Power system source-load-storage scheduling method considering demand response and related device

Abstract

The invention discloses a power system source-load-storage scheduling method considering demand response and a related device, and belongs to the technical field of power system scheduling. The method comprises the following steps: establishing each scheduling unit model of the novel power system; each scheduling unit model comprises a distributed power supply model, a thermal power generating unit model, an adjustable load demand response model, a composite energy storage model and a carbon transaction model; based on each scheduling unit model, establishing a power system source-load-storage scheduling model considering demand response; and solving the power system source-load-storage scheduling model considering the demand response to obtain an optimal scheduling scheme so as to regulate and control the output of each scheduling unit of the power system. According to the invention, multi-element adjustment of different load side resources is fully utilized to realize source and load coordinated interaction, the elasticity of the power grid and the capability of accepting new energy power generation are enhanced, and the construction of a novel power system with flexible interaction between new energy and demand side resources is promoted.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system dispatching, and relates to a power system source-load-storage dispatching method and related devices considering demand response. Background Art

[0002] In order to actively respond to climate change and resource and environmental constraints, it is necessary to accelerate the low-carbon transformation of energy. In recent years, with the year-on-year increase in power generation from renewable energy sources such as wind power and photovoltaics, this has raised the question of how to coordinate various adjustable resources to suppress the fluctuations of renewable energy and increase its absorption rate. Scholars first proposed a dispatching method that considers "source-load" coordination. Although in the past, the "source-load" coordination mode has shown certain advantages in traditional power system dispatching, with the development of energy storage technology, today's power system dispatching mode is no longer limited to this.

[0003] Nowadays, new energy, conventional units, load-side resources, and energy storage can all be used as resources for system dispatch, and coordinate and interact with each other to achieve a more optimized power system dispatch plan. Compared with the original "source-load" coordination, this "source-load-storage" coordinated dispatch mode has obvious advantages. By introducing energy storage, the flexibility and stability of the system are enhanced, the difference between supply and demand is balanced, and the economy of the system is improved. At the same time, this dispatch method will also help promote energy transformation, reduce carbon emissions, and promote the development of the power system in a clean and sustainable direction.

[0004] In this context, the coordinated economic dispatch mode of "source-load-storage" is particularly important. By coordinating various resources such as new energy power generation, load-side resources and energy storage, we can effectively improve energy utilization efficiency, smooth the load curve of the power system, and optimize the operation plan. However, the current optimization dispatch method is difficult to comprehensively consider the three factors of source (power supply), load (load), and storage (energy storage), and it is impossible to obtain the optimal dispatch plan, which hinders the subsequent regulation of the power system. Summary of the invention

[0005] The purpose of the present invention is to provide a power system source-load-storage scheduling method and related devices that take demand response into consideration, so as to solve the technical problem that the optimization scheduling method in the prior art is difficult to comprehensively consider the three factors of source, load and storage, thereby failing to obtain the optimal scheduling plan, affecting the subsequent power system regulation.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

[0007] In a first aspect, the present invention provides a method for dispatching a power system source-load-storage considering demand response, comprising the following steps:

[0008] Establishing the dispatching unit models of the new power system; the dispatching unit models include a distributed power supply model, a thermal power unit model, an adjustable load demand response model, a composite energy storage model and a carbon trading model;

[0009] Based on the models of each dispatch unit, a power system source-load-storage dispatch model considering demand response is established;

[0010] The power system source-load-storage dispatch model considering demand response is solved to obtain an optimal dispatch scheme, and the output of each dispatch unit of the power system is regulated based on the optimal dispatch scheme.

[0011] Furthermore, the distributed power supply model includes a photovoltaic power generation model and a wind power generation model;

[0012] The expression of the photovoltaic power generation model is:

[0013]

[0014] Where P PV,j,t is the output power of photovoltaic generator set j at time t; P PVmax is the maximum output power of the photovoltaic unit; G t is the light intensity at time t; G N is the rated light intensity; k is the temperature coefficient; T c,t is the temperature of the photovoltaic cell at time t; T N is the rated temperature of the photovoltaic cell; T en,t is the ambient temperature of the photovoltaic equipment;

[0015] The expression of the wind power generation model is:

[0016]

[0017]

[0018] In the formula, is the upper limit of the output of wind farm j in period t; is the lower limit of the output of wind farm j in period t; is the actual value of wind power output; is the predicted value of wind power output; μ is the robust conservative adjustment factor.

[0019] Furthermore, the expression of the thermal power unit model is:

[0020]

[0021] In the formula, C G is the total cost of the thermal power unit; is the total number of thermal power units in the dispatching area; ui,t is the start / stop status of thermal power unit i in period t, which is 1 when it is started and 0 when it is stopped; F i is the operating cost of thermal power unit i; a i , b i 、c i They are the coefficients of the secondary term, the primary term and the constant term of coal consumption of thermal power units respectively; is the output of thermal power unit i in period t; S i is the start-up and shutdown cost of thermal power unit i;

[0022] The constraints of the thermal power unit model include the output constraints of thermal power units in the region, the ramp constraints of thermal power units in the sending end region, the minimum start-stop time constraints of thermal power units in the sending end region, and the positive and negative spinning reserve constraints in the sending end region.

[0023] The expression of the output constraint of thermal power units in the region is:

[0024]

[0025] In the formula, is the upper limit of the output of thermal power unit i in period t; is the lower limit of the output of thermal power unit i in period t;

[0026] The expression of the ramp constraint of the thermal power unit in the sending end area is:

[0027]

[0028] In the formula, r i u is the ramp rate limit of thermal power unit i in period t; r i d is the ramp-down rate limit of thermal power unit i in period t;

[0029] The expression for the minimum start-stop time constraint of the thermal power unit in the sending end area is:

[0030]

[0031] In the formula, and are the continuous operation and shutdown time of thermal power unit i in the period t-1 respectively; T i on and T i off are the minimum operation and shutdown time of thermal power unit i respectively;

[0032] The expressions for the positive and negative spinning reserve constraints in the sending end region are as follows:

[0033] The positive spinning reserve constraint is:

[0034]

[0035] In the formula, is the positive spinning reserve capacity required by region A during period t; is the maximum possible output of thermal power unit i in period t, which must simultaneously satisfy the output upper limit constraint and the ramp constraint of the thermal power unit in this period, and take the minimum of the two; T 0 is the positive spinning reserve response time; ω D is the demand coefficient of the load in the sending-end power grid for positive spinning reserve; ω u is the demand coefficient of wind power and photovoltaic power for positive spinning reserve in the sending-end grid;

[0036] The negative spinning reserve constraint is:

[0037]

[0038] In the formula, is the negative spinning reserve capacity required by region A during period t; is the minimum possible output of thermal power unit i in period t, which must simultaneously satisfy the output lower limit constraint and down-slope constraint of the thermal power unit in this period, and the larger of the two is taken; ω d is the demand coefficient of wind power and photovoltaic power for negative spinning reserve in the sending-end power grid.

