A method and system for optimal configuration of a multi-energy flow system considering source-load uncertainty

Abstract

The application belongs to the technical field of multi-energy flow systems, and provides a multi-energy flow system optimization configuration method and system considering source and load uncertainty. The method comprises the following steps: determining an equivalent uncertainty fluctuation range; based on information gap decision and conditional risk value theory, combining a deterministic optimization configuration model, a multi-energy flow system optimization configuration model is constructed; taking the maximum loss that a decision maker can bear as a limiting condition, according to the set risk coefficient and avoidance coefficient, a target function of total planning cost is constructed; based on the electric heat cold gas load data, the wind power photovoltaic output data and the equipment parameter data, under the condition that the equivalent uncertainty fluctuation range is the largest, according to the target function of total planning cost and the uncertain variable model, combining the constraint condition and the multi-energy flow system optimization configuration model, a multi-energy flow system optimization configuration model considering source and load uncertainty is constructed; the multi-energy flow system optimization configuration model considering source and load uncertainty is solved, and a multi-energy flow system optimization configuration scheme is obtained.

Description

Technical Field

[0001] This invention belongs to the technical field of multi-energy flow systems, and particularly relates to a method and system for optimizing the configuration of multi-energy flow systems that takes into account source load uncertainty. 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] The intertwining of energy security, environmental pollution, and climate change has led to significant changes in people's energy consumption patterns and utilization methods. Integrated Energy Systems (IES) are based on the power grid, coupled with other energy structures such as natural gas networks, cooling / heating networks, and distributed energy networks. At the same time, they rely on Internet technology to coordinate, efficiently manage, and flexibly interact with multi-energy flow systems.

[0004] However, the current optimized configuration of integrated energy systems faces the following problems:

[0005] (1) The impact of short-term uncertainty of renewable energy output or long-term uncertainty of load on planning costs was only considered unilaterally, resulting in the cost of load growth exceeding the maximum loss that investors can bear.

[0006] (2) Multi-energy flow coupling is an important feature of IES, and existing source-load uncertainty analysis has not been combined with multi-energy flow systems. Summary of the Invention

[0007] This invention addresses the source load uncertainty problem caused by wind and solar power output prediction deviations and load growth prediction deviations in the optimal configuration of multi-energy flow systems. It provides a method and system for optimizing the configuration of multi-energy flow systems that takes into account source load uncertainty. This invention constructs a model using avoidance coefficients, risk coefficients, and the fluctuation range of uncertainties. It only requires the investor's ability to accept or averse to risk and the fluctuation range of uncertainties to ensure that the cost generated by load growth does not exceed the maximum loss that the investor can bear. This results in lower planning costs and a more reasonable configuration scheme.

[0008] To achieve the above objectives, the present invention adopts the following technical solution:

[0009] The first aspect of the present invention provides a method for optimizing the configuration of a multi-energy flow system that takes into account source load uncertainty.

[0010] A method for optimizing the configuration of a multi-energy flow system considering source-load uncertainties includes:

[0011] Acquire data on electricity, heat, cooling, and gas loads, wind and solar power output, and equipment parameters;

[0012] Calculate the equivalent uncertainty fluctuation range based on the actual fluctuation range of the electric heating and cooling load;

[0013] Based on information gap decision-making and conditional risk value theory, and combined with deterministic optimal allocation model, an optimal allocation model for multi-energy flow system is constructed.

[0014] Using the maximum loss that decision-makers can tolerate as a constraint, and based on the set risk coefficient and avoidance coefficient, construct an objective function for the total planning cost;

[0015] Based on electricity, heat, cooling, and gas load data, wind and solar power output data, and equipment parameter data, under the condition of the largest fluctuation range of equivalent uncertainty, according to the objective function of total planning cost and the uncertainty variable model, combined with constraints and multi-energy flow system optimization configuration model, a multi-energy flow system optimization configuration model considering source load uncertainty is constructed.

[0016] Solve the multi-energy flow system optimization configuration model that takes into account source load uncertainties to obtain the multi-energy flow system optimization configuration scheme.

[0017] Furthermore, the fluctuation range of the equivalent uncertainty is:

[0018]

[0019] And ξ1+ξ2+ξ3+ξ4=1

[0020] In the formula, For the equivalent uncertain variable fluctuation range, ξ1, ξ2, ξ3, and ξ4 represent the weighting coefficients for the fluctuation range of the electric heating and cooling loads, respectively, and α e α h α c α g These represent the actual fluctuation range of the electric heating and cooling loads, respectively.

[0021] Furthermore, the deterministic optimization configuration model includes:

[0022] Considering the overall economic efficiency of the planning scheme, the optimization objective is to minimize the total planning cost;

[0023]

[0024] In the formula, C represents the total planned cost within year T, C inv,t Let C be the total investment cost in year t. op,t τ represents the total operating cost in year t; t Let τ be the coefficient of the cash discount rate r in year t. t =(1+r) -t C inv,t The definition is as follows:

[0025]

[0026] In the formula, σ k P represents the investment cost per unit capacity of the power generation unit k. k For the installed capacity of power unit k, N k,t ρ represents the number of power supply units k that have been put into operation in year t; m E represents the investment cost per unit capacity of energy storage equipment. m For the energy storage device m's installed capacity, Y m,t C represents the number of energy storage devices (m) already in operation in year t; op,t The definition is as follows:

[0027]

[0028]

[0029]

[0030]

[0031]

[0032] In the formula, N d Let d be the number of days in a typical day of the year. There are a total of D typical days in a year. Let $t$ represent the operating cost of the power supply unit, the operating cost of the energy storage equipment, the cost of electricity purchased from the external power grid, and the cost of gas purchased from the external natural gas grid for the energy hub during the $h$ time period on the $d$-th typical day in year $t$. This represents the operating cost per unit power of the power supply unit k. This represents the total operating power of unit k during time period h; This represents the operating cost per unit power of the energy storage unit m. These represent the total charging power and discharging power of energy storage device m during time period h, respectively. This indicates the market electricity price for time period h. This indicates the amount of active power exchanged between the energy hub and the external power grid; This indicates the market price per unit of natural gas during time period h. This indicates the amount of natural gas that the energy hub purchases from external sources.

