Station-and-network-coordinated planning of integrated energy system considering integrated demand response

0 Introduction

With the increasing shortage of fossil energy,the integrated energy system (IES) has become an effective method of energy supply to alleviate the current energy crisis and environmental problems [1].Unlike the traditional power grids,the interdependence between energy production,transmission,storage,and consumption in the system is considered in the planning process of an IES.In addition,the characteristics of mutual coupling and mutual substitution between energy sources enable the demand side to change the energy-use methods among the different energy flows and provide a research background for the integrated demand response (IDR) [2].Compared to the traditional demand response (DR),the IDR of largescale energy storage (ES),traffic loads of electric vehicles(EVs),and flexible loads (FLs),coupling the energy flows of electricity,gas,and heat,have strengthened the complementary adjustment capabilities of the different forms of energy [3].This has enriched the operation modes of the IES and increased the margin of planning and operation.However,it has also increased the difficulty of IES planning [4].Therefore,it has become necessary to study the planning of the IES with interconnected and interactive EVs,ESs,and FLs,considering the IDR on the demand side to optimize the resource allocation.

The existing research on IES planning,mainly covers the planning of equipment capacity and transmission network.An optimized planning model for the IES can be established considering the load uncertainties,based on a unified model of the energy hub,aiming at economy,environmental protection,and reliability [5-6].A distribution network expansion planning model can be established considering the IES-optimized operation to achieve joint optimization of operation and planning [7-9].In reference [10],an optimized planning model for the IES was established considering the influence of the peakvalley difference of the tie line on the power grid.In reference [11],a planning model for the coordinated optimal configuration of a multiregion CCHP system capacity was established.Multienergy generation-grid-load collaborative planning based on energy hubs was discussed in reference[12].In reference [13],a robust planning method for the regional IES,considering the uncertainty of the multienergy loads of cooling,electricity,and heating was proposed.A distribution network planning strategy based on integrated energy collaborative optimization was studied in reference[14].A planning study on an IES with electricity and gas interconnections was presented in reference [15-16].An electricity-gas IES planning model considering the cost of reward and punishment tiered carbon transaction was established in reference [17].In reference [18],a multienergy system optimal configuration planning method considering distributed renewable energy was proposed.However,most of the existing IES planning studies were carried out independently for any one energy station and network; coordinated planning of energy stations and networks,from an overall perspective,was rarely done.In terms of the IDR,the existing research mainly focuses on load modeling,potential analysis,operational reliability,and multiple randomness [19-23].The load models of multienergy flow,multiform,and multiresponse types can be established to analyze the response potential and response behaviors of users [24].The impact of randomness of user participation in the IDR on the system security and reliability can be analyzed through the IES optimization configuration planning model considering the uncertainty[25].In reference [26-27],a gas-electric coordinated optimization operation strategy considering IDR resources at the microgrid level was proposed.The energy consumption characteristics of cold,heat,and electric loads and different IDR implementation mechanisms were used as constraints for the optimal operation of multienergy systems to analyze how the demand side resources responded and the response behavior of each demand subject under the condition of energy prices.In reference [28],an IDR interaction optimization model based on heat-electric multienergy complementarity was proposed from the perspective of industrial users.In reference [29],the IDR project for an IES was studied by taking a typical power-natural gas coupled energy system as an example.The interactive mechanism of the IES of industrial parks for multiagents was proposed by analyzing the interactive mode of the IDR in reference [30].However,it is rare to combine IDR with the charging and discharging of ESs and EVs at the level of IES planning.There are few applications of IDR involving FLs,ESs,and EVs in IES planning.

In response to the above problems,this study researches the station-and-network-coordinated planning of the IES,considering the IDR of FLs,ESs,and EVs to achieve optimal allocation of resources.The main contributions of this study are as follows.

• A collaborative planning method for energy stations and energy networks of IES,considering IDR,is proposed based on modeling the energy stations,including EVs,ESs,and FLs.This is conducive to reducing the long-term investment cost of the energy stations and energy networks of the IES.

• The influence of IDR on planning schemes,planning costs,and load curves in IES planning is analyzed.It is beneficial to provide suggestions for the long-term investment prediction of IES.