[0039] Furthermore, the establishment of the adjustable load demand response model includes the following steps:

[0040] The electricity price during normal hours is set as the average electricity price, and the electricity price during peak and valley hours is expressed as:

[0041]

[0042] In the formula, φ f is the peak-time electricity price; p is the electricity price during normal hours; g is the electricity price during off-peak hours; α is the upward floating ratio; β is the downward floating ratio;

[0043] The gap ratio and time-of-use electricity price ratio are defined as follows:

[0044] χ=α / β

[0045] μ=(1+α) / (1-β)

[0046] In the formula, χ is the pull-off ratio, which indicates the ratio of the fluctuation range of the peak-period electricity price to the valley-period electricity price based on the average electricity price; μ is the time-of-use electricity price ratio, which indicates the amplitude ratio of the peak-period electricity price to the valley-period electricity price after the implementation of the time-of-use electricity price;

[0047] The conditions for x to be satisfied are:

[0048]

[0049] Where P Lg is the total load during the valley period; P Lf is the total load during peak hours;

[0050] The constraints of the adjustable load demand response model include time-of-use electricity price related constraints and interruptible load related constraints; the expression of the time-of-use electricity price related constraints is as follows:

[0051] The upper and lower limits of load reduction for time-of-use electricity prices are:

[0052]

[0053] In the formula, is the load reduction under TOU; is the maximum load reduction in the period t after TOU; I t I is a 0-1 variable that represents whether load reduction is performed during period t after TOU. t =1 means that load reduction is carried out during the period t after TOU, I t =0 means no load reduction is performed during the period t after TOU;

[0054] The load reduction ramping constraint under time-of-use electricity prices is:

[0055]

[0056] In the formula, It is the upper limit of load reduction under time-of-use electricity price; It is the lower limit of load reduction under time-of-use electricity price;

[0057] The expression of the interruptible load related constraint is as follows:

[0058] The total interruption duration of IL users within a day is constrained as follows:

[0059]

[0060] In the formula, is the interruption duration of the jth IL user; Indicates interruption. Indicates not to be interrupted; The upper limit of the total interruption duration of the jth IL user in a day;

[0061] The single interruption duration of IL users is constrained as follows:

[0062]

[0063] In the formula, is the upper limit of a single interruption duration for the jth IL user.

[0064] Furthermore, the composite energy storage model includes a battery energy storage model and an electric vehicle energy storage model;

[0065] The expression of the battery energy storage model is as follows:

[0066] The investment cost of battery energy storage is:

[0067]

[0068] In the formula, C 1 represents the energy storage investment cost; d is the annual discount rate of energy storage; L is the service life of energy storage; C p and C e are the investment costs per unit power and per unit capacity of energy storage respectively; P ESS and E ESS Respectively represent the rated power and rated capacity of energy storage;

[0069] The operating costs of battery energy storage are:

[0070] C 2 =C cha P cha,t -C dis P dis,t

[0071] In the formula, C 2 Represents the operating cost of energy storage; C cha and C dis Respectively represent the charging cost coefficient and discharging benefit coefficient of energy storage; P cha,t and P dis,t are the charging and discharging power of the energy storage in the tth period respectively;

[0072] The constraints of the battery energy storage model for each group of batteries are as follows:

[0073] The capacity constraint is:

[0074]

[0075] E min ≤E t ≤E ESS

[0076] E 0 =E T

[0077] In the formula, E t is the remaining capacity of the battery in the tth period; η cha and ηdis are the charging and discharging efficiency of the battery; P cha,t and P dis,t are the charging and discharging power of the battery in the tth period respectively; Δt is the unit time interval; E min Indicates the minimum remaining battery power; E 0 and E T They are the remaining power of the battery at the beginning and end of the dispatching cycle respectively;

[0078] The working state constraints are:

[0079] X t +Y t ≤1

[0080] Where, X t and Y t are all 0-1 variables, representing the charging state and discharging state of the battery in the tth period respectively; when the battery is charging, X t =1,Y t =0; when the battery is discharged, X t =0, Y t =1; when the battery is in a state of neither charging nor discharging, X t =Y t =0;

[0081] The charge and discharge power constraints are:

[0082] 0≤P cha,t ≤λ cha P ESS X t

[0083] 0≤P dis,t ≤λ dis P ESS Y t

[0084] In the formula, λ cha is the margin factor of charging power; dis is the margin factor of discharge power;

[0085] The charge and discharge times are constrained as follows:

[0086] W t =max(0,X t -X t-1 )

[0087] Z t =max(0,Y t -Y t-1 )

[0088]

[0089] Where, T is the total number of scheduling periods; W t and Z t are all 0-1 variables, which are the signs that the battery enters the charging and discharging states in the tth period respectively; when the battery starts charging in the tth period, W t =1, in other cases W t =0; when the battery starts to discharge in the tth period, Z t =1, in other cases Z t =0; N 1 and N 2 are the maximum number of charging and discharging times of the battery in the dispatching cycle respectively;

[0090] The constraints of the electric vehicle energy storage model are as follows:

[0091] The power constraint during the charging and discharging process of electric vehicles is:

[0092] P ch,min ≤P ch,t ≤P ch,max

[0093] P di,min ≤P di,t ≤P di,max

[0094] Where P ch,t , P di,t are the charging and discharging power of electric vehicles respectively; P ch,max , P ch,min are the upper and lower limits of the charging power of electric vehicles respectively; P di,max , P di,min They are the upper and lower limits of the discharge power of electric vehicles respectively;

[0095] The line transmission power constraint is:

[0096] P l,t ≤P lmax

[0097] Where P l,t and P lmax are the load of line l and the line safety load respectively.