[0033] Furthermore, the multi-energy flow system optimization configuration model includes:

[0034]

[0035]

[0036]

[0037]

[0038] In the formula, To account for the operating costs in year t of the conditional risk assessment, The expected value of annual operating costs is given; a scene set of wind speed and light intensity on a typical day d in year t is established using Latin hypercube sampling; n w n s π(n) represents the number of corresponding scenes. w ), π(n s ) represents the probability of each scenario; For the nth time on a typical day d in year t,... w n s Operating costs of energy supply equipment, operating costs of energy storage equipment, electricity purchase costs, and gas purchase costs under different wind speed and light intensity scenarios.

[0039] Furthermore, the objective function for the total planning cost is:

[0040]

[0041] C′=C+μCVaR

[0042] In the formula, C′ represents the cost function taking into account CVaR; C(·) represents the investment planning cost function; σ represents the aversion coefficient, which represents the proportion of investment costs that investors can accept that are higher than the predicted value; μ represents the risk coefficient, which reflects the level of investors' aversion to risk; d represents the decision variable; and X represents the uncertain variable.

[0043] Furthermore, the uncertain variable model includes:

[0044]

[0045] In the formula, α represents the fluctuation range of the uncertain quantity; C(·) represents the investment planning cost function; σ represents the avoidance coefficient, which represents the proportion of the investment cost that investors can accept higher than the predicted value; d represents the decision variable; X represents the uncertain variable; h and g represent the equality constraint and inequality constraint, respectively; Γ represents the uncertainty variable X around its predicted value. The set of fluctuation ranges.

[0046] Furthermore, the constraints include:

[0047] 1) Energy hub constraints

[0048] The relationship between the upper and lower limits of the power output of the power supply unit is as follows:

[0049]

[0050]

[0051] In the formula, P k,min P is the lower limit of the active power output of power unit k. k,max This represents the upper limit of the active power output of the power supply unit k; This represents the maximum transmission power between the energy hub and the external power grid.

[0052] 2) CHP unit ramp-up power

[0053]

[0054] In the formula, For the electrical power of the combined heat and power unit, θ up θ down These represent the upward and downward ramp rates for the combined heat and power (CHP) unit, respectively.

[0055] 3) Constraints on the operation and charging / discharging of energy storage devices

[0056]

[0057]

[0058]

[0059]

[0060] Q t,d,0 =Q t,d,H

[0061] In the formula, Q t,d,h To store energy, The charging and discharging efficiency of energy storage devices;

[0062] 4) Supply and demand matching constraints

[0063] Supply and demand matching constraints ensure that the installed capacity of the electric heating and cooling units in year t is greater than or equal to the peak electric heating and cooling load.

[0064]

[0065]

[0066]

[0067]

[0068] In the formula, Let represent the peak values ​​of the electric heating and cooling loads in year t, respectively. These represent the capacity of the energy hub output to the load in year t.

[0069] 5) Other constraints

[0070] N k,0 ≤N k,t-1 ≤N k,t ≤N k,T

[0071] Y k,0 ≤Y k,t-1 ≤Y k,t ≤Y k,T

[0072] In the formula, N k,0 N k,T Y k,0 Y k,T These represent the initial and maximum investment amounts for energy supply and energy storage equipment, respectively.

[0073] A second aspect of the present invention provides an optimization configuration system for a multi-energy flow system that takes into account source load uncertainty.

[0074] A multi-energy flow system optimization configuration system considering source-load uncertainty includes:

[0075] The data acquisition module is configured to acquire electricity, heat, cooling, and gas load data, wind and solar power output data, and equipment parameter data.

[0076] The uncertainty fluctuation range determination module is configured to: calculate the equivalent uncertainty fluctuation range based on the actual fluctuation range of the electric heating and cooling load;

[0077] The module for constructing the optimal configuration model of a multi-energy flow system is configured to: construct an optimal configuration model of a multi-energy flow system based on information gap decision-making and conditional risk value theory, combined with a deterministic optimal configuration model;

[0078] The objective function determination module is configured to: construct an objective function for the total planning cost based on the set risk coefficient and avoidance coefficient, with the maximum loss that the decision-maker can bear as a constraint;

[0079] The module for constructing a multi-energy flow system optimization configuration model that takes into account source load uncertainty is configured as follows: based on electricity, heat, cooling, and gas load data, wind and solar power output data, and equipment parameter data, under the condition of the largest fluctuation range of equivalent uncertainty, according to the objective function of total planning cost and the uncertainty variable model, combined with constraints and the multi-energy flow system optimization configuration model, constructing a multi-energy flow system optimization configuration model that takes into account source load uncertainty.

