Optimum control strategies for short term load forecasting in smart grids. (December 2019)

• Record Type: Journal Article
• Authors: Ali, Mansoor; Adnan, Muhammad; Tariq, Muhammad
• Is Part Of: International journal of electrical power & energy systems. Volume 113(2019)
• Page Start: 792
• Page End: 806
• Publication Date: 2019-12
• Subjects: Short term load forecasting -- Power flow control -- Overloading -- Fuzzy control; Electrical engineering -- Periodicals; Electric power systems -- Periodicals; Électrotechnique -- Périodiques; Réseaux électriques (Énergie) -- Périodiques; Electric power systems; Electrical engineering; Periodicals; 621.3
• DOI: 10.1016/j.ijepes.2019.06.010
• Languages: English
• ISSNs: 0142-0615

Abstract

Highlights: STLF is performed using fuzzy logic in order to maintain balance between supply and demand but excluding unexpected disturbances like faults and overloading in power system network. In case of these disturbances fuzzy controller is implemented in closed loop to balance the actual and forecasted load profile. Probabilistic model including variation and controller in developed for STLF and is tested under fault and overloading modes. Abstract: Nonlinearity in load profile and variations in demand due to error margin in short term load forecasting cause power network overloading. The state of a power system is more severe when a fault occurs in the power system network that leads to overloading. Analyzing the effect due to these disturbances on power system network is an important feature of this work. This paper proposes a control algorithm that focuses on sophisticated fuzzy logic approach. Advanced fuzzy control takes overloading and variation in demand profile as input, which mitigate these disturbances by incorporating optimal power dispatch of renewable energy resources (RERs). To show the effectiveness and validity of the proposed model and fuzzy control design, 9 Bus test system of the transmission network is adopted. Not only normal mode but fault and overloading modes are used to verify the proposed approach. Competitiveness of the proposed control design in terms of reliability and optimal utilization of RERs are verified through simulation results.