Industrial park multi-time scale unit scheduling optimization method and system considering source-load-storage matching

Abstract

The invention relates to the field of industrial park scheduling optimization, in particular to an industrial park multi-time scale unit scheduling optimization method and system considering source-load-storage matching, and the method comprises the steps: employing a pre-constructed day-ahead pre-scheduling model to initialize a scheduling plan of an energy supply unit on that day; a pre-built intra-day rolling correction scheduling model is adopted to correct the scheduling plan every first preset time, meanwhile, a pre-built real-time feedback scheduling model is adopted to correct the output of the energy supply unit in operation every second preset time, and the time length of the first preset time is larger than that of the second preset time. According to the method, the day-ahead pre-scheduling model is firstly adopted to roughly plan scheduling, the intra-day rolling correction scheduling model is adopted to perform scheduling optimization in the specific scheduling process, and the real-time feedback scheduling model is adopted to perform real-time correction, so that finer adjustment is realized, and real-time adjustment and control can be performed according to the operation condition of the industrial park.

Description

Technical Field

[0001] The present invention relates to the field of industrial park scheduling optimization, and particularly relates to a multi-time scale unit scheduling optimization method and system for an industrial park considering the matching of power sources, loads, and energy storage. Background Art

[0002] In recent years, new energy has been strongly recommended and developed. With the large-scale grid connection of new energy, the safe and stable operation of the power system has been greatly affected. The energy system of industrial parks can make full use of the complementary characteristics among electricity, gas, cold, and heat energy, thereby reducing the regional energy supply cost and improving the stability of terminal energy supply. However, since the energy system of industrial parks integrates all aspects of the production, conversion, storage, transmission, and use of electricity, gas, cold, and heat energy, the energy system of industrial parks is not only affected by the fluctuations in the demand for electricity, heat energy, and cold energy from users, but also affected by the uncertainties in the output of distributed renewable energy such as wind energy and solar energy. This has led to an exacerbation of the imbalance between supply and demand in the park and an increase in the difficulty of scheduling and operation control, resulting in an exacerbation of the imbalance between supply and demand in the park and an increase in the difficulty of scheduling and operation control. Multi-energy storage technologies are regarded as an effective way to solve the problem of source-load mismatch. The cost and technical threshold of energy storage technologies have become the main constraints for their widespread application and participation in operation management in industrial parks. Therefore, it is urgent to carry out research on the optimization scheduling method for industrial parks considering multi-energy storage and source-load matching. Summary of the Invention

[0003] To solve the problems of the increasing complexity of the energy system in industrial parks in the prior art, which lead to an exacerbation of the imbalance between supply and demand in the park and an increase in the difficulty of scheduling and operation control, in the first aspect of the present invention, a multi-time scale unit scheduling optimization method for an industrial park considering the matching of power sources, loads, and energy storage is proposed, including:

[0004] Initializing the scheduling plan of the energy supply units for the current day by using a pre-constructed day-ahead pre-scheduling model;

[0005] Revising the scheduling plan by using a pre-constructed intra-day rolling correction scheduling model every first preset time, and at the same time, revising the output of the energy supply units in operation by using a pre-constructed real-time feedback scheduling model every second preset time, where the time length of the first preset time is greater than that of the second preset time;

[0006] Wherein, the day-ahead pre-scheduling model is constructed by performing an economic analysis on the interaction architecture among multi-energy storage, power sources, and loads in the industrial park; the intra-day rolling correction scheduling model is constructed with the goal of minimizing the operating cost of the energy supply units and the adjustment cost of adjustable energy supply units; the real-time feedback scheduling model is constructed with the goal of minimizing the total adjustment amount of the output of the energy supply units in the industrial park.

[0007] Preferably, the construction steps of the day-ahead pre-scheduling model are:

[0008] Obtain the power operation data of the source side to be scheduled on the day before in the interactive architecture of diversified energy storage and source-load in the industrial park;

[0009] Build a day-ahead pre-scheduling model with the goal of minimizing the operating cost under the operating constraints of the energy supply units.

[0010] Preferably, the operating cost includes the start-up cost of the source-side generator sets, the fuel cost of the gas turbines, the cost of the gas boilers, the use and maintenance cost of the diversified energy storage devices, and the electricity cost of the external large power grid.

[0011] Preferably, it is characterized in that the day-ahead pre-scheduling model is:

[0012]

[0013] Among them, f1 is the total operating cost, is the electric power of the i MT th gas turbine at time t, is the fuel cost of the i MT th gas turbine at time t, is the power of the i GB th gas boiler at time t, is the fuel cost of the i GB th gas boiler at time t, is the start-stop status quantity of the i GB th gas boiler at time t, is the start-stop status quantity of the i GB th gas boiler at time t-1, is the start-stop status quantity of the i MT th gas turbine at time t, is the start-stop status quantity of the i MT th gas turbine at time t-1, is the start-up cost of the i MT th gas turbine at time t, the start-up cost of the i GB th gas boiler at time t, f(C t ) is the usage cost of the energy storage device at time t, P grid is the electricity cost of the external large power grid, and represent operation when it is 1 and stop when it is 0, N MT is the total number of gas turbines in the industrial park, N GB is the total number of gas boilers in the industrial park, and T is the total time.

[0014] Preferably, the fuel cost of the i MT th gas turbine at time t The calculation formula is:

[0015]

[0016] Among them, is the cost coefficient of the gas turbine.

[0017] Preferably, the fuel cost GB of the i-th gas boiler at time t The calculation formula is:

[0018]

[0019] Among them, is the cost coefficient of the gas boiler.

[0020] Preferably, the usage cost f(C t ) of the multi-energy storage device at time t is calculated as:

[0021]

[0022] Among them, ε ES , ε HS , ε CS are the depreciation cost coefficients of electricity, heat, and cold per unit of energy storage respectively, P t ES.C is the energy storage power of the battery at time t, P t ES.D is the energy release power of the battery at time t, is the energy storage power of the heat storage tank at time t, P t HS.D is the energy release power of the heat storage tank at time t, is the energy storage power of the cold storage tank at time t, is the energy release power of the cold storage tank at time t.

[0023] Preferably, the operating constraint conditions of the energy supply unit include electric energy balance constraint, heat energy balance constraint, and cold energy balance constraint.

[0024] Preferably, the expression of the electric energy balance constraint is:

[0025] P t PV +P t wind +P t grid +P t MT +P t ES =P t D +P tAC +P t EB

[0026] Among them, P t PV is the photovoltaic output, P t wind is the wind power output, P t grid is the electric power obtained by the industrial park from the power grid, P t MT is the electric power output of the gas turbine, P t ES is the electric power released by the battery, P t D is the electric load required by the users on the load side of the industrial park, P t AC is the electric power required by the electric chiller, P t EB is the electric power required by the electric boiler.

[0027] Preferably, the expression of the thermal energy balance constraint is:

[0028]

[0029] Among them, is the thermal power of the gas turbine at time t, is the thermal power of the gas boiler at time t, is the thermal power of the electric boiler at time t, is the thermal power output by the heat storage tank at time t, is the thermal load required by the users on the load side of the industrial park at time t.

[0030] Preferably, the expression of the cold energy balance constraint is:

[0031]

[0032] Among them, is the cold power of the lithium bromide absorption chiller at time t, is the output of the electric chiller at time t, is the cold power released by the ice storage tank at time t, is the cold load of the industrial park at time t.

[0033] Preferably, the intra-day rolling correction scheduling model is:

[0034]

[0035] Among them, f2 is the total operating cost within the day, N MT is the total number of gas turbines in the industrial park, N GBis the total number of gas boilers in the industrial park, is the electrical power of the i MT th gas turbine, is the fuel cost of the i MT th gas turbine, is the power of the i GB th gas boiler, is the fuel cost of the i GB th gas boiler, is the corrected electrical power of the i MT th gas turbine, is the corrected cost of the i MT th gas turbine, The corrected power of the i GB th gas boiler, is the corrected cost of the i GB th gas boiler, P grid is the electricity cost of the external large power grid.

[0036] Preferably, the corrected cost of the i MT th gas turbine has the following expression:

[0037]

[0038] where λ MT is the unit power adjustment cost coefficient of the gas turbine unit.

[0039] Preferably, the corrected cost of the i GB th gas boiler has the following expression:

[0040]

[0041] where λ GB is the unit power adjustment cost coefficient of the gas boiler unit.

