Multi-time scale intelligent energy station collaborative operation control method and device

Abstract

The application provides a multi-time-scale intelligent energy station cooperative operation control method and device, which is used for optimizing scheduling of the intelligent energy station by considering renewable energy consumption. The method constructs an optimization scheduling model of the intelligent energy station by considering operation cost and green certificate-carbon trading cost. Then, operation of the intelligent energy station is divided into three scales of day-ahead optimization scheduling, rolling optimization scheduling and real-time control, and specific control algorithms are respectively formulated. Then, according to equipment characteristics of the intelligent energy station, equipment in the station is divided into three categories according to start-stop flexibility, and is involved in control of different time scales with different margins. Finally, the operation state of the intelligent energy station is monitored in the operation day, and the optimization scheduling model is solved rolling to obtain an optimal scheduling plan of the intelligent energy station. The application overcomes the problems of not being able to fully encourage renewable energy consumption and carbon emission reduction, and not fully considering renewable energy output uncertainty and flexibility of the comprehensive energy system.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent energy station collaborative operation control, in particular, especially relates to a multi-time scale intelligent energy station collaborative operation control method and device. BACKGROUND

[0002] The proportion of renewable energy generation access is increasing. At the same time, the intermittency, volatility and uncertainty of renewable energy generation output bring certain hidden dangers to the safe and stable operation of the power system, and also cause the occurrence of "wind curtailment and light curtailment". Under this background, many methods of renewable energy generation consumption have been proposed, among which the intelligent energy station based on multi-station integration construction mode can realize more flexible renewable energy consumption through energy storage and energy conversion equipment. For example, during the peak period of renewable energy output, the intelligent energy station can consume it by converting electrical energy into other energy for use and storage; during the trough period of renewable energy output, more natural gas can be used to meet its own operation demand.

[0003] Although the optimization scheduling of intelligent energy station has many related researches and project practices, and most of the optimization scheduling researches take the minimum operation cost as the optimization target, but it does not bring economic incentives to promote the consumption of renewable energy by intelligent energy station; at the same time, under the access of high proportion of renewable energy generation, the limited renewable energy output prediction accuracy greatly limits the economy and environmental protection of the optimization scheduling scheme in actual operation. SUMMARY

[0004] In view of the problems in the above content, the present application provides a multi-time scale intelligent energy station collaborative operation control method and device to solve the problems that the existing other comprehensive energy system optimization scheduling model cannot fully encourage renewable energy consumption and carbon emission reduction, and does not fully consider the uncertainty of renewable energy output and the flexibility of comprehensive energy system.

[0005] In order to achieve the above purpose, the present application provides the following technical solutions:

[0006] A multi-time scale intelligent energy station collaborative operation control method for optimizing scheduling of an intelligent energy station considering renewable energy consumption, the method comprising:

[0007] Considering the operation cost and green certificate-carbon trading cost of the intelligent energy station, an optimization scheduling model of the intelligent energy station is established according to the device composition of the intelligent energy station;

[0008] The optimization scheduling model and specific control algorithm of the intelligent energy station under the three scales of day-ahead optimization scheduling, rolling optimization scheduling and real-time control are constructed;

[0009] According to the equipment characteristics of the smart energy station, the equipment in the smart energy station is divided into three categories according to start-stop flexibility, and participates in control of different time scales with different margins.

[0010] The operation state of the smart energy station is continuously monitored within a running day, and the optimal scheduling model is solved to obtain the optimal scheduling plan of the smart energy station.

[0011] A multi-time-scale smart energy station cooperative operation control device is used for optimal scheduling of a smart energy station considering renewable energy consumption, and the device comprises:

[0012] A first construction unit is used for establishing an optimal scheduling model of the smart energy station by considering the operation cost and green certificate-carbon trading cost of the smart energy station and combining the equipment configuration of the smart energy station.

[0013] A second construction unit is used for constructing the optimal scheduling model and specific control algorithm of the smart energy station under three scales of day-ahead optimal scheduling, rolling optimal scheduling and real-time control.

[0014] A third construction unit is used for dividing the equipment in the smart energy station into three categories according to start-stop flexibility according to the equipment characteristics of the smart energy station, and participating in control of different time scales with different margins.

[0015] A solving unit is used for continuously monitoring the operation state of the smart energy station within a running day, and solving the optimal scheduling model to obtain the optimal scheduling plan of the smart energy station.

[0016] A storage medium comprises a stored program, wherein the device where the storage medium is located is controlled to execute the multi-time-scale smart energy station cooperative operation control method as described above when the program is running.

[0017] An electronic device comprises at least one processor and at least one memory connected with the processor through a bus; wherein the processor, the memory and the bus complete mutual communication; the processor is used for calling program instructions in the memory to execute the multi-time-scale smart energy station cooperative operation control method as described above.

