Optimal configuration method of source and storage capacity for integrated energy system considering comprehensive demand response

Abstract

The present invention discloses a method for optimizing the configuration of source and storage capacity of an integrated energy system taking into account comprehensive demand response, and relates to the technical field of optimized configuration of capacity of an integrated energy system. The specific steps are: collecting annual wind speed data, annual irradiation data, and electricity price plan data; processing the annual wind speed data and annual irradiation data using the confidence interval method to obtain a wind speed confidence curve and a photovoltaic confidence curve, and processing the electricity price plan data using the comprehensive demand response model to obtain a load curve; on the basis of the wind speed confidence curve, the photovoltaic confidence curve, and the load curve, taking into account the uncertainty of wind and solar output and the comprehensive demand response of electricity and heat, a two-layer optimization configuration model is constructed; the two-layer optimization configuration model is solved to obtain the optimal configuration solution. The method of the present invention can effectively reduce the total cost of system planning, meet the energy demand of electric and heat users, and improve energy utilization efficiency and the efficiency of intelligent planning of the integrated energy system.

Description

Technical Field

[0001] The present invention relates to the technical field of capacity optimization configuration of integrated energy systems, and more particularly to a method for optimizing source and storage capacity configuration of integrated energy systems taking comprehensive demand response into consideration. Background Art

[0002] In recent years, the contradiction between the rapid growth of load demand and the deterioration of environmental conditions has become increasingly prominent. At the same time, the shortage of fossil energy and environmental problems have prompted countries around the world to adjust their energy production and consumption methods. It is imperative to promote the development and utilization of new power systems based on renewable energy.

[0003] Compared to traditional single-energy production methods, integrated energy systems (IES) can flexibly manage distributed resources such as electricity, heat, and gas through storage and energy conversion equipment, improving the overall efficiency of the energy supply system and promoting the complementarity of IES across different temporal and spatial scales. Energy storage, as a key component of IES, can collaboratively optimize IES, meet the system's requirements for ensuring energy supply security and reliability, and improve the energy efficiency and cost-effectiveness of IES.

[0004] To address the imbalance between supply and demand in IES (Integrated Energy Systems), some studies have introduced demand response (DR) at the planning level to guide users to adjust their electricity usage. By leveraging the adjustable characteristics of flexible loads in IES, the potential role of various demand-side management measures can be leveraged, guiding users to actively participate in demand response and proactively adjust their energy usage, thereby effectively reducing peak-to-valley load variations and lowering system investment and operating costs. However, traditional DR only analyzes electricity price information while ignoring the relationship between electricity and heat coupling. To adjust users' demand for multiple energy types, such as electricity and heat, the coupling characteristics of different energy sources in IES, such as electricity, heat, and natural gas, can be fully utilized. Traditional DR can be expanded to integrated demand response (IDR). By integrating multiple energy forms such as electricity, heat, and natural gas, IDR improves the interaction between supply and demand in the IES network, flexibly switches energy sources, and actively participates in IDR while ensuring user comfort, promoting the transformation of consumers into prosumers.

[0005] When analyzing the IES optimization planning problem, the uncertainty of wind and solar power and energy prices involve economic cost issues for both producers and consumers. For those skilled in the art, how to use the idea of ​​IDR to establish an IES optimization planning method to better analyze the interactive relationship between the subjects and obtain a more reasonable optimization configuration plan is an urgent problem to be solved. Summary of the Invention

[0006] In view of this, the present invention provides a method for optimizing the configuration of source and storage capacity of an integrated energy system taking into account comprehensive demand response, thereby overcoming the shortcomings of the existing IES source and storage capacity optimization configuration technology.

