Academic Journal of Science and Technology 
ISSN: 2771-3032 | Vol. 11, No. 3, 2024 

 

264 

Overview	of	Optimized	Load	Scheduling	of	Energy	Hub	
under	Energy	Internet	Environment	 	
Yucheng Wang 

School of Economics and Management, Southwest Petroleum University, Chengdu, Sichuan, 610500, China 

 

Abstract: Energy hubs (EH) play a key role in the energy Internet environment, especially in the load optimization scheduling 
of multi-energy systems. As an input-output port model of multi-energy systems, EH realizes real-time monitoring and intelligent 
analysis through the Internet of Things, big data and artificial intelligence technologies, promoting the efficient use of energy 
and the reduction of environmental pollution. This model uses the coupling matrix to describe the conversion, storage and 
transmission relations between energy sources, which provides strong support for the planning and operation of multi-energy 
systems. With the development of the energy Internet, the importance of EH is becoming increasingly prominent. By the 
introduction of EH, the coordinated operation of power, natural gas, heat and other energy systems can be realized, and the 
flexibility and stability of the system can be improved. Current research focuses on stochastic optimization of energy systems 
and load optimization scheduling to cope with the uncertainties in energy prediction and price. However, user behavior and 
demand response still face challenges in energy system planning and scheduling. Future research needs to deeply explore the 
changes in user behavior and introduce demand response mechanisms to improve the accuracy and effectiveness of energy 
system planning and scheduling. In the future, the optimal scheduling of energy hub load will develop in the direction of 
intelligence, self-adaptability and multi-energy collaborative optimization. Using big data, cloud computing and other 
technologies to achieve higher levels of intelligence and adaptability, in-depth research on the collaborative optimization of 
various energy sources, to promote the efficient operation of the energy system and green and low-carbon development, to 
provide strong support for the sustainable development of the energy sector. 

Keywords: Energy Hub; Multi-energy System; Energy Internet; Demand Response; Optimized Operation. 
 

1. Introduction 
With the rapid growth of the global economy and the 

acceleration of industrialization, energy demand continues to 
grow, and the complexity of energy supply is becoming 
increasingly prominent. The traditional energy system is 
faced with multiple problems such as low efficiency, serious 
environmental pollution and single energy structure, which 
makes the energy hub as the key node in the energy system 
become particularly important. 

With the development of energy Internet and the increase 
of people's demand for comprehensive energy services, the 
coordinated and efficient utilization of electricity, gas, cold, 
heat and other energy sources has attracted more and more 
attention, and energy hubs emerge at the historic moment. 
Energy hub is a kind of used to describe the energy system of 
energy supply and load demand coupling relationship 
between input-output dual port model [1][2], using the 
coupling matrix to describe the various forms of energy 
distribution, conversion relationship, without detailed 
calculation of tide equation, in the optimization of energy 
system configuration and operation optimization has played 
an important role in the research. The study on load 
optimization scheduling of energy hub is to make full use of 
the flexibility of multi-energy coordination and 
complementarity, and solve the problem of energy 
distribution and scheduling by comprehensively considering 
the energy demand of end users under the condition of 
meeting the system operation constraints and user load 
demand. 

In recent years, with the rapid development of the Internet 
of Things, big data, artificial intelligence and other 
technologies, new ideas and methods are provided for the 

optimal load dispatching of energy hubs. Through the 
introduction of advanced information technology means, real-
time monitoring, data collection and intelligent analysis of the 
operation status of the energy hub can be realized, thus to 
provide powerful data support for optimal scheduling. At the 
same time, combined with mathematical planning, artificial 
intelligence and other optimization algorithms, a more 
accurate and efficient energy hub load optimization 
scheduling model can be built, to realize the optimal 
allocation and efficient utilization of energy resources. 

Scholars at home and abroad have carried out detailed 
research on energy hubs, including the modeling of energy 
hubs and the application of energy hubs in the planning and 
operation of multi-energy systems. This paper aims to 
summarize the research status, methods, challenges and 
prospects of energy hub load optimization scheduling, 
analyze the application effect of different methods in energy 
hub load optimization scheduling, and explore the 
shortcomings of existing research. Through comprehensive 
analysis, it aims to provide valuable reference and reference 
for the research of energy hub load optimization dispatching, 
and promote the sustainable development and green 
transformation of energy system. 

