A method and system for robust optimization configuration of integrated energy system planning

Abstract

The present application belongs to the field of comprehensive energy planning, and provides a kind of comprehensive energy system planning robust optimization configuration method and system.The method comprises, obtaining the resource condition and load demand parameter of comprehensive energy system, establish the IES optimization configuration model with the maximum internal rate of return as optimization target;Establish the uncertain set model of wind and light output and cold and heat load demand parameter, based on IGDT combined with IES optimization configuration model, construct IES robust optimization model facing investment income;Simplify IES robust optimization model, solve the maximum uncertainty fluctuation radius and the uncertain scene parameter of the corresponding worst scenario;Under the condition that the uncertain scene parameter is determined, the optimization configuration scheme of maximizing investment income is solved;According to different expected income deviation calculation forms IES optimization configuration scheme set, adopt fuzzy decision, select optimal scheme.Maximize the investment rate of return as optimization configuration target, and based on information gap decision, under the premise of reaching expected income, solve configuration scheme.

Description

Technical Field

[0001] This invention belongs to the field of integrated energy planning, and in particular relates to a robust optimization configuration method and system for integrated energy system planning. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] With the continuous advancement of the energy revolution and low-carbon transformation, clean and efficient energy supply and utilization have become a research hotspot in academia. Integrated Energy Systems (IES), as an energy supply and utilization model that deeply couples, plans, and complementarily dispatches multiple energy sources such as electricity, heat, cooling, and gas, can significantly improve the overall energy utilization efficiency, promote the local consumption of renewable energy, and reduce system carbon emissions. It is an important direction for the future low-carbon energy transformation.

[0004] Reasonable configuration planning for integrated energy systems is a crucial prerequisite for fully leveraging their advantages and improving investment returns. While there is some research on integrated energy system planning and optimization technologies both domestically and internationally, most studies focus on modeling and planning energy systems with specific scenarios and combinations. The uncertainty of renewable energy output (such as wind and solar) and diverse energy load demands within integrated energy systems often makes it difficult for planning configuration schemes under certain scenarios to achieve the expected optimization results. Research on integrated energy system planning under uncertain conditions is relatively limited; stochastic optimization and robust optimization are currently commonly used methods. Stochastic programming methods generally require accurate probability density distributions of uncertain variables, and the planning results depend on the probability density model of the uncertain quantities. However, accurate probability models are often difficult to obtain, and the computation time for stochastic optimization increases rapidly with the number of scenarios. Robust optimization, on the other hand, does not require knowledge of the probability distributions of uncertain variables, only the range of the uncertainty set. The solution can adapt to the worst-case scenario, but this often leads to conservative planning results.

[0005] Summarizing the current research and application status of robust optimization planning technology for integrated energy systems, existing methods have the following problems: (1) Existing technologies often only focus on the impact of a single uncertain factor on integrated energy system planning, and there are few studies that take into account multiple uncertainties on both the source and load sides; (2) Under the condition of a given set of uncertainties, optimization decisions are made for the worst scenario, which often leads to the setting of robust parameters being too subjective or the planning results being too conservative, and cannot accurately reflect the investment expectations of decision-makers; (3) Existing research on economic indicators basically takes the minimization of the annualized total cost of the planning system as the decision objective, ignoring the impact of uncertain factors on system revenue. In particular, the uncertainty fluctuations on the load side often directly cause changes in revenue. However, from the perspective of actual engineering investment decision-making process, compared with the annualized cost indicator, decision-makers pay more attention to the internal rate of return of system investment and regard it as the key indicator of the feasibility of the planning scheme. Summary of the Invention

[0006] To address the technical problems mentioned above, this invention provides a robust optimization configuration method and system for integrated energy system planning. This method aims to maximize the rate of return on investment and, based on information gaps, solves for robust optimization configuration schemes while ensuring the expected returns for decision-makers. This maximizes the mitigation of uncertainties and provides a valid basis for integrated energy system investment and construction.

[0007] To achieve the above objectives, the present invention adopts the following technical solution:

[0008] The first aspect of this invention provides a robust optimization configuration method for integrated energy system planning.

[0009] A robust optimization allocation method for integrated energy system planning includes:

[0010] Obtain the resource conditions and load demand parameters of the integrated energy system, and establish an IES optimization allocation model with the goal of maximizing the internal rate of return of the project.

[0011] An uncertain set model of wind and solar power output and cooling, heating and power load demand parameters is established. Based on IGDT combined with the IES optimization configuration model, an IES robust optimization model oriented towards investment returns is constructed.

