Multi-energy coupling system meshless reconstruction and optimal scheduling method thereof

Abstract

The invention discloses a multi-energy coupling system meshless reconstruction and optimization scheduling method based on topological structures and parameters of a power system, a natural gas system and a heat supply system and parameters of energy storage equipment. Constructing a multi-energy coupling system optimization scheduling model containing partial differential equation description by considering the dynamic characteristics of the natural gas system and the heat supply system; performing solution space transformation according to a Bernstein polynomial interpolation method to realize meshless reconstruction of the multi-energy coupling system model; the enhanced matrix is derived to improve solution space transformation, the flexibility loss in the solution space transformation process is reduced, and the potential flexible adjustment capability of the multi-energy coupling system is activated; and finally, solving the meshless reconstruction model based on improved solution space transformation to obtain a solving result. According to the method, the power change condition in the time period, the distribution condition of the state of the gas-heat system along the pipeline and the change condition along with time can be described, and the flexible adjustment capacity of the system is improved by improving the solution space transformation process.

Description

Technical Field

[0001] The present invention belongs to the field of energy, and in particular relates to a gridless reconstruction and optimization scheduling method of a multi-energy coupling system based on electricity-gas-heat-storage. Background Art

[0002] In order to cope with the energy crisis, global warming and other issues, the world is vigorously developing integrated energy systems to improve the comprehensive utilization efficiency of energy, promote the consumption of new energy, reduce energy consumption and reduce operating costs. Electricity, natural gas and heating as the main forms of energy utilization have received extensive attention and research, and the operating characteristics of natural gas systems and heating systems are very different from those of power systems, which brings new challenges to the operation optimization of multi-energy coupled systems. The present invention focuses on the modeling and economic operation of the electricity-gas-heat-storage multi-energy coupling system, and realizes the description of the system operation status and the optimization of the scheduling strategy.

[0003] On the one hand, the construction of natural gas and heating system models can be divided into two categories: steady-state models and dynamic models. The steady-state model of the natural gas system ignores its pipeline storage characteristics, and the steady-state model of the heating system ignores the transmission delay of the heat medium. The dynamic model of the natural gas and heating system described by partial differential equations can more accurately describe the dynamic operation process of the medium in the pipeline, so it is also widely used. For the natural gas and heating system models described by partial differential equations, the difference method is often used for discretization, and the model is coupled with the power system operation model to achieve model solution. However, the accuracy of the model will be affected by the difference step size, and the appropriate difference step size is different under different conditions, so the model accuracy is also difficult to guarantee. For this, it is necessary to embed the partial differential equation directly into the scheduling model to improve the model accuracy and describe the distribution of the gas and heat system state along the pipeline and its changes over time.

[0004] On the other hand, traditional power system optimization and dispatching models mostly use discrete time modeling, which makes it difficult to ensure the balance between system capacity and regulation demand within discrete time periods. With the increase in the penetration rate of new energy such as wind power in the power system, the system net load changes more dramatically, and the contradiction between supply and demand of the system regulation capacity will become more prominent. Reducing the length of the discrete dispatching time period can more fully consider the system regulation capacity, activation and call the inherent flexibility of the system. When the discrete time period is reduced to infinitesimal, the dispatching plan such as unit output will become a continuous function of time, and the model will also be established in the function space.

[0005] By establishing variables such as power in the power system, gas pressure and flow in the natural gas system, and temperature in the heating system in the function space, the multi-energy coupling scheduling optimization model can be reconstructed to achieve the construction of a gridless model. The solution space can be transformed through the Bernstein polynomial interpolation method to achieve model solving. However, the solution space transformation process will reduce the feasible domain of the problem, resulting in the loss of the system's potential regulation capacity, which may increase operating costs and affect the consumption of new energy. Summary of the invention

[0006] In view of the shortcomings of the prior art, the present invention realizes the characterization of the power changes within time periods in multi-energy coupling systems, the distribution of gas-heat system states along pipelines and their changes over time through a gridless reconstruction method, and improves the solution space transformation process of the model after gridless reconstruction, thereby further activating the system flexibility potential, improving the system's flexible adjustment capabilities, and avoiding flexibility loss.

