Robust optimization configuration method and device for low-carbon integrated energy system based on source-load uncertainty

Abstract

The present invention discloses a method and device for robust optimization configuration of a low-carbon integrated energy system based on source-load uncertainty, including establishing a random energy consumption behavior prediction model for a regional building complex based on the random characteristics of air-conditioning start-stop, lighting, and equipment (televisions and computers); establishing a park load prediction model based on the obtained random energy consumption behavior time series; establishing a capacity model of each device in the low-carbon integrated energy system under variable operating conditions based on the change of equipment capacity efficiency / performance ratio with load rate; taking investment and total operating cost as optimization targets, and establishing a two-stage robust optimization configuration model of a low-carbon integrated energy system taking into account source-load uncertainty based on the variable operating condition characteristics of equipment; the present invention is suitable for capacity configuration and optimized operation of a low-carbon regional integrated energy system considering source-load uncertainty.

Description

Technical Field

[0001] The present invention relates to the fields of building energy conservation, energy system optimization operation, and in particular to a method and device for robust optimization configuration of a low-carbon integrated energy system based on source-load uncertainty. Background Art

[0002] The uncertainty on both the supply and demand sides poses a major challenge to the optimal configuration of low-carbon integrated energy systems. On the one hand, the uncertainty of user behavior on the demand side greatly increases the difficulty of load forecasting for low-carbon integrated energy systems, thus affecting the configuration of low-carbon integrated energy systems. Current research on building load simulation based on random energy behavior mainly focuses on single buildings, but there are great differences in the energy behavior patterns of users in different types of buildings. At present, there is a lack of research on load forecasting for regional building complexes that comprehensively considers multiple types of buildings and multiple random energy behaviors.

[0003] On the other hand, the uncertainty of photovoltaic capacity and the variable operating characteristics of equipment have a great impact on the accuracy of capacity prediction, operating costs and energy supply reliability of low-carbon integrated energy systems. At present, many studies have taken into account the uncertainty of photovoltaic capacity in the optimal configuration of low-carbon integrated energy systems, but there is a lack of research that takes into account the uncertainty on both the source and load sides. At the same time, in the energy system configuration modeling, the capacity efficiency of other capacity equipment is usually simplified to a constant, or only the variable operating characteristics of some equipment are considered. Ignoring the variable operating characteristics of equipment will affect the accuracy of the equipment capacity model, and then affect the accuracy of the entire system configuration plan.

[0004] Therefore, coordinating the uncertainties on both the source and load sides is the focus of current research. Comprehensively considering the uncertainties on both the source and load sides will improve the accuracy of the optimal configuration of the low-carbon integrated energy system and ensure the safe and stable operation of the low-carbon integrated energy system. Summary of the invention

[0005] The purpose of the present invention is to address the deficiencies of the prior art and propose a method and device for robust optimization configuration of a low-carbon integrated energy system based on source-load uncertainty, so as to solve the problem that the current traditional deterministic method cannot reflect the impact of source-load uncertainty on equipment operating characteristics and energy production and consumption, thereby affecting the accurate configuration of low-carbon integrated energy systems in regional building complexes under actual conditions. The present invention establishes a demand-side building complex random load prediction model and a variable operating condition capacity model for each device, and introduces them into the optimal configuration of a low-carbon integrated energy system, thereby establishing a robust optimization configuration method for a low-carbon integrated energy system based on source-load uncertainty.

[0006] The technical solution adopted by the present invention to solve the technical problem is as follows: a robust optimization configuration method for a low-carbon integrated energy system based on source-load uncertainty, comprising the following steps:

[0007] Step 1, obtaining random operation data of energy-consuming equipment of various building users in the park;

[0008] Step 2: Based on the random operation data of energy-consuming equipment, the time series of energy consumption behavior of building users is obtained, and the annual dynamic load of the regional building complex is predicted through the load prediction model;

[0009] Step 3, determining the fluctuation of the photovoltaic panel temperature and solar radiation intensity of the photovoltaic production equipment and the relationship between the relative efficiency / performance ratio of the production capacity of other production equipment except photovoltaic and the load change of the energy-consuming equipment;

[0010] Step 4: Based on the results of Steps 2 and 3, the uncertainty of source and load of the low-carbon integrated energy system is analyzed, and a two-stage robust optimization model is established. In the first stage, the annual dynamic load is used as input, and the objective function is to minimize the equivalent cost of equipment investment at the beginning of the year in the low-carbon integrated energy system to determine the upper-level capacity configuration of the equipment in the low-carbon integrated energy system. In the second stage, based on the upper-level capacity configuration, the annual dynamic load, photovoltaic capacity uncertainty parameters and other variable operating conditions of capacity equipment are coupled, and the objective function is to minimize the annual operation and maintenance cost of capacity equipment. The optimal capacity configuration plan is obtained by iterative solution to ensure that the low-carbon integrated energy system can maintain supply and demand balance under the interference of source and load uncertainty parameters.

