Modern distribution networks face an increasing number of challenges in maintaining balanced grid voltages because of the rapid increase in single-phase distributed generators. Because of the proliferation of inverter-based resources, such as photovoltaic (PV) resources, in distribution networks, a novel method is proposed for mitigating voltage unbalance at the point of common coupling by tuning the volt–var curve of each PV inverter through a day-ahead deep reinforcement learning training platform with forecast data in a digital twin grid. The proposed strategy uses proximal policy optimization, which can effectively search for a global optimal solution. Deep reinforcement learning has a major advantage in that the calculation time required to derive an optimal action in the smart inverter can be significantly reduced. In the proposed framework, multiple agents with multiple inverters require information on the load consumption and active power output of each PV inverter. The results demonstrate the effectiveness of the proposed control strategy on the modified IEEE 13 standard bus systems with time-varying load and PV profiles. A comparison of the effect on voltage unbalance mitigation shows that the proposed inverter can address voltage unbalance issues more efficiently than a fixed droop inverter.

The proliferation of renewable energy resources (RES), especially solar photovoltaic (PV) generation resources, causes overvoltage and line overloading in distribution networks. This study proposes a two-level volt–var control method based on multiple timescales. The on-load tap changer (OLTC) operates on an hourly timescale, to regulate the voltage on the secondary winding. In the 15-minutes timescale, PV-connected smart inverters and static var compensators (SVCs) are obliged to compensate the reactive power for the voltage control at the point of common coupling. In the multi-timescale voltage control framework, this study proposes a new multi-sectional volt–var curve (MSVVC) of a PV inverter. The objective of the MSVVC is to minimize the energy loss in the network, improve the voltage profile, and obtain the operational margin of other reactive power compensation devices. In the process of determining the optimal parameters of the MSVVC, stochastic modeling-based load flow analysis is utilized to consider the intermittency and uncertainty of RES generation. The effectiveness of the proposed method is verified on the IEEE 33-bus system in comparison with the conventional volt–var curve cases.

This paper presents a 137-GHz bandwidth highlinearity CMOS distributed amplifier (DA) for ultra-wide bandwidth power amplification applications. Utilizing intrastack coupling and input-output transmission lines (TLs), the proposed DA achieves ultra-wide bandwidth and improved linearity. Folding the input and output TLs enables efficient transformer implementation without the need for additional large areas. Furthermore, the frequency-dependent coupling between the input and output TLs increases bandwidth up to 137 GHz. Each common source (CS) stage is capacitively coupled to the input TLs, allowing for different mode operations and enhancing linearity. The proposed CMOS DA boasts the widest bandwidth of 137-GHz among reported CMOS DAs, along with 16.5/15.2 dBm peak $\mathrm{P}_{\mathrm{s}\mathrm{a}\mathrm{t}}/$PidB and 15.1% peak PAE at 26 GHz. The proposed CMOS DA also achieves 505.9 GHz, $\mathrm{P}_{\mathrm{s}\mathrm{a}\mathrm{t}}\times \mathrm{B}\mathrm{W}\times \mathrm{P}\mathrm{A}\mathrm{E}/\mathrm{P}_{\mathrm{D}\mathrm{C}}$ figure-of-merit (FoM). To the best of our knowledge, our proposed CMOS DA achieves the highest FoM.

The integration of photovoltaic generators introduces significant uncertainties in distribution network (DN) operations. This study presents a novel distributionally robust energy management strategy for multiple DNs, addressing inherent variabilities in renewable energy generation and load demands. To achieve the optimal energy management across the entire system under uncertainty, we introduce a novel concept: the adjusted cost-influencing participation factor (ACPF). The distribution system operator incorporates critical components such as the net power of the entire system, the locational marginal prices of the DNs, and the rated capacities of the generation units to compute the ACPF. We further reformulate the proposed distributionally robust chance constraint into a deterministic linear constraint by employing duality theory and the nested bisection search method. Comprehensive case studies on modified IEEE test systems validate the proposed methodology, demonstrating enhanced operational efficiency and reduced generation costs under various parameters and scenarios. The approach offers a robust framework for optimizing energy transactions across multiple DNs.