Energy system configuration method and device considering carbon trading price uncertainty

Abstract

The application discloses an energy system configuration method and device considering carbon transaction price uncertainty, and the method comprises the following steps: establishing a carbon-energy coupling model based on the multi-energy flow of an energy hub and the carbon transaction volume; performing uncertainty modeling based on a normal distribution with zero mean, and generating a scenario set according to the modeling result; establishing a system planning total cost model according to the carbon-energy coupling model and the scenario set; establishing a risk value model under each confidence level based on the system planning total cost model; and solving to obtain a configuration scheme with the minimum risk value model as the target, so that the technical problem of planning total cost fluctuation risk can be solved.

Description

Technical Field

[0001] This invention relates to an energy system configuration method and apparatus that takes into account the uncertainty of carbon trading prices, belonging to the field of system planning technology. Background Technology

[0002] Integrated energy systems (IES) can break down the technical barriers between electricity, gas, cooling, and heating energy subsystems, enabling multi-energy complementarity and synergistic optimization, and effectively contributing to the clean and low-carbon development of the energy industry.

[0003] The carbon trading market is currently in its early stages, and carbon trading prices are highly uncertain. Furthermore, the inherent power fluctuations of renewable energy generation within IES (Engineering Industries for Science) directly impact the potential for stable participation in carbon trading. The total planned cost of an IES typically includes equipment investment costs, fuel costs, maintenance costs, and carbon trading costs. Due to the uncertainties in both carbon trading prices and renewable energy generation, the total planned cost is highly susceptible to deviations from the expected value, i.e., the risk of fluctuations in the total planned cost. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of the prior art and provide an energy system configuration method and apparatus that takes into account the uncertainty of carbon trading prices, thereby solving the technical problem of the risk of fluctuation in total planning cost.

[0005] To achieve the above objectives, the present invention is implemented using the following technical solution:

[0006] In a first aspect, the present invention provides an energy system configuration method that considers the uncertainty of carbon trading prices, comprising:

[0007] A carbon energy coupling model based on multi-energy flow and carbon trading volume of energy hubs;

[0008] Uncertainty modeling is performed based on a normal distribution with zero mean, and a scenario set is generated based on the modeling results.

[0009] A total cost model for system planning is established based on the carbon energy coupling model and scenario set.

[0010] Establish risk value models at various confidence levels based on the total cost model of systems planning;

[0011] The goal is to minimize the risk-value model and obtain the allocation scheme.

[0012] Optionally, the carbon energy coupling model based on multi-energy flow and carbon trading volume using energy hubs includes:

[0013] Establish the basic model of an energy hub:

[0014]

[0015] In the formula, L n For the nth form of energy output in the energy hub, P m For the m-th type of energy input in the energy hub, c nm H represents the coupling factor for the nth and mth forms of energy; u Let k be the charging and discharging power of the u-th type of energy storage. nu Let be the energy storage coupling factor for the nth and uth forms of energy;

[0016] By incorporating carbon trading volume into the basic model of the energy hub, a carbon-energy coupling model is established:

[0017]

[0018] In the formula, α m β m Let f(m) be the carbon emission coefficient and the free carbon allowance coefficient for the m-th form of energy in the energy hub. ρ u Let be the carbon emission coefficient and the free carbon quota coefficient of the u-th type of energy storage in the energy hub; E is the carbon trading volume of the energy hub.

[0019] Optionally, the uncertainty modeling based on the zero-mean normal distribution includes:

[0020] Constructing a probability distribution model for carbon trading prices:

[0021]

[0022] In the formula, X e Let μ be a random variable representing carbon trading prices. e σ e The mean and standard deviation of carbon trading prices;

[0023] Construct a probability distribution model for light intensity:

[0024]

[0025] In the formula, G t Let μ be a random variable representing light intensity. t σ t The mean and standard deviation of the light intensity;

[0026] Construct a probability distribution model for wind speed:

[0027]

[0028] In the formula, G v Let μ be a random variable representing light intensity. v σv The mean and standard deviation of the wind speed are given.

