Optimized scheduling of integrated energy systems for low carbon economy considering carbon transaction costs

0 Introduction

Global energy demand shows a continuous growth trend with the rapid increase in the world population and the constant improvement of living standards [1,2].Climate change and the ambient pollution problems caused by the massive use of fossil fuels are becoming increasingly prominent,making efficient and clean energy supply networks urgent [3].The rapid development of integrated energy systems (IESs) provides a solution for such problems.As a typical IES,a combined cooling,heating,and power (CCHP) system is coupled with the functional networks of the power grid,natural gas,refrigeration,and heating.It can simultaneously satisfy the power,cooling,and heating load demands of users [4] and provide an effective way to improve the energy utilization rate and realize a comprehensive utilization of energy [5].

To improve the economy of a CCHP system,extensive research has been conducted on the source-networkload integration systems using the system model.Wang et al.[6] established a bilayer optimization model to study the economics of the CCHP system by considering the maximum annual net return of the system as the upper optimization objective and by taking the fluctuation rate of the daily output of the system and the minimum difference between the peaks and valleys of its daily output as the lower optimization objective.Cui et al.[7] used a hybrid thermal-electric energy storage scheduling method to reduce the amount of wind abandonment due to insufficient consumption of new energy in the northern region.Liu et al.[8] established a model for the additional potential benefits of electric energy storage,which reduced the economic cost of a CCHP system in a single building.The existing optimal scheduling of a CCHP system is mainly based on the lowest operating cost of the system as the optimization goal,ignoring the environmental cost of the system in the operation process.

Scholars have focused on the economic optimization of the CCHP system,taking carbon emissions into consideration since the notion of “carbon peaking and carbon neutrality”was adopted.The concepts of demandside response and carbon trading offer a new way of thinking in the search for a CCHP system scheduling model that can consider economic and environmental benefits and reduce carbon emissions.For instance,Kou et al.[9] developed an integrated low-carbon optimal dispatch model involving wind power systems and carbon-capture power plants.Examples from the simulations demonstrate that the model has a larger edge when it comes to efficiently lowering a system’s carbon emissions.He et al.[10] proposed a coherence-based low-carbon economic dispatch strategy for integrated energy systems,and the proposed distributed dispatch method had a positive effect on reducing the total system operating costs and carbon emissions.Shi et al.[11]constructed overall and regional energy system models for different energy sources,considered the penalty cost for carbon emissions from equipment,and explored the system performance.Qin et al.[12] proposed a stepwise carbon trading cost calculation method for an IES combining electricity,heat,and gas supplies.Wang et al.[13] proposed a carbon emission factor measurement method based on the LCA energy chain,and investigated the impact of carbon trading on ES energy efficiency.Wei et al.[14] introduced a carbon-trading mechanism into a traditional economic dispatch model and realized a reduction in carbon emissions from thermal power units.Ding et al.[15] analyzed the performance of the system by comprehensively considering the demand-side response and carbon emission constraints but did not consider the carbon emissions from the demandside response.Li et al.[16] proposed a multiple-energy demand-side response and a CCHP system considering carbon emissions to establish a three-stage,multi-timescale optimization model for day-ahead-intraday-real-time.

Although the above literature considered demandside response and carbon emission quotas,the rational dispatch of demand-side flexible loads and the role that demand response can play require further study.Demandside flexible loads are characterized by flexible scheduling,which is effective in alleviating the mismatch between supply and user demand and promoting the consumption of renewable energy.Jia et al.[17] reduced the operating cost of a system and decreased network losses by introducing demand-side flexible loads into a distribution network.Wang et al.[18] planned electricity consumption periods for demand-side flexible loads through time-sharing tariffs,which improved the electricity consumption efficiency and renewable energy consumption capacity of the distribution network.Zou et al.[19] improved the economy and environmental friendliness of electric energy production by constructing an optimal scheduling model of a combined electricity-heat system,considering the dual objectives of economy and low carbon emissions and including the scheduling of flexible loads on the demand side to realize the peaking of the power grid.

