An electric cold and hot gas comprehensive energy optimization configuration method, system and device

Abstract

The embodiment of the application discloses a kind of electric cold and hot gas comprehensive energy optimization configuration method, system and device, the method includes the following steps: carbon price and renewable energy output power after considering carbon price and renewable energy output power uncertainty are calculated, and the typical scenario of carbon price and renewable energy output power is generated;Multi-energy flow model is established and carbon trading cost model is established by combining carbon price after considering carbon price uncertainty;On the basis of the typical scenario, an optimization configuration model is established, the optimization configuration model is converted into a mixed integer linear programming model using smoothing method, the optimization configuration model is solved using branch and bound algorithm, and the planning scheme under a given confidence level is obtained.The present application aims to introduce carbon price and renewable energy output power uncertainty into the planning total cost fluctuation risk modeling, in addition, due to the strong power fluctuation of renewable energy, the negative carbonization is possible.

Description

Technical Field

[0001] This invention belongs to the field of integrated energy system planning technology, and in particular relates to a method, system and device for optimizing the configuration of integrated electric, cooling, heating and gas energy. Background Technology

[0002] my country has proposed a "dual carbon" goal: to peak carbon emissions before 2030 and achieve carbon neutrality before 2060, and has subsequently released action plans for "peaking carbon and achieving carbon neutrality." The energy sector is my country's largest source of carbon emissions, and the core of the energy sector's contribution to achieving the dual carbon goal is to promote the low-carbon transformation of energy. Integrated Energy Systems (IES) can break down the technical barriers between electricity, gas, cooling, and heating subsystems, achieving multi-energy complementarity and synergistic optimization, effectively contributing to the clean and low-carbon development of the energy sector, and thus supporting the achievement of the dual carbon goal. Integrated energy systems (Electricity, Cooling, Heating, and Gas) are a typical example of multi-energy coupling and combined energy supply on the user side. These systems often feature high energy consumption and high energy density, and possess the potential to integrate and utilize various renewable energy sources, making them one of the important carriers for achieving my country's low-carbon development.

[0003] The low-carbon development of integrated energy systems (electricity, cooling, heating, and gas) can be achieved through participation in carbon trading and the development of renewable and clean energy. Participation of integrated energy systems in the carbon trading market to reduce carbon emissions is a crucial component of achieving my country's "dual carbon" goals. Furthermore, integrated energy systems can respond to national policies by allocating large-scale renewable and clean energy resources to contribute to the achievement of "dual carbon" goals. Simultaneously, the large-scale development of renewable and clean energy also provides integrated energy systems with significant profit potential when participating in carbon trading.

[0004] However, my country's carbon trading market, established in 2017, is still in its initial stage. Carbon prices are highly uncertain, and the inherent power fluctuations of renewable energy generation within integrated power-cooling-heating-gas (EPCH) systems directly impact their potential for stable participation in carbon trading. The total planned cost of an ECH system typically includes equipment investment, fuel costs, maintenance costs, and carbon trading costs. Due to the uncertainties surrounding carbon prices and renewable energy generation, the total planned cost is highly likely to deviate from the expected value, leading to insufficient system capacity and an inability to reliably supply the various load demands required by the system. Summary of the Invention

[0005] The purpose of this invention is to propose a method, system, and device for optimizing the allocation of integrated electric, cooling, heating, and gas energy. It aims to incorporate the uncertainty of carbon price and renewable energy output power into the modeling of the total planned cost fluctuation risk. A model for optimizing the allocation of integrated electric, cooling, heating, and gas energy systems based on CVaR risk measurement is proposed to address the risk of total planned cost fluctuation and solve the problem of insufficient system equipment capacity configuration, which makes it impossible to stably and reliably supply the various load demands required by the system.

[0006] To achieve this objective, the present invention provides a method for optimizing the allocation of integrated electric, cooling, heating, and gas energy, the method specifically comprising the following steps:

[0007] S1, calculate the carbon price and renewable energy output power after considering the uncertainty of carbon price and renewable energy output power, and generate a typical scenario of the carbon price and renewable energy output power;

[0008] S2, Establish a multi-energy flow model and combine it with carbon price after considering carbon price uncertainty to establish a carbon trading cost model;

[0009] S3. Based on the typical scenario, establish an optimal configuration model for an integrated energy system of electricity, cooling, heating, and gas based on CVaR. Import the electricity, heating, and cooling load data, the typical carbon price scenario obtained after clustering, and the typical renewable energy output power scenario into the optimal configuration model. Use the smoothing method to transform the optimal configuration model into a mixed integer linear programming model. Use the branch and bound algorithm to solve the mixed integer linear programming model to obtain the planning scheme under a given confidence level.

[0010] A system for optimizing the allocation of integrated electric, cooling, heating, and gas energy includes a computing module and a processing module.

[0011] The calculation module calculates the carbon price and renewable energy output power after considering the uncertainty of carbon price and renewable energy output power, and generates typical scenarios of the carbon price and renewable energy output power. It establishes a multi-energy flow model and combines it with the carbon price after considering the uncertainty of carbon price to establish a carbon trading cost model. Based on the typical scenarios, it establishes an optimal configuration model of an integrated energy system of electricity, cooling, heating and gas based on CVaR. It imports the electricity, heating and cooling load data, the typical carbon price scenarios obtained after clustering and the typical renewable energy output power scenarios into the optimal configuration model.

[0012] The processing module uses a smoothing method to transform the optimization configuration model into a mixed-integer linear programming model, and uses a branch and bound algorithm to solve the mixed-integer linear programming model to obtain a planning scheme under a given confidence level.

[0013] A device for optimizing the allocation of integrated electric, cooling, heating, and gas energy includes:

[0014] Memory, used to store computer programs;

[0015] A processor is used to execute the steps of the method for optimizing the configuration of integrated electric, cooling, heating, and gas energy.

