Sustainable Hybrid Energy Systems E-Book

Jiuping Xu

• Herausgeber: Wiley-VCH GmbH
• Kategorie: Wissenschaft und neue Technologien
• Sprache: Englisch
• Veröffentlichungsjahr: 2024

Seitenzahl: 700

Beschreibung

Offers the latest research and practical strategies on hybrid energy systems with multiple energy carriers as input and electricity carrier as output.

Leseprobe

Sustainable Hybrid Energy Systems

Carbon Neutral Approaches, Modeling, and Case Studies

Jiuping Xu and Fengjuan Wang

Authors

Prof. Jiuping XuSichuan UniversityInstitute of New Energy and Low‐Carbon TechnologyNo. 24 South Section 1Yihuan RoadChengdu, 610064China

Dr. Fengjuan WangSichuan UniversityInstitute of New Energy and Low‐Carbon TechnologyNo. 24 South Section 1Yihuan RoadChengdu, 610064China

Cover image: © The img/Shutterstock

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List of Figures

Figure 1.1

Research papers on hybrid energy systems (in order of year).

Figure 1.2

Research papers on hybrid energy systems (in order of country).

Figure 1.3

Cluster of references.

Figure 1.4

Definition of hybrid energy systems (this book focus on multi‐input single‐output hybrid energy systems, shown in gray).

Figure 1.5

Co‐located hybrid energy system.

Figure 1.6

Co‐combusted hybrid energy system.

Figure 1.7

Co‐operated hybrid energy system.

Figure 1.8

Structure of book chapters.

Figure 2.1

The hybrid energy system retrofitting scheme of a wastewater treatment park.

Figure 2.2

Decarbonization potential dataset of WWTPs.

Figure 2.3

Modeling of the optimized potential application of renewable energy.

Figure 2.4

Model‐solving flowchart.

Figure 2.5

Location and wastewater treatment capacity of cities in the Yangtze River Delta region.

Figure 2.6

LCOE and SSR of each province and scale.

Figure 3.1

Component and structure of the proposed grid‐connected hybrid wind–PV–battery system for drinking water treatment plant.

Figure 3.2

Operating strategy flowchart.

Figure 3.3

Optimization scheme of sustainable hybrid wind–solar‐storage system.

Figure 3.4

The location of the studied drinking water treatment plant. Source: Kekyalyaynen/Adobe Stock.

Figure 3.5

Model input data. (a) Global horizontal irradiation (Wh/m

2

), (b) air temperature °C, (c) wind speed (m/s), and (d) electricity demand (kWh).

Figure 3.6

LCOE and created jobs under different SSRs with varying generation sources: wind+storage, PV+storage, and wind+PV+storage. (a) Levelized cost of electricity and (b) jobs created.

Figure 3.7

Typical daily energy balance results of the hybrid wind–solar‐storage system. (a) Typical daily energy balance results – Spring, (b) typical daily energy balance results – Summer, (c) typical daily energy balance results – Autumn, and (d) typical daily energy balance results – Winter.

Figure 3.8

Results under different electricity price. (a) LCOE under fixed price (FP) and time‐of‐use price (TOU) and (b) Sensitive analysis of LCOE on the changing TOU price.

Figure 3.9

Results under changing natural resources. (a) LCOE under different wind speed scenarios and (b) LCOE under different solar irradiation scenarios.

Figure 4.1

Structure, advantages, and operation modes of the proposed hybrid energy system.

Figure 4.2

Fuzzy membership function and transformation process.

Figure 4.3

Flowchart of the bi‐level structure. Source: peteri/Adobe Stock.

Figure 4.4

Model solving and associated results analysis strategy.

Figure 4.5

Industrial park location.

Figure 4.6

The typical power supply in spring, summer, autumn, and winter. (a) Power supply on typical days in spring. (b) Power supply on typical days in summer. (c) Power supply on typical days in autumn. (d) Power supply on typical days in winter.

Figure 4.7

The typical power supply in spring, summer, autumn, winter under different scenarios. (a) Scenario 2: Recovery rate on typical days in spring. (b) Scenario 2: Recovery rate on typical days in summer. (c) Scenario 2: Recovery rate on typical days in autumn. (d) Scenario 2: Recovery rate on typical days in winter. (e) Scenario 3: Recovery rate on typical days in spring. (f) Scenario 3: Recovery rate on typical days in summer. (g) Scenario 3: Recovery rate on typical days in autumn. (h) Scenario 3: Recovery rate on typical days in winter. (i) Scenario 4: Recovery rate on typical days in spring. (j) Scenario 4: Recovery rate on typical days in summer. (k) Scenario 4: Recovery rate on typical days in autumn. (l) Scenario 4: Recovery rate on typical days in winter.