[0029] Optionally, generating a scene set based on the modeling results includes:

[0030] Based on the probability distribution models of carbon trading prices, light intensity, and wind speed, Monte Carlo sampling method was used to obtain the carbon trading price set, light intensity set, and wind speed set.

[0031] The K-means clustering algorithm was used to cluster the carbon trading price set, the light intensity set, and the wind speed set respectively.

[0032] Carbon trading prices, light intensity, and wind speed are extracted from the clustered carbon trading price set, light intensity set, and wind speed set, respectively, and combined to form a scenario.

[0033] Get all the scene sets corresponding to the combinations.

[0034] Optionally, the clustering includes:

[0035] Randomly select k elements from the set as the initial centroids;

[0036] Calculate the Euclidean distance between each element in the set and the initial cluster center;

[0037] Each element in the set is assigned to the initial centroid corresponding to the minimum Euclidean distance, forming a cluster.

[0038] Calculate the centroid of each cluster, use the calculated centroid as the initial centroid and iterate through the above steps until the calculated centroid converges, and output the final cluster.

[0039] Optionally, obtaining the value of k includes:

[0040] The sum of squared errors (SSE) is calculated based on the final clusters:

[0041]

[0042] In the formula, μ a For the a-th cluster h a The centroid of h, ω is the centroid of the a-th cluster. a Elements in;

[0043] The value of k is calculated with the goal of minimizing the sum of squared errors (SSE).

[0044] Optionally, the total cost model for system planning is as follows:

[0045]

[0046] In the formula, These represent the investment cost, maintenance cost, fuel cost, and carbon trading cost for the s-th scenario, respectively.

[0047]

[0048]

[0049]

[0050]

[0051] In the formula, l j For the lifespan of the j-th device, For the capacity of the j-th device in the s-th scenario, c inv,j Let r be the unit capacity price of the j-th device, r be the discount rate, and J be the number of devices. Let c be the output power of the j-th device in the s-th scenario during the t-th time period, where T is the number of time periods. main,j The maintenance cost per unit power for the j-th device; λ represents the system's natural gas purchases and electricity purchases from the external network in the s-th scenario. gas , λ elec These represent the prices of natural gas and electricity, respectively, with Δt being the system's operating time interval; E tr For the carbon trading volume of the system, Let be the carbon trading price in the s-th scenario.

[0052] Optionally, the Value at Risk (VaR) model is:

[0053]

[0054]

[0055] In the formula, CVaR θ Value at conditional risk at confidence level θ The value at risk of the total cost of system planning at confidence level θ. Let N be the total system planning cost in the s-th scenario, and N be the number of scenarios.

[0056] Secondly, the present invention provides an energy system configuration device that considers the uncertainty of carbon trading prices, the device comprising:

[0057] The first model building module is used to establish a carbon energy coupling model based on the multi-energy flow and carbon trading volume of energy hubs.

[0058] The scene generation module is used to perform uncertainty modeling based on a zero-mean normal distribution and generate a scene set based on the modeling results.

[0059] The second model building module is used to build a total cost model for system planning based on the carbon energy coupling model and the scenario set.

[0060] The third model building module is used to build risk value models at various confidence levels based on the total cost model of system planning.

[0061] The model solving module is used to solve for the configuration scheme with the goal of minimizing the value at risk model.

[0062] Thirdly, the present invention provides an energy system configuration device that takes into account the uncertainty of carbon trading prices, including a processor and a storage medium;

[0063] The storage medium is used to store instructions;

[0064] The processor is used to perform the steps of the above method according to the instructions.

[0065] Compared with the prior art, the beneficial effects achieved by the present invention are as follows:

[0066] This invention provides an energy system configuration method and apparatus that considers the uncertainty of carbon trading prices. Compared with traditional modeling methods, modeling the multi-energy flow and carbon trading volume of an independent system is cumbersome and cannot intuitively reflect the actual situation where carbon trading volume depends on multi-energy flow. The carbon-energy coupling model of multi-energy flow and carbon trading volume based on energy hubs proposed in this invention can characterize the actual situation where carbon trading volume depends on multi-energy flow. The total cost model of system planning based on CVaR risk value model proposed in this invention can cope with the risk of fluctuation in total planning cost, while making it possible for IES to develop negative carbonization. Finally, configuration schemes under different confidence levels are provided, which can provide a reference for engineering planners. Attached Figure Description

