Modern power electronics emit high frequency disturbances which are the remnants of their internal switching circuits. The electromagnetic interference, induced by these high frequency disturbances, can cause household equipment and utility assets to malfunction. Due to the lack of standardization in the frequency range of 2-150 kHz, power electronic devices have been designed to satisfy emission limits at lower frequencies but instead have increased emission at this higher frequency range. This paper presents an up-to-date literature survey on these high frequency disturbances in the 2-150 kHz range, a.k.a. ‘supraharmonics’. It includes classification, standardization, equipment interaction, propagation, and mitigation methods. This survey shows that most research conducted on this topic has been empirical or using simple models. However, analytical/physical models with sufficient detail have to be developed for equipment and low-voltage networks in this frequency range to increase understanding of the practical impact on end-user equipment and assets in the distribution grid. Supraharmonics are a relatively new power quality problem and this emission is expected to increase progressively due to the growing number of high frequency emitting devices, and the increasing number of susceptible loads. Hence, more research towards higher frequency harmonics is warranted.

This chapter provided an overview of DSM and especially, DR. In the past years, a wide range of DR programs have been developed in different power systems across the world. These programs aim at engaging all the types of consumers: from large industrial customers to residential end-users with relatively small electricity consumption, considering also special types of consumers such as the EV and data centers. The primary motive for developing DR programs is that by enabling the participation of the demand side in electricity markets significant benefits are anticipated: more efficient and sustainable system planning, enhancement of the operation of the distribution system, lower and more stable electricity prices in the long run, mitigation of the market power of several participants and promotion of competition, economic benefits for the consumers, and increased operational flexibility. Increased operational flexibility is directly linked to accommodating the handicaps of the trend that indicates that significant amount of variable RES generation will be introduced in power systems in the future.

The objective of this preliminary study was to investigate if voltage dips induced by different root causes have specific time-frequency patterns in their respective voltage dip waveforms by which the underlying root cause can be uniquely identified. Finding specific frequency patterns in the voltage dip waveform provides an innovative basis for the automatic classification of voltage dips based on their probable root cause. Ultimately, progressive insight into voltage dips allows more effective mitigation responses and thereby, conserves economic capital. Time-frequency parameters were obtained using short-time Fourier transforms. Subsequently, cluster analyses was performed to determine if time-frequency parameters from different voltage dip root causes were distinct. The outcome of the cluster analysis indicated that voltage dips which originate from different root causes have distinctive time-frequency patterns within the recorded voltage dip waveform.

Voltage dips (VDs) contribute significantly to the total annual cost resulting from poor power quality. This power quality disturbance can be induced by several root causes such as short circuits, transformer energizing, or due to the start-up of large electrical loads. The aim of this study was to develop a classifier which is able to automatically identify the probable root cause of a VD based on characteristic features contained within its corresponding RMS voltage curve. To this aim, mathematical functions were fitted through the characteristic section of VD RMS measurements. These measurements were obtained from the real-life distribution network. Subsequently, the coefficients of the fitting functions served as features for supervised pattern recognition schemes. In this study, 4 classifiers were developed and compared. The proposed approaches provided effective identification of VD root causes. Ultimately, effective classification schemes are a preliminary step to automatically localize VD sources.

It is anticipated that the growing number of distributed energy resources and other cyber physical components of smart grids will make the management of the distribution grid more complex. In this survey paper, four discernible challenges related to big data and the enablement of autonomous grid operation are investigated: (1) the technical readiness level of cloud computing services, (2) limitations of wireless telecommunication technology, (3) smart meter related privacy issues and (4) the intrinsic uncertainty in data analytics. The investigated challenges indicate that the current performance of cloud computing and wireless telecommunication technology do not readily enable autonomous decentralized secondary control of power systems. Moreover, technical and legislative solutions have to be developed to ensure consumer privacy, prior to applying data analytics on smart meter data.

In this paper, discernible challenges of autonomous grid operation via wireless machine-to-machine communication are surveyed. The objective was to gain insight into the feasibility of a self-regulating, autonomous smart grid which depends largely on wireless machine-to-machine communication technology. This technology is envisioned to coordinate numerous distributed energy resources and other cyber physical components for the substance of stable and secure grid operation. This survey showed that the technology readiness level of wireless telecommunication is currently insufficient to uphold autonomous control of power systems. Moreover, standard frameworks must be developed in order to advance the practicality of machine-to-machine communication technology regarding secure and autonomous grid operation.