[0007] The present invention proposes a gridless reconstruction and optimization scheduling method for a multi-energy coupling system, wherein the multi-energy coupling system includes a power system, a natural gas system, a heating system, and energy storage devices; the method comprises the following steps:

[0008] Step 1: Collect the topological structures and parameters of the power system, natural gas system, heating system, and parameters of energy storage and other equipment;

[0009] Step 2: Considering the dynamic characteristics of the natural gas system and the heating system, an optimal scheduling model of the electricity-gas-heat-storage multi-energy coupling system described by partial differential equations is constructed;

[0010] Step 3: Perform solution space transformation according to the Bernstein polynomial interpolation method to achieve gridless reconstruction of the optimal scheduling model of the electricity-gas-heat-storage multi-energy coupling system;

[0011] Step 4: Derive the enhancement matrix to achieve improved solution space transformation, reduce the flexibility loss in the solution space transformation process, and activate the potential flexibility of the electricity-gas-heat-storage multi-energy coupling system;

[0012] Step 5: Solve the meshless reconstruction model based on the improved solution space transformation and obtain the solution result.

[0013] Preferably, in step 1, it is also necessary to collect new energy output and various load forecast data;

[0014] Preferably, the following data are collected in step 1:

[0015] (1) The topological structure and mutual coupling relationship of the power system, natural gas system, and heating system;

[0016] (2) Parameters and operating status limits of power system lines, natural gas system pipelines, and heating system pipelines;

[0017] (3) Technical parameters of various equipment in the power system, natural gas system, and heating system, including energy storage equipment;

[0018] (4) Wind power output forecast curve and system electricity, gas and heat load curves.

[0019] Preferably, in step 2, the optimization scheduling model of the electricity-gas-heat-storage multi-energy coupling system is established in the function space, including the operation models of the power system, the natural gas system, the heating system and various equipment, with the goal of minimizing the operating cost of the system.

[0020] Preferably, in step 3, the optimization scheduling model of the electricity-gas-heat-storage multi-energy coupling system established in step 2 is reconstructed without grid by using cubic Bernstein polynomial interpolation, and the reconstruction functions of the (·)(τ) and (·)(τ,x) functions in the variables correspond to Bezier curves and Bezier surfaces, respectively.

[0021] Preferably, in step 3, according to the properties of the equation transformation, inequality equation transformation, differential / partial differential term transformation, and integral term transformation of the Bezier curve and Bezier surface equations, the equivalent transformation of the equality constraints, inequality constraints, differential / partial differential terms, and integral terms of the continuous function expression in the model can be achieved, and the time variable and the space variable can be eliminated, thereby converting the optimization problem in the function space to the Bernstein polynomial interpolation coefficient space, that is, performing a transformation process of the solution space, and the transformed model is a mixed integer linear programming model, which can be directly solved.

[0022] Preferably, in step 4, De Casteljau algorithm is used. This method uses De Casteljau algorithm to derive enhancement matrices of (·)(τ) and (·)(τ,x) functions to improve the solution space transformation process and activate the potential flexibility of the system.

[0023] Preferably, the enhanced matrix without redundant items is obtained by removing corresponding items in the enhanced matrix, thereby reducing redundant constraints in the model.

[0024] Preferably, in step 5, by solving the gridless reconstruction model after the improved solution space transformation in step 4, the optimal gridless interpolation coefficients corresponding to the (·)(τ) function and the (·)(τ,x) function can be obtained, and the spatiotemporal continuous values ​​of each operating variable in the system can be further calculated, that is, the inverse transformation of the solution space transformation is performed.

[0025] Compared with the prior art, the above technical solution proposed by the present invention can achieve the following beneficial effects:

[0026] 1. The gridless reconstruction and optimal scheduling method of the multi-energy coupling system provided by the present invention, by expressing and constructing the model in the function space, reflects the power changes in the power system during the period, the distribution of the gas and heat system status along the pipeline and its changes over time, and more finely characterizes the operating status of the multi-energy coupling system.