Figure 4.8

Financial losses of industrial parks facing extreme disasters under different scenarios (10

4

 CNY). (a) Financial losses of industrial parks facing extreme disasters under scenario 2. (b) Financial losses of industrial parks facing extreme disasters under scenario 3. (c) Financial losses of industrial parks facing extreme disasters under scenario 4.

Figure 4.9

Cost change under different electricity (a) and natural gas (b) prices.

Figure 5.1

Schematic of coal and biomass co‐combusted hybrid energy system.

Figure 5.2

Process of handling uncertain decision‐making environment.

Figure 5.3

Bi‐level decision‐making structure of coal and biomass co‐combusted hybrid energy system.

Figure 5.4

Algorithmic flowchart.

Figure 5.5

Case region and CPP locations.

Figure 5.6

Carbon emissions under different α values when β is 0.440.

Figure 5.7

Revenue and carbon emissions when β = 0.44. (a) Authority revenue and (b) total emission.

Figure 5.8

Revenue and carbon emissions when β = 0.44. (a) Fengxian CPP, (b) Ganyu CPP, (c) Baoying CPP, and (d) total.

Figure 5.9

Revenue and carbon emissions when β = 0.44 and α is changing.

Figure 5.10

Carbon emissions under different α values and β are changing. (a) Fengxian CPP, (b) Ganyu CPP, and (c) Baoying CPP.

Figure 6.1

Schematic of coal and municipal solid waste co‐combustion hybrid energy system.

Figure 6.2

Process to handle uncertain parameters with fuzzy theory.

Figure 6.3

Hierarchical structure of each participant.

Figure 6.4

The flowchart of the computation process.

Figure 6.5

The geographical position of the case study region.

Figure 6.6

The quantity of carbon emissions in IPPs (unit: ×10

3

tonnes).

Figure 6.7

The carbon emissions of different wastes in IPPs (unit: ×10

3

tonne).

Figure 6.8

IPPs' economic performances (unit: ×10

7

 CNY). (a) Luodai IPP, (b) Jiujiang IPP, (c) Xiangfu IPP, and (d) Wanxing IPP.

Figure 6.9

The profit trend of subsidy variations (unit: ×10

7

CNY).

Figure 7.1

Schematic of coal and sludge co‐combusted hybrid energy system.

Figure 7.2

Fuzzy membership function and transformation process.

Figure 7.3

Flowchart of the bi‐level structure.

Figure 7.4

Description of the case study.

Figure 7.5

Economic benefits, carbon emission intensity, and sewage sludge utilization under different sets of objective weights.

Figure 7.6

Carbon emission quotas under different sets of objective weights (unit: 10

6

 tonnes).

Figure 7.7

Carbon emission quotas of every coal‐fired power plants with α reducing from inside to outside under balanced objective weights (unit: 10

6

 tonnes).

Figure 8.1

Compensations of the hybrid solar–hydro power generation system.

Figure 8.2

Trade‐off between reliability and economy in the hybrid solar–hydro power system.

Figure 8.3

Method to handle renewable energy associated uncertainties.

Figure 8.4

Location of Longyang Gorge Project. Source: Adapted from (Pang and Zhang 2017).

Figure 8.5

Maximum technical output under different scenarios.

Figure 8.6

Quantities and ratios of total hydroand solar power output in different scheduling scenarios.

Figure 8.7

Hourly power supply and demand of different scenarios.

Figure 9.1

Structure and characteristics of the proposed hybrid solar–wind–gas power generation system.

Figure 9.2

Methods to solve renewable energy uncertainties.

Figure 9.3

Methods to solve renewable energy uncertainties.

Figure 9.4

Physical model for hybrid power generation in solar–wind–gas bundling mode in Golmud.

Figure 9.5

Typical uncertain data. (a) Typical power demand data, (b) typical temperature in sunny days, (c) typical solar irradiation in spring, and (d) typical wind speed in sunny days.

Figure 9.6

Optimal solutions for different scheduling scenarios. (a) Spring–sunny, (b) spring–cloudy, (c) spring–rainy, (d) summer–sunny, (e) summer–cloudy, (f) summer–rainy, (g) autumn–sunny, (h) autumn–cloudy, (i) autumn–rainy, (j) winter–sunny, (k) winter–cloudy, and (l) winter–rainy.

Figure 9.7

Power output ratios of solar, wind, and natural gas in optimal solutions under 12 scheduling scenarios (MW). (a) Spring, (b) summer, (c) autumn, and (d) winter.

Figure 9.8

Maximized economic benefits under 12 scenarios (MW). (a) Spring, (b) summer, (c) autumn, and (d) winter.

