Optimal operation method and apparatus for multi-category energy subsystem of urban energy internet

Abstract

Disclosed in the present invention are an optimal operation method and apparatus for a multi-category energy subsystem of an urban energy internet. The method comprises: dividing an urban energy internet into a main power grid layer and a micro-energy subsystem layer considering energy classification, and respectively establishing corresponding optimal operation models for the main power grid layer and the micro-energy subsystem layer considering energy classification; using prediction data of a load and a renewable energy output in the urban energy internet to solve the optimal operation model corresponding to the main power grid layer, so as to obtain an optimal operation parameter of a main power grid; using a model prediction control method and the optimal operation parameter of the main power grid to solve, multiple times, the optimal operation model corresponding to the micro-energy subsystem layer considering energy classification, so as to obtain an optimal operation parameter sequence of micro-energy subsystems after each solution, and ending the solution after an optimal scheduling scheme for one scheduling period is generated. The present invention aims to effectively solve the scheduling operation problem of an urban energy internet by means of considering energy classification.

Description

Optimized operation method and device for multi-category energy subsystems of urban energy internet Technical Field

[0001] The present invention belongs to the field of control and scheduling of urban energy internet, and more specifically, relates to an optimized operation method and device for multi-category energy subsystems of urban energy internet. Background Art

[0002] As a next-generation energy management model for efficient energy utilization and optimized allocation, the Urban Energy Internet relies on digital, intelligent, and integrated approaches, leveraging information technology, to organically integrate decentralized energy systems and facilities. By integrating and managing various renewable energy and micro-energy subsystems, it enables intelligent and optimized energy scheduling, diversified energy flow configuration and trading, and precise matching of energy use and production, thereby achieving efficient urban energy supply and low-carbon emissions. However, the randomness and volatility of renewable energy sources, when connected to the grid, can lead to problems such as unbalanced power supply, reduced power quality, and unstable operation, increasing the difficulty of urban energy operation and management.

[0003] Existing energy management strategies treat energy as a homogeneous product. However, it has been discovered that micro-energy subsystems are willing to buy and sell energy at different prices depending on factors such as the source and destination of the energy. Given that micro-energy subsystems have different preferences for different energy sources, energy should not be treated as a homogeneous product but rather classified into different categories based on its specific attributes, which are valuable to prosumers. Traditional micro-energy subsystems use a single energy category management approach, which fails to reflect the unique characteristics of electric energy products and also faces the challenges of intermittent output from distributed power sources and the bidirectional flow of electricity. Summary of the Invention

[0004] In response to the problems existing in the prior art, the present invention provides a method and device for optimizing the operation of multi-category energy subsystems of an urban energy internet. Its purpose is to effectively solve the scheduling and operation problems of the urban energy internet by considering energy classification, so as to cope with the challenges brought by high-penetration distributed renewable energy.

[0005] In order to solve the above technical problems, the present invention is implemented through the following technical solutions:

[0006] According to a first aspect of the present invention, a method for optimizing the operation of a multi-category energy subsystem of an urban energy internet is provided, comprising:

[0007] The urban energy internet is divided into a main power grid layer and a micro energy subsystem layer considering energy classification, and corresponding optimization operation models are established for the main power grid layer and the micro energy subsystem layer considering energy classification;

[0008] Using the forecast data of load and renewable energy output in the urban energy internet, the optimization operation model corresponding to the main grid layer is solved to obtain the optimized operation parameters of the main grid;

[0009] Utilizing a model predictive control method and the optimized operating parameters of the main power grid, the optimized operating model corresponding to the micro-energy subsystem layer considering energy classification is solved multiple times, each solution obtaining a sequence of optimized operating parameters for each micro-energy subsystem, and the solution is terminated after an optimized scheduling plan for one scheduling cycle is generated, wherein the optimized scheduling plan for one scheduling cycle includes the first value of the sequence of optimized operating parameters for each micro-energy subsystem obtained by each solution;

[0010] The operation of multiple energy subsystems of the urban energy internet is controlled based on the optimized scheduling plan of the one scheduling cycle.

[0011] In a possible implementation of the first aspect, establishing corresponding optimization operation models for the main power grid layer and the micro energy subsystem layer considering energy classification includes:

[0012] Taking the lowest energy supply cost of the urban energy internet main grid as the optimization goal and considering the first constraint condition, an optimization operation model corresponding to the main grid layer is established. The first constraint condition includes power balance constraint, unit output constraint, reserve constraint, unit ramp constraint, start-stop time constraint, and line capacity constraint;

[0013] Taking the maximization of the comprehensive benefit of the micro-energy subsystem layer considering energy classification as the optimization goal, and considering the second constraint condition, an optimization operation model corresponding to the micro-energy subsystem layer considering energy classification is established. The second constraint condition includes the upper and lower limit constraints of each micro-energy subsystem, the supply and demand balance constraints within the node, the constraints of each micro-energy subsystem and the equipment climbing constraints.