[0027] 2. The present invention improves the solution space transformation process of the function space model, reduces the conservatism of the transformation process while achieving model solution, thereby activating the potential flexibility of the multi-energy coupling system, avoiding flexibility loss, and improving flexible adjustment capabilities. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 A flow chart of the gridless reconstruction and optimized scheduling method of the multi-energy coupling system provided by the present invention;

[0029] Figure 2 A schematic diagram of the Bezier curve and its convexity enhancement (De Casteljau algorithm) provided by the present invention;

[0030] Figure 3 A schematic diagram of the Bezier surface and its convexity enhancement (De Casteljau algorithm) provided by the present invention;

[0031] Figure 4 A schematic diagram of a test system provided by the present invention;

[0032] Figure 5 The power load and wind power output curve diagram provided by the present invention;

[0033] Figure 6 A natural gas load curve diagram provided by the present invention;

[0034] Figure 7 A heat load curve diagram provided by the present invention;

[0035] Figure 8 A method for starting a power system unit provided by the present invention;

[0036] Fig. 9 The temperature time-space distribution diagram of the water supply pipeline 1-2 provided by the present invention;

[0037] Fig.10 A temporal and spatial distribution diagram of the gas flow of the natural gas pipeline 1-2 provided by the present invention;

[0038] Fig.11 This is the output curve of the power-to-gas unit provided by the present invention. DETAILED DESCRIPTION

[0039] In order to make the purpose, technical solution and advantages of the present invention clearer, the present invention is further described below in conjunction with the accompanying drawings and examples. It should be understood that the specific implementation examples described herein are only used to explain the present invention and are not used to limit the present invention.

[0040] The present invention provides a gridless reconstruction and optimization scheduling method of a multi-energy coupling system, wherein the multi-energy coupling system includes an electric power system, a natural gas system, a heating system, and energy storage equipment; and comprises the following steps:

[0041] Step 1: Collect the topological structure and parameters of the power system, natural gas system, heating system, and parameters of equipment such as energy storage. The data collected in the above steps are used to build the electricity-gas-heat-storage multi-energy coupling system model.

[0042] In a possible implementation, step 1 also requires collecting the new energy output of each system and various load forecast data.

[0043] The data to be collected in step 1 specifically include the following:

[0044] (1) The topological structure and mutual coupling relationship of the power system, natural gas system, and heating system;

[0045] (2) Parameters and operating status limits of power system lines, natural gas system pipelines, and heating system pipelines;

[0046] (3) Technical parameters of various equipment in the power system, natural gas system, and heating system, including energy storage equipment;

[0047] (4) Wind power output forecast curve and system electricity, gas and heat load curves.

[0048] Step 2: Taking into account the dynamic characteristics of the natural gas system and the heating system, an optimal scheduling model for the electricity-gas-heat-storage multi-energy coupling system described by partial differential equations is constructed, considering the operating constraints of the power system, natural gas system, heating system and various equipment, and minimizing the operating cost of the system.

[0049] Different from the modeling method of the electric-gas-heat-storage multi-energy coupling system based on discrete grids, the optimization scheduling model of the electric-gas-heat-storage multi-energy coupling system in the method of the present invention is established in the function space, including the operation models of the power system, natural gas system, heating system and various equipment, with the goal of minimizing the operating cost of the system.

[0050] (1) Objective function

[0051]

