Low-carbon economic operation of IES based on life cycle method and hydrogen energy utilization

Abstract

The Integrated Energy System (IES) that coordinates multiple energy sources can effectively improve energy utilization and is of great significance to achieving energy conservation and emission reduction goals. In this context, a low-carbon and economic dispatch model for IES is proposed. Firstly, a hydrogen energy-based IES (H2-IES) is constructed to refine the utilization process of hydrogen energy. Secondly, the carbon emissions of different energy chains throughout their life cycle are analyzed using the life cycle assessment method (LCA), and the carbon emissions of the entire energy supply and demand chain are considered. Finally, a staged carbon trading mechanism is adopted to promote energy conservation and emission reduction. Based on this, an IES low-carbon and economic dispatch model is constructed with the optimization goal of minimizing the sum of carbon trading costs, energy procurement costs, and hydrogen sales revenue, while considering network constraints and constraints on key equipment. By analyzing the model under different scenarios, the introduction of life cycle assessment, staged carbon trading, and hydrogen energy utilization is shown to promote low-carbon and economic development of the comprehensive energy system.

1 Introduction

Currently, the use of fossil fuels has led to serious problems of energy depletion and environmental pollution. Wind and photovoltaic power generation, as renewable clean energy sources, can improve the energy structure and reduce carbon emissions (Yang et al., 2017).

Hydrogen energy has the characteristics of high calorific value and low pollution, and can be coupled with electricity and heat in the integrated energy system (IES) to form an integrated energy system with hydrogen energy (H2-IES). This has important implications for improving overall energy utilization and achieving a reduction in carbon emissions (Xu et al., 2019; Lin et al., 2020; Wan et al., 2021).

There are a large number of literatures on integrated energy systems (IES) research both domestically and abroad. WU (Wu et al., 2021) established an optimization and scheduling model for an IES system that takes into account power-to-gas (P2G) devices and combined cooling, heating, and power (CCHP) units, and verified that introducing diversified energy conversion equipment is beneficial to improving wind power consumption and energy utilization efficiency. ZHENG (Zheng et al., 2021) considered the uncertainty of renewable energy generation and energy demand, and proposed a data-driven stochastic cooperative timing model for electric and gas integrated energy systems, and addressed the energy price formation and settlement issues in an uncertain market by proposing expected locational marginal prices, and demonstrated that the flexibility of P2G can help hedge against uncertainty. QIU (Qiu et al., 2022) explored the advantages of a mixed hydrogen-natural gas transportation system in improving energy utilization efficiency and reducing costs. SHI (Shi et al., 2018) coordinated demand-side flexible loads with P2G scheduling, and verified the optimal scheduling of the system under four scenarios. The scheduling models in these references focus on the operation optimization of IES and ignore the current low-carbon development background.

Currently, carbon trading is regarded as an important mechanism to reduce carbon emissions. LU (Lu et al., 2021) proposes a wind power curtailment strategy that takes into account the thermal and electrical characteristics of Combined Heat and Power (CHP) units based on the carbon trading mechanism. CUI (Cui et al., 2021) analyzes the principle of carbon trading mechanism and introduces an electric-thermal integrated energy system to analyze the impact of carbon trading prices on the system’s carbon emissions. ZHANG (Zhang et al., 2020) introduces a reward-penalty carbon trading mechanism in the IES planning model, and uses a two-stage robust optimization model to deal with the uncertainty of electric-thermal loads, verifying the low-carbon and economic performance of the model. HUANG (Huang et al., 2023) proposes a double-layer trading framework with regional carbon emission constraints based on the Stackelberg game theory, in which a virtual power plant with zero carbon emissions is the leader that sets flexible carbon emission permit prices, and analyzes the superiority of this trading mechanism in carbon emissions reduction. WANG, CHENG and WANG (Cheng et al., 2020; Wang et al., 2020; Wang et al., 2022) calculate the carbon emission responsibility borne by the demand side based on the theory of carbon emission flow, and verify that the model that takes into account the demand side’s carbon emissions responsibility can stimulate the willingness of the constructed system to reduce emissions.

