Model building of urban energy system and effect analysis of green low‐carbon transition under the background of carbon peaking and carbon neutrality

Under the carbon peaking and carbon neutrality goals background in China, cities are the main force of energy consumption and carbon emission, and their low‐carbon transition development has become the primary task of urban planning. The transitional development of cities not only puts forward higher requirements for the output proportion of new energy, but also puts forward higher requirements for the flexibility of energy system. This paper proposes an optimization model of urban energy system considering the uncertainty output of new energy and new energy vehicles. The uncertainty and operational flexibility of electric vehicles are taken into account at the planning level by using stochastic optimization method. The model considers the expansion and retirement of new energy units and various conversion devices in urban energy system, and ensures the achievement of carbon peaking and carbon reduction goals by imposing constraints on electric vehicles. The case results show that this model can effectively improve the economy and new energy consumption rate of urban energy system, and can meet the carbon reduction demand.

5][6] Cities are not only centers of energy load but also major sources of carbon emissions.They are also key regions for transitional development.In China, 70% of carbon emissions come from cities, which will reach 80% by 2030. 7Therefore, urban energy system transition can play a key role in achieving carbon peaking and carbon neutrality goals.
The energy flow range of urban energy system is relatively extensive, among which the energy flow that has a great influence on carbon emission is the urban thermal flow, and a large part of the total heat demand comes from buildings. 8he combination of buildings and distributed energy can make efficient use of energy and reduce energy waste.Meanwhile, the popularity of charging piles in cities can closely integrate electric vehicles and other new energy vehicles with buildings, coordinate with small and medium-sized comprehensive energy systems, and effectively reduce carbon emissions, contributing to the realization of carbon peaking and carbon neutrality. 9The realization of low-carbon cities cannot be achieved without technological progress and development. 10Large-scale grid connection of electric vehicles can provide considerable adjustable resources to cope with energy demand in peak periods and reduce energy supply pressure of the energy system through orderly coordination.By the end of 2020, the Beijing-Tianjin-Tangshan regional power grid has conducted a test on the participation of electric vehicles in power grid peak regulation. 11400,000 electric vehicles can provide the regional power grid with a total peak regulation power of nearly 3 million kW, which has huge potential.As an important tool to maintain the safe operation of power grid in the future, they can guide various adjustable load side resources such as new energy vehicles and distributed energy storage.
Energy flow analysis plays an important role in the transition of urban system.Yu H put forward the relationship between energy consumption and urban transition, established the analysis model of the influence of energy consumption on transition, and proposed a simple energy flow analysis. 12Ang, Y. Q. analyzed the internal relationship between energy consumption and carbon emission changes.Transition and development have a great impact on urban planning and development. 13Bekirsky N. proposed that the application of new energy technology has a certain law in influencing the distribution of urban energy flow. 14Chen, Z. proposed a mechanism of distributed equipment affecting the energy flow of the energy system, through which the transition analysis of the energy system can be deepened. 15he current research focus on urban energy system transition is concentrated on the user side.Chenic, A. Ş considered low-carbon elements in distribution network planning and analyzed the emission reduction potential of grid measurement. 16Fan, P. focused on reconstructing the user side in distribution network planning, but did not take into account the output of electric vehicles and other distributed energy sources. 17Ghobadi, A studied the relationship between transportation industry and urban energy system, and analyzed the potential of collaborative planning in solving traffic congestion. 18orak, D. proposed a multistage planning, and put forward different investment schemes for urban energy system transition. 19However, if the supply side is missing in the planning process, it will have a great impact on model simulation. 20Lo Franco, F. proposed to optimize the connection relationship of equipment investment demand, and analyzed the energy utilization efficiency and renewable energy consumption under this condition. 21Mustafa, J. and Shiravani, F. considered that urban energy system with high proportion of new energy will have higher requirements for flexibility, and included energy storage devices into their research scope. 22Trairat, P. and Yu H. et al. also took demand side response into consideration, which is a more reasonable measure. 23Ang Y. Q. considered carbon storage equipment and studied its impact on the overall model. 24ased on the information obtained from the review of relevant literature, there are still some shortcomings in the current research: First, it does not take into account the actual development trend of the transportation industry, and ignores the impact of electrification of the transportation industry on urban energy system transition.Second, it does not consider that urban transition development is accompanied by high flexibility of urban energy system.Finally, it ignores the huge potential of electric vehicles as energy storage devices.
To address the above shortcomings, this paper establishes an urban energy system transition optimization model based on stochastic optimization, considering the uncertainty caused by load growth and wind-solar fluctuation, taking into account the development constraints of electric vehicles, considering various constraints in the transition process, using multi-energy complementary system to increase renewable energy consumption rate, and improving the economic performance of the system.The case results verify the effectiveness of the model established in this paper.
The rest of this paper is divided into several sections: Section 2 introduces the model and the constraints.Section 3 presents the data and the results of simulation.Section 4 discusses the main findings in this study.Finally, the conclusion and policy recommendations are drawn in Section 5.
The definitions and abbreviations of some terms involved in this paper are shown in

CH
The running state of an energy storage device, namely the charging.