[0098] Furthermore, the carbon trading model is expressed as follows:

[0099] The initial quota of carbon emission rights is:

[0100]

[0101] In the formula, E IES Carbon emission quota for the integrated energy system; E gridCarbon emission quota for purchasing electricity from the upper power grid; E GT Carbon emission quota for gas turbines; E GB is the carbon emission quota of gas boiler; E gload is the carbon emission quota of gas load; e , χ h are the carbon emission quotas per unit of electrical power and per unit of thermal power respectively; gload is the carbon emission quota per unit gas load consumed; e,h is the electrical-to-heat coefficient; P grid (t) is the amount of electricity purchased from the upper power grid during period t; P GT,e (t), P GT,h (t) are the electrical power and thermal power generated by the gas turbine during period t; P GB,h (t) is the thermal power generated by the gas boiler during period t; P g,Load (t) is the gas load consumed in period t; T is the scheduling period;

[0102] The actual carbon emissions are:

[0103] E IESa =E grd,a +E gtgb,a +E gload,a -E p2G,a

[0104]

[0105] P gtgb (t) = P GT,e (t)+P GT,h (t)+P GB,h (t)

[0106]

[0107] In the formula, E IESa is the actual total carbon emissions of the comprehensive energy system; E grd,a Actual carbon emissions from coal-fired power units purchased for the upper power grid; E gtgb,a is the total actual carbon emissions of gas turbines and gas boilers; E gload,a is the actual carbon emission of gas load; E p2G,a Carbon emissions absorbed by power-to-gas equipment; P gtgb (t) is the total output power of the gas turbine and gas boiler during period t; a 1 , b 1 、c 1 is the carbon emission calculation coefficient of coal-fired power units, a 2 , b 2 、c 2 is the carbon emission coefficient of natural gas energy supply; ξ is the equivalent carbon emission coefficient of unit gas load; is the carbon emission coefficient absorbed by the power-to-gas equipment; P P2G,g (t) is the natural gas power discharged by the power-to-gas equipment during period t;

[0108] The carbon emissions of IES participating in carbon trading are:

[0109] E IES,t =E IES,a -E IES

[0110] The tiered carbon trading costs are:

[0111]

[0112] In the formula, is the carbon trading cost of the comprehensive energy system; λ is the carbon trading base price; α is the price increase; l is the length of the carbon emission interval.

[0113] Furthermore, the objective function expression of the power system source-load-storage scheduling model considering demand response is:

[0114]

[0115] In the formula, is the predicted value of the photovoltaic unit output; ρ j is the unit capacity compensation price for the jth IL user; is the capacity called by the jth IL user in period t; C cha and C dis Respectively represent the charging and discharging cost coefficients of the battery per unit power; P cha,t and P dis,t are the charging and discharging power of the battery in the tth period respectively; C ch and C di Respectively represent the charging and discharging cost coefficients of electric vehicles per unit power; P ch,t and P di,t are the charging and discharging power of the electric vehicle in the tth period respectively;

[0116] The constraints of the power system source-load-storage dispatch model considering demand response include power balance constraints and node voltage constraints;

[0117] The expression of the power balance constraint is:

[0118]

[0119] In the formula, is the original load of the system;

[0120] The expression of the node voltage constraint is:

[0121] U min ≤U i ≤U max

[0122] Where U i is the voltage at node i; U max and U min are the upper and lower limits of the node voltage respectively.

[0123] In a second aspect, the present invention provides a power system source-load-storage dispatching system considering demand response, comprising:

[0124] A dispatch unit model establishment module is used to establish various dispatch unit models of the new power system; the dispatch unit models include a distributed power supply model, a thermal power unit model, an adjustable load demand response model, a composite energy storage model and a carbon trading model;

[0125] A comprehensive dispatch model building module is used to build a power system source-load-storage dispatch model that takes demand response into consideration based on the models of each dispatch unit;

[0126] The optimal solution solving module is used to solve the power system source-load-storage scheduling model considering demand response, obtain the optimal scheduling solution, and regulate the output of each scheduling unit of the power system based on the optimal scheduling solution.

[0127] In a third aspect, the present invention provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above method when executing the computer program.

[0128] In a fourth aspect, the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0129] Compared with the prior art, the present invention has the following beneficial effects:

[0130] The present invention discloses a source-load-storage dispatching method and related devices for a power system considering demand response. By establishing various dispatching unit models of a new power system, including a distributed power supply model, a thermal power unit model, an adjustable load demand response model, a composite energy storage model and a carbon trading model, a comprehensive and detailed reference basis is provided for the dispatching of the power system. These models not only cover the key elements in traditional power dispatching, but also introduce emerging concepts such as demand response and carbon trading, so that the dispatching scheme is more in line with the actual needs and development trends of modern power systems. Then, based on these dispatching unit models, a source-load-storage dispatching model for a power system considering demand response is established. This model can comprehensively consider the three factors of the source (power source), load (load), and storage (energy storage) of the power system, as well as the regulatory role of demand response, so as to formulate a more reasonable and optimized dispatching scheme. The present invention makes full use of the multi-dimensional regulation of different load-side resources to realize the coordinated interaction of source and load, enhance the elasticity of the power grid and the ability to accept new energy power generation, and promote the construction of a new power system with flexible interaction between new energy and demand-side resources. The model includes composite energy storage power stations such as battery energy storage and electric vehicle energy storage, which helps to solve the problem of poor economic efficiency of single energy storage participating in power system regulation, and lays a solid foundation for achieving high-proportion consumption of new energy and sustainable development. It also promotes the transformation of the traditional "source-load" coordination model to the "source-load-storage" coordination model, and promotes the rational planning and optimized operation of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0131] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0132] Figure 1 is a flow chart of the method of the present invention;

[0133] Figure 2 is a schematic diagram of the system of the present invention;

[0134] Figure 3 A flow chart of model solving in an embodiment of the present invention;

[0135] Figure 4 It is a schematic diagram of the computer device structure of the present invention. DETAILED DESCRIPTION

[0136] The present invention will be described in detail below with reference to the accompanying drawings and in combination with embodiments. It should be noted that the embodiments and features in the embodiments of the present invention can be combined with each other without conflict.

[0137] The following detailed description is an exemplary description, which is intended to provide further detailed description of the present invention. Unless otherwise specified, all technical terms used in the present invention have the same meaning as those generally understood by those skilled in the art to which the present invention belongs. The terms used in the present invention are only for describing specific embodiments, and are not intended to limit the exemplary embodiments according to the present invention.