[0042] Preferably, the real-time feedback scheduling model is specifically constructed based on the real-time output of photovoltaic power generation and the real-time load of the industrial park, with the goal of minimizing the total adjustment amount of the output of the energy supply units in the industrial park.

[0043] Preferably, the expression for the goal of minimizing the total adjustment amount of the output of the energy supply units in the industrial park is:

[0044]

[0045] where ΔP Rt is the total adjustment amount of the output of the energy supply units in the industrial park, ΔP t MT is the output adjustment amount of the gas turbine at time t, is the total adjustment amount of the gas boiler output at time t, is the adjustment amount of the lithium bromide absorption chiller output at time t, is the adjustment amount of the electric chiller cooling output at time t, ΔP t grid is the adjustment amount of the external large power grid output at time t, is the maximum output power of the gas turbine, is the maximum output power of the gas boiler, is the maximum output power of the electric chiller, is the maximum output power of the lithium bromide absorption chiller, is the maximum output of the external power grid.

[0046] Preferably, the expression of the real-time feedback scheduling model is:

[0047] P t MT = P t MT.E + ΔP t AC + ΔP t EB - ΔP t grid + ΔP t D + ΔP t PV

[0048] P t grid = P t grid.E + ΔP t grid

[0049] P t AC = P t AC.E + ΔP t AC

[0050] P t EB = P t EB.E + ΔP t EB

[0051] where, P t MT is the output of the gas turbine at time t, P t MT.E is the planned power of the gas turbine in the previous stage at time t, ΔP t ACis the real-time changing power of the electric chiller at time t, ΔP t EB is the real-time changing power of the electric boiler at time t, ΔP t grid is the adjustment amount of the output of the external large power grid at time t, ΔP t D is the random deviation of the load of the park users at time t, ΔP t PV is the random deviation of the photovoltaic at time t, P t grid is the output of the external large power grid at time t, P t grid.E is the planned power of the external large power grid in the previous stage at time t, P t AC is the required electric power of the electric chiller at time t, P t AC.E is the power of the electric chiller in the previous stage at time t, ΔP t AC is the real-time changing power of the electric chiller at time t, P t EB is the required electric power of the electric boiler at time t, P t EB.E is the power of the electric boiler in the previous stage at time t, ΔP t EB is the real-time changing power of the electric boiler at time t.

[0052] Preferably, the interaction architecture between the multiple energy storages and the source-load in the industrial park is obtained by analyzing the energy flow correlation relationship between the multiple energy storages and the source-load by using the correlation analysis method.

[0053] In the second aspect of the present invention, an industrial park multi-time scale unit scheduling optimization system considering source-load-storage matching is provided, including:

[0054] Coarse scheduling module: used to initialize the scheduling plan of the energy supply units on the current day by using a pre-constructed day-ahead pre-scheduling model;

[0055] Fine adjustment module: used to correct the scheduling plan by using a pre-constructed intra-day rolling correction scheduling model every first preset time, and at the same time correct the output of the energy supply units in operation by using a pre-constructed real-time feedback scheduling model every second preset time, where the time length of the first preset time is greater than that of the second preset time;

[0056] Among them, the pre-dispatch model for the day ahead is constructed by conducting an economic analysis on the interaction framework between the diversified energy storage in the industrial park and the source and load; the rolling correction dispatch model for the day is constructed with the goal of minimizing the operating cost of the energy supply units and the adjustment cost of the adjustable energy supply units; the real-time feedback dispatch model is constructed with the goal of minimizing the total adjustment amount of the output of the energy supply units in the industrial park.

[0057] Preferably, the steps for constructing the pre-dispatch model for the day ahead in the coarse dispatch module are as follows:

[0058] Obtain the power operation data of the source side to be dispatched for the day ahead in the interaction framework between the diversified energy storage in the industrial park and the source and load;

[0059] Construct a pre-dispatch model for the day ahead with the goal of minimizing the operating cost under the operating constraints of the energy supply units.

[0060] Preferably, the operating cost in the coarse dispatch module includes the start-up cost of the generator sets on the source side, the fuel cost of the gas turbines, the cost of the gas boilers, the usage and maintenance cost of the diversified energy storage devices, and the electricity cost of the external large power grid.

[0061] Preferably, the pre-dispatch model for the day ahead in the coarse dispatch module is:

[0062]

[0063] Among them, f1 is the total operating cost, is the electric power of the i MT th gas turbine at time t, is the fuel cost of the i MT th gas turbine at time t, is the power of the i GB th gas boiler at time t, is the fuel cost of the i GB th gas boiler at time t, is the start-stop status quantity of the i GB th gas boiler at time t, is the start-stop status quantity of the i MT th gas turbine at time t, is the start-up cost of the i MT th gas turbine at time t, The start-up cost of the i GB th gas boiler at time t, f(C t ) is the usage cost of the energy storage device at time t, P grid is the electricity cost of the external large power grid, and represent operation when it is 1 and stop when it is 0, N MT is the total number of gas turbines in the industrial park, N GB$N$ is the total number of gas boilers in the industrial park, and $T$ is the total time.

[0064] Preferably, the fuel cost of the $i$-th MT gas turbine at time $t$ is calculated by the formula:

[0065]

[0066] where is the cost coefficient of the gas turbine.

[0067] Preferably, the fuel cost of the $i$-th GB gas boiler at time $t$ is calculated by the formula:

[0068]

[0069] where is the cost coefficient of the gas boiler.

[0070] Preferably, the usage cost $f(C$ t ) of the multi - energy storage device at time $t$ in the coarse scheduling module is calculated by the formula:

[0071]

[0072] where $\varepsilon$ ES , $\varepsilon$ HS , $\varepsilon$ CS are the depreciation cost coefficients per unit of electricity, heat, and cold energy storage respectively, $P$ t ES.C is the energy storage power of the battery at time $t$, $P$ t ES.D is the discharging power of the battery at time $t$, is the energy storage power of the heat storage tank at time $t$, $P$ t HS.D is the discharging power of the heat storage tank at time $t$, is the energy storage power of the cold storage tank at time $t$, is the discharging power of the cold storage tank at time $t$.

[0073] Preferably, the operating constraint conditions of the energy supply unit in the coarse scheduling module include electrical energy balance constraint, thermal energy balance constraint, and cold energy balance constraint.

[0074] Preferably, the expression of the electrical energy balance constraint in the coarse scheduling module is:

[0075] $P$ t PV +$P$ t wind +$P$t grid +P t MT +P t ES =P t D +P t AC +P t EB

[0076] Among them, P t PV is the photovoltaic output, P t wind is the wind power output, P t grid is the electric power obtained by the industrial park from the power grid, P t MT is the electric power output of the gas turbine, P t ES is the electric power released by the battery, P t D is the electrical load required by the industrial park's load-side users, P t AC is the electric power required by the electric chiller, P t EB is the electric power required by the electric boiler.

[0077] Preferably, the expression of the thermal energy balance constraint in the coarse scheduling module is:

[0078]

[0079] Among them, is the thermal power of the gas turbine at time t, is the thermal power of the gas boiler at time t, is the thermal power of the electric boiler at time t, is the thermal power output by the heat storage tank at time t, is the thermal load required by the industrial park's load-side users at time t.

[0080] Preferably, the expression of the cold energy balance constraint in the coarse scheduling module is:

[0081]

[0082] Among them, is the cooling power of the lithium bromide absorption chiller at time t, is the output of the electric chiller at time t, is the cooling power released by the ice storage tank at time t, is the cooling load of the industrial park at time t.

[0083] Preferably, the intra-day rolling correction scheduling model in the fine adjustment module is as follows:

[0084]

[0085] where f2 is the total operating cost within a day, N MT is the total number of gas turbines in the industrial park, N GB is the total number of gas boilers in the industrial park, is the electric power of the i MT th gas turbine, is the fuel cost of the i MT th gas turbine, is the power of the i GB th gas boiler, is the fuel cost of the i GB th gas boiler, is the corrected electric power of the i MT th gas turbine, is the corrected cost of the i MT th gas turbine, the corrected power of the i GB th gas boiler, is the corrected cost of the i GB th gas boiler, P grid is the electricity cost of the external large power grid.

[0086] Preferably, the corrected cost MT of the i th gas turbine in the fine adjustment module has the following expression:

[0087]

[0088] where λ MT is the unit power adjustment cost coefficient of the gas turbine unit.