[0011] Furthermore, in step 2, the k-means clustering algorithm is used to cluster the daily hourly energy consumption behaviors of building users, and the occurrence probability of the corresponding categories is obtained; based on the clustering results and the occurrence probability, the Monte Carlo method is used to obtain the annual random energy consumption behavior schedule of each type of building user in the park; the building model and the corresponding random energy consumption behavior schedule are imported into the EnergyPlus software for load simulation to obtain the hourly air conditioning, lighting and equipment power loads of each building.

[0012] Furthermore, in step 3, the relative efficiency / performance ratio of other production capacity equipment is calculated, where the relative efficiency is the ratio of the equipment efficiency to the rated power, and the performance ratio is the ratio of the equipment performance coefficient to the rated performance coefficient. The relationship between the predicted load change of the energy-consuming equipment is fitted to construct the equipment capacity model.

[0013] Furthermore, the power generation of the photovoltaic capacity equipment fluctuates with the temperature of the photovoltaic panel and the intensity of solar radiation, and is calculated from the difference between the actual operating temperature of the photovoltaic panel and the standard test temperature and the ratio of the actual solar radiation intensity to the light intensity under standard test conditions.

[0014] Furthermore, in step 3, other energy-generating equipment besides photovoltaics includes gas turbines, gas boilers, waste heat boilers, absorption chillers and electric chillers. Other energy-generating equipment all take into account their variable operating conditions and establish a variable operating condition model in which their energy-generating efficiency / performance ratio changes with load rate.

[0015] Furthermore, the equipment in the low-carbon integrated energy system includes batteries in addition to production equipment. The battery charging and discharging model is established by the energy stored at the previous moment and the charging and discharging amount within the time step. Its electricity source is photovoltaic, gas turbine power generation and grid power.

[0016] Furthermore, in step 4, the uncertainty of source and load of the low-carbon integrated energy system is analyzed, specifically: for the uncertainty of photovoltaic production capacity on the source side, a photovoltaic production capacity uncertainty set is established; for the performance curves of variable operating conditions of other production capacity equipment, the performance curves are obtained through data fitting; for the uncertainty on the load side, the dynamic load throughout the year is predicted based on the load forecasting model.

[0017] Furthermore, in step 4, for the uncertainty of the photovoltaic capacity on the source side, the photovoltaic uncertainty adjustment parameter To control the fluctuation range of photovoltaic production capacity, a box-type uncertainty set is used to describe the uncertainty of photovoltaic production capacity, and the fluctuation range of photovoltaic is represented by the uncertainty set interval.

[0018] Furthermore, in step 4, the equipment configuration capacity constraint, the system energy balance constraint, the equipment output constraint, and the power purchase constraint from the external power grid are used as the second-stage constraints of the two-stage robust optimization model; the C&CG algorithm is used to solve the robust optimization problem, and in the solution process, the two-stage robust optimization problem is transformed into a main problem and a sub-problem through dual transformation, thereby reducing the complexity of the model solution, and outputting the optimal capacity configuration plan that can ensure that the low-carbon integrated energy system can still maintain supply and demand balance under the influence of source-load uncertainty parameters.

[0019] On the other hand, the present invention also provides a device for robust optimization configuration of a low-carbon integrated energy system based on source-load uncertainty, comprising a memory and one or more processors, wherein the memory stores executable code, and when the processor executes the executable code, it implements the method for robust optimization configuration of a low-carbon integrated energy system based on source-load uncertainty.

[0020] Compared with the prior art, the beneficial effects of the present invention are as follows: compared with the traditional deterministic method, the present invention has established a complete set of robust optimization configuration methods and devices for low-carbon integrated energy systems based on source-load uncertainty. The present invention extracts the energy consumption behavior patterns of building users through the k-means clustering algorithm, and uses the Monte Carlo method to generate an hourly energy consumption behavior schedule throughout the year, quantitatively characterizes the randomness of energy consumption behavior, and introduces it into load forecasting, which significantly improves the accuracy of regional building complex load forecasting. At the same time, the present invention takes into account the volatility of source-side photovoltaic production capacity and the variable operating characteristics of other equipment, and constructs a two-stage robust optimization model based on source-load uncertainty, ensuring that the system can still output the optimal capacity configuration plan that maintains supply and demand balance under the interference of source-load uncertainty parameters, effectively improving the robustness of the low-carbon integrated energy system configuration to uncertain factors. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 Shown is a flow chart of the robust optimization configuration method for a low-carbon integrated energy system based on source-load uncertainty provided by the present invention.