[0016] The present invention has the following advantages:

[0017] 1. This invention proposes to incorporate the uncertainty of carbon prices, especially the uncertainty of renewable energy output power, into the modeling of the total planned cost fluctuation risk, thereby proposing an optimal configuration model for an integrated energy system of electricity, cooling, heating and gas based on CVaR risk measurement to cope with the risk of total planned cost fluctuation.

[0018] 2. Because electric energy storage can cope with the strong power fluctuations of renewable energy, it makes it possible for integrated electric cooling, heating and gas energy systems to develop negative carbon emissions. This allows integrated electric cooling, heating and gas energy systems to sell surplus carbon emission rights in the carbon trading market to generate profits and increase the system's revenue.

[0019] 3. Due to the contradictory relationship between total planned cost and CVaR, integrated electric, cooling, heating and gas energy systems face a high risk of fluctuation in total planned cost when pursuing low total cost. Integrated electric, cooling, heating and gas energy systems need to select the corresponding planning scheme according to their own risk preferences. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of this invention or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 A schematic diagram of the working steps of the method of the present invention;

[0022] Figure 2 A typical structural diagram of the integrated electric cooling, heating, and gas energy system in this embodiment of the invention:

[0023] Figure 3 In this embodiment of the invention, the integrated electric cooling and heating energy system has a fixed electric cooling load.

[0024] Figure 4 1. Fixed wind speed and illumination diagram of the integrated electric cooling and heating energy system in the embodiments of the present invention;

[0025] Figure 5 A graph showing the relationship between carbon price and the number of SSE clustering scenarios in an embodiment of the present invention;

[0026] Figure 6 A graph showing the relationship between light intensity and the number of SSE clustering scenes in an embodiment of the present invention;

[0027] Figure 7 A diagram showing the relationship between wind speed and the number of SSE clustering scenarios in an embodiment of the present invention;

[0028] Figure 8 Typical scenario diagram of carbon price after clustering in the embodiments of the present invention;

[0029] Figure 9 Typical scene diagram of light intensity after the first clustering in this embodiment of the invention;

[0030] Figure 10 Typical scene diagram of light intensity after the second clustering in the embodiments of the present invention;

[0031] Figure 11 Typical scene diagram of light intensity after the third clustering in the embodiments of the present invention;

[0032] Figure 12 Typical wind speed scene diagram after the first clustering in the embodiments of the present invention;

[0033] Figure 13 Typical wind speed scene diagram after the second clustering in the embodiments of the present invention;

[0034] Figure 14 Typical wind speed scene diagram after third clustering in the embodiments of the present invention;

[0035] Figure 15 The gas and electricity purchases of the integrated electric cooling and heating system in this embodiment of the invention on a typical winter day;

[0036] Figure 16 The gas purchase volume and electricity purchase volume of the integrated electric cooling and heating gas energy system in this embodiment of the invention on a typical summer day;

[0037] Figure 17 The gas purchase volume and electricity purchase volume of the integrated electric cooling and heating gas energy system in this embodiment of the invention on typical days in spring and autumn;

[0038] Figure 18 A comparison of total cost and CVaR at different confidence levels in the embodiments of the present invention. Detailed Implementation

[0039] The embodiments of the present invention will be further described in detail below with reference to the accompanying drawings and specific examples.

[0040] like Figure 2As shown, a typical integrated energy system consists of a combined heat and power (CHP) unit, a gas boiler (GB), an electric chiller (EC), an electric boiler (EB), a battery energy storage system (BESS), a photovoltaic (PV) system, and a wind turbine (WT). This integrated energy system can participate in carbon trading to sell surplus carbon emission rights for profit.

[0041] The method for optimizing the integrated energy configuration of electricity, cooling, heating, and gas as designed in this embodiment of the invention has the following working steps: Figure 1 As shown, the specific steps include the following:

[0042] Step S1: Calculate the carbon price and renewable energy output power after considering the uncertainty of carbon price and renewable energy output power, and generate the typical scenarios of carbon price and renewable energy output power.

[0043] The specific steps of S1 are as follows:

[0044] S11. Based on a normal distribution, calculate the carbon price and renewable energy output power considering the uncertainty of carbon price and renewable energy output power, where renewable energy includes solar power and wind power, and the carbon price is the carbon trading price.

[0045] Before considering carbon price uncertainty, let the fixed carbon price be *a*, and assume that the uncertain carbon price follows a normal distribution. Therefore, the carbon price *a′* after considering uncertainty is:

[0046] a′=a+ξ1(1)

[0047] In the formula, ξ1 represents the carbon price prediction error, which follows a normal distribution N(0,δ1). In this embodiment, δ1 = 0.3a is taken.

[0048] Before considering the uncertainty of light intensity, we fix the light intensity as b, and assume that the uncertain light intensity follows a normal distribution. Therefore, the light intensity b′ after considering the uncertainty is:

[0049] b′=b+ξ2 (2)

[0050] In the formula, ξ2 represents the illumination prediction error, which follows a normal distribution N(0,δ2). In this embodiment, δ2 = 0.1b.

[0051] Before considering wind speed uncertainty, the wind speed is fixed at c, and it is assumed that the uncertain wind speed follows a normal distribution. Therefore, the wind speed c′ after considering uncertainty is:

[0052] c′=c+ξ3 (3)

[0053] In the formula, ξ3 is the wind speed prediction error, which follows a normal distribution N(0,δ3). In this embodiment, δ3 = 0.1c is taken.

[0054] S12. A scenario in which a large amount of carbon price, light intensity and wind speed are obtained by sampling method;

[0055] S13. K-means clustering method is used to cluster the obtained carbon price, light intensity and wind speed scenarios respectively. After clustering, carbon price scenario, light intensity scenario and wind speed scenario are obtained, which are the typical scenarios of carbon price, light intensity and wind speed.