[0089] Preferably, the corrected cost GB of the i th gas boiler in the fine adjustment module has the following expression:

[0090]

[0091] where λ GB is the unit power adjustment cost coefficient of the gas boiler unit.

[0092] Preferably, the real-time feedback scheduling model in the fine adjustment module is specifically constructed based on the real-time output of photovoltaic power generation and the real-time load of the industrial park, with the goal of minimizing the total adjustment amount of the output of the energy supply units in the industrial park.

[0093] Preferably, the expression for the objective of minimizing the total adjustment amount of the output of the energy supply units in the industrial park in the fine adjustment module is:

[0094]

[0095] where, ΔP Rt is the total adjustment amount of the output of the energy supply units in the industrial park, ΔP t MT is the adjustment amount of the output of the gas turbine at time t, is the total adjustment amount of the output of the gas boiler at time t, is the adjustment amount of the output of the lithium bromide absorption chiller at time t, is the adjustment amount of the cooling output of the electric chiller at time t, ΔP t grid is the adjustment amount of the output of the external large power grid at time t, is the maximum output power of the gas turbine, is the maximum output power of the gas boiler, is the maximum output power of the electric chiller, is the maximum output power of the lithium bromide absorption chiller, is the maximum output of the external power grid.

[0096] Preferably, the expression of the real-time feedback scheduling model in the fine adjustment module is:

[0097] P t MT = P t MT.E + ΔP t AC + ΔP t EB - ΔP t grid + ΔP t D + ΔP t PV

[0098] P t grid = P t grid.E + ΔP t grid

[0099] P t AC = P t AC.E + ΔP t AC

[0100] P t EB = Pt EB.E +ΔP t EB

[0101] where P t MT is the output of the gas turbine at time t, and P t MT.E is the planned power of the gas turbine in the previous stage at time t, and ΔP t AC is the real-time change power of the electric chiller at time t, and ΔP t EB is the real-time change power of the electric boiler at time t, and ΔP t grid is the adjustment amount of the output of the external large power grid at time t, and ΔP t D is the random deviation amount of the load of the park users at time t, and ΔP t PV is the random deviation amount of the photovoltaic at time t, and P t grid is the output of the external large power grid at time t, and P t grid.E is the planned power of the external large power grid in the previous stage at time t, and P t AC is the required electric power of the electric chiller at time t, and P t AC.E is the power of the electric chiller in the previous stage at time t, and ΔP t AC is the real-time change power of the electric chiller at time t, and P t EB is the required electric power of the electric boiler at time t, and P t EB.E is the power of the electric boiler in the previous stage at time t, and ΔP t EB is the real-time change power of the electric boiler at time t.

[0102] Preferably, the interaction architecture between the multiple energy storages and the source-load in the industrial park in the coarse scheduling module is obtained by analyzing the energy flow correlation relationship between the multiple energy storages and the source-load by using the correlation analysis method.

[0103] On the other hand, the present invention also provides a computing device, including: at least one processor and a memory;

[0104] The memory is used to store one or more programs;

[0105] When the one or more programs are executed by the one or more processors, the method for optimizing the multi-time scale unit scheduling in the industrial park considering the matching of the source-load-storage as described above is implemented.

[0106] On the other hand, the present application also provides a computer-readable storage medium with a computer program stored thereon. When the computer program is executed, it implements a multi-time-scale unit scheduling optimization method for industrial parks considering source-load-storage matching as described above.

[0107] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0108] The present invention provides a multi-time-scale unit scheduling optimization method and system for industrial parks considering source-load-storage matching, including initializing the scheduling plan of the energy supply units for the current day using a pre-constructed day-ahead pre-scheduling model; correcting the scheduling plan every first preset time using a pre-constructed intra-day rolling correction scheduling model, and at the same time correcting the output of the energy supply units in operation every second preset time using a pre-constructed real-time feedback scheduling model, where the time length of the first preset time is greater than that of the second preset time. The present invention first uses the day-ahead pre-scheduling model to make a rough plan for the scheduling, and in the specific scheduling process, uses the intra-day rolling correction scheduling model to optimize the scheduling, and at the same time uses the real-time feedback scheduling model for real-time correction to achieve more refined adjustment, and then can make real-time adjustment and control according to the operation conditions of the industrial park. BRIEF DESCRIPTION OF THE DRAWINGS

[0109] Figure 1 is a flowchart of the multi-time-scale unit scheduling optimization method for industrial parks considering source-load-storage matching proposed by the present invention;

[0110] Figure 2 is a schematic structural diagram of the interaction architecture between multiple energy storages and source-loads in an industrial park proposed by the present invention;

[0111] Figure 3 is a schematic energy flow structure diagram of an industrial park proposed by the present invention;

[0112] Figure 4 is a schematic application process diagram of the multi-time-scale unit scheduling optimization method for industrial parks considering source-load-storage matching proposed by the present invention;

[0113] Figure 5 is a schematic structural diagram of the multi-time-scale unit scheduling optimization system for industrial parks considering source-load-storage matching proposed by the present invention;

[0114] Figure 6 is a schematic structural diagram of the electronic device proposed by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0115] The present invention provides a multi-time-scale unit scheduling optimization method and system for industrial parks considering source-load-storage matching. Firstly, a day-ahead pre-scheduling model is used to make a rough plan for unit scheduling. During the specific scheduling process, an intraday rolling correction scheduling model is used to optimize the unit scheduling plan, and at the same time, a real-time feedback scheduling model is used for real-time correction to achieve more refined adjustment. Therefore, by combining day-ahead economic optimization scheduling with intraday ultra-short-term real-time rolling optimization, the optimization results are continuously rolled with the reduction of the time scale, and the deviation between the day-ahead pre-scheduling plan and the actual load is corrected. Finally, real-time feedback adjustment is carried out. Through the coordinated optimization of day-ahead pre-scheduling - intraday correction - real-time update, not only can a day-ahead unit output plan under economic objectives be formulated according to the short-term prediction data of multiple energy storages and source-load side loads, but also the unit output level can be adjusted minimally according to the actual situation to achieve the energy balance of multiple energy storages and source-loads, and realize the safety and stability objectives of industrial parks. The solutions in the present invention will be introduced in detail below with reference to the accompanying drawings.

[0116] Embodiment 1:

[0117] A multi-time-scale unit scheduling optimization method for industrial parks considering source-load-storage matching, as Figure 1 shown, includes steps S1 and S2.

[0118] S1: Initialize the scheduling plan of the energy supply units for the current day by using a pre-constructed day-ahead pre-scheduling model.

[0119] Among them, the day-ahead pre-scheduling model is constructed by conducting an economic analysis on the interaction framework between multiple energy storages and source-loads in the industrial park, and the specific introduction is as follows.

[0120] The day-ahead pre-scheduling model is mainly used for day-ahead economic pre-scheduling. Based on the day-ahead short-term prediction data such as source-side wind and solar power output and load, according to the economic principle, the output level or start-stop plan of the source-side units for each hour of the next 24 hours is optimized. If there is sufficient time for day-ahead economic scheduling optimization, under the constraints of the operation of the energy supply units, such as system energy supply and demand balance, unit start-stop, unit ramp-up, and spinning reserve, etc., by reasonably arranging the unit start-up, shutdown, and output plan, the system can achieve the purpose of economic optimization.

[0121] As Figure 2 shown, the energy supply system of an industrial park generally consists of multiple subsystems, including an energy supply network, an energy coupling system, a multiple energy storage system, and a terminal user demand side. Specifically, the energy supply network covers wind and solar power generation facilities (such as Figure 2in the power grid, photovoltaic power generation, wind power generation, etc.); the energy coupling system integrates facilities such as combined cooling, heat and power, absorption refrigeration units, electric refrigeration equipment, and electric boilers; the multi - energy storage system consists of different forms of energy storage facilities such as battery energy storage, thermal energy storage, and ice storage. These systems work together. Power generation facilities such as the power grid, photovoltaic power generation, wind power generation, and gas turbines provide electric energy for end - users. Electric energy can be converted into heat and cold through electric boilers and compression refrigerators respectively. Natural gas can also be converted into some heat and cold through gas turbines, gas boilers, and absorption heat pumps to meet the diverse energy demands of end - users for cold, heat, electricity, etc. Specifically:

[0122] (1) Wind and solar power generation

[0123] Considering the discontinuity and uncontrollability of wind resources, the power output of wind power supply units shows volatility and uncertainty, specifically manifested as large daily and seasonal change rates, showing strong seasonal and intermittent characteristics. Photovoltaic power generation is related to the light intensity at its location and depends to a large extent on weather conditions. The output of wind power and photovoltaic power is as uncertain as that of wind power.