[0052] In formulas (1)-(7), τ is the time variable, i is the node number, Γ is the scheduling duration, and ()(τ) represents the corresponding variable at time τ; the objective function (1) minimizes the total operating cost, which includes the cost of thermal power on / off. and fuel costs Energy storage operation and maintenance cost C BES , Wind abandonment penalty C wc , Cogeneration unit operating costs C CHP and the gas purchase cost of the natural gas system C gl , can be calculated according to formula (2)-(7) respectively; where S u and S d They are the action variables for starting and shutting down the thermal power unit, 1 means action, 0 means no action, c su and c sd are the cost of a single startup and shutdown respectively; N pw is the number of segments for calculating the fuel cost by piecewise linearization, k is the segment index; I TU It is the start-stop state variable of the thermal power unit. When it is 1, it means it is in the start state, and when it is 0, it means it is in the shutdown state. is the minimum fuel cost when the thermal power unit is started, and are the power of unit i when its output is in the kth segment and its corresponding fuel cost coefficient, P TU is the power generation capacity of the thermal power unit, “” and “” represent the upper and lower limits of the corresponding operating variables; c BES is the unit power operation and maintenance cost of energy storage, P cha and P dis are the energy storage charging and discharging power respectively; P wc is the wind power abandonment and the corresponding penalty cost coefficient; P CHP and Q CHP are the power generation and heat generation of the cogeneration unit, and are their corresponding fuel cost coefficients; c gas is the natural gas price, M LD 、M GFU and M P2G They are the natural gas load mass flow rate, the natural gas mass flow rate consumed by the gas-fired unit, and the natural gas mass flow rate produced by the power-to-gas unit.

[0053] (2) Power system operation constraints

[0054]

[0055]

[0056] In formula (8)-(17): Formula (8) is S u , Sd with I TU The logical constraints between - and τ + Respectively represent the time corresponding to the instant before and after the moment τ; Formula (9) is the minimum startup and shutdown state duration constraint of the unit, where t on and t off are the duration of startup and shutdown states respectively; Equations (10) and (11) are the operation constraints of thermal power units, where r u and r d are the maximum up and down climbing rates of the unit, r su and r sd are the maximum up and down ramp rates during the start and shutdown process respectively; Formula (12) is the power balance constraint, where P sup and P dem are node power and load power respectively, P GFU , P w , P l and P P2G are respectively the power generation power of the gas unit, the wind power, the load power and the power consumption of the power-to-gas unit; Formula (13) is the line capacity constraint, where j is the line number, and are the line transmission power and upper limit, S j,i is the sensitivity coefficient between the transmission power on line j and the injection power on node i; Equations (14)-(17) are the energy storage operation constraints, where E BES is the energy stored in the energy storage, η cha and η dis are the energy storage charging and discharging efficiency, is the upper limit of energy storage discharge power, I BES is the energy storage charging and discharging state variable, 1 indicates that it is in the charging state, and 0 indicates that it is in the discharging state. The energy values ​​stored at the beginning and end of energy storage.

[0057] (3) Natural gas system operation constraints

[0058]

[0059] p l,j (τ,x j )=c 2 ρ l,j (τ,x j ) (20)

[0060]

[0061]

[0062] In formulas (18)-(28): x j is the spatial position variable, and equations (18)-(20) are the dynamic equations of the gas in the pipeline, which are the momentum equation, material balance equation and gas state equation, respectively. Among them, d, A and λ are the pipeline diameter, cross-sectional area and friction coefficient, respectively, j is the pipeline number, M l,j 、p l,j , l,j and are respectively the gas mass flow rate, pressure, density and average flow velocity in the pipeline; c is the speed of sound, is the upper limit of pipeline flow rate; Formula (22) is the mass conservation and pressure continuity constraints at the pipeline connection node, where δ I (i) and δ O (i) represents the set of pipelines with node i as the inlet and outlet respectively; Formula (23) is the calculation formula for the gas mass flow rate at the end node of the pipeline, where, and are the load flow, gas consumption flow of the gas unit and gas production flow of the power-to-gas unit respectively; Equations (24)-(26) are the operating constraints at the gas source, where: is the pressure at the gas source, is the gas mass flow rate at the gas source; Equations (27) and (28) are combined with Equations (23) and (24) as boundary condition constraints for the gas dynamic equation.