[0022] Figure 2 Shown is the decision process for regulating a random sequence of behaviors throughout the year.

[0023] Figure 3 Shown is the energy flow diagram of a low-carbon integrated energy system.

[0024] Figure 4 Shown is the C&CG algorithm solution flow chart.

[0025] Figure 5 The figure shows the energy consumption behavior patterns of various equipment in apartment buildings.

[0026] Figure 6 The figure shows the energy consumption behavior pattern of air conditioning in office buildings.

[0027] Figure 7 Shown is the energy usage behavior pattern of air conditioning / lighting / equipment in hotel buildings.

[0028] Figure 8 Shown are the hourly cooling / heating loads for the park apartment buildings throughout the year.

[0029] Fig. 9 Shown is the hourly cooling / heating load of the park's office buildings throughout the year.

[0030] Fig.10 Shown is the hourly cooling / heating load of the hotel buildings in the park throughout the year.

[0031] Fig.11 Shown is the hourly electricity load of the park's apartment buildings throughout the year.

[0032] Fig.12 Shown is the hourly electricity load of the park's office buildings throughout the year.

[0033] Fig.13 Shown is the hourly electricity load of the hotel buildings in the park throughout the year.

[0034] Fig.14 Shown is the hourly hot water load for the park's apartment buildings throughout the year.

[0035] Fig.15 Shown is the hourly hot water load of the hotel buildings in the park throughout the year.

[0036] Fig.16 The results show the equipment capacity changing with uncertain parameters.

[0037] Fig.17 The figure shows a robust optimization configuration device for a low-carbon integrated energy system based on source-load uncertainty provided by the present invention. DETAILED DESCRIPTION

[0038] The specific implementation of the present invention is further described below in conjunction with the specific drawings.

[0039] The present invention provides a method for robust optimization configuration of a low-carbon integrated energy system based on source-load uncertainty, and the specific steps are as follows: Figure 1 shown.

[0040] 1) The daily hourly energy consumption behavior sequence of each user obtained through questionnaire survey and actual measurement is used as a data sample, including air conditioning start and stop, lighting, equipment (TV and computer), and hot water:

[0041]

[0042] in, is the status of air conditioning, lighting, equipment, and hot water at the tth hour of the i-th user's day. If it is turned on in that hour, it is recorded as 1, otherwise it is recorded as 0.

[0043] 2) Using the data analysis software IBM SPSS Statistics 20.0 and the k-means clustering algorithm, the daily hourly energy consumption behaviors of various types of equipment of building users are refined into several typical patterns, and the occurrence probability of the corresponding patterns is obtained.

[0044] Furthermore, based on the typical energy consumption behavior patterns of users and their occurrence probabilities, the Monte Carlo method is used to make daily decisions on the energy consumption behaviors of users throughout the year, and the annual random operation sequences of various adjustment behaviors are obtained, such as Figure 2 shown.

[0045] 3) Based on the obtained random operation sequences of various adjustment behaviors throughout the year, the corresponding building physical model is established using SketchUp software.

[0046] Furthermore, the building model and the corresponding energy behavior schedule were imported into the EnergyPlus software for load simulation, and the hourly air-conditioning load, as well as the hourly electrical load of lighting and equipment of each building were calculated.

[0047] Furthermore, the comprehensive cooling, heating and electricity loads of the park are obtained based on the building area and the load intensity of the individual buildings.

