Multi-region collaborative configuration method and system for electrical integrated energy system

Abstract

The invention provides a multi-region collaborative configuration method and system for an electrical integrated energy system. The method comprises the following steps: acquiring parameters of the electrical integrated energy system; substituting the parameters of the electrical integrated energy system into a pre-constructed multi-region configuration model, and performing hierarchical solution by adopting an ATC algorithm to obtain an optimal configuration strategy of the integrated energy system in each region; wherein the multi-region configuration model is constructed on the basis of considering the full life cycle cost by using an objective function constructed by taking the minimum economic cost and environmental cost as an objective, and constraint conditions. Layered solution is carried out through the ATC algorithm, the huge and complex IENGS multi-region collaborative configuration problem is decomposed into multiple layers of sub-problems, the optimal configuration strategy of the integrated energy system in each region is iteratively solved under the condition that the consistency constraint of the target variable and the response variable is met, and the calculation difficulty is reduced.

Description

Technical Field

[0001] The present invention relates to the field of energy system configuration, and particularly to a multi-region collaborative configuration method and system for an electrical integrated energy system. Background Art

[0002] With the increasing prominence of environmental pollution problems and the contradiction between energy supply and demand, the diversified and open energy supply system and the complementary and interconnected energy consumption mode have developed rapidly. Under the goals of carbon peak and carbon neutrality, it is crucial to integrate renewable energy in the existing energy system and consider the carbon dioxide emissions of the integrated energy system. Constructing an IENGS in which the power system and the natural gas system are coupled and coordinated is of great significance for improving energy utilization efficiency and giving full play to the flexibility of the multi-energy system. Under the low-carbon background, the configuration scheme of the IENGS has gradually changed from economy to low carbon.

[0003] At present, the configuration schemes of IENGS can be basically divided into two categories: equipment configuration and line planning. The relevant research on equipment configuration mostly focuses on the distribution network level and the microgrid level, and locates and sizes or locates and types single or multiple types of equipment, often ignoring the planning of network lines. The relevant research on line planning mostly focuses on the transmission network level and the distribution network level. At the transmission network level, it is often coordinated with generators or new energy power stations, and at the distribution network level, it is often coordinated with substations and gas distribution stations, ignoring the configuration of other types of equipment in the IENGS. Although there are also studies on the coordinated configuration of network lines and equipment, the types of equipment are still relatively single. To sum up, in the current research on the IENGS configuration scheme, the consideration of the types of configured equipment is not complete enough.

[0004] IENGS has the advantage of multi-energy complementarity. With the gradual construction and close interconnection of multi-energy systems, it is necessary to study the collaborative configuration strategy of IENGS in multiple regions to give full play to the flexibility of resource scheduling in energy systems in different regions. At present, there have been many studies on the multi-region operation and configuration of IENGS. However, at present, there are still some problems in the collaborative configuration of IENGS. Compared with traditional power networks, the network composition of IENGS is more complex. When performing multi-region modeling and solving, if a unified algorithm is used for solving, it is bound to face problems such as a complex and large model and poor information interaction ability between regions. As the number of regions increases, the modeling and solving difficulties caused by such problems will increase rapidly. Summary of the Invention

[0005] In order to solve the problem that the network composition of IENGS is more complex and the difficulty will increase rapidly when performing multi-region modeling and solving, the present invention proposes a multi-region collaborative configuration method for an electrical integrated energy system, including:

[0006] Obtain the parameters of the electrical integrated energy system;

[0007] Substitute the parameters of the electrical integrated energy system into the pre-constructed multi-region configuration model, and use the ATC algorithm to decompose the multi-region configuration model into multiple layers of sub-problems. Under the condition of satisfying the consistency constraints of the target variables and response variables, iteratively solve the optimal configuration strategy of the integrated energy system in each region;

[0008] Among them, the multi-region configuration model is a target function constructed with the goal of minimizing economic cost and environmental cost on the basis of considering the life cycle cost, as well as the constructed constraint conditions.

[0009] Preferably, the construction of the multi-region configuration model includes:

[0010] Calculate the economic cost and environmental cost of the electrical integrated energy system considering the life cycle cost;

[0011] Construct a target function with the goal of minimizing economic cost and environmental cost;

[0012] Set constraint conditions for the target function;

[0013] The constraint conditions include: grid line configuration constraint conditions, gas pipeline configuration constraint conditions, substation configuration constraint conditions, gas distribution station configuration constraint conditions, wind and light unit configuration constraint conditions, gas turbine configuration constraint conditions, P2G equipment configuration constraint conditions.

[0014] Preferably, the economic cost is shown as the following formula:

[0015]

[0016] In the formula, is the life cycle economic cost of equipment , respectively represent the construction cost, the operating cost in the kth year, and the maintenance cost in the kth year of equipment , R k is the discount factor of the equipment at the end of the planning period, is the residual value of equipment at the end of the planning period.

[0017] Preferably, the environmental cost is shown as the following formula:

[0018] f env =C CET +C GCT

[0019] In the formula, f env is the environmental cost, C CET is the carbon trading cost, C GCT is the green certificate trading cost.

[0020] Preferably, the multi-region configuration model is shown as follows:

[0021]

[0022] In the formula, x is a configuration variable, y is an operating variable; f is the total system cost; g is a vector composed of inequality constraints, and h is a vector composed of equality constraints.

[0023] Preferably, the ATC algorithm is used to decompose the multi-region configuration model into multiple sub-problems, including:

[0024] Ignoring the interconnection between the gas transmission network and the gas distribution network, the gas transmission network is equivalent to a cluster of gas distribution stations, and the problem of minimizing the total system cost is simplified to only contain sub-problems of the power transmission network, the power distribution network, and the gas distribution network.

[0025] Preferably, the ATC algorithm is used to decompose the multi-region configuration model into multiple optimal configuration sub-problems. Under the condition of satisfying the consistency constraints of the target variables and the response variables, the optimal configuration strategy of the integrated energy system in each region is iteratively solved, including:

[0026] (1) Initialize the number of outer loop iterations L = 0 and the number of inner loop iterations K = 0;

[0027] (2) Initialize the first-order coefficient of the penalty function The coefficient of the norm term And the target variable

[0028] (3) Start the inner loop;

[0029] (4) Solve each sub-problem of the gas distribution network, update the response variable of the q-th sub-problem of the gas distribution network And transfer it to the corresponding power distribution network;

[0030] (5) Solve each sub-problem of the power distribution network, update the target variable of the q-th sub-problem of the gas distribution network Transfer it to the corresponding gas distribution network, update the response variable of the q-th sub-problem of the power distribution network Transfer it to the power transmission network;

[0031] (6) Solve the sub-problem of the power transmission network, update the target variable of the q-th sub-problem of the power distribution network Transfer it to the corresponding power distribution network;

[0032] (7) Determine whether the objective function values of the q-th sub-problem in the p-th layer in the K-th and (K-1)-th inner loops satisfy the inner loop convergence condition. If satisfied, go to step (8); otherwise, return to step (2);

[0033] (8) Determine whether the difference between the shared variables in the L-th and (L-1)-th outer loops meets the set outer loop convergence condition. If it meets, exit the loop; otherwise, proceed to step (9).

[0034] (9) Update the outer loop count L = L + 1, and update the penalty function correlation coefficient according to the update relation.

[0035] (10) Let K = 0 and return to step (2), where is the target variable at the K-th time.

[0036] Preferably, the outer loop convergence condition is shown as the following formula:

[0037]

[0038] In the formula: represents the difference between the shared variables in the L-th outer loop; ε 2 and ε 3 are the outer loop convergence gaps; represents the difference between the shared variables in the (L-1)-th outer loop.

[0039] Preferably, the update relation is shown as the following formula:

[0040]

[0041] In the formula: β is the penalty function weight growth rate; γ takes the standard value of 0.25.

[0042] Preferably, the inner loop convergence condition is shown as the following formula:

[0043]

[0044] In the formula: represents the objective function value of the p-th layer and q-th sub-problem in the K-th inner loop; represents the objective function value of the p-th layer and q-th sub-problem in the (K-1)-th inner loop; ε 1 represents the inner loop convergence gap.

[0045] On the other hand, the present invention also provides a multi-region collaborative configuration system for an electrical integrated energy system, including:

[0046] An acquisition module for acquiring electrical integrated energy system parameters;

[0047] An optimal configuration module for substituting the electrical integrated energy system parameters into a pre-constructed multi-region configuration model and using the ATC algorithm for hierarchical solution to obtain the optimal configuration strategy of the integrated energy system in each region;

[0048] Among them, the multi-region configuration model is an objective function constructed with the goal of minimizing economic cost and environmental cost based on considering the life cycle cost, and is constructed with constraint conditions.