2. Basic Concept and Theoretical Basis 
of Optimal Load Dispatching in 
Energy Hub 

2.1. Definition and Function of the Energy 
Hub 

The Federal Institute of Technology Zurich (ETH Zurich) 
first proposed the concept [3] of the energy hub (energy hub, 
EH) in 2007 in the "Future Energy Network Vision" project 



 

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study. The energy hub is defined as an input-output port 
model for describing the relationship between energy, load, 
exchange and coupling between networks in a multi-energy 
system. The coupling matrix describing the input energy and 
output load ports can briefly represent the coupling relations 
between the conversion, storage and transmission of various 
forms of energy such as electricity, heat and gas, and play an 
important role in the planning and operation of multi-energy 
systems [4]. 

Energy hub concept from a very simple idea: no matter 
more energy system between electricity, heat, gas coupling is 
complex, and all need various forms of energy input, 
eventually converted into other forms of energy as the output 
of the system, then can be more energy system abstract as 
shown in figure 1 the input-output dual port network, the 
middle of the box analysis for the energy system, using energy 
hub abstract description [5]. 

 

 
Figure 1. Input-output port model for multiple energy systems 
 
The P vector at the left end of the energy hub represents the 

original energy input of the multi-energy system, and the L 
vector at the right end represents the energy output after 
conversion. Therefore, the energy hub at the mathematical 
level is a function of an input P to an output L: 

L = f ( P) 
Where the function f () can take into account the 

transmission, conversion, storage and other links of various 
forms of energy [6]. 

Because the energy hub modeling method has a high degree 
of abstraction, so as long as an energy system reasonable 
modeling, regardless of the size of the system, can be 
described with the energy hub, such as a single residential 
user [7-9], commercial buildings (e. g., airport, hospital) [10], 
factory (such as steel mills, paper mills) [11], conventional 
generating units (such as hydropower station, CHP, etc.) 
[12,13], a region or even a national energy system [14], etc.  

The basic components of the energy hub are mainly divided 
into three parts [5]: energy conduction equipment: no energy 
conversion, can realize direct energy transmission, such as 
cable, heat network pipeline, gas network pipeline, etc.; 
energy conversion equipment: realize the conversion and 
coupling between different energy forms, such as fuel cell, 
motor, steam and gas turbine, internal combustion engine, 
electrolytic cell, etc.; energy storage equipment: battery, 
pumping and storage power station, heat storage device, etc. 

2.2. Objectives and Principles of Load 
Optimization Scheduling 

Load optimization scheduling is an important optimization 
problem to support the economic and efficient operation of 
the energy system. Therefore, various studies on optimized 
load scheduling of multi-energy systems. Literature [15] 
establishes a day-ahead load scheduling model for 
community microgrid, and applies deep learning to load and 
renewable energy prediction. In the study of Afrashi et al. [16], 

a deterministic model for the management of a multi-carrier 
energy system was proposed to coordinate renewable energy, 
battery and cogeneration, thus minimizing the energy cost of 
the energy hub. AkbaiZadeh et al. [17] proposed an energy 
hub energy management model connecting electricity, gas 
and heating networks, which takes into account the load of 
electric vehicle (EV), energy price, renewable energy and 
energy demand. Heidari et al. [18] proposed a stochastic 
operation model, including renewable energy, heat and power 
generation (CCHP) system and ice storage, focusing on the 
impact of ice storage on the performance and efficiency of 
energy hubs. A large number of references provide a good 
theoretical basis for the optimal operation of energy hubs. 
However, the development of the energy Internet makes the 
multi-energy systems facing more and more uncertainties. On 
the one hand, a high proportion of renewable energy is 
continuously connected to the energy system, and the 
uncertainty of renewable energy has a great impact on the 
planning and operation of the energy system by [19]. On the 
other hand, the implementation of demand response brings 
inconvenience to users' life or production, which also brings 
uncertainty to users at different times. Therefore, how to solve 
the uncertainty in the operation of energy hub and realize the 
stable operation of system economy is a research hotspot in 
this field. 