[0063] (4) Heating system operation constraints

[0064]

[0065]

[0066] In formulas (29)-(34), pipe j is the pipe number, p is the pipe identifier, s represents the water supply pipe, and r represents the return pipe; formula (29) describes the heat conduction equation in the pipe, where: is the temperature of the heat medium in the pipeline, v, m j and r j are the daily heat medium flow rate, mass flow rate and pipeline thermal resistance, c w is the specific heat capacity of the heat medium, T a is the ambient temperature; formula (30) is the upper and lower limit constraints of the heat medium temperature in the pipeline; formula (31) is the power balance equation at the heat source, where: and are the heat generation power of the electric boiler and the combined heat and power unit, ψ s (i) is the set of pipelines connected to the heat source node i; Equation (32) is the power balance equation at the load, where: is the load power, ψ d(i) is the set of pipes connected to load node i; Equation (33) is the node temperature mixing equation, δ p,I (i) and δ p,O (i) are the supply and return pipe sets with node i as the inlet and outlet respectively; Equation (34) is the boundary condition of the heat conduction equation (29).

[0067] (5) Coupling equipment operation constraints

[0068]

[0069] In formulas (35)-(37): n is the number of each coupling device; and The energy conversion efficiency of the corresponding equipment is is the thermal-electric ratio of CHP.

[0070] Step 3: Perform solution space transformation according to the Bernstein polynomial interpolation method to achieve gridless reconstruction of the electricity-gas-heat-storage multi-energy coupling system model.

[0071] For the model established in the function space in step 2, cubic Bernstein polynomial interpolation is used for meshless reconstruction. For the (·)(τ) and (·)(τ,x) functions, their reconstruction functions correspond to Bezier curves and Bezier surfaces, respectively, as shown in the attached figure. Figure 2 and attached Figure 3 shown.

[0072] (1) (·)(τ) function without grid reconstruction

[0073] Taking electric power P(τ) as an example, other types of (·)(τ) functions are similar.

[0074]

[0075] In formulas (38)-(40): B 3,k (τ) is a cubic Bernstein polynomial, as shown in formula (39); Corresponding to B 3,k The interpolation coefficient of (τ) can be calculated according to formula (40); P B and B3(τ) are and B 3,k (τ) composed of a vector; The formula for the number of combinations.

[0076] The transformation properties of the Bezier curve corresponding to the integral term, differential term, equality equation, and inequality equation are shown in equations (41)-(44), respectively, where equation (44) is the convex hull property of the Bezier curve.

[0077]

[0078] Where: B 2,k (τ) is a quadratic Bernstein polynomial, B2(τ) is the vector of its components; W3 is the coefficient matrix of the differential term when the third-order Bernstein polynomial is interpolated; c is a constant.

[0079] (2) (·)(τ,x) function without grid reconstruction

[0080] The heating network temperature T(τ,x) is used as an example. Other types of (·)(τ,x) functions are similar.

[0081]

[0082] In formula (45) and (46): B 3,k (τ) and B 3,s (x) are all cubic Bernstein polynomials, B 3,s (x) as shown in formula (46); Corresponding to B 3,k (τ) and B 3,s (x) interpolation coefficient; T B , B3(τ) and B3(x) are B 3,k (τ) and B 3,s (x) is a vector composed of

[0083] The transformation properties of the Bezier surface corresponding to the integral term, partial differential term, equality equation, and inequality equation are shown in equations (47)-(50), respectively. Among them, equation (50) is the convex hull property of the Bezier curve.

[0084]

[0085] Where: D is the differential operation matrix, I and K are the operation matrices of the integral term.

[0086] According to equations (48) and (47), the calculation formula for the partial differential term in the Bezier surface equation can be derived, as shown in equations (51)-(52).

[0087]

[0088] (3) Solution space transformation

[0089] For the optimal scheduling model of the electricity-gas-heat-storage multi-energy coupling system constructed, the total scheduling time Γ is divided into 24 periods, each period length T is 1h, and x j =L j x' j, τ=Tτ', so that the spatial variable x' j and the range of the time variable τ' is [0,1]. On this basis, according to the transformation properties of the equations of Bezier curves and surfaces, the transformation of the equations of equality, inequality, differential / partial differential, and integral, the equivalent transformation of the equation constraints, inequality constraints, differential / partial differential, and integral terms of the continuous function expression in the model can be realized, the time variable τ and the space variable x can be eliminated, and the optimization problem of the function space can be converted to the Bernstein polynomial interpolation coefficient space, that is, the transformation process of the solution space is carried out. The transformed model is a mixed integer linear programming model, which can be solved directly.