[0056] S14. The number of typical scenarios for carbon price, light intensity, and wind speed is expressed using the sum of squared errors index, as shown below:

[0057] (4)

[0058] In the formula, Indicates the first The a-th class of random variables; express Sample data in; Indicates the first The centroid of the a-th class of random variables; Represents the carbon price as a random variable; υ represents a random variable representing light intensity; υ represents a random variable representing wind speed.

[0059] The typical scenarios of carbon price, light intensity, and wind speed are represented by vectors obtained through K-means clustering. , and This indicates that a typical scene set constructed from the scene vectors of these three entities is... , where N represents the total number of scenarios, and its value is the product of the number of typical scenarios for carbon price, light intensity, and wind speed.

[0060] Step S2: Establish a multi-energy flow model and combine it with a carbon price model that considers carbon price uncertainty to establish a carbon trading cost model. The specific steps are as follows:

[0061] S21. First, establish a multi-energy flow model. The multi-energy flow model described in this embodiment of the invention includes a combined heat and power unit, a gas-fired boiler, an electric chiller, an electric boiler, electric energy storage, photovoltaics, a wind turbine, and electric energy storage. The following sections will model each of these components:

[0062] ① Combined heat and power units

[0063] CHP produces electricity and heat by consuming natural gas, as shown in equations (5) and (6):

[0064] (5)

[0065] (6)

[0066] In the formula, and These represent the electrical output power and thermal output power of CHP, respectively. This indicates the amount of natural gas consumed by CHP; and These represent the electrical output efficiency and thermal output efficiency of CHP, respectively.

[0067] ② Gas-fired boiler

[0068] GB is another type of heating device that provides heat energy by burning natural gas, and its model is shown in equation (7):

[0069] (7)

[0070] In the formula, Indicates the thermal output power of GB; This indicates the amount of natural gas consumed by GB; This indicates the thermal output efficiency of GB.

[0071] ③ Electric boiler

[0072] EB generates heat by consuming electrical energy, as shown in equation (8):

[0073] (8)

[0074] In the formula, This indicates the thermal output power of EB; This indicates the electrical power consumed by EB; This indicates the thermal output efficiency of EB.

[0075] ④ Electric refrigeration unit

[0076] EC uses electrical energy for cooling, as shown in equation (9):

[0077] (9)

[0078] In the formula, This indicates the EC's cold output power; This indicates the electrical power consumed by the EC; This indicates the cold output efficiency of the EC.

[0079] ⑤ Photovoltaics

[0080] The electrical output power of a PV is mainly determined by factors such as the light intensity irradiating the PV surface and the physical parameters of the PV. The input-output relationship of a PV is shown in equation (10):

[0081] (10)

[0082] In the formula, P PV Q represents the electrical output power of photovoltaics; pv This indicates the peak capacity of the PV (kWp). The power derating factor for photovoltaics is used to characterize the decrease in output power caused by surface dirt and aging, and is typically taken as 0.9; G T Indicates actual light intensity (kW / m²) 2 ); G T，STC This represents the light intensity under standard test conditions, typically taken as 1 kW / m². 2 .

[0083] ⑥ Fan

[0084] The electrical output power of WT is mainly related to the wind speed. The input-output relationship of WT is shown in equation (11):

[0085] (11)

[0086] In the formula, P WT This indicates the electrical output power of the fan; This indicates the cut-in wind speed of the fan, typically taken as 3 m / s; This indicates the cut-out velocity of the fan, typically taken as 25 m / s; This indicates the rated wind speed of the fan, typically taken as 13.5 m / s; Q WT This indicates the installed capacity of the wind turbine.

[0087] ⑦ Energy storage

[0088] The energy storage relationship before and after charging and discharging in electrical energy storage is as follows: (12)

[0089] In the formula: This indicates the energy stored after the electrical energy storage device has been charged and discharged. This indicates the energy stored in the electrical energy storage system before charging and discharging. and These represent the charging / discharging power of the electrical storage system, respectively. and These represent the charging / discharging efficiency of electrical energy storage, respectively. This indicates the system's operating time interval, with a value of 1 hour.

[0090] S22, with Figure 2 Taking the typical integrated electric, cooling, heating, and gas energy system structure shown as an example, the total carbon emissions of the integrated electric, cooling, heating, and gas energy system during actual operation are calculated based on the typical integrated electric, cooling, heating, and gas energy system. Total free carbon emission allowances allocated by the government to integrated energy systems with electricity, cooling, heating and gas This invention considers that the electricity purchased from the external power grid by the integrated electric-cooling-heating-gas energy system comes entirely from thermal power plants. The CHP unit can simultaneously provide both electricity and heat; therefore, its power generation is converted into equivalent heat output, and carbon emission allowances are calculated based on the total heat output. EB and EC units only consume electricity for heating and cooling respectively, and are generally considered in current research not to generate carbon emissions. Considering the complex construction processes of photovoltaic and wind turbines and the associated carbon emissions, this paper considers the carbon emissions of photovoltaic and wind turbines throughout their entire life cycle for a more refined carbon emission model. Through normalization calculations, the equivalent carbon emissions of photovoltaic and wind turbines during their operation can be obtained.

