Distributed optimization operation method and system for electrical interconnection system

Abstract

The invention provides a distributed optimization operation method and system for an electrical interconnection system, and is applied to the technical field of new energy. The distributed optimization operation method for the electrical interconnection system comprises the following steps: acquiring real-time operation data of equipment in the electrical interconnection system; solving a mathematical model of the electrical interconnection system by adopting a distributed algorithm according to the real-time operation data and a comprehensive objective function and constraint conditions of the electrical interconnection system to obtain a distributed optimization operation scheme of the electrical interconnection system; and controlling the operation of the electrical interconnection system according to the distributed optimization operation scheme. According to the method, a distributed algorithm is used in the solving process, the problems existing in a centralized algorithm are solved, the problem of information transmission obstacles of an electric power system and a natural gas system is solved, and a more reasonable optimization scheduling scheme can be obtained.

Description

Technical Field

[0001] The present invention relates to the field of new energy technology, and in particular to a distributed optimization operation method and system for an electrical interconnection system. Background Art

[0002] As environmental pollution and the contradiction between energy supply and demand become increasingly prominent, diversified and open energy supply systems and complementary and interconnected energy consumption methods are developing rapidly. Integrating renewable energy into the existing energy system and considering the carbon dioxide emissions of the interconnected system have become an important means. Building an electrical interconnection system in which the power system and the natural gas system are coupled and coordinated is of great significance to improving energy utilization efficiency and giving full play to the flexibility of the multi-energy system.

[0003] Domestic and foreign researchers have conducted extensive research on the optimization operation methods of electric-gas coupled networks. When coordinating the optimization of multiple networks, due to the expansion of system types and scales, traditional centralized algorithms face problems such as high model complexity and heavy communication pressure. Summary of the invention

[0004] In order to overcome the defects of the above-mentioned traditional centralized algorithm, such as high model complexity and heavy communication pressure, the present invention provides a distributed optimization operation method for an electrical interconnection system, the method comprising:

[0005] Obtain real-time operating data of equipment in electrical interconnection systems;

[0006] According to the real-time operation data, the comprehensive objective function and constraint conditions of the electrical interconnection system, a distributed algorithm is used to solve the mathematical model of the electrical interconnection system to obtain a distributed optimization operation plan of the electrical interconnection system;

[0007] According to the distributed optimization operation plan, the operation of the electrical interconnection system is controlled.

[0008] Optionally, the mathematical model includes a mathematical model of the power system, a mathematical model of the natural gas system and a mathematical model of coupling equipment of the electrical interconnection system.

[0009] Optionally, the process of determining the comprehensive objective function includes:

[0010] determining an economic cost function and a carbon emission function of the electrical interconnection system;

[0011] Normalizing the economic cost function and the carbon emission function;

[0012] The normalized economic cost function and the normalized carbon emission function are summed according to the weight coefficient to determine the comprehensive objective function.

[0013] Optionally, the comprehensive objective function satisfies the following formula:

[0014]

[0015] where ω 1 and ω 2 are weight coefficients that satisfy ω 1 + ω 2 = 1, f 1,min and f 2,max are respectively the minimum economic cost and the maximum carbon emission of the electrical interconnected system when only considering the economic cost f 1 , and f 1,max and f 2,min are respectively the maximum economic cost and the minimum carbon emission of the electrical interconnected system during the planning period when only considering the carbon emission f 2 .

[0016] Optionally, the distributed algorithm includes the alternating direction multiplier algorithm.

[0017] Optionally, the method of using a distributed algorithm to solve the mathematical model of the electrical interconnected system according to the real-time operation data, the comprehensive objective function, and the constraint conditions of the electrical interconnected system to obtain the distributed optimal operation plan of the electrical interconnected system includes:

[0018] Splitting the comprehensive objective function into a power subsystem objective function and a natural gas subsystem objective function;

[0019] According to the real-time operation data and the constraint conditions, using the distributed algorithm to solve the mathematical model, the power subsystem objective function, and the natural gas subsystem objective function to determine a first solution of the power subsystem objective function and a second solution of the natural gas subsystem objective function;

[0020] If it is determined that the first solution and the second solution satisfy the convergence condition, then according to the first solution and the second solution, determine the distributed optimal operation plan of the electrical interconnected system;

[0021] If it is determined that the first solution and the second solution do not satisfy the convergence condition, then according to the first solution and the second solution, continue to use the distributed algorithm to solve the mathematical model, the power subsystem objective function, and the natural gas subsystem objective function until the convergence condition is satisfied.

[0022] Optionally, the step of continuing to use the distributed algorithm to solve the mathematical model, the power subsystem objective function, and the natural gas subsystem objective function according to the first solution and the second solution includes:

[0023] Update the auxiliary variable according to the first solution and the second solution;

[0024] Continue to solve the mathematical model, the objective function of the power subsystem, and the objective function of the natural gas subsystem by using the distributed algorithm according to the first solution, the second solution, and the updated auxiliary variable.

[0025] Optionally, the update of the auxiliary variable according to the first solution and the second solution satisfies the following formula:

[0026]

[0027] where u (k) is the auxiliary variable, u (k+1) is the updated auxiliary variable, k is the k-th iteration, x is a variable in the power system, y is a variable in the power system, A, B, and C are the constraint conditions, f(x) is the objective function of the power subsystem, g(y) is the objective function of the natural gas subsystem, and ρ is the dual update step size and ρ > 0.

[0028] Optionally, the convergence condition includes the dual residual and / or the primal residual.

[0029] Optionally, the dual residual and the primal residual satisfy the following formula:

[0030]

[0031] where s (k+1) is the dual residual, and r (k+1) is the primal residual.