[0049] Preferably, it further includes a module construction module for:

[0050] Considering the life cycle cost to calculate the economic cost and environmental cost of the integrated electrical energy system;

[0051] Constructing an objective function with the goal of minimizing economic cost and environmental cost;

[0052] Setting constraint conditions for the objective function;

[0053] The constraint conditions include: power grid line configuration constraint conditions, gas pipeline configuration constraint conditions, substation configuration constraint conditions, gas distribution station configuration constraint conditions, wind-solar unit configuration constraint conditions, gas turbine configuration constraint conditions, P2G equipment configuration constraint conditions.

[0054] Preferably, the economic cost is shown as the following formula:

[0055]

[0056] In the formula, is the life cycle economic cost of the equipment , respectively represent the construction cost, the operation cost in the kth year, and the maintenance cost in the kth year of the equipment , R k is the discount factor of the equipment at the end of the planning period, is the salvage value of the equipment at the end of the planning period.

[0057] Preferably, the environmental cost is shown as the following formula:

[0058] f env =C CET +C GCT

[0059] In the formula, f env is the environmental cost, C CET is the carbon trading cost, C GCT is the green certificate trading cost.

[0060] Preferably, the multi-region configuration model is shown as the following formula:

[0061]

[0062] In the formula, x is the configuration variable, y is the operation variable; f is the total system cost; g is the vector composed of inequality constraints, and h is the vector composed of equality constraints.

[0063] Preferably, the optimization configuration module is specifically configured to:

[0064] (1) Initialize the number of outer loop iterations \(L = 0\) and the number of inner loop iterations \(K = 0\);

[0065] (2) Initialize the coefficient of the first - order term of the penalty function The coefficient of the norm term and the target variable

[0066] (3) Start the inner loop;

[0067] (4) Solve each sub - problem of the gas distribution network, update the response variable of the \(q\) - th sub - problem of the gas distribution network and transfer it to the corresponding power distribution network;

[0068] (5) Solve each sub - problem of the power distribution network, update the target variable of the \(q\) - th sub - problem of the gas distribution network transfer it to the corresponding gas distribution network, update the response variable of the \(q\) - th sub - problem of the power distribution network transfer it to the transmission network;

[0069] (6) Solve the sub - problem of the transmission network, update the target variable of the \(q\) - th sub - problem of the power distribution network transfer it to the corresponding power distribution network;

[0070] (7) Determine whether the objective function values of the \(p\) - th layer and \(q\) - th sub - problem in the \(K\) - th and \((K - 1)\) - th inner loop iterations satisfy the inner - loop convergence condition. If satisfied, go to step (8); otherwise, return to step (2);

[0071] (8) Determine whether the difference between the shared variables in the \(L\) - th and \((L - 1)\) - th outer loop iterations satisfies the set outer - loop convergence condition. If satisfied, exit the loop; otherwise, go to step (9);

[0072] (9) Update the number of outer loop iterations \(L = L + 1\), update the penalty - function - related coefficients according to the update relationship

[0073] (10) Let \(K = 0\) and return to step (2), where is the target variable in the \(K\) - th iteration.

[0074] Preferably, the outer - loop convergence condition is shown as the following formula:

[0075]

[0076] In the formula: represents the difference between the shared variables in the \(L\) - th outer loop iteration; \(\varepsilon\) 2 and \(\varepsilon\) 3 are the outer - loop convergence gaps; Denotes the difference of the shared variable in the (L - 1)-th outer loop.

[0077] Preferably, the update relation is as follows

[0078]

[0079] In the formula: β is the growth rate of the penalty function weight; γ takes the standard value of 0.25; and respectively represent the penalty function coefficients of the L-th and (L - 1)-th outer loops; is the penalty function coefficient of the (L - 1)-th outer loop; Denotes the difference of the shared variable in the (L - 1)-th outer loop.

[0080] Optionally, the convergence condition of the inner loop is as follows:

[0081]

[0082] In the formula: represents the objective function value of the q-th sub-problem in the p-th layer in the K-th inner loop; represents the objective function value of the q-th sub-problem in the p-th layer in the (K - 1)-th inner loop; ε 1 represents the convergence gap of the inner loop.

[0083] On the other hand, the present application also provides an electronic device, including: at least one processor and a memory; the memory and the processor are connected by a bus;

[0084] The memory is used to store one or more programs;

[0085] When the one or more programs are executed by the at least one processor, the multi-region collaborative configuration method of an electrical integrated energy system as described above is implemented.

[0086] On the other hand, the present application also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed, the multi-region collaborative configuration method of an electrical integrated energy system as described above is implemented.

[0087] Compared with the prior art, the beneficial effects of the present invention are:

[0088] The present invention provides a multi-region collaborative configuration method for an electrical integrated energy system, including: obtaining the parameters of the electrical integrated energy system; substituting the parameters of the electrical integrated energy system into a pre-constructed multi-region configuration model, and using the ATC algorithm to decompose the multi-region configuration model into multiple layers of sub-problems, and iteratively solving the optimal configuration strategy of the integrated energy system in each region under the condition of satisfying the consistency constraints of the target variables and the response variables; wherein, the multi-region configuration model is a target function constructed with the goal of minimizing the economic cost and the environmental cost on the basis of considering the life-cycle cost, and the constraint conditions are constructed. The present invention decomposes the complex IENGS multi-region collaborative configuration problem into sub-problems that can be solved layer by layer through the ATC algorithm, reducing the computational difficulty. BRIEF DESCRIPTION OF THE DRAWINGS

[0089] Figure 1 It is a flowchart of a multi-region collaborative configuration method for an electrical integrated energy system of the present invention;

[0090] Figure 2 It is a schematic diagram of the structure of the multi-region IENGS of the present invention;

[0091] Figure 3 It is a schematic diagram of the three-layer structure of the collaborative configuration model of the present invention;

[0092] Figure 4 It is a flowchart of distributed solution based on ATC of the present invention;

[0093] Figure 5 It is a schematic diagram of the structure of an electronic device of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0094] In the present invention, the common device types in the current optimal operation of the electrical integrated energy system are summarized, and the collaborative planning of various types of devices such as power grid lines, gas pipelines, substations, gas distribution stations, wind-solar units, gas turbines, and P2G devices is realized. The configuration strategy aims to minimize the sum of the economic cost and the environmental cost of the system within the planning period, and pursues the unity of the economy and low carbon of the configuration scheme. Through the linkage of green certificates, the carbon trading mechanism and the green certificate trading mechanism are coupled with each other, and the carbon-green certificate joint trading cost is regarded as the environmental cost of the electrical integrated energy system, and the economic cost of the carbon emission benefit of the system is quantified.

[0095] The Analytical Target Cascading (ATC) algorithm brings new ideas for the multi-region configuration modeling and solution of the Integrated Electricity and Natural Gas System (IENGS). Currently, the ATC algorithm has been used to solve the optimization problems of multi-region energy systems. However, most of the power grid optimization models based on the ATC algorithm are two-layer problems, and the framework structure of the IENGS multi-region collaborative configuration model is more complex. In the present invention, based on the hierarchical solution principle of the ATC algorithm, the multi-region IENGS configuration problem is decomposed into sub-problems of gas distribution network, power distribution network, and transmission network that are easy to solve. Under the condition of satisfying the consistency constraints of target variables and response variables, the optimal configuration strategy of IENGS in each region is iteratively solved.

[0096] The present invention discloses a multi-region collaborative configuration method for an Integrated Electricity and Natural Gas System (IENGS) considering the full life cycle cost in a low-carbon environment. The present invention mainly focuses on the low-carbon economic collaborative configuration of the IENGS, establishes a multi-region collaborative configuration model considering the full life cycle cost, and targets the minimum of economic cost and environmental cost to conduct collaborative configuration of power grid lines, gas transmission pipelines, substations, gas distribution stations, wind-solar units, gas turbines, Power to Gas (P2G) equipment, etc. within each region.

[0097] Regarding the setting of the objective function: Economy should not only consider the initial construction cost of equipment, but also take into account the economic costs in the operation and maintenance, retirement and scrapping stages, to achieve accurate accounting of the full life cycle of costs and avoid the inconsistency between the configuration plan design and the economy of operation costs. Low carbon introduces the carbon trading mechanism and the green certificate trading mechanism, and proposes a carbon-green certificate joint trading mechanism based on the ladder carbon price. By linking carbon trading and green certificate trading through green certificates, the low-carbon benefits related to carbon emission accounting are associated with the two market transactions, and the environmental cost under the joint trading mechanism is further defined. The setting of the objective functions of economic cost and environmental cost realizes the economic configuration goal of the IENGS in a low-carbon environment.

[0098] During the solution process, using the Analytical Target Cascading (ATC) algorithm, the multi-region problem is decomposed into three sub-problems of transmission network, power distribution network, and gas distribution network. Under the condition of satisfying the consistency constraints of target variables and response variables, the optimal configuration strategy of the integrated energy system in each region is iteratively solved.