D
Real-time user demand power.

DCH
The running state of an energy storage device, namely the discharging.

DEM
A variety of energy demands, including electricity, heating, and cooling demands.

E
Generation coefficients, including E E , E H , E C , are used to unify energy data across different sectors.

E A
The energy dissipation level of motor A.

ELMT
Carbon emission quotas for various sectors.

EMS
Annual carbon emission value.

FUE
Fuel cost per unit calorific value.

G A
The generating capacity of generator A.

HED
The amount of electricity used to produce a unit of heat energy.

SAL
The cost of decommissioning units 1 year before the end of their service life is related to the unit of annual generating capacity.SOC State of charge.

GREEN AND LOW-CARBON TRANSITION STRUCTURE FOR URBAN ENERGY SYSTEM
Urban energy system refers to the system of production, transmission, distribution, consumption and management of various energy sources in urban areas, including power system, natural gas system, thermal system, wind energy system, solar energy system and so on, as well as their interaction and coordination.Urban energy system is an important foundation for urban development.It affects the economic, social and environmental aspects of urban areas.It also faces challenges such as energy security, energy efficiency and energy environment.
To cope with these challenges, urban energy systems need to innovate and transform, achieving diversification, decarbonization, digitalization and interconnection of energy.At the same time, energy storage should play a key role in the transition process. 25Energy storage can reduce the phenomenon of energy abandonment of renewable energy and realize distributed energy supply. 26At the same time, the development of electric vehicles has a great impact on energy storage.Electric vehicles can realize a good interaction with the power grid and reduce the energy supply pressure of the energy system. 27Electric vehicles will generate decommissioned power batteries.Large-scale stepwise utilization of power batteries is of great significance to the construction of energy storage stations and reduce the cost pressure of energy system transition. 28Improved urban infrastructure facilitates the development of electric vehicles, deepens the connection between electric vehicles and energy systems, and pushes cities to achieve the goal of carbon peak and carbon neutrality.
Urban energy system mainly includes three types of entities: (1) Power sector involves green and low-carbon power sources in urban circle, including new energy devices such as photovoltaic power generation, wind power and (mobile) energy storage devices.(2) Power users include residential buildings, commercial buildings, industrial and transportation sectors.(3) Space heating sector needs to consider the energy consumption of residential buildings and commercial buildings.When optimizing urban energy system, it is necessary to meet the energy demand of various types of users in urban energy system with minimal cost, so as to improve the efficiency of green and low-carbon transition of this system. 29ccording to the setting in Figure 1, considering the interaction between natural gas-wind-light Integrated Energy System (IES) and demand side, Urban Energy System (UES) consists of three parts: Renewable Energy Generation System (REGS), CCHP, other auxiliary equipment and Electric Vehicle (EV) on demand side.Among them, the energy flow includes natural gas, electricity, heat flow and cold flow.Considering offshore wind power (WOFF), onshore wind power (WON) and distributed photovoltaic power generation (PV), they are the main equipment of REHS.Gas Turbine (GT), Waste Heat Boiler (WHB) and Absorption Chiller (AC) are the key equipment of CCHP system.Gas-fired Boiler (GB) and Electric Refrigerator (ER) are used to meet the cooling and heating needs of users.In addition, intelligent power consumption technology is the basis of future distribution system operation control, and EV, distributed energy storage, commercial air conditioning, industrial users are the interactive subjects of source, load and storage, and the electricity market is an important means to maintain the power and electricity balance of the whole supply-side and demand-side system. 30,31esults of a variety of research and survey reports show that in various scenarios of daily use, private cars spend a relatively low proportion of their commuting time, and in more cases, cars will stay in a state of residence, as shown in Figure 2. When electric vehicles stay in a state of residence for a long time, and the place of stay is closely related to urban buildings, therefore, the special relationship between electric vehicles and urban buildings determines that electric vehicles have great potential to realize energy interaction with urban buildings and power grids and synergistic interaction with urban energy systems under the background of carbon peaking and carbon neutrality. 32igure 3 shows the typical residence characteristics of EV.The probability of EV staying in office and residence is about 90%, and the potential of EV as a mobile energy storage device is strongly correlated with the behavior and habits of EV.As a mobile energy storage device, EV has a medium-long residence state and is close to urban load center.There is great potential for energy storage to support peak and frequency modulation in power systems.
Electric vehicles will be an important part of terminal load in the future power system, and their energy use characteristics are related to the performance of electric vehicles, users' demand for vehicles, charging behaviors and habits.The charging power of electric vehicles is too large, and the disorder of charging will cause a large load impact on the power grid, especially the transmission power of the power grid. 33The method of orderly charging and intelligent charging can effectively relieve the pressure of power system.Figure 4 describes the typical charging time and charging duration requirements of electric vehicles in charging stations.
As can be seen from Figure 4, most users choose to charge in the morning and evening peak hours, and their charging behaviors and habits have certain rules with the charging time, which increases the load demand in the morning and evening peak hours.As for the power distribution system of urban buildings, it is characterized by long-term low-load operation.Therefore, the power distribution system of buildings and electric vehicles within the urban system have complementary operation characteristics, indicating that some periods and partial loads have great potential to adjust the terminal loads of buildings and electric vehicles. 34