As can be observed,the participation of flexible loads in scheduling makes an effective improvement in the economic and environmental benefits of the corresponding scheduling system.However,most existing studies on demand-side flexible loads only consider flexible electric loads,few studies focus on thermal load controllability,and the models of load characteristics are not perfect.Therefore,based on the aforementioned problems,this study proposes a low-carbon and economically optimal scheduling model for a CCHP system that considers the demand-side response.To minimize the total operating cost of the CCHP system,this study simultaneously considers the transferable and curtailable characteristics of electric and thermal flexible loads,and establishes an optimal dispatch model of the CCHP system that considers user-side flexible loads.The role of flexible loads in improving the economy of the cogeneration system was analyzed through examples,and the reasonableness and effectiveness of the study were verified through a comparison of different scenarios.

1 Model of integrated energy system

Based on the principle of “temperature counterpart,gradient utilization,”the CCHP system is coupled with key components.As shown in Fig.1,a typical CCHP system comprises power units,refrigeration units,heating units,and other auxiliary equipment.The supply side primarily comprises gas turbines,power grids,wind power,and photovoltaic (PV) units.The power unit is mainly a gas turbine,and the refrigeration and heating units are typically absorption chillers and heat exchangers,respectively.Gas is fed into the gas turbine for combustion,generating high-temperature gas to push the turbine to work,and the electricity generated provides electric loads for users.The gas discharged from the turbine can provide users with cold and heat loads by driving the absorption refrigeration unit and heat exchanger,and simultaneously provide users with cold,heat,and electricity load demands,thus realizing the gradual utilization of energy and improving the energy utilization rate.The excess electricity and heat can be supplied to energy storage devices.When insufficient waste heat from the gas turbine is available,heat energy is supplied through the gas boiler and storage device,and when the system provides insufficient electrical energy,the users supplement it by purchasing electricity from the grid and the storage device.The demand-side loads mainly consist of uncontrollable base loads and controllable flexible loads.

1.1 Demand-side response model

The CCHP system can be categorized into four types of loads according to the manner in which the user loads participate in the demand response: (1) base loads,leveling loads,curtailable loads,and transferable loads.

1) Base loads

A base load is an uncontrollable load that responds completely to the user demand,and the user energy use method and time are fixed and cannot be changed by the system.

2) Transferable load

The energy supply of the system can be modified according to the schedule,and the customer’s energy-use time crosses several scheduling periods;however,overall load shifting is required.To guarantee the reasonableness of the user load shifting,the system can shift the time constraints that must be set.Assuming that the scheduling time period is 1 h,the interval of the translatable time period is [ts h-,tsh+],and the duration of the translatable load is tD.The set of translatable starting time periods Sshift is shown in (1) as follows:

The cost to be compensated to the customer after leveling the load is shown in (2) as follows:

3) Reducible Loads

Curtailable loads reduce consumers’ electricity consumption and can withstand certain interruptions or power reductions.The load is partially or fully curtailed according to the matching of the supply and demand.Using the 0-1 variable γ to indicate the curtailment status of the curtailable load Lcut at a certain time slot τ and γτ=1 to indicate that Lcut is curtailed in the τ time slot,the power calculation of the τ time slot after considering the participation of the curtailable load in scheduling is shown in (3).

where θτ denotes the load curtailment factor at the τ time slot,and θτ ∈[0,1].denotes the power at the τ time slot before Lcut participates in scheduling.

It is necessary to constrain the upper and lower limits of the curtailment time and the number of curtailments in order to ensure that the load curtailment is carried out reasonably as well as to take into account the user satisfaction.The constraints on the maximum curtailment time,minimum curtailment time,and number of curtailments are shown in(4),(5) and (6),respectively:

The compensation cost of the load that can be cut is given by (7):

4) Load transfer

The energy consumption of users in each time period can be flexibly adjusted;however,the total energy consumption of the entire cycle after load transfer is equal to that before transfer.Assuming that [t tr-,ttr+] is the transfer time interval of transferable load Ltran,the 0-1 variable β indicates the transfer state of Ltran at a given time period of τ,and βτ=1 indicates that the power in Ltran is transferred at time period τ.The power constraints of the transfer are shown in (8):

To avoid frequent startup and shutdown of the equipment,it is necessary to constrain the minimum duration of the load transfer,as shown in (9).

The cost Ctran of compensating users after load transfer is determined using (10).