[0138] The present invention is further described in detail below in conjunction with the accompanying drawings:

[0139] See also Figure 1 The embodiment of the present invention discloses a power system source-load-storage scheduling method considering demand response, comprising the following steps:

[0140] S1, establishing each dispatching unit model of the new power system; each dispatching unit model includes a distributed power supply model, a thermal power unit model, an adjustable load demand response model, a composite energy storage model and a carbon trading model;

[0141] S1.1, Distributed Power Model

[0142] S1.1.1, Photovoltaic power generation model

[0143] The solar radiation intensity has a great influence on the photovoltaic power generation. The model of the photovoltaic unit is constructed, and its expression is:

[0144]

[0145] in:

[0146] Where: P PV,j,t is the output power of photovoltaic generator set j at time t; P PVmax is the maximum output power of the photovoltaic unit; G t is the light intensity at time t; G N is the rated light intensity; k is the temperature coefficient; T c,t is the temperature of the photovoltaic cell at time t; T N is the rated temperature of the photovoltaic cell; T en,t is the ambient temperature of the photovoltaic device.

[0147] S1.1.2, Wind Power Generation Model

[0148] First, the prediction interval of wind power output is given as:

[0149]

[0150] Where: and are the upper and lower limits of the output of wind farm j in period t respectively.

[0151] When the uncertainty of wind power output is considered, there is inevitably a certain deviation between the actual output of the wind turbine and the predicted value of the wind turbine output. The present invention adopts a box-type set with adjustable conservatism to describe the wind power output, and its specific representation is as follows:

[0152]

[0153] in:

[0154]

[0155] Where: are the actual value and predicted value of wind power output respectively; is an intermediate variable; Both are 0-1 variables, which are the identification quantities of positive and negative deviations in wind power prediction respectively; μ is the robust conservative adjustment factor, which is used to adjust the conservativeness of the robust optimization solution problem so that the wind power output in the worst case will not reach the boundary.

[0156] S1.2, Thermal Power Plant Model

[0157] S1.2.1, Total cost of thermal power generation units

[0158]

[0159] Where: C G is the total cost of the thermal power unit; is the total number of thermal power units in the dispatching area; u i,t is the start / stop status of thermal power unit i in period t, which is 1 when it is started and 0 when it is stopped; F i is the operating cost of thermal power unit i; a i 、b i 、c i They are the coefficients of the secondary term, the primary term and the constant term of coal consumption of thermal power units respectively; is the output of thermal power unit i in period t; S i is the start-up and shutdown cost of thermal power unit i;

[0160] As for the startup cost of thermal power units, it includes u i,t u i,t -1 In order to linearize the nonlinear product term in the form of , new integer variables and corresponding constraints need to be introduced. Here, let U i,t =u i,t u i,t-1 , you can see U i,t is a new integer variable, which is equivalent to the following linear constraint:

[0161]

[0162] S1.2.2, Constraints for Thermal Power Units

[0163] (1) Output constraints of thermal power units in the region

[0164]

[0165] Where: and are the upper and lower limits of the output of thermal power unit i in period t respectively.

[0166] (2) Climbing constraints of thermal power units in the sending end area

[0167]

[0168] Where: r i u and r i d are the rate limits for the up-ramp and down-ramp of thermal power unit i in period t, respectively.

[0169] (3) Minimum start and stop time constraints for thermal power units in the sending end area

[0170]

[0171] Where: and are the continuous operation and shutdown time of thermal power unit i in the period t-1 respectively; T i on and T i off are the minimum operating and shutdown time of thermal power unit i respectively.

[0172] (4) Positive and negative spinning reserve constraints in the sending end area

[0173] ① Positive spinning reserve constraints:

[0174]

[0175] In the formula, is the positive spinning reserve capacity required by region A during period t; is the maximum possible output of thermal power unit i in period t, which must simultaneously satisfy the output upper limit constraint and the ramp constraint of the thermal power unit in this period, and take the minimum of the two; T 0 is the positive spinning reserve response time; ω D is the demand coefficient of the load in the sending-end power grid for positive spinning reserve; ω u It is the demand coefficient of wind power and photovoltaic power for positive spinning reserve in the sending-end grid.

[0176] ② Negative spinning reserve constraints:

[0177]

[0178] Where: is the negative spinning reserve capacity required by region A during period t; is the minimum possible output of thermal power unit i in period t, which must simultaneously satisfy the output lower limit constraint and down-slope constraint of the thermal power unit in this period, and the larger of the two is taken; ω d is the demand coefficient of wind power and photovoltaic power for negative spinning reserve in the sending-end power grid.

[0179] S1.3, Adjustable load demand response model

[0180] S1.3.1, Time-of-use electricity price

[0181] The electricity price during normal hours is set as the average electricity price, and the electricity prices during peak and valley hours can be expressed as:

[0182]

[0183] Where: φ f ,φ p and φ g are the electricity prices during peak, normal and valley periods respectively; α and β are the proportions of upward and downward fluctuations respectively.

[0184] The gap ratio and time-of-use electricity price ratio are defined as:

[0185] χ=α / β (18)

[0186] μ=(1+α) / (1-β) (19)

[0187] In the formula: χ is the spread ratio, which indicates the ratio of the fluctuation range of the peak-period electricity price to the valley-period electricity price based on the average electricity price; μ is the time-of-use electricity price ratio, which indicates the amplitude ratio of the peak-period electricity price to the valley-period electricity price after the implementation of the time-of-use electricity price.

[0188] When the peak-time electricity price and the valley-time electricity price are equally widened (χ=1), if the average electricity purchase price of the user is to be reduced, it is only when the user's valley electricity consumption is greater than the peak electricity consumption. When the peak-time electricity price and the valley-time electricity price are not widened (χ≠1), if the peak-valley characteristics of the user are not changed, if the average electricity purchase price of the user is to be reduced, the widening ratio χ<1 must be satisfied first. Therefore, in order to balance the interests of both supply and demand, the conditions that χ needs to meet are:

[0189]

[0190] Where: P Lg and PLf are the total load during valley period and peak period respectively.

[0191] S1.3.2, Interruptible Loads

[0192] (1) Time-of-Use (TOU)-related constraints

[0193] ① Upper and lower limits of load reduction under TOU

[0194]

[0195] Where: is the load reduction under TOU; is the maximum load reduction in the period t after TOU; I t I is a 0-1 variable that represents whether load reduction is performed during period t after TOU. t =1 means that load reduction is carried out during the period t after TOU, I t =0 means that no load reduction is performed during the period t after TOU.