[0124] (2) Multi - energy storage

[0125] Battery: The performance of the battery is greatly affected by temperature. Usually, high temperature will accelerate the battery's decline and affect its energy storage capacity; low temperature may reduce its discharge capacity.

[0126] Heat storage tank: The heat capacity of the heat storage tank determines its energy storage capacity, which is usually related to the type, quantity, and temperature of the medium in the tank. The larger the heat capacity, the more heat energy can be stored.

[0127] Ice storage tank: Used to store cold.

[0128] (3) End - users

[0129] End - users consume industrial electricity loads, cooling loads, and heating loads, which can be divided into:

[0130] Interruptible load: Users can obtain relevant benefits by signing an interruptible contract with the power supply department. The power supply department can relieve the power supply pressure during peak electricity consumption periods through the implementation of the interruptible contract, thus achieving a win - win situation for both users and the power supply department.

[0131] Shiftable load: The shiftable load can be flexibly scheduled at any working time within the shiftable working period, as long as the overall electricity consumption within the shiftable working period remains unchanged.

[0132] Reducible load: According to the system's energy supply surplus at the current moment, a certain proportion of load can be directly reduced rather than transferred during the peak period of users. The direct reduction of this type of load will not have a serious impact on users' electricity consumption. Therefore, the power supply reliability requirements for reducible electrical loads are usually relatively low.

[0133] In a further optimized solution, the interaction architecture between the multiple energy storages and the source and load in the industrial park (as Figure 2 shown) can be obtained by analyzing the energy flow correlation relationship between the multiple energy storages and the source and load (as Figure 3 shown) using the correlation analysis method.

[0134] The industrial park converts the input energy such as natural gas and electricity into output energy such as electricity, heat, and cold to meet the diverse load demands of the load-side users. To maximize the absorption of new energy power generation and improve the environmental protection of the industrial park, distributed photovoltaic power generation equipment is usually connected to the industrial park. Considering the time-of-use electricity price mechanism of the power grid, the industrial park is connected to the superior distribution network, enabling two-way power interaction with the superior distribution network. That is, when the power grid is in the valley electricity price period, the industrial park purchases electricity from the power grid to supply the electricity load demand, and at the same time supplies part of the heat load and cold load demands to reduce the energy supply cost, and charges the battery or charges the heat storage tank and ice storage tank. When the power grid is in the peak electricity price period, the electric energy generated by the combined heat and power unit, the electric energy released by the battery, and the electric energy generated by photovoltaic power generation, in addition to supplying the electricity load demand, the excess electric energy is reversely transmitted to the power grid. The energy flow diagram is as Figure 3 shown.

[0135] In a further optimized solution, the steps for constructing the day-ahead pre-scheduling model include the following steps S21 and S22.

[0136] S21: Obtain the power operation data of the source side to be scheduled on the day-ahead in the interaction architecture between the multiple energy storages and the source and load in the industrial park. The day-ahead economic scheduling is to optimize the scheduling before a certain period. In the present invention, a 24-hour scheduling cycle is used for scheduling optimization. According to the photovoltaic processing and the day-ahead prediction data of the multiple loads, the start-stop and output plans of the source-side units for each time period are optimized and calculated on the day-ahead. The power operation data includes prediction data such as the output of wind and light on the source side and the load.

[0137] S22: Construct a day-ahead pre-scheduling model with the goal of minimizing the operation cost under the operating constraints of the energy supply units.

[0138] In a further optimized solution, the operation cost includes the start-up cost of the source-side generator set, the fuel cost of the gas turbine, the cost of the gas boiler, the use and maintenance cost of the multiple energy storage devices, and the electricity cost of the external large power grid.

[0139] In a further optimized solution, the day-ahead pre-scheduling model is:

[0140]

[0141] Among them, f1 is the total operating cost, is the electric power of the i MT th gas turbine at time t, is the fuel cost of the i MT th gas turbine at time t, is the power of the i GB th gas boiler at time t, is the fuel cost of the i GB th gas boiler at time t, is the start-stop status quantity of the i GB th gas boiler at time t, is the start-stop status quantity of the i GB th gas boiler at time t-1, is the start-stop status quantity of the i MT th gas turbine at time t, is the start-stop status quantity of the i MT th gas turbine at time t-1, is the start-up cost of the i MT th gas turbine at time t, The start-up cost of the i GB th gas boiler at time t, f(C t ) is the usage cost of the multi-energy storage device at time t, P grid is the electricity fee of the external large power grid, and When it is 1, it means running, and when it is 0, it means stopping. N MT is the total number of gas turbines in the industrial park, N GB is the total number of gas boilers in the industrial park, and T is the total time.

[0142] In a further optimized solution, the fuel cost MT of the i th gas turbine at time t

[0143]

[0144] Among them, is the cost coefficient of the gas turbine.

[0145] In a further optimized solution, the fuel cost GB of the i th gas boiler at time t

[0146]

[0147] Among them, is the cost coefficient of the gas boiler.

[0148] The usage cost f(C t ) of the multi - energy storage device at time t is calculated as follows:

[0149]

[0150] where ε ES , ε HS , ε CS are the depreciation cost coefficients of unit energy storage for electricity, heat, and cold respectively, P t ES.C is the energy storage power of the battery at time t, P t ES.D is the power discharge of the battery at time t, is the energy storage power of the heat storage tank at time t, P t HS.D is the power discharge of the heat storage tank at time t, is the energy storage power of the cold storage tank at time t, is the power discharge of the cold storage tank at time t.

[0151] In a further optimized solution, the operating constraint conditions of the energy supply unit include electric energy balance constraint, heat energy balance constraint, and cold energy balance constraint.

[0152] The expression of the electric energy balance constraint is:

[0153] P t PV +P t wind +P t grid +P t MT +P t ES =P t D +P t AC +P t EB

[0154] where P t PV is the photovoltaic output, P t wind is the wind power output, P t grid is the electric power obtained by the industrial park from the power grid, P t MT is the electric power output of the gas turbine, P t ES is the electric power released by the batteryt D is the electrical load required by the users on the load side of the industrial park, P t AC is the electric power required by the electric chiller, P t EB is the electric power required by the electric boiler.

[0155] The expression of the thermal energy balance constraint is:

[0156]

[0157] Among them, is the thermal power of the gas turbine at time t, is the thermal power of the gas boiler at time t, is the thermal power of the electric boiler at time t, is the thermal power output by the heat storage tank at time t, is the thermal load required by the users on the load side of the industrial park at time t.

[0158] The expression of the cold energy balance constraint is:

[0159]

[0160] Among them, is the cooling power of the lithium bromide absorption chiller at time t, is the output of the electric chiller at time t, is the cooling power released by the ice storage tank at time t, is the cooling load of the industrial park at time t.

[0161] S2: Every first preset time, use the pre-constructed intraday rolling correction scheduling model to correct the scheduling plan, and at the same time, every second preset time, use the pre-constructed real-time feedback scheduling model to correct the output of the energy supply units during operation, where the time length of the first preset time is greater than the second preset time; among them, the day-ahead pre-scheduling model is constructed by performing an economic analysis on the interaction architecture between the multiple energy storages and the source-load in the industrial park; the intraday rolling correction scheduling model is constructed with the goal of minimizing the operating cost of the energy supply units and the adjustment cost of the adjustable energy supply units; the real-time feedback scheduling model is constructed with the goal of minimizing the total adjustment amount of the output of the energy supply units in the industrial park.

[0162] This step mainly performs rolling correction on the intraday dispatch and makes real-time feedback adjustment to the unit output in the dispatch plan. Based on the short-term prediction results, the day-ahead dispatch plan is corrected intraday. For example, the output plan for the next time period can be corrected on a 15-minute scale, and real-time dynamic rolling optimization is carried out with the rolling time window as the control time domain. During the operation time of the industrial park, the current input information of the system (such as ultra-short-term prediction data of photovoltaic and load) is updated at the beginning of each 15-minute time scale, and the current unit output cost is solved. After a 15-minute cycle scale ends, the time window will move to the next 15-minute cycle, and the above process is repeated. By rolling optimization of the renewable energy and load prediction of the integrated energy system, real-time update in the intraday dispatch plan is achieved.

