Low-carbon economic optimization operation method of integrated energy system considering demand response

Abstract

The present invention discloses a method for optimizing the low-carbon economy of an integrated energy system taking into account demand response, which divides the user loads participating in demand response into vertical demand response and horizontal demand response, and fully considers the demand response for optimizing the low-carbon economy of IES. In view of the shortcomings of existing models in considering demand response, this paper divides multi-energy loads into vertical demand responses of the same energy type that can be transferred and reduced in time, and horizontal demand responses that can replace each other between different energy types according to the energy consumption characteristics of the loads, and models them. Through numerical simulation, the superiority of demand response in reducing system carbon emissions and promoting system economic operation is verified.

Description

Technical Field

[0001] The present invention belongs to the technical field of integrated energy optimization and scheduling, and specifically relates to a low-carbon economic optimization operation method for an integrated energy system taking demand response into consideration. Background Art

[0002] In terms of achieving the optimal operation of IES low-carbon economy, the carbon trading mechanism is currently considered to be one of the most effective means. In carbon trading, the government and relevant departments manage carbon emissions and encourage carbon emission entities to voluntarily reduce emissions in the market to achieve CO 2 Control of total emissions. On the other hand, with the deepening reform of the energy market, in the integrated energy system, in addition to the economic efficiency of energy supply achieved through energy coupling equipment on the supply side, the response characteristics of the load side cannot be ignored.

[0003] With the continuous development of integrated energy systems and the gradual opening of energy markets, demand response is increasingly involved in the optimization operation of IES. Demand response based on price and incentives and electricity price demand response are all taken into consideration when establishing the IES low-carbon economic optimization operation model. However, it can still be optimized from the following two aspects: 1) Demand response has the benefit of smoothing the load curve and reducing the peak-to-valley difference of the curve. Introducing it into IES can further optimize the economic efficiency of system operation; 2) In addition to considering electricity demand response in IES, the comprehensive demand response of multiple energy systems should also be paid attention to. At the same time, in the carbon trading market environment, the carbon reduction benefits of comprehensive demand response in the operation of integrated energy systems need to be further explored.

[0004] In summary, for the integrated energy operation model, the IES low-carbon economic optimization operation model based on demand response is studied to reduce the peak-to-valley difference of the load curve, optimize the load curve, and achieve the reduction of CO generated in the operation of IES. 2 While reducing emissions, it also improves the economy of system operation. Summary of the invention

[0005] The purpose of this invention is to propose a low-carbon economic optimization operation method for an integrated energy system considering demand response. In IES, multiple energy demand responses on the load side are considered, the low-carbon benefits of demand response in IES are further explored, and the low-carbon economic optimization operation of IES is promoted.

[0006] The technical solution adopted by the present invention is a method for optimizing the low-carbon economy of an integrated energy system considering demand response, which mainly includes establishing a vertical demand response model and a horizontal demand response model by analyzing the IES load characteristics and establishing an IES low-carbon economy optimization operation model considering demand response. The specific implementation is as follows:

[0007] Step 1: Analyze the load characteristics of the integrated energy system IES;

[0008] Step 2: Establish a vertical demand response model and a horizontal demand response model respectively;

[0009] Step 3: Construct a low-carbon economic optimization operation model for the integrated energy system considering demand response;

[0010] Step 4: Use the CPLEX solver to solve the above-mentioned low-carbon economic optimization operation model of the integrated energy system.

[0011] The specific implementation process of step 1 is as follows:

[0012] According to the energy characteristics of the load, the multi-energy load is divided into vertical demand response of the same energy type that can be transferred and reduced in time, and horizontal demand response of different energy types that can be replaced by each other. The specific classification is as follows:

[0013] 1) Vertical demand response includes shiftable electricity / gas demand response and curtailable electricity / gas demand response. This type of load uses only one type of energy and can achieve energy consumption shift and electricity consumption reduction based on electricity prices and incentive mechanisms.

[0014] 2) Horizontal demand response refers to alternative electricity / gas loads. This type of load can use a variety of energy sources and achieve flexible load adjustment through energy substitution.

[0015] The specific implementation process of step 2 is as follows:

[0016] Step 2.1, establish a vertical demand response model;

[0017] In IES, the power system and natural gas system use time-of-use electricity prices and time-of-use gas prices respectively to guide the transferable load to transfer electricity in the unit energy consumption time period, and keep the total load unchanged in the total time scale, so as to achieve load peak shifting and valley filling, and improve the reliability and economy of IES operation. The elasticity matrix method is used to model the transferable electricity / gas load. The elasticity matrix is

[0018]

[0019] In formula (1), ε is the elasticity coefficient, the diagonal is the self-elasticity, and the others are the mutual elasticity. Self-elasticity is negative, indicating that the corresponding load in this period is reduced; mutual elasticity is positive, indicating the load response to price changes in other periods.

[0020] The elastic modulus ε is

[0021]

[0022] In formula (2), p and q are the electricity quantity and time-of-use price of the transferable load; Δp and Δq are the change in electricity quantity and time-of-use price of the transferable load.

[0023] The mathematical model of transferable electricity and gas loads is:

[0024]

[0025]

[0026] In formula (3), P TRY , P TR is the initial load of the transferable electric load and the electric load after response; P TR1 …P TRt is the load after the transferable load response in each period from 1 to t; ΔP TR is the transfer amount of the transferable electric load; the diagonal of the elastic matrix is ​​the self-elasticity of the electric load, and the others are the mutual elasticity of the electric load; Δq E1 …Δq Et is the time-sharing price change of the transferable electric load in each period from 1 to t; q E1 …q Et is the time-of-use price of the transferable electric load in each period from 1 to t. In formula (4), E TRY 、E TR is the initial load of the transferable gas load and the gas load after response; E TR1 …E TRt is the gas load after the transferable gas load response in each period from 1 to t; ΔE TR is the transfer amount of the transferable gas load; the diagonal of the elastic matrix is ​​the self-elasticity of the gas load, and the others are the mutual elasticity of the gas load; Δq E1 …Δq Et is the time-sharing price change of the transferable gas load in each period from 1 to t; q E1 …q Et is the time-of-day price of the transferable gas load in each period from 1 to t;

[0027] The total amount of transferable electric / gas load remains unchanged within an operating cycle, and the load power per unit time is subject to upper and lower limits. The specific constraints are as follows:

[0028]

[0029]

[0030] In formula (5) and formula (6), P TRY,t , P TR,t is the initial load of the transferable electric load and the electric load after response in period t; E TRY,t 、E TR,t is the initial load of the transferable gas load and the gas load after response in period t; ΔP TR,max , ΔP TR,min is the upper and lower limits of the transferable electric load; ΔETR,max , ΔE TR,min The upper and lower limits of the transferable gas load.