[0032] On the other hand, the present invention also provides a distributed optimal operation system for an electrical interconnected system, including:

[0033] An acquisition module, configured to acquire real-time operation data of devices in the electrical interconnected system;

[0034] A determination module, configured to solve the mathematical model of the electrical interconnected system by using a distributed algorithm according to the real-time operation data, the comprehensive objective function, and the constraint conditions of the electrical interconnected system, and obtain a distributed optimal operation plan for the electrical interconnected system;

[0035] A control module, configured to control the operation of the electrical interconnected system according to the distributed optimal operation plan.

[0036] Optionally, the determination process of the comprehensive objective function includes:

[0037] Determine the economic cost function and the carbon emission function of the electrical interconnected system;

[0038] Normalize the economic cost function and the carbon emission function;

[0039] Sum the normalized economic cost function and the normalized carbon emission function according to the weight coefficient to determine the comprehensive objective function.

[0040] Optionally, the distributed algorithm includes the alternating direction multiplier algorithm.

[0041] Optionally, the determining module is specifically configured to split the comprehensive objective function into a power subsystem objective function and a natural gas subsystem objective function; according to the real-time operation data and the constraint conditions, use the distributed algorithm to solve the mathematical model, the power subsystem objective function, and the natural gas subsystem objective function to determine a first solution of the power subsystem objective function and a second solution of the natural gas subsystem objective function; if it is determined that the first solution and the second solution meet the convergence condition, then according to the first solution and the second solution, determine the distributed optimal operation plan of the electrical interconnected system; if it is determined that the first solution and the second solution do not meet the convergence condition, then according to the first solution and the second solution, continue to use the distributed algorithm to solve the mathematical model, the power subsystem objective function, and the natural gas subsystem objective function until the convergence condition is met.

[0042] Optionally, the determining module is specifically configured to update the auxiliary variable according to the first solution and the second solution; continue to use the distributed algorithm to solve the mathematical model, the power subsystem objective function, and the natural gas subsystem objective function according to the first solution, the second solution, and the updated auxiliary variable.

[0043] Optionally, the convergence condition includes the dual residual and / or the primal residual.

[0044] On the other hand, the present invention also provides a computer device, which is characterized by including: one or more processors;

[0045] The processor is used to store one or more programs;

[0046] When the one or more programs are executed by the one or more processors, the distributed optimal operation method of the electrical interconnected system described in any one of the above is implemented.

[0047] On the other hand, the present invention also provides a computer-readable storage medium, which is characterized in that a computer program is stored thereon, and when the computer program is executed, the distributed optimal operation method of the electrical interconnected system described in any one of the above is implemented.

[0048] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0049] The present invention provides a method for distributed optimal operation of an electrical interconnection system, including: obtaining real-time operation data of devices in the electrical interconnection system; according to the real-time operation data, the comprehensive objective function and constraint conditions of the electrical interconnection system, using a distributed algorithm to solve the mathematical model of the electrical interconnection system to obtain a distributed optimal operation plan for the electrical interconnection system; controlling the operation of the electrical interconnection system according to the distributed optimal operation plan. In the solution process of the present invention, a distributed algorithm is used, which solves the problems existing in the centralized algorithm, breaks the information transmission barrier problem between the power system and the natural gas system, and can obtain a more reasonable optimal scheduling plan. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 is a schematic flow chart of the method for distributed optimal operation of the electrical interconnection system of the present invention;

[0051] Figure 2 is a schematic structural diagram of the distributed optimal operation system of the electrical interconnection system of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0052] The following further elaborates on the specific embodiments of the present invention with reference to the drawings.

[0053] Embodiment 1:

[0054] A method for distributed optimal operation of the electrical interconnection system provided by the present invention, the schematic flow chart is as Figure 1 shown, including:

[0055] Step 101: Obtain real-time operation data of devices in the electrical interconnection system.

[0056] Step 102: According to the real-time operation data, the comprehensive objective function and constraint conditions of the electrical interconnection system, use a distributed algorithm to solve the mathematical model of the electrical interconnection system to obtain a distributed optimal operation plan for the electrical interconnection system.

[0057] Step 103: Control the operation of the electrical interconnection system according to the distributed optimal operation plan.

[0058] In the solution process of the embodiment of the present invention, a distributed algorithm is used, which solves the problems existing in the centralized algorithm, breaks the information transmission barrier problem between the power system and the natural gas system, and can obtain a more reasonable optimal scheduling plan.

[0059] The mathematical model of the electrical interconnection system includes the mathematical models of different devices, such as the mathematical model of the power system, the mathematical model of the natural gas system, and the mathematical model of the coupling devices of the electrical interconnection system. That is, in the embodiments of the present invention, different devices are respectively modeled, including the model construction of the power system, the natural gas system, and the coupling devices. In the above step 101, the real-time operation data of the devices in the electrical interconnection system can be specifically obtained, and in the subsequent solution process, the device data and mathematical model in the electrical interconnection system are solved.

[0060] Exemplarily, modeling the power system includes modeling the power network and related electrical devices.

[0061] Modeling the power network can include modeling the distribution network using the following formula (1):

[0062]

[0063] where P ji is the active power transmitted by the line from node j to node i in the distribution network, and Q ji is the reactive power transmitted by the line from node j to node i in the distribution network; V i is the voltage of node i; V j is the voltage of node j; r ji is the resistance, and x ji is the reactance.

[0064] When linearizing by ignoring the line model loss, it can be considered that the rightmost square term can be ignored, thus obtaining the linearized distribution network power flow equation, as shown in the following formula (2):

[0065]

[0066] where V 0 is the reference node voltage.