[0080] The solution module is configured to solve the optimal configuration model of the multi-energy flow system taking into account the uncertainty of source load, and obtain the optimal configuration scheme of the multi-energy flow system.

[0081] A third aspect of the present invention provides a computer-readable storage medium.

[0082] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in the method for optimal configuration of a multi-energy flow system taking into account source-load uncertainty as described in the first aspect above.

[0083] A fourth aspect of the present invention provides a computer device.

[0084] 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 of the multi-energy flow system optimization configuration method considering source-load uncertainty as described in the first aspect above.

[0085] Compared with the prior art, the beneficial effects of the present invention are:

[0086] This invention takes into account the impact of uncertainties on both the source and load sides on planning costs and quantifies the losses due to uncertainty risks, thereby greatly reducing the additional loss compensation for investors due to load growth.

[0087] This invention constructs a model using aversion coefficients, risk coefficients, and the fluctuation range of uncertain quantities. It only requires the investor's ability to accept or averse to risk and the fluctuation range of uncertain quantities, without needing the boundaries of uncertain quantities. It requires less information about uncertain quantities and less computation, thus reducing the amount of computation while ensuring more accurate calculation results.

[0088] The optimized configuration scheme for the electric heating and cooling multi-energy flow system designed in this invention has lower planning costs, a more reasonable configuration scheme, and reduces the conservatism of the planning strategy.

[0089] 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

[0090] 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.

[0091] Figure 1 This is a typical energy hub framework diagram shown in this invention;

[0092] Figure 2 This is a flowchart illustrating the method for optimizing the configuration of a multi-energy flow system that takes into account source load uncertainty, as shown in this invention.

[0093] Figure 3 This is a diagram illustrating the effect of the avoidance coefficient setting analysis shown in this invention;

[0094] Figure 4 This is a diagram illustrating the effect of risk coefficient setting analysis as shown in this invention. Detailed Implementation

[0095] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] Example 1

[0100] This embodiment provides an optimization configuration method for a multi-energy flow system considering source-load uncertainty. This embodiment uses the application of this method to a server as an example for illustration. It is understood that this method can also be applied to terminals, and can also be applied to systems including terminals, servers, and other components, and implemented through interaction between the terminal and the server. The server can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network servers, cloud communication, middleware services, domain name services, CDN security services, and big data and artificial intelligence platforms. The terminal can be a smartphone, tablet, laptop, desktop computer, smart speaker, smartwatch, etc., but is not limited to these. The terminal and server can be directly or indirectly connected via wired or wireless communication, which is not limited herein. In this embodiment, the method includes the following steps:

[0101] Acquire data on electricity, heat, cooling, and gas loads, wind and solar power output, and equipment parameters;

[0102] Calculate the equivalent uncertainty fluctuation range based on the actual fluctuation range of the electric heating and cooling load;

[0103] Based on information gap decision-making and conditional risk value theory, and combined with deterministic optimal allocation model, an optimal allocation model for multi-energy flow system is constructed.

[0104] Using the maximum loss that decision-makers can tolerate as a constraint, and based on the set risk coefficient and avoidance coefficient, construct an objective function for the total planning cost;

[0105] Based on electricity, heat, cooling, and gas load data, wind and solar power output data, and equipment parameter data, under the condition of the largest fluctuation range of equivalent uncertainty, according to the objective function of total planning cost and the uncertainty variable model, combined with constraints and multi-energy flow system optimization configuration model, a multi-energy flow system optimization configuration model considering source load uncertainty is constructed.

[0106] Solve the multi-energy flow system optimization configuration model that takes into account source load uncertainties to obtain the multi-energy flow system optimization configuration scheme.

[0107] The specific solution for this embodiment can be implemented by referring to the following content:

[0108] 1. Modeling of multi-energy flow systems

[0109] In an integrated energy system, various forms of energy flow, including electricity, heating, and cooling, effectively promote the complementary advantages and coordinated utilization of different energy sources. A typical energy hub (EH) architecture is as follows: Figure 1 As shown.

[0110] EH's internal power generation equipment includes wind turbines (WT) and photovoltaic (PV) units; energy storage equipment includes electrical storage (ES) and thermal storage (TS); electric boilers (EB) for electrothermal coupling conversion; electric chillers (EC) for electrocooling coupling conversion; combined heat and power (CHP) units for gas-electric-thermal coupling conversion; gas boilers (GB) for gas-thermal coupling conversion; and absorption chillers (AC) for heat-cooling coupling conversion.

[0111] The energy hub (EH) receives electricity and natural gas from upstream power grid and natural gas companies as input. After supplementation by internal production equipment, it rationally distributes these resources to various energy coupling devices and energy storage devices to achieve energy balance among electricity, heat, cooling, and gas flow, thus meeting the user's electricity, heat, cooling, and gas load demands. The input-output coupling relationship of the energy hub is as follows:

[0112] L = CP + E (1)

[0113] L = [L e L h L c L g ] T (2)

[0114]

[0115]

[0116] E = [E e E h 0 0] T (5)

[0117] In the formula: matrix L represents the output matrix of electric, heat, cooling, and gas loads; matrix C represents the energy conversion matrix; matrix P represents the energy input matrix; matrix E represents the energy storage power matrix; L e ,L h ,L c ,L g Indicates the electrical, heating, cooling, and gas loads; η1 represents the transformer efficiency. The electrical efficiency of the combined heat and power (CHP) unit is expressed as ω1 and ω2, which are the electrical energy distribution coefficients; α1 and α2 are the natural gas distribution coefficients, and η is the power efficiency. EB To improve the heating efficiency of electric boilers, For the heating efficiency of a combined heat and power unit, η GB η is the heating efficiency of the gas-fired boiler, λ is the heat load distribution coefficient; EC For the refrigeration efficiency of an electric refrigeration machine, η AC For absorption refrigeration, the refrigeration efficiency is improved. P is the electrical energy supplied to the energy hub by the external power grid. dg The electrical energy input for internal distributed generation (dg) power sources. Natural gas energy purchased from an external natural gas network; E e E represents the power of the energy storage device. h This refers to the power of the thermal storage equipment.