[0031] The curtailable electricity / gas load refers to the demand response load that can reduce electricity within a certain range according to the contract content. In the integrated energy system, this load can reduce the corresponding load power according to the system energy supply and demand to alleviate the energy shortage during operation. The mathematical model of curtailable electricity / gas load is:

[0032]

[0033] In formula (7), P CUY,t , P CU,t is the initial load and the load after response that can be reduced in period t; E CUY,t 、E CU,t is the initial load and the load after response that can reduce the gas load in period t; ΔP CU,t , ΔE CU,t is the amount of reduction in electricity load and gas load that can be reduced in period t; b CUE,t 、b CUG,t is the reduction state of the electricity / gas load that can be reduced. A value of 1 indicates that a reduction adjustment occurs during period t, and a value of 0 indicates that no reduction adjustment occurs; θ E,t ,θ G,t It is the reduction ratio of the electric / gas load that can be reduced. A value of 1 indicates that the load is completely reduced during period t, and a value of 0 indicates that no reduction adjustment occurs.

[0034] The power reduction range constraint that can be met for the electricity / gas load is

[0035]

[0036] In formula (8), ΔP CU,max , ΔP CU,min ΔE is the upper and lower limits of the amount of electricity load that can be reduced; CU,max , ΔE CU,min The upper and lower limits of the amount of gas load reduction that can be reduced.

[0037] Step 2.2: Establish a horizontal demand response model.

[0038] Under the guidance of different energy prices and different electricity demand, the replaceable electricity / gas load can achieve substitution between different energy sources through coupling between energy sources. The energy coupling expression is

[0039]

[0040] In formula (9), P SUY,t , P SU,t is the initial load and the load after response of the replaceable electric load; ESUY,t 、E SU,t is the initial load and the load after response of the replaceable gas load; ΔP SU , ΔE SU is the amount of replacement for electric load and gas load; An alternative state for an alternative electric load; It is the alternative state of the replaceable gas load; The amount of electrical energy substituted by electric load instead of gas load and the amount of electrical energy substituted by gas load instead of electric load; It is the amount of gas energy replaced when gas load replaces electrical load, and the amount of gas energy replaced when electrical load replaces gas load.

[0041] The alternative power range of the alternative electric / gas load meets the following constraints:

[0042]

[0043]

[0044] In formula (10) and formula (11), The upper and lower limits of the amount of electric energy that can be substituted for gas load by electric load; The upper and lower limits of the amount of electric energy replaced by gas load instead of electric load; The upper and lower limits of the gas energy substitution amount for electric load by gas load; The upper and lower limits of the gas energy replacement amount when electric load replaces gas load.

[0045] The specific implementation process of step 3 is as follows:

[0046] Step 3.1, considering the energy purchase cost, demand response cost and carbon trading cost of the upper power grid and gas wells to construct a low-carbon operation objective function;

[0047] The objective function of the IES low-carbon economic optimization operation model considering demand response is:

[0048] min f=f 1 +f 2 (12)

[0049]

[0050]

[0051] In formulas (12), (13) and (14), f1 is the system operation cost; f2 is the carbon trading cost; P E,t is the power generation capacity of the coal-fired unit during period t; ΔP CU , ΔE CU P is the amount of reduction in electricity load and gas load; GT,t is the active power of the gas turbine during period t;CUE , γ CUG is the unit reduction cost coefficient of the electricity / gas load that can be reduced; ξ is the incentive base price of carbon trading; ΔE is the length of the carbon emission interval; κ is the growth rate of carbon trading price in the penalty stage; λ is the growth rate of carbon trading price in the incentive stage; ρ E,t is the unit electricity price of the upper power grid during period t; G,t is the unit price of natural gas in period t; E IES is the total free carbon emission quota of the system; E IES,C is the total carbon emissions of the actual operation of the system.

[0052] Step 3.2: Consider the power network constraints, node voltage constraints, and natural gas network constraints during system operation. The specific constraints are as follows:

[0053] Power network constraints:

[0054]

[0055]

[0056]

[0057]

[0058] P EL,t =P TR,t +P CU,t +P SU,t (19)

[0059] In formulas (15), (16), (17), (18) and (19), P ij,t , Q ij,t is the active and reactive power of the ij branch; P jk,t , Q jk,t is the active and reactive power of the jk branch; P GT,t , Q GT,t P is the active power and reactive power of the gas turbine during period t; WT,t , Q WT,t P is the wind power active power and reactive power in period t; EC,t , Q EC,t P is the input active power and reactive power of the electrolytic cell during period t; EL,t , Q EL,t is the active power and reactive power of the electric load during period t; ΔP W,t , ΔQ W,t is the abandoned wind active power and abandoned wind reactive power in period t; r ij 、x ij is the resistance and reactance of the ij branch; U i,t , Uj,t is the voltage at nodes i and j; I ij,t , U ij,t is the current flowing through the ij branch and the voltage difference at the ij node.

[0060] Node voltage constraints:

[0061] U j,min ≤U j,t ≤U j,max (20)

[0062] In formula (20), U j,max , U j,min are the upper and lower limits of the voltage allowed at node j.