[0014] In a possible implementation of the first aspect, the objective function of the optimization operation model corresponding to the main power grid layer is:

[0015]

[0016]

[0017]

[0018]

[0019] Where, The power supply cost of the main grid layer, N gen Indicates the total number of units, i indicates the number of units, fi is the coal consumption cost function of unit i, which is a function related to power, t is the time, P i (t) represents the output of unit i at time t, T is the number of time periods within the prediction time, is the startup cost of unit i, is the shutdown cost of unit i, H i and J i is the cost of single startup and shutdown of unit i, and For the crew Number of starts and stops, a i 、b i and c i It's the crew Cost coefficient;

[0020] The constraints of the optimization operation model corresponding to the main power grid layer are:

[0021]

[0022]

[0023]

[0024]

[0025]

[0026]

[0027] Where, represents the predicted value of wind power output at time t, N load represents the total number of load nodes, represents the load of node n at time t, u i (t) is a 0-1 decision variable, indicating the start and stop status of unit i at time t. and is the upper and lower limits of the unit output, η is the system's thermal reserve coefficient, R u ,i and R d,i is the ramp-up and ramp-down rate of unit i, the minimum startup / shutdown time of the unit is TO / TS, is the number of the system branch, P l (t) is the power flowing through a branch calculated by the DC power flow method at time t, It is the upper limit of the power that can flow through this branch.

[0028] In a possible implementation of the first aspect, the objective function of the optimization operation model corresponding to the micro-energy subsystem layer considering energy classification is:

[0029]

[0030]

[0031]

[0032]

[0033]

[0034]

[0035]

[0036]

[0037]

[0038] Where, To consider the benefits of the micro-energy subsystem layer of energy classification, For the benefits of green electric micro energy subsystem, To subsidize the income of electric micro-energy subsystem, is the revenue from subsidized load, is the starting time, is the termination time, k is the type of micro energy subsystem, K is the set of micro energy subsystem types, , C k ,m,i (t) is the benefit of micro energy subsystem i exchanging power with the main grid of the urban energy Internet through the interconnection line, C k, L,i (t) is the income obtained by micro energy subsystem i from supplying power to its own load, N k is the number of k-type micro-energy subsystems, u is used to represent the utility coefficient of micro-energy subsystem i when it tends to use a certain type of energy, u≥1, C L,p is the unit price of electricity sold by micro energy subsystem i to users, C m,p is the wholesale price of electricity sold / purchased by micro energy subsystem i from the main grid of the urban energy internet, C g,p is the unit price of natural gas, C h,p The heat gain from the micro energy subsystem supplying heat to its own heat load, G MT,i is the gas consumption during the operation of the micro gas turbine, G b,iis the natural gas capacity converted from heat purchase by the micro-energy subsystem, H load,i is the heat load power of the micro energy subsystem, is the power generation efficiency of the micro gas turbine, Q LHV is the lower calorific value of natural gas, P MT,i (t) is the electric power generated by the micro gas turbine in the micro energy subsystem i at time t, H MT,i (t) is the thermal power generated by the recovered waste heat, H b,i (t) is the thermal power purchased by the micro-energy subsystem;

[0039] The constraints of the optimization operation model corresponding to the micro-energy subsystem layer considering energy classification are:

[0040]

[0041]

[0042]

[0043]

[0044]

[0045]

[0046]

[0047]

[0048]

[0049]

[0050]

[0051]

[0052]

[0053]

[0054]

[0055]

[0056]

[0057] Where, is the predicted value of the load power of micro energy subsystem i at a certain moment, is the predicted value of the load power of micro energy subsystem i at a certain moment, and a certain period in the prediction domain starting from time t is defined as , To predict the starting time, S ik ( ) For this prediction period, the capacity of the energy storage device in the micro energy subsystem i allocated to the k type of energy, E bi is the capacity of the energy storage device, P ESS ( ) is the charging and discharging power of the energy storage device at this time, is the length of the forecast period, A bi is the charge and discharge state matrix of the energy storage device, and Micro energy subsystem The upper and lower limits of energy storage equipment capacity, 、 is the upper limit of charging and discharging of energy storage equipment, α is the thermoelectric ratio, and are the upper and lower limits of the micro gas turbine, Micro Energy Subsystem The upper limit of the capacity of the interconnection line between the city's energy internet main grid.

[0058] In a possible implementation of the first aspect, the method of using a model predictive control method and the optimized operating parameters of the main power grid to solve the optimized operating model corresponding to the micro-energy subsystem layer considering energy classification multiple times includes:

[0059] The energy storage device in the micro-energy subsystem is used as the control object of the model predictive control. The charging and discharging state of the energy storage device in the micro-energy subsystem is used as the control state vector and output vector in a linear discrete form. The micro-energy subsystem performs online correction on the output vector to obtain the corrected actual value.

[0060] The corrected actual value is input into the optimization operation model corresponding to the micro-energy subsystem layer considering energy classification, and another round of rolling optimization solution is performed.

[0061] In a possible implementation of the first aspect, the charging and discharging states of the energy storage device in the micro energy subsystem are used as control state vectors and output vectors in a linear discrete form, and the micro energy subsystem performs online correction on the output vector, specifically as follows:

[0062]

[0063] Where, ΔP pre,ik It is the disturbance of the electric energy of type k in the energy storage device in micro-energy subsystem i due to the prediction error.

[0064] In a possible implementation of the first aspect, the rolling optimization solution is specifically:

[0065] The prediction domain is decomposed into a short-term prediction domain and a long-term prediction domain. The optimization solution of the optimization operation model corresponding to the micro-energy subsystem layer considering energy classification adopts a rolling optimization solution by combining the short-term prediction domain and the long-term prediction domain.

