Robust optimization based optimal chiller loading under cooling demand uncertainty

Fuzzy-based heat and power hub models for cost-emission operation of an industrial consumer using compromise programming

A framework to expedite joint energy-reserve payment cost minimization using a custom-designed method based on Mixed Integer Genetic Algorithm

A new wind power prediction method based on ridgelet transforms, hybrid feature selection and closed-loop forecasting

Short-term wind power forecasting based on support vector machine with improved dragonfly algorithm

In this paper short term power forecast of wind and solar power is proposed to evaluate the available output power of each production component. In this model, lead acid batteries used in proposed hybrid power system based on wind-solar power system. So, before the predicting of power output, a simple mathematical approach to simulate the lead–acid battery behaviors in stand-alone hybrid wind-solar power generation systems will be introduced. Then, the proposed forecast problem will be evaluated which is taken as constraint status through state of charge (SOC) of the batteries. The proposed forecast model includes a feature selection filter and hybrid forecast engine based on neural network (NN) and an intelligent evolutionary algorithm. This method not only could maintain the SOC of batteries in suitable range, but also could decrease the on-or-off switching number of wind turbines and PV modules. Effectiveness of the proposed method has been applied over real world engineering data. Obtained numerical analysis, demonstrate the validity of proposed method.

Farzaneh Mirzapour

Papers

A new prediction model of battery and wind-solar output in hybrid power system