Given the current installed capacity of new energy generation units, increasing the consumption of new energy can reduce carbon emissions, but the problem of wind power curtailment is prominent due to the anti-peak characteristics of wind power (Yang et al., 2013). WU and QIU (Wu et al., 2021; Qiu et al., 2022) has used P2G to improve wind power consumption, however, most existing studies only consider the conversion of electricity to natural gas in P2G modeling. CUI (Cui et al., 2020) points out that the efficiency of producing natural gas from electricity is 55%, while the efficiency of producing hydrogen from electricity can reach up to 70% (Wei et al., 2018), indicating a need for detailed consideration of the electricity-to-gas conversion process. Currently, we mainly study coal-fired units Carbon in CHP units, gas fired boilers, and hydrogen fuel cell vehicles Emissions, rarely analyzing energy production Carbon emissions from transportation and storage.

2 H2-IES analysis of a carbon trading mechanism with a stepped structure incorporating LCA energy chain analysis

IES meets its internal energy demands through multiple energy sources and supply equipment. This paper introduces hydrogen production equipment (HPE) and hydrogen fuel cells (HFC) to the traditional model, forming an H2-IES. The specific framework is shown in Figure 1.

FIGURE 1

2.1 Modeling of hydrogen utilization stage

HFC can realize the coupling between hydrogen energy, thermal energy, and electrical energy, strengthen the synergy between different forms of energy in the system, and convert hydrogen energy into electricity and thermal energy through HFC without producing pollutants and carbon dioxide as traditional CHP units do. Therefore, it can promote the clean operation of the system. The HFC model in this paper is as follows:where is the hydrogen energy consumed by HFC at the time period t; and are the generating power and heat generation power of HFC in time period t; respectively; and are the conversion efficiency of HFC into electricity and heat energy in t period, respectively; and represent the polynomial coefficients of the power generation and heat generation efficiency functions respectively; and are the upper and lower limits of HFC capacity respectively; and are the upper and lower limits of HFC.

In the formula: is the hydrogen energy consumed by HFC in time period t; and are the power generation and heat production power of HFC in time period t, respectively; and are the efficiency of HFC converting hydrogen energy into electrical and thermal energy in time period t; and are the polynomial coefficients of the power generation and heat production efficiency functions, respectively; and are the upper and lower limits of HFC capacity, respectively; is the ramp-up and ramp-down limit of HFC.

2.2 LCA energy chain analysis in IES

2.2.1 LCA energy chain analysis

Life Cycle Assessment (LCA) energy chain analysis consists of three implementation steps: classification, characterization, and quantification (Wang et al., 2019). During the energy production and transmission process in the H2-IES supply process, carbon emissions are generated. If only the carbon emissions during device usage are considered, the overall carbon emissions of the H2-IES cannot be fully reflected. Therefore, the total carbon emissions of each energy chain are calculated using LCA energy chain analysis, which includes the entire process from energy extraction to consumption, taking into account the accompanying effects of material conversion.

2.2.2 Carbon emission analysis of IES based on LCA

WANG (Wang et al., 2019) and HUANG (Huang et al., 2022) respectively analyzed the LCA energy chains of coal-fired power, new energy, natural gas, and energy storage, but did not quantify the carbon emissions of the hydrogen energy chain. The carbon emissions of the hydrogen energy LCA energy chain can be divided into two stages: production (raw material mining and processing) and transportation (raw material transportation and hydrogen transportation). The specific measurement process is as follows:where and are carbon emission coefficients of production link and transport link respectively; is the conversion coefficient of unit standard electricity quantity and energy consumption; is the unit energy consumption of the production process; is the carbon emission factor of production. is the unit loss rate in the process of raw material mining; is the unit loss in the processing process; I and J represent the sets of transportation methods and fuels, respectively; is the energy consumption of the ith type of transportation using the jth type of fuel; is the emission equivalent of CO₂ produced by ith type of transportation using the jth fuel; is the proportion of the transportation distance using the jth type of fuel for the ith type of transportation to the total transportation distance; is the average transportation distance of the ith type of transportation.