[0066] According to a second aspect of the present invention, there is provided an optimized operation device for a multi-category energy subsystem of an urban energy internet, comprising:

[0067] An optimization operation model establishment module is used to divide the urban energy internet into a main power grid layer and a micro energy subsystem layer considering energy classification, and to establish corresponding optimization operation models for the main power grid layer and the micro energy subsystem layer considering energy classification;

[0068] A first solving module is configured to solve the optimization operation model corresponding to the main grid layer using the predicted data of load and renewable energy output in the urban energy internet to obtain the optimized operation parameters of the main grid;

[0069] A second solving module is configured to solve the optimized operation model corresponding to the micro-energy subsystem layer considering energy classification multiple times by using a model predictive control method and the optimized operating parameters of the main power grid, each solving obtaining a sequence of optimized operating parameters for each micro-energy subsystem, and terminating the solving until an optimized scheduling scheme for one scheduling cycle is generated, wherein the optimized scheduling scheme for one scheduling cycle includes a first value of the sequence of optimized operating parameters for each micro-energy subsystem obtained by each solving;

[0070] A control module is used to control the operation of multiple energy subsystems of the urban energy internet based on the optimized scheduling plan of the one scheduling cycle.

[0071] According to a third aspect of the present invention, a device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of the method for optimizing operation of a multi-category energy subsystem of an urban energy Internet are implemented.

[0072] According to a fourth aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method for optimizing operation of a multi-category energy subsystem of an urban energy Internet are implemented.

[0073] Compared with the prior art, the present invention has at least the following beneficial effects:

[0074] The present invention provides an optimization operation method for a multi-category energy subsystem of an urban energy internet. By using an energy classification method, electric energy is divided into different categories according to its own characteristics, reflecting the characteristics of electric energy products when users use electric energy. By adopting model predictive control, the main power grid and each micro-energy system still maintain the ability to seek optimization in a time-varying environment. Not only can the output error be minimized as much as possible in predictive control, but it can also be adjusted through feedback of real-time information, so that the system completes closed-loop optimization and can perform self-correction, reducing the impact of the intermittent output of distributed power sources. By solving a two-layer optimization model, an optimization control scheme for the main power grid and each micro-energy system can be obtained, and the two-layer optimization model is interacted by transmitting power parameters, which can better connect the main power grid and each micro-energy system to cope with the two-way flow of electric energy. This method can achieve efficient operation of the urban energy internet.

[0075] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] In order to more clearly illustrate the technical solutions in the specific embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the specific embodiments. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0077] FIG1 is a flow chart of an optimized operation method of a multi-category energy subsystem of an urban energy internet according to an embodiment of the present invention.

[0078] FIG2 is a diagram showing energy classification in an optimized operation method for a multi-category energy subsystem of an urban energy internet according to an embodiment of the present invention.

[0079] FIG3 is a schematic diagram of a 30-node topology used in a test of an optimized operation method for a multi-category energy subsystem of an urban energy internet according to an embodiment of the present invention.

[0080] FIG4 is a schematic diagram of rolling optimization in an optimization operation method of a multi-category energy subsystem of an urban energy internet according to an embodiment of the present invention.

[0081] FIG5 is a flowchart of an optimization operation method for optimizing multi-category energy subsystems of an urban energy internet according to an embodiment of the present invention.

[0082] FIG6 shows the unit output under an optimized operation method of a multi-category energy subsystem of an urban energy internet according to an embodiment of the present invention.

[0083] FIG7 is a SOC distribution diagram of a subsidized electric energy micro-energy subsystem under an optimized operation method of a multi-category energy subsystem of an urban energy internet according to an embodiment of the present invention.

[0084] FIG8 shows the total charging and discharging power of the energy storage device of the green electric micro-energy subsystem under an optimized operation method of a multi-category energy subsystem of an urban energy internet according to an embodiment of the present invention.

[0085] FIG9 shows the exchange power of the interconnection line of the subsidized electric energy micro-energy subsystem under an optimized operation method of a multi-category energy subsystem of an urban energy Internet according to an embodiment of the present invention.

[0086] FIG10 shows the load distribution of the green electric micro-energy subsystem under an optimized operation method of a multi-category energy subsystem of an urban energy internet according to an embodiment of the present invention.

[0087] FIG11 shows the distribution of subsidized loads under an optimized operation method of a multi-category energy subsystem of an urban energy internet according to an embodiment of the present invention. Modes for Carrying Out the Invention

[0088] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0089] The optimized operation of multiple energy subsystems in the urban energy internet needs to take into account aspects such as the balance of energy supply and demand, energy storage and conversion, energy flow and trading, so as to achieve the optimized operation of multiple energy subsystems in the urban energy internet.

[0090] As shown in FIG1 , an embodiment of the present invention provides an optimized operation method for a multi-category energy subsystem of an urban energy internet, including the following steps:

[0091] S100: Divide the urban energy internet into a main power grid layer and a micro energy subsystem layer considering energy classification, and establish corresponding optimization operation models for the main power grid layer and the micro energy subsystem layer considering energy classification.

[0092] S200. Using the predicted data of load and renewable energy output in the urban energy internet, the optimized operation model corresponding to the main grid layer is solved to obtain the optimized operation parameters of the main grid.

[0093] S300. Utilize the model predictive control method and the optimized operating parameters of the main power grid to solve the optimized operating model corresponding to the micro-energy subsystem layer considering energy classification multiple times, and obtain the optimized operating parameter sequence of each micro-energy subsystem each time, until the optimized scheduling plan of a scheduling cycle is generated and the solving is terminated. The optimized scheduling plan of a scheduling cycle includes the first value of the optimized operating parameter sequence of each micro-energy subsystem obtained each time.