1.2 Carbon emissions trading model

On the one hand,carbon dioxide (CO2) emissions are currently the most important element of research under the “two-carbon”objective.Therefore,the study of carbon dioxide emissions is helpful for the implementation of the“dual-carbon”goal.On the other hand,in a CCHP system,the raw material used is natural gas,and the main emission from the combustion of natural gas is carbon dioxide.Thus,this paper mainly considers the carbon dioxide emission for its impact on the environment.

In addition,some researchers have also conducted relevant studies on other emissions in the CCHP system.For example,[20] proposed a new dynamic simulation model of the CCHP system,which aims to formulate the optimal capacity allocation scheme of the prime mover.It is unique in that it integrates CO2,NOx,and other emission factors.The analysis and calculation of this model yielded annual CO2 and NOx emissions of 1034 tons and 0.3666 tons,respectively.In [21],the potential of existing installed gas turbine units for cooling,heating,power generation,and process heat coproduction was investigated.It was shown that the CCHP system could reduce emissions by more than 52000 tons of CO2 and 44 tons of NOx per year.Reference [22] proposed a multicriteria selection function(MCSF) for designing an optimal capacity and operation strategy for the prime mover of a residential micro-CCHP system.The environmental evaluation criteria in this strategy included CO,CO2,and NOx emission reductions,which were calculated using a hierarchical partitioning method.The NOx emission reduction weight was 0.289,which was used only as a tertiary indicator.Reference [23]proposed a CCHP cycle scheme for recovering exhaust and compression heat from a 35.5 MW industrial gas turbine operating in summer.The results showed that this CCHP cycle scheme resulted in 87% and 13% reductions in the higher CO2 and lower NOx contents in an industrial gas turbine exhaust,respectively.Reference [24] established an evaluation index system based on distributed cooling,heating,and power energy systems in terms of economy,energy consumption,and environment.The CO2 and NOx emission factors of each piece of equipment in this system are listed in this paper,and the NOx emission factor was significantly smaller than the CO2 emission factor.The calculation results of the environmental indicators show that the minimum annual CO2 emissions of the system are 88.3 tons,whereas the minimum annual NOx emissions are only 1.33 tons.Based on the literature [20-24],although they both consider CO2 and NOx in the calculation of pollutant emissions,the calculation results that the emission of NOx in the CCHP system is much smaller than that of CO2.Therefore,to facilitate the analysis of the performance of the CCHP system,this study ignored the NOx emissions and only considered the emission of CO2.

Carbon trading is a market-based mechanism that facilitates buying and selling carbon emission rights.By transforming carbon emission rights into commodities,this mechanism encourages optimization of the energy sector and enhances energy efficiency,ultimately leading to a reduction in carbon dioxide emissions.Government agencies allocate carbon emission allowances to all emission sources.Entities operating within their assigned allowances can sell surplus allowances in the carbon trading market.Conversely,if carbon emissions surpass the allocated quota,the excess must be procured from the carbon trading market.Within the carbon trading framework,enterprises that emit carbon are incentivized to implement effective energy conservation and emission reduction measures to maximize economic benefits.

Based on the above mechanism,this study establishes a carbon trading cost model for the CCHP system,as shown in (11).

whereis the carbon trading cost;a positivemeans that carbon emissions are excessive and carbon credits have to be purchased;a negativemeans that carbon credits should be sold to obtain income.Ct denotes the prevailing market price of carbon trading for the corresponding day;and Eout,Eall are the total CO2 emissions and carbon emission quotas,respectively.

Considering that carbon emissions in the CCHP system arise from both the production and transportation phases as well as the utilization of various energy sources,this study employs a two-stage approach to quantify the carbon emissions of the CCHP system.The calculation method is outlined in (12).

where Ω denotes the energy supply or storage equipment,the carbon emission coefficient of the i-th energy equipment in unit g/kWh,the carbon emission coefficient of the i-th energy equipment in unite g/kWh,and P the operating power of the i-th energy equipment.

2 Low carbon economic dispatch model for the system

The economically optimized operation of the CCHP system considers the flexible loads on the user side as well as carbon trading.The purpose of realizing the economic cost of the CCHP system in daily operation is achieved by strategically adjusting the output of controllable units,flexibly scheduling the flexible loads on the user side,and utilizing energy storage technology.This is realized under the premise of meeting the constraints of the individual units of the energy system and the carbon trading mechanism,as well as the permission of wind power and PV minimization.The optimization process in this study was grounded in the forecast curves for the electric load,thermal load,wind power,and PV power over the next 24 h.