[0018] The multi-time scale intelligent energy station collaborative operation control method and device provided by the application are used for optimizing scheduling of the intelligent energy station considering renewable energy consumption, the method comprising: constructing an optimization scheduling model of the intelligent energy station considering operation cost and green certificate-carbon trading cost; dividing operation of the intelligent energy station into day-ahead optimization scheduling, rolling optimization scheduling and real-time control three scales, and formulating specific control algorithms respectively; according to equipment characteristics of the intelligent energy station, dividing equipment in the station into three categories according to start-stop flexibility, and participating in control of different time scales with different margins; monitoring operation state of the intelligent energy station in a running day, and rolling solving the optimization scheduling model to obtain an optimal scheduling plan of the intelligent energy station.

[0019] The optimization scheduling model of the intelligent energy station established by the application considers operation cost and green certificate-carbon trading cost of the intelligent energy station, and has the beneficial effect of giving economic incentives to renewable energy consumption and carbon emission reduction, and is beneficial to the intelligent energy station to form a scheduling plan with economic efficiency and environmental protection;

[0020] The application divides operation of the intelligent energy station into day-ahead optimization scheduling, rolling optimization scheduling and real-time control three time scales, and formulates optimization scheduling models and specific control algorithms respectively, and has the beneficial effect of fully considering uncertainty of renewable energy generation and the feature that renewable energy generation prediction accuracy improves with reduction of prediction cycle, and is beneficial to rolling correcting the scheduling plan of the intelligent energy station to obtain optimal operation effect;

[0021] The application divides equipment in the station into three categories according to start-stop flexibility according to equipment characteristics of the intelligent energy station, and participates in control of different time scales with different margins, and has the beneficial effect of fully considering flexibility difference of equipment in the intelligent energy station, and is beneficial to cooperating with control schemes of different time scales to obtain optimal operation effect;

[0022] The application linearizes the optimization scheduling model and rolling solves it to obtain the optimal scheduling plan of the intelligent energy station, and has the beneficial effect that a method capable of solving linear programming problems can obtain a global optimal solution without making the solving result fall into a local optimal solution.

[0023] The application can overcome problems that other existing comprehensive energy system optimization scheduling models cannot fully motivate renewable energy consumption and carbon emission reduction, and do not fully consider renewable energy output uncertainty and flexibility of the comprehensive energy system. BRIEF DESCRIPTION OF DRAWINGS

[0024] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description only constitute some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor.

[0025] Figure 1 A flowchart of a multi-time-scale intelligent energy station collaborative operation control method disclosed by an embodiment of the present application;

[0026] Figure 2 A structural diagram of a multi-time-scale intelligent energy station collaborative operation control device disclosed by an embodiment of the present application;

[0027] Figure 3 A structural diagram of an electronic device disclosed by an embodiment of the present application. DETAILED DESCRIPTION

[0028] The technical solutions in the embodiments of the present application will be described clearly and completely in the following with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments only constitute some embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0029] Please refer to the drawings in the embodiments of the present application Figure 1 A flowchart of a multi-time-scale intelligent energy station collaborative operation control method provided by an embodiment of the present application. As shown in Figure 1 The present application provides a multi-time-scale intelligent energy station collaborative operation control method, which is used for optimizing scheduling of an intelligent energy station considering renewable energy consumption. The method comprises the following steps:

[0030] S101: considering the operation cost and green certificate-carbon trading cost of the intelligent energy station, and according to the equipment composition of the intelligent energy station, an optimization scheduling model of the intelligent energy station is established;

[0031] In the embodiments of the present application, the above-mentioned consideration of the operation cost and green certificate-carbon trading cost of the intelligent energy station, and the establishment of the optimization scheduling model of the intelligent energy station according to the equipment composition of the intelligent energy station, specifically comprises: according to the equipment composition of the intelligent energy station, an optimization scheduling model of the intelligent energy station is established, which contains independent device unit modeling and coupled device unit modeling, the establishment of device output constraints, energy storage state constraints, energy conversion constraints and energy balance constraints in the optimization scheduling model, and the establishment of a target function in the optimization scheduling model including external energy cost, operation and maintenance cost, green certificate trading cost and carbon trading cost.

[0032] The output constraints of the above-mentioned equipment are specifically expressed as follows:

[0033]

[0034] In the formula: P i t This represents the output power of device i at time t, where device i includes equipment within the smart energy station such as photovoltaics, energy storage devices, thermal storage devices, cold storage devices, gas turbines, gas boilers, electric boilers, electric chillers, lithium bromide units, waste heat boilers, as well as external energy networks such as the power grid and natural gas network; This represents the capacity (state of charge, thermal energy storage, and cold energy storage) of device i at time t, where device i includes energy storage devices, thermal storage devices, and cold energy storage devices; P and These represent the upper and lower limits of the output power of device i, respectively. S i and These represent the upper and lower limits of the state of charge of device i, respectively.