2.3 Carbon trading model

Carbon trading is essentially establishing reasonable carbon emissions quotas and allowing market-based trading of carbon emissions quotas, thus controlling the trading mechanism of carbon emissions.

2.3.1 Carbon emissions quota model

For major carbon emission sources such as coal-fired power plants, CHP units, and gas boilers, the baseline method is used to determine their carbon emissions quotas (Qu et al., 2018); the carbon emissions quotas for renewable energy units can refer to YANG (Yang et al., 2015). Since the government has not yet set carbon quotas for energy storage devices, the carbon emissions quota for energy storage is set to 0 in this paper.

2.3.2 Actual carbon emission model

The LCA energy chain of H2-IES involves various energy production, transportation, and utilization stages. The carbon emission coefficients of the three stages are quantified using the following formula:

Where is the total carbon emissions coefficient of equipment e, g/kWh; is the total carbon emissions coefficient of the production stage for the corresponding energy type of equipment e; is the total carbon emissions coefficient of the transportation stage for the corresponding energy type of equipment e; is the total carbon emissions coefficient of the usage stage for the corresponding energy type of equipment e. Since carbon emissions are related to the operating output of the equipment, the total carbon emissions can be calculated as follows:where Ω is the set of energy supply and storage equipment; is the power of energy type e; is the actual carbon emissions.

Once the actual carbon emissions are obtained, the carbon emissions trading volume involved in the carbon trading market can be calculated.where represents the carbon emission quota of the system.

2.3.3 Staggered carbon trading model

The traditional carbon trading mechanism assumes a fixed carbon trading cost throughout the entire scheduling period. If the carbon emissions do not exceed the quota, the excess quota can be traded for profit. Otherwise, the excess carbon emissions must be offset by purchasing additional carbon quotas, which can be expressed as Eq. 7:where is the carbon trading cost and ϖ represents the unit price of carbon trading.

The cost of tiered carbon trading mechanism is determined by dividing the carbon emission quota into different intervals, with different carbon trading prices for each interval.where l is the length of carbon emission interval; χ is the price growth rate.

3 Consideration of the staircase carbon trading mechanism and the low-carbon economic dispatch model of H2-IES objective function

3.1 Objective function

The optimization objective of the low-carbon economic dispatch model of H2-IES is the sum of carbon trading cost , energy cost and hydrogen sales revenue :where is the system operating cost, and is the cost of purchasing and selling electricity; represents the output power of natural gas source at time t; is the heating value of natural gas, which is taken as 9.98 (kWh)/m³; is the price of natural gas at time t; ; are respectively the system electricity purchasing price and electricity selling price at time t; is the power purchased and sold at time t; if it is greater than 0, it is electricity purchasing, if it is less than 0, it is electricity selling; is the hydrogen energy sold at time t; ν is the calorific value of hydrogen, taking 4.1 (kWh)/m³; is the selling price of hydrogen at time t.

3.2 Constraint condition

3.2.1 Equipment constraints

According to JIANG (Jiang and Ai, 2019), the models of energy storage devices such as electric and thermal are similar, so this paper adopts a unified modeling approach for electric and thermal energy storage devices.

Where and are the charging and discharging power of the nth energy storage device in time period t; is the maximum power of single charge and discharge of the nth energy storage device; and are binary variables, which are charging and discharging state parameters of the nth energy storage device during t period, respectively; and means in the energized state, whereas said in can put condition; is a kind of energy storage device t time the final output power; and respectively the charge and discharge efficiency of energy storage device; is the capacity of the nth energy storage device; and are respectively the upper and lower limits of the capacity of the nth energy storage device.

3.2.2 Electrical, gas, and heat network constraints

Where represents the heat load during period t; and represent the charging and discharging power of the thermal energy storage during period t.

Natural gas equilibrium constraintwhere is the gas load at time period t.

3.3 Model linearization processing

The hydrogen production equipment and fuel cell models constructed in this paper are nonlinear models that require piecewise linearization before being solved using CPLEX.