[0048] 3) Build the basic framework of the park's low-carbon integrated energy system with electric energy storage, such as Figure 3 As shown. The low-carbon integrated energy system established in this paper includes the following equipment: solar photovoltaic, gas turbine, gas boiler, waste heat boiler, absorption chiller, electric chiller, battery. Quantify the relationship between the relative efficiency / performance ratio of gas turbine, gas boiler, electric chiller, and absorption chiller as the load rate changes, the relationship between photovoltaic power generation and ambient temperature and light radiation density, the relationship between battery storage energy and charging and discharging efficiency and power, and establish the variable operating condition capacity model of each device in the low-carbon integrated energy system. The models of each component of the system are as follows:

[0049] Solar photovoltaic capacity model, the formula is as follows:

[0050]

[0051] in, ——Installed capacity of solar photovoltaic, kW; ——The power generated by solar photovoltaic at time t, kW; ——Ambient temperature is 25℃, and the incident sunlight intensity is 1000W / m 2 Maximum test power of PV modules under standard test conditions (STC), kW; ——Light intensity under standard test conditions, 𝑊 / m 2 ; ——actual solar radiation intensity, (𝑊 / m 2 ); ——Actual operating temperature of the solar panel, °C; ——STC standard test temperature, °C; ——Power temperature coefficient: -0.47% / ℃.

[0052] The battery charging and discharging model, the formula is as follows:

[0053]

[0054] in, is the energy stored in the battery at time t, kW·h; and is the charging efficiency and discharging efficiency of the battery; and is the charging power and discharging power of the battery at time t, kW, generally 0.95, is the time step.

[0055] The gas turbine capacity model, the formula is as follows:

[0056]

[0057]

[0058]

[0059]

[0060] in, ——the load rate of the gas turbine at time t; — rated power generation efficiency of the gas turbine; ——The power generation efficiency of the gas turbine at time t; ——Thermoelectric ratio of the gas turbine at time t; ——Natural gas consumption rate at time t, m 3 / s; ——Natural gas calorific value, J / m 3 ; ——Generation power of gas turbine at time t, kW; ——Thermal power of the gas turbine at time t, kW.

[0061] The waste heat boiler capacity model, the formula is as follows:

[0062]

[0063] in, ——heat output per unit time of waste heat boiler, kW; ——Heat generation efficiency of waste heat boiler ——The amount of heat absorbed by the waste heat boiler at time t, kW;

[0064] Gas boiler capacity model, the formula is as follows:

[0065]

[0066]

[0067] in, ——The load rate of the gas boiler at time t; - rated efficiency of gas boiler; ——Efficiency of gas boiler at time t; ——Heat production of the gas boiler at time t, kW.

[0068] The electric refrigerator capacity model, the formula is as follows:

[0069]

[0070]

[0071] in, ——load rate of the electric refrigerator at time t; - rated coefficient of performance of the electric refrigerator; ——the coefficient of performance of the electric refrigerator at time t; ——The cooling capacity of the electric refrigerator at time t, kW; ——Electric power input to the electric refrigerator at time t, kW; K ECi (i=0,1,2)——is the fitting constant.

[0072] The capacity model of absorption chiller is as follows:

[0073]

[0074]

[0075] in, ——Load rate of absorption chiller at time t; ——Rated coefficient of performance of absorption chiller; ——The coefficient of performance of the absorption chiller at time t; ——The cooling capacity of the absorption chiller at time t, kW; ——The heat input to the chiller at time t, kW.

[0076] 4) Constructing the PV uncertainty set to represent the uncertainty of load measurement:

[0077] In order to make the model easier to solve, a box-type uncertainty set is used to construct the uncertainty set of photovoltaic capacity, as follows: ,in The predicted photovoltaic energy production without considering the uncertainty is: is the maximum offset of photovoltaic production capacity, and the formula is as follows:

[0078]

[0079]

[0080] in, is the actual production capacity of photovoltaic power, is the robust adjustment coefficient, It is the photovoltaic uncertainty adjustment parameter, which is affected by the daily sunshine duration. The maximum value is 14, which can be adjusted according to the planner's demand for system robustness.

[0081] 5) The hourly load of the regional building complex throughout the year obtained by simulating and predicting the random energy consumption behavior of users is used as the input parameter of the optimization configuration model to characterize the uncertainty of the load side of the system; the equipment capacity model established based on the uncertainty set of photovoltaic capacity and the equipment variable operating condition curve is used as the source side input parameter of the optimization configuration model to characterize the uncertainty of the source side of the system. A two-stage robust optimization model of a low-carbon integrated energy system based on source-load uncertainty is established:

[0082] The optimization goal of this model is to minimize the annual total cost of the system, that is, to minimize the annual investment cost and annual operating cost of the system while meeting the user's load demand. The first stage determines the capacity of each device in the system, and the second stage optimizes the output of each device based on the first stage. The objective function includes the annual equal investment cost of each device , and the operating costs of the equipment during operation as follows:

[0083]

[0084]

[0085]