[0067] Figure 1 This is a flowchart of an energy system configuration method considering the uncertainty of carbon trading prices provided in Embodiment 1 of the present invention;

[0068] Figure 2 This is an embodiment of the present invention providing electrical / heating / cooling load diagrams for typical days in different seasons;

[0069] Figure 3 This is a map of light intensity and wind speed on a typical day in different seasons provided in Embodiment 1 of the present invention;

[0070] Figure 4 This is a schematic diagram of typical daily gas and electricity purchases during winter, provided in one embodiment of the present invention.

[0071] Figure 5 This is a schematic diagram of typical daily gas and electricity purchases in summer provided by Embodiment 1 of the present invention;

[0072] Figure 6 This is a schematic diagram of typical daily gas and electricity purchases during spring and autumn seasons provided in Embodiment 1 of the present invention;

[0073] Figure 7 This is a comparison chart of total cost and CVaR at different confidence levels provided in Embodiment 1 of the present invention. Detailed Implementation

[0074] The present invention will be further described below with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and should not be used to limit the scope of protection of the present invention.

[0075] Example 1:

[0076] like Figure 1 As shown, this embodiment of the invention provides an energy system configuration method that considers the uncertainty of carbon trading prices, including the following steps:

[0077] S1. Establishing a carbon energy coupling model based on multi-energy flow and carbon trading volume of energy hubs; specifically including:

[0078] S11. Establish the basic model of the power hub:

[0079]

[0080] In the formula, L n For the nth form of energy output in the energy hub, P m For the m-th type of energy input in the energy hub, c nm H represents the coupling factor for the nth and mth forms of energy; u Let k be the charging and discharging power of the u-th type of energy storage. nu Let be the energy storage coupling factor for the nth and uth forms of energy;

[0081] S12. Incorporate carbon trading volume into the basic model of the energy hub to establish a carbon-energy coupling model:

[0082]

[0083] In the formula, α m β m Let f(m) be the carbon emission coefficient and the free carbon allowance coefficient for the m-th form of energy in the energy hub. ρ u Let be the carbon emission coefficient and the free carbon quota coefficient of the u-th type of energy storage in the energy hub; E is the carbon trading volume of the energy hub.

[0084] S2. Model uncertainty based on a normal distribution with zero mean, and generate a scenario set based on the modeling results;

[0085] S21. Uncertainty modeling based on a normal distribution with zero mean includes:

[0086] Constructing a probability distribution model for carbon trading prices:

[0087]

[0088] In the formula, X e Let μ be a random variable representing carbon trading prices. e σ e The mean and standard deviation of carbon trading prices;

[0089] Construct a probability distribution model for light intensity:

[0090]

[0091] In the formula, G t Let μ be a random variable representing light intensity. t σ t The mean and standard deviation of the light intensity;

[0092] Construct a probability distribution model for wind speed:

[0093]

[0094] In the formula, G v Let μ be a random variable representing light intensity. v σ v The mean and standard deviation of the wind speed are given.

[0095] S22. Generating a scene set based on the modeling results includes:

[0096] Based on the probability distribution models of carbon trading prices, light intensity, and wind speed, Monte Carlo sampling method was used to obtain the carbon trading price set, light intensity set, and wind speed set.

[0097] The K-means clustering algorithm was used to cluster the carbon trading price set, the light intensity set, and the wind speed set respectively.

[0098] Carbon trading prices, light intensity, and wind speed are extracted from the clustered carbon trading price set, light intensity set, and wind speed set, respectively, and combined to form a scenario.

[0099] Get all the scene sets corresponding to the combinations.

[0100] Clustering includes:

[0101] Randomly select k elements from the sets (carbon trading price set, light intensity set, and wind speed set) as the initial centroids;

[0102] Calculate the Euclidean distance between each element in the set and the initial cluster center;

[0103] Each element in the set is assigned to the initial centroid corresponding to the minimum Euclidean distance, forming a cluster.