[0035] The energy storage state constraints are specifically expressed as follows:

[0036]

[0037]

[0038]

[0039] In the formula: and Let σ represent the capacity, energy storage power, and energy release power of device i at time t, respectively. i η i,s and η i,r These represent the loss rate, energy storage efficiency, and energy release efficiency of device i, respectively. Here, device i includes energy storage devices, thermal storage devices, and cold storage devices.

[0040] The energy conversion constraint is specifically expressed as follows:

[0041]

[0042]

[0043] In the formula: η i It is the conversion efficiency of device i. and These represent the output power and input power of device i at time t, respectively. Here, device i includes energy coupling devices such as gas turbines, gas boilers, electric boilers, electric chillers, lithium bromide units, and waste heat boilers. auxiliary power at time t, P i t output power of device i at time t, k i auxiliary power coefficient corresponding to device i, where device i includes devices requiring auxiliary power such as water pump, gas turbine, gas boiler, electric heating boiler, electric refrigerator, cold storage device.

[0044] The energy balance constraint is specifically represented as follows:

[0045]

[0046]

[0047]

[0048]

[0049] wherein: and represent the grid electricity purchase power and the gas grid gas purchase power at time t, respectively; and represent the electricity load, the gas load, the heat load and the cold load at time t, respectively; represents the photovoltaic power generation power at time t; and represent the discharging power of the electric storage device, the heat storage device and the cold storage device at time t, respectively; and represent the charging power of the electric storage device, the heat storage device and the cold storage device at time t, respectively; and represent the gas consumption power and the power generation power of the gas turbine at time t, respectively; and represent the electricity consumption power and the heat generation power of the electric heating boiler at time t, respectively; and represent the electricity consumption power and the refrigeration power of the electric refrigerator at time t, respectively; represents the auxiliary power at time t; and represent the gas consumption power and the heat generation power of the gas boiler at time t, respectively; and represent the heating power and the refrigeration power of the lithium bromide unit at time t, respectively.

[0050] The objective function of the model is specifically represented as follows:

[0051] min f=C e +C op +C tgc +C em

[0052] In the formula: C e C represents the cost of external energy. op Indicates maintenance costs, C tgc C represents the transaction cost of green certificates. em This indicates the cost of carbon trading.

[0053] The external energy costs are specifically represented as follows:

[0054]

[0055] In the formula: and These represent the gas purchase cost and electricity purchase cost of the external energy grid at time t, respectively.

[0056] The specific maintenance costs are as follows:

[0057]

[0058] In the formula: ξ i,op This represents the unit output operation and maintenance cost of device i. Here, device i includes equipment within the smart energy station, such as photovoltaics, energy storage devices, thermal storage devices, cold storage devices, gas turbines, gas boilers, electric boilers, electric chillers, lithium bromide units, and waste heat boilers.

[0059] The specific costs of green certificate transactions are as follows:

[0060]

[0061] In the formula: k, u, and s are the non-hydro renewable energy consumption quota ratio, the actual renewable energy consumption ratio, and the penalty margin of the consumption ratio as stipulated by the National Energy Administration, respectively; ξ tgc and ξ p These represent the green certificate trading price and the penalty price, respectively. and These represent the total power generation, photovoltaic power consumption, and grid-purchased power at time t, respectively.

[0062] The carbon trading costs are specifically represented as follows:

[0063]

[0064]

[0065]

[0066] In the formula: ξ em Indicates the carbon trading price; and λ represents the actual carbon emissions and carbon emission allowance at time t, respectively;load represents the carbon emission coefficient corresponding to the electric load; λ grid , λ cchp and λ gb respectively represent the carbon emission quota coefficient corresponding to the purchased power, the combined heat and power unit and the gas boiler.

[0067] S102: build the optimization scheduling model and specific control algorithm of the intelligent energy station under the three scales of day-ahead optimization scheduling, rolling optimization scheduling and real-time control;

[0068] In the embodiment of the present application, the above-mentioned building of the optimization scheduling model and specific control algorithm of the intelligent energy station under the three scales of day-ahead optimization scheduling, rolling optimization scheduling and real-time control is specifically:

[0069] The operation of the intelligent energy station is divided into day-ahead optimization scheduling, rolling optimization scheduling and real-time control three stages; wherein, the day-ahead optimization scheduling establishes an optimization scheduling model according to the day-ahead prediction of renewable energy output and user energy load; the rolling optimization scheduling establishes an optimization scheduling model according to the rolling update of renewable energy output and user energy load prediction within a day; the real-time control formulates real-time control strategy according to different emergencies.

[0070] The specific steps for day-ahead optimization scheduling are as follows:

[0071] 1) collect long-term source and load data, and predict the demand curve of next day's electric, gas, heat, cold and other multi-energy flow load;

[0072] 2) collect weather information, and predict the upper and lower limits of wind power, photovoltaic and other intermittent renewable energy generator output under the confidence level ψ For renewable energy output, calculate according to the predicted lower limit in the case of only considering reliability, and calculate according to the predicted upper limit in the case of only considering environmental protection; the weights of reliability and environmental protection are determined by the energy station operator.