[0086] t=1,2,3…NT

[0087] Among them: the minimization of the outer layer is the first stage problem, the outer layer is , the optimization variable is x, which is the capacity of each device, including the photovoltaic configuration capacity , kW; gas turbine configuration capacity , kW; gas boiler configuration capacity , kW; waste heat boiler configuration capacity , kW; absorption chiller configuration capacity , kW; steam compression refrigerator configuration capacity , kW; battery configuration capacity , kWh; the maximum minimization of the inner layer is the second stage problem, which means the minimum operating cost under the worst conditions. , the optimization variable is y, which is the output of each device, including the amount of electricity purchased , kW; electricity sales , kW; battery charge , kW; battery discharge capacity , kW; Photovoltaic energy production , kW; power generation of gas turbine , kW; boiler heat output , kW; heat output of waste heat boiler , kW; cooling capacity of absorption chiller , kW; cooling capacity of electric refrigerator , kW; its value is the configuration variable obtained by the first stage solution and the photovoltaic uncertain variable u. Represents a given set The feasible domain of the optimization variable y.

[0088] In the second stage, the max layer aims to find u with the worst performance, while the min layer aims to find y that minimizes the objective function given x and u.

[0089] Among them, the annual equivalent investment cost The annualized investment cost of the photovoltaic power station, gas turbine, gas boiler, waste heat boiler, absorption chiller, steam compression chiller and energy storage device, that is, the total system investment cost is equally allocated to the cost value of each year in the operation cycle, which is calculated as follows:

[0090]

[0091] in, A collection of equipment types, including solar photovoltaic, gas turbine, gas boiler, waste heat boiler, absorption chiller, electric chiller and battery; is the rated capacity of the equipment, kWh; c i is the unit investment cost of the equipment, RMB / kWh; It is the conversion coefficient of the annual equal investment of the equipment.

[0092]

[0093] Among them, r is the annual interest rate, generally 5%, and N is the useful life of the equipment.

[0094] The operating costs include equipment operation and maintenance costs, fuel costs, and the cost of purchasing and selling electricity from the municipal power grid, which can be calculated using the following formula:

[0095]

[0096]

[0097] in, is the operation and maintenance cost of the equipment, RMB; is the fuel cost for system operation, RMB; The cost of purchasing and selling electricity from the power grid for the system, RMB; For different typical day types; The duration of different typical days; is the total daily operating hours of the equipment, h; is the unit operation and maintenance cost of the equipment, yuan; Yes Equipment In the Energy production per hour, kW; is the total number of input energy types; It is Energy in the Price per hour, yuan; It is Energy in the Hours of usage, m 3 ; It is The price of electricity purchased / sold from the municipal power grid per hour, RMB; It is Electricity purchased / sold from the municipal power grid per hour, kW.

[0098] The constraints of the low-carbon integrated energy system optimization configuration model include: equipment configuration capacity constraints, system energy balance constraints, equipment output constraints, and power purchase constraints from external power grids.

[0099] Capacity constraint: The constraint of the upper model is the capacity constraint of the equipment. Due to objective conditions such as the equipment footprint, it is necessary to control the equipment capacity not to exceed a certain limit to avoid excessive investment and unreasonable capacity installation.

[0100]

[0101]

[0102]

[0103]

[0104]

[0105]

[0106]

[0107] in, Configure capacity for solar PV, kW; is the maximum capacity of solar photovoltaic that can be installed, kW; Configure the capacity for the gas turbine, kW; is the maximum capacity of the gas turbine that can be installed, kW; Configure the capacity for the gas boiler, kW; The maximum capacity of the gas boiler that can be installed, kW; Configure the capacity for the waste heat boiler, kW; is the maximum capacity of the waste heat boiler that can be installed, kW; Configure the capacity for the absorption chiller, kW; is the maximum capacity of the absorption chiller that can be installed, kW; Configure the capacity of the electric refrigerator, kW; is the maximum capacity of the electric refrigerator that can be installed, kW; Configure the capacity for the battery, kWh; The maximum capacity of the battery that can be installed, in kWh.

[0108] Power balance constraints:

[0109]

[0110] in, is the energy efficiency ratio of the electric refrigerator at time t, / is the electric energy consumed by the electric refrigerator in period t, kW; is the electrical load of the system in period t, kW.

[0111] Heat balance constraints:

[0112]

[0113] in, is the heat load of the system in period t, kW; is the energy efficiency ratio of the absorption chiller at time t, is the heat energy consumed by the absorption chiller during period t, kW;

[0114] Cooling balance constraints:

[0115]

[0116] in, is the cooling load of the system in period t, kW.