Figure 10.1

Framework of the hybrid solar–wind–hydro system.

Figure 10.2

Optimization scheme of hybrid solar–wind–hydro system's coordinated operation.

Figure 10.3

Scenario details for power output comparison.

Figure 10.4

Location of hybrid solar–wind–hydro generation system.

Figure 10.5

Complementary effects of local wind and solar resources. Cijd means daily complementary rate, Cis means seasonal complementary rate, and f1 means yearly complementary rate.

Figure 10.6

Integrated capacity results under different system reliability and complementary rates. (a) λ=0%, β=100%, and α is changing. (b) λ=0%, β=99.99%, and α is changing. (c) λ=0%, β=99.9%, and α is changing.

Figure 10.7

Power supply profits under different system reliability and complementary rates (10

5

 CNY).

Figure 10.8

Integrated wind (a) and solar (b) power capacities under different curtailment rate scenarios.

Figure 10.9

Curtailed wind (a) and solar (b) power under different curtailment rate scenarios.

Figure 10.10

Power output ratios (a), power supply profits (b), and maximum load (c) results under different curtailment rate scenarios.

Figure 11.1

Electricity consumer's RPS allocation model.

Figure 11.2

Model solving and result discussion processes.

Figure 11.3

Some basic information about Guangdong province.

Figure 11.4

Electricity consumption of power users.

Figure 11.5

Electricity sources of Guangdong province (GWh).

Figure 11.6

Electricity tariff of power users.

Figure 11.7

Generation costs of different energies in provinces.

Figure 11.8

Generation cost comparison. (a) Total generation cost of different energies and (b) total generation cost in different provinces.

Figure 11.9

Electricity tariff in different scenarios.

Figure 11.10

Generation costs in different scenarios.

Figure 11.11

Generation CO

2

emissions in different scenarios.

Figure 12.1

Bi‐level relationship between central and local governments for RPS allocation.

Figure 12.2

Flowchart of the interactive algorithm.

Figure 12.3

Location and electricity transmission direction for provinces in South Grid.

Figure 12.4

Generation and trading plans of each provinces.

Figure 12.5

Equitable RPS target allocation and executed plans.

Figure 12.6

Generation plans under different equity scenarios (hydro included).

Figure 12.7

Generation plans under different equity scenarios (non‐hydro).

Figure 13.1

Interaction mechanism of power market coordinates with TGC and CET trading markets from the provincial government perspective.

Figure 13.2

A provincial power generation and trading plan.

Figure 13.3

Flowchart of solution process.

Figure 13.4

Basic description for Guangdong province.

Figure 13.5

Provincial installed capacity and utilization hour in 2018.

Figure 13.6

Power generation and trading plan for Guangdong province.

Figure 13.7

Pareto‐optimal fronts for provincial power generation and trading plan, considering TGC and CET policies.

Figure 13.8

Generation plan under different scenarios.

Figure 13.9

Power trading plans under different scenarios.

Figure 14.1

Classification of energy storage systems.

Figure 14.2

Keywords co‐occurrence map of hybrid energy storage related researches.

List of Tables

Table 1.1

The power generation structure of different countries in 2020.

Table 1.2

Proportion of global power generation by various power generation methods in 2050 under different scenarios.

Table 1.3

Proportion of different types of power generation in the world in 2020 and 2050.

Table 2.1

The index of wastewater treatment plants.

Table 2.2

Wind turbines parameters.

Table 2.3

PV panels parameters.

Table 2.4

Battery parameters.

Table 2.5

The electricity consumption parameters.

Table 2.6

Area coefficient parameters.

Table 2.7

Parameters of typical WWTPs.

Table 2.8

Results of typical WWTPs.

Table 2.9

Installed capacity and decarbonization potential.

Table 2.10

Economy, technology, and environment results of two scenarios.

Table 3.1

Cost parameters for each power supply component unit.

Table 3.2

Electricity price of industrial and commercial users.

Table 3.3

Objective values under different SSRs with varying system components.

Table 4.1

Technical and cost parameters of the hybrid system components.

Table 4.2

Typical daily power demand (MW), solar irradiation (kW/m

2

), and ambient temperature (°C) data of the studied industrial park located in Wuxi city.

Table 4.3

Sensitivity analysis on the industrial park's attitude toward resilience targets.

Table 4.4

Installation plans under different scenarios.

Table 5.1

Straw resource quantities, prices, and power demand.

Table 5.2

Fuzzy straw prices.

Table 5.3

Crisp parameters of CPPs.

Table 5.4

Other crisp parameters.

Table 5.5

Uncertain fuel properties in fuzzy form ().

Table 5.6

Pollutant emission factors and char burn‐out fraction.