2.1 Carbon emissions trading model

This study aims to control the operational cost for users of a CCHP system.The operation cost mainly includes the cost of purchasing electricitycost of selling electricitycost of purchasing gas Cgas,operation cost of renewable energy Cre,compensation cost of user-side flexible load optimization Cl,depreciation cost of energy storage devices Ce es、C tes,and the carbon trading costThe objective function is expressed by (13).

The power purchase cost for the CCHP system is shown in (14):

The cost of power sales from the CCHP system to the municipal grid is calculated as shown in (15):

The cost of gas for the CCHP system comes mainly from the gas turbines and gas boilers.The cost calculation is shown in (16).

The cost of operation of renewable energy for a CCHP system is calculated as shown in (17):

where Kw,Kpv denote the operating cost coefficients of the WTG and PV,respectively,anddenote the WTG and PV output powers,in kW,respectively.

The total compensation cost for the optimization of electric and thermal flexible loads of the CCHP system is calculated as shown in (18):

The depreciation costs of the system’s electricity and heat storage are calculated as shown in (19) and (20):

where Kees,Ktes indicate the depreciation coefficients of the power storage and heat storage,respectively;indicates the charging/discharging power of the power storage,in kW;andindicates the storage and discharging power of the heat storage,in kW.

2.2 Model constraints

To ensure secure and consistent operation of each CCHP system,its functioning must align with the supplydemand equilibrium between the system and user’s load requirements.This entails meeting the constraints of the electrical energy balance,thermal energy balance,and power balance associated with energy storage and release.

The power balance constraints between the CCHP system and user are shown in (21).

The thermal energy balance constraint is shown in (22).

The heat balance constraints are shown in (23).

whereis the waste heat energy produced by the prime mover of the CCHP system (kW),ηhe is the efficiency of the heat exchanger,R is the output thermoelectric ratio of the gas turbine,and ηhr is the efficiency of the waste heat recovery unit.

To maintain the stability of a CCHP system,its operation is bound by both the upper and lower limits of the output of each piece of equipment.The constraints associated with the upper and lower bounds of each CCHP system equipment are illustrated in (24)-(27),where (24) represents the prime mover constraint,(25) represents the gas boiler constraint,(26) represents the heat exchanger constraint,and (27)represents the grid power purchase constraint.

The upper and lower constraints for PV and wind energy are expressed in (28) and (29),respectively.

The energy storage constraints are shown in (30)-(35).

Based on a mixed-integer nonlinear model,this study developed a low-carbon economic optimization scheduling model for a CCHP system considering the demand response.The model was solved using the MATLAB environment,first using the YALMIP toolbox to establish the optimal scheduling model and finally calling the GUROBI commercial solver to solve the model.The detailed solution flow of the model is shown in Fig.2.

3 Case study

3.1 Carbon emissions trading model

In this study,a community CCHP system was considered as the research object.The cold load demand of a typical winter day was selected,and the time interval was taken as 1 h.The predicted outputs of PV and wind power,as well as the predicted loads of the user’s electricity and heat for a typical winter day,are shown in Fig.3.The parameters of each device of the CCHP system are listed in Table 1,and the parameters of each user’s flexible electricity and heat loads are listed in Tables 2 to 4.The carbon emission coefficients involved in the two-stage carbon emission measurement method and the carbon emission quota coefficients are listed in Table 5,where the total carbon emission coefficient is the sum of the two-stage carbon emission coefficients.We assume that the carbon trading price is 150 ¥/t.The municipal grid price adopts the time-sharing electricity price,as shown in Fig.4.

To verify the effect of flexible loads on the IES,five scenarios were established for comparative analysis.

Scenario 1: Carbon trading costs and flexible loads are not considered to participate in the system-optimized dispatch.

Scenario 2: Only the carbon trading cost,and not the flexible load,participates in the system-optimized dispatch.

Scenario 3: Carbon trading costs and only flexible electric loads are involved in the optimal system scheduling.

Scenario 4: Carbon trading costs and only flexible thermal loads participate in the system-optimized dispatch.