F I G U R E 2
Relationship between electric vehicles and buildings in the city.The cooling of CCHP combined cooling, heating and power system is achieved by refrigeration units (electric refrigeration machine, absorption refrigeration unit) to meet the cold load demand of the system.The utilization of waste heat is an important link in the CCHP system, which improves the comprehensive utilization rate of energy and realizes the step utilization of absorption chiller.The integration of IES and REPG can solve the problem of redundant power consumption and reduce energy waste to a certain extent.Literature 35 shows that EV, as a load, participates in the load regulation of the comprehensive energy system, which can enhance the flexibility and economy of IES.The demand for refrigeration and heating is closely related to urban buildings.The combination of CCHP units, electric vehicles and urban buildings can realize the full utilization of energy.The main source of carbon emissions includes heating, so it has a positive impact on reducing carbon emissions and realizing carbon peak as soon as possible.

OPTIMIZATION MODEL OF GREEN AND LOW-CARBON TRANSITION OF URBAN ENERGY SYSTEM
The transition and optimization of urban energy system is a complex, multi-dimensional and dynamic process, which involves the coordination and balance of various factors and objectives.From the perspective of cost, transition optimization needs to consider the cost of energy system construction, operation, maintenance, and other aspects, in order to achieve economic benefits of energy system.From the emission point of view, we need to consider carbon emissions and other pollutant emissions to achieve clean and environmentally friendly.From the perspective of elasticity, it is necessary to consider the resilience and resilience of the system to achieve reliability and security.From the perspective of innovation, we need to consider the innovation of technology, management, system, culture, and other aspects to realize intelligence and serviceability.
These views are not isolated, but interrelated.Therefore, the transition and optimization of urban energy system should be constrained from the above four perspectives.

Objective function
Taking cost as the objective function can improve the investment trend.Cost is not a short-term goal, but a long-term goal.Cost reduction and efficiency increase are conducive to the healthy operation of the system and promote the green and low-carbon transition of the city. 36he optimization model of urban energy system transition aims at minimizing the total cost of system transition, which consists of the discounted cumulative cost (cost i) of various technologies such as power generation technology and space heating technology, the inter-provincial electricity transaction cost (C s,y = C imp s,y × e imp s,y .Among them, C imp s,y is the unit cost of inter-provincial electricity transaction with S province in Y year, e imp s,y is the electricity amount obtained from S province transaction in Y year) and government technical incentive (inc i,y ).As shown in Formula (1): The C s,y represents the inter-provincial electricity transaction cost.The RATE indicates discount rate.The costie represents the discounted cost of various technologies in the power generation sector in the urban energy system, including: (1) various constraints and discounted costs in the power generation of renewable energy such as wind and light, which reflects the green transition process of the energy system; (2) Power generation constraints and discounted costs of fossil energy such as coal and oil reflect the low-carbon transition process of energy system.