Table 5.7

Fuel to carbon emissions parameters in fuzzy form (kg/tonne).

Table 5.8

Fuel to power parameters in fuzzy form (kWh/tonne).

Table 5.9

Fuel quality requirements and carbon to power parameter in fuzzy form.

Table 5.10

Results when and is changing.

Table 5.11

Results when and is changing.

Table 5.12

Results when and is changing.

Table 6.1

Parameters of fuels in fuzzy form.

Table 6.2

Fuel properties (, ).

Table 6.3

Carbon emission coefficient (/tonne).

Table 6.4

Electric transmission parameter (kWh/tonne).

Table 6.5

Operational management parameters.

Table 6.6

Macro data used in the proposed model.

Table 6.7

Results under different authority attitudes (S ).

Table 6.8

Results under different authority attitudes (S1: ).

Table 6.9

Results under different authority attitudes (S2: ).

Table 6.10

Results under different authority attitudes (S ).

Table 6.11

Results under different authority attitudes (S4: ).

Table 6.12

Results under different authority attitudes (S5: ).

Table 7.1

Certain data for the three coal‐fired power plants.

Table 7.2

Other certain parameters used in the model.

Table 7.3

Uncertain data for the three coal‐fired power plants in a trapezoidal fuzzy form.

Table 7.4

Results under balanced weights.

Table 7.5

Results for different under balanced weights (1/2).

Table 7.6

Results for different under balanced weights (2/2).

Table 7.7

Results for different under balanced weights (1/2).

Table 7.8

Results for different under balanced weights (2/2).

Table 7.9

Comparison between the bi‐level model and the single‐level model (1/2).

Table 7.10

Comparison between the bi‐level model and the single‐level model (2/2).

Table 8.1

Parameters of Longyang Gorge hydropower station.

Table 8.2

Parameters of hydro turbines.

Table 8.3

Solar irradiation data in fuzzy form (Normal Season).

Table 8.4

Temperature data in fuzzy form (Normal Season,).

Table 8.5

Typical daily power demand data of Longyang Gorge hybrid solar–hydro generation systems (MW) (Northwest Engineering Corporation Limited 2014).

Table 8.6

Changes of technical output under different scenarios (MW).

Table 8.7

Income of power supply of different scenarios ( CNY).

Table 9.1

Certain parameters for the hybrid solar–wind–gas power system.

Table 9.2

Economic benefits in optimal solution under 12 scheduling scenarios.

Table 9.3

Improvement on system reliability under income maximum with natural gas integration.

Table 10.1

Solar irradiation data in different weather conditions of each season at Talatan ().

Table 10.2

Temperature data for different weather conditions of each season in Gonghe County (C).

Table 10.3

Technical parameters of hydro turbines.

Table 10.4

Lower and upper bounds of hydropower output in each season.

Table 10.5

Reservoir storage capacity.

Table 10.6

Calculation results when , , and is changing.

Table 10.7

Calculation results when , , and is changing.

Table 10.8

Calculation results when , , and is changing.

Table 10.9

Calculation results when , , and is changing.

Table 10.10

Calculation results of hydro‐only system, solar–hydro system, and solar–wind–hydro system.

Table 10.11

Maximum load results of different generation systems.

Table 11.1

Parameters of different energies.

Table 11.2

Parameters of different provinces.

Table 11.3

Allocated hydro, thermal, and non‐hydro RE power consumption of power users from different provinces (GWh).

Table 11.4

Generation and power import of Guangdong in 2019 (GWh).

Table 11.5

Renewable electricity consumption ration allocation scheme.

Table 11.6

Electricity generation related emissions in each province.

Table 11.7

The quantity of non‐hydro RE of different provinces in different scenarios (GWh).

Table 11.8

Renewable energy allocation scheme.

Table 12.1

Data for upper level.

Table 12.2

Unit provincial generation cost data.

Table 12.3

Last production cycle's electricity generation data.

Table 12.4

Provincial generation technology utilization hour data.

Table 12.5

Provincial generation capacity parameters.

Table 12.6

Provincial transmission‐related data.

Table 12.7

Operation cost and generation capacity of each province.

Table 12.8

Minimum and maximum RPS for each region.

Table 12.9

Equity scenario 1: Equality = 1/3, Capacity = 1/3, and Responsibility = 1/3.

Table 12.10

Equity scenario 2: Equality = 1, Capacity = 0, and Responsibility = 0.

Table 12.11

Equity scenario Equality = 0, Capacity = 1, and Responsibility = 0.

Table 12.12

Equity scenario 4: Equality = 0, Capacity = 0, and Responsibility = 1.

Table 13.1

Average feed‐in‐tariff in 2018 (CNY/kWh).

Table 13.2

Policy‐related parameters.