[0094] S400. Control the operation of multiple energy subsystems of the urban energy internet based on the optimized scheduling plan of the one scheduling cycle.

[0095] It should be noted that the micro-energy subsystem layer considering energy classification includes green electricity micro-energy subsystem, subsidized electricity micro-energy subsystem, subsidized load and ordinary load.

[0096] It should also be noted that the forecast data for renewable energy output is generated based on historical data.

[0097] For example, as shown in Figure 2, grid electricity is divided into heterogeneous energy products based on its source and destination. Based on the different preferences of micro-energy subsystems for different energy products, users are divided into different energy users. The set of micro-energy subsystems is defined as N = {1, 2…, n}, and the set of energy categories within each user is defined as K = {green, grid, sub}, where green represents green electricity, grid represents grid-interactive electricity, and sub represents subsidized electricity. Accordingly, users can be divided into four categories based on their preferences for energy use or production:

[0098] (1) Green electricity micro-energy subsystem: can produce green electricity and tends to use green electricity.

[0099] (2) Subsidized electricity micro-energy subsystem: It can produce subsidized electricity and green electricity, and tends to send subsidies to subsidized users first.

[0100] (3) Subsidized load: This type of users can purchase subsidized electricity at a lower price and are therefore more inclined to use subsidized electricity.

[0101] (4) Normal load: There is no particular preference for the type of energy used.

[0102] In one embodiment, in step S100, corresponding optimization operation models are established for the main power grid layer and the micro energy subsystem layer considering energy classification, as follows:

[0103] a. With the lowest energy supply cost for the Urban Energy Interconnection main grid as the optimization objective, and taking into account the first constraint, an optimized operation model corresponding to the main grid layer is established. The first constraint includes power balance constraints, unit output constraints, reserve constraints, unit ramp constraints, start / stop time constraints, and line capacity constraints. It should be understood that the first constraint is a safety constraint.

[0104] For example, taking the lowest energy supply cost of the main grid of the urban energy Internet as the optimization goal, the mixed integer programming method is used to establish the optimization operation model corresponding to the main grid layer with safety constraints.

[0105] b. Taking the maximization of the comprehensive benefits of the micro-energy subsystem layer considering energy classification as the optimization goal, and considering the second constraint condition, an optimization operation model corresponding to the micro-energy subsystem layer considering energy classification is established. The second constraint condition includes the upper and lower limit constraints of each micro-energy subsystem, the supply and demand balance constraints within the node, the constraints of each micro-energy subsystem and the equipment climbing constraints.

[0106] It should be noted that the comprehensive benefits of the micro-energy subsystem layer considering energy classification include the social benefits and environmental benefits when different entities use energy.

[0107] In one embodiment, specifically, the optimization operation model corresponding to the main power grid layer is described in detail as follows:

[0108] The objective function of the optimization operation model corresponding to the main power grid layer is:

[0109]

[0110]

[0111]

[0112] Where, The power supply cost of the main grid layer, N gen Indicates the total number of units, i indicates the number of units, f i is the coal consumption cost function of unit i, which is a function related to power, t is the time, P i (t) represents the output of unit i at time t, T is the number of time periods within the prediction time, is the startup cost of unit i, is the shutdown cost of unit i, H i and J i is the cost of single startup and shutdown of unit i, and For the crew Number of starts and stops, a i 、bi and c i It's the crew Cost coefficient;

[0113] The constraints of the optimization operation model corresponding to the main power grid layer are:

[0114]

[0115]

[0116]

[0117]

[0118]

[0119]

[0120] Where, represents the predicted value of wind power output at time t, N load represents the total number of load nodes, represents the load of node n at time t, u i (t) is a 0-1 decision variable, indicating the start and stop status of unit i at time t. and is the upper and lower limits of the unit output, η is the system's thermal reserve coefficient, R u ,i and R d,i is the ramp-up and ramp-down rate of unit i, the minimum startup / shutdown time of the unit is TO / TS, is the number of the system branch, P l (t) is the power flowing through a branch calculated by the DC power flow method at time t, It is the upper limit of the power that can flow through this branch.

[0121] In one embodiment, specifically, the optimization operation model corresponding to the micro-energy subsystem layer considering energy classification is described in detail as follows:

[0122] The objective function of the optimization operation model corresponding to the micro-energy subsystem layer considering energy classification is:

[0123]

[0124]

[0125]

[0126]

[0127]

[0128]

[0129]

[0130]

[0131]