[0196] ② Load reduction ramping constraint under TOU

[0197]

[0198] Where: and They are the upper and lower limits of the load reduction under TOU.

[0199] (2) Interruptible load-related constraints

[0200] The present invention mainly considers the time response characteristics of the interruptible load from two aspects.

[0201] ① Constraints on the total interruption duration of IL users within a day

[0202] First, in order to ensure the basic electricity demand of IL users, the total interruption time on the user side shall not exceed The constraints are as follows

[0203]

[0204] Where: is the interruption duration of the jth IL user; Indicates interruption. Indicates not to be interrupted; is the upper limit of the total interruption duration of the j-th IL user in a day.

[0205] ②IL user single interruption duration constraint

[0206] Then, in order to reduce the economic losses caused by power outages to users, the interruption of load demand should not exceed The constraints are as follows:

[0207]

[0208] Where: is the upper limit of a single interruption duration for the jth IL user.

[0209] S1.4, Composite Energy Storage Model

[0210] S1.4.1, Battery Energy Storage Model

[0211] (1) Investment cost of battery energy storage

[0212]

[0213] Where: C 1 represents the energy storage investment cost; d is the annual discount rate of energy storage; L is the service life of energy storage; C p and C e are the investment costs per unit power and per unit capacity of energy storage respectively; P ESS and E ESS They represent the rated power and rated capacity of the energy storage respectively.

[0214] (2) Operating costs of battery energy storage

[0215] C 2 =C cha P cha,t -C dis P dis,t (26)

[0216] Where: C 2 Represents the operating cost of energy storage; C cha and C dis Respectively represent the charging cost coefficient and discharging benefit coefficient of energy storage; P cha,t and P dis,t are the charging and discharging power of the energy storage in the tth time period respectively.

[0217] For each group of batteries, the constraints that need to be met are as follows.

[0218] (3) Capacity constraints

[0219]

[0220] E min ≤E t ≤E ESS (28)

[0221] E0 =E T (29)

[0222] Where: E t is the remaining capacity of the battery in the tth period; η cha and η dis are the charging and discharging efficiency of the battery; P cha,t and P dis,t are the charging and discharging power of the battery in the tth period respectively; Δt is the unit time interval; E min Indicates the minimum remaining battery power; E 0 and E T They are respectively the remaining capacity of the battery at the beginning and end of the scheduling cycle.

[0223] (4) Working status constraints

[0224] X t +Y t ≤1 (30)

[0225] Where: X t and Y t are all 0-1 variables, representing the charging state and discharging state of the battery in the tth period. t =1,Y t =0; when the battery is discharged, X t =0, Y t =1; when the battery is in a state of neither charging nor discharging, X t =Y t =0.

[0226] (5) Charge and discharge power constraints

[0227] 0≤P cha,t ≤λ cha P ESS X t (31)

[0228] 0≤P dis,t ≤λ dis P ESS Y t (32)

[0229] Where: cha and λ dis are the margin factors of charge and discharge power respectively.

[0230] (6) Charge and discharge times constraints

[0231] W t =max(0,X t -X t-1 ) (33)

[0232] Z t =max(0,Y t -Y t-1 ) (34)

[0233]

[0234] Where: T is the total number of scheduling periods; W t and Z t are 0-1 variables, which are the signs of the battery entering the charging and discharging states in the tth period. When the battery starts charging in the tth period, W t =1, in other cases W t =0; when the battery starts to discharge in the tth period, Z t =1, in other cases Z t = 0. N 1 and N 2 They are the maximum number of charging and discharging times of the battery during the scheduling cycle.

[0235] S1.4.2, Electric vehicle energy storage model

[0236] (1) Electric vehicles must meet power constraints during charging and discharging

[0237] P ch,min ≤P ch,t ≤P ch,max (37)

[0238] P di,min ≤P di,t ≤P di,max (38)

[0239] Where: P ch,t , P di,t are the charging and discharging power of electric vehicles respectively; P ch,max , P ch,min are the upper and lower limits of the charging power of electric vehicles respectively; P di,max , P di,min They are the upper and lower limits of the discharge power of electric vehicles respectively.

[0240] (2) EVs are connected to the grid for charging, which may cause line overload. Therefore, the line transmission power constraint should be set:

[0241] P l,t ≤P lmax (39)

[0242] Where: P l,t and P lmax are the load of line l and the line safety load respectively.

[0243] (3) Preferably, other constraints are added in the same manner as in equations (26) to (36).

[0244] S1.5, Carbon trading model

[0245] S1.5.1, Initial quota of carbon emission rights

[0246] Considering the carbon emissions generated by gas load, it is also necessary to make an initial quota for the carbon emission rights of gas load, so that this part of carbon emissions can participate in carbon trading. Assuming that the electricity from the upper power grid is all generated by coal-fired power units, the carbon emission sources in the integrated energy system include: upper coal-fired power units, gas turbines, gas boilers and gas loads. The carbon quota is:

[0247]

[0248] Where: E IES 、E grid 、E GT 、E GB 、E gload are the carbon emission quotas of the integrated energy system, power purchase from the upper power grid, gas turbines, gas boilers and gas loads; e , χ h are the carbon emission quotas per unit of electrical power and per unit of thermal power respectively; gload is the carbon emission quota per unit gas load consumed; e,h is the electrical-to-heat coefficient; P grid (t) is the amount of electricity purchased from the upper power grid during period t; P GT,e (t), P GT,h (t) are the electrical power and thermal power generated by the gas turbine during period t; P GB,h (t) is the thermal power generated by the gas boiler during period t; P g,Load (t) is the gas load consumed in period t; T is the scheduling period.

[0249] S1.5.2, Actual carbon emissions

[0250] Power-to-gas equipment can absorb part of the carbon emissions by consuming electricity. At the same time, the gas load consumed on the demand side will also produce carbon emissions, which cannot be ignored. Therefore, the actual carbon emission framework is improved to take into account the carbon emissions caused by the gas load. The actual carbon emissions are:

[0251]

[0252] Where: E IESa is the actual total carbon emissions of the comprehensive energy system; E grd,a Actual carbon emissions from coal-fired power units purchased for the upper power grid; E gtgb,ais the total actual carbon emissions of gas turbines and gas boilers; E gload,a is the actual carbon emission of gas load; E p2G,a Carbon emissions absorbed by power-to-gas equipment; P gtgb (t) is the total output power of gas turbines and gas boilers during period t; a1, b1, c1 are the carbon emission calculation coefficients of coal-fired power units, a2, b2, c2 are the carbon emission coefficients of natural gas energy supply; ξ is the equivalent carbon emission coefficient per unit gas load; is the carbon emission coefficient absorbed by the power-to-gas equipment; P P2G,g (t) is the natural gas power discharged by the power-to-gas equipment during period t.

