Hossein Sohrabi Vafa, Fatemeh Noori, Morteza Ebadi,
Volume 16, Issue 3 (10-2013)
Abstract
Energy has essential role in the production process and social welfare and its demand prediction is esential for regulate the market and the supply. Due to volatilitys and non-linearity of energy demand and its variables, the non-linear models espicialy neural networks(NN) and paricle swarm optimization(PSO) have been more sucsees in this regard. As respects to their weaknesses such as imposing the specific form, necessity to the larg samples and failur to finding global optimum, in this study these shortcomings fixed by combining thes methods as hybrid algorithm. After applying and comparing this technique with common techniques on energy demand prediction between 1967 -2011, the results confirm higher predictive performance of hybrid technique and the explanatory power of the used variables.
Volume 16, Issue 4 (1-2014)
Abstract
Demand Side Management (DSM) is one of the most important methods that is used to maximize the benefits of electric power market participants. Demand Response (DR) are methods from DSM that can be defined as the changes in electric usage by end-use customers from their normal consumption patterns in response to changes in the price of electricity over time. This paper is focused on simultaneous implementation of security constraint unit commitment (SCUC) and Emergency Demand Response Program (EDRP) by using economic model. Considering that the simultaneous implementation of SCUC and EDRP is complex non-linear optimization problem with continuous and discrete variables, this paper is used linearization method to linearized the non-linear terms and then Mixed Integer Linear Programing (MILP) is used to solve the optimization problem. The software GAMS 23.6 has been used for this work which is fast and robust software for solving various types of optimization problems. The IEEE RTS 24 bus test system is used to simulate and evaluate the effectiveness of the proposed method.