[0104] Calculate the centroid of each cluster, use the calculated centroid as the initial centroid and iterate through the above steps until the calculated centroid converges, and output the final cluster.

[0105] The numerical value of k is obtained as follows:

[0106] The sum of squared errors (SSE) is calculated based on the final clusters:

[0107]

[0108] In the formula, μ a For the a-th cluster h a The centroid of h, ω is the centroid of the a-th cluster. a Elements in;

[0109] The value of k is calculated with the goal of minimizing the sum of squared errors (SSE).

[0110] S3. Establish a total cost model for system planning based on the carbon energy coupling model and scenario set;

[0111] The total cost model for system planning is as follows:

[0112]

[0113] In the formula, These represent the investment cost, maintenance cost, fuel cost, and carbon trading cost for the s-th scenario, respectively.

[0114]

[0115]

[0116]

[0117]

[0118] In the formula, l j For the lifespan of the j-th device, For the capacity of the j-th device in the s-th scenario, c inv,j Let r be the unit capacity price of the j-th device, r be the discount rate, and J be the number of devices. Let c be the output power of the j-th device in the s-th scenario during the t-th time period, where T is the number of time periods. main,j The maintenance cost per unit power for the j-th device; λ represents the system's natural gas purchases and electricity purchases from the external network in the s-th scenario. gas , λ elec These represent the prices of natural gas and electricity, respectively, with Δt being the system's operating time interval; E tr For the carbon trading volume of the system, Let be the carbon trading price in the s-th scenario.

[0119] S4. Establish risk value models at various confidence levels based on the total cost model of system planning;

[0120] The Value at Risk (VaR) model is as follows:

[0121]

[0122]

[0123] In the formula, CVaR θ Value at conditional risk at confidence level θ The value at risk of the total cost of system planning at confidence level θ. Let N be the total system planning cost in the s-th scenario, and N be the number of scenarios.

[0124] S5. Solve the value at risk model to obtain the configuration scheme.

[0125] PIES (Integrated Energy Systems for Industrial Parks) are often characterized by high energy consumption and high energy density, and have the potential to integrate and utilize various renewable energy sources. This embodiment uses PIES as an example:

[0126] Step 1: Establish the PIES model based on the carbon-energy coupling model:

[0127] A typical PIES consists of equipment such as 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). PIES can participate in carbon trading to sell surplus carbon emission rights for profit.

[0128] The carbon emissions from the PIES primarily originate from primary energy supply and energy storage devices, 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 employing life cycle analysis, normalized carbon emissions from PV, WT, and energy storage during operation can be obtained. The coupling relationship between multi-energy flows and carbon trading volume in the PIES is modeled as follows:

[0129]

[0130]

[0131]

[0132]

[0133]

[0134] In the formula: P grid P gas P PV and P WT These represent the power purchased from the external grid, the power injected from natural gas, and the power injected from PV and WT, respectively; P EL P CL P HL and E tr These represent the electricity / cooling / heating load and carbon trading volume, respectively; c1 represents the power allocation factor for supplying the electricity load; c2 represents the power allocation factor for supplying the EB; and c3 represents the power allocation factor for supplying the CHP. and These represent the CHP electrical efficiency, CHP thermal efficiency, GB thermal efficiency, EB thermal efficiency, and EC refrigeration efficiency, respectively; P ES This represents the charging / discharging power of electrical energy storage, when P ES When P is greater than 0, it indicates that the electrical energy storage is charging. ES When α is less than 0, it indicates that the stored energy is discharging; grid α PV α WT α gas and These represent the carbon emission coefficients per unit power for electricity purchased from the external grid, PV, WT, natural gas, and electric energy storage, respectively; β grid β PV β WT β gas and ρ ESLet E and F represent the unit power free carbon allowance coefficients for electricity purchased from the external grid, PV, WT, CHP, GB, and energy storage, respectively. In equation (27), the carbon trading volume E of the system is... tr This is the sum of carbon trading volumes for five components: electricity purchased from the external grid, PV, WT, natural gas, and energy storage (if the carbon emission coefficient of any component is greater than the carbon quota coefficient, the carbon trading volume for that component is positive; if the carbon emission coefficient of any component is less than the carbon quota coefficient, the carbon trading volume for that component is negative). When E tr When E is positive, the system needs to purchase carbon emission rights from the carbon trading market; when E is positive... tr When the value is negative, the system can sell excess carbon emission rights on the carbon trading market.