3. Research Status of Optimal Load 
Dispatching in Energy Hubs 

3.1. Research on Single Micro Grid Load 
Optimization Scheduling 

At the present stage, the research on micro-grid load 
optimization scheduling is mainly aimed at a single micro-
grid. The traditional problem of single micro-grid load 
optimization scheduling is for load management from the 
perspective of the supply side. From this perspective, the 
decision maker can achieve the dispatching goal by 
reasonably arranging the output of the distributed generation 
unit on the supply side, the charging and discharge power of 
the distributed energy storage unit and the transmission power 
between the micro grid and the main grid while meeting the 
energy demand of the demand side. 

Traditional energy systems, such as power systems, natural 
gas systems, and thermal systems, are mostly planned and 
operated separately. The lack of coordination between 
traditional energy systems affects the economical and 
efficient operation of the whole system [20]. With the 
development of energy Internet, the coupling of various 
energy sources (electricity, gas and heat) has been 
significantly strengthened, and the interaction between power 
supply, power grid and load has been continuously 
strengthened by [21]. Therefore, the coordinated operation of 
multiple energy sources becomes an increasingly urgent task. 

For multi-energy micro-grid systems, the supply and 
conversion of different energy sources can improve the 
flexibility and stability of the whole system. For example, in 
the case of power shortage, electricity can be generated by 
natural gas. In addition, multi-energy systems can improve 
the energy utilization efficiency of micro-grid systems by 
coupling different forms of energy. 

To effectively model multi-energy microgrid systems, the 
concept of energy hub (Energy Hub, EH) is proposed as [22]. 
Energy hub is an input-output two-port model describing the 
supply, conversion, storage and their coupling relationship of 



 

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different types of energy and load requirements in a multi-
energy system. It uses a coupling matrix to represent the 
interrelationship [23][24] between the conversion, 
distribution and storage of multiple energy sources. Energy 
hubs have been extensively studied in the planning and 
operation of multi-energy systems. Literature [25] proposes 
an energy management model for residential energy hub. The 
proposed model uses the house as a cold, heat and power 
generation system, the model uses electric vehicles as a 
storage unit, and considers distributed power sources such as 
heat storage and photovoltaic. The results show that 
participation in demand response projects, the use of heat 
storage and electric vehicles can increase cogeneration in 
energy supply and peaking capacity, and help reduce energy 
costs. Literature [26] proposed comprehensive considering 
the weather, electricity price and other information of 
commercial energy hub optimization model, the literature 
through Monte Carlo simulation, studied the performance of 
the model under different electricity price and weather 
conditions and change, the results show that even in the case 
of electricity price and weather conditions exist uncertainty, 
energy costs fell significantly. 

3.2. Research on Multi-microgrid Load 
Optimization and Scheduling  

In the existing research on collaborative scheduling 
between multiple energy systems, literature [27] studied three 
typical schemes for managing energy hub clusters on the 
demand side under the random load prediction framework, 
namely individual, shared and aggregation. Literature [28] 
proposes a thermoelectric integrated multi-microgrid hybrid 
energy sharing framework considering power generation cost, 
transaction cost, cost of thermal energy inconvenience, load 
characteristics and power consumption utility. Literature [29] 
proposes a multi-objective optimal energy flow management 
model for interconnected multi-energy hubs, aiming to 
minimize the energy cost, active power loss and natural gas 
loss of multi-energy hubs. Literature [30] proposes an 
optimized scheduling model for a multi-residential energy 
hub system considering battery life. Literature [31] analyzes 
the performance of multiple residential energy hub load 
scheduling models under tOU electricity price and dynamic 
electricity price, and studies the influence of electric energy 
storage units and renewable resources on the optimization of 
energy hub load. The results show that the proposed 
optimization model can reduce the daily cost of the energy 
hub, and the dynamic pricing scheme can better motivate the 
energy hub to adjust its daily operation mode. 

4. Key Technologies and Methods of 
Load Optimization Dispatching in 
Energy Hubs 

In existing studies, the main methods to deal with energy 
system uncertainties are stochastic optimization and robust 
optimization. 

