Considering uncertainty in the optimal energy management of renewable micro-grids including storage devices

• Published in: Renewable energy Vol. 59; pp. 158 - 166
• Main Authors: Baziar, Aliasghar, Kavousi-Fard, Abdollah
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01.11.2013; Elsevier
• Subjects: Algorithms; Applied sciences; case studies; Convergence; Devices; Energy; Energy management; Exact sciences and technology; fuel cells; Natural energy; Optimization; Probabilistic methods; Probability theory; renewable energy sources; Renewable micro-grid; Self adaptive modified θ-particle swarm optimization (SAM-θ-PSO); Storage device; Two point estimate method (PEM); Uncertainty; wind turbines; RES; Uncertainty; DG; Self adaptive modified θ-particle swarm optimization (SAM-θ-PSO); PV; MT; SAM-θ-PSO; NiMH-battery; PSO; Storage device; Two point estimate method (PEM); PEM; MG; Renewable micro-grid; FC; WT; Renewable energy; Energy management; Optimization
• ISSN: 0960-1481; 1879-0682
• DOI: 10.1016/j.renene.2013.03.026

This paper proposes a new probabilistic framework based on 2m Point Estimate Method (2m PEM) to consider the uncertainties in the optimal energy management of the Micro Girds (MGs) including different renewable power sources like Photovoltaics (PVs), Wind Turbine (WT), Micro Turbine (MT), Fuel Cell (FC) as well as storage devices. The proposed probabilistic framework requires 2m runs of the deterministic framework to consider the uncertainty of m uncertain variables in the terms of the first three moments of the relevant probability density functions. Therefore, the uncertainty regarding the load demand forecasting error, grid bid changes and WT and PV output power variations are considered concurrently. Investigating the MG problem with uncertainty in a 24 h time interval with several equality and inequality constraints requires a powerful optimization technique which could escape from the local optima as well as premature convergence. Consequently, a novel self adaptive optimization algorithm based on θ-Particle Swarm Optimization (θ-PSO) algorithm is proposed to explore the total search space globally. The θ-PSO algorithm uses the phase angle vectors to update the velocity/position of particles such that faster and more stable convergence is achieved. In addition, the proposed self adaptive modification method consists of three sub-modification methods which will let the particles choosel the modification method which best fits their current situation. The feasibility and satisfying performance of the proposed method is tested on a typical grid-connected MG as the case study. •We modeled the uncertainty effects in the optimal energy operation management of renewable MG.•A novel self adaptive modification approach based on θ-PSO algorithm was proposed.•Several renewable sources like PV, WT, FC and MT as well as storage devices are considered.•θ-PSO algorithm is used for the first time to solve MG operation management.