[0163] Real-time feedback makes adjustments on a shorter time scale based on the above day-ahead pre-dispatch - intraday correction plan. For example, every 5 minutes, based on the actual output of photovoltaic, the capacity of multiple energy storages and the actual demand of the load, the unit output is adjusted without changing the unit dispatch plan. The feedback adjustment only changes the unit output. In extreme cases, other variable output units are also considered as adjustment means. The goal is to minimize the total adjustment amount of the system and achieve the purpose of stable system operation.

[0164] The present invention takes 15 minutes as the intraday dynamic correction dispatch cycle and 5 minutes as the intraday ultra-short-term real-time feedback dispatch cycle. The intraday correction stage focuses on the local economic optimum within the 15-minute dispatch cycle. The multiple energy storage devices are scheduled and operated according to the day-ahead calculation, and an intraday rolling correction dispatch model with the goal of minimizing the unit operation cost and the adjustment cost of the short-term plan adjustable units is established.

[0165] In a further preferred solution, the intraday rolling correction dispatch model is:

[0166]

[0167] where f2 is the total operating cost intraday, N MT is the total number of gas turbines in the industrial park, N GB is the total number of gas boilers in the industrial park, is the electric power of the i MT th gas turbine, is the i MT th gas turbine fuel cost, is the power of the i GB th gas boiler, is the i GB th gas boiler fuel cost, is the corrected electric power of the i MT th gas turbine, is the i MTThe corrected cost of a gas turbine The i-th GB The corrected power of a gas boiler is the corrected cost of the i-th GB gas boiler, and P grid is the electricity cost of the external large power grid

[0168] In a further optimized solution, the corrected cost of the i-th MT gas turbine has the following expression:

[0169]

[0170] where λ MT is the unit power adjustment cost coefficient of the gas turbine unit

[0171] The corrected cost of the i-th GB gas boiler has the following expression:

[0172]

[0173] where λ GB is the unit power adjustment cost coefficient of the gas boiler unit

[0174] Similarly, the constraint conditions during the intraday correction process are:

[0175] P t PV +P t grid +P t MT +P t ES =P t D +P t AC +P t EB

[0176]

[0177] In a further optimized solution, the real-time feedback scheduling model is specifically constructed based on the real-time output of photovoltaic power generation and the real-time load of the industrial park, with the goal of minimizing the total adjustment amount of the output of the energy supply units in the industrial park

[0178] The expression for the goal of minimizing the total adjustment amount of the output of the energy supply units in the industrial park is:

[0179]

[0180] where ΔPRt is the total adjustment amount of the output of the energy supply units in the industrial park, ΔP t MT is the output adjustment amount of the gas turbine at time t, is the total output adjustment amount of the gas boiler at time t, is the output adjustment amount of the lithium bromide absorption chiller at time t, is the cooling output adjustment amount of the electric chiller at time t, ΔP t grid is the output adjustment amount of the external large power grid at time t, is the maximum output power of the gas turbine, is the maximum output power of the gas boiler, is the maximum output power of the electric chiller, is the maximum output power of the lithium bromide absorption chiller, is the maximum output of the external power grid.

[0181] The feedback adjustment state of the system energy supply units, that is, the expression of the real-time feedback scheduling model is:

[0182] P t MT = P t MT.E + ΔP t AC + ΔP t EB - ΔP t grid + ΔP t D + ΔP t PV

[0183] P t grid = P t grid.E + ΔP t grid

[0184] P t AC = P t AC.E + ΔP t AC

[0185] P t EB = P t EB.E + ΔP t EB

[0186] Changed to matrix form as:

[0187]

[0188] The left side of the formula is the final scheduling plan value. The first item on the right side is the plan value of the previous stage, the second item is the real-time change value, and the third item is the random deviation between the photovoltaic output and the load demand.

[0189] In summary, the solution of this application is as Figure 4 shown, mainly including three key steps, namely day-ahead prediction scheduling, intra-day correction scheduling, and real-time feedback adjustment. The day-ahead prediction scheduling is used to specify the day-ahead scheduling plan and perform day-ahead economic pre-scheduling. The day-ahead prediction scheduling is adopted at the starting stage of the day that needs to be scheduled. During the intra-day operation process, intra-day correction and update are performed to formulate an intra-day correction plan, and the operation is carried out according to the intra-day correction plan. During the operation process, real-time new energy and load data are detected, and a real-time adjustment plan for the unit output is formulated. Real-time output adjustment is performed every 5 minutes, and at the same time, intra-day correction adjustment is performed every 15 minutes.

[0190] Embodiment 2:

[0191] Based on the same inventive concept, the present invention also provides an industrial park multi-time scale unit scheduling optimization system considering the matching of sources, loads, and energy storage, as Figure 5 shown, including:

[0192] Coarse scheduling module: used to initialize the scheduling plan of the energy supply units on the current day by using a pre-constructed day-ahead pre-scheduling model;

[0193] Fine adjustment module: used to correct the scheduling plan by using a pre-constructed intra-day rolling correction scheduling model every first preset time, and at the same time correct the output of the energy supply units during operation by using a pre-constructed real-time feedback scheduling model every second preset time, where the time length of the first preset time is greater than that of the second preset time;

[0194] Among them, the day-ahead pre-scheduling model is constructed by performing economic analysis on the interaction architecture between the multiple energy storage systems and sources and loads in the industrial park; the intra-day rolling correction scheduling model is constructed with the goal of minimizing the operating cost of the energy supply units and the adjustment cost of the adjustable energy supply units; the real-time feedback scheduling model is constructed with the goal of minimizing the total adjustment amount of the output of the energy supply units in the industrial park.

[0195] In a further preferred solution, the construction steps of the day-ahead pre-scheduling model in the coarse scheduling module are:

[0196] Obtain the power operation data of the day-ahead source side in the interaction architecture between the multiple energy storage systems and sources and loads in the industrial park;

[0197] Construct a day-ahead pre-scheduling model with the goal of minimizing the operating cost under the operating constraints of the energy supply units.

[0198] In a further preferred solution, the operating cost in the coarse scheduling module includes the start-up cost of the power generation units on the source side, the fuel cost of the gas turbines, the cost of the gas boilers, the usage and maintenance cost of the multi-energy storage device, and the electricity cost of the external large power grid.

[0199] In a further preferred solution, the day-ahead pre-scheduling model in the coarse scheduling module is as follows:

[0200]

[0201] where f1 is the total operating cost, is the electric power of the i MT -th gas turbine at time t, is the fuel cost of the i MT -th gas turbine at time t, is the power of the i GB -th gas boiler at time t, is the fuel cost of the i GB -th gas boiler at time t, is the start-stop status variable of the i GB -th gas boiler at time t, is the start-stop status variable of the i GB -th gas boiler at time t-1, is the start-stop status variable of the i MT -th gas turbine at time t, is the start-stop status variable of the i MT -th gas turbine at time t-1, is the start-up cost of the i MT -th gas turbine at time t, the start-up cost of the i GB -th gas boiler at time t, f(C t ) is the usage cost of the multi-energy storage device at time t, P grid is the electricity cost of the external large power grid, and being 1 represents running and 0 represents stopping, N MT is the total number of gas turbines in the industrial park, N GB is the total number of gas boilers in the industrial park, and T is the total time.

[0202] In a further preferred solution, the fuel cost MT of the i -th gas turbine at time t in the coarse scheduling module

[0203]

[0204] where is the cost coefficient of the gas turbine.

[0205] In a further preferred embodiment, the fuel cost of the i-th GB gas boiler in the coarse scheduling module at time t is calculated as follows:

[0206]

[0207] where is the cost coefficient of the gas boiler.

[0208] In a further preferred embodiment, the usage cost f(C t ) of the multi-energy storage device in the coarse scheduling module at time t is calculated as follows:

[0209]

[0210] where ε ES , ε HS , ε CS are the depreciation cost coefficients of unit energy storage for electricity, heat, and cold respectively, P t ES.C is the energy storage power of the battery at time t, P t ES.D is the discharging power of the battery at time t, is the energy storage power of the heat storage tank at time t, P t HS.D is the discharging power of the heat storage tank at time t, is the energy storage power of the cold storage tank at time t, is the discharging power of the cold storage tank at time t.

[0211] In a further preferred embodiment, the operating constraint conditions of the energy supply unit in the coarse scheduling module include the electric energy balance constraint, the heat energy balance constraint, and the cold energy balance constraint.

