Large-Scale Integrated Energy Systems

Planning and Operation

verfasst von: Prof. Qing-Hua Wu, Jiehui Zheng, Zhaoxia Jing, Xiaoxin Zhou

Verlag: Springer Singapore

Buchreihe : Energy Systems in Electrical Engineering

Enthalten in: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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Über dieses Buch

This book discusses key issues in the planning and operation of large-scale integrated energy systems (LSIES). It establishes individual-based models for LSIES and develops multi-objective optimization algorithms and multi-attribute decision making support systems, which are applied to the planning and optimal operation of LSIES. It is a valuable reference work for researchers, students and engineers who are interested in energy systems, operation research and decision theory.

Inhaltsverzeichnis

Frontmatter

Chapter 1. Introduction

Abstract

Along with the popularity of distributed energy generation and hybrid energy appliances, the optimization of large-scale integrated energy systems (LSIES) combining various kinds of energy has attracted increasing attention. In this chapter, we discuss the main characteristics of LSIES. The LSIES can be depicted by the characteristics of heterogeneity, hierarchical structure, complex network topology, multimodal, hybrid variables, and complex dynamics. Additionally, conventional techniques on modeling, optimization, and decision-making of the integrated energy systems are investigated.

Qing-Hua Wu, Jiehui Zheng, Zhaoxia Jing, Xiaoxin Zhou

Chapter 2. Modeling of Large-Scale Integrated Energy Systems

Abstract

Large-scale integrated energy systems are networks of various energy flows, such as electricity, thermal energy, cooling energy, and natural gas flows. In a LSIES, energy can be transmitted not only in the form of electricity, but also in many other forms such as thermal energy and natural gas. A wide variety of models have drawn much attention, such as district heating and cooling systems, combined cooling heating and power systems, and energy hubs. Each of these models is described in detail in the following sections. This chapter presents the models of subsystems of IES, such as district heating and cooling systems, combined cooling heating and power systems. Moreover, we propose an individual-based model (IBM) for modeling LSIES. An individual is a basic unit consisting of a quintuple of input, knowledge, state, function, and output sets. It can make decisions independently according to accurate evolutionary mechanisms described by the function set. Additionally, the individuals interact with others through input and output sets in a unified form. In this way, a complex system can be decoupled into several independent individuals whose internal characteristics are fully specified and hidden from the external environment.

Qing-Hua Wu, Jiehui Zheng, Zhaoxia Jing, Xiaoxin Zhou

Chapter 3. Multi-objective Optimization Algorithms

Abstract

In the LSIES, multiple benefits of different operating interests are taken into consideration. Hence, the planning and operation of LSIES are formulated as multi-objective optimization problems, which should be tackled using the multi-objective optimization algorithms. This chapter presents three multi-objective optimization algorithms, i.e., the multi-objective group search optimizer with adaptive covariance and Lévy flights (MGSO-ACL), multi-objective group search optimizer with adaptive covariance and chaotic search (MGSOACC), and multi-objective evolutionary predator and prey strategy (EPPS). Simulation studies conducted on benchmark functions are also carried out to investigate the performance of these algorithms. In later chapters, these algorithms are employed to deal with the planning and operating problems of LSIES.

Qing-Hua Wu, Jiehui Zheng, Zhaoxia Jing, Xiaoxin Zhou

Chapter 4. Multi-attribute Decision-Making Support System

Abstract

This chapter presents three multi-attribute decision-making support methods, i.e., an improved entropy weight method, evidential reasoning, and interval evidential reasoning. The decision-making methods based on ER and IER approaches are used to determine a final optimal solution from the Pareto-optimal solutions obtained by multi-objective optimization algorithms. The selection of independent evidence for decision-making is investigated together with the study of multiple people and multiple attributes involved in the decision- making process. The decision-making method takes into account both the multiple objectives and the multiple evaluation criteria representing the economy and reliability interests of different operating parties in the LSIES. The performance of these methods are tested in an integrated energy system to find a final operation solution.

Qing-Hua Wu, Jiehui Zheng, Zhaoxia Jing, Xiaoxin Zhou

Chapter 5. Planning of the Large-Scale Integrated Energy Systems

Abstract

This chapter presents the planning problems of the LSIES considering the optimal unit sizing and the multi-stage contingency-constrained co-planning, respectively. First, a comprehensive framework including a multi-objective interval optimization model and evidential reasoning (ER) approach is introduced to solve the unit sizing problem of small-scale integrated energy systems, with uncertain wind and solar energies integrated. In the multi-objective interval optimization model, interval variables are introduced to tackle the uncertainties of the optimization problem. Aiming at simultaneously considering the cost and risk of a business investment, the average and deviation of life cycle cost (LCC) of the integrated energy system are formulated. Second, a multi-stage contingency-constrained co-planning for electricity-gas systems (EGS) interconnected with gas-fired units and power-to-gas (P2G) plants considering the uncertainties of load demand and wind power. The MCC model considers the long-term co-planning for EGS with the short-term operation constraints, while enabling systems to satisfy N-1 reliability criterion. These planning problems are solved utilizing the multi-objective optimization algorithms and decision-making support methods introduced in the previous chapters.

Qing-Hua Wu, Jiehui Zheng, Zhaoxia Jing, Xiaoxin Zhou

Chapter 6. Optimal Operation of Large-Scale Integrated Energy Systems

Abstract

The increasing share of variable renewable energy sources and the improving requirements on system security and reliability are calling for important changes in the LSIES. The synergies between energy supply networks are of great importance to satisfy the development of LSIES. Hence, this chapter presents the study of the coordinated scheduling strategy (CSS), in which, the models of the electricity network and gas network are developed in detail, and the operation constraints of the networks are fully considered. The purpose of the CSS is to optimize the conflicting benefits of the electricity network and gas network for daily operation of the LSIES, while satisfying the operation constraints. In the CSS, a multi-objective optimization algorithm is applied to obtain a Pareto-optimal solution set, and a multiple attribute decision analysis (MADA) using interval evidential reasoning (IER) is developed to determine a final optimal daily operation solution for the LSIES.

Qing-hua Wu, Jiehui Zheng, Zhaoxia Jing, Xiaoxin Zhou

Titel: Large-Scale Integrated Energy Systems
verfasst von: Prof. Qing-Hua Wu
Jiehui Zheng
Zhaoxia Jing
Xiaoxin Zhou
Verlag: Springer Singapore
Electronic ISBN: 978-981-13-6943-8
Print ISBN: 978-981-13-6942-1
DOI: https://doi.org/10.1007/978-981-13-6943-8