4 Case study analysis

4.1 Case study parameters

To validate the effectiveness of the proposed scheduling strategy, simulation is conducted based on the following load, energy, and equipment data. The various loads of the IES and wind power output prediction results are shown in Figure 2. The parameters of time-of-use electricity prices, various equipment, and energy storage are shown in Supplementary Appendix Tables A1–A3. The price of natural gas is 0.35 yuan per cubic meter, and that of hydrogen is 3.6 yuan per cubic meter. Interval length = 2t, price growth = 25%, carbon trading base ϖ = 250 yuan/t. is given in advance as 1% of the maximum capacity of the linearized model device.

FIGURE 2

This paper performs optimization using CPLEX and sets up four operating scenarios for analysis.

Scenario 1: The operational cost of H2-IES without considering carbon trading costs under the tiered carbon trading mechanism; Scenario 2: H2-IES considers the cost of carbon trading under the traditional carbon trading mechanism; Scenario 3: H2-IES considering carbon trading costs under the tiered carbon trading mechanism (the optimization method proposed in this paper); Scenario 4: The traditional combined heat and power system considering carbon trading costs under the tiered carbon trading mechanism.

The total carbon emission coefficient obtained by the LCA energy chain analysis is shown in Table 1. The introduction of carbon trading mechanism is to study the carbon emissions in economic terms. The selection of the carbon emission quota coefficient per unit of electricity output in this paper refers to the relevant data on carbon emission quota allocation issued by the National Development and Reform Commission, and the specific data is shown in Table 2.

4.2 Analysis of dispatch results in different optimization scenarios

Based on the four scenarios mentioned above, the system operating costs were obtained as shown in Table 3. Among them, scenarios 1, 2, and 3 have the same energy supply structure and equipment, and in scenario 4, traditional P2G devices replace HPE and HFC.

FIGURE 3

FIGURE 4

FIGURE 5

FIGURE 6

In Scenario 1, with the goal of optimizing traditional economic operations, the system will purchase as much natural gas as possible to produce electricity and heat through CHP, as the gas price is cheaper than the electricity price during all time periods. Before 06:00, wind power is sufficient and the efficiency of GB in producing heat is higher than that of CHP, so most of the heat load is supplied by GB, while CHP is in a state of heat-determined electricity. As the electricity load increases and wind power gradually becomes insufficient, CHP needs to supply more electricity, so it gradually reaches full load and produces the maximum electricity and heat power. The period from 23:00 to 24:00 is the same as before 06:00. In order to maximize profits from selling hydrogen, HPE operates at maximum capacity throughout the day, leading to a minimal sum of energy purchase and hydrogen sales revenue. However, a large amount of energy purchases lead to actual carbon emissions far exceeding the carbon emission quota, requiring the purchase of a large amount of carbon emission quotas from the carbon trading market, resulting in the highest total cost.

In Scenario 2, traditional carbon trading is considered in the optimization process. Due to CHP being cleaner than thermal power and GB, CHP is prioritized in providing electricity and heat power, which is why CHP operates at full capacity throughout the day. In addition, unlike Scenario 1, during the peak load period from 07:00 to 22:00, the revenue from selling hydrogen is lower than the cost of carbon trading, so CHP operates near its lower limit, resulting in a decrease in energy purchase cost.

In Scenario 3, due to the tiered carbon trading mechanism, the revenue from selling hydrogen becomes lower than the cost of purchasing carbon emission quotas from the carbon trading market more quickly, resulting in a further reduction in energy purchase.

FIGURE 7

Scenario 4 includes a P2G system, which can also absorb excess wind power and convert it into natural gas to supply the GB, CHP, or gas load, resulting in the lowest total purchasing cost. However, the P2G conversion efficiency is not high, and the hydrogen undergoes multiple processes of loss after being synthesized into natural gas through a methane reactor before being delivered to the GB or CHP. Although P2G can absorb some of the carbon dioxide, burning natural gas will also emit carbon dioxide. In contrast, in the H2-IES system, the HFC uses hydrogen for thermal and electrical production, reducing losses and directly utilizing clean energy, thus reducing carbon emissions. Since all the hydrogen is converted into natural gas in the P2G system, there is a lack of revenue from selling hydrogen, and the economic cost has not decreased.

