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Über dieses Buch

This book introduces several simple analytical approaches to aid the seamless integration of renewable distributed generation. It focuses on the idea of intelligent integration, which involves locating and developing suitable operational characteristics of renewable distributed generation. After reviewing the options available, the best location should be chosen, an appropriately sized operation should be installed and the most suitable operational characteristics should be adopted. Presenting these simple analytical approaches, their step-by-step implementation and a number of cases studies using test distribution systems, the book clearly demonstrates the technical, economic and environmental benefits of intelligent integration.

Inhaltsverzeichnis

Frontmatter

Chapter 1. Introduction

Abstract
Distributed Generation (DG) has become particularly popular since the introduction of electricity market and policy instruments to promote the usage of renewable energy. This chapter first provides a brief coverage of DG history starting from the era of the first oil crisis and the trend of DG penetration into power systems, followed by DG definitions and its technologies widely adopted. The chapter then highlights the major benefits of DG integration, which can be broadly classified into economic, environmental and technical values. How each of these benefits can be better captured through an approach of intelligent integration is explained in detail along with various methodologies reported in the literature. The chapter also discusses how battery energy storage has been considered as an inevitable element to enable high renewable penetration into power distribution systems. Finally, before the book is outlined, the chapter briefly covers the major grid codes associated with DG integration.
Nadarajah Mithulananthan, Duong Quoc Hung, Kwang Y. Lee

Chapter 2. Distribution System Modelling

Abstract
This chapter presents distribution system modelling required for Distributed Generation (DG) integration studies. The model of distribution systems covers loads and generation sources. Loads modelled using time-varying voltage-dependent loads or time-varying loads associated with exponents for voltage-dependent loads are highlighted in this chapter. From the generation-side, how to model different technologies, including biomass, solar Photovoltaic (PV) and battery energy storage is clearly explained to better represent their P-Q capacity characteristics. PV output power represented by a probability density function is also thoroughly presented with mathematical expressions. Finally, three test distribution networks used in this book to verify various methodologies and showcase the benefits achieved from intelligent integration of renewable DG units are described in detail.
Nadarajah Mithulananthan, Duong Quoc Hung, Kwang Y. Lee

Chapter 3. Biomass DG Integration

Abstract
This chapter provides a comprehensive coverage of the intelligent integration of biomass Distributed Generation (DG) from a power and energy loss point of view. The intelligent integration that determines the optimal location, size, operating power factor and operational characteristics of DG units is presented in a step-by-step manner considering time-varying load demand. Firstly, analytical expressions are developed to calculate the optimal sizes of DG units at various locations in a distribution network to minimise the overall power and energy losses. Secondly, the optimal sizes of active and reactive power are calculated separately and then combined based on an analytical expression to specify the optimal power factor. Finally, the methodologies are developed to identify the optimal locations along with optimal sizes and power factors. The results obtained on two test distribution systems show the importance of selecting the best locations, sizes and power factors in minimizing the power and energy losses in both the test systems. The major conclusions that can be drawn from this case study are also highlighted at the end of the chapter.
Nadarajah Mithulananthan, Duong Quoc Hung, Kwang Y. Lee

Chapter 4. PV Integration

Abstract
This chapter presents the integration of Photovoltaic (PV) sources into distribution systems. Firstly, different types of time-varying voltage-dependent load models are introduced along with solar PV modelling, where a probability distribution function is used to describe the uncertainty of PV generation with detailed steps for steady-state analyses. To incorporate PV outputs as multistate variables in the problem formulation, a combined generation-load is also briefly explained. Secondly, various impact indices, namely active power loss, reactive power loss and voltage deviation indices to aid the integration of PV sources are introduced with their detailed mathematical model. A combination of these indices with appropriate weights based on priority to form a multi-objective index is also described in the model. Based on such an index, an expression for sizing PV units at various locations is also introduced along with a computational procedure to determine the best allocation for a PV unit. Finally, examples of application of the proposed methodologies on different distribution systems are presented in this chapter.
Nadarajah Mithulananthan, Duong Quoc Hung, Kwang Y. Lee

Chapter 5. PV and BES Integration

Abstract
This chapter discusses the integration of Solar Photovoltaic (PV) and Battery Energy Storage (BES) units for reducing energy loss and enhancing voltage stability. In this chapter, each nondispatchable PV unit is converted into a dispatchable source with a combination of PV and BES units. New multiobjective index-based analytical expressions are proposed to capture the size and power factor of the combination of PV and BES units. A Self-Correction Algorithm (SCA) is also developed for sizing multiple PV and BES units while considering the time-varying demand and probabilistic generation. The power factors of PV and BES units are optimally dispatched at each load level. The simulation results show that operation of PV and BES units with optimal power factors can reduce energy losses and enhance voltage stability significantly compared to that with unity power factor.
Nadarajah Mithulananthan, Duong Quoc Hung, Kwang Y. Lee

Chapter 6. PV and EV Integration

Abstract
Electric Vehicle (EV) charging stations are usually considered to consume active power only in most planning studies. This chapter proposes an analytical approach to adopt plug-in hybrid EV charging stations that considers reactive power support in a medium voltage commercial distribution network with Photovoltaic (PV) units. In this study, expressions are derived from the apparent power loss to size charging stations with dispatchable power factors for minimizing voltage deviations. Such stations are employed to charge aggregated EV units parked in a public area. The developed expressions are then adapted to accommodate EV charging stations while considering the time-varying voltage-dependent load models and the probability of PV generation and EV charging. The simulation results show that properly adopted charging stations with dispatchable power factors can enhance probabilistic voltage profiles.
Nadarajah Mithulananthan, Duong Quoc Hung, Kwang Y. Lee

Chapter 7. Biomass Integration—A Cost Benefit Analysis

Abstract
This chapter presents new analytical expressions to efficiently capture the optimal power factor of each distributed Generation (DG) unit for reducing energy losses and enhancing voltage stability over a given planning horizon. These expressions are based on the derivation of a multiobjective index, which is formulated as a combination of active and reactive power loss indices. The decision for the optimal location, size and number of DG units is then obtained through a benefit–cost analysis. Here, the total benefit includes energy sales and additional benefits, namely energy loss reduction, network upgrade deferral and emission reduction. The total cost is a sum of capital, operation and maintenance costs. The simulation results show that the additional benefits are imperative. Inclusion of these in the analysis can yield faster DG investment recovery.
Nadarajah Mithulananthan, Duong Quoc Hung, Kwang Y. Lee

Backmatter

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