[0091] (13)

[0092] (14)

[0093] In the formula This represents the total carbon emissions of the integrated electric, cooling, heating, and gas energy system during actual operation. This refers to the total amount of free carbon emission allowances allocated by the government to integrated energy systems (electricity, cooling, heating, and gas). , , , and These represent the carbon emissions from electricity purchased from the external power grid by the integrated electric, cooling, heating, and gas energy system, the carbon emissions from CHP units, GB, photovoltaic, and wind turbine output power, respectively. , , and These represent the carbon emission coefficient per unit of electricity supplied by the external power grid, the carbon emission coefficient per unit of heat supplied, and the carbon emission coefficient per unit of output power of photovoltaic and wind turbines, respectively. This represents the conversion factor for converting the unit power generation of the CHP unit into heat supply. , , , and These represent the free carbon emission allowances for electricity purchased from the external grid by the integrated electric, cooling, heating, and gas energy system, respectively, and the free carbon emission allowances for the output power of CHP units, GB, and photovoltaic and wind turbines. , , and These represent the carbon emission quota coefficient per unit of electricity supplied by the external power grid, the carbon emission quota coefficient per unit of heat supplied, and the carbon emission quota coefficient per unit of output power of photovoltaic and wind turbines, respectively. , , , , and These represent the power purchased from the external power grid, the electrical output power of CHP, the thermal output power of CHP, the thermal output power of GB, the electrical output power of PV, and the electrical output power of WT, respectively.

[0094] S23. Based on the carbon price a′ after considering uncertainties, the total carbon emissions during the actual operation of the integrated energy system (electricity, cooling, heating, and gas) Total free carbon emission allowances allocated by the government to integrated energy systems with electricity, cooling, heating and gas A carbon trading cost model is established, including carbon emission quota allocation. The carbon emissions of this integrated energy system (electricity, cooling, heating, and gas) mainly originate from the primary energy supply side and energy storage equipment, namely, carbon emissions from purchasing electricity from the external grid, consuming natural gas, generating electricity from PV and WT, and energy storage. Although PV, WT, and energy storage have zero carbon emissions during operation, they generate significant carbon emissions during manufacturing and transportation. Therefore, by adopting life cycle analysis, the normalized carbon emissions of PV, WT, and energy storage during operation can be obtained. The coupling relationship between multi-energy flow and carbon trading volume in the integrated energy system is modeled as follows, i.e., the carbon trading cost model, where the carbon trading cost is the product of the carbon trading volume and the carbon price considering uncertainty:

[0095] =a′(E real -E rate (15)

[0096] In the formula, For carbon trading costs; when A negative value indicates a sale, and the integrated electric, cooling, heating, and gas energy system generates profit; when... When the value is positive, it indicates a purchase, representing the cost incurred by the integrated energy system of electricity, cooling, heating, and gas; a′ represents the carbon price after considering uncertainties. This represents the total carbon emissions of the integrated electric, cooling, heating, and gas energy system during actual operation. This refers to the total free carbon emission allowance allocated by the government to integrated energy systems that combine electricity, cooling, heating, and gas.

[0097] S3. Based on the typical scenarios, establish an optimal configuration model for an integrated energy system of electricity, cooling, heating and gas based on CVaR. Import the electricity, heating and cooling load data, the typical carbon price scenarios obtained after clustering, and the typical renewable energy output power scenarios into the optimal configuration model. Use the smoothing method to transform the optimal configuration model into a mixed integer linear programming model. Use the branch and bound algorithm to solve the mixed integer linear programming model to obtain the planning scheme under a given confidence level.

[0098] The method for establishing an optimal configuration model for an integrated electric cooling, heating, and gas energy system based on CVaR includes the following steps:

[0099] S31. Establish an objective function with the goal of minimizing the CVaR of the total planning cost, and simultaneously generate the constraints of the optimization configuration model. The optimization configuration model includes the objective function and the constraints, which include equality constraints and inequality constraints.

[0100] (16)

[0101] (17)

[0102] (18)

[0103] (19)

[0104] (20)

[0105] (twenty one)

[0106] =a (E) real -E rate ) (twenty two)

[0107] In the formula: This indicates the confidence level, reflecting the system's aversion to the risk of fluctuations in total planning costs. Indicates at confidence level Value at risk of total planning cost, where value at risk refers to the threshold of the maximum total planning cost not exceeding a given confidence level; This represents the total planning cost under scenario S, where scenario S is the typical scenario. This represents the equivalent annual cost of investment in scenario S; This represents the fuel cost in scenario S; This indicates the maintenance cost in scenario S; This represents the carbon trading cost under scenario S; when A negative value indicates a sale, and the integrated electric, cooling, heating, and gas energy system generates profit; when... When the value is positive, it indicates the cost of purchasing an integrated energy system (electric, cooling, heating, and gas); a Represents the carbon price in scenario S; r represents the discount rate, which is a constant in annual increments; J represents the candidate set of equipment; l j This represents the lifespan of the j-th device; This represents the capacity of the j-th device in scenario S; This represents the price per unit capacity of the j-th device; This represents the maintenance cost per unit power of the j-th device; This represents the output power of the j-th device at time t in scenario S; T represents the total annual operating time, which is 8760 hours. and Let represent the amount of natural gas purchased by the system and the amount of electricity purchased from the external power grid at time t in scenario S, respectively. and These represent the prices of natural gas and electricity, respectively. This indicates the system's operating time interval, with a value of 1 hour. Indicates at confidence level The CVaR value below.

[0108] In this embodiment of the invention, the equality constraints include the system's energy balance constraints and the equality relationships between the inputs and outputs of various devices in the multi-energy flow model, including electrical power balance constraints, thermal power balance constraints, cold power balance constraints, and energy storage constraints at the beginning and end of the operating cycle, as shown in the following equations:

[0109] (twenty three)

[0110] (twenty four)

[0111] (25) (26)

[0112] In the formula, Indicates the power purchased from the external power grid; Indicates electrical load power; Indicates heat load power; Indicates cooling load power; and These represent the stored energy at the beginning and end of the operating cycle of the electrical energy storage, respectively.

[0113] In this embodiment of the invention, the inequality constraints include upper and lower limits of the output power during device operation, constraints on the installed capacity of the device, and constraints on the maximum charging and discharging power and stored energy, as shown in the following formula:

[0114] (27)

[0115] (28)

[0116] (29)

[0117] (30)

[0118] (31)

[0119] (32)

[0120] In the formula: P j，t express This represents the output power of the j-th device at time t; This represents the installed capacity of the j-th device; Indicates the maximum transmission power of the tie line; This indicates the maximum installed capacity of device j; and These represent the maximum charging / discharging power of the electrical energy storage; The 0-1 variables introduced are used to control whether the energy storage device can charge and discharge simultaneously; W ES This refers to the energy stored in electrical energy storage; and These represent the minimum and maximum energy storage capacity of electrical energy storage, respectively.