[0063] Natural gas network constraints:

[0064]

[0065]

[0066] In formula (21) and formula (22), sgn(π i,t ,π j,t ) is a sign function. When the pressure at node i is higher than that at node j, the value is 1, otherwise it is -1; π i,t , π j,t is the natural gas pressure at node i and node j during period t; π p,t , π q,t fp is the natural gas pressure at node p and node q during period t; p,t is the pipeline flow rate of natural gas pipeline p during period t; φ p is the gas flow transmission parameter of the natural gas pipeline p.

[0067] Node airflow balance constraints:

[0068]

[0069] P GL,t =E TR,t +E CU,t +E SU,t (twenty four)

[0070] In equations (23) and (24), V(n) and U(n) are the gas pipeline sets with node n as the end point and the start node; X(n) and Z(n) are the methane reactor and gas turbine sets at node n; ψ H2G , GT is the conversion factor between the methane reactor and the gas turbine; P H2G,t is the methane reactor power during period t; P GL,t is the active power of gas load in period t.

[0071] Pipeline flow constraints:

[0072] fq mn,min ≤fq mn,t ≤fq mn,max (25)

[0073] In formula (25), fq mn,t is the pipeline mn flow; fq mn,max 、fq mn,min The upper and lower limits of the pipeline mn flow rate.

[0074] Node pressure constraint:

[0075] π m,min ≤π m,t ≤π m,max (26)

[0076] In formula (26), π m,t is the air pressure at node m; π m,max , π m,min are the upper and lower limits of the air pressure at node m.

[0077] Exchange power constraints with the upper power grid and natural gas network:

[0078]

[0079] In formula (27), P E,t is the amount of electricity purchased during period t; P G,t is the amount of natural gas purchased during period t; P Emax , P Emin P is the upper and lower limits of the system's power purchase; Gmax , P Gmin The upper and lower limits of the system's gas purchase volume.

[0080] Hydrogen energy balance constraints:

[0081]

[0082] In formula (28), Y(n) is the set of hydrogen storage tanks at node n; P P2H,t P is the volume of hydrogen generated by the electrolyzer; H2H,t is the volume of hydrogen consumed by the methane reactor; It is the gas intake and gas output of the hydrogen storage tank.

[0083] Output constraints of each unit:

[0084]

[0085] In formula (29), P WTmax , P WTmin P is the upper and lower limits of wind power output; EC,tis the input active power of the electrolytic cell during period t; E MRHmax 、E MRHmin E is the upper and lower limits of hydrogen input to the methane reactor; MRH,t is the hydrogen input to the methane reactor during period t; P WTmax , P WTmin P is the upper and lower limits of wind power output; WT,t is the wind power active power in period t; P GTmax , P GTmin P is the upper and lower limits of gas turbine output; GT,t is the active power of the gas turbine during period t; ΔP GTmax , ΔP GTmin The upper and lower limits of the gas turbine's climbing power.

[0086] The specific implementation process of step 4 is as follows:

[0087] Step 4.1, set the model input parameters: input the original load data of electric load and gas load, input the output data of wind power, and the corresponding parameters of each unit in the system;

[0088] Step 4.2: The constructed IES low-carbon economy optimization operation model is linearized by using second-order cone relaxation and incremental linearization, and the CPLEX solver is used in MATLAB to perform simulation and solve the problem, and the optimal cost under the operation scheme is obtained;

[0089] Step 4.3: If the constraint conditions in step 3.2 are met, stop the calculation; if the conditions are not met, adjust the parameters and recalculate;

[0090] Step 4.4: Obtain the operating status of each unit in the integrated energy system and determine the operating plan of the system.

[0091] The beneficial effects of the present invention are:

[0092] 1) The IES low-carbon economic optimization operation model of the integrated energy system constructed by the present invention divides the user load participating in demand response into vertical demand response and horizontal demand response, and effectively combines it with the reward and punishment ladder carbon trading mechanism;

[0093] 2) Consider that demand response can reduce the peak-to-valley difference of the load curve and optimize the load curve by changing the user's energy usage habits and energy usage methods;

[0094] 3) Under the reward-penalty ladder carbon trading mechanism, considering both vertical demand response and horizontal demand response can give full play to the flexible coordination performance of the load side and achieve the goal of reducing CO2 generated during IES operation. 2 While reducing emissions, it also improves the economy of system operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0095] Figure 1 It is a low-carbon economic optimization operation model diagram of the comprehensive energy system considering demand response of the present invention;

[0096] Figure 2 It is a load classification diagram of the integrated energy system of the present invention;

[0097] Figure 3 It is a flow chart of the model solving algorithm of the present invention;

[0098] Figure 4 It is a schematic diagram of the structure of the comprehensive energy system of the present invention;

[0099] Figure 5 It is a diagram of the operation results of the electric energy subsystem in each time period after the optimization operation of the present invention;

[0100] Figure 6 It is a diagram of the operation results of the natural gas subsystem in each time period after the optimization operation of the present invention;

[0101] Figure 7 It is a diagram of the operation results of the hydrogen energy subsystem in each time period after the optimization operation of the present invention;

[0102] Figure 8 This is a diagram of the change in electrical load in scenario 2 of the present invention;

[0103] Fig. 9 This is a diagram of the gas load changes in scenario 2 of the present invention;

[0104] Fig.10 This is a diagram showing the changes in the electric load and gas load in scenario 3 of the present invention;

[0105] Fig.11 It is a diagram of the change of electric load in scenario 1 and scenario 4 of the present invention;

[0106] Fig.12 This is a schematic diagram of carbon emissions for scenarios 1 to 4 of the present invention;

[0107] Fig.13 This is a schematic diagram of the amount of electricity purchased in scenario 1 and scenario 4 of the present invention;

[0108] Fig.14 It is a schematic diagram of the gas purchase volume in scenario 1 and scenario 4 of the present invention. DETAILED DESCRIPTION

[0109] The present invention is described in detail below with reference to the accompanying drawings and specific embodiments.