[0253] S1.5.3, Tiered carbon trading costs

[0254] The carbon emissions of IES participating in carbon trading are:

[0255] E IES,t =E IES,a -E IES (42)

[0256] In the traditional carbon trading mechanism, the base price of carbon trading is fixed, while the tiered carbon trading mechanism divides the carbon emissions involved in the transaction into several purchase intervals. As the carbon trading volume of IES continues to increase, the carbon trading cost will also increase. The tiered carbon trading cost is:

[0257]

[0258] Where: is the carbon trading cost of the comprehensive energy system; λ is the carbon trading base price; α is the price increase; l is the length of the carbon emission interval.

[0259] S2, based on the models of each dispatching unit, establish a power system source-load-storage dispatching model considering demand response;

[0260] S2.1, Objective Function

[0261]

[0262] Where: is the predicted value of the photovoltaic unit output; ρ j is the unit capacity compensation price for the jth IL user; is the capacity called by the jth IL user in period t; C cha and C dis Respectively represent the charging and discharging cost coefficients of the battery per unit power; P cha,t and P dis,t are the charging and discharging power of the battery in the tth period respectively; C ch and C diRespectively represent the charging and discharging cost coefficients of electric vehicles per unit power; P ch,t and P di,t are the charging and discharging power of the electric vehicle in the tth period respectively;

[0263] S2.2, Constraints

[0264] (1) Power balance constraints:

[0265]

[0266] Where: is the original load of the system.

[0267] (2) Node voltage constraints:

[0268] U min ≤U i ≤U max (46)

[0269] Where: U i is the voltage at node i; U max and U min are the upper and lower limits of the node voltage respectively.

[0270] (3) Preferably, other constraints similar to those in S2 may be added.

[0271] S3, solving the power system source-load-storage dispatch model considering demand response to obtain an optimal dispatch plan, thereby regulating the output of each dispatch unit in the power system.

[0272] This step uses the obtained dispatching plan to accurately control the output of each dispatching unit in the power system to ensure the safe and reliable operation of the power system. At the same time, due to the consideration of factors such as demand response and carbon trading, this plan can also reduce the operating cost of the power system to a certain extent, improve energy efficiency, and promote sustainable development.

[0273] See also Figure 2 The embodiment of the present invention discloses a power system source-load-storage dispatching system considering demand response, including a dispatching unit model building module, a comprehensive dispatching model building module and an optimal solution solving module.

[0274] It should be noted that the dispatching unit model establishment module is used to establish each dispatching unit model of the new power system; each dispatching unit model includes a distributed power supply model, a thermal power unit model, an adjustable load demand response model, a composite energy storage model and a carbon trading model; the comprehensive dispatching model establishment module is used to establish a power system source-load-storage dispatching model that considers demand response based on each dispatching unit model; the optimal solution solving module is used to solve the power system source-load-storage dispatching model that considers demand response to obtain the optimal dispatching solution, thereby regulating the output of each dispatching unit in the power system.

[0275] The comprehensive dispatch model establishment module of this embodiment further integrates the three major elements of source (power source), load (load), and storage (energy storage) in the power system on the basis of the established dispatch unit models, and deeply integrates the demand response mechanism. This module constructs a source-load-storage dispatch model that can fully reflect the dynamic characteristics and economy of the power system through complex algorithms and mathematical models. This model not only takes into account the supply and demand balance and stable operation of the power system, but also emphasizes the optimization of resource allocation, reduction of operating costs and improvement of system flexibility through demand response strategies. Finally, the comprehensive dispatch model is solved to find the optimal dispatching scheme that can maximize economic benefits and minimize carbon emissions under the premise of meeting the constraints of safe and stable operation of the power system. This scheme will directly guide the output adjustment of each dispatch unit of the power system, including the power generation plan of distributed power sources, the start and stop and output adjustment of thermal power units, the implementation of demand response measures on the load side, and the charging and discharging strategy of the energy storage system, so as to realize the refined management and intelligent dispatching of the power system. To sum up, the power system source-load-storage scheduling system considering demand response disclosed in the embodiment of the present invention provides a new solution for the optimal scheduling of the power system through modular design and application of efficient algorithms, which helps to promote the transformation, upgrading and sustainable development of the energy structure.

[0276] Example:

[0277] See also Figure 3, the embodiment of the present invention analyzes the source-load-storage scheduling model of the power system considering demand response, performs case simulation in the improved IEEE33 node system, adopts yalmip (Yet Another MATLAB-based Optimization Package) in MATLAB 2016 for programming, and calls GUROBI optimization software for solution. The simulation results show that the proposed scheduling strategy can effectively optimize scheduling, thereby reducing the overall regulation cost of the system and alleviating the impact of the volatility and uncertainty of grid-connected wind and solar power generation on the system. The strategy of this embodiment successfully achieves economical and efficient scheduling of the power system by intelligently coordinating power output, adjusting user load response, and optimizing the charging and discharging behavior of the energy storage system. This not only significantly reduces the overall regulation cost of the system, including fuel costs, operation and maintenance costs, and additional losses caused by mismatch between supply and demand, but also effectively alleviates the volatility and uncertainty of grid-connected wind and solar power generation due to changes in weather conditions, and significantly improves the stability and reliability of the power system.

[0278] In one embodiment of the present invention, see Figure 4 , a computer device is provided, the computer device includes a processor and a memory, the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, and is specifically suitable for loading and executing one or more instructions in the computer storage medium to implement the corresponding method flow or corresponding function; the processor described in the embodiment of the present invention can be used for the operation of the power system source-load-storage scheduling method considering demand response.

[0279] The present invention also provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device for storing programs and data. It is understandable that the computer-readable storage medium here can include both built-in storage media in a computer device and, of course, extended storage media supported by the computer device. The computer-readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by a processor are also stored in the storage space, and these instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory (non-volatile memory), such as at least one disk memory. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the corresponding steps of the power system source-load-storage scheduling method considering demand response in the above embodiment.