[0135] Step 2: Uncertainty Modeling and Scene Generation

[0136] This invention models the prediction errors of carbon trading prices and renewable energy output and superimposes the predicted values ​​to describe the actual carbon trading prices and renewable energy output power.

[0137] Using neural network prediction methods, it can be determined that the carbon trading price prediction error follows a normal distribution with zero mean. By superimposing this error with the predicted carbon trading price, the probability distribution of the actual carbon trading price can be obtained as shown in the following formula:

[0138]

[0139] In the formula, X e Let μ be a random variable representing carbon trading prices. e σ e The mean and standard deviation of carbon trading prices;

[0140] Probabilistic statistical analysis shows that the prediction errors for light intensity and wind speed follow a normal distribution with zero mean. Therefore, the probability distributions of the actual light intensity and wind speed are as follows:

[0141] Probability distribution model of light intensity:

[0142]

[0143] In the formula, G t Let μ be a random variable representing light intensity. t σ t The mean and standard deviation of the light intensity;

[0144] Probability distribution model of wind speed:

[0145]

[0146] In the formula, G v Let μ be a random variable representing light intensity. v σ vThe mean and standard deviation of the wind speed are given.

[0147] Based on formulas (2)-(4), a sampling method is used to obtain discrete scenarios to describe the possible scenarios of the three. Then, the generated similar scenarios are clustered using the K-means clustering method to reduce the number of scenarios.

[0148] The sum of square error (SSE) index is used to determine the number of clustered scenarios for carbon trading prices, light intensity, and wind speed.

[0149] Typical scenarios for obtaining carbon trading prices, light intensity, and wind speed are represented by vector S. e S t S v This means that a typical scene set constructed from the scene vectors of these three entities is S = {S...} e ,S t ,S v} N N represents the total number of scenarios. As a supplement to the above technical solution, the construction process of the PIES optimization configuration model based on CVaR is as follows:

[0150] Step 3: Establish a RIES runtime optimization model

[0151] 1. Objective function

[0152] This invention establishes a PIES optimization configuration model based on CVaR with the objective of minimizing the CVaR of the total planning cost:

[0153]

[0154]

[0155]

[0156]

[0157]

[0158]

[0159]

[0160] In the formula, CVaR θ The conditional value of risk at confidence level θ is defined as the threshold of the maximum total planning cost not exceeding the given confidence level. The value at risk of the total cost of system planning at confidence level θ. Let N be the total system planning cost in the s-th scenario, and N be the number of scenarios. These represent the investment cost, maintenance cost, fuel cost, and carbon trading cost for the s-th scenario, respectively; j For the lifespan of the j-th device, For the capacity of the j-th device in the s-th scenario, c inv,j Let r be the unit capacity price of the j-th device, r be the discount rate, and J be the number of devices. Let c be the output power of the j-th device in the s-th scenario during the t-th time period, where T is the number of time periods. main,j The maintenance cost per unit power for the j-th device; λ represents the system's natural gas purchases and electricity purchases from the external network in the s-th scenario. gas , λ elec These represent the prices of natural gas and electricity, respectively, with Δt being the system's operating time interval; E tr For the carbon trading volume of the system, Let be the carbon trading price in the s-th scenario.

[0161] 2. Constraints

[0162] The constraints of the PIES programming model include equality constraints and inequality constraints, as follows.

[0163] The equality constraints are the energy balance constraints of the system as shown in equation (1) and the equality relationships between the inputs and outputs of each device.

[0164] The inequality constraints serve as upper and lower limits for the equipment's output during operation, as well as constraints on the equipment's installed capacity, as shown in the following equation:

[0165] 0≤P j,t ≤Q j

[0166]

[0167]

[0168] In the formula: Indicates the maximum transmission power of the tie line; This indicates the maximum installed capacity of device j.