In randomized optimization studies, several works have 
been conducted to address the planning problem of multi-
energy systems. Literature [32] proposes an optimal planning 
model of energy hub to determine the type and capacity of the 
installed components in the hub, considering the reliability of 
maintaining power supply and heating, carbon dioxide 
emissions, and the physical limitations of electricity and gas 
networks; the model estimates the sensitivity to the 

uncertainty of electricity and gas prices using stochastic 
planning method. Literature [33] proposes a mid-term energy 
hub management model to minimize the total cost of the 
system, and the model uses a stochastic planning method to 
treat the uncertainty of electricity price. Literature [34] uses a 
two-stage stochastic method to solve the dynamic multi-
carrier microgrid planning problem under uncertainty. 
Literature [35] constructs a new stochastic framework for the 
planning study of energy systems, reducing the complexity of 
stochastic models by assessing the relative importance of 
uncertain sources and discarding sources with minimal 
impact. Furthermore, some researchers have applied 
stochastic optimization methods to the operational 
optimization of energy systems. Literature [36] proposes a 
stochastic optimization framework for complex microgrids, 
in which stochastic optimization is performed to retain the up 
/ down regulation capacity for the uncertainty of energy 
prediction, and then rearrange the initial scheme according to 
signal requests and economic quotations. Considering the 
uncertainty of renewable energy, electricity prices and electric 
vehicle demand, literature [37] proposes an optimization 
model of random multi-objective grid connection and 
unbalanced microgrid, which generates three uncertain 
scenarios using the roulette mechanism. In literature [38], a 
new stochastic model is proposed for optimizing the CCHP 
system under energy load and price uncertainty, where a 
Monte Carlo sampling technique is used to generate a set of 
probability scenarios with different energy loads and 
associated energy prices based on a given occurrence 
probability. Although stochastic optimization is a well-
established method to deal with uncertainty, its application 
requires obtaining the distribution function of uncertain 
variables, which is difficult to achieve in practice. Moreover, 
a large number of discrete scenarios generated by stochastic 
optimization often lead to a large computational amount and 
a long solving time. Although scenario reduction based on 
clustering methods can reduce computation, clustering 
scenarios may not be representative. 

Robust optimization has received much attention as 
another important approach to cope with uncertainty. Robust 
optimization methods only need to know the fluctuation range 
of the uncertainty variable, not its probability distribution, and 
the solution is robust to all uncertainty sets (including worst 
case). Thus, a large number of robust optimization models 
have been developed for the planning and scheduling of 
energy systems. In terms of energy system planning problems, 
literature [39] proposes a robust optimization model to 
determine the optimal energy structure, which considers 
various predicted power load, capacity factor, and energy 
price uncertainties. Literature [40] proposes a decentralized 
adaptive robust planning model to address the planning 
problem of multi-stakeholder integrated energy systems. 
Moreover, several studies have applied robust optimization to 
the optimal operation of energy systems. For example, 
literature [41] proposes the optimal scheduling model for 
interconnected multi-EH in the presence of electric vehicles, 
which adopts a robust optimization approach to cope with the 
uncertainty of electricity price, renewable energy, and power 
load. For the actual scheduling of island microgrid under 
source and load uncertainty, literature [42] proposes an 
adaptive robust optimization model with binary recourse 
variables. 



 

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5. The Inadequacy of Existing Studies 
(1) Uncertainty and volatility of renewable energy sources 
Renewable energy, such as wind energy and solar energy, 

has great uncertainty and volatility, which brings great 
challenges to the optimal load scheduling of energy hubs. 
Although the existing studies suggest some methods to deal 
with this uncertainty and volatility, there may be some 
limitations. For example, prediction methods based on 
historical data may not accurately predict future renewable 
energy generation; methods based on stochastic planning or 
robust optimization may be too conservative or too optimistic. 
Therefore, how to more accurately predict and manage the 
uncertainty and volatility of renewable energy sources, is a 
problem that should be emphasized in the future research. 

(2) Efficiency and practicability of the optimization 
algorithm 

In the optimal scheduling of the energy hub load, the 
selection and application of the optimization algorithm have 
an important influence on the quality of the scheduling results. 
However, the existing optimization algorithms may still have 
some problems with their efficiency or practicality. For 
example, optimization algorithms based on mathematical 
planning may have large computation and long solution time; 
optimization algorithms based on artificial intelligence or 
machine learning may require a lot of historical data and 
training time. Therefore, how to select the appropriate 
optimization algorithm according to the specific application 
scenarios and requirements, and improve its efficiency and 
practicability, is a problem that needs to be concerned about 
in the future research. 