[0090] Step 4: Derive the enhancement matrix to achieve improved solution space transformation, reduce the flexibility loss in the solution space transformation process, and activate the potential flexibility of the electricity-gas-heat-storage multi-energy coupling system.

[0091] In the process of solution space transformation in step 3, due to max{P(τ)} and max{T(τ,x)} and There is a gap between them, which will reduce the feasible domain of the optimization problem after transformation, resulting in loss of system flexibility (such as the additional Figure 2 and attached Figure 3 As shown in Figure 2, there is a large gap between the original control curve and the Bezier curve. In this regard, the De Casteljau algorithm is used to derive the enhancement matrices of the (·)(τ) and (·)(τ,x) functions to improve the solution space transformation process and activate the potential flexibility of the system.

[0092] (1) Derivation of the gridless reconstruction enhancement matrix of the (·)(τ) function

[0093] Taking electric power P(τ) as an example (other types of (·)(τ) functions are similar), the vertical coordinate of the control point after the solution space transformation is marked as Y (0) Select τ0∈[0,1] and call the De Casteljau algorithm once. The corresponding enhancement matrix A can be derived as shown in Equation (53). The ordinate Y of the control point after convexity enhancement once is (1) =AY (0) .

[0094]

[0095] By the attached Figure 2 It can be seen that the 4th and 5th rows in A correspond to the same control points, and this redundancy problem will be dealt with later. Repeatedly calling the De Casteljau algorithm can derive the enhancement matrix A corresponding to the mth convexity enhancement process (m) , as shown in (54), we can further know that relative to Y (0), the ordinate vector Y of the control point after m-time convexity enhancement (m) It can be calculated according to formula (55).

[0096]

[0097] Y (m) =A (m) …A (1) Y (0) =E (m) Y (0) (55)

[0098] In formula (54) and (55): E (m) That is, it is the enhancement matrix corresponding to the m-times convexity enhancement process.

[0099] As for the redundancy problem in the enhanced matrix, it can be seen from the derivation process that it corresponds to the repetition of the 4th and 5th rows in A, so E (m) The (4r+1) rows (r=1,2,…,2 r -1) is removed to obtain the enhanced matrix J without redundant items (m) , then Y (m) =J (m) Y (0) , the enhanced convex hull property is shown in formula (56), which reduces the inequivalence in the transformation process and activates the potential operational flexibility of the system.

[0100] max{P(τ)}≤max{J (m) P B} (56)

[0101] (2) (·)(τ,x) function gridless reconstruction enhancement matrix

[0102] Taking the heating network temperature T(τ,x) as an example (other types of (·)(τ,x) functions are similar), the z-axis coordinate of the control point after the solution space transformation is marked as Z (0) Select τ0∈[0,1], x0∈[0,1], and call the De Casteljau algorithm once to derive the corresponding time and space dimension enhancement matrix A t and A x , as shown in equations (57) and (58), the control point Z after convexity enhancement once is (1) =A t Z (0) A x .

[0103]

[0104] By the attached Figure 3 It can be seen that array A t In the 4th and 5th rows, Ax The 4th and 5th columns correspond to the same control points, and this redundancy problem will be dealt with later. Repeatedly calling the De Casteljau algorithm can derive the enhancement matrix corresponding to the mth convexity enhancement process. and As shown in (59), we can further know that relative to Z (0) , the control point z coordinate vector Z after m-th convexity enhancement (m) It can be calculated according to formula (60).

[0105]

[0106] In formula (60): and That is, it is the enhancement matrix corresponding to the m-times convexity enhancement process.

[0107] As for the redundancy problem in the enhanced matrix, it can be seen from the derivation process that it corresponds to A t In the 4th and 5th rows, A x The 4th and 5th columns are repeated, so The (4r+1) rows (r=1,2,…,2 r -1) and The (4c+1) columns (c=1,2,…,2 r -1) is removed to obtain an enhanced matrix without redundant items and That is, The enhanced convex hull property is shown in formula (61), which reduces the inequivalence in the transformation process and activates the potential operational flexibility of the system.