[0007] To achieve the above objectives, the present invention adopts the following technical solution: a method for optimizing the source and storage capacity of an integrated energy system considering comprehensive demand response, the specific steps of which include the following:

[0008] Collect annual wind speed data, annual irradiation data, and electricity price plan data;

[0009] The annual wind speed data and the annual irradiation data are processed using a confidence interval method to obtain a wind speed confidence curve and a photovoltaic confidence curve, and the electricity price plan data are processed using a comprehensive demand response model to obtain a load curve;

[0010] Based on the wind speed confidence curve, the photovoltaic confidence curve, and the load curve, a two-layer optimization configuration model is constructed taking into account the uncertainty of wind and solar power output and the comprehensive demand response of electricity and heat;

[0011] The two-layer optimization configuration model is solved to obtain the optimal configuration solution.

[0012] Optionally, the two-layer optimization configuration model includes a planning layer and an operation layer. In the planning layer, the goal is to minimize the total cost of planning and constructing the integrated energy system, and the optimization variable is the equipment installation capacity; in the operation layer, the goal is to minimize the operating cost, and the optimization variable is the equipment operating output.

[0013] Optionally, the annual total planning cost function of the planning layer is:

[0014] minC tot =C inv +C ope +C m ;

[0015] Among them, C inv is the annual equivalent cost of system investment, C m is the system operation and maintenance cost, including labor costs and maintenance costs, C ope The annual operating cost of the system.

[0016] Optionally, the operation cost function of the operation layer is:

[0017] minC ope =C ele +C gas +C en +C IDR ;

[0018] Among them, C eleis the interaction cost between the comprehensive energy system and the power grid, C gas is the natural gas purchase cost, C en is the environmental cost, C IDR Comprehensive demand response costs for user participation.

[0019] Optionally, the load curve expression is:

[0020]

[0021] in, Indicates the total system load demand corresponding to the comprehensive demand, Indicates uncontrollable electrical / heat load demand, represents the transferable electrical / heating load demand, Indicates that the load can be reduced. represents replaceable load; k=1, 2 represents electrical load and thermal load, and t represents time.

[0022] Optionally, the operation layer optimizes the operation scheduling of each device in the system with the goal of minimizing the operation cost, and feeds back the optimization results to the planning layer; the planning layer corrects the total cost according to the optimization results of the operation layer, solves the objective function through a mixed integer linear programming solver, achieves the goals of minimizing the annual investment cost and the annual economic cost of the integrated energy system, and obtains the optimal configuration plan.

[0023] Optionally, the annual equivalent cost of the system investment includes the initial investment cost of the equipment and the equipment operation and maintenance costs, and is expressed as:

[0024]

[0025]

[0026] Where i is the device type, N=6, is the unit investment cost of each equipment, Cap i is the planned capacity of each device, f i is the annual cost coefficient of equipment i, r is the base discount rate, Y i is the life span of device i.

[0027] Optionally, the expression for the interaction cost between the integrated energy system and the power grid is:

[0028]

[0029] in, and is the electricity purchase price and electricity sales price of the integrated energy system interacting with the power grid at time t, and is the power purchased and sold by the integrated energy system and the power grid at time t, and Δt is the unit time duration;

[0030] The expression of the natural gas purchase cost is:

[0031]

[0032] Among them, f gas is the purchase price of natural gas, H gas is the lower calorific value of natural gas, P CHP (t) is the output power of the CHP unit at time t; P GB (t) is the thermal power output of GB unit at time t; η GB The gas-to-heat conversion efficiency of the gas boiler;

[0033] The expression of environmental cost is:

[0034]

[0035] Among them, μ gas The environmental value cost of electricity produced by gas-fired units and the power grid;

[0036] The expression of the user's participation in the comprehensive demand response cost is:

[0037]

[0038] Among them, P t ele0 and P t ele P is the user's electricity load demand before and after the implementation of comprehensive demand response at time t; t heat0 and P t heat is the heat load demand of users before and after the implementation of comprehensive demand response at time t; ρ h The price at which EH sells heat to system users.