(3) Research on user behavior and demand response 
In the process of load optimization dispatching in the 

energy hub, the user behavior has a significant impact on the 
load fluctuation and the power demand. However, existing 
studies often lack in-depth analysis and modeling of user 
behavior. User behavior is affected by many factors, such as 
weather, electricity price, personal habits, etc. The change of 
these factors will directly affect users' behavior of electricity 
consumption. Therefore, constructing a model that can 
accurately reflect the change in user behavior is crucial to 
improve the accuracy and effectiveness of optimal load 
scheduling in energy hubs. Demand response is an important 
mechanism in the power system. By adjusting the electricity 
behavior of users to respond to the changes of the power 
system, so as to realize the balance of power supply and 
demand. However, the demand response mechanism is 
relatively less used in the study of optimized load scheduling 
in energy hubs. This is mainly because existing studies often 
focus on the optimal scheduling of the supply side, while 
ignoring the management of the demand side. In fact, through 
the introduction of the demand response mechanism, the 
electricity consumption behavior of the users can be adjusted 
more flexibly, so as to realize the optimal scheduling of the 
energy hub load. In fact, user behavior and demand response 
are interrelated, and their changes will directly affect the 
fluctuations of the energy hub load and power demand. 
Therefore, it is of great significance to construct a 
comprehensive optimized scheduling strategy that can 
comprehensively consider user behavior and demand 
response to improve the effect and efficiency of optimal 
energy hub load dispatching. 

6. Future Development Trend of 
Energy Hub Load Optimization 
Dispatching 

Under the background of energy transformation and the 
rapid development of smart grid technology, the optimal 
dispatching of energy hub load is facing unprecedented 
development opportunities. Future trends will focus more on 
intelligence, refinement, collaboration and sustainability to 
adapt to the complexity and variability of energy systems. 

Intelligence will become the core driving force of the 
optimal dispatching of energy hub load. With the help of 
advanced technologies such as big data, cloud computing and 
artificial intelligence, energy hubs will realize real-time 
monitoring, predictive analysis and intelligent decision-
making. Through the deep learning algorithm, the system can 
independently learn and adapt to the change law of energy 
load, and realize accurate load prediction and optimal 
scheduling. In addition, the intelligent scheduling algorithm 
can deal with the complex multi-energy coupling relationship, 
improve the energy utilization efficiency, and reduce the 
operating costs. 

Refinement will be an important development direction of 
energy hub load optimization dispatching. With the 
diversification and personalization of energy needs, energy 
hubs need to more finely manage various energy resources. 
By constructing high-precision energy model and load 
prediction model, the fine management of various energy 
sources is realized. At the same time, the refined optimization 
algorithm will develop more reasonable scheduling strategies 
according to different energy types and load requirements, 
and improve the energy utilization efficiency and service 
quality. 

Synergy is another important trend of load optimization 
dispatching of energy hubs. The future energy system will be 
diversified and distributed, and energy hubs need to be 
optimized together with multiple energy networks and 
distributed energy equipment. By building a multi-energy 
collaborative optimization platform, the information sharing 
and collaborative scheduling among different energy systems 
are realized. At the same time, the coordinated scheduling 
strategy will consider the complementarity and substitution of 
various energy sources, so as to realize the efficient utilization 
of energy and reduce energy waste. 

Sustainability will be an important goal of optimizing the 
load scheduling of energy hubs. In the context of global 
response to climate change and promoting green and low-
carbon development, energy hubs need to pay more attention 
to environmental protection and sustainable development. By 
optimizing the energy structure, improving the utilization rate 
of renewable energy, and reducing energy consumption and 
emissions, the green transformation and sustainable 
development of energy hubs are realized. At the same time, 
the sustainable scheduling strategy also needs to consider the 
social and economic impact of the energy system, and 
promote the comprehensive and sustainable development of 
the energy system. 

In the future, the load optimization scheduling of energy 
hubs will pay more attention to the development of 
intelligence, refinement, coordination and sustainability. This 
will provide strong support for the efficient operation of the 
energy system and green and low-carbon development, and 
promote the green transformation and sustainable 
development of the energy structure. 



