Potential Use of Technical Virtual Power Plant Approach in Distribution Network for Mitigation of Contingencies


Onsomu O. N., Terciyanli E., BAYINDIR K. Ç., YEŞİLATA B.

7th International Symposium on Innovative Approaches in Smart Technologies, ISAS 2023, İstanbul, Turkey, 23 - 25 November 2023 identifier

  • Publication Type: Conference Paper / Full Text
  • Doi Number: 10.1109/isas60782.2023.10391700
  • City: İstanbul
  • Country: Turkey
  • Keywords: distributed generation, distribution networks, joint chance-constrained optimization, model predictive control, renewables, technical virtual power plant
  • Ankara Yıldırım Beyazıt University Affiliated: Yes

Abstract

Distribution level networks experience frequent contingency issues, apparently caused by outages as a result of component failure or malfunctioning of substation circuit breakers, other extreme occurrences for example natural disasters affect the whole transmission network, causing huge techno-economic losses to power utility companies and other key stakeholders. Generally, outages are a product of normal voltage deviations, and abrupt failure of generation units. Faults are problematic and very catastrophic in power systems, as mentioned earlier, economic losses are mostly experienced at distribution level, if proper mitigation strategies are not factored in, replacement of these systems take time, and such delays impact the industrial complex zones negatively. As an efficient improvement to these persistent issues and in reference to future smart energy systems, a virtual power plant (VPP) with a technical aspect is therefore proposed here. A technical virtual power plant (TVPP) is cognizant of the whole system architecture, distributed generation (DG) units and technical evaluation in regard to optimal performance of generators, ramp rates, provision of capacity or reserves that ensure seamless operation of an entire power system. Main reasons for starting with A 33-bus electronic grid system for simulation in MATLAB are first discussed. Appropriate methodological solution series at the optimization level is identified by careful filtering of available earlier research tools in the relevant field.