[0110] The low-carbon economic optimization operation model of the integrated energy system considering demand response in the present invention is as follows: Figure 1 As shown, the specific implementation steps are:

[0111] Step 1: By analyzing the load characteristics of the integrated energy system, the load can be divided into non-adjustable rigid load and flexible load with adjustment potential;

[0112] According to the energy characteristics of the load, the multi-energy load is divided into vertical demand response of the same energy type that can be transferred and reduced in time, and horizontal demand response of different energy types that can be replaced by each other. The specific classification is as follows:

[0113] 1) Vertical demand response includes shiftable electricity / gas demand response and curtailable electricity / gas demand response. This type of load uses only one type of energy and can achieve energy consumption shift and electricity consumption reduction based on electricity prices and incentive mechanisms.

[0114] 2) Horizontal demand response refers to alternative electricity / gas loads. This type of load can use a variety of energy sources and achieve flexible load adjustment through energy substitution. Figure 2 shown.

[0115] Step 2: Establish a vertical demand response model and a horizontal demand response model respectively;

[0116] Step 2.1, establish a vertical demand response model;

[0117] In IES, the power system and natural gas system use time-of-use electricity prices and time-of-use gas prices respectively to guide the transferable load to transfer electricity in the unit energy consumption time period, and keep the total load unchanged in the total time scale, so as to achieve load peak shifting and valley filling, and improve the reliability and economy of IES operation. The elasticity matrix method is used to model the transferable electricity / gas load. The elasticity matrix is

[0118]

[0119] In formula (1), ε is the elasticity coefficient, the diagonal is the self-elasticity, and the others are the mutual elasticity. Self-elasticity is negative, indicating that the corresponding load in this period is reduced; mutual elasticity is positive, indicating the load response to price changes in other periods.

[0120] The elastic modulus ε is

[0121]

[0122] In formula (2), p and q are the electricity quantity and time-of-use price of the transferable load; Δp and Δq are the change in electricity quantity and time-of-use price of the transferable load.

[0123] The mathematical model of transferable electricity and gas loads is:

[0124]

[0125]

[0126] In formula (3) and formula (4), P TRY , P TR is the initial load of the transferable electric load and the electric load after response;

[0127] ΔP TR is the transfer amount of transferable electric load; E TRY 、E TR is the initial load of the transferable gas load and the gas load after the response; ΔE TR is the transfer amount of transferable gas load.

[0128] The total amount of transferable electric / gas load remains unchanged within an operating cycle, and the load power per unit time is subject to upper and lower limits. The specific constraints are as follows:

[0129]

[0130]

[0131] In formula (5) and formula (6), P TRY,t , P TR,t is the initial load of the transferable electric load and the electric load after response in period t; E TRY,t 、E TR,t is the initial load of the transferable gas load and the gas load after response in period t; ΔP TR,max , ΔP TR,min is the upper and lower limits of the transferable electric load; ΔE TR,max , ΔE TR,min The upper and lower limits of the transferable gas load.

[0132] The curtailable electricity / gas load refers to the demand response load that can reduce electricity within a certain range according to the contract content. In the integrated energy system, this load can reduce the corresponding load power according to the system energy supply and demand to alleviate the energy shortage during operation. The mathematical model of curtailable electricity / gas load is

[0133]

[0134] In formula (7), P CUY,t , P CU,t is the initial load and the load after response that can reduce the electric load; E CUY,t 、E CU,t is the initial load and the load after the response that can reduce the gas load; ΔP CU , ΔE CU is the amount of reduction in electrical load and gas load that can be reduced; b CUE,t , b CUG,tis the reduction state of the electric / gas load that can be reduced. A value of 1 indicates that a reduction adjustment occurs during period t, and a value of 0 indicates that no reduction adjustment occurs; θ E,t ,θ G,t It is the reduction ratio of the electric / gas load that can be reduced. A value of 1 indicates that the load is completely reduced during period t, and a value of 0 indicates that no reduction adjustment occurs.

[0135] The power reduction range constraint that can be met for the electricity / gas load is

[0136]

[0137] In formula (8), ΔP CU,max , ΔP CU,min ΔE is the upper and lower limits of the amount of electricity load reduction that can be achieved; CU,max , ΔE CU,min The upper and lower limits of the amount of gas load reduction that can be reduced.

[0138] Step 2.2: Establish a horizontal demand response model.

[0139] Under the guidance of different energy prices and different electricity demand, the replaceable electricity / gas load can achieve substitution between different energy sources through coupling between energy sources. The energy coupling expression is

[0140]

[0141] In formula (9), P SUY,t , P SU,t is the initial load and the load after response of the replaceable electric load; E SUY,t 、E SU,t is the initial load and the load after response of the replaceable gas load; ΔP SU , ΔE SU is the amount of replacement for electric load and gas load; An alternative state for an alternative electric load; It is the alternative state of the replaceable gas load; The amount of electrical energy substituted by electric load instead of gas load and the amount of electrical energy substituted by gas load instead of electric load; It is the amount of gas energy replaced when gas load replaces electrical load, and the amount of gas energy replaced when electrical load replaces gas load.

[0142] The alternative power range of the alternative electric / gas load meets the following constraints:

[0143]

[0144]

[0145] In formula (10) and formula (11), The upper and lower limits of the amount of electric energy that can be substituted for gas load by electric load; The upper and lower limits of the amount of electric energy replaced by gas load instead of electric load; The upper and lower limits of the gas energy substitution amount for electric load by gas load; The upper and lower limits of the gas energy replacement amount when electric load replaces gas load.