In Section 2,the energy station model including EVs,ESs,and FLs is established considering load substitution at the user side.In Section 3,a station-and-networkcoordinated planning model for the IES,considering the IDR,is constructed to minimize the total cost,including investment,operation and maintenance,and clean energy shortage penalty costs.In Section 4,the influences of the IDR on the costs and planning schemes of the IES for different scenarios are analyzed through simulations to verify the effectiveness of the proposed method.Section 5 summarizes the main conclusions of this study.

1 Energy station model considering IDR

The IDR studied in this paper mainly acts on energy stations,and include the responses of FLs,ESs,and EVs.Assuming that the energy station equipment mainly includes energy production equipment such as wind turbines (WTs),energy conversion equipment such as power electronic converters (PECs),power-to-gas (P2G) converters,combined heat and power (CHP),gas boilers (GB),energy storage equipment such as (EVs),electric energy storages (EESs),gas energy storages (GESs),and FLs of electricity and heat.The energy input and output conversion relationship of the energy station,based on the IDR,can be expressed as

where Le and Lh are the outputs of the electric and heat FLs,respectively.εe and εh are the powers of replacing the heat load by the electric load and electric load by the heat load,respectively.v1 and v2 are the distribution coefficients of the natural gas input to each device.ηPEC and ηGB are the PEC and GB,respectively.ηCHPGE and ηCHPGH are the electrical and heat efficiencies,respectively,of the CHP.Pe and Pg are the electrical power and natural gas power,respectively,at the input,which can be expressed as

where Pgrid is the electricity purchased from the main grid.PWT is the output of the WT.PEV is the charge and discharge power of the EV.Pstor is the charge and discharge power of the EES.PP2G is the input electric power of the P2G. Ftot is the gas purchased from the main grid. QP2G is the natural gas converted by the P2G. Fstor is the charging and discharging power of the GES.The models of EV,ES,P2G,CHP,and GB of the energy stations can be found in reference [31],which will not be repeated in this paper.

2 Station-and-network-coordinated planning model of IES considering IDR

2.1 Objective function

This study aims to minimize the total cost during the planning period of the IES,to optimize the IES planning,considering the IDR.The total cost of the system mainly includes the investment costs and operation and maintenance costs of power lines,natural gas networks,CHPs,GBs,P2Gs,and the clean energy shortages penalty costs.The objective function for cost minimization is specifically shown in equation (4).

where Ctotal is the total planning cost of the IES.Cinf,τ is the investment cost in the τth year.Ccon,τ is the operation and maintenance cost in the τth year.Cq,τ is the clean energy shortage penalty cost in the τth year.λ is the discount rate,and y is the number of planning years.

Equations (5) to (10) represent the investment cost of the system:

where CLinf,τ,CQinf,τ,CP2Ginf,τ,CCHPinf,τ,and CGBinf,τ are the investment costs of the power lines,natural gas pipelines,P2G,CHP,and GB,respectively,in the τth year.CinfL,j,CinfQ,k,CinfP2G,i,CinfCHP,i,and CinfGB,i are the unit capacity investment costs of the power lines,natural gas pipelines,P2G,CHP,and GB,respectively.IL,j,τ,IQ,k,τ,IP2G,i,τ,ICHP,i,τ,and IGB,i,τ are the construction statuses of the power line j,natural gas pipeline k,and the investment and construction statuses of the P2G,CHP,and GB in energy station i,which mean newly installed entities take the value of 1,while the others take the value of 0. QLmax,j,QQmax,k,QP2Gmax,i,QCHPmax,i,and QGBmax,i are the capacities of the corresponding planning objects.