[0212] In a further preferred embodiment, the expression of the electric energy balance constraint in the coarse scheduling module is:

[0213] P t PV +P t wind +P t grid +P t MT +P t ES =P t D +P t AC +P t EB

[0214] Among them, P t PV is the photovoltaic output, P t wind is the wind power output, P t grid is the electric power obtained by the industrial park from the power grid, P t MT is the electric power output of the gas turbine, P t ES is the electric power released by the battery, P t D is the electrical load required by the users on the load side of the industrial park, P t AC is the electric power required by the electric chiller, P t EB is the electric power required by the electric boiler.

[0215] In a further preferred solution, the expression of the heat energy balance constraint in the coarse scheduling module is:

[0216]

[0217] Among them, is the thermal power of the gas turbine at time t, is the thermal power of the gas boiler at time t, is the thermal power of the electric boiler at time t, is the thermal power output by the heat storage tank at time t, is the heat load required by the users on the load side of the industrial park at time t.

[0218] In a further preferred solution, the expression of the cold energy balance constraint in the coarse scheduling module is:

[0219]

[0220] Among them, is the cooling power of the lithium bromide absorption chiller at time t, is the output of the electric chiller at time t, is the cooling power released by the ice storage tank at time t, is the cooling load of the industrial park at time t.

[0221] In a further preferred solution, the intra-day rolling correction scheduling model in the fine adjustment module is:

[0222]

[0223] Among them, f2 is the total operating cost within the day, N MT is the total number of gas turbines in the industrial park, N GBis the total number of gas boilers in the industrial park, is the electrical power of the i MT th gas turbine, is the fuel cost of the i MT th gas turbine, is the power of the i GB th gas boiler, is the fuel cost of the i GB th gas boiler, is the corrected electrical power of the i MT th gas turbine, is the corrected cost of the i MT th gas turbine, the i GB th gas boiler's corrected power, is the i GB th gas boiler's corrected cost, P grid is the electricity cost of the external large power grid.

[0224] In a further preferred embodiment, the corrected cost of the i MT th gas turbine in the fine adjustment module has the following expression:

[0225]

[0226] where λ MT is the unit power adjustment cost coefficient of the gas turbine unit.

[0227] In a further preferred embodiment, the corrected cost of the i GB th gas boiler in the fine adjustment module has the following expression:

[0228]

[0229] where λ GB is the unit power adjustment cost coefficient of the gas boiler unit.

[0230] In a further preferred embodiment, the real-time feedback scheduling model in the fine adjustment module is specifically constructed based on the real-time output of photovoltaic power generation and the real-time load of the industrial park, with the goal of minimizing the total adjustment amount of the output of the energy supply units in the industrial park.

[0231] In a further preferred embodiment, the expression for the goal of minimizing the total adjustment amount of the output of the energy supply units in the industrial park in the fine adjustment module is:

[0232]

[0233] where ΔP RtΔP is the total adjustment amount of the output of the energy supply units in the industrial park. t MT ΔP is the output adjustment amount of the gas turbine at time t. ΔP is the total output adjustment amount of the gas boiler at time t. ΔC is the output adjustment amount of the lithium bromide absorption chiller at time t. t AC ΔP is the cooling output adjustment amount of the electric chiller at time t. t grid ΔP is the output adjustment amount of the external large power grid at time t. P is the maximum output power of the gas turbine. P is the maximum output power of the gas boiler. P is the maximum output power of the electric chiller. P is the maximum output power of the lithium bromide absorption chiller. P is the maximum output of the external power grid.

[0234] In a further optimized solution, the expression of the real-time feedback scheduling model in the fine adjustment module is:

[0235] P t MT = P t MT.E + ΔP t AC + ΔP t EB - ΔP t grid + ΔP t D + ΔP t PV

[0236] P t grid = P t grid.E + ΔP t grid

[0237] P t AC = P t AC.E + ΔP t AC

[0238] P t EB = P t EB.E + ΔP t EB

[0239] Among them, P tMT is the output of the gas turbine at time t, P t MT.E is the planned power of the gas turbine in the previous stage at time t, ΔP t AC is the real-time change power of the electric chiller at time t, ΔP t EB is the real-time change power of the electric boiler at time t, ΔP t grid is the adjustment amount of the output of the external large power grid at time t, ΔP t D is the random deviation amount of the load of the park users at time t, ΔP t PV is the random deviation amount of the photovoltaic at time t, P t grid is the output of the external large power grid at time t, P t grid.E is the planned power of the external large power grid in the previous stage at time t, P t AC is the required electric power of the electric chiller at time t, P t AC.E is the power of the electric chiller in the previous stage at time t, ΔP t AC is the real-time change power of the electric chiller at time t, P t EB is the required electric power of the electric boiler at time t, P t EB.E is the power of the electric boiler in the previous stage at time t, ΔP t EB is the real-time change power of the electric boiler at time t.

[0240] In a further optimized solution, the interaction architecture between the multiple energy storages and the source-load in the industrial park in the coarse scheduling module is obtained by analyzing the energy flow correlation relationship between the multiple energy storages and the source-load by using the correlation analysis method.

[0241] Embodiment 3

[0242] As Figure 6 shown, the present invention further provides an electronic device, which may be a computer device, a single-chip microcomputer device, a smart mobile device, etc. The electronic device in this embodiment may include a processor, a memory, a transceiver component, etc. The memory, the processor and the transceiver component are connected by a bus; the memory can be used to store an execution program, and an exemplary execution program may include instructions; the processor is used to execute the instructions stored in the memory. The memory can also be used to store data, and the data can be called and / or modified when the instructions are executed.

[0243] The processor may be a Central Processing Unit (CPU), or may also be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), 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, and is suitable for implementing one or more instructions. Specifically, it is suitable for loading and executing one or more instructions in the storage medium to implement the corresponding method flow or corresponding function, so as to implement the steps of a method for optimizing the unit scheduling of an industrial park considering source-load-storage matching in the above embodiments.

[0244] Embodiment 4

[0245] Based on the same inventive concept, the present invention also provides a readable storage medium, specifically an electronic device-readable storage medium (Memory). The electronic device-readable storage medium is a memory device in the electronic device, used to store programs and data. It can be understood that the storage medium here can include both the built-in storage medium in the electronic device and, of course, the extended storage medium supported by the electronic device. The storage medium provides a storage space, and this storage space stores the operating system of the terminal. And, one or more instructions suitable for being loaded and executed by the processor are also stored in this storage space. These instructions can be one or more execution programs (including program codes). It should be noted that the storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory. By the processor loading and executing one or more instructions stored in the storage medium, the steps of a method for optimizing the unit scheduling of an industrial park considering source-load-storage matching in the above embodiments can be implemented.

[0246] Those skilled in the art should understand that the embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memories, CD-ROMs, optical memories, etc.) containing computer-usable program codes.

[0247] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and combinations of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to produce a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or means for implementing the functions specified in one block or multiple blocks.

[0248] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory produce a manufactured article including instruction means that implement the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or means for implementing the functions specified in one block or multiple blocks.

[0249] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to produce a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or means for implementing the functions specified in one block or multiple blocks.

[0250] The above are only embodiments of the present invention and are not used to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention are included within the scope of the claims of the present invention pending approval.

Claims

1. A multi-time scale unit dispatch optimization method for an industrial park considering source-load-storage matching, characterized in that: include: The pre-built day-ahead dispatch model is used to initialize the dispatch plan of the energy supply units on that day; The scheduling plan is corrected by using a pre-built intraday rolling correction scheduling model at first preset time intervals, and the output of the energy supply unit in operation is corrected by using a pre-built real-time feedback scheduling model at second preset time intervals, wherein the first preset time is longer than the second preset time; The day-ahead pre-dispatch model is constructed by conducting an economic analysis on the interactive architecture between the multi-energy storage and source-load of the industrial park; the intra-day rolling correction dispatch model is constructed based on the goal of minimizing the operating cost of the energy supply unit and the adjustment cost of the adjustable energy supply unit; The real-time feedback scheduling model is constructed with the goal of minimizing the total output adjustment of the energy supply units in the industrial park.

2. The method for optimizing the dispatching of multi-time scale units in an industrial park considering source-load-storage matching according to claim 1 is characterized in that: The steps for constructing the day-ahead pre-scheduling model are as follows: Obtain the power operation data of the day-ahead source to be dispatched in the interactive architecture between the multi-energy storage and source-load of the industrial park; A day-ahead scheduling model is constructed under the operating constraints of the energy supply units with the goal of minimizing operating costs.