[0067] Modeling the related electrical devices can include modeling the energy storage device using the following formula:

[0068]

[0069]

[0070]

[0071]

[0072] HS start =HS end (7)

[0073] where HS t+1is the energy state in the energy storage device at time t+1, HS t is the energy state in the energy storage device at time t; is the power for the energy storage device to store energy, is the power for the energy storage device to release energy; H max is the upper limit of the charge and discharge power, H min is the lower limit of the charge and discharge power; represents the charging state of the energy storage device, represents the discharging state of the energy storage device, and are both binary variables, used to ensure that charging and discharging cannot occur simultaneously at the same time; HS start is the energy state in the energy storage device at the initial moment of the scheduling period, HS end is the energy state in the energy storage device at the final moment of the scheduling period.

[0074] In yet another example, when modeling a natural gas system, considering that natural gas has dynamic characteristics and gas compression effects, there is a certain delay in its transmission process, and the gas flow velocity is much smaller than the electromagnetic wave velocity. Therefore, a model that can characterize the dynamic process of the natural gas system can be established for the natural gas system. For example, the mathematical model of the natural gas system includes the following momentum equation (8), mass balance equation (9), and state equation (10):

[0075]

[0076]

[0077] p = c 2 ρ (10)

[0078] where M is the mass flow rate of natural gas; λ is the damping factor; p is the pipeline pressure; ρ is the density of natural gas, c is a constant; S is the cross-sectional area of the pipeline; d is the pipeline diameter; t represents the time interval, and x represents the space interval. Since partial differential equations are difficult to utilize in optimization problems, in the embodiments of the present invention, a simplified method based on Wendroff difference (a calculation method for solving partial differential equations) can be used to convert to the difference form of differential equations for solution, see the following formulas (11) and (12):

[0079]

[0080]

[0081] where L is the pipeline length; M out is the gas mass flow rate at the end of the pipeline, M in is the gas mass flow rate at the beginning of the pipeline; p outThe electric power at the end of the pipeline, p in is the electric power at the beginning of the pipeline; λ is the damping factor; ω is the air flow velocity; Δt represents the time interval.

[0082] In another example, modeling the coupling device of the electrical interconnection system includes modeling a gas turbine and a P2G (Power to Gas) device.

[0083] As the core coupling element of the electrical interconnection system, the gas turbine has the advantages of high energy utilization efficiency and high environmental protection. When modeling the gas turbine, considering that the gas turbine produces electric energy by consuming natural gas, a mathematical model as shown in the following formula (13) can be constructed according to the relationship between its gas consumption and power generation:

[0084] P MT = η MT M MT (13)

[0085] Where, P MT is the output electric power; η MT is the conversion efficiency; M MT is the gas consumption.

[0086] The P2G technology realizes the conversion of electric energy into natural gas through electrolytic water hydrogen production and methanation reactions, and can serve as both a gas source and an electric load at the same time, enhancing the degree of electrical coupling. Therefore, a mathematical model as shown in the following formula (13) can be constructed for the P2G device:

[0087] M P2G = η P2G P P2G (14)

[0088] Where, M P2G is the natural gas mass flow rate; P P2G is the electric power; η P2G is the energy conversion efficiency.

[0089] In the above example, the embodiments of the present invention realize the overall scheduling of various types of devices such as wind-solar power generation, gas turbines, P2G devices, and energy storage devices by mathematically modeling various types of devices in the optimal operation of the electric-gas interconnected energy system.

[0090] In a possible implementation manner, the determination process of the comprehensive objective function obtained in the above step 101 includes:

[0091] Determine the economic cost function and carbon emission function of the electrical interconnection system;

[0092] Normalize the economic cost function and the carbon emission function;

[0093] Sum the normalized economic cost function and the normalized carbon emission function according to the weight coefficient to determine the comprehensive objective function.

[0094] Traditional optimal scheduling of integrated energy systems is mainly economic scheduling. To further control carbon emissions, in the embodiments of the present invention, the normalized weighting method is introduced to transform the original single economic scheduling into low-carbon economic scheduling, and a comprehensive optimization objective function including the system operation cost and carbon dioxide emissions is established, which can effectively balance the economy and environmental protection of the system. This implementation method can set a comprehensive objective function considering economy and low carbon. The comprehensive objective function includes two parts: the economic cost and carbon emissions of the electrical interconnected system.

[0095] Exemplarily, the economic cost can refer to the operation cost of the electrical interconnected system, including the gas purchase cost and the electricity purchase cost. For example, the electricity purchase cost can be determined according to the electricity purchase price and the total grid load at each moment, and the gas purchase cost can be determined according to the gas purchase price and the total gas network load at each moment. Specifically, it can satisfy the following formula:

[0096]

[0097] Among them, f 1 is the economic cost, c P,t is the electricity purchase price at time t, c G,t is the gas purchase price at time t, P t is the total grid load at time t, F t is the total gas network load at time t.

[0098] Another exemplarily, the carbon emissions include the carbon emissions generated by traditional coal-fired power generation equivalent to the purchase of electricity from the superior, the carbon emissions generated by gas turbine power generation, and the reduced carbon emissions during the operation of the power-to-gas device. For example, the carbon emissions can be determined according to the carbon emissions generated by traditional coal-fired power generation equivalent to the purchase of electricity from the superior, the carbon emissions generated by gas turbine power generation, and the reduced carbon emissions during the operation of the power-to-gas device at each moment. Specifically, it can satisfy the following formula:

[0099]

[0100] Among them, f 2 is the carbon emissions, is the carbon emissions generated by traditional coal-fired power generation equivalent to the purchase of electricity from the superior, is the carbon emissions generated by gas turbine power generation, is the reduced carbon emissions during the operation of the power-to-gas device.

[0101] For example, based on the total grid load at time t and the carbon emissions equivalent generated per unit of electricity, the carbon emissions generated by traditional coal-fired power generation equivalent to the purchased electricity from the upper level can be determined, specifically satisfying the following formula:

[0102]

[0103] Among them, is the carbon emissions equivalent generated per unit of electricity.