Relation constraints
The initialization of continuous variables included in the optimization process of urban energy system is shown in Formula (2) The optimization of urban energy transition system cannot be separated from the development and iteration of related technologies.During the development planning period of urban system, it is assumed that there is a correlation between the existing capacity level and decommissioned capacity of units with related technologies applied for two consecutive years.The existing capacity of a certain technology in a department with a Y -year life of T should be equal to the difference between the technical capacity of the corresponding department with a y−1 life of t−1 and the technical de-commissioned capacity of the corresponding department with a y-year life of T, which is expressed by Formula (3): e ext ie, t, y = e ext ie, t−1, y − 1 − e dis ie, t, y (3) Among them, dis stands for decommissioning, while ie stands for power generation, H stands for heating, C stands for cooling, B stands for energy storage, and T stands for the service life of units applying a certain technology.Different units have different service lives, and their changes are synchronized with Y .The letter represents the form of energy conversion.These transition constraints indicate that the existing capacity of technology i with a y-year life of t is equal to the existing capacity with a y−1-year life of t−1, and does not include the decommissioning capacity corresponding to y-year.
In addition to the capacity setting and calculation of the supply side, the supply side of the energy system is set as follows, and the equipment is required to be retired within the service life, in which LIFE i is used to indicate the service life of the equipment, which is a constant, and I is an element in the technology collection used by the energy system.Formula (4) indicates that the retirement of the generator set in Y year should be: The calculation formula of unit decommissioning in heating, refrigeration, energy storage and other departments is the same as Formula (4).

Renewable resource constraints
The "green transition" in the transition of the energy system with cities (urban agglomerations) as a unit responds to the increase in the application scale of renewable energy power generation technology in the national "14th Five-Year Plan" so as to reduce the proportion of fossil energy in the system.The level of renewable energy generation is shown in Formula (5): Where RPS G y represents the renewable energy portfolio quota based on renewable energy generation technology in the power sector of the urban energy system in y year.Ire represents renewable energy power generation technology, which is a subset of power generation technology Ie in the power sector.
In the system, the technological development of distributed photovoltaic power generation and wind power is one of the important ways to realize "double carbon".Ideally, the expected cumulative generation of distributed photovoltaic power generation and wind power generation in 2025 should be greater than or equal to the power generation target of the planning target, and the distributed solar photovoltaic (PV) and offshore wind power (WOFF) or onshore wind power (WON) can be optimized respectively.
The total target capacity of distributed photovoltaic (PV) is shown in Formula (6): Among them, CF PV,t,2025 represents the capacity factor of distributed photovoltaic (PV) units, HOUR PV,t,2025 represents the annual utilization hours of distributed photovoltaic (PV) in 2025, and TAR PV,y represents the target planned capacity of distributed photovoltaic (PV) power generation in urban energy system in 2025.
The target for the total power generation capacity of WON or WOFF is shown in Formula (7): Thereinto, CF WOFF/WON,t,2025 represents the capacity factor of wind power (PV) units, HOUR WOFF/WON,t,2025 represents the annual utilization hours of wind power (WOFF/WON) units in 2025, and TAR WOFF/WON,y represents the target deployment capacity of wind power (WON/WOFF) in power sector of urban energy system in 2025.

Emission constraints
The "low-carbon transition" in the system responds to the development content of "High emission and High energy consumption" projects in the "14th Five-Year Plan" of the state, and achieves the goal of "Double control" of total energy consumption and intensity.Taking cities (urban agglomerations) as a unit, considering the regulatory requirements of carbon emission in energy load centers, the carbon emission level in Y year should be less than or equal to the carbon emission limit of urban generator sets in Y year, which is expressed by Formula (8): Where E E i,t,y represent the carbon emission factor of power generation, and E LMT j=e represents the carbon emission limit of power generation of the whole urban energy system.The CO 2 emission constraints of space heating and cooling departments are similar to those of power generation departments, and should not be greater than the annual CO 2 emission limits of their respective departments.The emission constraints are expressed by Formulas ( 9) and (10).
The annual CO 2 emissions of the energy system consist of three parts, so the annual CO 2 emissions are calculated as shown in Formula (11).

Demand constraints
In the process of energy transition, whether the energy system can provide enough energy security for the city and meet the basic production and living needs is a key point, otherwise it will affect the normal economic operation and social activities of the system.The thermal energy demand of space heating in Y is recorded as DEM H y , and the total annual thermal energy production of existing space heating units in Y is limited by the upper limit of technology market share.The annual space heating demand constraint is shown in Formula ( 12): Among them, h ext i,t,y are the calorific value of space heating mode i h in Y year (t = 0), and t = 0 indicates the new units deployed in Y year.I h represents the collection of all space heating technologies, and MS i h ,y represents the upper limit of market share of i h , the space heating technology in y year.
Similarly, the constraints of annual space cooling and annual total electricity storage demand can be obtained, as shown in Formulas ( 13) and ( 14): There into, c ext i,t,y is the cooling capacity of space cooling mode i c in y year (t = 0), b ext i,t,y is the energy storage capacity of energy storage mode i b in y year (t = 0), and t = 0 indicates the new equipment and new units deployed in y year.The power generation capacity of the system should be equal to the comprehensive power demand of users in the system in year Y , including the direct power demand of users and indirect power demand such as cooling and heating of buildings.Direct electricity demand includes daily electricity demand and mobile energy storage device EV.The annual total electricity demand constraint in the planning period is shown in Formula (15).
Among them, HED i,t,y represents the electric power consumption generated by technology I in Y year at the production unit heat energy of life T, CED i,t,y represent the electric power consumption generated by technology I in Y year at the production unit cooling capacity of life T, and b ext iev,t,y represent the storage capacity that can be provided by the mobile energy storage device EV in the current storage equipment in Y year energy system.