[0099] To better understand the present invention, the content of the present invention will be further described below in conjunction with the accompanying drawings of the specification and embodiments.

[0100] Embodiment 1:

[0101] A multi-region collaborative configuration method for an integrated electrical energy system, as Figure 1 shown, includes:

[0102] Step S1: Obtain the parameters of the integrated electrical energy system;

[0103] Step S2: Substitute the parameters of the integrated electrical energy system into a pre-constructed multi-region configuration model, and use the ATC algorithm to decompose the multi-region configuration model into multiple layers of sub-problems. Under the condition of satisfying the consistency constraints of the target variables and response variables, iteratively solve the optimal configuration strategy of the integrated energy system in each region;

[0104] Among them, the multi-region configuration model is a target function constructed with the goal of minimizing economic cost and environmental cost on the basis of considering the life cycle cost, and the constraint conditions are constructed.

[0105] The present invention provides a multi-region collaborative configuration method for an integrated electrical energy system. The technical solution adopted by the present invention is:

[0106] The collaborative configuration of the IENGS includes the multi-region overall configuration of equipment such as power grid lines, gas pipelines, substations, gas distribution stations, wind turbines, photovoltaic units, P2G equipment, gas turbines, etc. Its economy is reflected in the life cycle cost in the system configuration stage and operation stage, and its low carbon is reflected in the environmental cost defined by the carbon-green certificate joint trading mechanism based on the ladder carbon price. The multi-region solution problem is realized through the ATC algorithm, which mainly includes the following steps:

[0107] Step S1: Obtain the parameters of the integrated electrical energy system, including:

[0108] The parameters of the integrated electrical energy system include: the index of configured equipment or lines, the discount factor, the average annual depreciation cost, the cost of equipment, the cost of CET, the base price of CET, the total actual carbon emissions of the system, the total carbon emission trading quota of the system, the step interval growth rate of the CET price, the active power of the equipment at the corresponding node, the reactive power of the equipment at the corresponding node, the carbon emissions per unit of active power output, the carbon emission trading quota per unit of active power output of the equipment, the green certificate quota quantity of the system, the number of green certificates obtained by renewable energy power generation enterprises through verification, the green certificate quota ratio of the system, the electrical load power of the node at a certain moment, the GCT cost, the unit green certificate trading price, the actual carbon emissions of the system under the carbon - green certificate joint trading mechanism, the carbon emissions offset by redundant green certificates, the natural gas flow at the head end of the gas transmission pipeline, the upper and lower limits of the natural gas flow in the gas transmission pipeline, the maximum gas load, the virtual power of the gas transmission pipeline, etc. Among them, CET is carbon emission trading, and GCT is green certificate trading.

[0109] Before step S2, it also includes: the construction of a multi - regional configuration model. The construction process of this multi - regional configuration model includes:

[0110] Considering the life - cycle cost to calculate the economic cost and environmental cost of the integrated electrical energy system;

[0111] Constructing an objective function with the minimum of economic cost and environmental cost as the goal;

[0112] Setting constraint conditions for the objective function;

[0113] The constraint conditions include: power grid line configuration constraint conditions, gas transmission pipeline configuration constraint conditions, substation configuration constraint conditions, gas distribution station configuration constraint conditions, wind - solar unit configuration constraint conditions, gas turbine configuration constraint conditions, P2G equipment configuration constraint conditions.

[0114] Furthermore, the economic cost is shown as the following formula:

[0115]

[0116] In the formula, is the life - cycle economic cost of equipment , respectively represent the construction cost, the operation cost in the k - th year, and the maintenance cost in the k - th year of equipment , R k is the discount factor of the equipment at the end of the planning period, is the salvage value of the equipment at the end of the planning period , K total is the total number of years, and k is the serial number of the k - th year.

[0117] Furthermore, the environmental cost is shown as the following formula:

[0118] f env = C CET + C GCT

[0119] In the formula, f env is the environmental cost, C CET is the carbon trading cost, and C GCT is the green certificate trading cost.

[0120] The multi-region configuration model is shown as the following formula:

[0121]

[0122] In the formula, x is the configuration variable, y is the operation variable; f is the total system cost; g is the vector composed of inequality constraints, and h is the vector composed of equality constraints.

[0123] The construction process of the multi-region configuration model specifically includes:

[0124] Step 1: Define the economic cost and environmental cost of the IENGS configuration problem considering the full life cycle cost in a low-carbon environment.

[0125] Step 2: Determine the collaborative configuration constraints of IENGS, including power grid line configuration constraints, gas pipeline configuration constraints, substation configuration constraints, gas distribution station configuration constraints, wind-solar unit configuration constraints, gas turbine configuration constraints, P2G equipment configuration constraints, etc.

[0126] Step 3: Determine the objective function of the IENGS economic low-carbon configuration problem. According to the economic cost and environmental cost determined by the model established in Step 1, determine the objective function of the IENGS multi-region configuration model considering the full life cycle cost.

[0127] Step S2: Substitute the parameters of the electrical integrated energy system into the pre-constructed multi-region configuration model, and use the ATC algorithm to decompose the multi-region configuration model into multiple layers of sub-problems. Under the condition of satisfying the consistency constraints of the target variables and response variables, iteratively solve the optimal configuration strategy of the integrated energy system in each region, including:

[0128] The use of the ATC algorithm to decompose the multi-region configuration model into multiple layers of sub-problems includes:

[0129] Ignore the interconnection between the gas transmission network and the gas distribution network, equivalent the gas transmission network to a gas distribution station cluster, and simplify the problem of minimizing the total system cost to only include a power transmission network sub-problem, a power distribution network sub-problem, and a gas distribution network sub-problem.

[0130] Further, under the condition of satisfying the consistency constraint of the target variable and the response variable, iteratively solving the optimal configuration strategy of the integrated energy system in each region includes:

[0131] (1) Initialize the number of outer loop iterations \(L = 0\) and the number of inner loop iterations \(K = 0\);

[0132] (2) Initialize the coefficient of the first - order term of the penalty function the coefficient of the norm term and the target variable

[0133] (3) Start the inner loop;

[0134] (4) Solve each sub - problem of the gas distribution network, update the response variable of the \(q\) - th sub - problem of the gas distribution network and transfer it to the corresponding power distribution network;

[0135] (5) Solve each sub - problem of the power distribution network, update the target variable of the \(q\) - th sub - problem of the gas distribution network transfer it to the corresponding gas distribution network, and update the response variable of the \(q\) - th sub - problem of the power distribution network transfer it to the transmission network;

[0136] (6) Solve the sub - problem of the transmission network, update the target variable of the \(q\) - th sub - problem of the power distribution network transfer it to the corresponding power distribution network;

[0137] (7) Judge whether the objective function values of the \(q\) - th sub - problem at the \(p\) - th layer in the \(K\) - th and \((K - 1)\) - th inner loop iterations satisfy the inner - loop convergence condition. If satisfied, go to step (8); otherwise, return to step (2);

[0138] (8) Judge whether the difference between the shared variables in the \(L\) - th and \((L - 1)\) - th outer loop iterations satisfies the set outer - loop convergence condition. If satisfied, exit the loop; otherwise, go to step (9);

[0139] (9) Update the number of outer loop iterations \(L = L + 1\), and update the penalty - function - related coefficients according to the update relationship

[0140] (10) Let \(K = 0\) and return to step (2), where is the target variable at the \(K\) - th time.

[0141] Further, the outer - loop convergence condition is shown as the following formula:

[0142]

[0143] In the formula: represents the difference between the shared variables in the \(L\) - th outer loop; \(\varepsilon\) 2 \(\varepsilon\)3 are different outer-loop convergence gaps; represents the difference in shared variables in the (L - 1)-th outer loop.

[0144] Furthermore, the update relation formula is as follows

[0145]

[0146] In the formula: β is the penalty function weight growth rate; γ takes the standard value of 0.25; and respectively represent the penalty function coefficients in the L-th and (L - 1)-th outer loops; is the penalty function coefficient in the (L - 1)-th outer loop; is the penalty function coefficient in the L-th outer loop; represents the difference in shared variables in the (L - 1)-th outer loop; is the difference in shared variables in the (L - 2)-th outer loop.

[0147] Furthermore, the inner-loop convergence condition is as follows:

[0148]

[0149] In the formula: represents the objective function value of the q-th sub-problem in the p-th layer in the K-th inner loop; represents the objective function value of the q-th sub-problem in the p-th layer in the (K - 1)-th inner loop; ε 1 represents the inner-loop convergence gap.

