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Optimal Day-Ahead Renewable Power Generation Scheduling of Hybrid Electrical Power System

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Microgrid: Operation, Control, Monitoring and Protection

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 625))

Abstract

In this chapter, new optimal generation scheduling problem is solved in a hybrid power system by considering trade-off between system operating cost and risk level and provides best-fit schedules by optimizing the real-time and day-ahead deviation costs. The hybrid system considered in this work consists of wind, solar photovoltaic (PV), and conventional thermal generators. In this chapter, a set of batteries is considered for storing the energy. The intermittency of these renewable energy resources (RERs) creates imbalances between generation and load demand as the renewable power output cannot be known with certainty for the next few hours. Therefore, to handle uncertainties involved in these RERs, the optimal scheduling strategies are required to adapt to these requirements. The imbalance between generation and load demand is due to the forecast errors, and they need additional generation capacity (i.e., ancillary service) to handle this issue. In proposed approach, uncertainties in renewable power generations are handled by using anticipated real-time deviation bids, i.e., by considering the spinning reserves in the system. In this work, the spinning reserve offers from thermal units are considered. The intermittent nature of wind power is represented by Weibull distribution function and the solar PV power by bimodal distribution function. The proposed problem is solved by using multi-objective-based non-dominated sorting genetic algorithm-II (NSGA-II).

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Abbreviations

Doubly fed induction generators:

DFIGs

Energy storage systems:

ESSs

National renewable energy laboratory:

NREL

Non-dominated sorting genetic algorithm-II:

NSGA-II

Pareto archived evolution strategy:

PAES

Photo voltaic:

PV

Probability density function:

PDF

Renewable energy resources:

RERs

Spinning reserves:

SRs

Strength Pareto evolutionary algorithm:

SPEA

System operator:

SO

Total operating cost:

TOC

Ultra-high voltage:

UHV

Wind energy generators:

WEGs

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Author information

Authors and Affiliations

  1. Department of Railroad Electrical Systems, Woosong University, Daejeon, Republic of Korea

    Surender Reddy Salkuti

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  1. Surender Reddy Salkuti
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Correspondence to Surender Reddy Salkuti .

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Editors and Affiliations

  1. Department of Electrical Engineering, Veer Surendra Sai University of Technology, Sambalpur, Odisha, India

    Papia Ray

  2. Department of Electrical Engineering, National Institute of Technology, Raipur, Raipur, Chhattisgarh, India

    Monalisa Biswal

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Salkuti, S.R. (2020). Optimal Day-Ahead Renewable Power Generation Scheduling of Hybrid Electrical Power System. In: Ray, P., Biswal, M. (eds) Microgrid: Operation, Control, Monitoring and Protection. Lecture Notes in Electrical Engineering, vol 625. Springer, Singapore. https://doi.org/10.1007/978-981-15-1781-5_2

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  • DOI: https://doi.org/10.1007/978-981-15-1781-5_2

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  • Online ISBN: 978-981-15-1781-5

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