[0012] A simplified IES robust optimization model is used to solve for the maximum uncertainty fluctuation radius and the uncertainty parameters corresponding to the worst-case scenario.

[0013] Given a scenario with fixed parameters but uncertain conditions, find the optimal allocation scheme that maximizes investment returns.

[0014] An IES optimal configuration scheme set is formed based on different expected return deviations, and the optimal scheme is selected by fuzzy decision-making.

[0015] A second aspect of the present invention provides a robust optimization configuration system for integrated energy system planning.

[0016] A robust optimization configuration system for integrated energy system planning includes:

[0017] The model building module is configured to: acquire the resource conditions and load demand parameters of the integrated energy system, and establish an IES optimization configuration model with the goal of maximizing the project's internal rate of return;

[0018] The model optimization module is configured to: establish an uncertain set model of wind and solar power output and cooling, heating and power load demand parameters; and construct an IES robust optimization model oriented towards investment returns based on IGDT combined with the IES optimization configuration model.

[0019] The model simplification module is configured to: simplify the IES robust optimization model and solve for the maximum uncertainty fluctuation radius and the uncertainty parameters corresponding to the worst scenario;

[0020] The solution module is configured to: solve for the optimal configuration scheme that maximizes investment returns under the condition that the parameters of an uncertain scenario are determined;

[0021] The output module is configured to: calculate an IES optimized configuration scheme set based on different expected return deviations, and select the optimal scheme using fuzzy decision-making.

[0022] A third aspect of the present invention provides a computer-readable storage medium.

[0023] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in the robust optimization configuration method for integrated energy system planning as described in the first aspect above.

[0024] A fourth aspect of the present invention provides a computer device.

[0025] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the program, implements the steps in the robust optimization configuration method for integrated energy system planning as described in the first aspect above.

[0026] Compared with the prior art, the beneficial effects of the present invention are:

[0027] Compared with traditional IES planning and optimization methods, the IES robust optimization configuration method mentioned in this invention takes a practical engineering investment decision-making perspective, comprehensively considering the multiple impacts of uncertainties on investment costs and revenues on both the source and load sides. Based on IGDT, it solves for the maximum robustness level and the corresponding optimal configuration scheme under the premise of meeting the expected rate of return on investment. On the one hand, it avoids the one-sidedness of previous system planning that only considers cost targets or only considers the impact of uncertainties on investment costs, and overcomes the subjectivity and conservatism of traditional robust optimization methods. On the other hand, through fuzzy decision-making, it can effectively balance the economy and robustness of the optimization scheme, and can be directly used in IES planning and engineering project investment decisions. It has higher practical application value than traditional robust optimization configuration methods and can provide an effective basis for IES investment and construction.

[0028] Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0029] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0030] Figure 1 This is a flowchart illustrating the robust optimization configuration method for integrated energy system planning in Embodiment 1 of the present invention. Detailed Implementation

[0031] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0032] It should be noted that the following detailed description is illustrative and intended to provide further explanation of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0033] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0034] It should be noted that the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of methods and systems according to various embodiments of this disclosure. It should be noted that each block in a flowchart or block diagram may represent a module, segment, or portion of code, which may include one or more executable instructions for implementing the logical functions specified in the various embodiments. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than that shown in the drawings. For example, two consecutively represented blocks may actually be executed substantially in parallel, or they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the flowcharts and / or block diagrams, and combinations of blocks in the flowcharts and / or block diagrams, may be implemented using a dedicated hardware-based system that performs the specified functions or operations, or using a combination of dedicated hardware and computer instructions.

[0035] Example 1

[0036] This embodiment provides a robust optimization configuration method for integrated energy system planning.

[0037] like Figure 1 As shown, a robust optimization allocation method for integrated energy system planning includes:

[0038] Obtain the resource conditions and load demand parameters of the integrated energy system, and establish an IES optimization allocation model with the goal of maximizing the internal rate of return of the project.

[0039] An uncertain set model of wind and solar power output and cooling, heating and power load demand parameters is established. Based on IGDT combined with the IES optimization configuration model, an IES robust optimization model oriented towards investment returns is constructed.

[0040] A simplified IES robust optimization model is used to solve for the maximum uncertainty fluctuation radius and the uncertainty parameters corresponding to the worst-case scenario.

[0041] Given a scenario with fixed parameters but uncertain conditions, find the optimal allocation scheme that maximizes investment returns.

[0042] An IES optimal configuration scheme set is formed based on different expected return deviations, and the optimal scheme is selected by fuzzy decision-making.