3. The method for optimizing the dispatching of multi-time scale units in an industrial park considering source-load-storage matching according to claim 2 is characterized in that: The operating costs include the startup costs of the source-side energy supply units, gas turbine fuel costs, gas boiler costs, multi-energy storage device use and maintenance costs, and external large power grid electricity costs.

4. The method for optimizing the dispatching of multi-time scale units in an industrial park considering source-load-storage matching according to claim 2 or 3 is characterized in that: The day-ahead scheduling model is: Among them, f1 is the total operating cost, For the i MT The electrical power of a gas turbine at time t is: For the i MT The fuel cost of a gas turbine at time t is, For the i GB The power of a gas boiler at time t is, For the i GB The fuel cost of a gas boiler at time t is, for i GB The start and stop state of a gas boiler at time t, for i GB The start and stop state of a gas boiler at time t-1, For the i MT The start and stop state of a gas turbine at time t, For the i MT The start and stop state of a gas turbine at time t-1, For the i MT The startup cost of a gas turbine at time t is, i-th GB The startup cost of a gas boiler at time t, f(C t ) is the use cost of the multi-energy storage device at time t, P grid The electricity fee for the external large power grid is and When it is 1, it means running, and when it is 0, it means stopping. MT is the total number of gas turbines in the industrial park, N GB is the total number of gas boilers in the industrial park, and T is the total time.

5. The method for optimizing the dispatching of multi-time scale units in an industrial park considering source-load-storage matching according to claim 4 is characterized in that: i-th MT The fuel cost of a gas turbine at time t The calculation formula is: in, is the cost factor of the gas turbine.

6. The multi-time scale scheduling optimization method for industrial parks considering source-load-storage matching according to claim 4 is characterized in that: i-th GB The fuel cost of a gas boiler at time t The calculation formula is: in, is the cost coefficient of the gas boiler.

7. The method for optimizing the dispatching of multi-time scale units in an industrial park considering source-load-storage matching according to claim 4 is characterized in that: The cost of using the multi-energy storage device at time t (C t ) is calculated as: Among them, ε ES , ε HS , ε CS are the depreciation cost coefficients of energy storage per unit for electricity, heat and cold, respectively. is the energy storage power of the battery at time t, is the discharge power of the battery at time t, is the energy storage power of the heat storage tank at time t, is the energy release power of the heat storage tank at time t, is the energy storage power of the cold storage tank at time t, is the energy release power of the cold storage tank at time t.

8. The method for optimizing the dispatching of multi-time scale units in an industrial park considering source-load-storage matching according to claim 2 is characterized in that: The operating constraints of the energy supply unit include electric energy balance constraints, thermal energy balance constraints and cold energy balance constraints.

9. The method for optimizing the dispatching of multi-time scale units in an industrial park considering source-load-storage matching according to claim 8 is characterized in that: The expression of the electric energy balance constraint is: in, For photovoltaic power output, To contribute to wind power, is the electric power obtained by the industrial park from the power grid, For gas turbine electrical power output, is the electrical power released by the battery, The power load required by users on the load side of the industrial park. is the electrical power required by the electric refrigerator, The electric power required for the electric boiler.

10. The method for optimizing the dispatching of multi-time scale units in an industrial park considering source-load-storage matching according to claim 8, characterized in that: The expression of the thermal energy balance constraint is: in, is the thermal power of the gas turbine at time t, is the thermal power of the gas boiler at time t, is the thermal power of the electric boiler at time t, is the thermal power output of the heat storage tank at time t, is the heat load required by users on the load side of the industrial park at time t.

11. The method for optimizing the dispatch of multi-time scale units in an industrial park considering source-load-storage matching according to claim 8, characterized in that: The expression of the cold energy balance constraint is: in, is the cooling power of the lithium bromide absorption refrigerator at time t, is the output of the electric refrigerator at time t, is the cooling power released by the ice storage tank at time t, is the cooling load of the industrial park at time t.

12. The method for optimizing the dispatch of multi-time scale units in an industrial park considering source-load-storage matching according to claim 1, characterized in that: The intraday rolling correction scheduling model is: Among them, f2 is the total operating cost within a day, N MT is the total number of gas turbines in the industrial park, N GB is the total number of gas boilers in the industrial park, For the i MT The electrical power of a gas turbine, For the i MT The fuel cost of a gas turbine, For the i GB The power of a gas boiler, For the i GB Fuel cost of a gas boiler, For the i MT Corrected electrical power of each gas turbine, For the i MT The corrected cost of a gas turbine, i-th GB Corrected power of a gas boiler, For the i GB Corrected cost of a gas boiler, P grid Electricity charges for external large power grid.

13. The method for optimizing the dispatching of multi-time scale units in an industrial park considering source-load-storage matching according to claim 12, characterized in that: The i MT Corrected cost per gas turbine The expression is: Among them, λ MT Adjust the cost factor per unit power for the gas turbine unit.

14. The method for optimizing the dispatch of multi-time scale units in an industrial park considering source-load-storage matching according to claim 12 or 13, characterized in that: The i GB Correction cost of a gas boiler The expression is: Among them, λ GB It is the unit power adjustment cost coefficient of the gas boiler unit.

15. The method for optimizing the dispatch of multi-time scale units in an industrial park considering source-load-storage matching according to claim 1, characterized in that: The real-time feedback scheduling model is specifically constructed based on the real-time output of photovoltaic power generation and the real-time load of the industrial park, and with the goal of minimizing the total output adjustment of the energy supply units in the industrial park.

16. The method for optimizing the dispatch of multi-time scale units in an industrial park considering source-load-storage matching according to claim 15, characterized in that: The expression for minimizing the total adjustment of the output of the energy supply units in the industrial park is as follows: Where ΔP Rt It is the total output adjustment of the energy supply units in the industrial park. is the output adjustment of the gas turbine at time t, is the total output adjustment of the gas boiler at time t, is the output adjustment of the lithium bromide absorption refrigerator at time t, is the cooling output adjustment of the electric refrigerator at time t, is the output adjustment of the external large power grid at time t, is the maximum output power of the gas turbine, is the maximum output power of the gas boiler, is the maximum output power of the electric refrigerator, is the maximum output power of lithium bromide absorption refrigerator, It is the maximum output of the external power grid.

17. The method for optimizing the dispatch of multi-time scale units in an industrial park considering source-load-storage matching according to claim 15 or 16, characterized in that: The expression of the real-time feedback scheduling model is: in, is the output of the gas turbine at time t, is the planned power of the gas turbine in the previous stage at time t, is the real-time power change of the electric refrigerator at time t, is the real-time power change of the electric boiler at time t, is the output adjustment of the external large power grid at time t, is the random deviation of the park user load at time t, is the random deviation of photovoltaic power generation at time t, is the external large power grid output at time t, is the planned power of the external large power grid in the previous stage at time t, is the electric power required by the electric refrigerator at time t, is the power of the lithium bromide absorption refrigerator in the previous stage at time t, is the real-time power change of the electric refrigerator at time t, is the electric power required by the electric boiler at time t, is the power of the electric boiler in the previous stage at time t, The real-time power change of the electric boiler at time t.

18. The method for optimizing the dispatch of multi-time scale units in an industrial park considering source-load-storage matching according to claim 1, characterized in that: The interactive architecture between the multi-energy storage and the source load in the industrial park is obtained by analyzing the energy flow correlation relationship between the multi-energy storage and the source load using a correlation analysis method.

19. An industrial park multi-time scale unit scheduling optimization system considering source-load-storage matching, characterized in that: include: Rough scheduling module: used to initialize the scheduling plan of the energy supply units on the day using the pre-built day-ahead scheduling model; A fine adjustment module: used to modify the scheduling plan by using a pre-built intraday rolling modification scheduling model at a first preset time interval, and modify the output of the energy supply unit in operation by using a pre-built real-time feedback scheduling model at a second preset time interval, wherein the first preset time is longer than the second preset time; The day-ahead pre-dispatch model is constructed by conducting an economic analysis on the interactive architecture between the multi-energy storage and source-load of the industrial park; the intra-day rolling correction dispatch model is constructed based on the goal of minimizing the operating cost of the energy supply unit and the adjustment cost of the adjustable energy supply unit; The real-time feedback scheduling model is constructed with the goal of minimizing the total output adjustment of the energy supply units in the industrial park.