Table 13.3

Power generation plan for Guangdong Province ( GWh).

Table 13.4

Power trading plan for Guangdong Province ( GWh).

Table 13.5

Optimization models under different scenarios.

Table 13.6

Total cost and carbon emission of different scenarios in 2018.

Table 14.1

Properties of different storage technologies

Table 14.2

The possible applications of hybrid energy storage systems in energy sectors

Preface

Humans cannot rely only on a single energy source but on a mixture of multiple energy sources. Hybridization is the eternal theme of human energy utilization. For example, in the agricultural era, humans used natural power such as animal power, wind power, and hydro power; in the industrial age, firewood, coal, and electric energy were used at the same time; in the information age, oil, natural gas, and coal were the main energy sources; now and in the future, nuclear energy, wind energy, and solar energy and other low‐carbon energy sources will gradually become mainstream. High‐quality hybridization of different energy resources is closely related to energy security and stability, and requires systematic research. Since 2020, the carbon‐neutral mission has put forward new requirements for energy hybridization, and how to build a sustainable hybrid energy system under this goal has become a key issue of global concern.

Fully considering the global mission of achieving carbon neutrality and the global passion for promoting energy transition, this book discusses the comprehensive approaches to building sustainable hybrid energy systems. The hybrid energy system considered in this book is multi‐input (wind, solar, coal, etc.) and single‐output (electricity). We clearly distinguish it into three kinds, the co‐located hybrid energy system, the co‐combusted hybrid energy system, and the co‐operated hybrid energy system. We also explore four groups of comprehensive approaches to exploring sustainable hybrids, including deployment optimization, emission quota allocation, scheduling coordination, and optimal policy implementation. This book contains six parts and including 14 chapters.

The first part introduces background in Chapter 1. The global mission of achieving carbon neutrality, the global passion for promoting energy transition, and the global status of developing hybrid energy systems are first described; then the general definition of hybrid energy systems is presented and the detailed characteristics of co‐located hybrid energy systems, co‐combusted hybrid energy systems, and co‐operated hybrid energy systems are identified separately; advantages of energy system hybridization are also introduced. A logical relationship diagram of all the chapters of the book is also presented.

The second part presents the deployment optimization of co‐operated hybrid energy systems, including Chapters 2–4. Co‐operated hybrid energy systems deployed next to customers are significantly important nowadays as they can avoid long‐distance transmission loss and provide flexible choices. In these three chapters, the optimal deployment problem of distributed level co‐operated hybrid energy systems is discussed from three aspects, the industrial decarbonization‐oriented deployment, the sustainable operation‐oriented deployment, and the disaster resilience‐oriented deployment. The general mathematical optimization models are proposed, and three case studies are conducted to demonstrate the practicability and effectiveness of the proposed models.

The third part provides emission quota allocation of co‐combusted hybrid energy systems, as seen in Chapters 5–7. Due to the fossil fuel crisis, generation fuel hybridization has played and will continue to play an important role in generating electricity from thermal power plants. In these three chapters, the optimal emission quota allocation problem of co‐combusted hybrid energy systems is discussed, and three kinds of different generation sources co‐combustion scenarios, including coal‐biomass co‐combustion, coal‐municipal solid waste co‐combustion, and coal‐sewage co‐combustion, are explored. These studies are conducted from concentrated power plant level, three bi‐level emission quota allocation optimization models are proposed, and related case studies are conducted.

The fourth part grants the scheduling coordination of co‐operated hybrid energy systems, as seen in Chapters 8–10. Due to the inherent uncertainties and the unstorable features, the coordinated scheduling problem of conventional and new energy sources is very complex. In these three chapters, short‐term scheduling problems of hybrid solar–hydro, solar–hydro–wind, and solar–hydro–gas systems under reliable, economical, and social equilibrium requirements are explored. These studies are conducted from concentrated multi‐power plants level, the problems are described in detail and the corresponding multi‐objective optimization model are proposed and solved, and related case studies are also conducted.

The fifth part covers policy mechanism toward hybrid energy systems, as seen in Chapters 11–13. Policy mechanism is one of the most efficient tools to integrate new energy into traditional power systems, thus forming the hybrid power systems. Nowadays, the renewable portfolio standards policy has been widely applied. In these three chapters, three multi‐objective renewable portfolio standards implementation strategies, the strategy considering both the power suppliers and users, considering the equity and economy equilibrium, and considering the carbon trade and green certificate trade are separately studied. These studies are conducted from the regional grid level, the corresponding mathematical optimization model are proposed, and related case studies are conducted.