[0146] Step 3: Construct a low-carbon economic optimization operation model for the integrated energy system considering demand response;

[0147] Step 3.1, considering the energy purchase cost, demand response cost and carbon trading cost of the upper power grid and gas wells to construct a low-carbon operation objective function;

[0148] The objective function of the IES low-carbon economic optimization operation model considering demand response is:

[0149] min f=f 1 +f 2 (12)

[0150]

[0151]

[0152] In formulas (12), (13) and (14), f 1 is the system operation cost; f 2 is the carbon trading cost; P E,t is the power generation capacity of the coal-fired unit during period t; ΔP CU , ΔE CU P is the amount of reduction in electrical load and gas load that can be reduced; GT,t is the power generation of the gas turbine during period t; CUE , γ CUG is the unit reduction cost coefficient of the electricity / gas load that can be reduced; ξ is the incentive base price of carbon trading; ΔE is the length of the carbon emission interval; κ is the growth rate of carbon trading price in the penalty stage; λ is the growth rate of carbon trading price in the incentive stage; ρ E,t is the unit electricity price of the upper power grid during period t; G,t is the unit price of natural gas in period t; E IES is the total free carbon emission quota of the system; E IES,C is the total carbon emissions of the actual operation of the system;

[0153] Step 3.2: Consider the power network constraints, node voltage constraints, and natural gas network constraints during system operation. The specific constraints are as follows:

[0154] Power network constraints:

[0155]

[0156]

[0157]

[0158]

[0159] P EL,t =P TR,t +P CU,t +P SU,t (19)

[0160] In equations (15), (16), (17), (18) and (19), v(j) and u(j) are the branches with node j as the first and last nodes; z(j), x(j), and y(j) are the gas turbine, fan and electrolyzer at node j; P ij,t , Q ij,t is the active and reactive power of the ij branch; P jk,t , Q jk,t is the active and reactive power of the jk branch; P GT,t , Q GT,t P is the active power and reactive power of the gas turbine during period t; WT,t , Q WT,t P is the wind power active power and reactive power in period t; EC,t , Q EC,t P is the input active power and reactive power of the electrolytic cell during period t; EL,t , Q EL,t is the active power and reactive power of the electric load during period t; ΔP W,t , ΔQ W,t is the abandoned wind active power and abandoned wind reactive power in period t; r ij 、x ij is the resistance and reactance of the ij branch; U i,t , U j,t is the voltage at nodes i and j; I ij,t , U ij,t is the current flowing through the ij branch and the voltage difference at the ij node;

[0161] Node voltage constraints:

[0162] U j,min ≤U j,t ≤U j,max (20)

[0163] In formula (20), U j,max , U j,min are the upper and lower limits of the voltage allowed at node j.

[0164] Natural gas network constraints:

[0165]

[0166]

[0167] In formula (21) and formula (22), sgn(π i,t ,π j,t ) is a sign function. When the pressure at node i is higher than that at node j, the value is 1, otherwise it is -1; π i,t , π j,t is the natural gas pressure at node i and node j during period t; π p,t , π q,t fp is the natural gas pressure at node p and node q during period t; p,t is the pipeline flow rate of natural gas pipeline p during period t; φ p is the gas flow transmission parameter of the natural gas pipeline p;

[0168] Node airflow balance constraints:

[0169]

[0170] P GL,t =E TR,t +E CU,t +E SU,t (twenty four)

[0171] In equations (23) and (24), V(n) and U(n) are the gas pipeline sets with node n as the end point and the start node; X(n) and Z(n) are the methane reactor and gas turbine sets at node n; fq mn,t 、fq nl,t is the flow rate of pipes mn and nl during period t; ψ H2G , GT is the conversion factor between the methane reactor and the gas turbine; P H2G,t is the methane reactor power during period t; P GL,t is the active power of gas load in period t;

[0172] Pipeline flow constraints:

[0173] fq mn,min ≤fq mn,t ≤fq mn,max (25)

[0174] In formula (25), fq mn,max 、fq mn,min The upper and lower limits of the pipeline mn flow rate.

[0175] Node pressure constraint:

[0176] π m,min ≤π m,t ≤πm,max (26)

[0177] In formula (26), π m,max , π m,min are the upper and lower limits of the air pressure at node m.

[0178] Exchange power constraints with the upper power grid and natural gas network:

[0179]

[0180] In formula (27), P Emax , P Emin P is the upper and lower limits of the system's power purchase; Gmax , P Gmin The upper and lower limits of the system's gas purchase volume.

[0181] Hydrogen energy balance constraints:

[0182]

[0183] In formula (28), Y(n) is the set of hydrogen storage tanks at node n; P P2H,t P is the volume of hydrogen generated by the electrolyzer; H2H,t is the volume of hydrogen consumed by the methane reactor; It is the gas intake and gas output of the hydrogen storage tank.

[0184] Output constraints of each unit:

[0185]

[0186] In formula (29), P WTmax , P WTmin P is the upper and lower limits of wind power output; EC,t is the input active power of the electrolytic cell during period t; E MRHmax 、E MRHmin E is the upper and lower limits of hydrogen input to the methane reactor; MRH,t is the hydrogen input to the methane reactor during period t; P WTmax , P WTmin P is the upper and lower limits of wind power output; WT,t is the wind power active power in period t; P GTmax , P GTmin P is the upper and lower limits of gas turbine output; GT,t is the active power of the gas turbine during period t; ΔP GTmax , ΔP GTmin are the upper and lower limits of the gas turbine's climbing power;

[0187] Step 4: Solve the above optimization scheduling model.

[0188] The IES low-carbon economy optimization operation model constructed by the present invention is a non-convex nonlinear model. After the nonlinear terms in the model are processed by second-order cone relaxation and linearization, the IES low-carbon economy optimization operation model is a mixed integer linear programming model, and the result is obtained by using the CPLEX solver. The main process of model solution is as follows: Figure 3 shown.

[0189] Step 1: Set the model input parameters. Input the original load data of the electric load and gas load, the output data of the wind power, and the corresponding parameters of each unit in the system;

[0190] Step 2: The constructed IES low-carbon economy optimization operation model is linearized by using second-order cone relaxation and incremental linearization, and the CPLEX solver is used in MATLAB to perform simulation and solve the problem, and the optimal cost under the operation scheme is obtained;

[0191] Step 3: If the constraint conditions are met, stop the calculation; if the conditions are not met, adjust the parameters and recalculate;

[0192] Step 4: Obtain the operating status of each unit in the integrated energy system and determine the operating plan of the system.

[0193] Example

[0194] The integrated energy system is used as an example for simulation analysis. The structure of the integrated energy system is as follows: Figure 4 Table 1 is the price elasticity matrix of alternative electricity / gas demand response.