Equations (11) to (17) represent the operating cost of the system:

where CLcon,τ,CQcon,τ,and Cfcon,τ are the operation and maintenance costs of the power lines,natural gas pipelines,and equipment in the τth year.Cebuy,τ and Cgbuy,τ are the purchase costs of electricity and gas energy in the τth year.Cde,τ is the cost of the IDR in the τth year.CconL,n and CconQ,n are the unit operation and maintenance costs of the power lines and natural gas pipelines,respectively.Cconf,n is the unit operation cost of the equipment,including the installed equipment and newly built equipment.N is the number of pieces of equipment.Nd is the number of days.Cbuye,τ,t and Cbuyg,τ,t are the purchase prices of electricity and gas energy in the τth year at time t.α,β,and γ are the unit costs of the IDR reduction,transfer,and substitution,respectively.Pcut,i,t,Pmov,i,t,and Ptran,i,t are the IDR amounts of reduction,transfer,and substitution at time t. QL,n,τ,QQ,n,τ,and Qf,nτ are the operating powers of the corresponding planning objects.

Equation (18) represents the penalty cost of the clean energy shortage of the system:

2.2 Constraints

The constraints mainly include investment and construction constraints,energy station constraints,and operation constraints of the power system and natural gas system.

(1) Investment and construction constraints

In the IES,all types of candidate equipment,including transmission lines and natural gas pipelines,must be installed within the planned period of time,as shown in the following formula:

where Is,τ is the installation variable of equipment s.In other words,each piece of equipment can only be installed once within the planned period.

(2) Energy station constraints

The equipment of the energy station needs to meet the capacity constraints,energy conversion constraints,and node heat-power balance constraints during operation.The specific description can be found in reference [32-33].

where Ldh,τ,t is the heat load after IDR at time t in the τth year.QGBh,τ,t and QCHPh,τ,t are the heating powers of the GB and CHP,respectively.

(3) Power system constraints

The power system constraints mainly include the conventional generator output constraints,WT operation constraints,and power network constraints.A specific description can be found in reference [34].

where PL,ij,τ,t is the power of branch ij at time t in the τth year.θi,τ,t and θj,τ,t are the phase angles of nodes i and j at time t in the τth year.xij is the impedance of branch ij.A is a fairly large constant,which can be taken as the total installed capacity of the system.IL,ij,τ is the line construction state variable,newly added as 1; otherwise,it is 0. QL,ij,max is the upper limit of the active power of the line.Lde,i,τ,t is the electric load after IDR at time t in the τth year.QCHPe,i,τ,t is the electric power of the CHP generator.

(4) Natural gas system constraints

Natural gas system constraints mainly include gas source constraints,steady-state flow constraints,transmission flow constraints,and node natural gas balance constraints.A specific description can be found in reference [35].

where fP,mn,τ,t is the flow of natural gas pipeline mn at time t in the τth year. fP,mn,max is the upper limit of the flow of natural gas pipeline mn.Ldg,m,τ,t is the natural gas load after IDR at time t in the τth year.QCHP,m,τ,t and QGB,m,τ t are the natural gas consumption flows of CHP and GB,respectively.

2.3 Solution process

To reduce the difficulty of solving the model,the nonlinear part of the model is linearized.First,because the model contains the absolute value part,a linear simulation cannot be performed.The positive and negative uncertainties of the IDR are processed by introducing non-negative auxiliary variables [36].Second,the nonlinear model of the natural gas pipeline is linearized by the incremental linearization method [37].Finally,based on the above model,the CPLEX solver is used in the GAMS platform to solve the problem of mixed-integer linear programming.The solution process used in this study is shown in Fig.1.First,the basic data,such as the equipment parameters to be selected,energy network parameters,and load are entered.Second,the non-negative auxiliary variables are set to deal with the positive and negative problems of the IDR.The incremental linearization method is used to linearize the nonlinear constraints of the energy network.Then,the configuration plan of the system is initialized and marked as i.According to the configuration plan,the operating mode of the system is calculated considering IDR.Then,

where cr is the penalty coefficient for the lack of clean energy planning.Pr,τ,t are the amount of the lack of clean energy planning.the system investment cost,operation and maintenance cost,and clean energy shortage penalty cost are calculated,and the total planning cost is synthesized.The configuration scheme i is iterated with the goal of minimizing the total planning cost.Finally,the planning scheme is output.