[0118] 2. Load output uncertainty modeling based on IGDT

[0119] Given the characteristics of low prediction accuracy and limited information on uncertainties, this embodiment employs information gap decision theory to address the long-term uncertainties arising from the growth of cooling, heating, and power loads. The cost optimization problem model belongs to the IGDT robust model under a risk-averse strategy, which assumes that load output uncertainty will significantly worsen the system cost function objective. A negative worst-case boundary objective is set to ensure that the objective remains within this boundary, and the maximum fluctuation of uncertain parameters is sought.

[0120] The basic model of this theory is as follows:

[0121]

[0122] In the formula, f is the objective function, d represents the decision variable, and X represents the uncertain variable; h and g represent the equality constraint and inequality constraint, respectively; Γ represents the uncertain variable X around its predicted value. The set of fluctuation ranges.

[0123] The model for the uncertain variables described using envelope constraints is as follows:

[0124]

[0125] In the formula, α represents the fluctuation range of the uncertain quantity, C(·) represents the investment planning cost function, σ represents the avoidance coefficient, which represents the proportion of the investment cost that the investor can accept that is higher than the predicted value.

[0126] For IGDT modeling with multiple uncertain variables, existing techniques generally use a method of assigning weights to the fluctuations of the multiple uncertain variables to form an equivalent range of uncertain variable fluctuations. The equivalent range of uncertain fluctuations can then be expressed as:

[0127]

[0128] And we have ξ1+ξ2+ξ3+ξ4=1 (9)

[0129] In the formula, For the equivalent uncertain variable fluctuation range, ξ1, ξ2, ξ3, and ξ4 represent the weighting coefficients for the fluctuation range of the electric heating and cooling loads, respectively, and α e α h α c α g These represent the actual fluctuation range of the electric heating and cooling loads, respectively.

[0130] 3. Uncertainty Modeling of Wind and Solar Power Output Based on CVaR

[0131] To leverage the relatively accurate probabilities of renewable energy output, this embodiment employs conditional value at risk theory to adjust the modeling scheme's adaptability to short-term uncertainties in wind and solar power. This embodiment uses the scenario set method to simulate the uncertainties in wind and solar turbine output.

[0132] 3.1 Construction of the Scenery Output Set

[0133] This embodiment uses Latin hypercube sampling to establish a scene set for wind speed and light intensity. Latin hypercube sampling is a stratified random sampling method that accurately reflects the theoretical distribution of random variables and avoids duplication. Its equidistant sampling characteristic ensures that the sampling area is uniformly covered by the sampling points. Assume x1, x2, ... x k Given k independent uncertain variables, and N scenarios generated for each uncertain variable, the process of generating the nth scenario is as follows:

[0134]

[0135] In the formula, It is the inverse function of the cumulative distribution function of the k-th uncertain variable.

[0136] For multi-random variable sampling, N samples for each variable need to be randomly combined with samples from other variables, resulting in a total of N scenarios. k To reduce the solution burden and improve solution efficiency, a synchronous back-substitution elimination method is used to reduce the number of scenarios.

[0137] 3.2 Construction of CVaR Model

[0138] CVaR evolved from VaR. VaR is a risk value assessment model in the financial field that refers to the maximum possible loss of an investment at a given confidence level.

[0139] The mathematical expression for VaR is:

[0140]

[0141] In the formula, β is the confidence level, f represents the loss function due to fluctuations in the uncertain variable, d represents the decision variable, X represents the uncertain variable, and the integral... This represents the probability that the loss function f(d,X) is not greater than α.

[0142] Value at Risk (VaR) describes the maximum possible loss an investor can make at a given confidence level, but it does not characterize losses exceeding that confidence level and lacks a measure of tail risk, thus affecting risk loss decisions. Conditional Value at Risk (CVaR), on the other hand, refers to the expected value of systematic risk losses exceeding VaR at a given confidence level and time frame.

[0143] The mathematical expression for CVaR is:

[0144]

[0145] In the formula, Γ represents the uncertainty variable X around its predicted value. The set of fluctuation ranges.

[0146] Conditional Risk Value (CVaR) represents the expected value of the portion of loss exceeding the maximum possible VaR, thus compensating for VaR's inability to measure tail risk. The optimal configuration model for a multi-energy flow system based on CVaR theory is modeled as follows:

[0147] C′=C+μCVaR (13)

[0148] In the formula, C′ represents the cost function taking into account CVaR, C is the expected value of operating costs, and μ represents the risk coefficient, reflecting the investor's level of risk aversion.

[0149] 4. Optimization configuration model considering uncertainty

[0150] 4.1 Deterministic Optimization Configuration Model

[0151] 4.1.1 Objective Function

[0152] Considering the overall economic efficiency of the planning scheme, the optimal configuration model of the electric heating and cooling multi-energy flow system takes the minimum total planning cost as the optimization objective, including investment cost and operating cost.