[0043] Under diversified power generation and load conditions, a typical Integrated System of Resources (IES) generally includes distributed power sources (photovoltaics, wind power, etc.), combined cooling, heating, and power (CCHP) units (composed of gas turbines, waste heat boilers, absorption chillers, etc.), gas boilers, heat pumps, electric chillers, energy storage devices (batteries, dual cooling and heating tanks, etc.), diversified CHP loads, and transmission and distribution networks. For investment decision-makers, the planning and configuration of IES is based on the project's load requirements and resource endowment, maximizing investment returns through the optimal configuration of equipment types and capacities.

[0044] The technical solution involved in this embodiment includes the following steps:

[0045] 1. Based on the resource conditions and load demand parameters of the integrated energy system to be built, establish an IES optimization allocation model with the goal of maximizing the project's internal rate of return;

[0046] The objective function of the model is expressed as:

[0047]

[0048] In the formula, N represents the lifespan of the IES, including the construction and operation periods; CI n and CO n Let $\mathbf$ be the project's cash inflow and cash outflow in year n, respectively, and their calculation formulas are as follows:

[0049]

[0050]

[0051] In the formula, N c The construction period for the IES is in years; and These represent the revenue from electricity, heat, and cooling sales in year n, respectively; D is the set of typical planning days; N d Let d be the number of days corresponding to a typical day in year n. and P t grid,s These represent the unit price and the amount of electricity sold to the grid during time period t, respectively. and P represents the unit price for selling electricity, heat, and cooling to users during time period t; t el P t hl and P t cl These represent the load demand for electricity, heat, and cooling on the energy consumption side during time period t.

[0052]

[0053]

[0054] In the formula, C inv Investment during the IES construction period; and These represent the operating costs, management and maintenance costs, and taxes in year n of the IES's operating period; p i and X i These represent the unit installed capacity cost and the construction capacity of equipment i, respectively; and P t grid ,b These represent the unit price and quantity of electricity purchased from the grid during time period t, respectively. and V t in These represent the gas purchase price and gas volume for time period t, respectively. The annual management and maintenance fee rate for year n; and These are the value-added tax, surtax, and income tax for year n, respectively.

[0055]

[0056] In the formula, τ1, τ2, τ3 and τ4 are the value-added tax rate, the input tax deduction rate, the value-added tax surcharge rate and the income tax rate, respectively.

[0057] The constraints of IES optimization configuration planning include: equipment investment capacity constraints, equipment performance constraints, energy balance constraints, and internal and external network interaction power constraints.

[0058] (1) Equipment investment capacity constraints

[0059]

[0060] In the formula, denoted as the maximum allowable investment capacity of device i; {GT,WB,AC,EC,GB,PV,WT,HP,ES,WS} represents the set of candidate devices, with the device order corresponding to gas turbine, waste heat boiler, absorption chiller, electric chiller, gas boiler, photovoltaic, wind power, heat pump, electric energy storage, and dual hot and cold water storage tank.

[0061] (2) Equipment performance constraints

[0062] For energy-coupled devices in IES, the constraints include energy coupling conversion relationship constraints, device operating power constraints, and device ramp-up power constraints, as follows:

[0063]

[0064]

[0065]

[0066] in, and η represents the input power and output power of device i during time period t, respectively. i Δ represents the energy conversion efficiency of device i. P i out and These correspond to the lower and upper limits of the device's ramp power, respectively. For multiple-input multiple-output devices, all the above symbols are vectors.

[0067] For energy storage devices in IES, their charging and discharging power and stored energy must meet the energy storage state transition constraints, periodic utilization constraints, and upper and lower limit constraints on stored energy, stored energy power, and released energy power:

[0068]

[0069] S i,0 =S i,T (12)

[0070]

[0071]

[0072]

[0073] In the formula, S i,t δ represents the energy stored in energy storage device i at the end of time period t; i , and Let i be the energy loss coefficient, energy storage efficiency, and energy release efficiency of energy storage device i. and These represent the energy storage power and energy release power of energy storage device i during time period t, respectively. and These are the charge / discharge limit rates for energy storage device i.

[0074] For devices that operate in multiple modes, such as heat pumps that can both cool and heat, and dual-storage devices that can store cold and heat, only one mode can be operated at any given time, and the operating constraints are as follows:

[0075] Heat pump:

[0076] Dual-energy cold and hot energy storage device:

[0077] In the formula, and These are 0-1 variables representing the operating modes of multi-mode device i; and The output power of device i is respectively in heating and cooling modes; M i It is a very large constant, which can generally be set as the maximum output value corresponding to the maximum investment capacity limit of equipment i.

