The electrification of the transport industry is expected to have a major impact on the operation of future distribution grids which includes overloading of components, voltage unbalance and power quality issues. This paper proposes a novel approach to utilize the ripple injection load control signals for the control of EV charging load, considering a large integration of PV systems in the grid. Currently, ripple injection signals are widely used by distribution network service providers (DNSPs) around the world for control of loads such as streetlights and hot water systems. Ripple Injection Signals or Audio Frequency Injection Control (AFIC) signals are the applications of a high-frequency signal superimposed on the 50/60 Hz supply. The proposed control will make use of the existing infrastructure and offers a viable solution where a smart grid solution is not available. AFIC signals can be encoded in binary to carry different information which can be used to set the maximum allowabl
Extreme incidents can cause diverse and dynamic disruptions to active distribution networks (ADNs). Thus, ADN operators (DNOs) must plan to prepare for and adapt to changes in conditions to withstand and quickly recover from such disruptions. This paper proposes a cooperative planning framework to design optimal microgrids (MGs) in smart ADNs integrated with virtual power plants (VPPs) to quickly reconfigure and recover during such events. The proposed framework has two parts: 1) optimal partitioning of ADNs with integrated VPPs into supply-sufficient MGs; and 2) the scheduling of the partitioned MGs. Since the VPPs are autonomous, a smart DNO and VPP interface and operating model for dynamic operating envelopes (DOEs) is proposed to quantify and integrate the supply capacity of VPPs in the operation of MGs. As part of this framework, three optimization models are formulated including VPP optimization, ADN partitioning, and a partitioned ADN scheduling model, where the uncertainty of r
Modern active distribution networks (ADNs) have become more resilient to service interruptions due to the flexibility offered by integrated distributed energy resources (DERs). Compared to network-owned DERs, directly controlled by the distribution system operator (DSO), a vast majority of DERs controlled by independent virtual power plants (VPPs) are much more difficult to coordinate. The coordination with the VPPs and the management of uncertainty are the two major obstacles in the utilization of these DERs in the ADN restoration (ADNR). This paper proposes a hierarchical framework for the sequential ADNR to overcome these two obstacles. The proposed framework adaptively integrates the sequential ADNR planning and the VPP scheduling models based on a comprehensive DSO and VPP coordination scheme to quantify and include the flexibility of VPPs in the ADNR. Furthermore, to address the uncertainty of renewable DERs and loads, a chance-constraint (CC) approach is used in these models. As
In this article, an advanced nonlinear control scheme is proposed for a three-phase grid-connected inverter and a solar photovoltaic (PV) system connected dc–dc converter. The proposed control scheme is based on a nonlinear adaptive integral backstepping approach. The robustness of the proposed control scheme is achieved through maintaining stability of the overall system, extacting maximum power from the solar PV, and controlling active power. Moreover, the injection of less harmonic components into the grid, the superior performance against different atmospheric conditions, and external disturbances enduring capability are also key features of the controller. The stability of the overall system is achieved through the negative-definite control Lyapunov functions of the proposed control scheme. An adaptive law of the proposed control scheme on external disturbances provides the undergoing capability against the external disturbances. Maximum dc power from the solar PV system is obta
Abstract
Soft actuators are versatile and can readily perform various functions and interact safely with humans and the environment owing to their deformability. Shape memory alloys (SMAs) have advantages of high power-to-weight ratio, silent operation, and high response speed, which make them suitable for fabricating soft, compact, and muscle-like soft actuators with various transformation capabilities. However, it is challenging to precisely control SMA-based soft actuators and robots because of the lack of explicit dynamics-based control method. In this study, a linear phase transition model of SMA is derived to express the dynamic model of an SMA planar actuator (SPA) in an explicit form. Then, a model-based feedback controller considering constraints of strain of the SMA, temperature increment of the SPA and load increment, was built. Strain gauges are used to obtain the bending angle of the SPA as the feedback signal for the controller. Various capabilities of the SPA, such as