Planning constraints
In order to get a reasonable and realistic energy transition forecast, it should also include the scheduled changes of the energy system.The proposed increase of generator and power storage facilities is provided by Formulas ( 16) and ( 17) respectively.Among them, PRO G i,y indicates that technology i was proposed to increase the power generation capacity in y, and PRO B i,y indicates that technology i is planned to increase the energy storage capacity in y.

Energy storage constraint
In order to avoid unrealistic subversive changes in the system, additional constraints need to be added, including deployment smoothing constraint, maximum growth constraint, market share ceiling and maximum interprovincial electricity transaction.Among them, the capacity target of energy storage is shown in Formula ( 18), which is very important for the power grid with high intermittent power penetration.TARS y is the energy storage capacity target for Y year.
The mobile energy storage in the city has high penetration into the power grid and has the characteristics of distributed energy storage.When it reaches a certain scale, it will affect the overall level of energy storage in the energy system.Therefore, the setting of the development scale of electric vehicles can be expressed by Formula (19): Considering that a considerable part of EV batteries of electric vehicles are made of lithium, the annual energy storage level of part of mobile energy storage devices is expressed by the energy storage of lithium electronic batteries.
Power generation plan and renewable energy quota will affect the energy storage level of urban energy system.At the same time, the total capacity of energy storage departments should be greater than the peak demand of urban energy system.According to Formulas ( 20) and ( 21), REQ P represents the peak demand percentage, Peak represents the peak electricity consumption in Y , REQ M i represents the marginal increase capacity percentage of renewable energy power generation technology, CF i,t,y represents the capacity factor, and HOUR represents the annual utilization hours.

Economic constraints
Economic cost is the cost generated in the process of electric power production, trading and storage, space cooling and heating.The cost of electricity includes power generation cost, electricity transaction cost and electricity storage cost.
The discounted total cost of power generation includes the capital cost of newly built units, the operation and maintenance cost of existing units, the decommissioning cost of power plants and the social cost of carbon dioxide emissions from power generation activities, which is expressed by Formula (22).
Among them, C CAP i,t,y represents the unit capital cost of building new technology unit i in y, which is equal to zero when t ≠ 0. C OPT is the annual operation and maintenance costs, normalized to the unit energy production, including fixed and variable operation and maintenance costs.C SAL refers to the retirement cost of the retired unit using technology i at the end of its service life y, which is related to the annual energy production per unit, and C CAR y refers to the unit cost of carbon emission.The electric power transaction cost is expressed by Formula ( 23), C IMP s,y represents the interprovincial transaction cost of s unit electric power in y year, and eimp s, y represents the electric power transaction volume of each province and city.
The power storage process does not directly discharge greenhouse gases into the atmosphere, and does not consider the social cost of carbon dioxide.The energy storage cost is expressed by Formula (24).C CAP ib represents the unit capital cost of new technology i energy storage, C OPT represents the annual operation and maintenance cost related to the unit energy output, and C SAL represents the unit decommissioning cost generated in the y year when the service life of applied technology i ends.
Space heating cost includes capital cost, fuel cost, electricity cost, retirement cost, and greenhouse gas emission cost.The discounted total cost of space heating mode ih during the system conversion period is shown in Formula (25), including capital cost, fuel cost (including electricity) and retirement cost.C FUE represents the fuel cost per unit calorific value of technology i in y year, and E H represents the annual greenhouse gas emissions per unit thermal energy when technology i is used in t year.
Space cooling cost can be divided into capital cost, fuel cost, electricity cost, retirement cost and greenhouse gas emission cost.Formula ( 26) is used to calculate the discounted total cost of space cooling mode ic at the time of energy system conversion.These include capital costs, fuel costs (including electricity) and decommissioning costs.E C represents the annual greenhouse gas emissions per unit cooling capacity.
The government increases the budget of new technology, stimulates the development of technology and adjusts the energy structure.The incentive amount is related to the effect of technology.The incentive amount of technology I in Y year (recorded as inc i,y ) is calculated by Formula (27).IR i,y is the incentive rate of unit technical quantity I deployment in Y year.As shown in Formula (28), the total incentive amount is limited by the incentive plan budget, which is described by Bgt i .inc ie,y = IR ie,y ⋅ e ext ie, 0, y ( In addition, it is stipulated that the continuous variables such as e ext , e dis , e imp , h ext , h dis , b ext , and b dis involved in the optimization model of green and low-carbon transition of urban energy system are all non-negative.