[0073] 3) use the above-mentioned intelligent energy station optimization scheduling model to formulate the multi-objective optimal A, B and C device operation curve and purchased energy strategy;

[0074] 4) carry out day-ahead scheduling of the intelligent energy station according to the established scheme.

[0075] The specific steps for rolling optimization scheduling are as follows:

[0076] 1) update the renewable energy output prediction from t to T time period at t time;

[0077] 2) take the day-ahead optimization scheme from 0 to t as new equation constraints, keep the operation curves of all kinds of devices and the external energy purchase power from 0 to t consistent with the day-ahead scheduling;

[0078] 3) take the operation curve of the A-type device from t to T as new equation constraints, keep the operation curve of the A-type device from t to T consistent with the day-ahead scheduling;

[0079] 4) take the updated renewable energy output prediction as new input parameters, correct the operation curve of the device and the external energy purchase power from t to T by using the above-mentioned intelligent energy station optimization scheduling model;

[0080] 5) in the time period from t to t+1, schedule the intelligent energy station according to the corrected scheduling scheme;

[0081] 6) update the renewable energy output prediction from t+1 to T at t+1, and cycle steps 1) to 5) to perform rolling optimization scheduling of the intelligent energy station.

[0082] The specific steps of the real-time control strategy are as follows:

[0083] 1) real-time monitor the operation state of the intelligent energy station, and determine whether the renewable energy output mutation needs to be responded to by comparing the mutation of the renewable energy with the response threshold. If the mutation of the renewable energy output is greater than the response threshold of the real-time control, a real-time control scheme is generated to correct the operation curve of the intelligent energy station in the time scale of minutes or seconds.

[0084] 2) if it is determined to start the real-time control response, update the source and load data from t to t0 at t;

[0085] 3) take the rolling optimization scheme from 0 to t as new equation constraints, keep the operation curves of all kinds of devices and the external energy purchase power from 0 to t consistent with the rolling optimization scheduling;

[0086] 4) take the operation curves of the A-type and B-type devices from t to t0 as new equation constraints, keep the operation curves of the A-type and B-type devices from t to t0 consistent with the rolling optimization scheduling;

[0087] 5) take the updated source and load data as new input parameters, and form the real-time control scheme from t to t0 by using the above-mentioned intelligent energy station optimization scheduling model;

[0088] 6) repeat steps 1) to 5) until the emergency is over, and the intelligent energy station returns to the original scheduling curve.

[0089] S103: According to the device characteristics of the smart energy station, the devices in the smart energy station are divided into three categories according to start-stop flexibility, and participate in control of different time scales with different margins;

[0090] In the embodiments of the present application, the devices in the smart energy station are divided into three categories according to start-stop flexibility according to the device characteristics of the smart energy station, and participate in control of different time scales with different margins, specifically:

[0091] According to the device characteristics of the smart energy station, the devices in the smart energy station are divided into three categories according to start-stop flexibility according to the device characteristics of the smart energy station, and participate in control of different time scales with different margins, specifically:

[0092] S104: Continuously monitor the operation state of the smart energy station in a running day, and solve the optimization scheduling model to obtain the optimal scheduling plan of the smart energy station.

[0093] In the embodiments of the present application, the devices in the smart energy station are divided into three categories according to start-stop flexibility according to the device characteristics of the smart energy station, and participate in control of different time scales with different margins, specifically:

[0094] Before a running day, the optimization scheduling model is solved according to the day-ahead prediction of renewable energy output and user load to obtain a day-ahead optimization scheduling plan at the hour level;

[0095] In a running day, the operation state of the smart energy station is continuously monitored, the prediction of renewable energy output and user load is updated, and the optimization scheduling model is solved to obtain a rolling optimization scheduling plan at the minute level;

[0096] In real-time operation, the real-time control strategy is executed according to the emergency scenario to obtain a real-time control scheduling plan at the minute level;

[0097] In the process of solving the optimization model, the energy optimization model is linearized, the product of the decision variables in the original model is converted into a linear expression of multiple equality or inequality constraints, the original nonlinear mixed integer programming problem is converted into a linear mixed integer programming problem, and the algorithm of linear programming problem is used for global optimal solution to realize the optimal control of the smart energy station.