[0039] It can be seen from the above technical solution that, compared with the existing technology, the present invention discloses a method for optimizing the configuration of source and storage capacity of an integrated energy system taking into account comprehensive demand response, which has the following beneficial technical effects: it solves the problem of supply and demand balance on the source and load sides in the planning of the integrated energy system, effectively reduces the total cost of system planning, meets the energy needs of electric and thermal users, and improves energy utilization efficiency. It has the advantages of scientific and reasonable methods, strong applicability, good effects, and can improve the efficiency of intelligent planning of the integrated energy system and tap the potential of multiple energy users to participate in comprehensive demand response. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0041] Figure 1 This is a structural diagram of the energy hub of the present invention;

[0042] Figure 2 This is a diagram of the double-layer optimized configuration structure of the integrated energy system of the present invention;

[0043] Figure 3 This is a schematic diagram of the confidence interval method of the present invention;

[0044] Figure 4 This is a transition season wind power output curve diagram of the present invention;

[0045] Figure 5 This is a summer wind power output curve diagram of the present invention;

[0046] Figure 6 This is a winter wind power output curve diagram of the present invention;

[0047] Figure 7 This is a transition season photovoltaic output curve diagram of the present invention;

[0048] Figure 8 This is a summer photovoltaic output curve diagram of the present invention;

[0049] Figure 9 This is a winter photovoltaic output curve diagram of the present invention;

[0050] Figure 10 The three typical seasonal electric load curves of the present invention are as follows;

[0051] Figure 11 The heat load curve diagrams of three typical seasons of the present invention are as follows;

[0052] Figure 12 The real-time electricity price curves for three typical seasons of the present invention are as follows;

[0053] Figure 13 This is a diagram showing the effect of the summer electricity load and winter heat load of the present invention before and after IDR is considered;

[0054] Figure 14 This is a comparison chart of the capacity configurations of the two modes of the present invention;

[0055] Figure 15 This is a comparison chart of the economic costs of the two models of the present invention. DETAILED DESCRIPTION

[0056] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0057] The embodiment of the present invention discloses a method for optimizing the source and storage capacity configuration of an integrated energy system considering comprehensive demand response, and the specific steps include the following:

[0058] S1. Collect annual wind speed data, annual irradiation data and electricity price data;

[0059] S2. The annual wind speed data and annual irradiation data are processed using the confidence interval method to obtain the wind speed confidence curve and the photovoltaic confidence curve. The electricity price plan data is processed using the comprehensive demand response model to obtain the load curve.

[0060] S3. Based on the wind speed confidence curve, photovoltaic confidence curve, and load curve, taking into account the uncertainty of wind and solar power output and the comprehensive demand response of electricity and heat, a two-layer optimization configuration model is constructed;

[0061] S4. Solve the two-layer optimization configuration model to obtain the optimal configuration solution.

[0062] Reference Figure 1 Taking the integrated energy system structure diagram shown in the figure as an example, this paper analyzes the source and storage capacity optimization configuration method of the integrated energy system considering comprehensive demand response. The components to be planned include: wind turbines (WT), photovoltaics (PV), combined heat and power (CHP), gas boilers (GB), electrical energy storage (EES), and thermal energy storage. The integrated energy system is responsible for supplying electricity and heat loads to users in the region. The production capacity within the energy system mainly supplies energy to users in the region and generates revenue by connecting surplus power to the grid. When the system cannot meet the regional electricity and heat load demand, it can purchase energy from the power grid company, energy storage system, and gas company to supplement the regional electricity and heat load supply.

[0063] The optimal configuration of source and storage capacity for an integrated energy system first analyzes the models and coupling relationships of various equipment components within an energy hub (EH). A comprehensive demand response model is then constructed to optimize the load curve based on real-time market electricity price scenarios. The confidence interval method is then used to analyze the uncertainty of wind and solar power in the integrated energy system, determining wind and solar power output curves at different confidence levels. Based on this, a two-layer optimization model is developed that considers both source and storage capacity configuration and operational optimization. This model proposes an optimal configuration of source and storage capacity for an integrated energy system (TES) that considers comprehensive demand response.