4.3 Benefit analysis under different carbon trading prices

With the development of society, in order to reduce emission intensity, there is a possibility of changes in the carbon trading base price, price growth rate, and interval length in the stepped carbon trading mechanism. Changes in these parameters will affect the output of various units in the system. Figure 8 shows the impact trend of changes in carbon trading base price on system carbon emissions and total cost in Scenario 3.

FIGURE 8

According to Figure 8, as the carbon trading base price increases, the weight of carbon trading costs in the total system cost increases, and the carbon trading mechanism imposes stronger restrictions on carbon emissions. Although the carbon emissions of both Scenario 2 and Scenario 3 will decrease as the carbon trading price increases, the decrease in Scenario 3 is faster than that in Scenario 2. When the carbon trading price is less than 45 yuan/t, both Scenario 2 and Scenario 3 provide heat and electricity to the system by increasing the output of relatively clean CHP, thereby reducing carbon emissions. When the carbon trading price is greater than 145 yuan/t, the system reduces its electricity purchases and increases the output of HFC for heat and electricity to reduce carbon emissions. In contrast, Scenario 2 can only affect the purchased electricity when the carbon trading price is greater than 285 yuan/t, indicating that considering the stepped carbon trading is more favorable for the low-carbon operation of the system. In Scenario 3, when the carbon trading base price increases to 245 yuan/t, the output distribution of each equipment in the system tends to be stable, and the carbon emissions also tend to be stable, so the carbon emissions are less affected by changes in the carbon trading base price; due to the increase in carbon trading costs, the total system cost also increases.

According to the above analysis, it can be seen that when the carbon trading base price is greater than a certain value, the system tends to be stable and carbon emissions reach their minimum, indicating that simply raising the base price cannot further reduce emissions, but instead leads to cost increases. Therefore, in Scenario 3, a carbon trading base price of 165 yuan per ton should be selected to balance emissions reduction and economic feasibility.

Based on the analysis between carbon emissions reduction and system costs, high-emission entities face the cost of paying for carbon quotas or fines under carbon trading policies, or buying low-carbon technologies from low-emission entities. If there is no technological innovation, simply increasing the carbon trading price cannot further reduce carbon emissions but instead leads to cost escalation. In contrast, low-emission entities can compensate for their technological innovation and production costs by selling surplus quotas and low-carbon technologies, which will encourage them to increase their investment in low-carbon technologies and create a virtuous circle. Therefore, under carbon trading policies, carbon emitters can only achieve reduced carbon emissions and reduced costs through technological improvements.

4.4 Impact of relative price fluctuations of hydrogen and natural gas on system economics

When analyzing the impact of relative price fluctuations between hydrogen and natural gas on the system economy, it is assumed that the natural gas price remains constant while the hydrogen price fluctuates up and down by 3.6 yuan/m³. The resulting relative economic relationship is shown in Figure 9.

FIGURE 9

According to Figure 9, when the relative price of hydrogen and natural gas fluctuates in a large range (0.6–1.6), it does not affect the output of CHP units. When the relative price is at 1.2, if the price of hydrogen decreases, the output of the HFC equipment increases, because the equipment has no carbon emissions during the entire operation process, which means that the carbon trading cost is higher than the hydrogen sales revenue. If the consumption potential of hydrogen energy in China continues to increase, leading to changes in the supply and demand relationship of hydrogen energy and the price rises, the system will increase the purchase of electricity and reduce the output of HFC to ensure hydrogen sales revenue.