[0098] wherein the linearization process is specifically as follows:

[0099] The charge-discharge state constraint in the optimal scheduling model of the smart energy station is specifically as follows:

[0100]

[0101] The product of the decision variables and is contained in the formula, which leads to the nonlinearity of the problem. Therefore, the binary indicator variable is introduced, so that the charge-discharge state constraint originally containing the nonlinear term can be rewritten as the following linear expression:

[0102]

[0103] The method provided by the embodiment of the application is used for the optimal scheduling of the smart energy station considering renewable energy consumption. The optimal scheduling model of the smart energy station is constructed by considering the operation cost and green certificate-carbon trading cost. Then, the operation of the smart energy station is divided into day-ahead optimal scheduling, rolling optimal scheduling and real-time control three scales, and specific control algorithms are respectively formulated. Then, according to the equipment characteristics of the smart energy station, the equipment in the station is divided into three categories according to the start-stop flexibility, and is involved in the control of different time scales with different margins. Finally, the operation state of the smart energy station is monitored in the operation day, and the optimal scheduling model is solved rolling to obtain the optimal scheduling plan of the smart energy station. The embodiment of the application can overcome the problems that the renewable energy consumption and carbon emission reduction cannot be fully stimulated, and the renewable energy output uncertainty and the flexibility of the comprehensive energy system are not fully considered.

[0104] Please refer to Figure 2 , based on the multi-time scale smart energy station collaborative operation control method disclosed in the above embodiment, the embodiment corresponds to disclose a multi-time scale smart energy station collaborative operation control device, which is used for the optimal scheduling of the smart energy station considering renewable energy consumption. The device comprises:

[0105] The first construction unit 201 is used for considering the operation cost and green certificate-carbon trading cost of the smart energy station, combining the equipment configuration of the smart energy station, and establishing the optimal scheduling model of the smart energy station.

[0106] The second construction unit 202 is used for constructing the optimal scheduling model and specific control algorithm of the smart energy station under the day-ahead optimal scheduling, rolling optimal scheduling and real-time control three scales.

[0107] The third construction unit 203 is configured to divide the devices in the smart energy station into three categories according to the device characteristics of the smart energy station, and participate in the control of different time scales with different margins.

[0108] The solving unit 204 is configured to continuously monitor the operation state of the smart energy station in a running day, and solve the optimal scheduling model to obtain the optimal scheduling plan of the smart energy station.

[0109] Further, the first construction unit 201 is specifically configured to:

[0110] According to the device composition of the smart energy station, the optimal scheduling model of the smart energy station is established, including independent device unit modeling and coupled device unit modeling, establishing the device output constraint, energy storage state constraint, energy conversion constraint, and energy balance constraint in the optimal scheduling model, and establishing the objective function in the optimal scheduling model including external energy cost, operation and maintenance cost, green certificate transaction cost, and carbon transaction cost.

[0111] Further, the second construction unit 202 is specifically configured to:

[0112] The operation of the smart energy station is divided into three stages of day-ahead optimal scheduling, rolling optimal scheduling, and real-time control, wherein the day-ahead optimal scheduling establishes an optimal scheduling model according to the day-ahead prediction of renewable energy output and user energy load; the rolling optimal scheduling establishes an optimal scheduling model according to the rolling updated renewable energy output and user energy load prediction in a day; and the real-time control formulates a real-time control strategy according to different sudden events.

[0113] Further, the third construction unit 203 is specifically configured to:

[0114] According to the device characteristics of the smart energy station, the devices in the station are divided into three categories according to the scheduling level according to the start-stop flexibility, wherein the A-type devices are the devices that bear the base load in the station and the devices that are not suitable for frequent start-stop or power adjustment, such devices participate in the day-ahead optimal scheduling with a lower margin, do not participate in the rolling optimal scheduling and real-time control; the B-type devices include some devices with strong flexibility, which can update the output plan in a running day, such devices participate in the day-ahead optimal scheduling with a higher margin, participate in the rolling optimal scheduling with a lower margin, and do not participate in the real-time control; the C-type devices are the most flexible devices for responding to sudden situations, such devices participate in the day-ahead optimal scheduling and rolling optimal scheduling with a higher margin, and participate in the real-time control with a lower margin.

[0115] Further, the solving unit 204 is specifically configured to:

[0116] In one operation day, the optimization scheduling model is solved according to the day-ahead prediction of renewable energy output and user load to obtain a day-ahead optimization scheduling plan at an hourly level;

[0117] In one operation day, the operation state of the smart energy station is continuously monitored, the prediction of renewable energy output and user load is continuously updated, and the optimization scheduling model is continuously solved to obtain a rolling optimization scheduling plan at a minute level;

[0118] In real-time operation, the real-time control strategy is executed according to the emergency scenario to obtain a minute-level real-time control scheduling plan;

[0119] In the process of solving the optimization model, the energy optimization model is linearized, the decision variable product in the original model is converted into a linear expression of multiple equality or inequality constraints, so that the original nonlinear mixed integer programming problem is converted into a linear mixed integer programming problem, and the algorithm of linear programming problem is used for global optimal solution, so as to realize the optimal control of the smart energy station.

[0120] The multi-time-scale smart energy station collaborative operation control device includes a processor and a memory, and the first processing unit, the second processing unit, the third processing unit and the like are stored in the memory as program units. The corresponding functions are realized by the processor executing the program units stored in the memory.