[0117] Waste heat balance constraints:

[0118]

[0119] in, is the unused waste heat of the gas turbine at time t, kW; is the heat-to-electricity ratio of the gas turbine at time t.

[0120] Battery storage energy constraints:

[0121]

[0122]

[0123]

[0124]

[0125]

[0126] in, and is the charging power and discharging amount of the battery at time t, kW; The maximum amount of battery charge and discharge allowed, kW; It is the charging and discharging status bit of the energy storage power station. It is a 0-1 variable. When the value is 1, it means discharging, and when the value is 0, it means charging. is the initial storage energy of the energy storage power station, kW, and is the charging efficiency and discharging efficiency of the battery, N T is the scheduling period, which is 24h. S (0) is the capacity of energy storage at the initial time of dispatch, kW·h, and is the maximum / minimum remaining capacity allowed in the energy storage dispatch process, kW·h, is the energy stored in the battery at time t, kW·h, For the battery Energy stored at any given moment, kW·h.

[0127] Equipment operating power constraints:

[0128]

[0129]

[0130]

[0131]

[0132]

[0133]

[0134] in, is the maximum power of photovoltaic power generation, kW, is the maximum power generated by the gas turbine, kW; is the maximum power of gas boiler to generate heat, kW; is the maximum power of heat generation by the waste heat boiler, kW; is the maximum cooling power of the absorption chiller, kW; The maximum cooling power of the electric refrigerator, kW.

[0135]

[0136]

[0137] in, It represents the maximum value of the exchange power between the low-carbon integrated energy system and the distribution network. In order to ensure that the grid connection of the energy system will not have an impact on the power grid, it is necessary to comprehensively consider factors such as the capacity of the transformer and specific policies to determine the value; It is a 0-1 variable, representing the state of purchasing and selling electricity to the power grid. When the value is 1, it means buying electricity, and when the value is 0, it means selling electricity. The main purpose of this formula is to ensure that the system does not purchase and sell electricity at the same time.

[0138] 6) For the above robust optimization model, the present invention adopts the C&CG algorithm to solve and obtain the capacity configuration strategy and optimized operation strategy that meet the specific optimization target:

[0139] Based on the MATLAB2021b platform and YALMIP toolbox, the CPLEX12.8 solver is called to solve the proposed two-stage robust optimization configuration model. The solution process is as follows: Figure 4 shown.

[0140] Example:

[0141] This paper takes a science and technology park in Hangzhou as an example to explore a low-carbon integrated energy system based on source-load uncertainty. The park contains three types of buildings: hotel buildings, office buildings and apartment buildings, including 6 office buildings, 1 hotel building and 2 apartment buildings.

[0142] Through questionnaire survey and field measurement analysis, based on step 2, the energy consumption behavior patterns of various buildings and their corresponding occurrence probabilities are obtained as follows: Figure 5-Figure 7 As shown. Figure 5-Figure 7 Based on the typical timetable and occurrence probability in the , the Monte Carlo method is used to obtain the random operation sequences of air conditioning start and stop, lighting, and equipment (TV and computer) throughout the year.

[0143] The building envelope parameters of this case are shown in Table 1. The obtained annual random operation sequence is imported into the EnergyPlus software for load simulation, and the annual hourly air conditioning load and the electrical load of lighting and TV / computer equipment of the three types of buildings in the park are obtained. Figure 8-Figure 15 .

[0144] Table 1 Case building envelope parameters

[0145]

[0146] Based on step 4, with the goal of minimizing the annualized total cost of the system, the capacity of each device in the system is determined in the first stage. In the second stage, the output of each device is optimized based on the first stage to explore the optimal design solution that meets the goal. The parameters of each device in this case were obtained through market research, as shown in Table 2. The system capacity configuration results under 2000 are shown in Table 3. The system economic cost is shown in Table 4, and the change trend of equipment capacity with uncertain parameters is shown in Table 5. Fig.16 .

[0147] Table 2 Equipment parameters

[0148]

[0149] Table 3 Configuration results of equipment capacity with uncertain parameters

[0150]

[0151] Table 4 System economic cost changes with uncertain parameters

[0152]

[0153] Through this method, a two-stage robust optimization configuration scheme for a low-carbon integrated energy system based on source-load uncertainty is proposed, which solves the difficult problems of capacity optimization configuration and optimal operation of a low-carbon integrated energy system considering source-load uncertainty and equipment variable operating condition modeling.