[0153]

[0154] In the formula, C represents the total planned cost within year T, C inv,t Let C be the total investment cost in year t. op,t τ represents the total operating cost in year t; t Let τ be the coefficient of the cash discount rate r in year t. t =(1+r) -t C inv,tThe definition is as follows:

[0155]

[0156] In the formula, σ k P represents the investment cost per unit capacity of the power generation unit k. k For the installed capacity of power unit k, N k,t ρ represents the number of power supply units k that have been put into operation in year t; m E represents the investment cost per unit capacity of energy storage equipment. m For the energy storage device m's installed capacity, Y m,t Let C be the number of energy storage devices (m) already in operation in year t. op,t The definition is as follows:

[0157]

[0158]

[0159]

[0160]

[0161]

[0162] In the formula, N d Let d be the number of days in a typical day of the year. There are a total of D typical days in a year. Let $t$ represent the operating cost of the power supply unit, the operating cost of the energy storage equipment, the cost of electricity purchased from the external power grid, and the cost of gas purchased from the external natural gas grid for the energy hub during the $h$ time period on the $d$-th typical day in year $t$. This represents the operating cost per unit power of the power supply unit k. This represents the total operating power of unit k during time period h; This represents the operating cost per unit power of the energy storage unit m. These represent the total charging power and discharging power of energy storage device m during time period h, respectively. This indicates the market electricity price for time period h. This indicates the amount of active power exchanged between the energy hub and the external power grid; This indicates the market price per unit of natural gas during time period h. This indicates the amount of natural gas that the energy hub purchases from external sources.

[0163] 4.1.2 Constraints

[0164] 1) Energy hub constraints

[0165] The relationship between the upper and lower limits of the power output of the power supply unit is as follows:

[0166]

[0167]

[0168] In the formula, P k,min P is the lower limit of the active power output of power unit k. k,max This represents the upper limit of the active power output of the power supply unit k; This represents the maximum transmission power between the energy hub and the external power grid.

[0169] 2) CHP unit ramp-up power

[0170]

[0171] In the formula, For the electrical power of the combined heat and power unit, θ up θ down These represent the upward and downward ramp rates for the combined heat and power (CHP) unit, respectively.

[0172] 3) Constraints on the operation and charging / discharging of energy storage devices

[0173]

[0174]

[0175]

[0176]

[0177] Q t,d,0 =Q t,d,H (28)

[0178] Equations (24) to (25) represent the upper and lower limits of energy storage charge and discharge constraints, Q t,d,h To store energy, The energy storage device's charging and discharging efficiency.

[0179] 4) Supply and demand matching constraints

[0180] Supply and demand matching constraints ensure that the installed capacity of the electric heating and cooling units in year t is greater than or equal to the peak electric heating and cooling load.

[0181]

[0182]

[0183]

[0184]

[0185] In the formula, Let represent the peak values ​​of the electric heating and cooling loads in year t, respectively. These represent the capacity of the energy hub output to the load in year t.

[0186] 5) Other constraints

[0187] N k,0 ≤N k,t-1 ≤N k,t ≤N k,T (33)

[0188] Y k,0 ≤Y k,t-1 ≤Y k,t ≤Y k,T (34)

[0189] In the formula, N k,0 N k,T Y k,0 Y k,T These represent the initial and maximum investment amounts for energy supply and energy storage equipment, respectively.

[0190] 4.2 Optimization Configuration Model Considering Uncertainty

[0191] From equations (11) to (13), combined with the deterministic optimal configuration model, we can obtain the optimal configuration model for a multi-energy flow system based on CVaR theory:

[0192]

[0193]

[0194]

[0195]

[0196] In the formula, To account for the operating costs in year t of the conditional risk assessment, The expected value of annual operating costs is given; a scene set of wind speed and light intensity on a typical day d in year t is established using Latin hypercube sampling; n w n s π(n) represents the number of corresponding scenes. w ), π(n s ) represents the probability of each scenario; For the nth time on a typical day d in year t,... w n s Operating costs of energy supply equipment, operating costs of energy storage equipment, electricity purchase costs, and gas purchase costs under different wind speed and light intensity scenarios.

[0197] Given the low prediction accuracy and limited information on uncertainties, an IGDT model with a cost optimization function is constructed to handle the long-term uncertainties brought about by the growth of cooling, heating, and power loads. Taking advantage of the relatively accurate characterization of renewable energy output probabilities, based on a set of typical prediction scenarios, conditional value at risk theory is applied to assess the conditional risk of output fluctuation losses during the operation of multi-energy flow systems.

[0198] The final optimal configuration model for the multi-energy flow system, taking into account source load uncertainties, is shown below:

[0199]

[0200] The fluctuation range of the equivalent uncertain variable for the electric heating and cooling load. The decision variable for the model is N. k,t Y m,t , VaR. Model planning process as follows: Figure 2 As shown.

[0201] 5. Case Analysis:

[0202] 5.1 Parameter Settings

[0203] Taking a commercial district as the research object, a numerical example is set up, and the structure of the energy hub is as follows: Figure 1 As shown. Input wind and solar power output data, various equipment parameters, various load data, time-of-use electricity prices, and natural gas price data. The example is written and solved in the Cplex solver.

