A strategic bi-level framework for wind power investment considering grid strength index: A stochastic programming approach

Abstract

Increasing the penetration level of renewable energy sources (RESs) has been a key energy policy for international societies seeking to reduce carbon dioxide emissions. However, the growing integration of wind power into the electric network would alter the system characteristics since the inverter-based RESs weaken the grid strength. Therefore, it becomes necessary to include the system strength in the wind power investment problem (WPIP). In this regard and with being subject to wind production and electric load uncertainty, this paper presents a stochastic programming approach for WPIP considering the grid strength index. For this purpose, a bi-level model is presented whose upper level seeks to maximize the profit of a private investor while respecting the system strength requirement. In the lower level, the market-clearing problem is formulated under different operating conditions. Unlike the common approaches, which employ a DC model to represent the electric network, an approximated yet efficient AC model is utilized. Karush–Kuhn–Tucker​ (KKT) conditions are employed to transform the stochastic bi-level problem into a bilinear single-level problem which is solved using Konno’s cutting plane algorithm. IEEE 118-bus system is used to exhibit the efficiency of the proposed model for wind power investment. • A model for wind power investment based on bilevel optimization is presented. • A linearized AC-OPF framework is utilized to clear the electricity market. • The uncertainty of wind and demand is modeled using stochastic programming. • System strength index is incorporated in the investment model. • The bilevel model is solved using KKT conditions and Konno’s algorithm.