[0064] In the optimization planning of key equipment in integrated energy systems, in addition to the economic considerations of investment and operating costs, the uncertainty of the output of new energy equipment such as wind power and photovoltaics within the integrated energy system, as well as the time-shifting characteristics of energy storage devices such as electrical and thermal energy storage, are also worthy of attention. Integrated demand response, as an extension of traditional electricity demand response within integrated energy systems and integrated energy markets, has the primary advantage of not only adjusting demand to shift traditional electricity loads, but also allowing users to adjust energy conversion methods based on the coupling characteristics of various energy prices in the integrated energy market and electricity, heat, and other energy sources within the integrated energy system, achieving an equivalent response effect without changing their energy usage patterns. On this basis, planning at the planning level considers the uncertainty of integrated demand response and wind and solar output, achieving environmentally friendly energy supply while ensuring regional energy supply and economic efficiency.

[0065] Specifically, such as Figure 2 As shown in Figure 1, the two-layer optimization configuration model consists of a planning layer and an operation layer. In the planning layer, the goal is to minimize the total cost of planning and constructing the integrated energy system, and the optimization variable is the equipment installed capacity. In the operation layer, the goal is to minimize operating cost, and the optimization variable is the equipment operating output. The operation layer optimizes the operation scheduling of each device within the system with the goal of minimizing operating cost and feeds the optimization results back to the planning layer. The planning layer adjusts the total cost based on the optimization results of the operation layer and solves the objective function using a mixed integer linear programming solver to achieve the goals of minimizing the annual investment cost and annual economic cost of the integrated energy system, thereby obtaining the optimal configuration solution.

[0066] (1) Planning takes into account the uncertainty of wind and solar power output

[0067] Furthermore, the uncertainty of wind and solar power output is taken into account in the planning layer. In dealing with the uncertainty of wind and solar power output, the current confidence interval method, compared with the typical day analysis method, robust optimization method and other methods, can consider the impact of extreme weather on wind power and photovoltaic output, and can comprehensively evaluate the impact of reliability and economy on renewable energy capacity configuration.

[0068] like Figure 3The figure shows the principle diagram of the confidence interval method. In this example, based on historical output data such as wind speed and photovoltaic radiation intensity throughout the year for a certain region in China, a wind turbine with a single unit rated capacity of 100kW and a photovoltaic panel with a single unit rated capacity of 0.26kW were selected. The year was divided into three typical seasons: June to August as summer, December to February as winter, and the remaining months as transitional seasons. The three typical seasons accounted for 0.25, 0.25, and 0.5 of the annual total, respectively. The wind and solar sample data for each season was divided into 24 sets of time-series output curves based on the daily hourly time. The wind and solar output values ​​at the hourly time were sorted in ascending order to obtain the sequential output curves for each season.

[0069] In order to obtain the daily output power curve of each season, the power value is divided into small intervals with equal intervals according to the season. The wind and solar output frequency r(i) in each small interval is calculated. The r(i) of the accumulated interval at each moment is accumulated to obtain the wind and solar probability distribution at that moment as α(∑r(i)). The confidence level at the corresponding moment is 1-α(∑r(i)). Different confidence levels are selected respectively, and the power values ​​on the wind and solar probability distribution curve corresponding to the corresponding confidence levels are calculated. The power values ​​at each hour are connected in chronological order and normalized to obtain the wind and solar output curves at different confidence levels in each season as shown below. Figures 4 to 9 shown.

[0070] (2) Planning and integrated demand response model

[0071] Economic factors such as energy prices, incentive compensation, and penalty policies can motivate consumers to adjust their energy consumption patterns based on their needs. These economic factors are determined by the demand response model employed. For this purpose, two main types of load are considered: uncontrollable load and adjustable load. Base load corresponds to uncontrollable load, which primarily has a fixed electricity usage time and is non-delayed. Adjustable load corresponds to flexible load, which can be flexibly adjusted according to the time of use and includes shiftable load, curtailable load, and replaceable load.