The last part gives emerging hotspot of developing hybrid energy storage systems in Chapter 14. The urgent need for energy storage in future energy systems characterized by a high percentage of renewable energy sources is presented; existing electrical, electrochemical, chemical, mechanical, and thermal energy storage technologies and their properties in terms of power and energy are described; and the advantages and future challenges of hybrid energy storage systems are presented.

Many people contributed directly or indirectly to this book; we wish to give special thanks to the Wiley publishing team (Lifen Yang, Monica Chandra Sekar, Merlyn Hema Daniel, and Shiji Sreejish) for their professional work. The authors would like to thank the anonymous referees for their insightful comments and suggestions to improve this book, and the authors listed in the references for their excellent work. The authors would also like to thank the Major Programs of the National Social Science Foundation of China (22&ZD142), the Youth Programs of the National Natural Science Foundation of China (No. 72301185), and the Postdoctoral Science Foundation of China (2023M732420) for providing us foundation support. We would also like to thank the members of our research group for their efforts in studying sustainable hybrid energy system–related research, which provide important reference and solid support for this book, including Chengwei Lv, Qian Huang, Chuangdang Zhao, Mengyuan Zhu, Liying Liu, Yalou Tian, Hongyan Tao, and Guocan Yang.

15th July 2023Chengyi Building

Jiuping Xu and Fengjuan Wang

1Introduction

To undertake rapid reductions thereafter in accordance with best available science, so as to achieve a balance between anthropogenic emissions by sources and removals by sinks of greenhouse gases in the second half of this century.

Article 4 of the Paris Agreement Apr. 2016

As mankind cannot rely only on a single energy source but on a mixture of multiple energy sources, hybridization is the eternal theme of human energy utilization, and the restructuring of the energy system is related to global security and stability, which requires systematic research. This chapter introduces the background of constructing hybrid energy systems (HESs), provides a detailed definition and classification of HESs, describes the motivation for hybridization of energy systems, and gives a logical relationship diagram of the whole book chapters.

1.1 Background

This section introduces the global mission of achieving carbon neutrality, the global passion of promoting energy transition, and the global status of developing HESs.

1.1.1 Global Mission of Achieving Carbon Neutrality

In the past few decades, global population has continued to grow, social economy has developed rapidly, living standards have improved significantly, electricity supply and demand have increased dramatically, and a large amount of fossil energy has been continuously exploited and utilized (Chen et al. 2016a). According to the BP Statistical Review of World Energy 2021, global power generation reached 2.68 billion kilowatt hour in 2020, of which coal, natural gas, and oil power generation accounted for 35.12%, 23.37%, and 2.82%, respectively. However, if these fossil fuels are still mined at 2020 levels, the world's remaining reserves of coal, natural gas, and oil will only last 139, 48.8, and 50 years (BP Global 2021). Not only that, the excessive burning of fossil fuels has resulted in massive emissions of sulfur dioxide, carbon dioxide, nitrogen oxides, and other gases, causing and exacerbating environmental problems such as global acid rain, greenhouse effect, and photochemical pollution (Liu et al. 2017). Statistics released by the International Energy Agency (IEA) in 2021 show that in 2019, the world's carbon emissions from fuel combustion reached 34.234 billion tonnes, of which 14.068 billion tonnes were directly attributable to heat and power production (International Energy Agency 2021a). Therefore, climate change caused by carbon dioxide and other greenhouse gas emissions has become the biggest non‐traditional security risk in the twenty‐first century, and reducing greenhouse gas emissions from energy use has become a common global issue.

In recent decades, the international community has made a series of efforts to address the climate crisis. In 1992, more than 150 countries and the European Economic Community signed the United Nations Framework Convention on Climate Change (UNFCC) in Rio de Janeiro, Brazil, and agreed to control the concentration of greenhouse gases in the atmosphere at a stable level. In 1997, the third Conference of the Parties to the Convention (COP3) was held in Kyoto, Japan, and formulated the first document in human history, the Kyoto Protocol, to limit greenhouse gas emissions in the form of regulations. The protocol sets greenhouse gas emission reduction targets for developed countries and countries with economies in transition but does not impose greenhouse gas emission reduction obligations for developing countries (The Ministry of Foreign Affairs of the People's Republic of China 2008). In 2009, at the 15th Meeting of the Parties to the Convention, relevant authorities from 193 countries and regions signed the Copenhagen Accord as a follow‐up plan after the expiration of the Kyoto Protocol. The Copenhagen Accord, although not legally binding, lays the foundation for the first truly global agreement to limit and reduce greenhouse gas emissions in the future, the Paris Agreement (United Nations 2009). In 2015, the Paris Agreement, the first global emission reduction agreement covering nearly 200 countries and regions in human history, was finally reached, becoming the second legally binding agreement after the Kyoto Protocol. The Paris Agreement made a unified arrangement for the global response to climate change after 2020, and put forward the goal of controlling the global temperature rise within the range of 2C and working toward 1.5C.