[0104] Again, for example, based on the total gas network load at time t and the carbon emissions generated per unit of electricity of the gas turbine unit, the carbon emissions generated by the gas unit power generation can be determined, specifically satisfying the following formula:

[0105]

[0106] Among them, is the carbon emissions generated per unit of electricity of the gas turbine unit.

[0107] Again, for example, based on the electric power of the P2G device at time t and the amount of carbon dioxide consumed per unit of electricity consumed by the P2G device, the reduced carbon emissions during the operation of the power-to-gas device can be determined, specifically satisfying the following formula:

[0108]

[0109] Among them, P t P2G is the electric power of the P2G device at time t, is the amount of carbon dioxide consumed per unit of electricity consumed by the P2G device.

[0110] When establishing a comprehensive objective function considering economy and low carbon, considering that the economic cost f 1 and the carbon emissions f 2 cannot be directly added due to differences in units and orders of magnitude. To coordinate the two objective functions, in this implementation method, the two objective functions are first normalized, and then weighted to facilitate obtaining the optimal solution in the subsequent process. For example, the comprehensive objective function satisfies the following formula:

[0111]

[0112] Among them, ω 1 , ω 2 are weight coefficients, satisfying ω 1 +ω 2 =1; f 1,min , f 2,max are respectively the minimum economic cost and the maximum carbon emissions of the electrical interconnected system when only considering the economic cost f 1 , f 1,max , f2,min They are respectively the maximum economic cost and the minimum carbon emission of the electrical interconnected system during the planning period when only considering the carbon emission f 2 , that is, f 1,max , f 1,min , f 2,max , f 2,min They respectively represent the maximum value and the minimum value of f 1 , f 2 under the unified solution method, f 1,min , f 2,max is the economic cost and carbon emission of the system during the planning period when the planning goal only considers the economic cost f 1 of the system, f 1,max , f 2,min is the economic cost and carbon emission of the system during the planning period when the planning goal only considers the carbon emission f 2 of the system. The two weight coefficients can be determined according to requirements. That is to say, the established operation model comprehensively considers the economic cost and carbon emission of the system. The decision maker can freely choose the weight coefficients of the two goals according to requirements to obtain a planning scheme that meets the requirements. In this formula, first, the objective function is normalized through fuzzy processing to respectively represent the satisfaction degree of each objective function with respect to its own solution, and then the optimal compromise solution can be obtained through the weighted satisfaction index method.

[0113] The above comprehensive objective function comprehensively considers the economic cost and carbon emission of the system. The decision maker can freely choose the weight coefficients of the two goals, so as to obtain an optimal dispatching scheme that meets the requirements.

[0114] In one implementation, the constraint conditions can be to build constraint conditions suitable for the safe operation of the electrical interconnected system according to the above constructed mathematical model. For example, the constraint conditions can be the constraint matrices A, B, and C of the variables in the equality constraint conditions. Based on the objective function and the equipment model, the constraint conditions are set to constrain the objective function so that the obtained optimal solution is more in line with the actual situation and the effectiveness of the objective function is improved.

[0115] In one implementation, the distributed algorithm includes but is not limited to: the ADMM (Alternating Direction Method of Multipliers) algorithm. Considering that the centralized optimization dispatching model in the traditional scheme ignores that the power grid and the gas grid belong to different control centers, resulting in obstacles in information transmission and other aspects and increasing the difficulty of joint optimization, while the ADMM algorithm can solve large-scale problems in a distributed manner. Therefore, in the embodiments of the present invention, the ADMM algorithm is applied to the electrical interconnected system to break the information transmission obstacle problem between the power system and the natural gas system, and a more reasonable distributed dispatching scheme can be obtained.

[0116] ADMM can achieve distributed solution of large-scale problems, with the advantages of simple form, good convergence and easy solution. By alternately iterating on relevant variables, it can achieve the effect of common convergence. For example, the optimization problems in ADMM are shown in equations (21) and (22):

[0117] min f(x)+g(y) (21)

[0118] s.t.Ax+By=C (22)

[0119] In the formula: f(x) and g(y) are two convex optimization sub-problems; x and y are variables in the power grid and gas grid respectively; A, B, and C are the constraint matrices of the variables in the equality constraint conditions. The augmented Lagrangian function is as follows:

[0120]

[0121] In the formula: λ is the Lagrange multiplier; ρ is the dual update step size and ρ>0; ||·|| 2 represents the two-norm.

[0122] In the iterative process of ADMM, it is necessary to update three parts: variable x, variable y, and Lagrange multiplier λ. The standard iterative update format for the (k + 1)-th time is:

[0123]

[0124] where, L ρ () is the augmented Lagrangian function. Here, it is necessary to normalize λ. First, define the following two auxiliary variables r and u:

[0125] r=Ax+By-C (25)

[0126] u=(1 / ρ)λ (26)

[0127] Thus, the canonical form of ADMM is obtained:

[0128]

[0129] where, u (k) is the auxiliary variable, u (k+1) is the updated auxiliary variable, k is the k-th iteration, x is the variable in the power system, y is the variable in the power system, A, B, and C are the constraint conditions, f(x) is the objective function of the power subsystem, g(y) is the objective function of the natural gas subsystem, and ρ is the dual update step size and ρ>0.