[0078] (3) Energy balance constraint

[0079]

[0080]

[0081]

[0082]

[0083] In the formula, μ i,t The power output of distributed power generation (photovoltaic, wind power) equipment i with unit capacity in time period t; and These represent the heat storage power, heat release power, cold storage power, and cold release power of the dual-storage cold and heat storage device during time period t.

[0084] (4) Internal and external network connection constraints

[0085] This includes constraints on the internal process system network of the IES, the capacity constraints of the interaction between the IES and the external power grid, and the upper limit constraints on the power interaction between the IES and the external power grid.

[0086]

[0087]

[0088]

[0089] 2. To address the uncertainties on both the power source and load sides, an uncertainty set model for wind and solar power output and cooling, heating and power load demand parameters is established, and an IES robust optimization model oriented towards investment returns is constructed based on IGDT.

[0090] According to IGDT theory, the robust optimization allocation of IES for investment returns aims to maximize the range of uncertainty fluctuations in wind and solar power output and cooling, heating, and power (CHP) loads while ensuring that the rate of return on investment is not lower than the expected value. First, an uncertainty set model of the wind and solar power output and CHP load demand parameters is established, expressed as:

[0091]

[0092] In the formula, The predicted output value of distributed power source i with unit capacity in time period t; α represents the demand forecast for load type j during time period t; i and βj These represent the uncertainty fluctuation radius of power output i and load type j, respectively. The larger this value, the stronger the robustness of the corresponding planning scheme.

[0093] Let the above uncertain set be denoted as U(α). PV ,α WT ,β el ,β hl ,β cl Let μ PV μ WT P el P hl and P cl These represent the sets of uncertain parameters for photovoltaic power output, wind power output, electrical load, heat load, and cooling load demand at different times, where μ = [μ PV ,μ WT ,P el ,P hl ,P cl ] T Let represent a column vector formed by a set of uncertain parameters. Then, the robust optimal placement model based on IGDT can be represented as:

[0094]

[0095] In the formula, x is the decision variable vector, corresponding to the configuration planning scheme and operation scheme of IES; F(μ) is the feasible solution space based on constraints (7)-(24); IRR exp Let IRR be the expected rate of return on investment for policymakers. cer This represents the maximum rate of return on investment for the system configuration without considering uncertainties. It's easy to see that a necessary condition for the above model to have a solution is:

[0096] IRR exp = (1-σ)·IRR cer ,σ≥0 (27)

[0097] In the formula, σ represents the expected return deviation coefficient.

[0098] The five optimization objectives of the above model correspond to the prediction deviations of wind power and photovoltaic output and cooling, heating and electricity load demands, respectively. These objectives are contradictory in the multi-objective optimization process, meaning that maximizing one objective will lead to a decrease in the others. On the other hand, the five deviations are all determined based on current prediction technology and available project information. When planning system configuration, the proportional relationship between the deviations is basically determined. Therefore, by assigning different proportional weights to each deviation, they can be unified to a benchmark prediction deviation, thereby transforming the multi-objective optimization problem into a single-objective optimization problem.

[0099]

[0100] In the formula, ξ is the radius of fluctuation of the reference deviation; ρ i These are the weighting coefficients for each prediction deviation. In practice, one deviation can be selected as the benchmark, and other deviations are assigned weights based on proportional relationships.

[0101] 3. Based on the definition of return on investment, the IES robust optimization model is simplified into a two-level optimization problem by equivalent transformation and dual transformation. A step-by-step solution strategy is adopted. First, under the constraints of the model, the maximum uncertainty fluctuation radius and the uncertainty parameters corresponding to the worst scenario are solved. Then, under the condition that the parameters of the uncertain scenario are determined, the optimal allocation scheme that maximizes the return on investment is solved.