5 Conclusion

References

• 1

  ChengY. H.ZhangN.ZhangB. S.KangC. Q.XiW. M.FengM. S. (2020). Low-carbon operation of multiple energy systems based on energy-carbon integrated prices. IEEE Trans. smart grid11 (2), 1307–1318. 10.1109/TSG.2019.2935736

  • CrossRef
  • Google Scholar
  • View reference in article

• 2

  CuiY.YangS.ZhongW. Z.WangZ.ZhangP.ZhaoY. T. (2020). Optimaal thermoelectric dispatching of regional integrated energy system with power-to-gas. Power Syst. Technol.44 (11), 4254–4264. 10.13335/j.1000-3673.pst.2019.2468

  • CrossRef
  • Google Scholar
  • View reference in article

• 3

  CuiY.ZengP.ZhongW. Z.CuiW. L.ZhaoY. T. (2021). Low-carbon economic dispatch of electricity-gas-heat integrated energy system based on ladder-type carbon trading. Electr. Power Autom. Equip.41 (03), 10–17. 10.16081/j.epae.202011030

  • CrossRef
  • Google Scholar
  • View reference in article

• 4

  HuangH. X.LiZ. M.Mohasha Isuru SampathL. P.YangJ. W.NguyenH. D. (2023). Blockchain-enabled carbon and energy trading for network-constrained coal mines with uncertainties. IEEE Trans. Sustain. Energy2023, 1–12. 10.1109/TSTE.2023.3240203

  • CrossRef
  • Google Scholar
  • View reference in article

• 5

  HuangJ. g.XiongH. j.LiZ. X.WangT. (2022). Capacity configuration optimization of buileding inttegrated engrgy system considering fine energy storage mode. Electr. Measurement& Instrum.59 (03), 82–91. 10.19753/j.issn1001-1390.2022.03.011

  • CrossRef
  • Google Scholar
  • View reference in article

• 6

  JiangC. F.AiX. (2019). Integrated energy system operation opitimization model considering uncertainty of multi-energy coupling units. Power Syst. Technol.43 (08), 2843–2854. 10.13335/j.1000-3673.pst.2019.0197

  • CrossRef
  • Google Scholar
  • View reference in article

• 7

  LiX. S.SuiQ.LinX. N.WangZ. X.WuC. T.WeiF. R.et al (2020). A flexible load control strategy for power grid considering fully consumption of surplus wind power and global benefits. Proceeding CSEE40 (18), 5885–5897. 10.13334/j.0258-8013.pcsee.191028

  • CrossRef
  • Google Scholar
  • View reference in article

• 8

  LinZ. H.JiangC. W.ChenM. H.ShangH. Y.ZhaoH. W.YangZ.et al (2020). Low-carbon economic operation of integrated energy systenm considering flexible loads. Electr. Power Constr.41 (05), 9–18. 10.12204/j.issn.1000-7229.2020.05.002

  • CrossRef
  • Google Scholar
  • View reference in article

• 9

  LuH. P.XieL. R.GaoW. (2021). Cogeneration-storage-electric boiler wind power consumption strategy with carbon trading. Electr. Measurement& Instrum.2021, 1–11. Available at: http://kns.cnki.net.shiep.vpn358.com/kcms/detail/23.1202.TH.20210831.1125.002.html (Accessed: February 13, 2020).

  • Google Scholar
  • View reference in article

• 10

  QiuB.MuH. B.WangK.ZhangZ. C.YangZ. (2022). An optimal scheduling model of hydrogen coupling IES considering the mixed transportation of hydrogen and natural gas. Proc. CSU-EPSA.34 (8), 51–59. 10.19635/j.cnki.csu-epsa.000935

  • CrossRef
  • Google Scholar
  • View reference in article

• 11

  QuK. P.HuangL. N.YuT.ZhangX. S. (2018). Decentralized dispatch of multi-area integrated energy systems with carbon trading. Proc. CSEE38 (03), 697–707. 10.13334/j.0258-8013.pcsee.170602

  • CrossRef
  • Google Scholar
  • View reference in article

• 12

  ShiS. S.WangH. J.FangC.LingZ.YangX.LiY. (2018). “Optimal scheduling of integrated energy system combined with demand side flexible loads,” in Proceedings of the international conference on information Technology and electrical engineering (New York, NY: Assoc Computing Machinery). 10036-9998 USA, 1–6. 10.1145/3148453.3306290

  • CrossRef
  • Google Scholar
  • View reference in article

• 13

  WanW. X.JiY. N.YinL.WuH. (2021). Application and prospect of carbon trading in the planning and operation of integrated energy system. Electr. Meas. &Instrumentation58 (11), 39–48. 10.19753/j.issn1001-1390.2021.11.006

