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The integrated community energy system (ICES) has emerged as a promising solution for enhancing the efficiency of the distribution system by effectively coordinating multiple energy sources. However, the concept and modeling of ICES still remain unclear, and operational optimization of ICES is hindered by the physical constraints of heterogeneous integrated energy networks. This paper, therefore, provides an overview of the state-of-the-art concepts for techno–economic modeling of ICES by establishing a Multi-Network Constrained ICES (MNC-ICES) model. The proposed model underscores the diverse energy devices at community and consumer levels and multiple networks for power, gas, and heat in a privacy-protection manner, providing a basis for practical network-constrained community operation tools. The corresponding operational optimization in the proposed model is formulated into a constrained Markov decision process (C-MDP) and solved by a Safe Reinforcement Learning (RL) approach. A novel Safe RL algorithm, Primal-Dual Twin Delayed Deep Deterministic Policy Gradient (PD-TD3), is developed to solve the C-MDP. By optimizing operations and maintaining network safety simultaneously, the proposed PD-TD3 method provides a solid backup for the ICESO and has great potential in real-world implementation. The non-convex modeling of MNC-ICES and the optimization performance of PD-TD3 is demonstrated in various scenarios. Compared with benchmark approaches, the proposed algorithm merits training speed, higher operational profits, and lower violations of multi-network constraints. Potential beneficiaries of this work include ICES operators and residents who could be benefited from improved ICES operation efficiency, as well as reinforcement learning researchers and practitioners who could be inspired for safe RL applications in real-world industry.

Artificial intelligence of things (AIoT)-enabled intelligent automatic train operation (iATO) is an urgently needed technology to expand the capability of ATO in addressing the real-time responsiveness and dynamic online challenges to energy-efficient train trajectory optimization (TTO) and its associated ride-comfort, punctuality, and safety issues in modern urban rail transit networks. This paper proposes a three-step supervised reinforcement learning-based intelligent energy-efficient train trajectory optimization (SRL-IETTO) approach for iATO by hybrid-integrating deep reinforcement learning (DRL) and supervised learning. First, multiple objectives are formulated based on real-time train operation and systematically integrated into the RL algorithm by a binary function-based goal-directed reward design method. Second, an IETTO model is established to handle uncertain disturbances in real-time train operation and generate optimal energy-efficient train trajectories online by optimizing energy efficiency and receiving supervisory information from trajectories of pre-trained TTO models. Finally, numerical simulations are implemented to validate the effectiveness of the SRL-IETTO using in-service subway line data. The results demonstrate the superiority and improved energy saving of the proposed approach and confirm its adaptability to online trip time adjustments within the practical running time range under uncertain disturbances with less trip time error compared to other intelligent TTO algorithms.

This paper demonstrates the risk of a new control interaction phenomenon between a large-scale type-4 wind park (WP) and a 500 kV transmission grid in a super-synchronous frequency range. This study aims at (i) using the impedance-based stability assessment (IBSA) method to investigate this new interaction phenomenon, (ii) analyzing the impact of various transmission grid topologies, WP operating conditions, and wind turbine (WT) converter control parameters on the severity of the interaction. For ultimate accuracy, the frequency-dependent impedance characteristics of the WP are extracted using the electromagnetic transient (EMT)-type impedance scanning instead of a simplified analytical model. The performed analyses demonstrate that the WP generation capacity, length of the transmission lines, applied series and shunt compensation levels, presence of parallel lines, and grid side converter (GSC) control parameters of the WT can significantly affect the identified instability risk. The obtained results are validated through detailed EMT simulations. Although the type-4 WP is considered in this paper, the presented results can be generalized to any inverter-based resource (IBR) with a full-size converter (FSC), such as photovoltaic (PV) power stations.

Abstract The shoot‐through (ST) states are essential for the operation of sinusoidal pulse‐width‐modulation (SPWM) based Z‐source inverters. However, the insertion of shoot‐through state unavoidably leads to output harmonics which is greatly affected by its distribution, especially for bipolar modulation schemes. With quantitative analysis, this paper investigates the mathematical relationship between the output harmonics and shoot‐through states for single‐phase Z‐source inverters. A modified double‐Fourier‐transformation‐based calculation scheme is proposed to estimate the output harmonics with different shoot‐through states insertion methods. Simulations and hardware experiments with a 200 W single‐phase Z‐source converter under two bipolar modulation control methods verified the accuracy of the proposed theory. The quantitative analysis would be helpful for the design of Z‐source converter's control strategy and modulation scheme.