[0102] According to the definition of return on investment, for any x, if its corresponding return on investment IRR ≥ IRR exp This is equivalent to taking the IRR at the benchmark return. exp When the net present value (NPV) is ≥ 0, there exists an x ​​such that IRR ≥ IRR in a given scenario with any given μ. exp This is equivalent to taking the IRR at the benchmark return. exp At that time, there exists an x ​​such that the net present value (NPV) ≥ 0. Based on this, the robust optimal allocation model in step 2 is transformed into:

[0103]

[0104] For the above multi-layered optimization problem, the objective function and constraint conditions of its inner-layer optimization problem are matrixed:

[0105]

[0106] In the formula, b T Let g and g be the row vector of coefficients and the constant term of x after matrix transformation of the objective function; A and c are the column vectors composed of the variable coefficient matrix and the right-hand side terms after matrix transformation of the inequality constraints in step 1; B and d are the column vectors composed of the variable coefficient matrix and the right-hand side terms after matrix transformation of the inequality constraints in step 1. Based on duality theory, a dual transformation is performed on the inner-layer solution problem, transforming the inner-layer optimization problem into a minimum value solution problem:

[0107]

[0108] In the formula, χ and γ are the dual variables of x. The inner problem is denoted as SP1:

[0109]

[0110] Since B, d, and g are all related to μ, the inner optimization problem SP1 is a typical quadratic programming problem, which can be solved using the GUROBI solver. The outer optimization problem can be solved using a bisection search algorithm, with the specific steps as follows:

[0111] Step (1) Initialize the lower bound L of the outer problem decision variable ξ. B =0 and upper bound U B Generally speaking, 0 B <1.

[0112] Step (2) Verify the upper bound U B Validity. Let ξ = U B Substituting into the inner layer problem SP1, the optimal objective value O is obtained. * If O * ≥0, then L B =U B U B =2×U B Repeat the verification; otherwise, proceed to step (3).

[0113] Step (3) Let ξ = (U B +L B Substituting 2 into the inner problem SP1, we obtain the optimal objective value O. * and the corresponding optimal solution μ * If O * ≥0, then L B =ξ, update record μ * Otherwise, U B =ξ.

[0114] Step (4) If U B -L B If it is less than the verification error ε, then L B If the solution is the optimal solution to the outer problem, proceed to step (5); otherwise, proceed to step (3).

[0115] Step (5) will μ * Substitute the following inner-layer original maximization problem SP2, and use the algorithm for solving the optimal IRR configuration scheme under given uncertain parameters to solve the optimal configuration scheme x under the corresponding uncertain fluctuation scenario.

[0116]

[0117] The algorithm for finding the optimal IRR configuration under given uncertain parameters is as follows:

[0118] Step (5-1) Initialize the upper and lower bounds of the IRR. and

[0119] ​Step (5-2) Substitute the solution into problem SP2 and call the solver to obtain the optimal objective value ν;

[0120] Step (5-3): If ν < 0, then update. Otherwise, update

[0121] Step (5-4) if If the value is less than the stopping gap threshold, then output the optimization result. And the corresponding planning scheme x; otherwise, go to step (5-2).

[0122] It should be noted that, The optimal IRR obtained using the above algorithm is the maximum return on investment (IRR) of IES without considering uncertainties. cer .

[0123] 4. Based on different expected return deviations, an IES optimal allocation scheme set is formed. Using fuzzy decision-making method, the most satisfactory compromise between economy and robustness is selected from the optimal allocation scheme set.

[0124] Fuzzy decision-making uses piecewise linear membership functions to represent the satisfaction level of each attribute of the solution, and applies the weighted sum of the satisfaction levels of each attribute as the overall satisfaction level of the solution:

[0125]

[0126]

[0127]

[0128] In the formula, and ms k σ represents the satisfaction levels for economic efficiency, robustness, and overall satisfaction for scheme k, respectively; max σ min ξ represents the upper and lower limits of σ in the set of optimized allocation schemes, corresponding to the maximum and minimum values ​​of the expected return deviation coefficient acceptable to the decision-maker; max ξ min ξ represents the upper and lower limits of the optimized configuration scheme set, respectively; σ represents the lower and upper limits of the optimized configuration scheme set, respectively. k ξ k σ and ξ represent the expected return deviation coefficient and robustness level corresponding to scheme k, respectively; w and 1-w are the weight coefficients of economy and robustness in decision evaluation, respectively, reflecting the decision-maker's preference and pursuit of different evaluation indicators.

[0129] Example 2

[0130] This embodiment provides a robust optimization configuration system for integrated energy system planning.

[0131] A robust optimization configuration system for integrated energy system planning includes:

[0132] The model building module is configured to: acquire the resource conditions and load demand parameters of the integrated energy system, and establish an IES optimization configuration model with the goal of maximizing the project's internal rate of return;

[0133] The model optimization module is configured to: establish an uncertain set model of wind and solar power output and cooling, heating and power load demand parameters; and construct an IES robust optimization model oriented towards investment returns based on IGDT combined with the IES optimization configuration model.