Fig.1 Solution process

3 Simulation results and analysis

3.1 Basic data

This section uses the revised IEEE 14-node power system and the 11-node natural gas system to form an IES for simulation.Node 4 of the power system and node 6 of the natural gas system are connected through an ES.The equipment to be selected in the IES includes P2G,GB,and CHP.The IES structure is shown in Fig.2.Fig.3 shows the forecasted total load data of electricity and gas for each planned year.The y-axis label indicates the number of years.The candidate equipment and branch candidate capacity parameters are listed in Table1 and Table2 [36].The E,η,UIC,and UOC are the equipment,efficiency,unit investment cost,and unit operating cost.The capacities of the EES and GES are 2 MW and 1.18 MW,respectively,and their efficiencies are 90% and 95%,respectively.The rated charge and discharge power of the EVs is 25 kW.Their charge and discharge efficiencies are 90%.The number of EVs is 300.A planning cycle is given as 10 years.The discount rate is 6%.The initial annual electricity load and heat load data of the energy station are 21900 MW and 21611 MW,respectively.The average annual growth rate of electricity,gas,and heat load during the planning period is 6%.The FLs are 5% of the original load and increase yearon-year following the growth of the load curve.

Fig.2 IES structure

Fig.3 Typical annual load forecast curve

Table1 Data of equipment to be selected for energy station

EηTypeCapacity/MW UIC/(103 $/MW)UOC/(103 $/MW)CHP 0.4,0.35 CHP Ⅰ 4 917 0.0865 CHP Ⅱ 3 917 0.0865 P2G 0.6 P2G Ⅰ 3 800 0.08 P2G Ⅱ 4 800 0.08 GB 0.75 GB Ⅰ 3 1262 0.11 GB Ⅱ 4 1262 0.11

Table2 Parameters of the power lines and natural gas pipelines to be selected

Network Line/pipeline to be selected Unit investment cost/(103 $/MW)Power system Line 1 - Line 20 80 Natural gas system Pipe 1 - Pipe 14 100

3.2 Effectiveness analysis

In order to verify the effectiveness of the IDR for the station-and-network-coordinated planning of IES in this paper,the following three scenarios are analyzed:

S1:coordinated planning of the energy station and network of the IES,without considering the IDR.

S2:coordinated planning of energy station and network of the IES,considering DR.

S3:coordinated planning of energy station and network of the IES,considering IDR.

(1) Influence of IDR on planning schemes

Table3 shows the optimal planning schemes for the IES under different scenarios.For example,[2,5](1) in the power line column means that the line between nodes 2 and 5 will be expanded in the first year.The timing and cost of investment for the station and network are as shown in Fig.4.It can be seen from Table3 that,in the entire planning period,when compared with the results of S1,the planning results of the power lines in S2 change significantly from the 7th and 10th years,and the planning results of the power lines in S3 undergo significant changes from the 6th and 10th years.In S2,the natural gas pipeline [2,3]is delayed for two years.The natural gas pipeline [2,3]in S3 is delayed for five years.The construction of GB of ES in S3 is delayed for three years,compared with S1.Some lines are not constructed.Overall,the investment and construction year in S3 is generally delayed,which is the result of a combination of two factors.First,the utilization rate of WTs during low-load periods has increased.Second,the implementation of IDR has reduced the peak loads and the pressure caused by load growth,thereby delaying,or even reducing the system’s investment in new power lines and natural gas pipelines.

Table3 Planning schemes in different scenarios

S Power lines Natural gas pipelines Equipment in energy station S1[2,5](1),[2,6](1),[8,9](1),[1,2](5),[4,13](9)[1,2](1),[2,3](1),[9,10](10)[GB Ⅰ](1),[GB Ⅱ](1),[P2G Ⅰ](1),[P2GⅡ](1),[CHP Ⅰ](4)S2[2,6](1),[8,9](1),[1,2](7),[5,7](10),[4,13](10)[1,2](1),[2,3](3),[9,10](10)[GB Ⅱ](1),[P2G Ⅰ](1),[P2G Ⅱ](1),[CHPⅠ](3)S3[8,9](1),[8,10](1),[3,11](10),[5,7](10),[13,14](10)[1,2](1),[2,3](6),[9,10](10)[CHP Ⅰ](1),[P2G Ⅰ](1),[P2G Ⅱ](1),[GBⅡ](4)

Fig.4 Comparison of planning results in the three scenarios

(2) Influence of IDR on planning costs

Table4 shows a comparison of the total planning costs under different scenarios in the IES.