[0130] Exemplarily, through an iterative process of alternately iterating relevant variables, it can be an iterative update of the solution to the comprehensive objective function. In the above step 102, the comprehensive objective function can be split into a power subsystem objective function and a natural gas subsystem objective function; according to real-time operation data and constraint conditions, a distributed algorithm is used to solve the mathematical model, the power subsystem objective function, and the natural gas subsystem objective function to determine the first solution of the power subsystem objective function and the second solution of the natural gas subsystem objective function; if it is determined that the first solution and the second solution meet the convergence conditions, then according to the first solution and the second solution, a distributed optimal operation plan for the electrical interconnected system is determined; if it is determined that the first solution and the second solution do not meet the convergence conditions, then according to the first solution and the second solution, the distributed algorithm is continued to solve the mathematical model, the power subsystem objective function, and the natural gas subsystem objective function until the convergence conditions are met. In this example, in the process of solving the optimal operation plan, when using the ADMM algorithm, it is necessary to decouple the original problem, that is, the comprehensive objective function, and regard the objective functions corresponding to the electrical system and the natural gas system as different optimization sub-problems to be solved and iteratively solved respectively, and establish a connection for distributed solution through the coupling variables, and finally obtain the optimal scheduling plan at the convergence of the objective function.

[0131] When continuing to use the distributed algorithm to solve the mathematical model, the power subsystem objective function, and the natural gas subsystem objective function according to the first solution and the second solution, the auxiliary variables can be updated according to the first solution and the second solution; according to the first solution, the second solution, and the updated auxiliary variables, continue to use the distributed algorithm to solve the mathematical model, the power subsystem objective function, and the natural gas subsystem objective function. Exemplarily, updating the auxiliary variables according to the first solution and the second solution satisfies the above formula (27).

[0132] The above convergence conditions can include the dual residual and / or the primal residual. Exemplarily, the dual residual and the primal parameter satisfy the following formula:

[0133]

[0134] where s (k+1) is the dual residual, and r (k+1) is the primal residual. If the formula (28) is satisfied, the iteration is stopped.

[0135] When splitting the comprehensive objective function into the power subsystem objective function and the natural gas subsystem objective function above, the premise of using ADMM to solve the optimal operation problem of the electrical interconnected system by a distributed method is to decompose the distribution network and the natural gas network. The gas turbine consumes natural gas M MT to produce electric power P MT , and the P2G device consumes electric power P P2G to produce natural gas M P2GTherefore, the electrical interconnection system is decomposed at the above-mentioned coupling variables, while other variables and related constraints belong to their respective sub-problems, thus realizing the mutual separation of the two electrical subsystems.

[0136] Among them, the relevant constraints between the dual variables are determined by the models of the gas turbine and the P2G device. Thus, the original optimization model is split into two optimization sub-problems, and the comprehensive objective function is split into the objective functions of the power subsystem and the natural gas subsystem, as shown in the following formula:

[0137]

[0138]

[0139]

[0140]

[0141] Among them, ω 1 and ω 2 are weight coefficients, satisfying ω 1 + ω 2 = 1, f 1,min and f 2,max are respectively the minimum economic cost and the maximum carbon emission of the electrical interconnection system when only considering the economic cost f 1 , f 1,max and f 2,min are respectively the maximum economic cost and the minimum carbon emission of the electrical interconnection system during the planning period when only considering the carbon emission f 2 ; C ele is the objective function of the power subsystem, C gas is the objective function of the natural gas subsystem, is the power consumed by the P2G device for power conversion, and represent the power consumed by the P2G device for gas conversion, u P2G is the auxiliary variable corresponding to the P2G device, is the power consumed by the gas turbine for power conversion, is the power consumed by the gas turbine for gas conversion, u MT is the auxiliary variable corresponding to the gas turbine, f e is the electricity purchase cost, f g is the gas purchase cost, c P,t is the electricity purchase price at time t, c G,t is the gas purchase price at time t, P t is the total grid load at time t, F t is the total gas network load at time t.

[0142] Through S102, an optimal scheduling plan can be output, that is, a scheduling plan including the optimal solutions of the power system sub-problem and the natural gas system sub-problem. The distributed optimal operation plan corresponding to this optimal scheduling plan can be implemented in the electrical interconnected system to control the operation of the electrical interconnected system.

[0143] In the process of solving the optimal operation plan in the embodiments of the present invention, when using the ADMM algorithm, the original problem needs to be decoupled, and the electrical system and the natural gas system are solved and iteratively solved separately. The connection of distributed solution is established through the coupling variables, and finally the optimal scheduling plan is obtained at the convergence of the objective function.

[0144] The following uses a specific embodiment to illustrate the embodiments of the present invention, including the following steps:

[0145] Step 1: Model the power network and related electrical equipment.

[0146] Further, in step 1.1, model the distribution network as shown in the above formula (1).

[0147] When ignoring the line model loss for linearization, it can be considered that the rightmost square term can be ignored, and thus the linearized distribution network power flow equation is obtained, as shown in the above formula (2).

[0148] Step 2.2: Model the energy storage device as shown in the above formulas (3) to (7).

[0149] Step 2: Model the natural gas network. Natural gas has dynamic characteristics and gas compression effects, so there is a certain delay in its transmission process, and the gas flow velocity is much smaller than the electromagnetic wave velocity. The natural gas system network model in this paper adopts a model that can characterize the dynamic process of the natural gas system, including the momentum equation (8), mass balance equation (9) and state equation (10) as shown above. Since partial differential equations are difficult to use in optimization problems, a simplified method based on Wendroff difference can be used to convert them into the difference form of differential equations for solution, as shown in the above formulas (11) to (12).

[0150] Step 3: Model the coupling equipment.

[0151] Step 3.1: Model the gas turbine.

[0152] As the core coupling element of the electrical interconnected system, the gas turbine has the advantages of high energy utilization efficiency and high environmental protection. The gas turbine consumes natural gas to produce electric energy, and the relationship between its gas consumption and power generation is constructed by formula (13).

[0153] Step 3.2: Model the P2G device:

[0154] The P2G technology realizes the conversion of electrical energy into natural gas through electrolytic water hydrogen production and methanation reactions, and can serve as both a gas source and an electrical load, enhancing the degree of electrical coupling. The P2G operation process is as shown in the above formula (14).