[0134] The model simplification module is configured to: simplify the IES robust optimization model and solve for the maximum uncertainty fluctuation radius and the uncertainty parameters corresponding to the worst scenario;

[0135] The solution module is configured to: solve for the optimal configuration scheme that maximizes investment returns under the condition that the parameters of an uncertain scenario are determined;

[0136] The output module is configured to: calculate an IES optimized configuration scheme set based on different expected return deviations, and select the optimal scheme using fuzzy decision-making.

[0137] It should be noted that the model building module, model optimization module, model simplification module, solution module, and output module described above are the same examples and application scenarios implemented in Embodiment 1, but are not limited to the content disclosed in Embodiment 1. It should also be noted that these modules, as part of the system, can be executed in a computer system such as a set of computer-executable instructions.

[0138] Example 3

[0139] This embodiment provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps in the robust optimization configuration method for integrated energy system planning as described in Embodiment 1 above.

[0140] Example 4

[0141] This embodiment provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the steps in the robust optimization configuration method for integrated energy system planning as described in Embodiment 1 above.

[0142] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of hardware embodiments, software embodiments, or embodiments combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage and optical storage) containing computer-usable program code.

[0143] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0144] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0145] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0146] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0147] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A robust optimization allocation method for integrated energy system planning, characterized in that, include: Obtain the resource conditions and load demand parameters of the integrated energy system, and establish an IES optimization allocation model with the goal of maximizing the internal rate of return of the project. The constraints of IES optimization configuration planning include: equipment investment capacity constraints, equipment performance constraints, energy balance constraints, and internal and external network interaction power constraints. Equipment investment capacity constraints In the formula, For equipment The maximum allowable investment capacity; The set of candidate equipment corresponds to the following equipment in sequence: gas turbine, waste heat boiler, absorption chiller, electric chiller, gas boiler, photovoltaic, wind power, heat pump, electric energy storage, and hot and cold water storage tank. Equipment performance constraints, for energy-coupled equipment in IES, include constraints on energy coupling conversion relationships, equipment operating power constraints, and equipment ramp-up power constraints, as follows: ; ; ;in, and respectively equipment exist Input power and output power during the time period For equipment Energy conversion efficiency; and These correspond to the lower and upper limits of the equipment's ramp power, respectively; For energy storage devices in IES, their charging and discharging power and stored energy must meet the energy storage state transition constraints, periodic utilization constraints, and upper and lower limit constraints on stored energy, stored energy power, and released energy power: ; ; ; ; In the formula, For energy storage devices exist Energy storage at the end of the time period; , and For energy storage devices The energy loss coefficient, energy storage efficiency, and energy release efficiency; and Energy storage devices exist Energy storage capacity and energy release capacity during a given time period; and Energy storage devices Charge / discharge rate limitation; For equipment operating in multiple modes, such as heat pumps capable of both cooling and heating, and dual-mode cold and heat storage devices capable of storing cold and heat, only one mode can be operated at any given time. The operational constraints are as follows: Heat pump: Dual-energy cold and hot energy storage device: In the formula, and These are characterization devices for multiple modes. 0-1 variables representing the operating mode; and Equipment for heating and cooling modes respectively ; output power; It is a very large constant, and can generally be set as a device. The maximum output value corresponding to the maximum investment capacity limit; Energy balance constraints ； ； ; In the formula, Distributed power generation photovoltaic and wind power equipment with unit capacity exist Power generation output during a given time period; , , and The cold and hot storage devices are respectively located in The heat storage power, heat release power, cold storage power, and cold release power during each time period; Internal and external network connectivity constraints include constraints on the process network of the IES internal process system, constraints on the interaction capacity between the IES and the external power grid, and constraints on the upper limit of the interaction power between the IES and the external power grid: ; ; ; An uncertain set model of wind and solar power output and cooling, heating and power load demand parameters is established. Based on IGDT combined with the IES optimization configuration model, an IES robust optimization model oriented towards investment returns is constructed. The uncertain set model is as follows: In the formula, Distributed power supply with unit capacity exist Power output forecast for the time period; For load type exist Demand forecasts for the time period; and Power supply Output and load type The radius of fluctuation in demand uncertainty; The robust optimization model for IES is as follows: In the formula, This is a vector of decision variables, corresponding to the configuration planning scheme and operation scheme of IES; This represents the feasible solution space. The expected rate of return on investment by policymakers; This refers to the lifespan of the IES, including the construction and operation periods; and The first Annual project cash inflows and outflows; It is an uncertain set; A simplified IES robust optimization model is used to solve for the maximum uncertainty fluctuation radius and the uncertainty parameters corresponding to the worst-case scenario. Under the condition that the parameters of an uncertain scenario are fixed, the optimal allocation scheme that maximizes investment returns is solved. The specific process includes: Step (1) Initialize the outer problem decision variables lower bound and the Upper Realm ; Step (2) Verify the upper bound Validity; let Substituting the values ​​into the inner layer problem SP1, we obtain the optimal objective value. ,like ,but , If the verification is repeated, proceed to step (3); otherwise, proceed to step (3). Step (3) Let Substituting the values ​​into the inner layer problem SP1, we obtain the optimal objective value. and the corresponding optimal solution ,like ,but Update history ;otherwise, ; Step (4) if Less than the verification error Then If the solution is the optimal solution to the outer problem, proceed to step (5); otherwise, proceed to step (3). Step (5) will Substituting into the inner-layer original maximization problem SP2, and applying the optimal solution under given uncertain parameters... The algorithm for solving the configuration scheme finds the optimal configuration scheme under the corresponding uncertain fluctuation scenario. ; Among them, the inner problem SP1 is: The inner-layer original maximization problem SP2 is: In the formula, , for dual variables, This refers to the lifespan of the IES, including the construction and operation periods; and The first Annual project cash inflows and outflows; An IES optimal configuration scheme set is formed based on different expected return deviations, and the optimal scheme is selected by fuzzy decision-making.