[0108]

[0109] Step 5: Solve the meshless reconstruction model based on the improved solution space transformation and obtain the solution result.

[0110] By solving the gridless reconstruction model after the improved solution space transformation in step 4, the optimal gridless interpolation coefficients corresponding to the (·)(τ) function and the (·)(τ,x) function can be obtained. The spatiotemporal continuous values ​​of the operating variables in the system can be further calculated through equations (38) and (45), which is the inverse process of the solution space transformation. The result can reflect the power changes within the time period, the distribution of the gas-heat system state along the pipeline, and its changes over time. In addition, the improvement of the solution space transformation process can further activate the system flexibility potential and enhance the system's flexible adjustment capability.

[0111] The present invention also provides an electronic device, comprising one or more processors and one or more memories; wherein the one or more memories are coupled to the one or more processors, the one or more memories are used to store computer program codes, and the computer program codes include computer instructions. When the one or more processors execute the computer instructions, the electronic device executes the gridless reconstruction and optimized scheduling method of the multi-energy coupling system provided by the present invention.

[0112] The present invention also provides a computer storage medium, which stores a computer program, and the computer program includes program instructions. When the program instructions are run on an electronic device, the electronic device executes the gridless reconstruction and optimization scheduling method of the multi-energy coupling system provided by the present invention.

[0113] Consider attaching Figure 4 The parameters of the three thermal power units (TU1, TU2, and TU3) in the test system are shown in Table 1. The capacity of the energy storage (BES) is 20MW / 80MWh, the installed capacity of the gas unit (GFU) is 100MW, the efficiency is 19.75MW / (kg / s), the installed capacity of the power-to-gas unit (P2G) is 50MW, the efficiency is 0.0091(kg / s) / MW, and the η of the combined heat and power unit (CHP) is CHP , and 1.5, 300 and 250 yuan / MWh respectively. The electric load curve and wind power output curve are shown in the attached figure. Figure 5 The natural gas load and heat load curves are shown in the attached Figure 6 and attached Figure 7 As shown, the natural gas price is 3 yuan / kg and the wind curtailment penalty cost is set at 1,000 yuan / MWh.

[0114] Table 1 Parameters of thermal power units

[0115]

[0116] Through simulation, it is found that the start and stop modes of the units are the same when no convexity enhancement is performed and when convexity enhancement is performed once. Figure 8 (solid indicates that the unit is in the on state); the temperature time-space distribution diagram of the water supply pipeline 1-2 and the gas flow time-space distribution diagram of the natural gas pipeline 1-2 are shown in the attached Fig. 9 and 10 Taking the power-to-gas unit as an example, its output curve and the control curve before and after convexity enhancement are shown in the attached figure. Fig.11 The system operating costs are shown in Appendix 2. Fig. 9 , 10As shown in , 11, the method proposed in the present invention can characterize the power change within a time period, the distribution of the gas-heat system state along the pipeline and the change over time; as shown in the attached Fig.11 As shown in Figure 2, after the convexity is enhanced, the conservatism of the model is reduced, and the flexible adjustment ability of the system can be more fully utilized; as shown in Appendix 2, after the convexity is enhanced, the operating cost of the system is reduced by 14,506.57 yuan compared with the system without convexity enhancement, and wind power is also more effectively absorbed.

[0117] Table 2 System operating costs

[0118] Operating cost (yuan) No convexity enhancement Perform convexity enhancement once Thermal power units 380620.25 380429.65 Battery Energy Storage 3564.68 3564.68 Wind curtailment 13182.03 0 Combined heat and power unit 218030.52 218030.29 Gas Purchase 303354.374 302220.66 total 918751.85 904245.28