[0132] Where, To consider the benefits of the micro-energy subsystem layer of energy classification, For the benefits of green electric micro energy subsystem, To subsidize the income of electric micro-energy subsystem, is the revenue from subsidized load, is the starting time, is the termination time, k is the type of micro energy subsystem, K is the set of micro energy subsystem types, , C k ,m,i (t) is the benefit of micro energy subsystem i exchanging power with the main grid of the urban energy Internet through the interconnection line, C k, L,i (t) is the income obtained by micro energy subsystem i from supplying power to its own load, N k is the number of k-type micro-energy subsystems, u is used to represent the utility coefficient of micro-energy subsystem i when it tends to use a certain type of energy, u≥1, C L,p is the unit price of electricity sold by micro energy subsystem i to users, C m,p is the wholesale price of electricity sold / purchased by micro energy subsystem i from the main grid of the urban energy internet, C g,p is the unit price of natural gas, C h,p The heat gain from the micro energy subsystem supplying heat to its own heat load, G MT,i is the gas consumption during the operation of the micro gas turbine, G b,i is the natural gas capacity converted from heat purchase by the micro-energy subsystem, H load,i is the heat load power of the micro energy subsystem, is the power generation efficiency of the micro gas turbine, Q LHV is the lower calorific value of natural gas, P MT,i (t) is the electric power generated by the micro gas turbine in the micro energy subsystem i at time t, H MT,i (t) is the thermal power generated by the recovered waste heat, H b,i (t) is the thermal power purchased by the micro-energy subsystem;

[0133] The constraints of the optimization operation model corresponding to the micro-energy subsystem layer considering energy classification are:

[0134]

[0135]

[0136]

[0137]

[0138]

[0139]

[0140]

[0141]

[0142]

[0143]

[0144]

[0145]

[0146]

[0147]

[0148]

[0149]

[0150]

[0151] Where, is the predicted value of the load power of micro energy subsystem i at a certain moment, is the predicted value of the load power of micro energy subsystem i at a certain moment, and a certain period in the prediction domain starting from time t is defined as , To predict the starting time, S ik ( ) For this prediction period, the capacity of the energy storage device in the micro energy subsystem i allocated to the k type of energy, E bi is the capacity of the energy storage device, P ESS ( ) is the charging and discharging power of the energy storage device at this time, is the length of the forecast period, A bi is the charge and discharge state matrix of the energy storage device, ､ Micro energy subsystem The upper and lower limits of energy storage equipment capacity, 、 is the upper limit of charging and discharging of energy storage equipment, α is the thermoelectric ratio, , are the upper and lower limits of the micro gas turbine, Micro Energy Subsystem The upper limit of the capacity of the interconnection line between the city's energy internet main grid.

[0152] In one embodiment, in step S300, the optimized operation model corresponding to the micro-energy subsystem layer considering energy classification is solved multiple times using the model predictive control method and the optimized operation parameters of the main power grid, as follows:

[0153] a. The energy storage device in the micro-energy subsystem is used as the control object of the model predictive control. The charging and discharging state of the energy storage device in the micro-energy subsystem is used as the control state vector and output vector in a linear discrete form. The micro-energy subsystem performs online correction on the output vector to obtain the corrected actual value.

[0154] Among them, regarding the use of linear discrete form to take the charging and discharging state of the energy storage device in the micro energy subsystem as the control state vector and output vector, the micro energy subsystem performs online correction on the output vector. The specific formula is as follows:

[0155]

[0156] Where, ΔP pre,ik It is the disturbance of the electric energy of type k in the energy storage device in micro-energy subsystem i due to the prediction error.

[0157] Specifically, the micro-energy subsystem performs online correction on the output vector, reducing the disturbance caused by the error between the predicted value and the actual value.

[0158] b. Input the corrected actual value into the optimization operation model corresponding to the micro-energy subsystem layer considering energy classification, and perform another round of rolling optimization solution. Specifically, the rolling optimization solution is as follows: decompose the prediction domain into a short-term prediction domain and a long-term prediction domain. The optimization operation model corresponding to the micro-energy subsystem layer considering energy classification is optimized and solved using a rolling optimization solution combining the short-term prediction domain and the long-term prediction domain.

[0159] As shown in Figure 4, the exemplary rolling optimization method decomposes the forecast domain into a 5-minute forecast domain (i.e., short-term forecast domain) and a 2-hour forecast domain (i.e., long-term forecast domain). The optimization solution for the optimized operation model corresponding to the micro-energy subsystem layer, which considers energy classification, also adopts a combined 5-minute and 2-hour optimization solution. At the beginning of the solution, based on the generated 5-minute and 2-hour forecast values, at 00:00, the system performs a solution based on the forecast values ​​in both the 5-minute and 2-hour forecast domains to obtain a 5-minute scheduling plan. From 00:00 to 2:00, the 5-minute forecast domain is continuously regressed until the first 2-hour optimized schedule is completed. From 2:00 to 4:00, the 2-hour forecast domain begins to regress, and a 5-minute rolling optimization process begins from 2:00 to 4:00. This process continues until 22:00 to 24:00, at which point the 5-minute and 2-hour forecast domains overlap, and only the 5-minute forecast values ​​are used in the optimization solution. When the scheduling plan for the last control domain is generated, the day's optimization scheduling work is completed.

[0160] In one embodiment, a specific simulation case is provided as follows:

[0161] Step 1. Select an improved IEEE 30-node power system. To verify the control capabilities and scheduling effectiveness of the control method proposed in this paper, a simulation analysis of this system was performed using MATLAB software called CPLEX. First, the electrical energy in the power grid was divided into heterogeneous energy products based on its source and destination. Based on the different preferences of the micro-energy subsystem for different energy products, users were divided into different energy users, and the flow direction of different types of electrical energy was specified, as shown in Figures 2 and 3.