[0169] In addition, energy storage must also meet the energy storage relationship before and after charging and discharging, as well as the maximum charging and discharging power and energy storage constraints, as shown in the following formula:

[0170]

[0171]

[0172]

[0173]

[0174]

[0175] In the formula: This indicates the energy stored in the electrical energy storage system before charging and discharging. This indicates the energy stored after the electrical energy storage device has been charged and discharged. 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. and These represent the maximum charging / discharging power of the electrical energy storage, respectively; ζ ES The 0-1 variables are introduced to control whether the energy storage can be charged and discharged simultaneously; and These represent the minimum and maximum energy storage capacities of electrical energy, respectively. and These represent the stored energy at the beginning and end of the operating cycle of the electrical energy storage, respectively.

[0176] Since equation (5) is a nonlinear optimization problem, to facilitate the solution, an auxiliary solution variable zs is introduced, transforming equation (5) into equation (6) and two linear inequalities as shown in equations (7) and (8):

[0177]

[0178] z s ≥0(7)

[0179]

[0180] This invention uses the branch and bound algorithm to solve the above-mentioned optimization problem.

[0181] Application example:

[0182] This application example uses a park in northern my country, where the PIES operates in a "grid-connected but not grid-connected" mode. To accurately reflect the actual operation of the system, data from three typical days—summer, winter, and spring / autumn—are selected to represent the annual operation. A fixed natural gas price of RMB 2.55 / m³ is used. The lower calorific value of natural gas is 9.7 kWh / m³, resulting in a converted price of RMB 0.26 / kWh. The park 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 hourly rate is RMB 0.8 / kWh, the peak hour rate is RMB 1.6 / kWh, and the off-peak hour rate is RMB 0.4 / kWh. The economic and technical parameters of the equipment are shown in Table 1.

[0183] Table 1. Equipment Economic and Technical Parameters

[0184]

[0185] The typical daily electricity / heat / cooling load curves for PIES in summer, winter, and spring / autumn, as well as the predicted light intensity and wind speed curves, are shown below. Figure 2-3 As shown.

[0186] To verify the effectiveness of the method of the present invention, the following three scenarios were constructed for comparison:

[0187] Scenario I: Without considering energy storage, the carbon trading price and renewable energy output are determined;

[0188] Scenario II: Without considering energy storage, carbon trading prices and renewable energy output are uncertain, and θ is set to 90%;

[0189] Scenario III: Considering energy storage, the uncertainty of carbon trading prices and renewable energy output, θ is set to 90%.

[0190] The comparison results of the planning schemes for the three scenarios are shown in Table 2.

[0191] Table 2 Comparison of Planning Schemes in Three Scenarios

[0192]

[0193] As shown in Table 2, 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.

[0194] Table 2 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 4As shown in Figures 5 and 6, the summer and spring / autumn scenarios are as follows. In scenarios I and II, since the system lacks energy storage, scenario II, to address the risk of planned total cost fluctuations due to uncertainties in carbon trading prices and renewable energy output, requires a larger renewable energy capacity than scenario I to reduce system carbon emissions. Simultaneously, it necessitates increased gas purchases to boost CHP output and increased 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 increased by RMB 1,135,000 compared to 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.

[0195] By comparing scenarios I and II, it is shown that when the system is not equipped with energy storage, the proposed optimization configuration method can reduce the risk of fluctuation in the total planned cost of the system, but at the cost of increased carbon emissions and total cost.

[0196] 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 (Consumable Gas Pipeline) to reduce gas purchases and reducing electricity purchases from the external grid. Therefore, compared to Scenario II, Scenario III relatively reduces both gas and electricity purchases from the external grid. This results in a significant reduction in Scenario III's operation and maintenance costs, by approximately RMB 1.1903 million, far exceeding the RMB 318,500 increase in investment costs. Furthermore, the RMB 61,600 in negative carbon emissions generated by Scenario III contributes to the reduction in total system costs. Thus, the total cost of Scenario III is lower than that of Scenario II. Simultaneously, because energy storage addresses the uncertainty of renewable energy output, the CVaR (Cost Per Revenue) of Scenario III is RMB 726,800 lower than that of Scenario II, thus mitigating the risk of fluctuations in the total planned cost.