[0204] The planning period is 10 years, and the discount rate is 3%. The year is divided into three typical days: summer, winter, and transition season. Considering the short-term uncertainty of wind and solar power output, the probability distributions of distributed solar power system and wind power system output are set to satisfy Beta and Weibull distributions, respectively. For each typical day, 10 wind power scenarios and 10 solar power scenarios are generated, i.e., nw = ns = 10. Therefore, the total number of scenarios for the whole year is... After synchronous back-substitution and elimination, the number of scenarios was reduced to 18. Considering the impact of long-term uncertainties in the growth of various types of electricity, heating, and cooling loads on the planning results, the annual growth rate of various loads was set to 3%. The case study was solved by applying both a deterministic model and an optimization configuration model that takes uncertainty into account. The parameters were set as avoidance coefficient σ = 0.2, risk coefficient μ = 0.1, and the weighting ratio of the deviation coefficients of electricity, heating, and cooling loads was ξ1:ξ2:ξ3:ξ4 = 1:1:1:1.

[0205] 5.2 Comparison and Analysis of Different Methods

[0206] To verify the effectiveness and superiority of the proposed method, four scenarios were set up for simulation analysis. Scenario 1: Without considering the uncertainty of wind and solar power output and load forecasting, the deterministic model in this paper was used for simulation analysis; Scenario 2: Only considering load uncertainty, a robust planning method considering the uncertainty of multi-energy load was set up; Scenario 3: A robust optimization method was used to study the optimization configuration method for uncertainty; Scenario 4: The optimization configuration method for multi-energy flow systems considering uncertainty in this paper was used. The system equipment capacity selection is shown in Table 1, the planning results are shown in Table 2, the optimization configuration cost analysis is shown in Table 3, and the model calculation time comparison is shown in Table 4. The fluctuation range of the load growth uncertainty is 0.107. The total planning costs of the optimization configuration models in scenarios 1 to 4 are RMB 4.2679 million, RMB 4.5712 million, RMB 5.1058 million, and RMB 4.9865 million, respectively.

[0207] Table 1 Equipment Capacity Parameters

[0208]

[0209] Table 2 Results of Optimized Configuration Planning

[0210]

[0211]

[0212] Table 3 Cost Analysis of Optimized Configuration

[0213]

[0214] Table 4 Comparison of Model Calculation Time

[0215]

[0216] Overall, the increased load has led to a year-on-year increase in the configuration of energy supply and storage equipment in this commercial area. Comparing four scenarios: 1) Comparing scenarios 1 and 2 with scenarios 3 and 4, the short-term uncertainty of wind and solar power output has resulted in a reduction in wind and solar power installations and an increase in CHP (Consumer Power Supply) configurations to address power forecasting errors on the source side, leading to higher investment and equipment maintenance costs; 2) Comparing scenario 1 with scenario 2, the uncertainty of load growth has changed the GB (Gas Storage) construction plan, resulting in increased investment, operation, and external purchase costs; 3) Comparing scenario 3 with scenario 4, scenario 3's configuration includes an additional GB, leading to increased overall investment costs, increased gas purchase costs, and decreased electricity purchase costs, with the total cost slightly higher than that of scenario 4.

[0217] Due to its price advantage, WT becomes the most frequently added power supply equipment when the load increases; ES can cope with the fluctuations caused by the long-term uncertainty of load growth and can effectively mitigate the impact of the uncertainty of wind and solar power output on the system. It is usually built in conjunction with wind and solar power units; in scenario 4, the coupling equipment EC appears as a new configuration, possibly because its lower operating cost makes it more economical in dealing with fluctuations in electrical and cooling loads.

[0218] In terms of solution time, the complexity of the deterministic model, the model considering load uncertainty, and the model considering source load uncertainty increases sequentially, and the solution time increases sequentially. Compared with the robust optimization of scenario 3, the model constructed in scenario 4 does not require the boundary of the uncertainties and requires less information about the uncertainties, so its solution time is slightly faster than that of scenario 3.

[0219] In summary, the method for scenario 4 constructed in this embodiment has the following advantages compared with other planning methods: 1) It takes into account the impact of uncertainties on planning costs on both sides of the source and load and quantifies the loss due to uncertainty risk. In this embodiment, when the difference between the load growth and the predicted value is within 10.7%, the additional loss compensation for investors does not exceed 20%; 2) Compared with robust optimization in scenario 3, scenario 4 constructs a model with avoidance coefficient, risk coefficient, and uncertainty fluctuation range. It only requires the investor's ability to accept or averse to risk and the fluctuation range of uncertainty, without the need for the boundary of uncertainty, and requires less uncertainty information; 3) Compared with robust optimization in scenario 3, scenario 4 has lower planning costs, more reasonable configuration schemes, and reduces the conservatism of the planning strategy.

[0220] 5.3 Avoidance Coefficient Analysis

[0221] The aversion coefficient σ in the model represents the proportion of investment costs that investors can accept exceeding the predicted value, primarily characterizing the losses caused by the uncertainty of load growth. In this example, the value of σ is set to 0.2, and the simulation yields a value of α of 0.107, meaning that when the load growth differs from the predicted value by less than 10.7%, the additional loss compensation for investors will not exceed 20%. Changing the setting value of σ yields the following results for the load growth fluctuation range α: Figure 3 As shown, a larger σ value indicates that investors are willing to pay greater compensation for losses, resulting in a wider allowable range of load growth fluctuations, better system robustness, and higher planned investment. Conversely, a smaller σ value indicates poorer system robustness and lower planned investment.

