algorithm is proposed to guarantee hard constraints by converting a non-convex optimization problem into a finite iteration problem. Next, a DNN assisted projection method is proposed to accelerate the calculation of projection and achieve the practical implementation of hard constraints in DRL problem. Finally, a DNN Projection embedded twin-delayed deep deterministic policy gradient (DPe-TD3) method is proposed to achieve optimal operation of distribution grids with guaranteed 100% safety of the distribution grid. The safety of the DRL training is guaranteed via the embedded Projection DNN in TD3 with participation in gradient return process, which could smoothly and effectively project the DRL agent actions into the feasible area, thus guaranteeing the safety of data driven control and the optimal operation at the same time. The case studies and comparisons are conducted in the IEEE 33 bus system to show the effectiveness of the proposed method., QC 20240709

Hybrid energy storage system (HESS) is effective to compensate for fluctuation power in renewables and fast fluctuation loads in DC microgrids. To regulate DC bus voltage, a power management strategy is an essential issue. In the meantime, the increasing integration of constant power loads (CPLs) in DC microgrids brings great challenges to stable operation due to their negative incremental impedance. In this paper, a fast composite backstepping control (FBC) method is proposed for the HESS to achieve faster dynamics, smaller voltage variations, and large-signal stabilization. In the FBC method, a higher order sliding mode observer (HOSMO) is adopted to estimate the coupled disturbances. Furthermore, the FBC method is integrated with the droop control; so that the FBC-based decentralized power allocation (FBC-DPA) strategy for HESS in DC microgrids is developed. The proposed FBC method is designed based on the Lyapunov function to ensure its stability. Moreover, the design guidelines are provided to facilitate the application of the proposed method. Both simulation and experimental studies under different operating scenarios show that the proposed method achieves faster voltage recovery and smaller voltage variations than the conventional backstepping control method., QC 20240716

Hydrogen electrolyzers are promising tools for frequency regulation of future power systems with high penetration of renewable energies and low inertia. This is due to both the increasing demand for hydrogen and their flexibility as controllable load. The two main electrolyzer technologies are Alkaline Electrolyzers (AELs) and Proton Exchange Membrane Electrolyzers (PEMELs). However, they have trade-offs: dynamic response speed for AELs, and cost for PEMELs. This paper proposes the combination of both technologies into a Hybrid Hydrogen Electrolyzer System (HHES) to obtain a fast response for frequency regulation with reduced costs. A decentralized dynamic power sharing control strategy is proposed where PEMELs respond to the fast component of the frequency deviation, and AELs respond to the slow component, without the requirement of communication. The proposed decentralized approach facilitates a high reliability and scalability of the system, what is essential for expansion of hydrogen production. The effectiveness of the proposed strategy is validated in simulations and experimental results., QC 20240628