[0195] Table 1 Elasticity matrix coefficient

[0196]

[0197] In order to verify the advantages of demand response considered in this chapter, the following four scenarios are set for comparative analysis: 1) IES low-carbon economic optimization operation without considering demand response; 2) IES low-carbon economic optimization operation considering vertical demand response; 3) IES low-carbon economic optimization operation considering horizontal demand response; 4) IES low-carbon economic optimization operation considering vertical and horizontal demand response.

[0198] IES optimization operation results are as follows Figures 5 to 7 As shown. Among them, Figure 5 is the operation result of the electric energy subsystem, Figure 6 This is the operation result of the natural gas subsystem. Figure 7 This is the operating result of the hydrogen energy subsystem.

[0199] In scenario 2, the changes in electric load and gas load are as follows: Figure 8 and Fig. 9 As shown. Figure 8It can be seen that 0:00 to 6:00 is the period of low electricity prices, when the electricity load demand is relatively low. Under the guidance of time-of-use electricity prices, users actively transfer the electricity load from other periods with higher electricity prices to this period, and the transferable load is positive. At the same time, the wind power output is at its peak, and some wind power in the system is difficult to absorb, so P2G can be used to convert surplus wind power into hydrogen storage. During the period of high electricity prices from 11:00 to 22:00, users actively transfer the load in this period to other periods with lower electricity prices, and the transferable load is negative. At the same time, during the periods of 11:00, 15:00 and 19:00 to 20:00 when the load demand is relatively high, the system, while ensuring the economic efficiency of operation, in addition to transferring part of the electricity load, also appropriately reduces part of the electricity load, effectively reducing the peak-to-valley difference of the load curve, alleviating the energy supply pressure of IES when the power load is at its peak. Fig. 9 It can be seen that the gas load demand response of the system has similar longitudinal response characteristics to the electric load demand response. During the low gas price periods such as 0:00 to 5:00 and 14:00 to 16:00, users transfer the load from other periods to this period under the guidance of the time-sharing gas price, and the transferable load is positive. During the high gas price periods such as 7:00 to 12:00 and 18:00 to 20:00, the load can be transferred to other low gas price periods, and the transferable load is negative. At the same time, during the period of high gas load demand, in addition to the transferable load response, the system also reduces the peak-to-valley difference of the load curve by cutting part of the gas load, alleviating the energy supply pressure of the integrated energy system during the peak period of natural gas load.

[0200] The changes of electricity load and gas load in scenario 3 are as follows: Fig.10 As shown in the figure, during the period from 1:00 to 6:00, both electricity and gas prices are at their lowest point, but at this time the wind power output is high. In order to absorb wind power to the maximum extent, part of the natural gas load can be converted into electricity load, which effectively increases the system's electricity load. During the period from 10:00 to 22:00, both electricity and gas prices are high. Considering the high carbon emissions of purchasing electricity from the upper power grid and the lowest total system operation cost, part of the electricity load is replaced by the natural gas load.

[0201] The load changes in scenario 4 are as follows: Fig.11As shown in the figure. As can be seen from the figure, when the system does not consider demand response, the electric load curve has obvious peak-to-valley difference. At around 12:00 and 18:00, the electric load demand is at peak time, and at around 5:00 and 24:00, the electric load demand is at low time. However, wind power output has obvious anti-peak characteristics, with higher output at night and lower output during the day. The load curve without considering demand response is poorly matched with the wind power output characteristics, resulting in a relatively tight energy supply from IES to the load during the day. After the system considers demand response, under the incentive of time-of-use electricity prices and response mechanisms, the electric load smoothes the load curve by changing energy usage habits and adjusting energy usage methods. Flexible transferable loads in the power consumption period can transfer the energy consumption time of part of the system load from the peak of electricity prices to the valley. The reducible load can alleviate the system energy supply tension during peak power consumption by reducing the power of part of the load, and the replaceable load can achieve substitution between different energy sources. IES reduces the peak-to-valley difference of the load curve through the synergy of different demand responses and optimizes the load curve.

[0202] By comparing the various costs, carbon emissions and purchased energy of the integrated energy system operation scheme in the four scenarios, the demand response benefits of IES are analyzed. The various costs of scenarios 1 to 4 are shown in Table 2, and the carbon emissions at each moment of scenarios 1 to 4 are shown in Table 2. Fig.12 shown.

[0203] Table 2 Comparison of costs for scenarios 1 to 4

[0204]

[0205] As shown in Table 2, scenario 1 does not consider demand response, and the load cannot change its energy use strategy according to the time-of-use price information and response mechanism. When the load demand is at its peak, the system's energy supply pressure is relatively large. At this time, in order to meet the load demand, the system needs to purchase more electricity and natural gas, resulting in high electricity and gas purchase costs, which are 6431 yuan and 35794 yuan respectively. And due to the high amount of purchased energy, the system's carbon emissions and total costs are both high, which are 24204 kg and 47254 yuan respectively. Scenario 2 and scenario 3 consider vertical demand response and horizontal demand response respectively. Users can adjust their energy use behavior by responding to time-of-use electricity prices and time-of-use gas prices. The system's electricity and gas purchase costs are significantly reduced, and the system's total cost and carbon emissions are also effectively reduced. Compared with scenario 1, the system's carbon emissions are reduced by 24.4% and 28.1% respectively, and the system's total cost is reduced by 7.5% and 8.6% respectively. It can be seen that considering demand response can improve the system's optimal operation mode.

[0206] Fig.13 and Fig.14 The comparison of electricity and gas purchases in scenario 1 and scenario 4 is shown below. Fig.13 , Fig.14As shown in Table 2, after considering both demand responses in Scenario 4, the peak-to-valley difference of the load curve after the response is reduced and the curve is smoother due to the change of load power consumption period, load reduction, and replacement of load energy consumption mode. Through the guidance of time-of-use electricity prices and time-of-use gas prices, the system reduces the amount of electricity and gas purchased from the upper power grid and gas wells during the peak period of electricity and gas prices, and increases the amount of electricity and gas purchased during the low period of electricity and gas prices, so that the system's electricity purchase cost and gas purchase cost are reduced by 10.7% and 12.3% respectively compared with Scenario 1. Although Scenario 4 increases the demand response cost of 1,132 yuan compared with Scenario 1, the total system cost is reduced by 12.6% and the carbon emissions are reduced by 38.8% compared with Scenario 1. At the same time, compared with Scenario 2 and Scenario 3, the total system cost of Scenario 4 is reduced by 4.4% and 5.6% respectively, and the carbon emissions are reduced by 19.0% and 14.9% respectively.