[0119] It will be easily understood by those skilled in the art that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A gridless reconstruction and optimization scheduling method for a multi-energy coupling system, characterized in that: The multi-energy coupling system includes power system, natural gas system, heating system and energy storage equipment; The method comprises the following steps: Step 1: Collect the topological structures and parameters of the power system, natural gas system, heating system, and parameters of energy storage and other equipment; Step 2: Considering the dynamic characteristics of the natural gas system and the heating system, an optimal scheduling model of the electricity-gas-heat-storage multi-energy coupling system described by partial differential equations is constructed; Step 3: Perform solution space transformation according to the Bernstein polynomial interpolation method to achieve gridless reconstruction of the optimal scheduling model of the electricity-gas-heat-storage multi-energy coupling system; Step 4: Derive the enhancement matrix to achieve improved solution space transformation, reduce the flexibility loss in the solution space transformation process, and activate the potential flexibility of the electricity-gas-heat-storage multi-energy coupling system; Step 5: Solve the meshless reconstruction model based on the improved solution space transformation and obtain the solution result.

2. A gridless reconstruction and optimization scheduling method for a multi-energy coupling system according to claim 1, characterized in that: In step 1, it is also necessary to collect new energy output and various load forecast data.

3. A gridless reconstruction and optimization scheduling method for a multi-energy coupling system according to claim 2, characterized in that: In step 1, the following data is collected: (1) The topological structure and mutual coupling relationship of the power system, natural gas system, and heating system; (2) Parameters and operating status limits of power system lines, natural gas system pipelines, and heating system pipelines; (3) Technical parameters of various equipment in the power system, natural gas system, and heating system, including energy storage equipment; (4) Wind power output forecast curve and system electricity, gas and heat load curves.

4. A gridless reconstruction and optimization scheduling method for a multi-energy coupling system according to claim 1, characterized in that: In step 2, the optimization scheduling model of the electricity-gas-heat-storage multi-energy coupling system is established in the function space, including the operation models of the power system, natural gas system, heating system and various equipment, with the goal of minimizing the operating cost of the system.

5. The method for gridless reconstruction and optimization scheduling of a multi-energy coupling system according to claim 1, characterized in that: In step 3, the optimization scheduling model of the electricity-gas-heat-storage multi-energy coupling system established in step 2 is reconstructed gridlessly using cubic Bernstein polynomial interpolation. The reconstruction functions of the (·)(τ) and (·)(τ,x) functions in the variables correspond to Bezier curves and Bezier surfaces, respectively.

6. A gridless reconstruction and optimization scheduling method for a multi-energy coupling system according to claim 5, characterized in that: In step 3, according to the properties of the transformation of equations, inequality equations, differential / partial differential terms, and integral terms of the Bezier curve and Bezier surface equations, the equivalent transformation of the equality constraints, inequality constraints, differential / partial differential terms, and integral terms of the continuous function expression in the model can be realized, and the time variable and space variable can be eliminated, thereby converting the optimization problem in the function space to the Bernstein polynomial interpolation coefficient space, that is, performing a transformation process of the solution space. The transformed model is a mixed integer linear programming model, which can be directly solved.

7. A gridless reconstruction and optimization scheduling method for a multi-energy coupling system according to claim 1, characterized in that: In step 4, the De Casteljau algorithm is used. This method uses the De Casteljau algorithm to derive the enhancement matrices of the (·)(τ) and (·)(τ,x) functions to improve the solution space transformation process and activate the potential flexibility of the system.

8. A gridless reconstruction and optimization scheduling method for a multi-energy coupling system according to claim 7, characterized in that: By removing the corresponding items in the enhanced matrix, an enhanced matrix without redundant items is obtained, thereby reducing the redundant constraints in the model.

9. A gridless reconstruction and optimization scheduling method for a multi-energy coupling system according to claim 1, characterized in that: In step 5, by solving the gridless reconstruction model after the improved solution space transformation in step 4, the optimal gridless interpolation coefficients corresponding to the (·)(τ) function and the (·)(τ,x) function can be obtained, and the spatiotemporal continuous values ​​of each operating variable in the system can be further calculated, that is, the inverse transformation of the solution space transformation is performed.

10. A computer storage medium, characterized in that: The computer storage medium stores a computer program, wherein the computer program includes program instructions. When the program instructions are executed on an electronic device, the electronic device executes the method according to any one of claims 1 to 9.

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