Risk-based optimal scheduling of reconfigurable smart renewable energy based microgrids. (October 2018)

• Record Type: Journal Article
• Authors: Hemmati, M.; Mohammadi-Ivatloo, B.; Ghasemzadeh, S.; Reihani, E.
• Is Part Of: International journal of electrical power & energy systems. Volume 101(2018)
• Page Start: 415
• Page End: 428
• Publication Date: 2018-10
• Subjects: Reconfiguration -- Reconfigurable smart microgrid -- Scenario generation -- Time-varying acceleration coefficients particle swarm optimization -- Risk-measure -- Conditional value-at risk; 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.2018.04.005
• Languages: English
• ISSNs: 0142-0615

Abstract

Highlights: Short term scheduling of reconfigurable microgrids. Considering uncertainty in renewable power generation and energy prices. Considering risk of uncertainty using conditional value at risk criterion. Modeling limit of topology change in short term scheduling. Abstract: Due to penetration of renewable energy resources and volatility of market price, scheduling of microgrid is associated with risk. Reconfigurable smart microgrids (RSMGs) are a new generation of microgrids which require further investigations. In this paper, a daily risk-based optimal scheduling of RSMG in presence of wind turbine for microgrid operator profit maximization is presented. As a reward scheme for further use of wind, the price of selling power is considered different and more than the price of purchasing power. The wind speed, price of selling and purchasing power are considered as uncertain parameters and scenario generation based on ARMA model is used for simulation. To find the best combination of microgrid switches in each hour, TVAC-PSO algorithm is used and new constraint called maximum number of optimal topology constraint is added to limit the number of changes in the structure. Moreover, a risk measure is based on condition value-at risk (CVaR) is formulated. The proposed method is implemented on 10 and 32-bus test RSMG. Numerical results show that by assessing the risk, the expected profit of optimal scheduling problem will be improved and RSMG can achieve the greater revenue by selling power to upstream network in a longer time.