Power constraints
The energy transition process has a long time span and involves many units and energy types. 37The actual operation of units is restricted by various aspects.Considering the output of CCHP combined cold, thermal and electric power supply on the supply side, reasonable cooperation of various units is needed to reduce unnecessary energy consumption and meet the energy demand of users.Formulas from ( 29) to (39) are the power constraints generated by the interaction between the energy storage sector and energy use demand.
Where E is the power of the generator, G is the power of the motor, ch stands for charging, dch stands for discharge, and efficiency, corner label stands for the corresponding unit, SOC stands for the state of charge of the energy storage device, and so forth.
Operation constraints of conventional energy storage components in urban energy system are shown in Formulas from (40) to (42): u ch,t + u dis,t ≤ 1 (41) In terms of energy storage, in addition to conventional power storage technology, electric vehicles are mobile storage and discharge devices with high flexibility.In the microgrid structure, electric vehicles whose battery power reaches a certain value can serve as the supply side to provide energy storage for the microgrid; when the battery power level is lower than a certain value, electric energy can be obtained from the grid.Formula (43) restrains the charging and discharging power of electric vehicles: P ev,t min ≤ P ev,t ≤ P ev,t max (43) P ev,t represent the charging power of the electric vehicle at time t.P ev,t min represent the minimum charging power of the electric vehicle.P ev,t max represent the maximum charging power; positive value represents the electric energy obtained from the grid for charging; negative value represents the electric energy provided to the grid as an energy storage device.

Sensitivity analysis
In this study, sensitivity analysis was conducted to investigate the influence of potential deviation of input parameters.Specifically, the changes of the following factors are considered: space heating and cooling energy demand, electric vehicle energy use, minimum energy storage demand, power input capacity and power peak demand.

Data source and description
In this paper, the comprehensive energy system of an urban agglomeration composed of a medium-sized city and its surrounding small cities in East China is studied.According to time of use electricity price (Table 2), the charging time of electric vehicles on the power grid is divided, and the influence of electric vehicles as mobile energy storage devices on the energy storage level of the energy system is determined.Information and energy interaction with IES's main distribution network.New unit deployment, decommissioning cost, technical and operational parameters, unit maintenance parameters in urban system are derived from literatures 36,[38][39][40] and. 41The indicators related to urban energy system can be divided into power system indicators, emission indicators and technical application indicators.
The power system goal requires that by 2025, renewable energy will account for 45% of power generation.The emission target requires that carbon dioxide emissions should not exceed 3.5 million tons by 2025.The goal of technology deployment and technology application sets the minimum capacity requirements for distributed photovoltaic power generation (PV), wind power generation (WON/WOFF) and electric energy storage deployment in several target years.At the same time, considering the transition from fuel vehicles to electric vehicles and the deployment goal of mobile energy storage, it is necessary to increase the deployment of mobile energy storage equipment as much as possible to accelerate the electrification process of the transportation industry.The number of electric vehicles is required to reach 700,000 by 2025.In the optimization process, the economic value is discounted to 2021.