[0121] S32. Introduce auxiliary solution variable z S The objective function is then transformed into a mixed-integer linear programming model.

[0122] Since equation (16) is a nonlinear optimization problem, an auxiliary solution variable z is introduced to facilitate the solution. s Equation (16) is transformed into equation (33) and two linear inequalities as shown in equations (34) and (35):

[0123] (33)

[0124] (34)

[0125] (35)

[0126] In the formula, Indicates at confidence level The CVaR value below, Indicates the confidence level. Indicates at confidence level The risk value of the total planned cost, where N represents the total number of scenarios. This represents the total planning cost under scenario S, where scenario S is the typical scenario.

[0127] S33. Use Matlab software to call the branch and bound algorithm to solve the mixed integer linear programming model and obtain the planning scheme under the given confidence level.

[0128] The optimized configuration model for the above equations (16)-(35) is actually a mixed-integer linear optimization problem. In this embodiment of the invention, the branch and bound algorithm is called using Matlab software to solve the mixed-integer linear optimization problem, and a planning scheme under a given confidence level is obtained. Matlab software is a commercial mathematical software produced by MathWorks Inc. in the United States, used in fields such as data analysis, wireless communication, deep learning, image processing and computer vision, signal processing, quantitative finance and risk management, robotics, and control systems.

[0129] This invention also provides an integrated energy optimization configuration system for electricity, cooling, heating, and gas, which includes a calculation module and a processing module:

[0130] The calculation module calculates the carbon price and renewable energy output power after considering the uncertainty of carbon price and renewable energy output power, and generates typical scenarios of the carbon price and renewable energy output power. It establishes a multi-energy flow model and combines it with the carbon price after considering the uncertainty of carbon price to establish a carbon trading cost model. Based on the typical scenarios, it establishes an optimal configuration model of an integrated energy system based on CVaR, and imports the electric, heat and cooling load data, the typical carbon price scenarios obtained after clustering, and the typical renewable energy output power scenarios into the optimal configuration model.

[0131] The processing module uses a smoothing method to transform the optimization configuration model into a mixed-integer linear programming model, and uses a branch and bound algorithm to solve the mixed-integer linear programming model to obtain a planning scheme under a given confidence level.

[0132] This invention also provides an integrated energy optimization configuration device for electric cooling, heating, and gas systems, comprising: a memory for storing a computer program; and a processor for executing the computer program to implement the steps of the integrated energy optimization configuration method for electric cooling, heating, and gas systems.

[0133] The following is a specific embodiment. This embodiment uses a certain electric cooling and heating integrated energy system in northern my country. The system adopts the "grid-connected but not grid-connected" mode. Combined with the above formulas (1) to (35), the electric cooling and heating integrated energy is optimized. Figure 2 This is a schematic diagram of a typical integrated energy system combining electricity, cooling, heating, and gas. To accurately reflect the system's actual operation, data from three typical days—summer, winter, and spring / autumn—are selected to represent the annual operating conditions. A fixed natural gas price of 2.55 yuan / m³ is used. 3 The lower calorific value of natural gas combustion is 9.7 kWh / m³. 3 The converted natural gas price is 0.26 yuan / kWh. The system uses time-of-use pricing, dividing the day into off-peak hours (0:00-8:00), peak hours (8:00-12:00, 16:00-20:00), and normal hours (12:00-16:00, 20:00-24:00). The normal hour price is 0.8 yuan / kWh, the peak hour price is 1.6 yuan / kWh, and the off-peak hour price is 0.4 yuan / kWh.

[0134] Table 1 shows the carbon emission coefficients per unit of active power and the carbon quota coefficients allocated by the government for different energy types. It can be seen that the carbon emission coefficients per unit of power for wind turbines and photovoltaic power generation are less than the carbon quota coefficients. This means that when participating in carbon trading, integrated energy systems (electricity, cooling, heating, and gas) can offset their own carbon emissions through wind turbines and photovoltaic power generation, while simultaneously selling excess carbon emission rights in the carbon trading market for profit. The economic and technical parameters of the equipment are shown in Table 2.

[0135] Table 1 Carbon emission coefficients and carbon quota coefficients for different energy types

[0136]

[0137] Table 2 Equipment Economic and Technical Parameters

[0138]

[0139] The electricity, heat, and cooling load curves, as well as the curves for fixed light intensity and wind speed, of the integrated electric, cooling, heating, and gas energy system on typical days in summer, winter, and spring / autumn are shown below. Figures 3-4 As shown.

[0140] Based on historical carbon price data (2017-2021), and following a normal distribution, the carbon price and renewable energy output power are calculated, taking into account the uncertainties of carbon price and renewable energy output power. Renewable energy includes solar power and wind power, and the carbon price is the carbon trading price. ξ1 represents the carbon price prediction error, following a normal distribution N(0,δ1). In this embodiment, δ1 = 0.3a, where a is the fixed carbon price. That is, the average fixed carbon price is approximately 50 yuan / ton, and the standard deviation δ1 is 15 yuan / ton, meaning the carbon price considering uncertainty is 1.3 times the fixed carbon price. Figure 4 Using fixed light intensity and wind speed data as the mean, the standard deviation of the data is approximately 0.1 times the mean, meaning that the light intensity and wind speed after considering uncertainty are 1.1 times the fixed light intensity and wind speed. Furthermore, according to formulas (1)-(4), 500 carbon price, light intensity, and wind speed scenarios are sampled using a sampling method. The carbon price, light intensity, and wind speed scenarios are then clustered using K-means clustering. The resulting carbon price, light intensity, and wind speed scenarios are typical scenarios for carbon price, light intensity, and wind speed. The sum of squared errors (SSE) index is then used to express the number of typical carbon price, light intensity, and wind speed scenarios. According to formula (4), the relationship between the SSE of carbon price, light intensity, and wind speed and the number of clustered scenarios is obtained, as detailed below. Figures 5-7 As shown.