[0072] (a) Uncontrollable load

[0073] Uncontrollable load refers to the load that cannot be interrupted or reduced at will and has no ability to respond to electricity prices, usually including lighting facilities, heating facilities, etc. It can be expressed as:

[0074]

[0075] Where: is the uncontrollable electrical / heat load demand, k=1,2 refers to the electrical load and thermal load; is the proportion of uncontrollable electricity / heat load in the total electricity / heat load demand; is the system's electricity / heat load demand under the benchmark electricity price.

[0076] (b) Transferable load

[0077] Transferable load refers to a load with a fixed total energy consumption but can be flexibly adjusted in time. It is a load that users transfer during peak electricity price periods according to price signals within a certain time range. It usually includes electric vehicles, water heaters, etc. and can be expressed as:

[0078]

[0079] Where: is the proportion of transferable load in the total electricity / heat load demand; is the price elasticity coefficient of transferable load; and are the electricity purchase price and benchmark electricity price of the user at time t, respectively. In order to ensure that the user satisfaction does not decrease significantly over time, it is considered that the transferable load can only be transferred to the adjacent time period and decrease linearly within the continuous energy consumption time, which can be expressed as:

[0080]

[0081]

[0082] Where: is the transferable load value at time t; is the load value of the transferable load transferred from time t to time t'; is the attenuation coefficient of the load transfer, which represents the linear attenuation effect of the translation process of the transferable load over time; TR is the maximum duration of the transferable load transfer.

[0083] (c) Load reduction

[0084] Curtailable load refers to the load that users can voluntarily interrupt or increase based on their own load demand and price information. It usually includes air conditioning and lighting loads. It is subject to the maximum curtailable amount and can be expressed as:

[0085]

[0086]

[0087] Where: is the proportion of curtailable load in the total electricity load demand; is the price elasticity coefficient of load reduction, is the maximum amount of load that can be reduced at time t.

[0088] (d) Replaceable load

[0089] Alternative loads refer to loads with fixed energy consumption but variable energy sources, typically including hybrid electric / gas air conditioners and water heaters. Alternative loads are a key load type distinct from traditional demand response. Users can compare market electricity price signals with system heat prices and participate in comprehensive demand response based on their own energy needs. This can be expressed as:

[0090]

[0091] Where: is the proportion of replaceable load in the total electric load demand; is the price elasticity coefficient of alternative load; ρ h Hot selling price for the system.

[0092] Combining equations (1) to (7), the total system load demand taking into account the comprehensive demand response can be expressed as:

[0093]

[0094] (3) The annual total planning cost function of the planning layer is:

[0095] minC tot =C inv +C ope +C m ;

[0096] Among them, C inv is the annual equivalent cost of system investment, C m is the system operation and maintenance cost, including labor costs and maintenance costs, C ope The annual operating cost of the system.

[0097] 1) The annual equivalent cost of system investment includes the initial investment cost of the equipment and the equipment operation and maintenance costs, and the expression is:

[0098]

[0099]

[0100] Where i is the device type, N=6, is the unit investment cost of each equipment, Cap i is the planned capacity of each device, f i is the annual cost coefficient of equipment i, r is the base discount rate, Y i is the life span of device i.

[0101] 2) Operating costs

[0102] Considering that electricity and heat loads are affected by seasonal characteristics, three typical days in transition season, summer and winter are selected for optimization, which can be expressed as:

[0103]

[0104] Among them, ε r is the percentage of three typical days in the whole year, is the daily operating cost of the rth typical day.

[0105] (4) Operational layer cost function

[0106] The lower operation layer aims to minimize the typical daily operating cost, and the optimization variable is the operating power of each device in the system. The specific mathematical model can be expressed as:

[0107] minC ope =C ele +C gas +C en +C IDR ;

[0108] Among them, C ele is the interaction cost between the comprehensive energy system and the power grid, C gas is the natural gas purchase cost, C en is the environmental cost, C IDR Comprehensive demand response costs for user participation.