[0155] Step 4: Set a comprehensive objective function considering economy and low carbon.

[0156] Step 4.1, the objective function consists of the system economic cost f 1 and the carbon emission f 2 in two parts. The economic cost f 1 is the operating cost, including the gas purchase cost and the electricity purchase cost; the carbon emission f 2 includes the carbon emissions generated by traditional coal power generation equivalently generated by the superior's electricity purchase, the carbon emissions generated by gas turbine power generation, and the carbon emissions reduced during the operation of the power-to-gas device. The objective functions of these two parts are as shown in the above formulas (15) to (16).

[0157] Step 4.2, calculate the carbon emissions generated by traditional coal power generation equivalently generated by the superior's electricity purchase, the carbon emissions generated by gas turbine power generation, and the carbon emissions reduced during the operation of the power-to-gas device, as shown in the above formulas (17) to (19).

[0158] Step 4.3, establish a comprehensive objective function considering economy and low carbon. The economic cost f 1 and the carbon emission f 2 cannot be directly added due to differences in units and orders of magnitude. To coordinate the two objective functions, this paper first performs fuzzification to normalize the objective functions, respectively representing the satisfaction degrees of each objective function with respect to its own solution, and then obtains the optimal compromise solution through the weighted satisfaction index method. The final objective function is as shown in the above formula (22).

[0159] In this step 4, a comprehensive objective function including economy and low carbon and with weight coefficients is constructed. Research is carried out on the optimal operation of the electrical interconnected system to coordinate the operation conditions of the two systems. On the basis of considering the economy of the power network and the natural gas network as the basic requirement, the equivalent external carbon dioxide emissions of the system are calculated to reduce the system carbon emissions. And a normalization weighting method is proposed. In the face of the situation where the units of economy and carbon dioxide emissions are different, first use the unified solution method to normalize the calculation results of economy and carbon dioxide emissions to eliminate the influence of units. Secondly, the weights of economy and low carbon can be selected by adjusting the weight coefficients. Decision-makers can adjust the comprehensive optimization of economy and low carbon of the objective function according to the different proportions of economy and environmental protection in different situations.

[0160] Step 5: Use the ADMM algorithm for solution.

[0161] ADMM can achieve distributed solutions for large-scale problems, with the advantages of simple form, good convergence, and easy solution. By alternately iterating on relevant variables, a common convergence effect is achieved. The optimization problems are shown in the above equations (21) and (22). The augmented Lagrangian function is shown in the above equation (23).

[0162] During the iterative process, it is necessary to update three parts: variable x, variable y, and Lagrange multiplier λ. The standard iterative update formula for the (k + 1)-th time is shown in the above equation (24).

[0163] Here, it is necessary to normalize λ. First, two auxiliary variables are defined as shown in the above equations (25) to (26), so as to obtain the canonical form of ADMM, as shown in the above equation (27).

[0164] The convergence basis of ADMM consists of the dual residual s (k+1) and the primal residual r (k+1) , as shown in the above equation (28). If equation (28) is satisfied, the iteration stops.

[0165] Furthermore, the premise of using ADMM to solve the optimal operation problem of the electrical interconnected system by a distributed method is to decompose the distribution network and the natural gas network. The gas turbine consumes natural gas M MT to produce electric power P MT , while the P2G device consumes electric power P P2G to produce natural gas M P2G . Therefore, the electrical interconnected system is decomposed at the above coupling variables, and other variables and related constraint conditions belong to their respective sub-problems, thus realizing the mutual separation of the two electrical subsystems.

[0166] Among them, the relevant constraint conditions between the dual variables are determined by the models of the gas turbine and the P2G device. Thus, the original optimization model is split into two optimization sub-problems, and the comprehensive objective function is split into the objective functions of the power subsystem and the natural gas subsystem, as shown in the above equations (29) to (32).

[0167] Furthermore, the specific steps of the distributed optimal operation algorithm for the electrical interconnected system based on ADMM and considering economy and carbon emissions are as follows:

[0168] Step 5.1: Set the number of iterations as k = 0, and initialize the coupling variables of the electrical interconnected system (referring to the variables that couple the power system and the natural gas system, such as the above ) and u, and give the initial values of ρ and s (k) and r (k) .

[0169] Step 5.2: Solve the power system sub-problem based on M P2G (k) 、M MT (k) and u (k) obtained from the k-th iteration of the natural gas system sub-problem, and obtain the optimal solutions of P P2G (k+1) 、P MT (k+1) .

[0170] Step 5.3: Solve the natural gas system sub-problem based on P P2G (k+1) 、P MT (k+1) obtained in Step 2 and u (k) obtained from the k-th iteration, and obtain the optimal solutions of M P2G (k+1) and M MT (k+1) .

[0171] Step 5.4: Update the auxiliary variable u according to Equation (27) (k+1) .

[0172] Step 5.5: Judge whether the convergence criterion is satisfied according to the dual residual and the primal residual defined by Equation (28). If it converges, output the optimal scheduling scheme; otherwise, let k = k + 1 and perform a new round of calculation.

[0173] This optimal scheduling scheme can be implemented in the electrical interconnected system.

[0174] In this Step 5, the centralized optimization scheduling model commonly used ignores that the power grid and the gas grid belong to different control centers, so there are obstacles in information transmission and other aspects, increasing the difficulty of joint optimization. The present invention uses the ADMM algorithm to solve large-scale problems distributively. The present invention applies it to the electrical interconnected system to obtain a reasonable distributed scheduling scheme. During the iterative solution process, the coupling variables of the coupling devices are used for decoupling. When solving a certain system sub-problem, the sub-problem of the other system is fixed to achieve the purpose of iterative solution. Finally, the optimal scheme for the distributed optimal operation of the electrical interconnected system is realized.