[0150] Step S2 specifically includes:

[0151] Step 4: The multi-region collaborative configuration strategy based on the ATC algorithm. According to the IENGS collaborative configuration constraint conditions determined in Step 2 and the objective function determined in Step 3, use the ATC algorithm to solve the original problem layer by layer, decompose the multi-region problem into three sub-problems of the transmission network, distribution network, and gas distribution network, and iteratively solve the optimal configuration strategy of the integrated energy system in each region under the condition of satisfying the consistency constraints of the target variables and response variables.

[0152] Compared with the prior art, the advantages of the present invention are:

[0153] (1) Based on the traditional integrated energy system configuration problem, the present invention not only considers the initial construction cost of equipment, but also takes into account the economic costs in the operation and maintenance, retirement, and scrapping stages, realizes the accurate accounting of the full life cycle of costs, and avoids the inconsistency between the configuration scheme design and the economy of operation costs;

[0154] (2) The present invention further improves the calculation method of the economic cost of carbon dioxide emissions. Compared with the traditional carbon trading pricing mechanism, the stepped carbon trading mechanism has stronger binding force on carbon emissions; through the linkage of green certificates, the carbon trading mechanism and the green certificate trading mechanism are coupled, which can increase the promotion of renewable energy power generation on the basis of reducing system carbon emissions, and better guide low-carbon emission reduction;

[0155] (3) Through the ATC algorithm, the present invention decomposes the huge and complex IENGS multi-region collaborative configuration problem into sub-problems of gas distribution network, power distribution network, and transmission network that can be solved hierarchically, and iteratively solves the optimal configuration strategy of the integrated energy system in each region under the condition of satisfying the consistency constraints of target variables and response variables.

[0156] Example 2

[0157] Next, the technical solution of the present invention will be further specifically described.

[0158] Step 1: Define the economic cost and environmental cost of the IENGS configuration problem considering the full life cycle cost in a low-carbon environment.

[0159] Furthermore, in Step 1.1, the full life cycle economic cost is defined. For the convenience of expressing and calculating the configuration cost, the present invention introduces a present value coefficient as shown in Equation (1) to convert the cost generated each year within the planning period into the value corresponding to the starting point of the cash flow. Since the life cycle of the equipment is generally longer than the planning period of the IENGS, the remaining value of the equipment, i.e., the equipment salvage value, should also be considered and deducted at the end of the planning period. Using the straight-line depreciation method, first, the depreciation value of the equipment is averaged annually, and then the investment cost of the equipment is subtracted by the depreciation value within the corresponding planning period to calculate the equipment salvage value, as shown in Equation (2). Then, the full life cycle economic cost of the IENGS equipment is as shown in Equation (3).

[0160]

[0161] In the formula: is the index of the equipment or line to be configured in the IENGS; Ω Tf 、Ω Ts 、Ω Tpv 、Ω Tpw 、Ω Tg 、Ω Tp 、Ω Te 、Ω Tl 、Ω Tc respectively represent the sets of available power grid line models, substation models, photovoltaic power station models, wind turbine models, gas turbine models, P2G equipment models, energy storage equipment models, gas transmission pipeline models, and gas distribution station models; Rk represents the discount factor for the k-th year; r represents the discount rate; represents the equipment average annual depreciation cost within the service life; respectively represent the equipment construction cost, operating cost in the k-th year, and maintenance cost in the k-th year; represents the equipment net salvage rate; represents the equipment service life; represents the equipment total life cycle economic cost; R K represents the discount factor of the equipment at the end of the planning period; represents the equipment at the end of the planning period salvage value.

[0162] Furthermore, step 1.2 defines the environmental cost f env . It consists of two parts, the carbon trading cost C CET and the green certificate trading cost C GCT .

[0163] f env = C CET + C GCT (4)

[0164] Furthermore, in the formula f env = C CET + C GCT (4), the calculation method of the carbon trading cost C CET is as follows:

[0165]

[0166] In the formula: C CET represents the cost of CET; γ represents the base price of CET; E r represents the total actual carbon emissions of the system; E d represents the total carbon emission trading quota of the system; λ represents the purchase interval of the stepped CET; k cet represents the growth rate of the stepped interval of the CET price; Ω sub , Ω gt , Ω wt , Ω pv , Ω p2g respectively represent the sets of substations, gas turbines, wind turbines, photovoltaic power plants, and P2G equipment; respectively represent the active power outputs of the corresponding node substations, gas turbines, wind turbines, and photovoltaic power plants at time t; represents the active power consumed by the corresponding node P2G equipment at time t; σg , σ gt , σ wt , σ pv represent the carbon emissions under the unit active power output of thermal power units, gas turbines, wind turbines, and photovoltaic power stations respectively; σ p2g represents the mass of CO 2 utilized when the P2G device consumes unit electric energy and converts it into natural gas; δ g , δ gt , δ wt , δ pv represent the carbon emission trading quota amounts of thermal power units, gas turbines, wind turbines, and photovoltaic power stations under the unit active power output respectively. t is the time index of scheduling, T is a scheduling period, and i is a node, representing a power grid node here.

[0167] Furthermore, in the formula f of step 1.2.2 env = C CET + C GCT (4), the calculation method of the green certificate trading cost C GCT is as follows:

[0168] C GCT = ξ(B d - B o ) (8)

[0169]

[0170] C GCT = ξ(B d - B o ) (10)

[0171] In the formula: B d represents the green certificate quota quantity of the system; B o represents the number of green certificates obtained by renewable energy power generation enterprises through issuance; ω represents the system green certificate quota ratio; represents the electrical load power at node i at time t; Ω PL represents the set of power grid load nodes; C GCT represents the GCT cost; ξ represents the unit green certificate trading price.

[0172] Step 1.2.3 Carbon trading cost of the carbon - green certificate joint trading mechanism The specific relationship between the carbon emission trading quota quantity and the actual carbon emissions can be described as:

[0173]

[0174] δ E = σ g (B o - Bd ) (12)

[0175]

[0176] In the formula: represents the actual carbon emissions of the system under the carbon-green certificate joint trading mechanism; δ E represents the carbon emissions offset by redundant green certificates, σ g is the carbon emissions per unit active power output of the thermal power unit; E d represents the total amount of system carbon emission trading quotas, γ represents the base price of CET; λ represents the purchase interval of the stepped CET; k cet represents the stepped interval growth rate of the CET price.

[0177] Step 2: Determine the IENGS collaborative configuration constraints, including distribution network line configuration constraints, gas pipeline configuration constraints, substation configuration constraints, gas distribution station configuration constraints, wind-solar unit configuration constraints, gas turbine configuration constraints, P2G equipment configuration constraints, etc. In addition, since in the IENGS multi-region collaborative configuration model considered in the present invention, each regional subsystem is interconnected through the same power transmission network, it is necessary to add constraints related to the operation of the power transmission network.

[0178] Further, in Step 2.1, determine the distribution network collaborative configuration constraints, including voltage constraints of formulas (14) to (15), power balance constraints of formulas (16) to (17), line transmission power constraints of formula (18), distribution network line existence state constraints of formula (19), auxiliary variable constraints of formula (20), distribution network radial topology state constraints of formula (21), and power load and load reduction relationship formulas (22) to (23):

[0179]

[0180]

[0181] In the formula: is the square of the voltage at node i at time t, is the square of the voltage at node j at time t, f is the expansion substation type index, Ω Tf is the set of available power grid line types, r f is the resistance of the f-type power grid line, x f is the reactance, is the length of the line between nodes ij, V i min is the minimum voltage, is the square value of the voltage, V i max is the maximum voltage, is the substation power, is the power of the gas turbine, is the power of the fan, is the power of the photovoltaic, is the discharge power of the energy storage device, is the charging power of the energy storage, is the power consumption of the power - to - gas equipment, is the active load, is the reactive power of the substation, is the reactive power of the gas turbine, is the reactive load, Ω PN and Ω PGL represent the set of distribution network nodes and the set of distribution network lines respectively; μ ij,t is the auxiliary slack variable used to relax Equation (3 - 7) when the distribution network line ij does not exist; represent the active power, reactive power and their capacity upper limit values flowing through the f - type distribution network line respectively; is a 0 - 1 variable representing the new - built state, expansion state, existence state and operation state of the f - type distribution network line respectively; M takes a very large constant value; N PN represents the total number of nodes of the distribution network to be planned; represents the virtual power of the distribution network line ij; is the active power flowing through the distribution network line; is the reactive power flowing through the distribution network line; Ω SUB is the set of substation nodes; Ω Ts is the set of substation types; is the existence state of the s - type substation; is the active load reduction represents the data related to the typical scenario of the power load; represents the peak value of the active power load; ε l represents the power factor of the power load. Ω PN and Ω PGL represent the set of distribution network nodes and the set of distribution network lines respectively; μ ij,t is the auxiliary slack variable used to relax Equation (3 - 7) when the distribution network line ij does not exist; represent the active power, reactive power and their capacity upper limit values flowing through the f - type distribution network line respectively; is a 0 - 1 variable representing the new - built state, expansion state, existence state and operation state of the f - type distribution network line respectively; M takes a very large constant value; N PN represents the total number of nodes of the distribution network to be planned; represents the virtual power of the distribution network line ij.