[0197] 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.

[0198] Furthermore, taking Scenario III as an example, the impact of different confidence levels on the planning results is analyzed. The comparison results of planning schemes under different confidence levels are shown in Table 3.

[0199] Table 3 Comparison of Planning Schemes under Different Confidence Levels

[0200]

[0201] As shown in Table 3, as the confidence level increases, the system tends to avoid the risk of fluctuation in total planned cost. The system's energy storage capacity increases to cope with the uncertainty of renewable energy output, which leads to an increase in investment costs and thus an increase in the total cost of the system. However, the risk of fluctuation in total planned cost, as measured by CVaR, decreases.

[0202] Furthermore, as can be seen from Table 3, 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 prices to make profits, but this requires an increase in investment costs.

[0203] The comparison results of total cost and CVaR at different confidence levels are as follows: Figure 7 As shown. By Figure 7 It can be seen that 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. PIES should choose the corresponding planning scheme based on their own risk appetite.

[0204] In summary, this embodiment proposes an optimized allocation method for the integrated energy system of a park that considers the uncertainty of carbon trading prices. The conclusions are as follows:

[0205] 1) When considering the PIES optimization configuration with uncertainty in carbon trading prices and renewable energy output, carbon trading alone cannot effectively control the system's carbon emissions. When dealing with the risk of fluctuations in total planning costs, the system needs to take into account both system carbon emissions and the development of renewable energy.

[0206] 2) When the system is equipped with electrical energy storage, the optimization configuration method proposed in this paper can achieve PIES negative carbonization and reduce the risk of fluctuation in the total planned cost of the system. Moreover, the carbon trading revenue of the system increases with the increase of confidence level.

[0207] 3) There is a contradictory relationship between total planning cost and CVaR. When pursuing a low total cost, PIES faces a high risk of fluctuation in total planning cost. PIES needs to choose the corresponding planning scheme according to its own risk preference.

[0208] Example 2:

[0209] This invention provides an energy system configuration device that considers the uncertainty of carbon trading prices. The device includes:

[0210] The first model building module is used to establish a carbon energy coupling model based on the multi-energy flow and carbon trading volume of energy hubs.

[0211] The scene generation module is used to perform uncertainty modeling based on a zero-mean normal distribution and generate a scene set based on the modeling results.

[0212] The second model building module is used to build a total cost model for system planning based on the carbon energy coupling model and the scenario set.

[0213] The third model building module is used to build risk value models at various confidence levels based on the total cost model of system planning.

[0214] The model solving module is used to solve for the configuration scheme with the goal of minimizing the value at risk model.

[0215] Example 3:

[0216] Based on Embodiment 1, this embodiment of the invention provides an energy system configuration device that considers the uncertainty of carbon trading prices, including a processor and a storage medium;

[0217] Storage media are used to store instructions;

[0218] The processor is used to perform the steps of the above method according to instructions.

[0219] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application 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, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0220] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. 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... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0221] 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.

[0222] 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.