Scenario 5: Carbon trading costs and flexible electric and thermal loads participate in the system-optimized dispatch.

3.2 Analysis and discussion of results

3.2.1 Low carbon economics analysis

The economics of the system under the five scenarios are presented in Table 6.It can be observed that Scenario 1,which does not consider carbon trading costs or flexible load dispatch,has the highest total operating cost and total carbon emissions of 4047 ¥ and 1.44 tons,respectively.Compared to Scenario 1,Scenario 2,which only considers carbon trading costs,has a cost reduction of 9.83% and a total carbon emissions reduction of 56.25%.This shows that the total operating cost and carbon emissions of the IES can be reduced by considering carbon trading costs.Scenarios 3 and 4 consider flexible electric load and flexible thermal load scheduling on the basis of Scenario 2,respectively,and compared with Scenario 2,the system’s total operating cost under Scenarios 3 and 4 is reduced by 5.95% and 2.22%,respectively;and the total carbon emissions of the system are reduced by 17.46% and 3.17%,respectively.As can be observed,flexible load dispatch can further reduce both the total operating cost and carbon emissions of the system.The cost of power purchase in Scenario 4 is higher than that in Scenario 3;therefore,the total cost and carbon emissions of the system are higher than those in Scenario 3,that is,the impact on the system is greater when considering a flexible electric load dispatch.Scenario 5 considered both carbon trading costs and flexible electricity and heat load dispatches,and the total operating costs and carbon emissions of the system were the lowest among the five scenarios.Compared with the other four scenarios,the total operating cost of the system in Scenario 5 was reduced by 18.04%,9.1%,3.35%,and 7.03%,and the carbon emissions were reduced by 65.28%,20.63%,3.85%,and 18.03%,respectively.Considering the carbon trading costs and flexible electric and thermal load scheduling simultaneously reduces the system’s carbon trading costs,power purchase costs,and renewable energy operating costs to a great extent,thereby reducing the system’s total costs and total carbon emissions.

The distribution of system costs under the different scenarios is shown in Fig.5.The cost of the system under Scenario 1 mainly comes from the grid,followed by the gas cost and operating cost of renewable energy,whereas the energy storage cost has the smallest proportion.After considering carbon trading cost and flexible load dispatch,the operating cost of renewable energy in Scenarios 2 to 5 has the highest proportion,followed by gas cost,power purchase cost,energy storage cost and carbon trading cost.Considering carbon trading cost and flexible load dispatch promotes the consumption of renewable energy,reduces the power purchase cost of the grid,and increases the operating cost of renewable energy.

The investment costs of the system are shown in Fig.6.The investment cost of the system described in this study is the average daily investment cost of the CCHP system;that is,the total investment cost is spread evenly over each day.When calculating the investment cost,it was assumed that the life cycle of the CCHP system was 20 years and that of the energy storage equipment was 8 years.From Fig.6,it can be observed that the investment cost of the system is reduced when considering flexible load dispatch and carbon trading costs.Compared with Scenario 1,the investment costs of the system under Scenarios 2-5 were reduced by 1.71%,1.42%,2.85%,and 3.13%,respectively.In addition,the total cost of the system and the operating cost (the total operating cost of the system presented in Table 6) have similar trends,and the total cost of the system is the lowest when both flexible load dispatch and carbon trading costs are considered.

3.2.2 Impact of carbon trading on the system

This section focuses on the power output of each type of system equipment when only the carbon-trading cost is considered.Fig.7 shows the electric heating power of each system under Scenarios 1 and 2.From Fig.7(a) and Fig.7(b),it is clear that there is no wind power output in Scenario 2 from 01:00 to 07:00 when carbon trading is considered;because of the low grid price in this time period,the electric load demand is mainly met by the grid and gas turbines in this phase.In addition,from 10:00 onwards,most of the load demand in Scenario 1 is met by the grid and gas turbines,and the utilization rate of wind power is small,which increases the power purchase cost of the system.However,when carbon trading is considered,the customer load in Scenario 2 is mainly satisfied by wind power and gas turbines,which reduces the grid cost of the system.