[0280] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0281] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0282] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1A function specified in one or more boxes.

[0283] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0284] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A power system source-load-storage scheduling method considering demand response, characterized in that: The following steps are involved: Establishing the dispatching unit models of the new power system; the dispatching unit models include a distributed power supply model, a thermal power unit model, an adjustable load demand response model, a composite energy storage model and a carbon trading model; Based on the models of each dispatch unit, a power system source-load-storage dispatch model considering demand response is established; The power system source-load-storage dispatch model considering demand response is solved to obtain an optimal dispatch scheme, and the output of each dispatch unit of the power system is regulated based on the optimal dispatch scheme.

2. A power system source-load-storage scheduling method considering demand response according to claim 1, characterized in that: The distributed power supply model includes a photovoltaic power generation model and a wind power generation model; The expression of the photovoltaic power generation model is: Where P PV,j,t is the output power of photovoltaic generator set j at time t; P PVmax is the maximum output power of the photovoltaic unit; G t is the light intensity at time t; G N is the rated light intensity; k is the temperature coefficient; T c,t is the temperature of the photovoltaic cell at time t; T N is the rated temperature of the photovoltaic cell; T en,t is the ambient temperature of the photovoltaic equipment; The expression of the wind power generation model is: In the formula, is the upper limit of the output of wind farm j in period t; is the lower limit of the output of wind farm j in period t; is the actual value of wind power output; is the predicted value of wind power output; μ is the robust conservative adjustment factor.

3. A power system source-load-storage scheduling method considering demand response according to claim 1, characterized in that: The expression of the thermal power unit model is: In the formula, C G is the total cost of the thermal power unit; is the total number of thermal power units in the dispatching area; u i,t is the start / stop status of thermal power unit i in period t, which is 1 when it is started and 0 when it is stopped; F i is the operating cost of thermal power unit i; a i 、b i 、c i They are the coefficients of the secondary term, the primary term and the constant term of coal consumption of thermal power units respectively; is the output of thermal power unit i in period t; S i is the start-up and shutdown cost of thermal power unit i; The constraints of the thermal power unit model include the output constraints of thermal power units in the region, the ramp constraints of thermal power units in the sending end region, the minimum start-stop time constraints of thermal power units in the sending end region, and the positive and negative spinning reserve constraints in the sending end region. The expression of the output constraint of thermal power units in the region is: In the formula, is the upper limit of the output of thermal power unit i in period t; is the lower limit of the output of thermal power unit i in period t; The expression of the ramp constraint of the thermal power unit in the sending end area is: In the formula, is the ramp-up rate limit of thermal power unit i in period t; is the ramp-down rate limit of thermal power unit i in period t; The expression for the minimum start-stop time constraint of the thermal power unit in the sending end area is: In the formula, and are the time that thermal power unit i has been in continuous operation and shutdown in the period t-1 respectively; and are the minimum operation and shutdown time of thermal power unit i respectively; The expressions for the positive and negative spinning reserve constraints in the sending end region are as follows: The positive spinning reserve constraint is: In the formula, is the positive spinning reserve capacity required by region A during period t; is the maximum possible output of thermal power unit i in period t, which must simultaneously meet the output upper limit constraint and the ramp constraint of the thermal power unit in this period, and take the minimum of the two; T0 is the positive spinning reserve response time; ω D is the demand coefficient of the load in the sending-end power grid for positive spinning reserve; ω u is the demand coefficient of wind power and photovoltaic power for positive spinning reserve in the sending-end grid; The negative spinning reserve constraint is: In the formula, is the negative spinning reserve capacity required by region A during period t; is the minimum possible output of thermal power unit i in period t, which must simultaneously satisfy the output lower limit constraint and down-slope constraint of the thermal power unit in this period, and the larger of the two is taken; ω d is the demand coefficient of wind power and photovoltaic power for negative spinning reserve in the sending-end power grid.

4. A method for dispatching source-load-storage of a power system considering demand response according to claim 1, characterized in that: The establishment of the adjustable load demand response model includes the following steps: The electricity price during normal hours is set as the average electricity price, and the electricity price during peak and valley hours is expressed as: In the formula, φ f is the peak-time electricity price; p is the electricity price during normal hours; g is the electricity price during off-peak hours; α is the upward floating ratio; β is the downward floating ratio; The gap ratio and time-of-use electricity price ratio are defined as follows: χ=α / β μ=(1+α) / (1-β) In the formula, χ is the pull-off ratio, which indicates the ratio of the fluctuation range of the peak-period electricity price to the valley-period electricity price based on the average electricity price; μ is the time-of-use electricity price ratio, which indicates the amplitude ratio of the peak-period electricity price to the valley-period electricity price after the implementation of the time-of-use electricity price; The conditions for χ to be satisfied are: Where P Lg is the total load during the valley period; P Lf is the total load during peak hours; The constraints of the adjustable load demand response model include time-of-use electricity price related constraints and interruptible load related constraints; the expression of the time-of-use electricity price related constraints is as follows: The upper and lower limits of load reduction for time-of-use electricity prices are: In the formula, is the load reduction under TOU; is the maximum load reduction in the period t after TOU; I t I is a 0-1 variable that represents whether load reduction is performed during period t after TOU. t =1 means that load reduction is carried out during the period t after TOU, I t =0 means no load reduction is performed during the period t after TOU; The load reduction ramping constraint under time-of-use electricity prices is: In the formula, It is the upper limit of load reduction under time-of-use electricity price; It is the lower limit of load reduction under time-of-use electricity price; The expression of the interruptible load related constraint is as follows: The total interruption duration of IL users within a day is constrained as follows: In the formula, is the interruption duration of the jth IL user; Indicates interruption. Indicates not to be interrupted; The upper limit of the total interruption duration of the jth IL user in a day; The single interruption duration of IL users is constrained as follows: In the formula, is the upper limit of a single interruption duration for the jth IL user.