[0162] Step 2. Based on the characteristics of the supply, storage, and demand links in the energy system and the energy consumption trends of each micro-energy subsystem, the optimization goal is established to maximize the comprehensive benefits of each micro-energy subsystem. Constraints such as upper and lower limits of each energy entity, supply and demand balance constraints within the node, constraints on each energy system, and equipment ramp-up constraints are analyzed and constructed:

[0163] The specific system parameters are shown in Table 1:

[0164] Table 1 Parameters of the micro-energy subsystem optimization model

[0165]

[0166] Step 3. Taking the minimization of the main grid operation cost of the urban energy internet as the optimization goal, a mixed integer programming method is used to establish the optimal unit combination model with safety constraints. The power per unit value is taken as 100, and the unit parameters are shown in Table 2:

[0167] Table 2 Unit parameter values

[0168]

[0169] Step 4. Set the initial capacity of the energy storage device in the green power micro-energy subsystem to 60%, and the initial capacities of green power and grid energy to 30%. Set the initial capacity of the energy storage device in the subsidized power micro-energy subsystem to 60%, and the initial capacities of green power, grid energy, and subsidized power to 20%. Scheduling was performed according to the processes shown in Figures 4 and 5, resulting in 288 solutions. The power system operation was observed, and the simulation results are shown in Figures 6, 7, 8, 9, 10, and 11. As shown in Figure 6, to maximize the absorption of renewable energy, thermal power units must more flexibly adapt to changes in renewable energy output while meeting power supply requirements. Starting at 8:00 AM, photovoltaic output increased, while the output of each thermal power unit gradually decreased. When the reduced output of the operating units could no longer meet the renewable energy absorption requirements, units 2 and 3 were shut down to absorb the renewable energy. After 5:00 PM, fluctuations in renewable energy output were entirely driven by fluctuations in wind turbine output. The wind power output data at the site selected for the example shows a significant drop after 8:00 PM. To ensure peak power supply during this period, Unit 1's output is gradually increased, and Units 2 and 3 are also activated to meet user electricity needs. Figures 7, 8, and 9 illustrate how the microgrid subsystem stores different types of energy during periods of photovoltaic output and discharges them to meet power supply needs during peak demand periods. Due to the intermittent nature of renewable energy output, the charge and discharge power of the energy storage equipment fluctuates significantly, but still maintains a clear trend. This shows that through optimized scheduling, the energy storage equipment is able to respond to changes in load and electricity prices, achieving peak load shifting and valley filling capabilities, which is beneficial to the economic operation of the microgrid. Figures 10 and 11 illustrate the use of load-powered energy in various types of microgrids. It can be seen that because green electricity micro-grids tend to give priority to the use of green electricity, during the photovoltaic output period (6:00-17:00), the energy supply of users within the micro-grid is mainly met by green electricity. In the subsidized electricity micro-grid, the priority of using green electricity is lower than that of sending subsidized electricity. Therefore, the time range for the subsidized electricity micro-grid to use green electricity to supply internal users is narrower than that of the green electricity micro-grid in the same photovoltaic output period, and is concentrated in the period with the largest photovoltaic output (10:00-14:00).

[0170] Combined with the above simulation results, it is not difficult to find that the optimization control method of urban energy Internet multi-category energy subsystem based on model predictive control proposed in the present invention takes into account the energy consumption tendency of micro-energy subsystems, improves the absorption rate of renewable energy, realizes the intelligent charging and discharging of energy storage equipment, improves the benefits of micro-energy subsystems, reduces the power supply cost of the main power grid of the urban energy Internet, and meets the electricity demand of system users.

[0171] The present invention takes the information system as the link and the multi-category energy physical coupling nodes as the important nodes to control the urban regional energy Internet architecture with deep information and physical integration. It takes into account the typical characteristics of the subsystems, the optimization control method and the information data requirements. On the basis of considering the mutual coupling mechanism of the subsystems and the information interaction mechanism between subsystems, it constructs an edge-cloud collaborative architecture to maximize the absorption rate of renewable energy, realize the intelligent charging and discharging of energy storage equipment, increase the benefits of micro-energy subsystems, reduce the power supply cost of the main power grid of the urban energy Internet, and meet the electricity needs of system users.

[0172] The embodiment of the present invention provides an optimized operation device for multiple energy subsystems of a city energy internet, which is used to implement the above-mentioned optimized operation method, specifically including:

[0173] The optimization operation model establishment module is used to divide the urban energy Internet into a main power grid layer and a micro energy subsystem layer considering energy classification, and to establish corresponding optimization operation models for the main power grid layer and the micro energy subsystem layer considering energy classification.

[0174] The first solving module is used to solve the optimization operation model corresponding to the main power grid layer by using the predicted data of load and renewable energy output in the urban energy Internet to obtain the optimized operation parameters of the main power grid.

[0175] The second solving module is used to use the model predictive control method and the optimized operating parameters of the main power grid to solve the optimized operating model corresponding to the micro-energy subsystem layer considering energy classification multiple times, and obtain the optimized operating parameter sequence of each micro-energy subsystem each time. The solution is terminated after the optimized scheduling plan of a scheduling cycle is generated. The optimized scheduling plan of one scheduling cycle includes the first value of the optimized operating parameter sequence of each micro-energy subsystem obtained by each solution.

[0176] A control module is used to control the operation of multiple energy subsystems of the urban energy internet based on the optimized scheduling plan of the one scheduling cycle.

[0177] In one embodiment of the present invention, a computer device is provided, comprising a processor and a memory, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions to implement corresponding method processes or corresponding functions; the processor described in the embodiment of the present invention can be used to implement the operation of an optimized operation method for a multi-category energy subsystem of an urban energy internet.