[0222] 5.4 Risk Coefficient Analysis

[0223] The risk coefficient μ corresponds to the weighting coefficient of CVaR, representing the decision-maker's risk preference level, where μ ≥ 0. Generally, a μ ≥ 0.10 indicates that the decision-maker chooses a risk-averse strategy, exchanging a certain return for less risk. When μ ≤ 0.05, it indicates that the decision-maker chooses an opportunity-seeking strategy, using greater risk to obtain greater returns. In this example, the value of μ is set to 0.1, and the simulation yields a value of α of 0.107. Changing the setting value of μ yields the following results for the load growth fluctuation range α: Figure 4 As shown, the smaller the μ value, the more investors tend to avoid the risks caused by fluctuations in wind and solar power output and choose other power supply methods, resulting in better system robustness. Conversely, the larger the μ value, the more wind and solar power is deployed, the greater the investment risk, and the worse the system robustness.

[0224] This embodiment first analyzes the typical energy supply equipment, energy storage equipment, and energy coupling conversion equipment in the multi-energy flow power supply network of EH, and analyzes the physical mechanism and input-output characteristics of the power supply network. Secondly, it constructs the objective function of the optimization configuration model, analyzes the constraints such as EH operation, energy storage operation, and source-load supply-demand matching, uses information gap decision theory to deal with the prediction deviation caused by the growth of electric, heating, and cooling loads in the model, and uses conditional value at risk theory to adjust the modeling scheme to assess the risk loss caused by the prediction deviation of wind and solar power output.

[0225] Example 2

[0226] This embodiment provides an optimized configuration system for a multi-energy flow system that takes into account source load uncertainty.

[0227] A multi-energy flow system optimization configuration system considering source-load uncertainty includes:

[0228] The data acquisition module is configured to acquire electricity, heat, cooling, and gas load data, wind and solar power output data, and equipment parameter data.

[0229] The uncertainty fluctuation range determination module is configured to: calculate the equivalent uncertainty fluctuation range based on the actual fluctuation range of the electric heating and cooling load;

[0230] The module for constructing the optimal configuration model of a multi-energy flow system is configured to: construct an optimal configuration model of a multi-energy flow system based on information gap decision-making and conditional risk value theory, combined with a deterministic optimal configuration model;

[0231] The objective function determination module is configured to: construct an objective function for the total planning cost based on the set risk coefficient and avoidance coefficient, with the maximum loss that the decision-maker can bear as a constraint;

[0232] The module for constructing a multi-energy flow system optimization configuration model that takes into account source load uncertainty is configured as follows: based on electricity, heat, cooling, and gas load data, wind and solar power output data, and equipment parameter data, under the condition of the largest fluctuation range of equivalent uncertainty, according to the objective function of total planning cost and the uncertainty variable model, combined with constraints and the multi-energy flow system optimization configuration model, constructing a multi-energy flow system optimization configuration model that takes into account source load uncertainty.

[0233] The solution module is configured to solve the optimal configuration model of the multi-energy flow system taking into account the uncertainty of source load, and obtain the optimal configuration scheme of the multi-energy flow system.

[0234] It should be noted that the data acquisition module, the uncertainty fluctuation range determination module, the multi-energy flow system optimization configuration model construction module, the objective function determination module, the multi-energy flow system optimization configuration model construction module considering source load uncertainties, and the solution 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 the above modules, as part of the system, can be executed in a computer system such as a set of computer-executable instructions.

[0235] Example 3

[0236] This embodiment provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps in the multi-energy flow system optimization configuration method considering source-load uncertainty as described in Embodiment 1 above.

[0237] Example 4

[0238] 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 multi-energy flow system optimization configuration method considering source-load uncertainty as described in Embodiment 1 above.

[0239] 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.

[0240] 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.

[0241] 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.

[0242] 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.

[0243] 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.

[0244] 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 method for optimizing the configuration of a multi-energy flow system considering source-load uncertainty, characterized in that, include: Acquire data on electricity, heat, cooling, and gas loads, wind and solar power output, and equipment parameters; Calculate the equivalent uncertainty fluctuation range based on the actual fluctuation range of the electric heating and cooling load; Based on information gap decision-making and conditional risk value theory, and combined with deterministic optimal allocation model, an optimal allocation model for multi-energy flow system is constructed. Using the maximum loss that decision-makers can tolerate as a constraint, and based on the set risk coefficient and avoidance coefficient, construct an objective function for the total planning cost; The objective function for the total planning cost is: In the formula, Indicates taking into account The cost function; Express the cost function of investment planning; The aversion coefficient represents the proportion of investment costs that investors can accept being higher than the predicted value. This represents the risk coefficient, reflecting the investor's level of risk aversion. Represent decision variables; Represents an uncertain variable; Based on electricity, heat, cooling, and gas load data, wind and solar power output data, and equipment parameter data, under the condition of the largest fluctuation range of equivalent uncertainty, according to the objective function of total planning cost and the uncertainty variable model, combined with constraints and multi-energy flow system optimization configuration model, a multi-energy flow system optimization configuration model considering source load uncertainty is constructed. The uncertain variable model includes: In the formula, Indicates the range of fluctuation of an uncertain quantity; These represent equality constraints and inequality constraints, respectively. Representing uncertain variables Around its predicted value The set of fluctuation ranges; Solve the multi-energy flow system optimization configuration model that takes into account source load uncertainties to obtain the multi-energy flow system optimization configuration scheme.