[0109] 1) Interaction cost between EH and power grid:

[0110]

[0111] in, and is the electricity purchase price and electricity sales price of the integrated energy system interacting with the power grid at time t, and is the power purchased and sold by the integrated energy system and the power grid at time t, and Δt is the unit time duration;

[0112] 2) The expression for natural gas purchase cost is:

[0113]

[0114] Among them, f gas is the purchase price of natural gas, H gas is the lower calorific value of natural gas, P CHP (t) is the output power of the CHP unit at time t; P GB (t) is the thermal power output of GB unit at time t; η GB The gas-to-heat conversion efficiency of the gas boiler;

[0115] 3) The expression of environmental cost is:

[0116]

[0117] Among them, μ gas The environmental value cost of electricity produced by gas-fired units and the power grid;

[0118] 4) The expression of user participation in comprehensive demand response cost is:

[0119]

[0120] Among them, P t ele0 and P t ele P is the user's electricity load demand before and after the implementation of comprehensive demand response at time t; t heat0 and P t heat is the heat load demand of users before and after the implementation of comprehensive demand response at time t; ρ h The price at which EH sells heat to system users.

[0121] Furthermore, in order to illustrate the effectiveness of the method for optimizing the source and storage capacity of the integrated energy system considering the integrated demand response of the present invention, the impact of IDR on the planning and operation of the internal storage capacity of the EH is explored. The electricity and heat load curves of three typical days are as follows: Figure 10 、 Figure 11 By selecting 9 confidence levels (30% to 70%), two system source and storage configuration modes are set up under the consideration of IDR and the non-consideration of IDR, and the analysis is carried out.

[0122] Mode 1 (Mode 1, M1): IDR is not considered during EH planning;

[0123] Mode 2 (M2): IDR is considered during EH planning.

[0124] In order to more intuitively illustrate the impact of IDR on electric heating load, Figure 12 Based on the real-time electricity prices of the three typical seasons shown in the figure, the electricity / heat load curve is adjusted according to the IDR model. Taking the summer electricity load and winter heat load as examples, the load response characteristics of EH after considering IDR are analyzed. Figure 13 Figure 2 shows the effects of implementing IDR on electric and thermal loads before and after it is implemented. It clearly shows that based on electricity price signals, Mode 2 incorporates IDR on top of Mode 1, reducing the peak-to-valley difference in load. Compared to Mode 1, Mode 2 reduces the peak-to-valley difference in summer electric load by 8.94% and the peak-to-valley difference in winter thermal load by 12.59%. This demonstrates that IDR can effectively reduce the peak-to-valley difference, achieving peak shaving and valley filling, and smoothing the load curve.

[0125] The two modes are optimized and solved respectively, and the capacity optimization configuration results are shown in Table 1. Figure 14 The changing trend of each device data in Table 1. Figure 14 As can be seen in terms of equipment capacity configuration, under the assumption that the system meets the constraints, the required wind turbine and photovoltaic capacity for both modes increases with increasing confidence, while the required energy storage capacity shows a trend of initially increasing and then decreasing. Looking at the capacity configuration of each device, after implementing IDR, the wind turbine capacity in Mode 2 increases compared to Mode 1, while the photovoltaic capacity in Mode 2 decreases compared to Mode 1. For example, at a confidence level of 55%, Mode 2 increases wind power by 128 kW and decreases photovoltaic capacity by 110 kW compared to Mode 1, an increase of 8.65% for wind power and an decrease of 18.97% for photovoltaic capacity. Regarding energy storage configuration, the energy storage capacity configuration decreases significantly with increasing confidence. For example, at a confidence level of 60%, Mode 2 decreases photovoltaic capacity by 149 kW, a decrease of 20.96% compared to Mode 1. The capacity of the CHP coupled heat and power units decreases with increasing confidence, indicating that IDR implementation has reduced the size of the CHP units to a certain extent. The GB unit capacity configurations for the two modes are similar, which is related to the higher winter heat load. In terms of thermal energy storage configuration, Mode 2 has a higher thermal energy storage capacity than Mode 1. This is because the thermal energy storage capacity is related to the configuration scale of the CHP and GB units. When the CHP unit configuration scale is reduced, the thermal energy storage configuration scale is increased to meet the heat load balance. The low unit investment cost of thermal energy storage can effectively reduce the system investment cost. From the perspective of overall equipment optimization, the implementation of IDR can more reasonably optimize the capacity configuration of the EH internal source storage.