[0182] Further, in step 2.2, the configuration constraints of the gas transmission pipeline are determined, including establishing a steady-state model of the gas distribution network using the Weymouth equation (24), the gas network node flow balance constraint (25), the actual gas flow expression of the gas pipeline (26), the relationship between gas load and load reduction (27), the gas pressure constraint equation (28), the gas transmission pipeline flow constraint equation (29), the existence state constraint equation of the gas transmission pipeline (30), the auxiliary variable constraint equation (31), and the radial topological state constraint equation of the gas network (32):

[0183]

[0184] In the formula: represents the natural gas flow of the gas transmission pipeline mn at time t; C mn represents the relevant parameters of the gas transmission pipeline; represents the square value of the gas pressure at node m; represents the square value of the gas pressure at node n; μ mn,t is an auxiliary variable used to relax Equation (3-14) when the gas transmission pipeline mn does not exist; respectively represent the natural gas flows of the gas distribution station at node m, the P2G equipment, and the gas turbine at time t; represents the natural gas flow of the L-shaped gas transmission pipeline mn at time t; respectively represent the gas load and the gas load reduction value; n represents the node index of the gas transmission pipeline; l represents the alternative types of the gas transmission pipeline; represents the load reduction of the gas distribution station of model c at node m at time t; represents the relevant data of the typical gas load scenario; represents the gas load peak; Ω GN and Ω NGL respectively represent the set of nodes in the gas distribution network and the set of gas transmission pipelines in the gas distribution network; Ω GN (m) represents all the nodes connected to the gas network node m; is a 0-1 variable, representing the new construction state, expansion state, and existence state of the gas transmission pipeline mn respectively; represents the upper limit of the natural gas flow of the L-shaped gas transmission pipeline; respectively represent the upper and lower limits of the gas pressure at node m; represents the relevant data of the gas load; represents the maximum value of the gas load; is a 0-1 variable representing the existence state of the gas distribution station; N GN represents the total number of nodes in the gas distribution network to be planned; represents the virtual power of the gas transmission pipeline mn.

[0185] Furthermore, in step 2.2.1, since the Weymouth equation is non-linear, the piecewise linearization of Equation (24) is as follows:

[0186]

[0187] η mn,t,q+1 ≤δ mn,t,q ≤η mn,t,q (35)

[0188] 0≤δ mn,t,q ≤1 (36)

[0189] In the formula: represents the square value of the natural gas flow rate at the head end of the gas transmission pipeline mn at time t; represents the natural gas flow rate at the head end of the gas transmission pipeline mn at time t; represents the square value of the natural gas flow rate at the starting point of the q-th segment of the gas transmission pipeline mn at time t; represents the square value of the natural gas flow rate at the starting point of the (q + 1)-th segment of the gas transmission pipeline mn at time t; δ mn,t,q represents a continuous auxiliary variable; η mn,t,q represents a 0-1 type auxiliary variable; represents; represents as mentioned before; q represents the number of segments, Ω q represents the segment number index; represents the gas load after segmentation, but the segmentation point is at q + 1; represents the gas load after segmentation; represents the piecewise linearization auxiliary variable.

[0190] Furthermore, in step 2.3, the substation configuration constraint conditions are determined, including the capacity constraint Equation (37) and the existence state constraint Equation (38):

[0191]

[0192] In the formula: represents the active power of the substation at node i at time t, represents the construction state of the s-type substation at node i, represents the reactive power of the substation, s represents the substation type, Ω SS represents the set of nodes where substations can be built; represents the upper limit of the capacity of the s-type substation; is a 0-1 variable, representing the new construction state, expansion state, and existence state of the s-type substation respectively.

[0193] Furthermore, in step 2.4, the configuration constraints of the gas distribution station are determined, including the capacity constraint equation (39) and the existence state constraint equation (40):

[0194]

[0195] In the formula: represents the power of the gas distribution station at node m and time t; c represents the type of gas distribution station; Ω CG represents the set of nodes where the gas distribution station can be built; represents the upper limit of the flow rate of the c-type gas distribution station; is a 0-1 variable, representing the new construction state, expansion state, and existence state of the c-type gas distribution station respectively.

[0196] Furthermore, in step 2.5, the configuration constraints of the wind-solar units are determined, including the power relationship constraint equation (41) and the unit existence state constraint equation (42):

[0197]

[0198] In the formula: represents the power of the wind turbine at node m and time t; v represents the type of wind turbine; Ω CG represents the set of nodes where the gas distribution station can be built; represents the upper limit of the flow rate of the c-type gas distribution station; is a 0-1 variable, representing the new construction state, expansion state, and existence state of the v-type gas distribution station respectively, and Ω Tv represents the set of wind turbine nodes.

[0199] Furthermore, in step 2.6, the configuration constraints of the gas turbine are determined, including the operation constraints equations (43) to (47) and the existence state constraint equation (48):

[0200]

[0201] In the formula: represents the active power of the gas turbine at node i and time t; g represents the type of gas turbine; Ω Tg represents the index of the alternative types available for the gas turbine; H gas represents the calorific value of natural gas; represent the sets of nodes where the gas turbine can be built in the distribution network and the gas distribution network respectively; represents the natural gas flow rate consumed by the g-type gas turbine at time t; represents the active power and reactive power of the g-type gas turbine at time t; is a 0-1 variable, representing the new construction state, expansion state, and existence state of the g-type gas turbine respectively; respectively represent the upper power limit, operating efficiency, and power factor angle of the g-type gas turbine, represents the gas load of the gas turbine at node m at time t in the corresponding gas distribution network, represents the existence state of the g-type gas turbine at node m in the gas distribution network, represents the upper limit of the gas distribution power of the g-type gas turbine.

[0202] Furthermore, in step 2.7, the configuration constraints of the P2G device are determined, including the operation constraints of equations (43) to (47) and the existence state constraint of equation (48):

[0203]

[0204] In the formula: represents the active power of the power-to-gas device at node i at time t, p represents the type of the power-to-gas device, and Ω Tp represents the set of power-to-gas device models, represents the gas volume consumed and produced by the p-type power-to-gas device at node m at time t in the gas distribution network, represents the gas volume consumed and produced by the p-type power-to-gas device at node m at time t in the actual gas distribution network, represents the existence state of the p-type power-to-gas device at node m, represents the upper limit of gas production of the p-type power-to-gas device, respectively represent the sets of nodes where P2G devices can be built in the power distribution network and the gas distribution network; respectively represent the active power consumed by the p-type P2G device at the coupling node i in the power distribution network and the natural gas flow rate produced and injected into the coupling node m in the gas distribution network at time t; is a 0-1 variable, representing the new construction state, expansion state, and existence state of the p-type P2G device respectively; respectively represent the efficiency and upper limit of power operation of the p-type P2G device.

[0205] Furthermore, in step 2.8, the operation constraints of the transmission network are determined, including the power balance constraint of equation (54), the DC power flow constraint of equation (55), and the upper and lower limit constraints of the generator power, line transmission power, and transmission-distribution connection power of equation (56):

[0206]

[0207]

[0208] In the formula: Ω TPN and Ω TPGL respectively represent the set of transmission network nodes and the set of transmission network lines; Denote the active power output of the generator at node \(i\) at time \(t\); Denote the active power load; Denote the lower and upper limits of the active power output of the generator at node \(i\) respectively; \(Z\) on,t Denote the on / off state of the generator at node \(i\) at time \(t\); \(\theta\) i,t Denote the phase angle of the voltage at node \(i\) at time \(t\); \(\theta\) j,t Denote the phase angle of the voltage at node \(j\) at time \(t\); Denote the active power transmitted by the transmission line \(ij\) at time \(t\); Denote the lower limit of the active power output of the generator at node \(i\) in the transmission network; \(Z\) on,i,t Denote the on / off state of the generator, \(P\) G,i,t Denote the active power output of the generator at node \(i\) of the transmission network at time \(t\); \(b\) ij Denote the susceptance of the transmission line \(ij\); Denote the active power transmitted by the transmission line \(ij\) at time \(t\) and its upper limit respectively; Denote the connection power between the transmission network and the distribution network of the \(h\)-th IENGS at the coupling node and its upper limit at time \(t\) respectively.

[0209] Step 3: Determine the objective function of the IENGS economic and low-carbon configuration problem. Determine the objective function of the IENGS multi-region configuration model considering the full life cycle cost according to the economic cost and environmental cost determined by the model established in Step 1.