[0175] It can be seen that in the embodiments of the present invention, a distributed optimal operation method for an electric-gas interconnected system considering economy and carbon emissions is proposed. The research mainly focuses on the optimal operation of the electric-gas interconnected system, coordinates the operation status of the two systems, calculates the equivalent external carbon dioxide emissions of the system on the basis of considering economy as the basic requirement, reduces the system carbon emissions, and realizes the comprehensive optimization of the economic and low-carbon electric-gas interconnected energy system with an adjustable objective function. In addition, the normalization weighting method is proposed, which can select the weights of economy and low-carbon by adjusting the weight coefficients, so as to realize the comprehensive optimization of adjustable economy and low-carbon of the objective function. In addition, the centralized optimal scheduling model commonly used ignores that the power grid and gas network belong to different control centers, so there are obstacles in information transmission and other aspects, which increases the difficulty of joint optimization. ADMM can solve large-scale problems distributively and apply it to the electric-gas interconnected system to obtain a reasonable distributed scheduling scheme.

[0176] Embodiment 2:

[0177] Based on the same inventive concept, the present invention also provides a distributed optimal operation system for an electric-gas interconnected system. The structural schematic diagram is as Figure 2 shown, including:

[0178] An acquisition module, configured to acquire the real-time operation data of the devices in the electric-gas interconnected system;

[0179] A determination module, configured to solve the mathematical model of the electric-gas interconnected system by using a distributed algorithm according to the real-time operation data, the comprehensive objective function and the constraint conditions of the electric-gas interconnected system, and obtain a distributed optimal operation scheme of the electric-gas interconnected system;

[0180] A control module, configured to control the operation of the electric-gas interconnected system according to the distributed optimal operation scheme.

[0181] In a possible implementation manner, in the processing module, the determination process of the comprehensive objective function includes:

[0182] Determine the economic cost function and carbon emission function of the electric-gas interconnected system;

[0183] Normalize the economic cost function and the carbon emission function;

[0184] Sum the normalized economic cost function and the normalized carbon emission function according to the weight coefficient to determine the comprehensive objective function.

[0185] In a possible implementation manner, the distributed algorithm includes the alternating direction multiplier algorithm.

[0186] In a possible implementation, the determination module is specifically configured to split the comprehensive objective function into an objective function of the power subsystem and an objective function of the natural gas subsystem; according to the real-time operation data and the constraint conditions, use a distributed algorithm to solve the mathematical model, the objective function of the power subsystem, and the objective function of the natural gas subsystem, and determine a first solution of the objective function of the power subsystem and a second solution of the objective function of the natural gas subsystem; if it is determined that the first solution and the second solution meet the convergence condition, then according to the first solution and the second solution, determine a distributed optimal operation plan for the electrical interconnected system; if it is determined that the first solution and the second solution do not meet the convergence condition, then according to the first solution and the second solution, continue to use the distributed algorithm to solve the mathematical model, the objective function of the power subsystem, and the objective function of the natural gas subsystem until the convergence condition is met.

[0187] In a possible implementation, the determination module is specifically configured to update the auxiliary variables according to the first solution and the second solution; according to the first solution, the second solution, and the updated auxiliary variables, continue to use the distributed algorithm to solve the mathematical model, the objective function of the power subsystem, and the objective function of the natural gas subsystem.

[0188] In a possible implementation, the convergence condition includes the dual residual and / or the primal residual.

[0189] Embodiment 3:

[0190] Based on the same inventive concept, the present invention also provides a computer device, which includes a processor and a memory. The memory is used to store a computer program, and the computer program includes program instructions. 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 may also be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, and is suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions in the computer storage medium to implement the corresponding method flow or corresponding function, so as to implement the steps of a method for distributed optimal operation of an electrical interconnected system in the above embodiment.

[0191] Embodiment 4:

[0192] Based on the same inventive concept, the present invention also provides a storage medium, specifically a computer-readable storage medium (Memory). A computer-readable storage medium is a memory device in a computer device, used to store programs and data. It can be understood that the computer-readable storage medium here can include both the built-in storage medium in the computer device and, of course, the extended storage medium supported by the computer device. The computer-readable storage medium provides a storage space, and this storage space stores the operating system of the terminal. And, in this storage space, there is also stored one or more instructions suitable for being loaded and executed by the processor. 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, such as at least one disk memory. The one or more instructions stored in the computer-readable storage medium can be loaded and executed by the processor to implement the steps of a method for distributed optimal operation of an electrical interconnection system in the above-mentioned embodiments.

[0193] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memories, CD-ROMs, optical memories, etc.) containing computer-usable program codes.

[0194] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram, as well as the combination of flows 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 the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for implementing the specified functions in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0195] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including an instruction device, and this instruction device implements the specified functions in Figure 1 one flow or multiple flows and / or blocks Figure 1The functions specified in one or more boxes.

[0196] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process. Thus, the instructions executed on the computer or other programmable device provide for implementing in the process Figure 1 One or more processes and / or boxes Figure 1 The steps of the functions specified in one or more boxes.

[0197] 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 the scope of its protection. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: after reading the present invention, those skilled in the art can still make various changes, modifications or equivalent replacements to the specific implementation manners of the application. However, these changes, modifications or equivalent replacements are all within the scope of protection of the claims pending for the application.

Claims

1. A method for distributed optimal operation of an electrical interconnected system, characterized in that, it includes: Obtaining the real-time operation data of the devices in the electrical interconnected system; According to the real-time operation data, the comprehensive objective function and the constraint conditions of the electrical interconnected system, using a distributed algorithm to solve the mathematical model of the electrical interconnected system, and obtaining the distributed optimal operation plan of the electrical interconnected system; According to the distributed optimal operation plan, controlling the operation of the electrical interconnected system.