[0178] In one embodiment of the present invention, a method for optimizing the operation of a multi-category energy subsystem of an urban energy internet, if implemented in the form of a software functional unit and sold or used as an independent product, can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and when the computer program is executed by a processor, it can implement the steps of each of the above-mentioned method embodiments. The computer program includes computer program code, which can be in source code form, object code form, executable file, or some intermediate form. Computer-readable storage media include permanent and non-permanent, removable and non-removable media, and information storage can be achieved by any method or technology. Information can be computer-readable instructions, data structures, program modules, or other data.

[0179] The computer storage medium can be any available medium or data storage device that can be accessed by a computer, including but not limited to magnetic storage (such as floppy disks, hard disks, magnetic tapes, magneto-optical disks (MO)), optical storage (such as CDs, DVDs, BDs, HVDs, etc.), and semiconductor storage (such as ROMs, EPROMs, EEPROMs, non-volatile memories (NANDFLASH), solid-state drives (SSDs)), etc.

[0180] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0181] This application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products of the embodiments of the application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate a device for implementing the functions specified in one or more processes in the flowchart and / or one or more blocks in the block diagram.

[0182] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce a product including an instruction device that implements the functions specified in one or more processes in the flowchart and / or one or more boxes in the block diagram.

[0183] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more processes in the flowchart and / or one or more boxes in the block diagram.

[0184] In the present invention, the terms "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" mean that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and the features of different embodiments or examples without contradiction.

[0185] Finally, it should be noted that the above-described embodiments are only specific implementations of the present invention, which are used to illustrate the technical solutions of the present invention, rather than to limit them. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the above-described embodiments, those skilled in the art should understand that any person skilled in the art can modify or easily conceive of changes to the technical solutions described in the above-described embodiments within the technical scope disclosed by the present invention, or replace some of the technical features therein with equivalents. Such modifications, changes, or replacements do not deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A method for optimizing the operation of multi-category energy subsystems of urban energy internet, characterized in that: include: The urban energy Internet is divided into a main power grid layer and a micro energy subsystem layer considering energy classification, and corresponding optimization operation models are established for the main power grid layer and the micro energy subsystem layer considering energy classification respectively; Using the forecast data of load and renewable energy output in the urban energy internet, the optimization operation model corresponding to the main power grid layer is solved to obtain the optimization operation parameters of the main power grid; The optimized operation model corresponding to the micro-energy subsystem layer considering energy classification is solved multiple times by using the model predictive control method and the optimized operation parameters of the main power grid, and each solution obtains the optimized operation parameter sequence of each micro-energy subsystem, until the solution is terminated after the optimized scheduling scheme of a scheduling cycle is generated, and the optimized scheduling scheme of one scheduling cycle includes the first value of the optimized operation parameter sequence of each micro-energy subsystem obtained by each solution; The operation of multiple energy subsystems of the urban energy Internet is controlled based on the optimized scheduling plan of the said scheduling cycle.

2. According to claim 1, the optimization operation method of a multi-category energy subsystem of an urban energy internet is characterized in that: The establishing of corresponding optimization operation models for the main power grid layer and the micro energy subsystem layer considering energy classification respectively includes: Taking the lowest energy supply cost of the main power grid of the urban energy Internet as the optimization goal and considering the first constraint condition, an optimization operation model corresponding to the main power grid layer is established, wherein the first constraint condition includes power balance constraint, unit output constraint, standby constraint, unit climbing constraint, start-stop time constraint and line capacity constraint; Taking the maximization of the comprehensive benefit of the micro-energy subsystem layer considering energy classification as the optimization goal, and considering the second constraint condition, an optimization operation model corresponding to the micro-energy subsystem layer considering energy classification is established. The second constraint condition includes the upper and lower limit constraints of each micro-energy subsystem, the supply and demand balance constraints within the node, the constraints of each micro-energy subsystem and the equipment climbing constraints.

3. The method for optimizing the operation of a multi-category energy subsystem of an urban energy internet according to claim 2, characterized in that: The objective function of the optimization operation model corresponding to the main power grid layer is: In the formula, The power supply cost of the main grid layer, N gen represents the total number of units, i represents the number of units, f i is the coal consumption cost function of unit i, which is a function related to power, t is the time, P i (t) represents the output of unit i at time t, T is the number of time periods in the prediction time, is the startup cost of unit i, is the shutdown cost of unit i, H i and J i is the cost of a single startup and shutdown of unit i, and For the crew The number of starts and stops, a i 、b i and c i It's the crew Cost coefficient of The constraints of the optimization operation model corresponding to the main power grid layer are: In the formula, represents the predicted value of wind power output at time t, N load represents the total number of load nodes, represents the load of node n at time t, u i (t) is a 0-1 decision variable, indicating the start and stop status of unit i at time t. and is the upper and lower limits of the unit output, η is the system's hot standby coefficient, R u ,i and R d,i is the ramp-up and ramp-down rate of unit i, the minimum startup / shutdown time of the unit is TO / TS, is the number of the system branch, P l (t) is the power flowing through a branch calculated by the DC power flow method at time t, It is the upper limit of the power that can flow through this branch.

