Influence of inherent characteristic of PV plants in risk-based stochastic dynamic substation expansion planning under MILP framework


Yurtseven K., KARATEPE E.

IEEE Transactions on Power Systems, vol.37, pp.750-763, 2022 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 37
  • Publication Date: 2022
  • Doi Number: 10.1109/tpwrs.2021.3095266
  • Journal Name: IEEE Transactions on Power Systems
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, Applied Science & Technology Source, Business Source Elite, Business Source Premier, Communication Abstracts, Compendex, Computer & Applied Sciences, INSPEC, Metadex, Civil Engineering Abstracts
  • Page Numbers: pp.750-763
  • Keywords: Substations, Planning, Uncertainty, Load modeling, Stochastic processes, Probabilistic logic, Power system dynamics, Correlation, dynamic planning, mixed integer linear programming, photovoltaic, stochastic programming, substation expansion planning, PHOTOVOLTAIC SYSTEMS, SUB-TRANSMISSION, FAULT-DETECTION, UNCERTAINTY, OPTIMIZATION, IDENTIFICATION, ALGORITHM, DECISION, NETWORK, MODEL
  • Dokuz Eylül University Affiliated: Yes

Abstract

IEEEA suitable probabilistic scenario set of load demand and natural characteristics of renewable energy is becoming a crucial issue in power system planning studies. Properly addressing the impact of potentially thousands of residential PV plants on the resilience and reliability needs of substations necessitates the representation of inherent relations between photovoltaics and the load throughout the long-term planning period. The optimal planning of substation expansions is achievable through proper modeling of input parameters which describes the characteristics of the service areas. In this paper, the co-existence of PV plants and the load in a service area under three different states such as daytime with clear-sky and no-fault, daytime with abnormal events, and nighttime are incorporated into the stochastic dynamic optimization problem by using scenario-based approach. The scenario tree of the problem is branched from three different bases simultaneously instead of only one as in conventional approach. This paper also combines the risk-constrained stochastic dynamic SEP problem and Mixed Integer Linear Programming (MILP) framework under one roof. The comparison between integrating inherent characteristics of PV plants with and without considering abnormal events into the optimization is performed to show the impact of suitable probabilistic model on dynamic nature of investment decisions.