Recent developments in the electric power sector as well as in district heating and cooling systems has led to an increased interest in local energy systems and markets. In the electricity sector, this is driven by the integration of distributed resources such as solar power, electric vehicles and demand response. For district heating, sustainability and energy efficiency targets drives the development to further exploit small-scale heat sources. A closer integration of these energy carriers can also unlock potential flexibility, to the benefit of local as well as overlaying systems. In this respect, there is a need to further explore the possibilities to design local energy markets to facilitate the integration between electricity and district heating, as well as providing adequate instruments enabling flexibility. This paper therefore presents a market clearing design, based on optimization, for local energy markets incorporating multiple energy carriers and bid structures suitable for representing flexibility. The market clearing model is applied in a case study to illustrate and validate key design elements. One conclusion is that even though various elements can be added to the market clearing function, there is a challenge to interpret the results due to an increased complexity of the resulting optimization problem. 

This study aims to classify and compare AC and DC partial discharge (PD) based on PD pulse waveform analysis. To achieve this goal, we designed a testbed that enables precise measurements of individual PD pulses. The testbed is used for collecting data from four different types of PDs including cavity, surface, corona, and floating potential discharges generated by individual PD source samples. All samples were examined under AC, positive DC, and negative DC electrical stresses, through which we captured thousands of PD pulses. We classify the waveforms of each PD type into representative groups associated to their discharge mechanisms. The statistical data of the measured pulses are utilized to identify the differences between AC and DC PDs while the clustered patterns of PD amplitude versus their temporal characteristics serve as a means to classify the types of PDs under AC and DC electrical stresses.

Nowadays, transmission system operators require higher degree of observability in real-time to gain situational awareness and improve the decision-making process to guarantee a safe and reliable operation. Digitalization of energy systems allows utilities to monitor the system dynamic performance in real-time at fast time scales. The use of such technologies has unlocked new opportunities to introduce new data driven algorithms for improving the stability assessment and control of the system. Motivated by these challenges, the IEEE Task Force “Application of Big Data Analytic on Transmission Systems for Dynamic Security Assessment” have worked together to highlight and establish a baseline set of these common concerns within the power system community, which will be used as motivation to propose innovative analytics and data-driven solutions in future efforts. In this document, the results of a survey on 10 transmission system operators around the world are presented and it aims to understand the current practices of the participating companies, in terms of data acquisition, handling, storage, modelling and analytics. The overall objective of this document is to capture the actual needs from the interviewed utilities, thereby laying the groundwork for setting valid assumptions for the development of advanced algorithms in this field. © 2021 The Author(s)