The doubly-fed induction generator (DFIG) is considered to provide a low-reactance path in the negative-sequence system and naturally comply with requirements on the negative-sequence reactive current in emerging grid codes. This paper shows otherwise and how the control strategy of converters plays a key role in the formation of the active and reactive current components. After investigating the existing control strategies from the perspective of grid code compliance and showing how they fail in addressing emerging requirements on the negative-sequence reactive current, we propose a new coordinated control strategy that complies with reactive current requirements in grid codes in the positive- and negative-sequence systems. The proposed method fully takes advantage of the current and voltage capacities of both the rotor-side converter (RSC) and grid-side converter (GSC), which enables the grid code compliance of the DFIG under unbalanced three-phase voltages due to asymmetrical faults. The mathematical investigations and proposed strategy are validated with detailed simulation models using the Electric Power Research Institute (EPRI) benchmark system. The derived mathematical expressions provide analytical clarifications on the response of the DFIG in the negative-sequence system from the grid perspective.

Energy storage is essential for balancing the generation and load in power systems. Building a battery energy storage system (BESS) with retired battery packs from electric vehicles (EVs) or plug-in hybrid electric vehicles (PHEVs) is one possible way to subsidize the price of EV/PHEV batteries, and at the same time mitigating forecast error introduced by load and renewable energy sources in power systems. This paper proposes a detailed framework to evaluate end-of-life (EOL) EV/PHEV batteries in BESS application. The framework consists of three parts. A generalized model for battery degradation is first introduced. It is followed by modeling the battery retirement process in its first life. Two vehicle types—EV and PHEV—as well as two retirement modes—nominal and realistic modes—are considered. Finally, the application of the second-life BESS in power systems is modeled in a detailed economic dispatch (ED) problem. This is how second-life BESS’s performance translates into cost savings on power generation. An optimization problem is formulated to maximize total cost savings in power generation over the battery’s second life. This is done by striking a balance between short-term benefit (daily cost savings) and long-term benefit (cost savings through service years). Numerical results validate the effectiveness of the proposed framework/models. They show that battery usage and retirement criterion in its first life directly affect the performance in its second life application. In our case study, EV battery packs possess larger EOL energy capacities and consequently generate more cost savings in the second life. However, the BESS built from retired PHEV batteries has higher cost savings per MWh. It is because, with the proposed degradation model, battery health is better preserved in PHEV applications. Compared to nominal retirement mode, realistic retirement mode results in extra cost savings due to the reduced first-life service years.

Large-scale renewable photovoltaic (PV) and battery energy storage system (BESS) units are promising to be significant electricity suppliers in the future electricity market. A bidding model is proposed for PV-integrated BESS power plants in a pool-based day-ahead (DA) electricity market, in which the uncertainty of PV generation output is considered. In the proposed model, we consider the market clearing process as the external environment, while each agent updates the bid price through the communication with the market environment for its revenue maximization. A multiagent reinforcement learning (MARL) called win-or-learn-fast policy-hill-climbing (WoLF-PHC) is used to explore optimal bid prices without any information of opponents. The case study validates the computational performance of WoLF-PHC in the proposed model, while the bidding strategy of each participated agent is thereafter analyzed.

Traditional speed control of permanent magnet synchronous motors (PMSMs) includes a cascaded speed loop with proportional-integral (PI) regulators. The output of this outer speed loop, i.e. electromagnetic torque reference, is in turn fed to either the inner current controller or the direct torque controller. This cascaded control structure leads to relatively slow dynamic response, and more importantly, larger speed ripples. This paper presents a new dual cost function model predictive direct speed control (DCF-MPDSC) with duty ratio optimization for PMSM drives. By employing accurate system status prediction, optimized duty ratios between one zero voltage vector and one active voltage vector are firstly deduced based on the deadbeat criterion. Then, two separate cost functions are formulated sequentially to refine the combinations of voltage vectors, which provide two-degree-of-freedom control capability. Specifically, the first cost function results in better dynamic response, while the second one contributes to speed ripple reduction and steady-state offset elimination. The proposed control strategy has been validated by both Simulink simulation and hardware-in-the-loop (HIL) experiment. Compared to existing control methods, the proposed DCF-MPDSC can reach the speed reference rapidly with very small speed ripple and offset.