2. The method for optimizing the configuration of a multi-energy flow system considering source-load uncertainty according to claim 1, characterized in that, The fluctuation range of the equivalent uncertainty is: And there are In the formula, For the range of fluctuation of the equivalent uncertain variable, These represent the weighting coefficients for the fluctuation range of electric heating and cooling loads, respectively. These represent the actual fluctuation range of the electric heating and cooling loads, respectively.

3. The method for optimizing the configuration of a multi-energy flow system considering source-load uncertainty according to claim 1, characterized in that, The deterministic optimization configuration model includes: Considering the overall economic efficiency of the planning scheme, the optimization objective is to minimize the total planning cost; In the formula, express Total planned cost for the year For the first Total investment cost for the year, For the first Total annual operating costs; For the first Annual cash discount rate coefficient, ; The definition is as follows: In the formula, For power supply units Investment cost per unit capacity For power supply units The construction capacity, For the first Annual power supply units The number of projects already built; For energy storage devices Investment cost per unit capacity For energy storage devices The construction capacity, For the first Annual energy storage equipment The number of projects already built; The definition is as follows: In the formula, A typical day of the year The number of consecutive days, totaling [number] days in a year A typical day; , , , They represent the first Year, the On a typical day, the first Operating costs of power supply units during specific time periods, operating costs of energy storage equipment, electricity purchase costs of energy hubs from external power grids, and gas purchase costs from external natural gas grids; Indicates the power supply unit Operating cost per unit power express Time-of-use power supply units Total operating power; Indicates energy storage unit Operating cost per unit power , They represent Time-of-use energy storage equipment Total charging power and discharging power; express Market electricity price during the period This indicates the amount of active power exchanged between the energy hub and the external power grid; express The market price of natural gas per unit during the period This indicates the amount of natural gas that the energy hub purchases from external sources.

4. The method for optimizing the configuration of a multi-energy flow system considering source-load uncertainty according to claim 1, characterized in that, The multi-energy flow system optimization configuration model includes: In the formula, This represents the conditional value at risk theory. For taking into account the conditional risk assessment Annual operating costs The expected value of annual operating costs; Latin hypercube sampling is used to establish the first... Typical days of the year Scene set with wind speed and light intensity, , For the number of corresponding scenarios, The probability of each scenario; For the first Typical days of the year time Next , Operating costs of energy supply equipment, operating costs of energy storage equipment, electricity purchase costs, and gas purchase costs under different wind speed and light intensity scenarios.

5. The method for optimizing the configuration of a multi-energy flow system considering source-load uncertainty according to claim 1, characterized in that, The constraints include: 1) Energy hub constraints The relationship between the upper and lower limits of the power output of the power supply unit is as follows: In the formula, For power supply units The lower limit of meritorious contribution, For power supply units The upper limit of effective output; This represents the maximum transmission power between the energy hub and the external power grid. 2) CHP unit ramp-up power In the formula, For the electrical power of the combined heat and power unit, , These represent the upward and downward ramp rates for the combined heat and power (CHP) unit, respectively. 3) Constraints on the operation and charging / discharging of energy storage devices In the formula, This represents the total charging power of energy storage device m during time period h. This represents the total charging power of energy storage device m during time period h. To store energy, , The charging and discharging efficiency of energy storage devices; 4) Supply and demand matching constraints Supply and demand matching constraints ensure the first The annual installed capacity of the electric heating and cooling unit is greater than or equal to the peak electric heating and cooling load. In the formula, , , , They represent the first Peak annual electric heating and cooling load, , , , They represent the first Annual energy hub output capacity to the load section; 5) Other constraints In the formula, , , , These represent the initial and maximum investment amounts for energy supply and energy storage equipment, respectively.

6. A multi-energy flow system optimization configuration system considering source-load uncertainty using the method described in claim 1, characterized in that, include: The data acquisition module is configured to acquire electricity, heat, cooling, and gas load data, wind and solar power output data, and equipment parameter data. The uncertainty fluctuation range determination module is configured to: calculate the equivalent uncertainty fluctuation range based on the actual fluctuation range of the electric heating and cooling load; The module for constructing the optimal configuration model of a multi-energy flow system is configured to: construct an optimal configuration model of a multi-energy flow system based on information gap decision-making and conditional risk value theory, combined with a deterministic optimal configuration model; The objective function determination module is configured to: construct an objective function for the total planning cost based on the set risk coefficient and avoidance coefficient, with the maximum loss that the decision-maker can bear as a constraint; The module for constructing a multi-energy flow system optimization configuration model that takes into account source load uncertainty is configured as follows: based on electricity, heat, cooling, and gas load data, wind and solar power output data, and equipment parameter data, under the condition of the largest fluctuation range of equivalent uncertainty, according to the objective function of total planning cost and the uncertainty variable model, combined with constraints and the multi-energy flow system optimization configuration model, constructing a multi-energy flow system optimization configuration model that takes into account source load uncertainty. The solution module is configured to solve the optimal configuration model of the multi-energy flow system taking into account the uncertainty of source load, and obtain the optimal configuration scheme of the multi-energy flow system.

7. 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 method for optimizing the configuration of a multi-energy flow system taking into account source load uncertainty as described in any one of claims 1-5.

8. 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 multi-energy flow system optimization configuration method considering source load uncertainty as described in any one of claims 1-5.

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