[0121] The processor includes a core, and the core retrieves the corresponding program unit from the memory. The core can be set to one or more, and the problem that the existing other comprehensive energy system optimization scheduling model cannot fully stimulate renewable energy consumption and carbon emission reduction, and the problem that the renewable energy output uncertainty and the comprehensive energy system flexibility are not fully considered can be solved by adjusting the core parameters.

[0122] The embodiment of the application provides a storage medium, which stores a program, and the program is executed by a processor to realize the multi-time-scale smart energy station collaborative operation control method.

[0123] The embodiment of the application provides a processor, which is used for running a program, and the program is executed to perform the multi-time-scale smart energy station collaborative operation control method.

[0124] The embodiment of the application provides an electronic device, such as Figure 3As shown, the electronic device 30 comprises at least one processor 301, at least one memory 302 connected with the processor, and a bus 303; wherein the processor 301, the memory 302 complete mutual communication through the bus 303; the processor 301 is used to call the program instruction in the memory 302, to execute the above-mentioned multi-time scale intelligent energy station coordinated operation control method.

[0125] The electronic device herein can be a server, a PC, a PAD, a mobile phone, etc.

[0126] The application also provides a computer program product, which, when executed on a data processing device, is adapted to execute a program of the following method steps:

[0127] Considering the operation cost and green certificate-carbon trading cost of the intelligent energy station, an optimal scheduling model of the intelligent energy station is established according to the equipment composition of the intelligent energy station;

[0128] The optimal scheduling model and specific control algorithm of the intelligent energy station under the three scales of day-ahead optimal scheduling, rolling optimal scheduling and real-time control are constructed;

[0129] According to the equipment characteristics of the intelligent energy station, the equipment in the intelligent energy station is divided into three categories according to the start-stop flexibility, and participates in the control of different time scales with different margins;

[0130] The operation state of the intelligent energy station is continuously monitored within a running day, and the optimal scheduling model is solved rolling to obtain the optimal scheduling plan of the intelligent energy station.

[0131] Further, the consideration of the operation cost and green certificate-carbon trading cost of the intelligent energy station, and the establishment of the optimal scheduling model of the intelligent energy station according to the equipment composition of the intelligent energy station, are specifically:

[0132] According to the equipment composition of the intelligent energy station, the optimal scheduling model of the intelligent energy station is established, which contains independent device unit modeling and coupled device unit modeling, establishes the equipment output constraint, energy storage state constraint, energy conversion constraint and energy balance constraint in the optimal scheduling model, and establishes the objective function in the optimal scheduling model including external energy cost, operation and maintenance cost, green certificate trading cost and carbon trading cost.

[0133] Further, the construction of the optimal scheduling model and specific control algorithm of the intelligent energy station under the three scales of day-ahead optimal scheduling, rolling optimal scheduling and real-time control is specifically:

[0134] The operation of the smart energy station is divided into three stages of day-ahead optimal scheduling, rolling optimal scheduling and real-time control; wherein the day-ahead optimal scheduling establishes an optimal scheduling model according to the day-ahead prediction of renewable energy output and user energy load; the rolling optimal scheduling establishes an optimal scheduling model according to the rolling updated renewable energy output and user energy load within a day; the real-time control formulates a real-time control strategy according to different emergency events.

[0135] Further, according to the device characteristics of the smart energy station, the devices in the station are divided into three categories according to start-stop flexibility to participate in control of different time scales with different margins, specifically:

[0136] According to the device characteristics of the smart energy station, the devices in the station are divided into three categories according to start-stop flexibility according to the scheduling level, wherein the A-class devices are the devices that bear the base load in the station and the devices that are not suitable for frequent start-stop or power adjustment, such devices participate in day-ahead optimal scheduling with lower margin, do not participate in rolling optimal scheduling and real-time control; the B-class devices include some devices with stronger flexibility, which can update the output plan within the running day, such devices participate in day-ahead optimal scheduling with higher margin, participate in rolling optimal scheduling with lower margin, and do not participate in real-time control; the C-class devices are the most flexible devices for responding to emergency situations, such devices participate in day-ahead optimal scheduling and rolling optimal scheduling with higher margin, and participate in real-time control with lower margin.

[0137] Further, the running state of the smart energy station is continuously monitored within a running day, and the optimal scheduling model is solved rolling to obtain the optimal scheduling plan of the smart energy station, specifically:

[0138] Before a running day, the optimal scheduling model is solved according to the day-ahead prediction of renewable energy output and user load to obtain a day-ahead optimal scheduling plan at the hour level;

[0139] Within a running day, the running state of the smart energy station is continuously monitored, the prediction of renewable energy output and user load is updated rolling, and the optimal scheduling model is solved rolling to obtain a rolling optimal scheduling plan at the minute level;

[0140] In real-time operation, the real-time control strategy is executed according to the emergency event scenario to obtain a real-time control scheduling plan at the minute level;

[0141] In the process of solving the optimal model, the energy optimization model is linearized, the product of the decision variables in the original model is converted into a linear expression of multiple equality or inequality constraints, so that the original nonlinear mixed integer programming problem is converted into a linear mixed integer programming problem, and the algorithm of linear programming problem is used for global optimal solution to realize the optimal control of the smart energy station.