[0207] In summary, compared with considering only one of them, the system that simultaneously considers vertical demand response and horizontal integrated demand response can more effectively reduce the system's carbon emissions while reducing the total system cost, thereby optimizing the operation mode of the integrated energy system.

Claims

1. A low-carbon economic optimization operation method for an integrated energy system considering demand response, characterized in that: By analyzing the load characteristics of the integrated energy system IES, a vertical demand response model and a horizontal demand response model are established, and an IES low-carbon economic optimization operation model considering demand response is established; the specific implementation is as follows: Step 1: Analyze the load characteristics of the integrated energy system IES; the details are as follows: According to the energy characteristics of the load, the load of the integrated energy system IES is divided into vertical demand response of the same energy type that can be transferred and reduced in time, and horizontal demand response of different energy types that can be replaced by each other. The specific classification is as follows: 1) Vertical demand response includes shiftable electricity / gas demand response and curtailable electricity / gas demand response. This type of load uses only one type of energy and can achieve energy consumption shift and electricity consumption reduction based on electricity prices and incentive mechanisms; 2) Horizontal demand response refers to alternative electricity / gas loads, which can use a variety of energy sources and achieve flexible load adjustment through energy substitution; Step 2: Establish a vertical demand response model and a horizontal demand response model respectively; the specific process of step 2 is as follows: Step 2.1, establish a vertical demand response model; In IES, the power system and natural gas system use time-of-use electricity prices and time-of-use gas prices respectively to guide the transferable load to transfer electricity in the unit energy consumption time period. The elasticity matrix method is used to model the transferable electricity / gas load. The elasticity matrix is In formula (1), ε is the elasticity coefficient, the diagonal is the self-elasticity, and the others are the mutual elasticity. The self-elasticity is negative, which indicates that the corresponding load in this period is reduced; the mutual elasticity is positive, which indicates the load response to the price changes in other periods. The elastic modulus ε is In formula (2), p and q are the quantity and time-of-use price of the transferable load; Δp and Δq are the change in the amount of electricity and time-of-use price of the transferable load; The mathematical model of transferable electricity and gas loads is: In formula (3), P TRY , P TR is the initial load of the transferable electric load and the electric load after response; P TR1 …P TRt is the load after the transferable load response in each period from 1 to t; ΔP TR is the transfer amount of the transferable electric load; the diagonal of the elastic matrix is ​​the self-elasticity of the electric load, and the others are the mutual elasticity of the electric load; Δq E1 …Δq Et is the time-sharing price change of the transferable electric load in each period from 1 to t; q E1 …q Et is the time-of-use price of the transferable electric load in each period from 1 to t; in formula (4), E TRY 、E TR is the initial load of the transferable gas load and the gas load after response; E TR1 …E TRt is the gas load after the transferable gas load response in each period from 1 to t; ΔE TR is the transfer amount of the transferable gas load; the diagonal of the elastic matrix is ​​the self-elasticity of the gas load, and the others are the mutual elasticity of the gas load; Δq E1 …Δq Et is the time-sharing price change of the transferable gas load in each period from 1 to t; q E1 …q Et is the time-of-day price of the transferable gas load in each period from 1 to t; The total amount of transferable electric / gas load remains unchanged within an operating cycle, and the load power per unit time is subject to upper and lower limits. The specific constraints are as follows: In formula (5) and formula (6), P TRY,t , P TR,t is the initial load of the transferable electric load and the electric load after response in period t; E TRY,t 、E TR,t is the initial load of the transferable gas load and the gas load after response in period t; ΔP TR,max , ΔP TR,min is the upper and lower limits of the transferable electric load; ΔE TR,max , ΔE TR,min The upper and lower limits of the transferable gas load; In the integrated energy system, according to the system energy supply and demand, the energy shortage during operation can be alleviated by reducing the corresponding load power. The mathematical model for reducing the electricity / gas load is: In formula (7), P CUY,t , P CU,t is the initial load and the load after response that can be reduced in period t; E CUY,t 、E CU,t is the initial load and the load after response that can reduce the gas load in period t; ΔP CU,t , ΔE CU,t is the amount of reduction in electricity load and gas load that can be reduced in period t; b CUE,t , b CUG,t is the reduction state of the electric / gas load that can be reduced. A value of 1 indicates that a reduction adjustment occurs during period t, and a value of 0 indicates that no reduction adjustment occurs; θ E,t ,θ G,t is the reduction ratio of the electricity / gas load that can be reduced. A value of 1 indicates that the load is completely reduced during period t, and a value of 0 indicates that no reduction adjustment occurs. The power reduction range constraint that can be met for the electricity / gas load is In formula (8), ΔP CU,max , ΔP CU,min ΔE is the upper and lower limits of the amount of electricity load reduction that can be achieved; CU,max , ΔE CU,min The upper and lower limits of the amount of gas load reduction that can be reduced; Step 2.2: Establish a horizontal demand response model Under the guidance of different energy prices and different electricity demands, the alternative electricity / gas load can achieve substitution between different energy sources through coupling between energy sources. The energy coupling expression is: In formula (9), P SUY,t , P SU,t is the initial load and the load after response of the replaceable electric load; E SUY,t 、E SU,t is the initial load and the load after response of the replaceable gas load; ΔP SU , ΔE SU is the amount of replacement for electric load and gas load; An alternative state for an alternative electric load; It is an alternative state for alternative gas load; The alternative power range of the alternative electric / gas load meets the following constraints: In formula (10) and formula (11), The upper and lower limits of the amount of electric