This paper proposes a series of new control methods for single-phase Z-source inverters. A detailed description of the concept and principle of each method is first presented, then a comparison among them is conducted comprehensively. Afterwards, an optimized closed-loop control scheme with better harmonic elimination performance is derived. Experimental results obtained from a 1kW un-isolated Z-source inverter prototype have demonstrated the effectiveness of the proposed control method. Compared to the conventional boost control, the proposed scheme has better performance with reduced harmonics, more flexible voltage gain, and simple algorithm.

There is an increasing interest in low voltage direct current (LVDC) distribution grids due to advancements in power electronics enabling efficient and economical electrical networks in the DC paradigm. Power flow equations in LVDC grids are non-linear and non-convex due to the presence of constant power nodes. Depending on the implementation, power flow equations may lead to more than one solution and unrealistic solutions; therefore, the uniqueness of the solution should not be taken for granted. This paper proposes a new power flow solver based on a graph theory for LVDC grids having radial or meshed configurations. The solver provides a unique solution. Two test feeders composed of 33 nodes and 69 nodes are considered to validate the effectiveness of the proposed method. The proposed method is compared with a fixed-point methodology called direct load flow (DLF) having a mathematical formulation equivalent to a backward forward sweep (BFS) class of solvers in the case of radial distribution networks but that can handle meshed networks more easily thanks to the use of connectivity matrices. In addition, the convergence and uniqueness of the solution is demonstrated using a Banach fixed-point theorem. The performance of the proposed method is tested for different loading conditions. The results show that the proposed method is robust and has fast convergence characteristics even with high loading conditions. All simulations are carried out in MATLAB 2020b software.

Phase-shifted converters are practically important to provide high conversion efficiencies through soft-switching techniques. However, the limitation on a resonant inductor current in the converters often leads to a non-fulfillment of the requirement of minimum load current. This paper presents a new power electronics control technique to enable the dual features of bi-directional power flow and an extended load range for soft-switching in phase-shift-controlled DC-DC converters. The proposed technique utilizes two identical full bridge converters and inverters in conjunction with a new control logic for gate-driving signals to facilitate both Zero Current Switching (ZCS) and Zero Voltage Switching (ZVS) in a single phase-shift-controlled DC-DC converter. The additional ZCS is designed for light load conditions at which the minimum load current cannot be attained. The bi-directional phase-shift-controlled DC-DC converter can implement the function of synchronous rectification. Its fast dynamic response allows for quick energy recovery during the regenerative braking of traction systems in electrified trains.

Nanomedicines for treating chronic kidney disease (CKD) are on the horizon, yet their delivery to renal tubules where tubulointerstitial fibrosis occurs remains inefficient. We report a folic acid-conjugated gold nanoparticle that can transport into renal tubules and treat tubulointerstitial fibrosis in mice with unilateral ureteral obstruction. The 3-nm gold core allows for the dissection of bio-nano interactions in the fibrotic kidney, ensures the overall nanoparticle (~7 nm) to be small enough for glomerular filtration, and naturally inhibits the p38a mitogen-activated protein kinase in the absence of chemical or biological drugs. The folic acids support binding to selected tubule cells with overexpression of folate receptors and promote retention in the fibrotic kidney. Upon intravenous injection, this nanoparticle can selectively accumulate in the fibrotic kidney over the nonfibrotic contralateral kidney at ~3.6% of the injected dose. Delivery to the fibrotic kidney depends on nanoparticle size and disease stage. Notably, a single injection of this self-therapeutic nanoparticle reduces tissue degeneration, inhibits genes related to the extracellular matrix, and treats fibrosis more effectively than standard Captopril therapy. Our data underscore the importance of constructing CKD nanomedicines based on renal pathophysiology. [ABSTRACT FROM AUTHOR]