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    Öğe
    New Optimization Algorithms for Application to Environmental Economic Load Dispatch in Power Systems
    (Istanbul Univ, Fac Engineering, 2018) Akkas, Ozge Pinar; Cam, Ertugrul; Eke, Ibrahim; Arikan, Yagmur
    The determination of the most economical generation dispatch in an electrical power system has become a very important issue globally. However, economical dispatch can no longer be considered alone because of environmental problems that are derived from emissions such as nitrogen oxide, carbon dioxide, and sulfur dioxide. In this study, the problem of environmental economic load dispatch (EELD) in a power system of six generators is addressed both by neglecting and including line transmission losses using a modified genetic algorithm and a modified artificial bee colony optimization method. The results of these modified algorithms are compared with those of the unmodified versions. The results demonstrate that the proposed new methods have better economic and environmental distribution performances. Accordingly, it can be concluded that the new methods are more effective and should be adopted.
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    Öğe
    Risk-based Optimal Bidding and Operational Scheduling of a Virtual Power Plant Considering Battery Degradation Cost and Emission
    (Univ Suceava, Fac Electrical Eng, 2023) Akkas, Ozge Pinar; Cam, Ertugrul
    A virtual power plant (VPP) is a system combining various types of distributed energy resources (DERs) to provide a reliable power system operation. It provides the advantage of making changes in generation according to variety, price, and demand conditions with bringing renewable energy sources (RES) in a single portfolio and using their flexibility. In this study, it is tried to find optimal bidding and operational scheduling of a VPP containing Wind Power Plant (WPP), Photovoltaic Power Plant (PVPP), Heat-Only Unit (HOU), Battery Energy Storage System (BESS), Combined Heat and Power Plant (CHPP), and electrical/thermal demands and participating in the day-ahead electricity market in 24-h time interval. It is aimed to maximize profit and minimize emissions with considering the battery cost. A stochastic model is formed by considering the uncertainty arising from RES. In addition, CVaR (Conditional Value at Risk) as a risk measurement technique is applied against the risk arising from low profit scenarios. The proposed optimization problem formulated as a stochastic Mixed Integer Nonlinear Programming (MINLP) model and is solved by solver LINDO in GAMS (General Algebraic Modelling System). The case studies are implemented to show the applicability and effectiveness of the presented model.