Optimization results analysis
Figure 5 shows the predicted trend results by sector.The transition of urban energy system drives the development of all sectors.All kinds of demand will be on the rise from 2020 to 2025.The average annual growth rate of electricity demand during the planning period will be 5.5%.The proportion of urban electricity consumption will increase by about 1.3%.The annual growth rate of heat demand is about 7.3%, cooling demand is about 11.2%, and energy storage demand is about 18.1%.Over the planning period, cooling demand will grow slightly faster than heating demand.In addition, energy storage demand has the best growth effect, which may be due to the larger scale of new energy vehicles, which has improved the energy storage level of urban energy systems.The energy transition has increased the demand for electric energy, promoted the growth of electric energy demand and accelerated the replacement of electric energy.The urban energy transition has achieved initial results.
Figure 6 shows the structural change trend of power generation sector in urban agglomeration energy system in the next 5 years.In the early stage of the transition, the power generation sector mainly relied on coal, natural gas, wind energy, nuclear energy, solar energy, hydropower, and other renewable energy, which gradually improved its impact on the energy structure.The development of wind power is obvious.Onshore wind power and offshore wind power are growing rapidly, and the power generation in 2025 will be about 1.8 times of that in 2021.Natural gas has been significantly increased in the power generation structure, from 27,243 GWh in 2021 to 48,319 GWh in 2025, increasing about twice.Natural gas is the transitional energy in the process of transition and development, and plays an important role in the early and middle stages of transition.The improvement effect of solar power generation is obvious, with power generation capacity increased by 5.7 times, but the proportion is still at a low level, and energy storage technology is needed to cooperate with the development.In short, the multi-energy complementary effect is obvious in the planning period.The power generation level of new energy and renewable energy has been improved to varying degrees, while the growth of fossil energy is relatively small.Figure 7 shows the development trend of heating sector.The heating sector was transformed to rely on natural gas, and the proportion of natural gas increased from 27.9% to 33.1%, while the proportion of coal did not change much, and the total amount increased.It will take some time to observe a more obvious transition effect.The transition reduces the use of traditional fossil energy in the heating industry, and the complementary effect is obvious.
Figure 8 shows the optimization results of refrigeration sector.Refrigeration sector refrigeration capacity increased overall during the planning period.Waste heat refrigeration improved greatly, from 11,532 to 16,717 GWh, increased by 44.9%.Electric refrigeration technology improved by 13.7%, but absorption refrigeration did not improve significantly.The CCHP system delivered results over the planning period, saving energy, avoiding energy waste and improving energy efficiency.
Figure 9 shows the optimization results of the energy storage sector.The energy storage of lithium electronic batteries has increased obviously, and the mobile energy storage devices have increased slowly, which is about 21% higher than that of 2021.Intermittent problems caused by the potential high permeability of renewable energy can be alleviated to a certain extent with the assistance of energy storage sectors, which will drive the development of energy storage.The increase of energy storage, on the one hand, delays the phenomenon of abandoning wind and light.On the other hand, according to its characteristics, the mobile energy storage device can not only take the task of storing electric energy, but F I G U R E 10 Sensitivity analysis of input parameters with the impacts on the total system cost.also absorb some excess electric energy.The effect of electric energy substitution in the energy storage sector is good, and it can obviously promote the green and low-carbon transition.
Figure 10 shows the sensitivity analysis results of several input parameters to the total system cost influencing factors with an error of ±10%.It can be seen from the analysis results that heating demand has a significant impact on the forecast results, possibly because heating demand accounts for a relatively large proportion of the total energy demand and has a more significant impact on the cost.The most influential factors are the cooling demand, the energy use of electric vehicles and the lower limit of power storage.The influence of electricity trading volume has little influence on the forecast results, indicating that it can be ignored.
Figure 11 shows the predicted results of the number of new-energy vehicles scrapped and mobile energy storage capacity.The peak of scrapped electric vehicles is about 2024 to 2025.Due to the limitation of battery technology at that time, the first batch of electric vehicles in China have less advantages in terms of battery capacity and battery life.2030 will be the peak of hybrid vehicle scrapping.The surge of retired power batteries has brought abundant mobile energy storage resources for urban energy systems.In 2027, the mobile energy storage capacity that can be provided will reach nearly 500 MWh, which is a great progress compared with the optimization results of mobile energy storage from 2020 to 2025.Power batteries retired by 2040 will provide nearly 800 MWh of available mobile storage capacity for urban energy systems.At the same time, the increase in energy storage capacity is not instantaneous.The cumulative mobile energy storage capacity that can be provided is close to 8 GWh, which is likely to exceed the newly installed capacity of renewable energy.
As can be seen from the curve shown in Figure 12, during the planning period 2021-2025, the carbon emissions of urban energy system show an increasing trend year by year, with the rising rate slowing down and the decreasing trend not obvious.Due to the adjustment of urban energy structure, the annual increment of carbon emissions is decreasing year by year, from 200,000 tons to 50,000 tons.It is expected to reach the peak at the end of the planning period, bringing about the decline of carbon emissions.The reduction of carbon emission intensity indicates the realizability of "carbon dioxide emission peak" in urban energy system.The temporary increase in carbon emissions, partly due to the introduction of new technologies, did not bring the desired reduction in emissions to the system.
Figure 13 shows the forecast trend of carbon emissions of urban energy system.As can be seen from the figure, the carbon emission value has shown a stable trend since 2025, indicating that the transition of urban energy system has achieved initial results and the energy structure has been significantly improved.Around 2028, carbon emissions peak, and by 2029, carbon emissions begin to decline, and there will be a relatively rapid decline, lasting several years.By 2037,

F I G U R E 12
Carbon emission trend of urban energy system in planning period.