[0210] Furthermore, Step 3.1 determines the transmission network cost:

[0211]

[0212] In the formula: \(F\) TRA Denote the transmission network cost; Denote the electricity price, Denote the connection power between the transmission network and the gas distribution network, \(q\) represents the expected index, \(N\) q Denote the total number of gas distribution networks connected to the transmission network, \(i\) represents the transmission network node, \(t\) represents the scheduling time, \(T\) represents a scheduling period, \(\Omega\) TG Denote the set of generator nodes in the transmission network; \(N\) q Denote the total number of distribution networks connected to the transmission network; \(C\) g,i \(()\) represents the cost function of the generator at node \(i\); Denote the active power output of the generator at node \(i\) at time \(t\); \(a\) g,i ,\(b\) g,i ,\(c\) g,i Denote the correlation coefficients of the cost function of the generator at node \(i\) respectively.

[0213] Further, in step 3.2, the distribution network cost is determined, and the distribution network cost objective function F P Equation (58) consists of two parts, the life-cycle economic cost of the distribution network The environmental operation cost under the carbon-green certificate joint trading mechanism Among them It is calculated from the construction costs of all the devices and lines to be configured in the distribution network The operation cost in the k-th planning year The maintenance cost in the k-th planning year The salvage value cost The carbon-green certificate trading cost considering the life-cycle carbon emissions is calculated It is calculated based on Equations (10) and (13) as follows:

[0214]

[0215]

[0216] In the formula: respectively represent the new construction cost, expansion cost, maintenance cost, and equipment salvage value of the f-type distribution network line; respectively represent the new construction cost, expansion cost, maintenance cost, and equipment salvage value of the s-type substation; respectively represent the new construction cost, expansion cost, maintenance cost, and equipment salvage value of the g-type gas turbine; respectively represent the new construction cost, expansion cost, maintenance cost, and equipment salvage value of the p-type P2G equipment; respectively represent the new construction cost, expansion cost, maintenance cost, and equipment salvage value of the w-type wind turbine; respectively represent the new construction cost, expansion cost, maintenance cost, and equipment salvage value of the v-type photovoltaic unit; respectively represent the new construction cost, expansion cost, maintenance cost, and equipment salvage value of the e-type energy storage device; R K represents the discount factor of the equipment at the end of the planning period; represents the new construction status of the f-type power grid line, represents the new construction status of the s-type substation, represents the new construction status of the g-type gas turbine, represents the new construction status of the p-type power-to-gas equipment, represents the new construction status of the w-type wind turbine, represents the new construction status of the v-type photovoltaic, represents the new construction status of the e-type energy storage device, represents the expansion status of the f-type power grid line, represents the expansion status of the s-type substation, represents the expansion status of the g-type gas turbine, Indicates the expansion status of the p-type power-to-gas equipment Indicates the expansion status of the w-type fan The status of the photovoltaic unit of the type substation is indicated Indicates the expansion status of the e-type energy storage device, where e represents the type of energy storage device, Ω Te Indicates the set of alternative energy storage device models, Ω ESS Indicates the set of energy storage device construction nodes, Ω WT Indicates the set of fan construction nodes, Ω Tw Indicates the set of alternative fan types, Ω GT Indicates the set of gas turbine construction nodes, Ω PL Indicates the set of load nodes, L ij Indicates the length of line ij Indicates the salvage value of the s-type substation of the substation Indicates the salvage value of the g-type gas turbine Indicates the c salvage value of the p-type power-to-gas equipment Indicates the salvage value of the w-type fan Indicates the salvage value of the v-type photovoltaic unit Indicates the salvage value of the e-type energy storage device, Ω SS Indicates the set of substation construction nodes P2G Indicates the set of power-to-gas equipment construction nodes, Ω PV Indicates the set of photovoltaic unit construction nodes, Ω Tv Indicates the set of alternative photovoltaic unit types Indicates the environmental cost in the p distribution network h scenario, C CET Indicates the carbon trading cost, C GCT Indicates the green certificate trading cost Is a 0-1 variable indicating the existence status of the f-type distribution network line Is the existence status of the s model substation Is a 0-1 variable indicating the existence status of the g-type gas turbine Indicates the existence status of the p model power-to-gas equipment at node i Is a 0-1 variable indicating the existence status of the w-type wind turbine unit Indicates the existence status of the v-type photovoltaic unit Indicates the existence status of the e-type energy storage device

[0217] Furthermore, in step 3.3, the gas distribution network cost is determined. Similar to the distribution network, the life-cycle economic cost of the gas distribution network Is composed of the construction costs of all the equipment and lines to be configured in the gas distribution network and The operating cost in the kth planning year The maintenance cost in the kth planning year The salvage value cost Calculated as follows:

[0218]

[0219] Where: respectively represent the new construction cost, expansion cost, maintenance cost, and equipment residual value of the L-type gas transmission pipeline; respectively represent the new construction cost, expansion cost, maintenance cost, and equipment residual value of the C-type gas distribution station, F G represents the gas distribution network cost, Ω NGL represents the set of gas transmission pipelines, Ω Tl represents the set of alternative gas transmission pipeline types, represents the new construction status of the L-type gas transmission pipeline, represents the expansion status of the L-type gas transmission pipeline, L mn represents the length of the gas transmission pipeline mn, Ω CG represents the set of gas distribution stations, represents the new construction status of the C-type gas distribution station, represents the expansion status of the C-type gas distribution station, Ω Tc represents the set of alternative gas distribution station models, m represents the gas distribution network node index, represents the existence status of the C-type gas distribution station, represents the existence status of the L-type gas transmission pipeline, c represents the gas distribution station model, mn represents the gas transmission pipeline index, represents the output gas load of the m-type gas distribution station at time t, represents the reduced gas load, l represents the gas transmission pipeline model, represents the operating cost of the gas distribution station at time t, represents the cost of reducing the load of the gas distribution station at time t.

[0220] Step 4: Multi-region collaborative configuration strategy based on the ATC algorithm. According to the IENGS collaborative configuration constraint conditions determined in Step 2 and the objective function determined in Step 3, use the ATC algorithm to hierarchically solve the original problem, decompose the multi-region problem into three sub-problems of the power transmission network, power distribution network, and gas distribution network, and iteratively solve the optimal configuration strategy c of the integrated energy system within each region under the condition of satisfying the consistency constraints of the target variables and response variables.

[0221] Furthermore, Step 4.1 Model equivalent simplification based on the ATC algorithm. As Figure 2 shown, the IENGS multi-region collaborative configuration model can be expressed as an optimization problem in the unified form shown in Equation (70):

[0222]

[0223] Where: x and y are vectors composed of decision variables, x is the configuration variable, and y is the operation variable; f is the total system cost; g is the vector composed of inequality constraints, and h is the vector composed of equality constraints.

[0224] Using the ATC algorithm to Figure 2 simplify and decouple the multi-region IENGS collaborative configuration problem shown in Figure 3 as follows: Ignoring the interconnection between the gas transmission network and the gas distribution network, the gas transmission network is equivalent to a cluster of gas distribution stations, and the original problem is simplified to a three-layer sub-problem containing only the power transmission network, the power distribution network, and the gas distribution network. Among them, p is the number of layers, q is the number of elements, and t and r are the target variables and response variables between the corresponding layers.

[0225] According to the network structure of each region's IENGS, decouple the operation variable y in the original multi-region collaborative configuration model (70) into two parts according to whether it only exists in a single sub-problem: local variable and coupling variable The local variable only exists in a single sub-problem, while the coupling variable exists in two adjacent sub-problems at the same time. Decouple the coupling and decompose it into the target vector t pq and the response variable r pq . For the convenience of the unity of the penalty function expression writing in the later stage, the number of layers p of t pq is defined as the number of layers of its corresponding r pq . Define D pq ={q 1 , q 2 ... q n} as the set of serial numbers of the sub-problems of the (p + 1)-th layer coupled with the q-th problem of the p-th layer, and q n is the sub-problem with the serial number n.

[0226] After the above simplification and decoupling process, the multi-region IENGS collaborative configuration problem can be transformed into a three-level problem including the optimal operation of the power transmission network, the optimal configuration of the power distribution network, and the optimal configuration of the gas distribution network, with a total of 2N q +1 sub-problems, including 1 optimal operation problem of the power transmission network, N q optimal configuration problems of the power distribution network, and N q optimal configuration problems of the gas distribution network.

[0227] The mathematical model of each optimal sub-problem can be expressed in the form of equation (71):

[0228]

[0229] Where: f pq is the original objective function of the q-th sub-problem of the p-th layer; gpq is the inequality constraint column vector for the q-th sub-problem of the p-th layer; h pq is the equality constraint column vector for the q-th sub-problem of the p-th layer; x pq is the configuration variable for the q-th sub-problem of the p-th layer; is the operating variable for the q-th sub-problem of the p-th layer; σ pq is the difference of the outer loop shared variable, i.e., the slack target variable t pq and the response variable r pq The difference of, see Equation 74, Φ pq is the penalty function for the transmission-distribution network layer, see Equation 75, Φ (p+1)q is the penalty function for the distribution-gas network layer, σ (p+1)q is Equation 74 for the corresponding layer, is the internal variable of the sub-problem, r pq is the response variable, is the first slack target variable of the (p + 1)-th layer and the q1-th one, is the n-th slack target variable of the (p + 1)-th layer and the qn-th one.