Table4 Comparison of total cost under different scenarios

S Cinf,τ /(106 $) Ccon,τ /(106 $) Cq,τ /(106 $) Ctotal /(106 $)S1 21.08 153.10 0.21 174.39 S2 17.07 153.58 0.18 170.83 S3 16.32 153.76 0.10 170.18

It can be seen from Table4 that,compared to S1 and S2,S3 has lower investment cost,which is 4.01×106 $ and 4.76×106 $.This is because the implementation of IDR can delay or even reduce the investment and construction of equipment in energy stations and lines of network.At the same time,the clean energy shortage penalty cost is reduced by 0.03×106 $ and 0.11×106 $,indicating that IDR can improve the clean energy absorption capacity of the system.Thus,considering IDR for collaborative planning is more advantageous than not considering IDR and considering only DR for planning,in terms of delaying investment,improving clean energy consumption,and reducing total planning costs.

(3) Influence of IDR on planning load curves

In an IES considering IDR,multienergy users adjust the energy demand by reducing the load,shifting the load,and substituting the load,which can optimize the energy use curve.In this section,the analysis of IDR is carried out on the load of the energy station,and the implementation effect of the IDR is shown from the two aspects of FLs and the charging and discharging response of a single EV.Taking S3 as an example,the response of each time period of a typical day in the 10th year of planning is studied,as shown in Fig.5 and Fig.6.The curve of the multienergy load of the ES before and after IDR optimization is shown in Fig.7.

It can be seen from Fig.5 that,during the low period of the electric load,it is more economical to substitute the electric load for heat load.The load substitution value varies in different periods.Overall,there are more loads to replace heat with electricity,which is about 2 MW.Therefore,the implementation of load substitution is not to blindly substitute the electrical and heat loads,but to determine the amount of substitution that can obtain the best economic benefits according to the energy supply and load level while ensuring the balance of system supply and demand.

Fig.5 Response of FLs

Fig.6 Response of EVs

In Fig.6,the positive and negative values represent the charging and discharging,respectively,of the EVs.It can be seen that EVs have a mobile energy storage capacity.EVs can achieve energy transfer through valley charging and peak discharging,and the response is more flexible.

Fig.7 Curve of multienergy load before and after IDR optimization

It can be seen from Fig.7 that,compared to the IES not considering IDR,the peak-to-valley difference in the energy consumption curve of multienergy users considering IDR is reduced.The variances of the electric load curve and heat load curve are reduced by 8.24% and 6.29%,respectively.The energy consumption curve is optimized.It can be seen that the load curve becomes more stable.The IDR can improve the peak-to-valley difference of the multienergy load curve.

4 Conclusion

Based on the multienergy complementary structure of IES,this study first built an energy station model of IES with IDR,including ESs,EVs,and FLs.Second,a coordinated station-and-network optimization planning model was established with the goal of minimizing the total cost,considering the IDR.Based on the simulation results obtained in this study,the following conclusions were drawn:

(1) The coordinated planning of an IES that considers IDR can reduce the investment and delay the construction of equipment in an energy station and new lines of network.When compared with general DR,the investment cost is found to be reduced by 4.39%.

(2) The IDR with multienergy subjects in IESs can reduce the total cost of the system,optimize the multienergy curve,and promote the consumption of renewable energy.The variances of the electric load curve and heat load curve were reduced by 8.24% and 6.29%,respectively.

(3) The planning method in this study is optimized based on a typical annual load forecast curve.In a future work,the uncertainty of user load will be considered,and the reliability and economic benefit analysis of IES planning will be carried out.

Acknowledgements

This work was supported in part by the National Key R&D Program of China (2018YFB0905000) and the Science and Technology Project of the State Grid Corporation of China (SGTJDK00DWJS1800232).

Declaration of Competing Interest

We declare that we have no conflict of interest.