[0126] Table 1

[0127]

[0128]

[0129] The economic comparison of the two models is shown in Table 2. Figure 15 The changing trend of each cost in Table 2. Figure 15It can be seen that in terms of investment costs, as the confidence level increases, the investment costs of both models gradually increase. However, the implementation of IDR reduces the system's investment costs. For example, at a confidence level of 40%, after implementing IDR, the investment cost of Model 2 is reduced by 85,900 yuan compared to Model 1, a decrease of 4.83%, indicating that the implementation of IDR can effectively reduce the system's equipment investment costs. In terms of grid interaction costs, at a confidence level of 30% for Model 2, the electricity sales revenue from the interaction between EH and the grid is higher than the electricity purchase cost, so the grid interaction cost is negative. As the confidence level increases, the electricity sales volume gradually decreases, the electricity purchase volume gradually increases, and the grid interaction cost gradually decreases. At a confidence level of 35%, the electricity purchase cost is higher than the electricity sales revenue, and the grid interaction cost is positive. The grid interaction cost of Model 2 is lower than that of Model 1, indicating that the implementation of IDR helps reduce the system's grid interaction costs. The annual operating cost and annual planning total cost of the system both increase with the increase of confidence. In terms of annual operating cost, although the implementation of IDR results in an additional IDR cost of RMB 104,900 for Mode 2, the annual operating cost of IDR is still lower than that of Mode 1. In terms of annual total planning cost, in order to reflect the economic impact of IDR implementation on system planning and operation, the improvement rate is used as an economic indicator. For example, when the confidence level is 35%, the annual total planning cost after IDR implementation is reduced by RMB 127,300 compared with Mode 1 without IDR implementation, and the annual total planning cost improvement rate is 3.42%, indicating that the economic benefits of the planning scheme system obtained by considering IDR have been significantly improved.

[0130] Table 2

[0131]

[0132] In the aforementioned study, various load types were assumed to account for a fixed proportion of energy demand, with the electricity and thermal loads composed of 60% uncontrollable loads, 20% shiftable loads, 15% curtailable loads, and 5% replaceable loads. However, in reality, due to differences in user energy usage habits and load composition, the resulting demand response benefits vary.

[0133] To illustrate the impact of integrated demand response on EH planning and operation, a simulation was conducted using a wind and solar output curve with a 45% confidence level. The system's IDR load was assumed to consist of three scenarios: Scenario 1: 15% shiftable load + 10% curtailable load + 15% replaceable load; Scenario 2: 15% shiftable load + 15% curtailable load + 10% replaceable load; and Scenario 3: 20% shiftable load + 15% curtailable load + 5% replaceable load. In each of the three scenarios, the uncontrollable and controllable loads were assumed to account for 60% and 40% of the total load demand, respectively. A comparison of the optimal planning solutions is shown in Table 3.

[0134] Table 3

[0135]

[0136] The calculation results in Table 3 show that the IDR load types in different scenarios contribute differently to the economic benefits of EH. The ranking of the overall annual planned total cost improvement rate shows that Scenario 3 has the highest economic improvement rate of 3.3% compared to the scenario without IDR, and Scenario 3 has the lowest IDR cost. The economic benefits of the three scenarios are ranked from highest to lowest as follows: Scenario 3, Scenario 2, and Scenario 1. The above solutions show that, while satisfying system operating constraints, the complementarity of the response characteristics of different IDR load types can be utilized, and that better economic benefits can be achieved when IDR is used in system scenarios with a high proportion of transferable loads and a low proportion of replaceable loads.