[0230] Furthermore, Step 4.2 Coupled Variable Decomposition and Penalty Function Modeling:

[0231] Regard the energy flow coupled by the substation power, P2G equipment, and gas turbine as the coupled variable between the corresponding levels. The optimization problems of each level are interconnected through the coupled variable. Among them, the coupled vector between the transmission-distribution network levels is decomposed into the target variable t 2q of the transmission network and the response variable r 2q of the distribution network, as shown in Equation (72):

[0232]

[0233] In the formula: represents the active power connected to the distribution network from the perspective of the transmission network at time t, where t takes values 1, 2... T; represents the active power connected to the transmission network from the perspective of the distribution network at time t, where t takes values 1, 2... T.

[0234] The coupled vector between the distribution-gas network levels is decomposed into the target variable t 3q of the distribution network and the response variable r 3q of the gas network, as shown in Equation (73):

[0235]

[0236] In the formula: represents the active power connected to the gas network from the perspective of the distribution network at time t, where t takes values 1, 2... T; Denote the active power connected to the distribution network at the valve timing network angle at time t, where t takes values 1, 2, …, T.

[0237] For the slack target variable t pq and the response variable r pq of the consistency constraint formula (74), introduce the corresponding penalty function Φ pq (σ pq ), as shown in formula (75):

[0238] σ pq = t pq - r pq = 0 (74)

[0239]

[0240] In the formula: v pq , w pq respectively represent the coefficient vectors related to the calculation of the corresponding penalty function; the symbol represents the Hadamard operation symbol.

[0241] Furthermore, in step 4.3, solve the sub-problems layer by layer:

[0242] Furthermore, in step 4.3.1, solve the transmission network sub-problem:

[0243] The transmission network sub-problem aims to minimize the sum of the total operating cost of the transmission network and the penalty function cost, as shown in formula (76):

[0244]

[0245] In the formula: f 11 represents the objective function of the transmission network optimal operation sub-problem; F tra represents the total operating cost of the transmission network calculated based on formula (3 - 51); Φ 2q represents the penalty function of the transmission network sub-problem.

[0246] Furthermore, in step 4.3.2, solve the distribution network sub-problem:

[0247] The distribution network sub-problem aims to minimize the sum of the total configuration cost over the entire life cycle of the system and the penalty function cost, as shown in formula (77):

[0248]

[0249] In the formula: f 2q represents the objective function of the q-th distribution network optimal configuration sub-problem; F P,2q represents the total configuration cost from the perspective of the entire life cycle of the q-th distribution network calculated based on formula (3 - 52); Φ 2q + Φ 3qDenote the penalty function of the \(q\)-th distribution network sub-problem.

[0250] Furthermore, in step 4.3.3, solve the gas network sub-problem:

[0251] The gas network sub-problem aims to minimize the sum of the total configuration cost of the system over its entire life cycle and the penalty function cost, as shown in Equation (78):

[0252]

[0253] In the formula: \(f\) 3q Denotes the objective function of the \(q\)-th optimal configuration sub-problem of the gas network; \(F\) G,3q Denotes the total configuration cost of the \(q\)-th gas network from the perspective of its entire life cycle calculated based on Equation (3 - 59); \(\varPhi\) 3q Denote the penalty function of the \(q\)-th gas network sub-problem.

[0254] Furthermore. The solution process of the ATC algorithm in step 4.4 is as Figure 4 shown:

[0255] (1) Initialize the number of outer loop iterations \(L = 0\) and the number of inner loop iterations \(K = 0\);

[0256] (2) Initialize the penalty function coefficient and the target variable

[0257] (3) Start the inner loop;

[0258] (4) Solve each gas network sub-problem, update the response variable and pass it to the corresponding distribution network;

[0259] (5) Solve each distribution network sub-problem, update the target variable pass it to the corresponding gas network, and update the response variable pass it to the transmission network;

[0260] (6) Solve the transmission network sub-problem, update the target variable pass it to the corresponding distribution network;

[0261] (7) Determine whether the inner loop convergence condition Equation (79) holds; if it holds, go to step (8), otherwise return to step (2);

[0262]

[0263] In the formula: Denotes the objective function value of the \(q\)-th sub-problem at the \(p\)-th layer in the \(K\)-th inner loop; Denotes the objective function value of the \(q\)-th sub-problem at the \(p\)-th layer in the \((K - 1)\)-th inner loop; \(\varepsilon\) 1 Denotes the inner loop convergence gap.

[0264] (8) Determine whether the outer loop convergence condition formula (80) holds; if it holds, exit the loop; otherwise, proceed to step (9).

[0265]

[0266] Where: represents the difference in shared variables in the L-th outer loop; represents the difference in shared variables in the (L-1)-th outer loop; ε 2 and ε 3 are the outer loop convergence gaps.

[0267] (9) Update the outer loop count L = L + 1; update the penalty function related coefficients according to formulas (81) to (82)

[0268]

[0269] Where: β is the penalty function weight growth rate; γ takes the standard value of 0.25; and represent the penalty function coefficients in the L-th and (L-1)-th outer loops respectively; is the penalty function coefficient in the (L-1)-th outer loop; is the penalty function coefficient in the L-th outer loop; represents the difference in shared variables in the (L-1)-th outer loop; is the difference in shared variables in the (L-2)-th outer loop.

[0270] (10) Let K = 0 and return to step (2).

[0271] Embodiment 3

[0272] Based on the same inventive concept, the present invention also provides a multi-region collaborative configuration system for an electrical integrated energy system, including:

[0273] An acquisition module for acquiring electrical integrated energy system parameters;

[0274] An optimal configuration module for substituting the electrical integrated energy system parameters into a pre-constructed multi-region configuration model and performing hierarchical solution using the ATC algorithm to obtain the optimal configuration strategy for the integrated energy system in each region;

[0275] Among them, the multi-region configuration model is a target function constructed with the goal of minimizing economic cost and environmental cost on the basis of considering the full life cycle cost, and constraint conditions are constructed.

[0276] Preferably, it further includes a module construction module for:

[0277] Consider the economic cost and environmental cost of the integrated electrical energy system by calculating the life-cycle cost;

[0278] Construct an objective function with the minimum of economic cost and environmental cost as the goal;

[0279] Set constraint conditions for the objective function;

[0280] The constraint conditions include: grid line configuration constraint conditions, gas pipeline configuration constraint conditions, substation configuration constraint conditions, gas distribution station configuration constraint conditions, wind-solar unit configuration constraint conditions, gas turbine configuration constraint conditions, P2G equipment configuration constraint conditions.

[0281] Preferably, the economic cost is shown as the following formula:

[0282]

[0283] In the formula, is the life-cycle economic cost of equipment , respectively represent the construction cost, the operation cost in the kth year, and the maintenance cost in the kth year of equipment , R k is the discount factor of the equipment at the end of the planning period, is the salvage value of the equipment at the end of the planning period.

[0284] Preferably, the environmental cost is shown as the following formula:

[0285] f env =C CET +C GCT

[0286] In the formula, f env is the environmental cost, C CET is the carbon trading cost, C GCT is the green certificate trading cost.

[0287] Preferably, the multi-region configuration model is shown as the following formula:

[0288]

[0289] In the formula, x is the configuration variable, y is the operation variable; f is the total system cost; g is the vector composed of inequality constraints, and h is the vector composed of equality constraints.

[0290] Preferably, the optimization configuration module is specifically used for:

[0291] (1) Initialize the number of outer loop times L = 0 and the number of inner loop times K = 0;

[0292] (2) Initialize the coefficient of the first - order penalty function Norm term coefficient With the target variable

[0293] (3) Start the inner loop;

[0294] (4) Solve each sub - problem of the gas distribution network, update the response variable of the q - th sub - problem of the gas distribution network And transfer it to the corresponding power distribution network;

[0295] (5) Solve each sub - problem of the power distribution network, update the target variable of the q - th sub - problem of the gas distribution network Transfer it to the corresponding gas distribution network, update the response variable of the q - th sub - problem of the power distribution network Transfer it to the transmission network;

[0296] (6) Solve the sub - problem of the transmission network, update the target variable of the q - th sub - problem of the power distribution network Transfer it to the corresponding power distribution network;

[0297] (7) Judge whether the objective function values of the q - th sub - problem of the p - th layer in the K - th and (K - 1) - th inner loops meet the inner - loop convergence condition. If they meet, go to step (8); otherwise, return to step (2);

[0298] (8) Judge whether the difference between the shared variables in the L - th and (L - 1) - th outer loops meets the set outer - loop convergence condition. If it meets, exit the loop; otherwise, go to step (9);

[0299] (9) Update the outer - loop count L = L + 1, update the penalty - function - related coefficients according to the update relationship

[0300] (10) Let K = 0 and return to step (2), where is the target variable of the K - th time.