[0141] from Figures 5-7 It can be seen that the SSE (Solution-Oriented Sequence) for carbon price, light intensity, and wind speed decreases with the increase of the number of clustering scenarios. As shown in the figure, when the number of clustering scenarios for carbon price, light intensity, and wind speed is greater than 14, 12, and 14 respectively, the rate of SSE decrease becomes very slow. Therefore, in this embodiment, the final number of typical scenarios for carbon price, light intensity, and wind speed selected are 14, 12, and 14 respectively. Figures 8-14 As shown, this is a typical scenario for carbon price, light intensity, and wind speed after clustering. For easier viewing, the light intensity and wind speed scenarios are divided into three images, as follows: Figures 9-14 As shown, the total number of scenarios N, consisting of typical scenarios of carbon price, light intensity, and wind speed, is... There are 1 / 2352 chances for each scenario.

[0142] To verify the effectiveness of the method in the embodiments of the present invention, the following three scenarios are constructed for comparison:

[0143] Scenario I: Without considering energy storage, a given carbon price and renewable energy output power;

[0144] Scenario II: Without considering energy storage, carbon prices and renewable energy output power are uncertain. Set to 90%;

[0145] Scenario III: Considering energy storage, carbon prices, and uncertainties in renewable energy output power, Set it to 90%.

[0146] The comparison results of the planning schemes for the three scenarios are shown in Table 3, where the units for investment cost, operation and maintenance cost, carbon cost, total cost and CVaR are all in RMB 10,000.

[0147] Table 3 Comparison of Planning Schemes in Three Scenarios

[0148]

[0149] As shown in Table 3, in Scenario II, the capacities of CHP, EB, PV, and WT are increased by 298kW, 314kW, 1470kWp, and 649kW respectively compared to Scenario I, while the capacity of GB is decreased by 622kW. In Scenario III, the capacities of WT, EB, and GB are increased by 534kW, 204kW, and 78kW respectively compared to Scenario II, while the capacities of CHP and PV are decreased by 678kW and 5964kWp respectively. The following analysis will first examine the comparison results of the planning schemes from an economic perspective.

[0150] Table 3 shows that the total cost of Scenario II is 1.5347 million yuan higher than that of Scenario I. The following analysis focuses on investment costs, operation and maintenance costs, and carbon trading costs. Typical daily gas and electricity purchase volumes for the three scenarios in winter are shown below. Figure 15 As shown, the conditions in summer and spring / autumn are as follows: Figures 16-17 As shown. From Figure 15 As can be seen, in scenarios I and II, since the system lacks energy storage, scenario II, to address the risk of planned total cost fluctuations caused by the uncertainty of carbon prices and renewable energy output power, requires a larger renewable energy capacity than scenario I to reduce system carbon emissions. Simultaneously, it necessitates increasing gas purchases to maximize CHP output power and increasing external grid power purchases to cope with renewable energy fluctuations. This results in increased operation and maintenance costs and carbon trading costs of RMB 355,300 and RMB 44,600, respectively, in scenario II. Due to the increased capacity of CHP, PV, and WT in scenario II, the investment cost is RMB 1,135,000 higher than in scenario I, ultimately leading to a higher total cost in scenario II compared to scenario I. However, scenario II considers the risk of planned total cost fluctuations during optimized configuration, resulting in a CVaR reduction of RMB 1,190,100 compared to scenario I. The CVaR value reflects the level of planned total cost fluctuation risk faced by the system; therefore, compared to scenario I, scenario II reduces the risk of planned total cost fluctuations.

[0151] By comparing scenarios I and II, it is shown that when the system is not equipped with energy storage, the optimized configuration method proposed in this invention can reduce the risk of fluctuation in the total planned cost of the system, but this comes at the cost of increased carbon emissions and total cost.

[0152] On the other hand, compared to Scenario II, Scenario III reduces the total cost by RMB 1.6392 million. This is because Scenario III incorporates energy storage to address the uncertainty of renewable energy output, eliminating the need for CHP (Consumer Gas Storage) to reduce gas purchases and eliminating electricity purchases from the external grid. Therefore, compared to Scenario II, Scenario III relatively reduces both gas and electricity purchases from the external grid. Figure 15 As shown, this leads to a significant reduction in the operation and maintenance costs of Scenario III, with a decrease of approximately RMB 1.1903 million, far exceeding the increase in investment costs of RMB 318,500. Furthermore, the RMB 61,600 profit generated from negative carbon emissions in Scenario III helps reduce the total system cost. Therefore, the total cost of Scenario III is lower than that of Scenario II. Simultaneously, because configuring energy storage can address the uncertainty of renewable energy output power, the CVaR of Scenario III is reduced by RMB 726,800 compared to Scenario II, thus mitigating the risk of fluctuations in the total planned cost.

[0153] By comparing scenarios II and III, it is shown that when the system is equipped with energy storage, energy storage can not only reduce the risk of fluctuation in the total planned cost of the system, but also achieve negative carbonization of the system, that is, the system generates equivalent negative carbon emissions and obtains carbon trading revenue.

[0154] Furthermore, taking Scenario III as an example, we analyze the impact of different confidence levels on the planning results. The comparison results of planning schemes under different confidence levels are shown in Table 4.