2. The method according to claim 1, characterized in that, the mathematical model includes a power system mathematical model, a natural gas system mathematical model, and a coupling device mathematical model of the electrical interconnected system.

3. The method according to claim 1 or 2, characterized in that, the determination process of the comprehensive objective function includes: Determining the economic cost function and the carbon emission function of the electrical interconnected system; Normalizing the economic cost function and the carbon emission function; According to the weight coefficient, summing the normalized economic cost function and the normalized carbon emission function to determine the comprehensive objective function.

4. The method according to claim 3, characterized in that, the comprehensive objective function satisfies the following formula: Among them, ω 1 and ω 2 are weight coefficients, satisfying ω 1 + ω 2 = 1, f 1,min and f 2,max are respectively the minimum economic cost and the maximum carbon emission of the electrical interconnection system when only considering the economic cost f 1 . f 1,max and f 2,min are respectively the maximum economic cost and the minimum carbon emission of the electrical interconnection system during the planning period when only considering the carbon emission f 2 .

5. The method according to claim 1 or 2, characterized in that, the distributed algorithm includes the alternating direction multiplier algorithm.

6. The method according to claim 5, characterized in that, the step of using a distributed algorithm to solve the mathematical model of the electrical interconnected system according to the real-time operation data, the comprehensive objective function and the constraint conditions of the electrical interconnected system to obtain the distributed optimal operation plan of the electrical interconnected system includes: Splitting the comprehensive objective function into a power subsystem objective function and a natural gas subsystem objective function; According to the real-time operation data and the constraint conditions, using the distributed algorithm to solve the mathematical model, the power subsystem objective function and the natural gas subsystem objective function, and determining the first solution of the power subsystem objective function and the second solution of the natural gas subsystem objective function; If it is determined that the first solution and the second solution meet the convergence condition, then according to the first solution and the second solution, determining the distributed optimal operation plan of the electrical interconnected system; If it is determined that the first solution and the second solution do not meet the convergence condition, then according to the first solution and the second solution, continuing to use the distributed algorithm to solve the mathematical model, the power subsystem objective function and the natural gas subsystem objective function until the convergence condition is met.

7. The method according to claim 6, characterized in that, the step of continuing to use the distributed algorithm to solve the mathematical model, the power subsystem objective function and the natural gas subsystem objective function according to the first solution and the second solution includes: Updating the auxiliary variables according to the first solution and the second solution; According to the first solution, the second solution and the updated auxiliary variables, continuing to use the distributed algorithm to solve the mathematical model, the power subsystem objective function and the natural gas subsystem objective function.

8. The method according to claim 7, wherein, the updating of the auxiliary variable according to the first solution and the second solution satisfies the following formula: where, u (k) is the auxiliary variable, u (k+1) is the updated auxiliary variable, k is the k-th iteration, x is a variable in the power system, y is a variable in the power system, A, B, and C are the constraint conditions, f(x) is the power subsystem objective function, g(y) is the natural gas subsystem objective function, and ρ is the dual update step size and ρ > 0.

9. The method according to claim 6, wherein, the convergence condition includes the dual residual and / or the primal residual.

10. The method according to claim 9, wherein, the dual residual and the primal residual satisfy the following formula: where s (k+1) is the dual residual and r (k+1) is the primal residual.

11. A distributed optimal operation system for an electrical interconnection system, wherein, it includes: an acquisition module for acquiring real-time operation data of devices in the electrical interconnection system; a determination module for solving the mathematical model of the electrical interconnection system by using a distributed algorithm according to the real-time operation data, the comprehensive objective function and the constraint conditions of the electrical interconnection system to obtain a distributed optimal operation plan for the electrical interconnection system; a control module for controlling the operation of the electrical interconnection system according to the distributed optimal operation plan.

12. The system according to claim 11, wherein, the determination process of the comprehensive objective function includes: determining the economic cost function and the carbon emission function of the electrical interconnection system; performing normalization processing on the economic cost function and the carbon emission function; summing the normalized economic cost function and the normalized carbon emission function according to the weight coefficient to determine the comprehensive objective function.

13. The system according to claim 11 or 12, wherein, the distributed algorithm includes the alternating direction multiplier algorithm.

14. The system according to claim 13, wherein, the determination module is specifically configured to split the comprehensive objective function into a power subsystem objective function and a natural gas subsystem objective function; solve the mathematical model, the power subsystem objective function and the natural gas subsystem objective function by using the distributed algorithm according to the real-time operation data and the constraint conditions to determine a first solution of the power subsystem objective function and a second solution of the natural gas subsystem objective function; if it is determined that the first solution and the second solution satisfy the convergence condition, then determine the distributed optimal operation plan of the electrical interconnection system according to the first solution and the second solution; if it is determined that the first solution and the second solution do not satisfy the convergence condition, then continue to solve the mathematical model, the power subsystem objective function and the natural gas subsystem objective function by using the distributed algorithm according to the first solution and the second solution until the convergence condition is satisfied.

15. The system according to claim 14, wherein, the determination module is specifically configured to update the auxiliary variable according to the first solution and the second solution; continue to solve the mathematical model, the power subsystem objective function and the natural gas subsystem objective function by using the distributed algorithm according to the first solution, the second solution and the updated auxiliary variable.

16. The system according to claim 14, wherein, the convergence condition includes the dual residual and / or the primal residual.

17. A computer device, wherein, it includes: One or more processors; The processor is configured to store one or more programs; When the one or more programs are executed by the one or more processors, the distributed optimization operation method of the electrical interconnection system as described in any one of claims 1 to 10 is implemented.

18. A computer-readable storage medium, characterized in that, a computer program is stored thereon, and when the computer program is executed, the distributed optimization operation method of the electrical interconnection system as described in any one of claims 1 to 10 is implemented.

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