  • CrossRef
  • Google Scholar
  • View reference in article

• 14

  WangY. Q.QiuJ.TaoY. C.ZhangX.WangG. B. (2020). Low-carbon oriented optimal energy dispatch in coupled natural gas and electricity systems. Appl. energy280 (15), 115948. 10.1016/j.apenergy.2020.115948

  • CrossRef
  • Google Scholar
  • View reference in article

• 15

  WangZ. S.ShiY.TangY. M.MenX. Y.CaoJ.WangH. J. (2019). Low carbon economy operation and energy efficiency analysis of integrated energy systems considering LCA energy chain and carbon trading mechanism. Proceeding CSEE39 (06), 1614–1626+858. 10.13334/j.0258-8013.pcsee.180754

  • CrossRef
  • Google Scholar
  • View reference in article

• 16

  WangQiuY. Q. J.TaoY. C. (2022). Robust energy systems scheduling considering uncertainties and demand side emission impacts. Energy239, 122317. 10.1016/j.energy.2021.122317

  • CrossRef
  • Google Scholar
  • View reference in article

• 17

  WeiF. R.SuiQ.LinX. N.LiL.ChenL.ZhaoB.et al (2018). Energy control scheduling optimization strategy for coal-wind- hydrogen energy grid under consideration of the efficiency featyres of hydrogen production equipment. Proceeding CSEE38 (05), 1428–1439. 10.13334/j.0258-8013.pcsee.170044

  • CrossRef
  • Google Scholar
  • View reference in article

• 18

  WuJ.deG.TanZ. F.ZhangS. (2021). Multi-objective coordinated optimization model for integrated energy systems with power-to-gas and combined-cooling-heating-power technologies. Electr. Measurement& Instrum.58 (05), 20–30. 10.19753/j.issn1001-1390.2021.05.004

  • CrossRef
  • Google Scholar
  • View reference in article

• 19

  XuH.DongS. F.HeZ. X.ShiY. S.WangL.LiuY. Q. (2019). Electro-thermal comprehensive demand response based on muti-energy complementarity. Power Syst. Technol.43 (02), 480–489. 10.13335/j.1000-3673.pst.2018.2234

  • CrossRef
  • Google Scholar
  • View reference in article

• 20

  YangD.LiuJ. R.YangJ. X.DingJ. (2015). Carbon footprint of wind turbine by life cycle assessment. Acta Sci. Cricumstantiae35 (03), 927–934. 10.13671/j.hjkxxb.2014.0906

  • CrossRef
  • Google Scholar
  • View reference in article

• 21

  YangJ. G.LiuW. M.LiS. X.DengT. H.ShiZ. P.HuZ. C. (2017). Optimal operation scheme and Benefit analysis of wind-hydrogen power systems. Electr. Power Constr.38 (01), 106–115. 10.3969/j.issn.1000-7229.2017.01.014

  • CrossRef
  • Google Scholar
  • View reference in article

• 22

  YangN.WangB.LiuD. C.ZhaoJ.WangH. (2013). An integrated supply-demand stochastic optimization method considering large-scale wind power and flexible load. Proc. CSEE33 (16), 63–69+17. 10.13334/j.0258-8013.pcsee.2013.16.015

  • CrossRef
  • Google Scholar
  • View reference in article

• 23

  ZhangX. H.LiuX. Y.ZhongJ. Q. (2020). Integrated energy system planning considering a reward and punishment ladder-type carbon trading and electric-thermal transfer load uncertainty. Proc. CSEE40 (19), 6132–6142. 10.13334/j.0258-8013.pcsee.191302

  • CrossRef
  • Google Scholar
  • View reference in article

• 24

  ZhengX. D.XuY.LiZ. M.ChenH. Y. (2021). Co-optimisation and settlement of power-gas coupled system in day-ahead market under multiple uncertainties. IET Renew. power Gener.15 (8), 1632–1647. 10.1049/rpg2.12073

  • CrossRef
  • Google Scholar
  • View reference in article