[0137] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Reference can be made to the common and similar parts between the various embodiments. For the devices disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the method description.

[0138] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not limited to the embodiments shown herein but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for optimizing the source and storage capacity of an integrated energy system considering comprehensive demand response, characterized in that: The specific steps include the following: Collect annual wind speed data, annual irradiation data, and electricity price plan data; The annual wind speed data and the annual irradiation data are processed using a confidence interval method to obtain a wind speed confidence curve and a photovoltaic confidence curve, and the electricity price plan data are processed using a comprehensive demand response model to obtain a load curve; Based on the wind speed confidence curve, the photovoltaic confidence curve, and the load curve, a two-layer optimization configuration model is constructed taking into account the uncertainty of wind and solar power output and the comprehensive demand response of electricity and heat; Solving the two-layer optimization configuration model to obtain an optimal configuration solution; The two-layer optimization configuration model includes a planning layer and an operation layer. In the planning layer, the goal is to minimize the total cost of planning and constructing an integrated energy system, and the optimization variable is the equipment installation capacity; In the operation layer, the goal is to minimize the operating cost, and the optimized variable is the equipment operating output; The annual total planning cost function of the planning layer is: minC tot =C inv +C ope +C m ; Among them, C inv is the annual equivalent cost of system investment, C m is the system operation and maintenance cost, including labor costs and maintenance costs, C ope is the annual operating cost of the system; The operating cost function of the operating layer is: minC ope =C ele +C gas +C en +C IDR ; Among them, C ele is the interaction cost between the comprehensive energy system and the power grid, C gas is the natural gas purchase cost, C en is the environmental cost, C IDR Comprehensive demand response costs for user participation; The expression of the load curve is: in, Indicates the total system load demand corresponding to the comprehensive demand, Indicates uncontrollable electrical / heat load demand, represents the transferable electrical / heating load demand, Indicates that the load can be reduced. represents the replaceable load; k = 1, 2 represents the electrical load and thermal load, and t represents the time; Optimize the operation and scheduling of each device in the system and feed the optimization results back to the planning layer; the planning layer corrects the total cost based on the optimization results of the operation layer, solves the objective function through a mixed integer linear programming solver, achieves the goals of minimizing the annual investment cost and the annual economic cost of the integrated energy system, and obtains the optimal configuration plan.

2. The method for optimizing the source and storage capacity of an integrated energy system considering comprehensive demand response according to claim 1, characterized in that: The annual equivalent cost of the system investment includes the initial investment cost of the equipment and the equipment operation and maintenance costs, and the expression is: Where i is the device type, N=6, is the unit investment cost of each equipment, Cap i is the planned capacity of each device, f i is the annual cost coefficient of equipment i, r is the base discount rate, Y i is the life span of device i.

3. The method for optimizing the source and storage capacity of an integrated energy system considering comprehensive demand response according to claim 1, characterized in that: The expression of the interaction cost between the integrated energy system and the power grid is: in, and is the electricity purchase price and electricity sales price of the integrated energy system interacting with the power grid at time t, and is the power purchased and sold by the integrated energy system and the power grid at time t, and Δt is the unit time duration; The expression of the natural gas purchase cost is: Among them, f gas is the purchase price of natural gas, H gas is the lower calorific value of natural gas, P CHP (t) is the output power of the CHP unit at time t; P GB (t) is the thermal power output of GB unit at time t; η GB The gas-heat conversion efficiency of the gas boiler; The expression of environmental cost is: Among them, μ gas The environmental value cost of electricity produced by gas-fired units and the power grid; The expression of the user's participation in the comprehensive demand response cost is: in, and The electricity load demand of users before and after the implementation of comprehensive demand response at time t; and is the heat load demand of users before and after the implementation of comprehensive demand response at time t; ρ h The price at which EH sells heat to system users.

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