5. A method for dispatching source-load-storage of a power system considering demand response according to claim 1, characterized in that: The composite energy storage model includes a battery energy storage model and an electric vehicle energy storage model; The expression of the battery energy storage model is as follows: The investment cost of battery energy storage is: In the formula, C1 represents the energy storage investment cost; d is the annual discount rate of energy storage; L is the service life of energy storage; C p and C e are the investment costs per unit power and per unit capacity of energy storage respectively; P ESS and E ESS Respectively represent the rated power and rated capacity of energy storage; The operating costs of battery energy storage are: C2=C cha P cha,t -C dis P dis,t Where C2 represents the operating cost of energy storage; C cha and C dis Respectively represent the charging cost coefficient and discharging benefit coefficient of energy storage; P cha,t and P dis,t are the charging and discharging power of the energy storage in the tth period respectively; The constraints of the battery energy storage model for each group of batteries are as follows: The capacity constraint is: AND min ≤E t ≤E ESS E0=E T In the formula, E t is the remaining capacity of the battery in the tth period; η cha and η dis are the charging and discharging efficiency of the battery; P cha,t and P dis,t are the charging and discharging power of the battery in the tth period respectively; Δt is the unit time interval; E min Indicates the minimum remaining capacity of the battery; E0 and E T They are the remaining power of the battery at the beginning and end of the dispatching cycle respectively; The working state constraints are: X t +Y t ≤1 Where, X t and Y t are all 0-1 variables, representing the charging state and discharging state of the battery in the tth period respectively; when the battery is charging, X t =1,Y t =0; when the battery is discharged, X t =0, Y t =1; when the battery is in a state of neither charging nor discharging, X t =Y t =0; The charge and discharge power constraints are: 0≤P cha,t ≤λ cha P ESS X t 0≤P dis,t ≤λ dis P ESS AND t In the formula, λ cha is the margin coefficient of charging power; dis is the margin factor of discharge power; The charge and discharge times are constrained as follows: W t =max(0,X t -X t-1 ) Z t =max(0,Y t -AND t-1 ) Where T is the total number of scheduling periods; W t and Z t are all 0-1 variables, which are the signs that the battery enters the charging and discharging states in the tth period respectively; when the battery starts charging in the tth period, W t =1, in other cases W t =0; when the battery starts to discharge in the tth period, Z t =1, in other cases Z t =0; N1 and N2 are the maximum number of charging and discharging times of the battery in the dispatching cycle respectively; The constraints of the electric vehicle energy storage model are as follows: The power constraint during the charging and discharging process of electric vehicles is: P ch,min ≤P ch,t ≤P ch,max P di,min ≤P di,t ≤P di,max Where P ch,t , P di,t are the charging and discharging power of electric vehicles respectively; P ch,max , P ch,min are the upper and lower limits of the charging power of electric vehicles respectively; P di,max , P di,min They are the upper and lower limits of the discharge power of electric vehicles respectively; The line transmission power constraint is: P l,t ≤P lmax Where P l,t and P lmax are the load of line l and the line safety load respectively.

6. A method for dispatching source-load-storage of a power system considering demand response according to claim 1, characterized in that: The carbon trading model is expressed as follows: The initial quota of carbon emission rights is: In the formula, E IES Carbon emission quota for the integrated energy system; E grid Carbon emission quota for purchasing electricity from the upper power grid; E GT Carbon emission quota for gas turbines; E GB is the carbon emission quota of gas boiler; E gload is the carbon emission quota of gas load; e , χ h are the carbon emission quotas per unit of electrical power and per unit of thermal power respectively; gload is the carbon emission quota per unit gas load consumed; e,h is the electrical-to-heat coefficient; P grid (t) is the amount of electricity purchased from the upper power grid during period t; P GT,e (t), P GT,h (t) are the electrical power and thermal power generated by the gas turbine during period t; P GB,h (t) is the thermal power generated by the gas boiler during period t; P g,Load (t) is the gas load consumed in period t; T is the scheduling period; The actual carbon emissions are: AND IESa =And grd,a +E gtgb,a +E gload,a -AND p2G,a P gtgb (t)=P GT,e (t)+P GT,h (t)+P GB,h (t) In the formula, E IES a is the actual total carbon emissions of the comprehensive energy system; E grd,a Actual carbon emissions from coal-fired power units purchased for the upper power grid; E gygb,a is the total actual carbon emissions of gas turbines and gas boilers; E gload,a is the actual carbon emission of gas load; E p2G,a Carbon emissions absorbed by power-to-gas equipment; P gtgb (t) is the total output power of gas turbines and gas boilers during period t; a1, b1, c1 are the carbon emission calculation coefficients of coal-fired power units, a2, b2, c2 are the carbon emission coefficients of natural gas energy supply; ξ is the equivalent carbon emission coefficient per unit gas load; P is the carbon emission coefficient absorbed by the power-to-gas equipment; P2G,g (t) is the natural gas power discharged by the power-to-gas equipment during period t; The carbon emissions of IES participating in carbon trading are: AND IES,t =And IES,a -AND IES The tiered carbon trading costs are: In the formula, is the carbon trading cost of the comprehensive energy system; λ is the carbon trading base price; α is the price increase; l is the length of the carbon emission interval.

7. A method for dispatching source-load-storage of a power system considering demand response according to claim 1, characterized in that: The objective function expression of the power system source-load-storage scheduling model considering demand response is: In the formula, is the predicted value of the photovoltaic unit output; ρ j is the unit capacity compensation price for the jth IL user; is the capacity called by the jth IL user in period t; C cha and C dis Respectively represent the charging and discharging cost coefficients of the battery per unit power; P cha,t and P dis,t are the charging and discharging power of the battery in the tth period respectively; C ch and C di Respectively represent the charging and discharging cost coefficients of electric vehicles per unit power; P ch,t and P di,t are the charging and discharging power of the electric vehicle in the tth period respectively; The constraints of the power system source-load-storage dispatch model considering demand response include power balance constraints and node voltage constraints; The expression of the power balance constraint is: In the formula, is the original load of the system; The expression of the node voltage constraint is: IN min ≤U i ≤U max Where U i is the voltage at node i; U max and U min are the upper and lower limits of the node voltage respectively.

8. A power system source-load-storage dispatching system considering demand response, characterized in that: include: A dispatch unit model establishment module is used to establish various dispatch unit models of the new power system; the dispatch unit models include a distributed power supply model, a thermal power unit model, an adjustable load demand response model, a composite energy storage model and a carbon trading model; A comprehensive dispatch model building module is used to build a power system source-load-storage dispatch model that takes demand response into consideration based on the models of each dispatch unit; The optimal solution solving module is used to solve the power system source-load-storage scheduling model considering demand response, obtain the optimal scheduling solution, and regulate the output of each scheduling unit of the power system based on the optimal scheduling solution.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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