[0301] Preferably, the outer - loop convergence condition is shown as the following formula:

[0302]

[0303] In the formula: represents the difference between the shared variables in the L - th outer loop; ε 2 、ε 3 are the outer - loop convergence gaps.

[0304] Preferably, the update relationship is shown as the following formula

[0305]

[0306] Where: β is the penalty function weight growth rate; γ takes the standard value of 0.25.

[0307] Preferably, the inner loop convergence condition is shown as follows:

[0308]

[0309] Where: represents the objective function value of the qth sub-problem at the pth layer in the Kth inner loop; represents the objective function value of the qth sub-problem at the pth layer in the (K - 1)th inner loop; ε 1 represents the inner loop convergence gap.

[0310] Embodiment 4

[0311] As Figure 5 shown, the present invention also provides an electronic device, which may be a computer device, a single-chip microcomputer device, a smart mobile device, etc. The electronic device in this embodiment may include a processor, a memory, a transceiver component, etc. The memory, the processor, and the transceiver component are connected through a bus; the memory can be used to store an execution program, and an exemplary execution program may include instructions; the processor is used to execute the instructions stored in the memory. The memory can also be used to store data, and this data can be called and / or modified when the instructions are executed.

[0312] 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, it is suitable for loading and executing one or more instructions in the storage medium to implement the corresponding method flow or corresponding function, so as to implement the steps of a multi-region collaborative configuration method for an electrical integrated energy system in the above embodiments.

[0313] Embodiment 5

[0314] Based on the same inventive concept, the present invention also provides a readable storage medium, specifically an electronic device-readable storage medium (Memory). The electronic device-readable storage medium is a memory device in the electronic device and is used to store programs and data. It can be understood that the storage medium here can include both the built-in storage medium in the electronic device and, of course, the extended storage medium supported by the electronic device. The storage medium provides a storage space, and this storage space stores the operating system of the terminal. Moreover, in this storage space, there is also stored one or more instructions suitable for being loaded and executed by the processor, and these instructions can be one or more executable programs (including program codes). It should be noted that the storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory. By the processor loading and executing one or more instructions stored in the storage medium, the steps of a multi-region collaborative configuration method for an electrical integrated energy system in the above embodiments can be implemented.

[0315] 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.

[0316] The present invention is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the 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 functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0317] 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 product including an instruction device, and the instruction device implements the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0318] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus, so that a series of operation steps are executed on the computer or other programmable apparatus to generate a computer-implemented process, thereby the instructions executed on the computer or other programmable apparatus provide steps for implementing the functions specified in one process or a plurality of processes and / or one block or a plurality of blocks in the flow Figure 1 one process or a plurality of processes and / or Figure 1 steps of the functions specified in one block or a plurality of blocks.

[0319] The above are only embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention are included within the scope of the claims of the present invention pending approval.

Claims

1. A multi-region collaborative configuration method for an electrical integrated energy system, characterized in that: include: Obtaining electrical integrated energy system parameters; The parameters of the electric integrated energy system are introduced into a pre-built multi-region configuration model, and the multi-region configuration model is decomposed into multi-layer sub-problems using the ATC algorithm. Under the condition of satisfying the consistency constraints of the target variables and the response variables, the optimal configuration strategy of the integrated energy system in each region is iteratively solved; The multi-region configuration model is constructed based on an objective function and constraint conditions with the goal of minimizing economic and environmental costs on the basis of considering the full life cycle costs.

2. The method according to claim 1, characterized in that The construction of the multi-region configuration model includes: Calculate the economic and environmental costs of the electrical integrated energy system by taking into account the full life cycle costs; Construct an objective function with the goal of minimizing economic and environmental costs; Setting constraints for the objective function; The constraints include: grid line configuration constraints, gas pipeline configuration constraints, substation configuration constraints, gas distribution station configuration constraints, wind and solar unit configuration constraints, gas turbine configuration constraints, and P2G equipment configuration constraints.

3. The method according to claim 2, characterized in that The economic cost is shown in the following formula: In the formula, For equipment The economic cost of the entire life cycle, Respectively represent devices The construction cost, operation cost in the kth year, and maintenance cost in the kth year, R k is the discount factor for the equipment at the end of planning, Planning for end-of-life equipment The residual value is K, the total number of years, and k is the serial number of the year.

4. The method according to claim 2, characterized in that The environmental cost is shown in the following formula: f env =C CET +C GCT In the formula, f env is the environmental cost, C CET is the carbon trading cost, C GCT The transaction cost of green certificates.

5. The method according to claim 1, characterized in that The multi-region configuration model is shown in the following formula: Where x is the configuration variable, y is the operating variable, f is the total system cost, g is the vector of inequality constraints, and h is the vector of equality constraints.

6. The method according to claim 1, characterized in that The ATC algorithm is used to decompose the multi-region configuration model into multi-layer sub-problems, including: Ignoring the interconnection between the gas transmission network and the gas distribution network, the gas transmission network is equivalent to a cluster of gas distribution stations, and the problem of minimizing the total system cost is simplified to only contain the transmission network sub-problem, the distribution network sub-problem, and the gas distribution network sub-problem.

7. The method according to claim 6, characterized in that The optimal configuration strategy of the integrated energy system in each region is iteratively solved under the condition that the consistency constraints of the target variable and the response variable are met, including: (1) Initialize the outer loop times L = 0 and the inner loop times K = 0; (2) Initialize the penalty function linear coefficient Norm term coefficient With the target variable (3) Start the internal circulation; (4) Solve each gas distribution network sub-problem and update the response variable of the qth sub-problem of the gas distribution network And transmit it to the corresponding distribution network; (5) Solve each distribution network sub-problem and update the target variable of the qth sub-problem of the gas distribution network Pass it to the corresponding gas distribution network to update the response variable of the qth sub-problem of the distribution network to the transmission grid; (6) Solve the transmission network sub-problem and update the objective variable of the qth sub-problem of the distribution network Transmit to the corresponding distribution network; (7) Determine whether the objective function value of the qth subproblem of the pth layer in the Kth and K-1th inner loops meets the inner loop convergence condition. If so, proceed to step (8); otherwise, return to step (2); (8) Determine whether the difference between the shared variables of the Lth and L-1th outer loops meets the set outer loop convergence condition. If so, exit the loop; otherwise, go to step (9); (9) Update the number of outer loops L = L + 1, and update the penalty function correlation coefficient according to the update relation (10) Order K=0 Return to step (2), where is the K-th target variable.

8. The method according to claim 7, characterized in that The outer loop convergence condition is as follows: Where: represents the difference of the shared variable in the Lth outer loop; ε2 and ε3 are different outer loop convergence gaps; Indicates the difference of the shared variable in the L-1th outer loop.

9. The method according to claim 7, characterized in that The update relationship is shown as follows Where: β is the penalty function weight growth rate; γ takes the standard value of 0.25; and Respectively represent the penalty function coefficients of the Lth and L-1th outer loops; is the penalty function coefficient of the L-1th outer loop; is the penalty function coefficient of the Lth outer loop; Indicates the difference of the shared variable in the L-1th outer loop; It is the difference of the shared variable in the L-2nd outer loop.

10. The method according to claim 7, characterized in that The inner loop convergence condition is as follows: Where: Represents the objective function value of the qth subproblem of the pth layer in the Kth inner loop; It represents the objective function value of the qth subproblem of the pth layer in the K-1th inner loop; ε1 represents the inner loop convergence gap.

11. A multi-region collaborative configuration system for an electrical integrated energy system, characterized in that: include: An acquisition module is used to obtain parameters of the electrical integrated energy system; An optimization configuration module is used to bring the parameters of the electrical integrated energy system into a pre-built multi-region configuration model, and use the ATC algorithm to perform hierarchical solution to obtain the optimal configuration strategy of the integrated energy system in each region; The multi-region configuration model is constructed based on an objective function and constraint conditions with the goal of minimizing economic and environmental costs on the basis of considering the full life cycle costs.

12. An electronic device, characterized in that: include: at least one processor and memory; The memory and the processor are connected via a bus; The memory is used to store one or more programs; When the one or more programs are executed by the at least one processor, a multi-region collaborative configuration method for an electrical integrated energy system as described in any one of claims 1 to 10 is implemented.

13. A readable storage medium, characterized in that: An execution program is stored thereon, and when the execution program is executed, a multi-region collaborative configuration method for an electrical integrated energy system as described in any one of claims 1 to 10 is implemented.

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