From the thermal power output in Fig.7(c) and Fig.7(b),it can be observed that the heat load demand of the users in Scenario 2 is mainly satisfied by the waste heat of the gas turbine,and some of the insufficient heat is satisfied by the gas boiler and heat storage equipment at 1:00-5:00,11:00-15:00,and 16:00-23:00.In contrast,most of the heat load demand of the users in Scenario 1 from 6:00 to 10:00 and from 21:00 to 22:00 is supplemented by gas boilers,thus increasing the cost of gas purchase.Taking the above optimization results together,it is clear that the cost of the system can be reduced and the consumption of renewable energy can be promoted when considering the cost of carbon trading.

3.2.3 Impact of flexible electric and heating loads on system cost

Fig.8 shows the various electric and thermal load demands of the users before and after the optimization of Scenario 5.The distribution of the electric loads before optimization is shown in Fig.8(a),including the base electric load,transferable electric load,curtailable electric load,and leveled electric load.The flexible load and base electric load of the system after optimization,considering the carbon trading cost and flexible load response,are shown in Fig.8(b).When the carbon trading cost and flexible load response scheduling are considered,the flexible electric loads from 10:00 to 15:00 and 19:00 to 21:00,when the grid price is high,are transferred to 01:00 to 09:00,when the grid price is relatively low,to reduce the amount of electric power purchased from the grid and reduce costs.

Fig.8(c) shows the base heat load demand,curtailable heat load,and levelizable heat load of the users before optimization.Fig.8(d) shows the base heat load and flexible heat load after optimization using Scenario 5.After considering the carbon trading cost and flexible heat load response scheduling,the peak heat load,which is originally in the range of 18:00-21:00,is shifted to 8:00-10:00 through scheduling,thus reducing the peak heat load demand.

The output of each device of the CCHP system after the optimized scheduling of Scenario 5 and the change curves of the users’ electric and thermal load demands before and after the optimization are shown in Fig.9.As shown in Fig.9(a),the electric load demand of the system is mainly satisfied by the grid and gas turbines from 01:00 to 07:00,when the grid price is low.The electrical load of the user is primarily satisfied by renewable energy sources and gas turbines.The gas turbines operate at full load at all times,except from 06:00 to 07:00,when the gas turbines operate at partial load rates,thus reducing the power purchase cost of the system.In addition,in the stage of higher grid price,the peak load of users is shifted through flexible load dispatch,and the load changes smoothly compared with the preoptimization period,which is beneficial for the favorable operation of each unit of the CCHP system.From the optimized heat load shown in Fig.9(b),it can be observed that the user’s heat load demand is mainly satisfied by the waste heat of the gas turbine,and only a small portion of it is satisfied by the gas boiler and energy storage equipment.This significantly reduces the system’s purchased gas cost,thus reducing the system’s total cost.

3.2.4 Carbon emissions by equipment

The curves of the hourly carbon emissions under different scenarios are shown in Fig.10.As can be observed in Fig.10,compared with Scenario 1,carbon trading increases the carbon emissions of the system in the other scenarios from 01:00 to 07:00 and at 13:00.Combined with Fig.7(a),it is clear that considering carbon trading costs and flexible load dispatch at this stage increases the amount of electricity purchased by the grid,and therefore,increases carbon emissions.The grid purchases less electricity and,therefore,emits less carbon at other times,as the customer’s electric load demand is mainly met by gas turbines,renewable energy sources,and energy storage devices.The negative values at 08:00,11:00,19:00,21:00,23:00,and 24:00 indicate that the carbon trading cost is negative at these times,indicating profitability.In addition,the carbon emissions of the system in Scenario 5 are lower than those in the other scenarios,which verifies the feasibility of flexible load dispatch considering carbon trading costs.

The hour-by-hour carbon trading volume of each device of the system under Scenario 5 is shown in Fig.11,and the carbon trading of the system under the other scenarios is shown in Figs.A1-A4.Because of the lower grid price at 01:00-07:00,the demand for electric loads of users was mainly provided by the grid at this stage;therefore,the carbon trading volume was higher.In the other stages,the carbon trading volumes caused by gas,PV,and energy storage are low.From 08:00 to 24:00,the carbon trading of the PV is negative,which means that the PV has excess carbon trading credits and can profit from selling carbon credits,whereas other equipment has excess carbon trading and needs to buy carbon credits.