[0155] Table 4 Comparison of Planning Schemes under Different Confidence Levels

[0156]

[0157] As shown in Table 4, with increasing confidence levels, the system increasingly avoids the risk of planned total cost fluctuations. This leads to increased energy storage capacity to address the uncertainty of renewable energy output power, resulting in higher investment costs and consequently higher total system costs. However, the planned total cost fluctuation risk, as measured by CVaR, decreases. The aforementioned operation and maintenance costs are fuel costs plus maintenance costs.

[0158] Furthermore, as can be seen from Table 4, when the system is configured with a high proportion of renewable energy and a certain capacity of energy storage, the system generates negative carbon emissions. Moreover, as the confidence level increases, the carbon trading revenue of the system increases. The system actively uses the uncertainty of carbon trading costs to make a profit, but this requires an increase in investment costs.

[0159] The comparison results of total cost and CVaR at different confidence levels are as follows: Figure 18 As shown in the figure, as the confidence level increases, the total cost of the system increases while the CVaR decreases, indicating a contradictory relationship between the total cost and CVaR. Between the 85% and 90% confidence levels, there is an intersection point A between the total cost and CVaR. To the left of intersection point A, when the system selects planning schemes at 80% and 85% confidence levels, the CVaR curve is above the total cost curve, indicating a "high-risk, low-cost" planning scheme. To the right of intersection point A, when selecting planning schemes at 90%, 95%, and 99% confidence levels, the total cost curve is above the CVaR curve, indicating a "high-cost, low-risk" planning scheme. Integrated energy systems (electricity, cooling, heating, and gas) need to select appropriate planning schemes based on their own risk preferences.

[0160] In summary, this invention proposes a method for optimizing the integrated energy configuration of electricity, cooling, heating, and gas, with the following conclusions:

[0161] 1) When optimizing the configuration of an integrated energy system that combines electricity, cooling, heating and gas, considering the uncertainty of carbon trading costs and renewable energy output power, carbon trading alone cannot effectively control the system's carbon emissions. When dealing with the risk of fluctuations in the total planned cost, the system needs to take into account both system carbon emissions and the development of renewable energy.

[0162] 2) When the system is equipped with electric energy storage, the optimized configuration method proposed in this invention can realize the negative carbonization of the integrated energy system of electricity, cooling, heating and gas, and can reduce the risk of fluctuation in the total planned cost of the system. Moreover, as the confidence level increases, the carbon trading revenue of the system increases.

[0163] 3) There is a contradictory relationship between total planned cost and CVaR. When pursuing a low total cost, the integrated energy system of electricity, cooling, heating and gas faces a high risk of fluctuation in total planned cost. The integrated energy system of electricity, cooling, heating and gas needs to choose the corresponding planning scheme according to its own risk preference.

[0164] The above-described invention merely illustrates implementation methods of the present invention and should not be construed as limiting the scope of the invention patent, nor as imposing any form of limitation on the structure of the embodiments of the present invention. It should be noted that those skilled in the art can make various changes and improvements without departing from the concept of the embodiments of the present invention, and these all fall within the protection scope of the embodiments of the present invention.

Claims

1. A method for optimizing the allocation of integrated electric, cooling, heating, and gas energy, characterized in that, The method specifically includes the following steps: Calculate the carbon price and renewable energy output power after considering the uncertainty of carbon price and renewable energy output power, and generate typical scenarios of the carbon price and renewable energy output power; A multi-energy flow model is established, and a carbon trading cost model is built by combining a carbon price that takes into account the uncertainty of carbon prices. Based on the aforementioned typical scenario, an optimal configuration model for an integrated energy system of electricity, cooling, heating, and gas based on CVaR is established. The data on electricity, heat, and cooling loads, typical carbon price scenarios obtained after clustering, and typical renewable energy output power scenarios are imported into the optimization configuration model. The smoothing method is used to transform the optimization configuration model into a mixed-integer linear programming model. The branch and bound algorithm is used to solve the mixed-integer linear programming model to obtain the planning scheme under a given confidence level. The method for establishing an optimal configuration model for an integrated electric cooling, heating, and gas energy system based on CVaR includes the following steps: An objective function is established with the goal of minimizing the CVaR of the total planning cost. At the same time, the constraints of the optimization configuration model are generated. The optimization configuration model includes the objective function and the constraints, which include equality constraints and inequality constraints. The CVaR characterizes the measure of the volatility risk of the total planning cost. By introducing auxiliary variables, the objective function is transformed into a mixed-integer linear programming model; The mixed-integer linear programming model was solved using the branch and bound algorithm in Matlab software to obtain the planning scheme under a given confidence level.

2. The method for optimizing the allocation of integrated electric, cooling, heating, and gas energy according to claim 1, characterized in that, The steps of calculating carbon price and renewable energy output power after considering the uncertainty of carbon price and renewable energy output power, and generating typical scenarios of carbon price and renewable energy output power, include: Based on the normal distribution, calculate the carbon price and renewable energy output power after considering the uncertainty of carbon price and renewable energy output power, wherein renewable energy includes solar power and wind power. Multiple scenarios with carbon price, light intensity, and wind speed were obtained using a sampling method; K-means clustering was used to cluster the obtained carbon price, light intensity and wind speed scenes respectively, resulting in carbon price scene, light intensity scene and wind speed scene; The number of typical scenarios for carbon price, light intensity, and wind speed is expressed using the sum of squared errors index.

3. The method for optimizing the allocation of integrated electric, cooling, heating, and gas energy according to claim 1, characterized in that, The method for establishing a carbon trading cost model includes the following steps: A multi-energy flow model is established, which includes a combined heat and power unit, a gas boiler, an electric chiller, an electric boiler, an electric energy storage, a photovoltaic system, a wind turbine, and an electric energy storage system. The output power of the combined heat and power unit, the gas boiler, the electric chiller, the electric boiler, the photovoltaic system, and the wind turbine, as well as the charging and discharging power and stored energy of the electric energy storage are calculated respectively. Calculate the total carbon emissions of the typical integrated electric cooling, heating and gas energy system during actual operation and the total free carbon emission quota allocated by the government to the integrated electric cooling, heating and gas energy system based on the typical integrated electric cooling, heating and gas energy system. A carbon trading cost model is established based on the carbon price after taking uncertainty into account, the total carbon emissions of the integrated energy system (electricity, cooling, heating, and gas) during actual operation, and the total free carbon emission allowance allocated by the government to the integrated energy system. This model includes the allocation of carbon emission allowances.