energy that can be substituted for gas load by electric load; The upper and lower limits of the amount of electric energy replaced by gas load instead of electric load; The upper and lower limits of the gas energy substitution amount for electric load by gas load; The upper and lower limits of gas energy substitution for electric loads replacing gas loads; Step 3: Construct a low-carbon economic optimization operation model for an integrated energy system considering demand response; the specific process of step 3 is as follows: Step 3.1, considering the energy purchase cost, demand response cost and carbon trading cost of the upper power grid and gas wells to construct a low-carbon operation objective function; The objective function of the IES low-carbon economic optimization operation model considering demand response is: minf=f1+f2 (12) In formulas (12), (13) and (14), f1 is the system operation cost; f2 is the carbon trading cost; P E,t is the power generation capacity of the coal-fired unit during period t; ΔP CU , ΔE CU P is the amount of reduction in electricity load and gas load; GT,t is the power generation of the gas turbine during period t; CUE , γ CUG is the unit reduction cost coefficient of the electricity / gas load that can be reduced; ξ is the incentive base price of carbon trading; ΔE is the length of the carbon emission interval; κ is the growth rate of carbon trading price in the penalty stage; λ is the growth rate of carbon trading price in the incentive stage; ρ E,t is the unit electricity price of the upper power grid during period t; G,t is the unit price of natural gas during period t; E IES is the total free carbon emission quota of the system; E IES,C is the total carbon emissions of the actual operation of the system; Step 3.2: Consider the power network constraints, node voltage constraints, and natural gas network constraints during system operation. The specific constraints are as follows: Power network constraints: P EL,t =P TR,t +P CU,t +P SU,t (19) In formulas (15), (16), (17), (18) and (19), P ij,t , Q ij,t is the active and reactive power of the ij branch; P jk,t , Q jk,t is the active and reactive power of the jk branch; P GT,t , Q GT,t P is the active power and reactive power of the gas turbine during period t; WT,t , Q WT,t P is the wind power active power and reactive power in period t; EC,t , Q EC,t P is the input active power and reactive power of the electrolytic cell during period t; EL,t , Q EL,t is the active power and reactive power of the electric load during period t; ΔP W,t , ΔQ W,t is the abandoned wind active power and abandoned wind reactive power in period t; r ij 、x ij is the resistance and reactance of the ij branch; U i,t , U j,t is the voltage of nodes i and j; I ij,t , U ij,t is the current flowing through the ij branch and the voltage difference at the ij node; Node voltage constraints: IN j,min ≤U j,t ≤U j,max (20) In formula (20), U j,max , U j,min are the upper and lower limits of the voltage allowed at node j; Natural gas network constraints: In formula (21) and formula (22), sgn(π i,t ,π j,t ) is a sign function. When the pressure at node i is higher than that at node j, the value is 1, otherwise it is -1; π i,t , π j,t is the natural gas pressure at node i and node j during period t; π p,t , π q,t fp is the natural gas pressure at node p and node q during period t; p,t is the pipeline flow rate of natural gas pipeline p during period t; φ p is the gas flow transmission parameter of the natural gas pipeline p; Node airflow balance constraints: P GL,t =And TR,t +E CU,t +E SU,t (24) In equations (23) and (24), V(n) and U(n) are the gas pipeline sets with node n as the end point and the start node; X(n) and Z(n) are the methane reactor and gas turbine sets at node n; ψ H2G , GT is the conversion factor between the methane reactor and the gas turbine; P H2G,t is the methane reactor power during period t; P GL,t is the active power of gas load in period t; Pipeline flow constraints: fq mn,min ≤fq mn,t ≤fq mn,max (25) In formula (25), fq mn,t is the pipeline mn flow; fq mn,max 、fq mn,min The upper and lower limits of the pipeline mn flow; Node pressure constraint: π m,min ≤π m,t ≤π m,max (26) In formula (26), π m,t is the air pressure at node m; π m,max , π m,min are the upper and lower limits of the air pressure allowed at node m; Exchange power constraints with the upper power grid and natural gas network: In formula (27), P E,t is the amount of electricity purchased during period t; P G,t is the amount of natural gas purchased during period t; P Emax , P Emin P is the upper and lower limits of the system's power purchase; Gmax , P Gmin The upper and lower limits of the system's gas purchase volume; Hydrogen energy balance constraints: In formula (28), Y(n) is the set of hydrogen storage tanks at node n; P P2H,t P is the volume of hydrogen generated by the electrolyzer; H2H,t is the volume of hydrogen consumed by the methane reactor; is the gas inlet and outlet volume of the hydrogen storage tank; Output constraints of each unit: In formula (29), P WTmax , P WTmin P is the upper and lower limits of wind power output; EC,t is the input active power of the electrolytic cell during period t; E MRHmax 、E MRHmin The upper and lower limits of hydrogen input to the methane reactor; E MRH,t is the hydrogen input to the methane reactor during period t; P WTmax , P WTmin P is the upper and lower limits of wind power output; WT,t is the wind power active power in period t; P GTmax , P GTmin P is the upper and lower limits of gas turbine output; GT,t is the active power of the gas turbine during period t; ΔP GTmax , ΔP GTmin are the upper and lower limits of the gas turbine's climbing power; Step 4: Use the CPLEX solver to solve the low-carbon economic optimization operation model of the integrated energy system.

2. The low-carbon economic optimization operation method of the integrated energy system considering demand response according to claim 1 is characterized in that: The specific process of step 4 is as follows: Step 4.1, set the input parameters of the low-carbon economic optimization operation model of the integrated energy system, input the original load data of the electric load and gas load, input the output data of the wind power, and the corresponding parameters of each unit in the system; Step 4.2: The constructed IES low-carbon economy optimization operation model is linearized by using second-order cone relaxation and incremental linearization, and the CPLEX solver is used in MATLAB to perform simulation and solve the problem, and the optimal cost under the operation scheme is obtained; Step 4.3: If the constraint conditions of step 3.2 are met, stop the calculation; if the conditions are not met, adjust the parameters and recalculate; Step 4.4: Obtain the operating status of each unit in the integrated energy system and determine the operating plan of the system.

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