[0154] Corresponding to the aforementioned embodiment of a method for robust optimization configuration of a low-carbon integrated energy system based on source-load uncertainty, the present invention also provides an embodiment of a device for robust optimization configuration of a low-carbon integrated energy system based on source-load uncertainty.

[0155] See also Fig.17 An embodiment of the present invention provides a device for robust optimization configuration of a low-carbon integrated energy system based on source-load uncertainty, comprising a memory and one or more processors, wherein the memory stores executable code, and when the processor executes the executable code, it is used to implement a method for robust optimization configuration of a low-carbon integrated energy system based on source-load uncertainty in the above embodiment.

[0156] An embodiment of a device for robust optimization configuration of a low-carbon integrated energy system based on source-load uncertainty provided by the present invention can be applied to any device with data processing capabilities, and the device with data processing capabilities can be a device or apparatus such as a computer. The device embodiment can be implemented through software, or through hardware or a combination of software and hardware. Taking software implementation as an example, as a device in a logical sense, it is formed by the processor of any device with data processing capabilities in which it is located reading the corresponding computer program instructions in the non-volatile memory into the internal memory for execution. From a hardware perspective, if Fig.17 As shown, it is a hardware structure diagram of any device with data processing capability where a low-carbon integrated energy system robust optimization configuration device based on source-load uncertainty provided by the present invention is located, except Fig.17 In addition to the processor, memory, network interface, and non-volatile memory shown, any device with data processing capabilities in which the apparatus in the embodiments is located may also include other hardware, generally based on the actual functions of the device with data processing capabilities, which will not be described in detail.

[0157] The implementation process of the functions and effects of each unit in the above-mentioned device is specifically described in the implementation process of the corresponding steps in the above-mentioned method, and will not be repeated here.

[0158] For the device embodiment, since it basically corresponds to the method embodiment, the relevant parts can refer to the partial description of the method embodiment. The device embodiment described above is only schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of the present invention. Ordinary technicians in this field can understand and implement it without paying creative work.

[0159] An embodiment of the present invention also provides a computer-readable storage medium having a program stored thereon. When the program is executed by a processor, a robust optimization configuration method for a low-carbon integrated energy system based on source-load uncertainty in the above embodiment is implemented.

[0160] The computer-readable storage medium may be an internal storage unit of any device with data processing capability described in any of the aforementioned embodiments, such as a hard disk or a memory. Furthermore, the computer-readable storage medium may include both an internal storage unit of any device with data processing capability and an external storage device. The computer-readable storage medium is used to store the computer program and other programs and data required by any device with data processing capability, and may also be used to temporarily store data that has been output or is to be output.

[0161] The present invention also provides a computer program product, including a computer program, which, when executed by a processor, implements the method for robust optimization configuration of a low-carbon integrated energy system based on source-load uncertainty.

[0162] The above embodiments are used to illustrate the present invention rather than to limit the present invention. Any modification and change made to the present invention within the spirit of the present invention and the protection scope of the claims shall fall within the protection scope of the present invention.

Claims

1. A robust optimization configuration method for a low-carbon integrated energy system based on source-load uncertainty, characterized in that: The steps include: Step 1, obtaining random operation data of energy-consuming equipment of various building users in the park; Step 2: Based on the random operation data of energy-consuming equipment, the time series of energy consumption behavior of building users is obtained, and the annual dynamic load of the regional building complex is predicted through the load prediction model; Step 3, determining the fluctuation of the photovoltaic panel temperature and solar radiation intensity of the photovoltaic production equipment and the relationship between the relative efficiency / performance ratio of the production capacity of other production equipment except photovoltaic and the load change of the energy-consuming equipment; Step 4: Based on the results of Step 2 and Step 3, the uncertainty of the source and load of the low-carbon integrated energy system is analyzed, and a two-stage robust optimization model is established. In the first stage, the annual dynamic load is used as input to characterize the uncertainty of the load side of the system, and the upper-level capacity configuration of the equipment in the low-carbon integrated energy system is determined with the lowest equivalent cost of the equipment investment at the beginning of the year as the objective function. In the second stage, based on the upper-level capacity configuration, the annual dynamic load, the uncertainty parameters of photovoltaic production capacity and other variable operating conditions of production capacity equipment are coupled to characterize the uncertainty of the source side of the system, and the annual operation and maintenance cost of the production capacity equipment is minimized as the objective function. The optimal capacity configuration scheme that ensures that the low-carbon integrated energy system can still maintain the balance of supply and demand under the interference of the source and load uncertainty parameters is obtained through iterative solution; for the uncertainty of the photovoltaic production capacity on the source side, the fluctuation range of the photovoltaic production capacity is controlled by the photovoltaic uncertainty adjustment parameter Γ, and the box-type uncertainty set is used to describe the uncertainty of the photovoltaic production capacity, and the fluctuation range of the photovoltaic is represented by the uncertainty set interval; specifically: in, The predicted photovoltaic energy production without considering the uncertainty is: is the maximum offset of photovoltaic capacity, u is the actual photovoltaic capacity, g is pv is the robust adjustment coefficient, Γ is the photovoltaic uncertain adjustment parameter, which is affected by the daily sunshine duration.