According to the 2018 report of the United Nations Intergovernmental Panel on Climate Change (IPCC), to achieve the goal of no more than 2C, the world needs to reach carbon neutrality around 2070. To achieve the 1.5 C target, carbon neutrality needs to be brought forward to around 2050. Therefore, since 2018, many parties have made carbon‐neutral commitments. According to data from the World Resources Institute (WRI), at least 83 countries around the world have made carbon‐neutrality commitments through legislation, promulgation of policies, and submission of statements of intent to the United Nations Framework Convention on Climate Change (Climate Watch 2022). China is the world's largest energy producer, the world's largest energy consumer, and the world's largest carbon emitter (International Energy Agency 2021a). At the federation conference in September 2020, China also made a solemn commitment to achieve carbon peaking by 2030 and carbon neutrality by 2060. According to data from the WRI, the common transition period in developed countries is 40–60 years from carbon peaking to carbon neutrality. For example, the United Kingdom and France achieved carbon peaks in 1991 and committed to carbon neutrality by 2050, with a transition period of 59 years. The U.S. reached its carbon peak in 2007 and pledged to achieve carbon neutrality by 2050, with a 43‐year transition period. Japan achieved carbon peak in 2012, pledge to achieve carbon neutrality by 2050, with a transition period of 38 years.

The global mission of achieving carbon neutrality is pressuring energy transition from fossil fuel‐dominated energy systems toward renewable energy‐dominated energy systems; details are described in the following part.

1.1.2 Global Passion for Promoting Energy Transition

Due to the rapid increase in consumption of fossil energy and the increasingly severe impact of climate change, renewable energy has ushered in an unprecedented development opportunity; the third round of the energy revolution characterized by large‐scale utilization of renewable energy is booming around the world. According to the IEA, the main sources of global carbon dioxide are the electricity and heat sector (power generation, 44%), the transportation sector (land, shipping, air transport, 26%), the industrial sector (metal smelting and chemical manufacturing, 20%), and the construction sector (building construction and home life, 9%) (International Energy Agency 2021b). Since electricity and heat production is the industry with the highest carbon emission in the world, if power supply cannot be very clean, the emission reduction target will be difficult to achieve (Energy Foundation 2021). Converting the dependence of electricity production on traditional fossil energy to clean energy and ensuring a diversified, stable, efficient, and clean power supply are becoming increasingly crucial to achieving the net‐zero emission goal and promoting the transformation of the power structure.

Table 1.1 shows the power generation structure of several countries in 2020. Overall, the proportion of fossil fuel power generation in developing countries like China and India is very high, 60.75% and 70.56% of which comes from less clean coal, far exceeding the world's average coal power generation ratio of 33.79%. Although in terms of proportion, the proportion of fossil energy power generation in Japan is also close to 70%, more than 30% is from gas power with a higher degree of cleanliness. The same situation applies to the United States, although the proportion of fossil energy power generation in the United States also exceeds 60%, but more than 40% is from gas power. From the perspective of renewable energy power, European Union (EU) has a large number of wind and hydropower stations, and the proportion of renewable energy power generation (38.16%) is the highest around the world.

Based on the existing power supply structure, many organizations have forecast global power supply structure in 2050, as shown in Table 1.2. Among them, the World Energy Scenarios (2013) publication World Energy Scenario: 2050 Energy Future was released before the Paris Agreement was signed in 2013, the International Renewable Energy Agency publication Global Energy Transition: 2050 Roadmap was released after the agreement was signed in 2018, and the International Energy Agency's (2020) publication Net Zero Emissions to 2050: A Roadmap for the Global Energy Sector comes at a time when parties to the agreement have made carbon‐neutral commitments in 2021. The three documents are staggered in time and can be used to compare changes due to international policy. As can be seen from Table 1.2, in the World Energy Council's Energy Sustainability Forecast Scenario, the power generation ratio of non‐renewable energy power is 52% (coal power 17.7%, gas power 19.9%, nuclear power 14.5%), the proportion of renewable energy power generation is 48%, of which photovoltaic (PV) and hydropower have the highest proportions, both exceeding 16.2%. The International Renewable Energy Agency's Renewable Energy Pathway Scenario (IREA‐REmap) is based on the premise that the global temperature rise is kept below 2C. The proportion of wind power and PV power generation will rise to 36% and 26%, respectively. The IEA's net‐zero emissions scenario (IEA‐Netzero) is based on the premise of a temperature control of 1.5C. Under this premise, net‐zero emissions need to be achieved in 2050, and the proportion of renewable energy in the power structure exceeds 87.4%. As can be seen from the data, renewable energy will become the main force of power supply in 2050.