20. The industrial park multi-time scale unit dispatch optimization system considering source-load-storage matching according to claim 19, characterized in that: The steps for constructing the day-ahead pre-scheduling model in the rough scheduling module are as follows: Obtain the power operation data of the day-ahead source to be dispatched in the interactive architecture between the multi-energy storage and source-load of the industrial park; A day-ahead scheduling model is constructed under the operating constraints of the energy supply units with the goal of minimizing operating costs.

21. The industrial park multi-time scale unit dispatch optimization system considering source-load-storage matching according to claim 20, characterized in that: The operating costs in the rough scheduling module include the startup cost of the source-side generator set, the fuel cost of the gas turbine, the cost of the gas boiler, the use and maintenance cost of the multi-energy storage device, and the electricity cost of the external large power grid.

22. The industrial park multi-time scale unit dispatch optimization system considering source-load-storage matching according to claim 20 or 21, characterized in that: The day-ahead pre-scheduling model in the rough scheduling module is: Among them, f1 is the total operating cost, For the i MT The electrical power of a gas turbine at time t is: For the i MT The fuel cost of a gas turbine at time t is, For the i GB The power of a gas boiler at time t is, For the i GB The fuel cost of a gas boiler at time t is, for i GB The start and stop state of a gas boiler at time t, for i GB The start and stop state of a gas boiler at time t-1, For the i MT The start and stop state of a gas turbine at time t, For the i MT The start and stop state of a gas turbine at time t-1, For the i MT The startup cost of a gas turbine at time t is, i-th GB The startup cost of a gas boiler at time t, f(C t ) is the use cost of the multi-energy storage device at time t, P grid The electricity fee for the external large power grid is and When it is 1, it means running, and when it is 0, it means stopping. MT is the total number of gas turbines in the industrial park, N GB is the total number of gas boilers in the industrial park, and T is the total time.

23. The industrial park multi-time scale unit scheduling optimization system considering source-load-storage matching according to claim 22 is characterized in that: The i-th MT The fuel cost of a gas turbine at time t The calculation formula is: in, is the cost factor of the gas turbine.

24. The industrial park multi-time scale unit dispatch optimization system considering source-load-storage matching according to claim 22, characterized in that: The i-th GB The fuel cost of a gas boiler at time t The calculation formula is: in, is the cost coefficient of the gas boiler.

25. The industrial park multi-time scale unit dispatch optimization system considering source-load-storage matching according to claim 22, characterized in that: The usage cost f(C t ) is calculated as: Among them, ε ES , ε HS , ε CS are the depreciation cost coefficients of energy storage per unit for electricity, heat and cold, respectively. is the energy storage power of the battery at time t, is the discharge power of the battery at time t, is the energy storage power of the heat storage tank at time t, is the energy release power of the heat storage tank at time t, is the energy storage power of the cold storage tank at time t, is the energy release power of the cold storage tank at time t.

26. The industrial park multi-time scale unit dispatch optimization system considering source-load-storage matching according to claim 20, characterized in that: The operating constraints of the energy supply units in the rough scheduling module include electric energy balance constraints, thermal energy balance constraints and cold energy balance constraints.

27. The industrial park multi-time scale unit dispatch optimization system considering source-load-storage matching according to claim 26 is characterized in that: The expression of the electric energy balance constraint in the rough scheduling module is: in, For photovoltaic power output, To contribute to wind power, is the electric power obtained by the industrial park from the power grid, For gas turbine electrical power output, is the electrical power released by the battery, The power load required by users on the load side of the industrial park. is the electrical power required by the electric refrigerator, The electric power required for the electric boiler.

28. The industrial park multi-time scale unit dispatch optimization system considering source-load-storage matching according to claim 26, characterized in that: The expression of the thermal energy balance constraint in the rough scheduling module is: in, is the thermal power of the gas turbine at time t, is the thermal power of the gas boiler at time t, is the thermal power of the electric boiler at time t, is the thermal power output of the heat storage tank at time t, is the heat load required by users on the load side of the industrial park at time t.

29. The industrial park multi-time scale unit dispatch optimization system considering source-load-storage matching according to claim 26, characterized in that: The expression of the cold energy balance constraint in the rough scheduling module is: in, is the cooling power of the lithium bromide absorption refrigerator at time t, is the output of the electric refrigerator at time t, is the cooling power released by the ice storage tank at time t, is the cooling load of the industrial park at time t.

30. The industrial park multi-time scale unit dispatch optimization system considering source-load-storage matching according to claim 19, characterized in that: The intraday rolling correction scheduling model in the fine adjustment module is: Among them, f2 is the total operating cost within a day, N MT is the total number of gas turbines in the industrial park, N GB is the total number of gas boilers in the industrial park, For the i MT The electrical power of a gas turbine, For the i MT The fuel cost of a gas turbine, For the i GB The power of a gas boiler, For the i GB Fuel cost of a gas boiler, For the i MT Corrected electrical power of each gas turbine, For the i MT The corrected cost of a gas turbine, i-th GB Corrected power of a gas boiler, For the i GB Corrected cost of a gas boiler, P grid Electricity charges for external large power grid.

31. The industrial park multi-time scale unit dispatch optimization system considering source-load-storage matching according to claim 30 is characterized in that: The i-th MT Corrected cost per gas turbine The expression is: Among them, λ MT Adjust the cost factor per unit power for the gas turbine unit.

32. The industrial park multi-time scale unit dispatch optimization system considering source-load-storage matching according to claim 30 or 31, characterized in that: The i-th GB Correction cost of a gas boiler The expression is: Among them, λ GB It is the unit power adjustment cost coefficient of the gas boiler unit.

33. The industrial park multi-time scale unit dispatch optimization system considering source-load-storage matching according to claim 19 is characterized in that: The real-time feedback scheduling model in the fine adjustment module is specifically constructed based on the real-time output of photovoltaic power generation and the real-time load of the industrial park, and with the goal of minimizing the total output adjustment of the energy supply units in the industrial park.

34. The industrial park multi-time scale unit dispatch optimization system considering source-load-storage matching according to claim 33 is characterized in that: The expression for minimizing the total adjustment amount of the output of the energy supply units in the industrial park in the fine adjustment module is as follows: Where ΔP Rt It is the total output adjustment of the energy supply units in the industrial park. is the output adjustment of the gas turbine at time t, is the total output adjustment of the gas boiler at time t, is the output adjustment of the lithium bromide absorption refrigerator at time t, is the cooling output adjustment of the electric refrigerator at time t, is the output adjustment of the external large power grid at time t, is the maximum output power of the gas turbine, is the maximum output power of the gas boiler, is the maximum output power of the electric refrigerator, is the maximum output power of lithium bromide absorption refrigerator, It is the maximum output of the external power grid.

35. The industrial park multi-time scale unit dispatch optimization system considering source-load-storage matching according to claim 33 or 34, characterized in that: The expression of the real-time feedback scheduling model in the fine adjustment module is: in, is the output of the gas turbine at time t, is the planned power of the gas turbine in the previous stage at time t, is the real-time power change of the electric refrigerator at time t, is the real-time power change of the electric boiler at time t, is the output adjustment of the external large power grid at time t, is the random deviation of the park user load at time t, is the random deviation of photovoltaic power generation at time t, is the external large power grid output at time t, is the planned power of the external large power grid in the previous stage at time t, is the electric power required by the electric refrigerator at time t, is the power of the electric refrigerator in the previous stage at time t, is the real-time power change of the electric refrigerator at time t, is the electric power required by the electric boiler at time t, is the power of the electric boiler in the previous stage at time t, The real-time power change of the electric boiler at time t.

36. The industrial park multi-time scale unit dispatch optimization system considering source-load-storage matching according to claim 19 is characterized in that: The interactive architecture between the multi-energy storage and the source load in the industrial park in the rough scheduling module is obtained by analyzing the energy flow correlation relationship between the multi-energy storage and the source load using a correlation analysis method.

37. A computer device, characterized in that: include: at least one processor and memory; The memory and the processor are connected via a bus; The memory is used to store one or more programs; When the one or more programs are executed by the at least one processor, the multi-time-scale unit scheduling optimization method for an industrial park considering source-load-storage matching as described in any one of claims 1 to 18 is implemented.

38. A computer-readable storage medium, characterized in that: An execution program is stored thereon, and when the execution program is executed, the multi-time-scale unit scheduling optimization method for an industrial park considering source-load-storage matching as described in any one of claims 1 to 18 is implemented.

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