2. According to claim 1, a method for robust optimization configuration of a low-carbon integrated energy system based on source-load uncertainty is characterized in that: In step 2, the k-means clustering algorithm is used to cluster the daily hourly energy consumption behaviors of building users, and the occurrence probability of the corresponding categories is obtained; based on the clustering results and the occurrence probability, the Monte Carlo method is used to obtain the annual random energy consumption behavior schedule of each type of building user in the park; The building model and the corresponding random energy consumption behavior schedule were imported into the EnergyPlus software for load simulation to obtain the hourly air conditioning, lighting and equipment electrical loads of each building.

3. According to claim 1, a method for robust optimization configuration of a low-carbon integrated energy system based on source-load uncertainty is characterized in that: In step 3, the relative efficiency / performance ratio of other production capacity equipment is calculated, where the relative efficiency is the ratio of the equipment efficiency to the rated power, and the performance ratio is the ratio of the equipment performance coefficient to the rated performance coefficient. The relationship between the predicted load change of the energy-consuming equipment is fitted to construct the equipment capacity model.

4. According to claim 1, a method for robust optimization configuration of a low-carbon integrated energy system based on source-load uncertainty is characterized in that: The power generation power of photovoltaic capacity equipment fluctuates with the temperature of photovoltaic panels and the intensity of solar radiation, and is calculated by the difference between the actual working temperature of the photovoltaic panels and the standard test temperature, and the ratio of the actual solar radiation intensity to the light intensity under standard test conditions.

5. According to claim 1, a method for robust optimization configuration of a low-carbon integrated energy system based on source-load uncertainty is characterized in that: In step 3, other energy-generating equipment besides photovoltaic power generation includes gas turbines, gas boilers, waste heat boilers, absorption chillers and electric chillers. The variable operating characteristics of other energy-generating equipment are considered to establish a variable operating condition model in which the energy-generating efficiency / performance ratio changes with the load rate.

6. The method for robust optimization configuration of a low-carbon integrated energy system based on source-load uncertainty according to claim 1 is characterized in that: In addition to production equipment, the equipment in the low-carbon integrated energy system also includes batteries. The battery charging and discharging model is established by the energy stored at the previous moment and the charging and discharging amount within the time step. Its electricity source is photovoltaic, gas turbine power generation and grid power.

7. According to claim 1, a method for robust optimization configuration of a low-carbon integrated energy system based on source-load uncertainty is characterized in that: In step 4, the uncertainty of source and load of the low-carbon integrated energy system is analyzed. Specifically, for the uncertainty of photovoltaic production capacity on the source side, a photovoltaic production capacity uncertainty set is established; for the performance curves of variable operating characteristics of other production capacity equipment, the performance curves are obtained through data fitting; for the uncertainty on the load side, the dynamic load throughout the year is predicted based on the load forecasting model.

8. According to claim 1, a method for robust optimization configuration of a low-carbon integrated energy system based on source-load uncertainty is characterized in that: In step 4, the equipment configuration capacity constraint, the system energy balance constraint, the equipment output constraint, and the power purchase constraint from the external power grid are used as the second-stage constraints of the two-stage robust optimization model; the C&CG algorithm is used to solve the robust optimization problem. During the solution process, the two-stage robust optimization problem is transformed into a main problem and a sub-problem through dual transformation, which reduces the complexity of the model solution and outputs the optimal capacity configuration plan that can ensure that the low-carbon integrated energy system can maintain supply and demand balance under the influence of source-load uncertainty parameters.

9. A low-carbon integrated energy system robust optimization configuration device based on source-load uncertainty, comprising a memory and one or more processors, wherein the memory stores executable code, characterized in that: When the processor executes the executable code, a robust optimization configuration method for a low-carbon integrated energy system based on source-load uncertainty as described in any one of claims 1-8 is implemented.

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