4. The method for optimizing the operation of a multi-category energy subsystem of an urban energy internet according to claim 2 is characterized in that: The objective function of the optimization operation model corresponding to the micro-energy subsystem layer considering energy classification is: In the formula, To consider the benefits of the micro-energy subsystem layer of energy classification, For the benefits of green power micro energy subsystem, To subsidize the revenue of the electric micro-energy subsystem, is the revenue of the subsidized load, is the starting time, is the termination time, k is the type of micro energy subsystem, K is the set of micro energy subsystem types, , C k ,m,i (t) is the benefit of micro energy subsystem i exchanging power with the main grid of the urban energy Internet through the interconnection line, C k, L,i (t) is the income obtained by micro energy subsystem i from supplying power to its own load, N k is the number of k-type micro-energy subsystems, u is used to represent the utility coefficient of micro-energy subsystem i when using a certain type of energy, u≥1, C L,p is the unit price of electricity sold by micro energy subsystem i to users, C m,p is the wholesale price of electricity sold / purchased by micro energy subsystem i from the main grid of the urban energy Internet, C g,p is the unit price of natural gas, C h,p The heat gain from the micro energy subsystem supplying heat to itself, G MT,i is the gas consumption of the micro gas turbine during operation, G b,i is the natural gas capacity converted from the heat purchased by the micro-energy subsystem, H load,i is the heat load power of the micro energy subsystem, is the power generation efficiency of the micro gas turbine, Q LHV is the lower calorific value of natural gas, P MT,i (t) is the electric power generated by the micro gas turbine in the micro energy subsystem i at time t, H MT,i (t) is the thermal power generated by the waste heat recovery, H b,i (t) is the thermal power purchased by the micro-energy subsystem; The constraints of the optimization operation model corresponding to the micro-energy subsystem layer considering energy classification are: In the formula, is the predicted value of the load power of micro energy subsystem i at a certain moment, is the predicted value of the load power of micro energy subsystem i at a certain time, and a certain period in the prediction domain starting from time t is defined as ， To predict the starting time, For this prediction period, the capacity of the energy storage device in the micro energy subsystem i allocated to the k type of energy, E bi is the capacity of the energy storage device, P ESS ( ) is the charging and discharging power of the energy storage device at this time, is the length of the forecast period, A bi is the charge and discharge state matrix of the energy storage device, and Micro Energy Subsystem The upper and lower limits of energy storage equipment capacity, 、 is the upper limit of charging and discharging of energy storage equipment, α is the thermal-electric ratio, and are the upper and lower limits of the micro gas turbine, Micro Energy Subsystem The upper capacity limit of the interconnection line with the main power grid of the urban energy Internet.

5. The method for optimizing the operation of a multi-category energy subsystem of an urban energy internet according to claim 4, characterized in that: The method of using the model predictive control method and the optimized operating parameters of the main power grid is used to solve the optimized operating model corresponding to the micro energy subsystem layer considering energy classification for multiple times, including: The energy storage device in the micro energy subsystem is taken as the control object of the model predictive control, and the charging and discharging state of the energy storage device in the micro energy subsystem is taken as the control state vector and the output vector in a linear discrete form. The micro energy subsystem performs online correction on the output vector to obtain the corrected actual value; The corrected actual value is input into the optimization operation model corresponding to the micro-energy subsystem layer considering energy classification, and another round of rolling optimization solution is performed.

6. The method for optimizing the operation of a multi-category energy subsystem of an urban energy internet according to claim 5, characterized in that: The linear discrete form is used to take the charging and discharging state of the energy storage device in the micro energy subsystem as the control state vector and the output vector, and the micro energy subsystem performs online correction on the output vector, specifically: In the formula, ΔP pre,ik It is the disturbance of the electric energy of type k in the energy storage device i in the micro-energy subsystem i due to the prediction error.

7. The method for optimizing the operation of a multi-category energy subsystem of an urban energy internet according to claim 5, characterized in that: The rolling optimization solution is specifically as follows: The prediction domain is decomposed into a short-term prediction domain and a long-term prediction domain. The optimization solution of the optimization operation model corresponding to the micro-energy subsystem layer considering energy classification adopts a rolling optimization solution by combining the short-term prediction domain with the long-term prediction domain.

8. An optimized operation device for multi-category energy subsystems of urban energy internet, characterized in that: include: An optimization operation model establishment module is used to divide the urban energy Internet into a main power grid layer and a micro energy subsystem layer considering energy classification, and establish corresponding optimization operation models for the main power grid layer and the micro energy subsystem layer considering energy classification respectively; The first solving module is used to solve the optimization operation model corresponding to the main power grid layer by using the prediction data of load and renewable energy output in the urban energy Internet to obtain the optimization operation parameters of the main power grid; A second solving module is used to solve the optimized operation model corresponding to the micro-energy subsystem layer considering energy classification multiple times by using the model predictive control method and the optimized operation parameters of the main power grid, and obtain the optimized operation parameter sequence of each micro-energy subsystem each time, until the optimized scheduling scheme of a scheduling cycle is generated and the optimized scheduling scheme of one scheduling cycle includes the first value of the optimized operation parameter sequence of each micro-energy subsystem obtained by each solution; A control module is used to control the operation of multiple energy subsystems of the urban energy Internet based on the optimized scheduling plan of the scheduling cycle.

9. A device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, it implements the steps of the method for optimizing operation of a multi-category energy subsystem of an urban energy Internet as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method for optimizing operation of a multi-category energy subsystem of an urban energy Internet as described in any one of claims 1 to 7 are implemented.

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