2. The robust optimization configuration method for integrated energy system planning according to claim 1, characterized in that, The objective function of the IES optimization configuration model is: In the formula, In the formula, The construction period for the IES is in years; , and The first Annual revenue from electricity, heat, and cooling sales; To plan typical day sets; For the first Mid-year planning typical day The corresponding number of days; and They are respectively The unit price and quantity of electricity sold to the grid during the specified time period; , and They are respectively The unit price for selling electricity, heat, and cooling to users during specific time periods; , and They are respectively The load demand for electricity, heat, and cooling during different time periods; In the formula, Investment during the IES construction period; , and These are the first [number] ... Annual operating costs, management and maintenance costs, and taxes; and respectively equipment Unit installed capacity cost and construction capacity; and They are respectively The unit price and quantity of electricity purchased from the power grid during the specified time period; and They are respectively Gas purchase price and volume for different time periods; For the first Annual management and maintenance fee rate; , and The first Value-added tax, surtaxes and income tax for the year; In the formula, , , and These are the VAT rate, input tax deduction rate, VAT surcharge rate, and income tax rate.

3. The robust optimization configuration method for integrated energy system planning according to claim 1, characterized in that, The optimal condition under given uncertain parameters The algorithm for solving the configuration scheme is as follows: Step (5-1) Initialization upper and lower boundaries and ; Step (5-2) Substitute the original inner-layer maximization problem SP2, and call the solver to obtain the optimal objective value. ; Step (5-3) if Then update Otherwise, update. ; Step (5-4) if If the value is less than the stopping gap threshold, then output the optimization result. and corresponding planning schemes Otherwise, proceed to step (5-2).

4. The robust optimization configuration method for integrated energy system planning according to claim 1, characterized in that, The specific schemes for calculating the IES optimized configuration scheme set based on different expected return deviations and selecting the optimal scheme using fuzzy decision-making include: Fuzzy decision-making uses piecewise linear membership functions to represent the satisfaction level of each attribute of the solution, and applies the weighted sum of the satisfaction levels of each attribute as the overall satisfaction level of the solution: In the formula, , and Each represents a scheme Corresponding economic efficiency, robustness satisfaction, and overall satisfaction; , These represent the optimized configuration schemes. The upper and lower limits correspond to the maximum and minimum values ​​of the expected return deviation coefficient that the decision-maker can accept. , These represent the optimized configuration schemes. The upper and lower limits; , Each represents a scheme Corresponding expected return deviation coefficient and robustness level ; and These are the weighting coefficients for economy and robustness in decision evaluation, respectively.