F I G U R E 13
Further prediction of annual carbon emissions from urban energy system.the rate of decline in carbon emissions will start to slow down, and the transition effect will be obvious.The city's carbon peak goal has been achieved ahead of schedule, and it has entered the early stage of carbon neutral development.To achieve carbon neutrality, it still needs a certain amount of time to accumulate technology and adjust the structure of urban energy system.

DISCUSSION
In the context of sustainable low-carbon development, this paper proposes an urban energy system transition optimization model that considers multiple agents and new energy vehicles in the process of urban energy system transition.Compared with previous studies, the model proposed in this paper has the following characteristics: This paper considers the large-scale grid connection of new energy vehicles and the grid flexibility demand caused by new energy and renewable energy access to the system when considering the demand for urban energy system transition optimization, combined with the development characteristics of China's transportation industry.
This paper extends multiple devices with multi-energy conversion, and improves the consumption of new energy and the sustainable low-carbon transition of urban energy system through multi-energy complementarity.
The numerical results show that considering short-term operational flexibility in the planning model can reduce the curtailment rate and carbon emissions of new energy sources by 5.3% and 16.7%, respectively.The reduction of system cost in the planning period is about 2.74%, which is not very significant.The investment capacity of new energy units in urban energy system planning decisions depends on low-carbon transition requirements.At the same time, to meet the operational flexibility under high proportion of new energy penetration, urban energy systems need to invest in multi-energy conversion equipment.Future research can further analyze the impact of expansion of electric heating and cooling storage, line pipelines and other facilities on operational flexibility, assess the impact of multi-stage investment planning on results, and provide diversified planning paths for urban energy transition from a low-carbon perspective.

CONCLUSIONS
The results show that the low-carbon transition development of urban energy system needs to do a good job in the optimization management of industries with high energy consumption, which is of great help to adjust the energy structure and reduce the excessive use and waste of energy.

F I G U R E 3
Typical residence characteristics of electric vehicles.

4
Charging time and charging duration probability of EV in a typical scenario (a) Charging duration probability of EVs in typical scenarios (b) Probability of EV charging time in typical scenarios.

F I G U R E 5
Development trend of four sectors of urban energy system.

F I G U R E 6
Development trend of power sector of urban energy system.F I G U R E 7 Development trend of heating sector of urban energy system.

F I G U R E 8
Development trend of cooling sector of urban energy system.F I G U R E 9 Development trend of energy storage sector of urban energy.

F I G U R E 11
Mobile energy storage capacity and scrap number of new energy vehicles.

Table
Nomenclature.electricity generation technologies, subset of I, including bituminous coal (BIT), fuel oil (FO), methane from biogas (MTE), refuse of solid waste (REF), utility solar PV (SUNU), distributed solar PV (SUND), nuclear power (UR), hydropower (WAT), on-land wind (WON), offshore wind (WOFF), natural gas combined cycle (NGCC), natural gas combustion turbine (NGGT), natural gas steam turbine (NGST), natural gas combined cycle with carbon capture and storage (NCCS) TA B L E 1T A set of ages for all technologies, indexed by t, and t = 0 indicates that the technology has just been deployed in this year Y A set of years, indexed by y, from the initial year to the end of planning horizon TAR A variety of unit deployment objectives for different departments Parameters Bgt Government annual technology incentive budget.C Represents various costs, distinguished by the lower corner mark.CAR Estimates of the cost of carbon emissions per unit of society.CAP Unit construction cost of a new unit.CF Capacity factor, represents the capacity utilization efficiency of the unit.
Basic structure and energy flow diagram of urban energy system.
. Inini is the abbreviation of ININITIAL, which represents the initial quantity, where INI E i,t represents the power generation of the initial year when technology I was applied in YINI for T years.INI H i,t represents the heat of t years when method I applied technology I in Y INI years.INI, t means that the time of applying technology I by method I in Y INI is the refrigerating capacity of t years.Ini, t represents the initial energy storage capacity of the urban energy system where technology I has been applied for t years.Thus, the power generation capacity, space heating heat energy production capacity, cooling capacity and electricity storage capacity of the whole city's energy system in the initial year are defined respectively.Ext means existing.
e ext ie, t, Y INI = INI E ie, t t, y CF i,t,y ⋅ HOUR i,t,y(21) Charging parameters.