[0142] The computer program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other processing devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other processing devices to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions specified in the flowchart block or blocks. Figure 1 The flowchart and / or block diagram in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments. In this regard, each flowchart block and / or block in the Figures can represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical functions. It should also be noted that, in some alternative implementations, the flowchart blocks and / or blocks in the Figures can represent a Figure 1 The apparatus can be a special purpose or general-purpose computer including various computer hardware, that can be programmed to implement the functions specified in the flowchart block or blocks or in combination. In this regard, each block in the flowchart of FIGS. 1-4 can represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical functions.

[0143] In a typical configuration, the apparatus includes one or more processors (CPUs), memory, and a bus. The apparatus can also include input / output interfaces, network interfaces, and the like.

[0144] The memory can include non-persistent memory in the computer-readable medium, random access memory (RAM), and / or non-volatile memory, such as read-only memory (ROM) or flash memory, among others. The memory includes at least one memory chip. The memory is an example of computer-readable media.

[0145] Computer-readable media includes permanent and non-permanent, removable and non-removable media implemented in any method or technology for storage of information such as computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD), or other optical storage, magnetic cassettes, magnetic tapes, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information accessible to computing devices. According to the definition herein, computer-readable media does not include transitory media, such as modulated data signals and carrier waves.

[0146] The detailed description set forth below in connection with the appended drawings describes exemplary embodiments and does not represent all exemplary embodiments that can be implemented in accordance with the application. The description below sets forth numerous specific details to provide a thorough understanding of the exemplary embodiments. The description below

[0147] The terminology used in the description herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in this description and the appended claims, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will also be understood that the term "and / or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.

[0148] It is also to be noted that the term "comprising" or "comprises" when used in this specification is taken to mean the inclusion from the listed integral claimed steps or elements that it directly enumerates together with any additional optional steps or elements. However, the indefinite articles "an" and "a" are used herein in a generic sense, not as a definite article unless specifically so defined follow the first occurrence of "an" or "a" when used in this specification.

[0149] Those skilled in the art will appreciate that embodiments of the present application can be devised for a method, a system or a computer program product. Accordingly, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application can take the form of a computer program product on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage devices, etc.) embodying computer-readable program code.

[0150] The embodiments of the application described hereinabove are implemented as software, hardware, or a combination thereof, and any kind of computer program product. The software can be computer program code, routines or instructions written in any computer language. The hardware can be a circuit, an integrated circuit, a logic circuit, a microprocessor, a microcomputer, a programmable logic device, a memory device, a computer or the like. The hardware can be a circuit, an integrated circuit, a logic circuit, a microprocessor, a microcomputer, a programmable logic device, a memory device, a computer or the like.

Claims

1. A method for collaborative operation control of a smart energy station across multiple time scales, characterized in that, The method for optimizing the scheduling of smart energy stations, taking into account the consumption of renewable energy, includes: Considering the operating costs and green certificate-carbon trading costs of the smart energy station, an optimized scheduling model for the smart energy station is established based on the equipment composition of the smart energy station. The operation of the smart energy station is divided into three stages: day-ahead optimization scheduling, rolling optimization scheduling, and real-time control. The day-ahead optimization scheduling establishes an optimization scheduling model based on the day-ahead forecast of renewable energy output and user energy load. The rolling optimization scheduling establishes an optimization scheduling model based on the daily updated renewable energy output and user energy load forecast. The real-time control formulates real-time control strategies based on different emergencies. Based on the equipment characteristics of the smart energy station, the equipment in the smart energy station is divided into three categories according to its start-stop flexibility, and participates in the control of different time scales with different margins; The operating status of the smart energy station is continuously monitored within a single operating day, and the optimal scheduling model is solved continuously to obtain the optimal scheduling plan for the smart energy station. Taking into account the operating costs and green certificate-carbon trading costs of the smart energy station, and based on the equipment composition of the smart energy station, an optimized scheduling model for the smart energy station is established, specifically as follows: Based on the equipment composition of the smart energy station, an optimized scheduling model for the smart energy station is established, including independent equipment unit modeling and coupled equipment unit modeling. Equipment output constraints, energy storage status constraints, energy conversion constraints, and energy balance constraints are established in the optimized scheduling model. Objective functions including external energy costs, operation and maintenance costs, green certificate trading costs, and carbon trading costs are established in the optimized scheduling model. The process involves continuously monitoring the operational status of the smart energy station within a single operating day, and continuously solving the optimization scheduling model to obtain the optimal scheduling plan for the smart energy station. Specifically: On a given operating day, the optimal scheduling model is solved based on the day-ahead forecasts of renewable energy output and user load to obtain an hourly day-ahead optimal scheduling plan. Within a single operating day, the operating status of the smart energy station is continuously monitored, the forecasts of renewable energy output and user load are updated on a rolling basis, and the optimization scheduling model is solved on a rolling basis to obtain a rolling optimization scheduling plan at the minute level. In real-time operation, the real-time control strategy is executed according to the emergency scenario to obtain a minute-level real-time control scheduling plan; In solving the optimization scheduling model, the optimization scheduling model is linearized, and the product of decision variables in the original model is transformed into a linear expression of multiple equality or inequality constraints. This transforms the original nonlinear mixed integer programming problem into a linear mixed integer programming problem, and the algorithm for linear programming problems is used to solve the global optimum, so as to achieve the optimal control of the smart energy station.