4. The method for optimizing the allocation of integrated electric, cooling, heating, and gas energy according to claim 1, characterized in that, The formula for the objective function is as follows: =a (AND real -AND rate ) In the formula Indicates at confidence level Value at risk of total planning cost, where value at risk refers to the threshold of the maximum total planning cost not exceeding a given confidence level; This represents the total planning cost in scenario S; This represents the equivalent annual cost of investment in scenario S; This represents the fuel cost in scenario S; This indicates the maintenance cost in scenario S; This represents the carbon trading cost under scenario S; when A negative value indicates a sale, and the integrated electric, cooling, heating, and gas energy system generates profit; when... When the value is positive, it indicates the cost of purchasing an integrated energy system (electric, cooling, heating, and gas); a Represents the carbon price in scenario S; r represents the discount rate, which is a constant in annual increments; J represents the candidate set of equipment; l j This represents the lifespan of the j-th device; This represents the capacity of the j-th device in scenario S; This represents the price per unit capacity of the j-th device; This represents the maintenance cost per unit power of the j-th device; This represents the output power of the j-th device at time t in scenario S; T represents the total annual operating time, which is 8760 hours. and Let represent the amount of natural gas purchased by the system and the amount of electricity purchased from the external grid at time t in scenario S, respectively. and These represent the prices of natural gas and electricity, respectively. This indicates the system's operating time interval, with a value of 1 hour. Indicates at confidence level The CVaR value below; The confidence level reflects the system's aversion to the risk of fluctuations in the total planning cost. This represents the total carbon emissions of the integrated electric, cooling, heating, and gas energy system during actual operation. This refers to the total free carbon emission allowance allocated by the government to integrated energy systems that combine electricity, cooling, heating, and gas.

5. The method for optimizing the allocation of integrated electric, cooling, heating, and gas energy according to claim 4, characterized in that: The equality constraints include the system's energy balance constraints and the equality relationships between the inputs and outputs of various devices in the multi-energy flow model. These equality relationships include electrical power balance constraints, thermal power balance constraints, cold power balance constraints, and the relationship between energy storage at different stages. The equality relationships are shown below: In the formula, Indicates the power purchased from the external power grid, Indicates the electrical output power of CHP, Indicates PV power output, Indicates the electrical output power of WT, Indicates electrical load power, Indicates the electrical power consumed by the EC. Indicates the electrical power consumed by EB. Indicates the thermal output power of CHP, Indicates the thermal output power of GB, Indicates the thermal output power of EB, Indicates heat load power, Indicates the EC's cold output power, Indicates cooling load power, and These represent the stored energy at the beginning and end of the operating cycle of the electrical energy storage, respectively.

6. The method for optimizing the allocation of integrated electric, cooling, heating, and gas energy according to claim 5, characterized in that: The inequality constraints include upper and lower limits of the output power during equipment operation, the installed capacity of the equipment, and the maximum charge / discharge power and energy storage constraints. The constraint formulas for the upper and lower limits of the output power during equipment operation, the installed capacity of the equipment, and the maximum charge / discharge power and energy storage constraints are shown below: In the formula: P j，t express This represents the output power of the j-th device at time t; This represents the installed capacity of the j-th device; Indicates the maximum transmission power of the tie line; This indicates the maximum installed capacity of device j; and These represent the charging / discharging power of the electrical storage system, respectively. and These represent the maximum charging / discharging power of the electrical energy storage; The 0-1 variables introduced are used to control whether the energy storage device can charge and discharge simultaneously; W ES This refers to the energy stored in electrical energy storage; and These represent the minimum and maximum energy storage capacity of electrical energy storage, respectively.

7. The method for optimizing the allocation of integrated electric, cooling, heating, and gas energy according to claim 6, characterized in that, By introducing auxiliary variables, the objective function is transformed into a mixed-integer linear programming model, as shown below: In the formula, Indicates at confidence level The CVaR value below, Indicates the confidence level, reflecting the system's aversion to the risk of fluctuations in total planning cost, z S To solve for the variables, Indicates at confidence level The risk value of the total planned cost, where N represents the total number of scenarios. This represents the total planning cost under scenario S, where scenario S is the typical scenario.

8. A system based on the integrated energy optimization configuration method of electric cooling, heating and gas according to claim 1, characterized in that, Includes a calculation module and a processing module: The calculation module calculates the carbon price and renewable energy output power after considering the uncertainty of carbon price and renewable energy output power, and generates typical scenarios of the carbon price and renewable energy output power. It establishes a multi-energy flow model and combines it with the carbon price after considering the uncertainty of carbon price to establish a carbon trading cost model. Based on the typical scenarios, it establishes an optimal configuration model of an integrated energy system based on CVaR, and imports the electric, heat and cooling load data, the typical carbon price scenarios obtained after clustering, and the typical renewable energy output power scenarios into the optimal configuration model. The processing module uses a smoothing method to transform the optimization configuration model into a mixed-integer linear programming model, and uses a branch and bound algorithm to solve the mixed-integer linear programming model to obtain a planning scheme under a given confidence level.

9. A device for optimizing the allocation of integrated electric, cooling, heating, and gas energy, characterized in that: include: Memory, used to store computer programs; A processor, configured to execute the computer program to implement the steps of the integrated energy optimization configuration method for electric cooling, heating, and gas as described in any one of claims 1 to 7.

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