5. A robust optimization configuration system for integrated energy system planning, characterized in that, include: The model building module is configured to: acquire the resource conditions and load demand parameters of the integrated energy system, and establish an IES optimization configuration model with the goal of maximizing the project's internal rate of return; The constraints of IES optimization configuration planning include: equipment investment capacity constraints, equipment performance constraints, energy balance constraints, and internal and external network interaction power constraints. Equipment investment capacity constraints In the formula, For equipment The maximum allowable investment capacity; The set of candidate equipment corresponds to the following equipment in sequence: gas turbine, waste heat boiler, absorption chiller, electric chiller, gas boiler, photovoltaic, wind power, heat pump, electric energy storage, and hot and cold water storage tank. Equipment performance constraints, for energy-coupled equipment in IES, include constraints on energy coupling conversion relationships, equipment operating power constraints, and equipment ramp-up power constraints, as follows: ; ; ;in, and respectively equipment exist Input power and output power during the time period For equipment Energy conversion efficiency; and These correspond to the lower and upper limits of the equipment's ramp power, respectively; For energy storage devices in IES, their charging and discharging power and stored energy must meet the energy storage state transition constraints, periodic utilization constraints, and upper and lower limit constraints on stored energy, stored energy power, and released energy power: ; ; ; ; In the formula, For energy storage devices exist Energy storage at the end of the time period; , and For energy storage devices The energy loss coefficient, energy storage efficiency, and energy release efficiency; and Energy storage devices exist Energy storage capacity and energy release capacity during a given time period; and Energy storage devices Charge / discharge rate limitation; For equipment operating in multiple modes, such as heat pumps capable of both cooling and heating, and dual-mode cold and heat storage devices capable of storing cold and heat, only one mode can be operated at any given time. The operational constraints are as follows: Heat pump: Dual-energy cold and hot energy storage device: In the formula, and These are characterization devices for multiple modes. 0-1 variables representing the operating mode; and Equipment for heating and cooling modes respectively ; output power; It is a very large constant, and can generally be set as a device. The maximum output value corresponding to the maximum investment capacity limit; Energy balance constraints ； ； ; In the formula, Distributed power generation photovoltaic and wind power equipment with unit capacity exist Power generation output during a given time period; , , and The cold and hot storage devices are respectively located in The heat storage power, heat release power, cold storage power, and cold release power during each time period; Internal and external network connectivity constraints include constraints on the process network of the IES internal process system, constraints on the interaction capacity between the IES and the external power grid, and constraints on the upper limit of the interaction power between the IES and the external power grid: ; ; ; The model optimization module is configured to: establish an uncertain set model of wind and solar power output and cooling, heating, and power load demand parameters; and construct an IES robust optimization model oriented towards investment returns based on IGDT combined with the IES optimization configuration model; the uncertain set model is as follows: In the formula, Distributed power supply with unit capacity exist Power output forecast for the time period; For load type exist Demand forecasts for the time period; and Power supply Output and load type The radius of fluctuation in demand uncertainty; The robust optimization model for IES is as follows: In the formula, This is a vector of decision variables, corresponding to the configuration planning scheme and operation scheme of IES; This represents the feasible solution space. The expected rate of return on investment by policymakers; This refers to the lifespan of the IES, including the construction and operation periods; and The first Annual project cash inflows and outflows; It is an uncertain set; The model simplification module is configured to: simplify the IES robust optimization model and solve for the maximum uncertainty fluctuation radius and the uncertainty parameters corresponding to the worst scenario; The solution module is configured to: find the optimal allocation scheme that maximizes investment returns under the condition that the parameters of an uncertain scenario are determined. The specific process includes: Step (1) Initialize the outer problem decision variables lower bound and the Upper Realm ; Step (2) Verify the upper bound Validity; let Substituting the values ​​into the inner layer problem SP1, we obtain the optimal objective value. ,like ,but , If the verification is repeated, proceed to step (3); otherwise, proceed to step (3). Step (3) Let Substituting the values ​​into the inner layer problem SP1, we obtain the optimal objective value. and the corresponding optimal solution ,like ,but Update history ;otherwise, ; Step (4) if Less than the verification error Then If the solution is the optimal solution to the outer problem, proceed to step (5); otherwise, proceed to step (3). Step (5) will Substituting into the inner-layer original maximization problem SP2, and applying the optimal solution under given uncertain parameters... The algorithm for solving the configuration scheme finds the optimal configuration scheme under the corresponding uncertain fluctuation scenario. ; Among them, the inner problem SP1 is: The inner-layer original maximization problem SP2 is: In the formula, , for dual variables, This refers to the lifespan of the IES, including the construction and operation periods; and The first Annual project cash inflows and outflows; The output module is configured to: calculate an IES optimized configuration scheme set based on different expected return deviations, and select the optimal scheme using fuzzy decision-making.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps in the robust optimization configuration method for integrated energy system planning as described in any one of claims 1-4.

7. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps in the robust optimization configuration method for integrated energy system planning as described in any one of claims 1-4.

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