Table 1.1 The power generation structure of different countries in 2020.

Non‐renewable power

Renewable power

Country

Coal (%)

Natural gas (%)

Oil (%)

Nuclear (%)

Hydro (%)

Wind (%)

Solar (%)

Others (%)

Total (%)

World

33.79

22.8 

4.36

10.12

16.85

 6.15

3.27

2.71

28.98

EU 27

13.16

19.94

3.97

24.77

12.6 

14.34

5.24

5.98

38.16

Japan

29.09

31.28

8.77

 4.57

 9.02

 1.13

8.97

7.17

26.29

USA

19.11

40.23

0.71

19.5 

 7.06

 8.31

3.27

1.8 

20.44

India

70.56

 3.88

0.02

 3.32

12.19

 4.5 

4.38

1.05

22.22

China

60.75

 3.32

2.1 

 4.8 

17.78

 6.12

3.42

1.7 

29.02

Table 1.2 Proportion of global power generation by various power generation methods in 2050 under different scenarios.

Non‐renewable power

Renewable power

Scenarios

Coal (%)

Natural gas (%)

Nuclear (%)

Hydro (%)

Biomass (%)

Wind (%)

Solar (%)

Geothermal (%)

WEC‐Symphony

17.7

19.9

14.5

16.1

 5.7

 8.4

16.2

 1.4

IREA‐REmap

1

10

4

12

4

36

26

3

IEA‐Netzero

0.9

 1.3

 7.7

11.9

 4.6

34.8

34.9

 1.2

Table 1.3 shows the proportion of power generation by different power generation methods in the world in 2020 and 2050. It can be seen that from 2020 to 2050, the global power structure will require major adjustments. The most obvious change is that the proportion of coal‐fired power generation will be significantly reduced, from 33.79% to 0.9–17.7% globally. The proportion of gas‐electric power generation in the world will decrease from 22.8% to 1.3–17.7%. On the whole, the proportion of hydropower generation will be slightly reduced. This is because the development of hydropower is relatively mature and will be close to saturation. The increased power demand in the future will be mainly provided by new energy sources such as solar and wind power. It can be seen from the table that the proportion of wind power and PV power generation in the world will increase to 35–36%. In general, in order to achieve the goal of net zero emissions as much as possible, the electricity supply will show a trend of high cleanliness by 2050. The proportion of coal power supply would have dropped sharply, and clean energy such as solar, wind power, and hydropower will take over 60–90% of the power supply tasks in the world.

Table 1.3 Proportion of different types of power generation in the world in 2020 and 2050.

World

Category

Generation

2020 (%)

2050 (%)

Non‐renewable

Coal

33.79

0.9–17.7

Natural gas

22.8

1.3–19.9

Nuclear

10.12

4.0–14.5

Renewable

Hydro

16.85

11.9–16.1

Wind

6.15

8.4–36.0

Solar

3.27

16.2–34.9

It can be seen that converting the dependence of electricity production on traditional fossil energy to clean energy has become an irreversible trend. As an effective solution to mitigate this issue, establishing HESs becomes the most feasible option and has drawn wide attention around the world, as shown in the following part.

1.1.3 Global Status of Developing Hybrid Energy Systems

Here we use the Web of Science (WOS) database to search HES‐related researches to find out the global development status.

The search codes in WOS are “hybrid energy system*” (Topic) or “hybrid renewable energy system” (Topic) or “multi‐energy system*,” and the article type includes “Article” or “Review Article” or “Proceeding Paper” or “Early Access,” and totally 2585 papers written in English were extracted from Science Citation Index Expanded (SCI‐Expanded) and Social Sciences Citation Index (SSCI) databases. The authors conducted the search on 30 June 2023.

Figure 1.1 Research papers on hybrid energy systems (in order of year).

Figure 1.2 Research papers on hybrid energy systems (in order of country).

Ranking the retrieved literature by their publication year, the results can be seen in Figure 1.1. It can be seen that the first literature appeared in 1984, and since then a small amount of literature has focused on HESs every year, but until 2008, the number of papers per year was still less than 10; since 2008, the number of papers has increased, but still less than 100 papers per year; since 2016, the number of papers has increased rapidly, and in 2022 the number of papers in just one year reached 483.

Ranking the retrieved literature by their publication country, the results can be seen in Figure 1.2. As can be seen from the figure, developing countries are the main force in HESs research, with China, India, and Iran authors accounting for the top three places among all countries, and China leading the way with 665 papers accounting for 26% of the total literature. Developed countries USA, Canada, and UK also published 130–210 relevant papers, respectively, ranking 4th to 6th.