NDLI: Optimal day ahead scheduling of combined heat and power units with electrical and thermal storage considering security constraint of power system

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Optimal day ahead scheduling of combined heat and power units with electrical and thermal storage considering security constraint of power system

Content Provider	Semantic Scholar
Author	Kia, Mohsen Nazar, Mehrdad Setayesh Sepasian, Mohammad Sadegh Siano, Pierluigi
Copyright Year	2018
Abstract	The use of Combined Heat and Power (CHP) with an overall efficiency from 70 to 90% is one of the most effective solutions to optimize the energy consumption. Mainly due to interdependence of the power and heat in these systems, the optimal operation of CHP systems is a complex optimization problem that needs powerful solutions. This paper addresses optimal day-ahead scheduling of CHP units with Electric Storage Systems (ESSs) and Thermal Storage Systems (TSSs) considering security constraints. Basically, the optimal scheduling of CHP units problem is a Mixed Integer Non Linear (MINLP) problem with many stochastic and deterministic variables. In this paper, linearization techniques are adopted to linearize equations and a two-stage Stochastic Mixed-Integer Linear Programming (SMILP) model is utilized to solve the problem. The first stage models behavior of operation parameters and minimizes the operation costs meanwhile the second stage considers the system's stochastic contingency scenarios. The proposed method is applied to 18-bus, 24-bus IEEE test systems. The effectiveness of the proposed algorithm has
File Format	PDF HTM / HTML
Alternate Webpage(s)	http://isiarticles.com/bundles/Article/pre/pdf/137537.pdf
Language	English
Access Restriction	Open
Subject Keyword	Addresses (publication format) Entity Name Part Qualifier - adopted Financial cost Integer (number) Interdependence Linear programming Lymphoma, Mixed-Cell, Follicular Mathematical optimization Optimization problem Scheduling (computing) Scheduling - HL7 Publishing Domain Solutions Stage level 1 Stage level 2 algorithm
Content Type	Text
Resource Type	Article

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