To analyze the impact of changes in the carbon market on the total system cost and carbon emissions in depth,this study further discusses carbon base price changes;the calculation results are shown in Fig.12.As shown in Fig.12(a),with an increase in the carbon-trading base price,the total cost of the system increases because of the increase in the carbon-trading costs.The trend of carbon emissions is opposite to that of the total cost,as shown in Fig.12(b).As the carbon trading base price increases (i.e.,the greater the weight of the carbon emission target cost,the stronger is the role of carbon trading cost),the system has to reduce carbon emissions to minimize the carbon trading cost,so the carbon emissions gradually decrease.When the carbon trading base price factor continues to increase to a certain value(the carbon trading base price factors for Scenarios 2-5 are 2.6,3.6,3.0,and 3.6,respectively.),the distribution of the various equipment outputs of the system tends to stabilize as the carbon trading base price increases,and the carbon emission level also tends to stabilize.Therefore,carbon emissions are less affected by the change in the carbon trading base price.In addition,the total cost and carbon emissions of the system are minimized when the flexible load dispatch and carbon trading costs are considered simultaneously.

The impacts of the grid power purchase tariff on the overall system cost and carbon emissions are shown in Fig.13.As shown in Fig.13(a),an increase in electricity prices correlates with a gradual increase in the total system cost.Notably,when the electricity price factor surpasses 1.6,the total system cost stabilizes in Scenarios 3 and 5.The results from Scenarios 3 and 4 indicate that the influence on the total system cost is less pronounced than that of the thermal load when factoring in electrical load considerations.The heightened electricity price contributes to an increase in the power purchase expenses of the system,thereby leading to a gradual increase in the total system cost.Incorporating demand response mitigates the cost of purchased electricity.Additionally,with an escalating electricity price factor,the carbon emissions of the system progressively decrease until a consistent trend is observed.When the electricity price factor exceeds 1.6,the carbon emissions exhibit a steady decline,signifying a minimal impact of the grid power purchase price on the system’s carbon emissions during this period.

The gas turbine in the CCHP system is the core of the entire system,and its installed capacity directly affects the overall performance of the system.Therefore,this paper discusses and analyzes the impact of the change in the capacity of the gas turbine on the performance of the system,as shown in Fig.14.The total operating cost (Fig.14(a))and carbon emissions (Fig.14(b)) of the system gradually decrease with a gradual increase in the installed capacity of the gas turbine.When the variation factor of the gas turbine installed capacity is 1.8,the change in the total operating cost and carbon emissions of the system tend to stabilize.As the installed capacity of the gas turbine increases,the load demand of the users is gradually satisfied by the CCHP unit.This reduces the amount of purchased power from the grid,and thus reduces the cost of purchased power;thus,the total operating cost of the system gradually decreases.As the installed capacity of the gas turbine increases further,the system’s CHP supply is in equilibrium with the customer’s load demand;therefore,the total operating cost and carbon emissions of the system no longer change.

5 Conclusions

In this study,a low-carbon and economic scheduling model of a multi-characteristic,multi-load CCHP system was established by simultaneously considering the carbon trading cost and flexible electricity and heat load scheduling.The cost of the CCHP system was analyzed by setting different scenarios,and the following main conclusions were obtained:

1) The total cost and total carbon emissions of the system were effectively reduced by simultaneously considering the carbon trading cost and demand-side flexible electric and thermal load responses.Compared with the other four scenarios,the total cost of the system under Scenario 5 was reduced by 18.04%,9.1%,3.35%,and 7.03%,and the total carbon emissions of the system were reduced by 65.28%,20.63%,3.85%,and 18.03%,respectively.

2) The flexible electrical and thermal loads did not have the same impact on the system performance.In the analyzed case,the total cost and carbon emissions of the system when only the flexible electrical load response was considered were lower than those when only the flexible thermal load response was taken into account.

3) An excess of carbon trading credits was found for PV,which can be profit by selling them,whereas an excess of carbon trading was observed for grid,gas,PV,and energy storage,which requires the purchase of carbon credits.

Appendix A

Time-by-time carbon trading change curves in Scenarios 1 to 4.

Acknowledgments

This work was supported by State Grid Shanxi Electric Power Company Science and Technology Project “Research on key technologies of carbon tracking and carbon evaluation for new power system”(Grant: 520530230005).

Declaration of Competing Interest

We declare that we have no conflict of interest.