2. The method according to claim 1, characterized in that, Based on the equipment characteristics of the smart energy station, the equipment within the station is divided into three categories according to its start-stop flexibility, and participates in control at different time scales with different margins, specifically: Based on the characteristics of the equipment in the smart energy station, the equipment within the station is divided into three categories according to the scheduling level based on its start-stop flexibility. Category A equipment consists of equipment that bears the base load and equipment that is not suitable for frequent start-stop or power adjustment. This type of equipment participates in day-ahead optimized scheduling with a lower margin and does not participate in rolling optimized scheduling or real-time control. Category B equipment includes some highly flexible equipment that can update its output plan on a rolling basis within the operating day. This type of equipment participates in day-ahead optimized scheduling with a higher margin and in rolling optimized scheduling with a lower margin, and does not participate in real-time control. Category C equipment is the most flexible equipment used to cope with emergencies. This type of equipment participates in day-ahead optimized scheduling and rolling optimized scheduling with a higher margin and participates in real-time control with a lower margin.

3. A multi-timescale smart energy station collaborative operation control device, characterized in that, The device is used for optimized scheduling of smart energy stations that takes into account the consumption of renewable energy. The first construction unit is used to consider the operating cost and green certificate-carbon trading cost of the smart energy station, and to establish an optimized scheduling model for the smart energy station in combination with the equipment composition of the smart energy station. The second building unit is used to divide the operation of the smart energy station into three stages: day-ahead optimization scheduling, rolling optimization scheduling, and real-time control. The day-ahead optimization scheduling establishes an optimization scheduling model based on the day-ahead forecast of renewable energy output and user energy load. The rolling optimization scheduling establishes an optimization scheduling model based on the daily updated renewable energy output and user energy load forecast. The real-time control formulates real-time control strategies based on different emergencies. The third building unit is used to classify the equipment in the smart energy station into three categories based on the start-stop flexibility according to the equipment characteristics of the smart energy station, and to participate in the control of different time scales with different margins. The solution unit is used to continuously monitor the operating status of the smart energy station within a working day, and to solve the optimization scheduling model in a rolling manner to obtain the optimal scheduling plan of the smart energy station; The first construction unit is specifically used for: Based on the equipment composition of the smart energy station, an optimized scheduling model for the smart energy station is established, including independent equipment unit modeling and coupled equipment unit modeling. Equipment output constraints, energy storage status constraints, energy conversion constraints, and energy balance constraints are established in the optimized scheduling model. Objective functions including external energy costs, operation and maintenance costs, green certificate trading costs, and carbon trading costs are established in the optimized scheduling model. The solution unit is specifically used for: On a given operating day, the optimal scheduling model is solved based on the day-ahead forecasts of renewable energy output and user load to obtain an hourly day-ahead optimal scheduling plan. Within a single operating day, the operating status of the smart energy station is continuously monitored, the forecasts of renewable energy output and user load are updated on a rolling basis, and the optimization scheduling model is solved on a rolling basis to obtain a rolling optimization scheduling plan at the minute level. In real-time operation, the real-time control strategy is executed according to the emergency scenario to obtain a minute-level real-time control scheduling plan; In solving the optimization scheduling model, the optimization scheduling model is linearized, and the product of decision variables in the original model is transformed into a linear expression of multiple equality or inequality constraints. This transforms the original nonlinear mixed integer programming problem into a linear mixed integer programming problem, and the algorithm for linear programming problems is used to solve the global optimum, so as to achieve the optimal control of the smart energy station.

4. A storage medium, characterized in that, The storage medium includes a stored program, wherein, when the program is running, it controls the device where the storage medium is located to execute the multi-timescale smart energy station collaborative operation control method as described in any one of claims 1 to 2.

5. An electronic device, characterized in that, The electronic device includes at least one processor, and at least one memory and a bus connected to the processor; wherein the processor and the memory communicate with each other through the bus; the processor is used to call program instructions in the memory to execute the multi-timescale smart energy station collaborative operation control method as described in any one of claims 1 to 2.

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