ORIGINAL ARTICLE Security-constrained optimal power flow with wind and thermal power generators using fuzzy adaptive artificial physics optimization algorithm Kiran Teeparthi 1 • D. M. Vinod Kumar 1 Received: 9 January 2016 / Accepted: 6 July 2016 Ó The Natural Computing Applications Forum 2016 Abstract In this paper, a new fuzzy adaptive artificial physics optimization (FAAPO) algorithm is used to solve security-constrained optimal power flow (SCOPF) problem with wind and thermal power generators. The stochastic nature of wind speed is modeled as a Weibull probability density function. The production cost is modeled with the overestimation and underestimation of available wind energy and included in the conventional SCOPF. Wind generation cost model comprises two components, viz. reserve capacity cost for wind power surplus and penalty cost for wind power shortage. The selection of optimal gravitational constant (G) is a tedious process in conven- tional artificial physics optimization (APO) method. To overcome this limitation, the gravitational constant (G) is fuzzified in this work. Therefore, based upon the require- ment, the gravitational constant changes adaptively. Hence, production cost is reduced, settles at optimum point and takes less number of iterations. The proposed algorithm is tested on IEEE 30-bus system and Indian 75-bus practical system, including wind power in both the test systems. It is observed that FAAPO can outperform BAT algorithm and APO algorithm. Hence, the proposed algorithm can be used for integration of wind power with thermal power generators. Keywords Security-constrained optimal power flow  Wind power  Reserve cost  Penalty cost  Fuzzy-based artificial physics optimization 1 Introduction Due to high penetration of renewable energy sources, electric power system operation and control have become a complex and challenging issue for energy management systems (EMS). Security assessment and enhancement are the key issues in EMS [1, 2]. The main part involved in security assessment is contingency analysis. It combines with an optimal power flow called SCOPF for enhancing the system security. This endeavors to make changes to the rescheduling of active power generation as well as other adjustments such as phase shifter positions, HVDC line MW transfer, switching of FACTS devices and load shedding, so that no contingencies can result in violation of equality and inequality constraints. The conventional SCOPF problem involved only thermal power generators, which use conventional resources such as fossil fuels. Growing environmental concerns are forcing the power system to minimize the fuel cost and dependency on con- ventional resources, thus bringing renewable energy sour- ces to the mainstream of the power sector. Among all renewable energy sources, wind energy is the most proven around the world [3]. The main feature of wind energy is that once a wind farm is installed it requires zero produc- tion cost and minimal maintenance cost unlike conven- tional thermal power plants. It also reduces carbon footprint and earns carbon credits. The main problem involved with SCOPF problem is that one needs to know details of stochastic wind power gen- eration. Several investigations predicted the wind power based on wind speed. These investigations are based on neural networks [4], fuzzy logic [5] and time series [6] methods. The main focus of this paper is security enhancement by re-dispatching active power generations and not through wind forecasting. John Hetzer et al. [7] & Kiran Teeparthi kiran.t39@nitw.ac.in; kiran.t39@gmail.com 1 Department of Electrical Engineering, National Institute of Technology Warangal, Warangal, India 123 Neural Comput & Applic DOI 10.1007/s00521-016-2476-4