 

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7. Conclusion and Outlook 
With the continuous growth of global energy demand and 

the increase of energy supply complexity, as a key node in the 
multi-energy system, the load optimization scheduling 
problem of the energy hub is particularly important. This 
paper summarizes the research status, method challenges and 
prospects of the optimal load scheduling of energy hub in the 
energy Internet environment, aiming to provide valuable 
reference for promoting the sustainable development and 
green transformation of energy system. Energy hub as the 
coupling system between energy supply and load demand of 
input-output two-port model, the coupling matrix and 
describes the energy distribution between electricity, heat, gas 
and other forms, and coupling relationship, provides an 
important tool for the optimal configuration and operation 
optimization of multi-energy system. Through the study of the 
optimal scheduling of energy hub load, the flexibility of 
multi-energy coordination and complementarity can be fully 
utilized to realize the optimal allocation and efficient 
utilization of energy resources. With the rapid development of 
the Internet of Things, big data, artificial intelligence and 
other technologies, new ideas and methods are provided for 
the load optimization scheduling of energy hubs. By 
introducing advanced information technology, the operation 
status of the energy hub can be monitored in real time, with 
data collection and intelligent analysis, providing powerful 
data support for optimal scheduling. At the same time, 
combined with mathematical planning, artificial intelligence 
and other optimization algorithms, a more accurate and 
efficient energy hub load optimization scheduling model can 
be built to realize the intelligent management and control of 
the energy system. 

The optimal load dispatching of energy hub is one of the 
important areas of energy system research, which is of great 
significance for promoting the sustainable development and 
green transformation of energy system. In the future, with the 
continuous progress of technology and the deepening of 
research, the optimal load scheduling of energy hub load will 
play a more important role in improving energy utilization 
efficiency, promoting the development of renewable energy 
and promoting the reform of energy market. 

In the future, the optimized load scheduling of energy hubs 
will show the following development trends: 

Intelligent and adaptive promotion: through big data, cloud 
computing, emerging technologies such as artificial 
intelligence constantly development and innovation, the 
energy hub load optimization scheduling has achieved a new, 
high level of intelligent and adaptability, through the new 
means to real-time monitoring of energy supply and demand 
changes, through any time monitoring and analysis data 
automatically adjust optimization strategy, and break through 
the existing mode of new ways to deal with various complex, 
huge challenges, so as to better promote the development of 
the industry and progress. 

More energy collaborative optimization in-depth study: 
further research will focus on collaborative optimization 
between a variety of energy, including solar energy, wind 
energy, hydropower, geothermal energy and other forms of 
energy interaction and mutual coordination, through the 
advantages and disadvantages of complementary, in order to 
maximize energy efficiency and reduce the purpose of the 
energy waste. In future studies, more attention will be paid to 
the collaborative optimization between multiple energy 

sources to achieve efficient energy utilization and reduce 
energy waste. Through the construction of multi-energy 
collaborative optimization platform, the information sharing 
and collaborative scheduling between different energy 
systems are realized, so as to realize the efficient utilization 
of energy and reduce energy waste. 

Technological innovation of renewable energy 
consumption and utilization: Through continuous 
development and improvement of technology, future research 
will pay more attention to the consumption and utilization of 
renewable energy. How to improve the utilization rate and 
stability of renewable energy has become a key topic in the 
academic circle. The introduction of advanced energy storage 
technology, demand side response and other means provides 
new ideas and methods for this topic, which helps to improve 
the absorption capacity and stability of renewable energy, so 
as to better promote the development and application of 
renewable energy. 

The synergistic research of the energy market and policy: 
With the rapid development of the global energy market and 
the need of the sustainable energy system, the synergistic 
research of the future energy market and policy will also 
become the trend in the future. How to guide the development 
of the energy market, promote the coordination of market 
mechanisms, establish a more reasonable and suitable for the 
sustainable development of the energy system, so as to help 
guide the direction of energy consumption and investment, 
and promote the green transformation of the energy structure. 
Therefore, in the future, we not only need to pay attention to 
the continuous innovation and breakthrough of technology, 
but also the mutual collaborative research between policy and 
market is one of our future research directions. We must 
conduct more comprehensive research and analysis in order 
to contribute to the sustainable development of the energy 
system. 

Deep integration of energy hub and smart grid: The deep 
integration of energy hub and smart grid is one of the 
important ways to realize the intelligence, automation and 
efficiency of energy system. With the continuous 
development of smart grid technology, its influence on the 
optimal load dispatching of energy hubs will gradually 
expand. Future research should further explore the deep 
integration of energy hubs and smart grids, so as to achieve 
more efficient, flexible and reliable energy supply and 
demand matching, and promote the sustainable development 
of the energy system. 

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