[0223] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for energy system configuration considering carbon trading price uncertainty, characterized in that, The method comprises the following steps: a carbon-energy coupling model is established based on multi-energy flow and carbon trading volume of an energy hub; uncertainty modeling is performed based on a normal distribution with zero mean, and a scenario set is generated according to the modeling result; a system planning total cost model is established according to the carbon-energy coupling model and the scenario set; a risk value model under each confidence level is established based on the system planning total cost model; a configuration scheme is obtained by solving the risk value model with the minimum value as the target; the method further comprises the following steps: a basic model of the energy hub is established; ； In the formula, The first in the energy hub Various forms of energy output, The first in the energy hub Various forms of energy input, For the first Coupling factors for various forms of energy; For the first Various forms of energy storage charging and discharging power, For the first Energy storage coupling factor for formal energy sources; the carbon-energy coupling model is established by introducing the carbon trading volume into the basic model of the energy hub; ； In the formula, is the carbon emission coefficient of the first form of energy in the energy concentrator, and the free carbon quota coefficient; is the carbon emission coefficient of the first form of energy in the energy concentrator, and the free carbon quota coefficient; is the carbon emission coefficient of the first form of energy in the energy concentrator, and the free carbon quota coefficient; is the carbon emission coefficient of the first form of energy in the energy concentrator, and the free carbon quota coefficient; is the carbon emission coefficient of the first form of energy in the energy concentrator, and the free carbon quota coefficient; the uncertainty modeling based on the normal distribution with zero mean comprises the following steps: a probability distribution model of the carbon trading price is constructed; ； wherein is the carbon trading price random variable, is the mean and standard deviation of the carbon trading price; a probability distribution model of the light intensity is constructed; ； wherein is the light intensity random variable, is the mean and standard deviation of the light intensity; a probability distribution model of the wind speed is constructed; ； wherein is the light intensity random variable, is the mean and standard deviation of the wind speed; the scenario set is generated according to the modeling result, and the method comprises the following steps: carbon trading price sets, light intensity sets and wind speed sets are obtained by using the Monte Carlo sampling method according to the probability distribution models of the carbon trading price, the light intensity and the wind speed; the carbon trading price sets, the light intensity sets and the wind speed sets are clustered by using the K-means clustering algorithm; carbon trading prices, light intensities and wind speeds are combined into scenarios from the clustered carbon trading price sets, light intensity sets and wind speed sets; a scenario set is generated by combining all the scenarios.

2. The method for energy system configuration considering carbon price uncertainty according to claim 1, characterized in that, The clustering comprises the following steps: Randomly select from the set Each element is used as the initial centroid; the Euclidean distances between each element in the set and the initial cluster center are calculated; each element in the set is classified into the initial cluster center corresponding to the minimum Euclidean distance to form a cluster; the cluster centers of the clusters are calculated, and the calculated cluster centers are used as the initial cluster centers to replace the initial cluster centers in the above steps for iterative operation until the calculated cluster centers converge, and the final cluster centers are output.

3. The method for energy system configuration considering carbon price uncertainty according to claim 2, characterized in that, The The numerical acquisition includes: calculating the sum of squared errors from the final cluster : ； In the formula, For the first Clusters The center of mass, For the first Clusters Elements in; to minimize the sum of the squares of the errors the value of is calculated. 4.The method of Claim 1, wherein The system planning total cost model is as follows: ； In the formula, The investment cost, maintenance cost, fuel cost, and carbon trading cost in the first scenario, respectively. ； ； ； ； wherein, is the lifetime of the th device, is the capacity of the th device under the th scenario, is the unit capacity price of the th device, is the discount rate, is the number of devices; is the output power of the th device under the th scenario in the th time period, is the number of time periods, is the unit power maintenance cost of the th device; are the natural gas purchase amount and the electricity purchase amount from the external grid of the system under the th scenario, respectively, are the natural gas price and the electricity price, respectively, is the operation time interval of the system; is the carbon trading amount of the system, is the carbon trading price under the th scenario.

5. The method for energy system configuration considering carbon price uncertainty according to claim 4, wherein, The risk value model is as follows: ； ； wherein is the conditional value at risk at a confidence level of the system planning total cost, is the value at risk at a confidence level of the system planning total cost, is the system planning total cost at the th scenario, is the number of scenarios.

6. An energy system configuration device considering carbon trading price uncertainty, characterized by, The device is configured to perform the steps of the method according to any one of claims 1-5, and the device comprises: a first model establishing module configured to establish a carbon-energy coupling model based on multi-energy flow and carbon trading volume of an energy hub; a scenario generating module configured to perform uncertainty modeling based on a normal distribution with zero mean, and generate a scenario set according to the modeling result; a second model establishing module configured to establish a system planning total cost model according to the carbon-energy coupling model and the scenario set; a third model establishing module configured to establish a risk value model under each confidence level based on the system planning total cost model; a model solving module configured to solve the risk value model with the minimum value as the target to obtain a configuration scheme.

7. An energy system configuration device considering carbon trading price uncertainty, characterized by, The device comprises a processor and a storage medium. The storage medium is used to store instructions. The processor is used to operate according to the instructions to perform the steps of the method according to any one of claims 1-5.

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