Multi-power-supply short-term combined operation method and device considering deep peak regulation of thermal power generating unit

Abstract

The invention relates to the technical field of novel electric power system optimization operation, in particular to a multi-power-supply short-term combined operation method and device considering deep peak regulation of a thermal power generating unit, and the method comprises the steps: in a day-ahead scheduling operation stage, considering the deep peak regulation state of the thermal power generating unit, determining a day-ahead optimization target of a multi-power-supply combined system, and constructing a day-ahead optimization scheduling model according to the at least one decision variable and the constraint condition to obtain a day-ahead optimization result, and in the intra-day rolling optimization stage, performing rolling updating on the day-ahead optimization result by using the power supply output of the multi-power-supply power system based on a preset rolling optimization model to obtain a target scheduling optimization result, therefore, optimized operation of the multi-power-supply combined system is realized based on the comprehensive decision. According to the embodiment of the invention, the day-ahead scheduling result is dynamically corrected through intra-day rolling optimization, the accuracy and reliability of scheduling plan arrangement of the multi-power-supply combined system are improved, and coordinated optimization operation of the thermal power deep scheduling unit under a short-term time scale can be realized.

Description

Technical Field

[0001] The present application relates to the technical field of optimized operation of a novel power system, and in particular to a method and device for short-term joint operation of multiple power sources taking into account deep peak regulation of thermal power units. Background Art

[0002] With the deep transformation of energy structure and the rapid development of new power systems, the flexibility transformation of traditional thermal power units and the participation of thermal power in deep peak regulation of new power systems are increasingly being used in engineering practice.

[0003] In the related technologies, the day-ahead optimal dispatching of the power system involving flexible thermal power units is mainly based on the day-ahead load forecast and renewable energy power generation forecast results to formulate the unit combination and spare capacity allocation for the next 24 hours.

[0004] However, in the related technology, the existing optimization scheduling method does not conduct a comprehensive analysis of the peak-shaving and valley-filling effect of the energy storage power station and the response to the energy consumption requirements when determining the unit combination, resulting in reduced flexibility of the unit combination and difficulty in maximizing the efficiency of energy storage resource utilization. It also fails to fully consider the uncertainty of renewable energy forecasts and load forecasts in the power grid, resulting in insufficient or excessive configuration of backup capacity, reducing the stability and economy of the power grid. At the same time, there is a neglect of the effectiveness of the configuration of rotating reserve capacity and the effectiveness of the reserve capacity in the N-1 safety verification mode, which affects the safe operation of the power grid and needs to be solved urgently. Summary of the invention

[0005] The present application provides a method and device for short-term joint operation of multiple power sources taking into account deep peak regulation of thermal power units, in order to solve the problem in the related art that the existing optimization scheduling method does not conduct a comprehensive analysis of the peak-shaving and valley-filling effect of the energy storage power station and the response to the energy consumption requirements when determining the unit combination, resulting in reduced flexibility of the unit combination, making it difficult to maximize the efficiency of energy storage resource utilization, and failing to fully consider the uncertainty of renewable energy forecasts and load forecasts in the power grid, resulting in insufficient or excessive configuration of spare capacity, reducing the stability and economy of the power grid, and at the same time ignoring the effectiveness of the configuration of the rotating spare capacity and the effectiveness of the spare capacity in the N-1 safety verification mode, affecting the safe operation of the power grid, and other problems.

[0006] The first aspect of the present application provides a method for short-term joint operation of multiple power sources taking into account deep peak regulation of thermal power units, comprising the following steps: in the day-ahead scheduling operation stage, considering the deep peak regulation state of the thermal power units, determining the day-ahead optimization target of the multi-power joint system; constructing a day-ahead optimization scheduling model based on at least one decision variable, constraint condition and the day-ahead optimization target of the multi-power power system, and generating the day-ahead optimization result corresponding to the day-ahead scheduling operation stage by the day-ahead optimization scheduling model; in the intra-day rolling optimization stage, based on a preset rolling optimization model, rollingly updating the day-ahead optimization result using the power output of the multi-power power system to obtain a target scheduling optimization result, so as to control the multi-power power system to perform joint operation according to the target scheduling optimization result in the intra-day rolling optimization stage.

[0007] Optionally, in one embodiment of the present application, in the intraday rolling optimization stage, based on a preset rolling optimization model, the power output of the multi-power power system is used to roll over the day-ahead optimization result, including: obtaining the current power output of the multi-power power system in the current forecast control cycle of the intraday rolling optimization stage; solving the preset rolling optimization model based on the current power output and the historical operation scheduling optimization results of the previous forecast control cycle to obtain a target control instruction sequence for the current forecast control cycle; updating the historical operation scheduling optimization results based on the target control instruction sequence to obtain a target scheduling optimization result for the current forecast control cycle.

[0008] Optionally, in one embodiment of the present application, the constraints include: unit output constraints of thermal power units in the conventional peak-shaving stage; unit climbing constraints, minimum operating time constraints and shortest downtime constraints of thermal power units in the deep peak-shaving stage; output constraints and wind power abandonment constraints of wind power units; photovoltaic power abandonment constraints; power balance constraints, effective reserve capacity constraints, network static safety constraints and network N-1 safety constraints of the multi-power power system.

[0009] Optionally, in one embodiment of the present application, the day-ahead optimization objective is:

[0010]

[0011] Wherein, f is the output of the multi-power system, f 1 is the operating cost of thermal power units in the conventional peak load regulation stage, f 2 is the start-up and shutdown cost of thermal power units in the conventional peak load regulation stage, f l is the loss cost of thermal power units in the deep peak regulation stage without oil injection, f t is the oil cost of thermal power units in the oil-intensive peak-shaving stage, f curtis the new energy consumption capacity of the multi-power system, P i,min is the minimum technical output of thermal power units, P i,max is the maximum output of the thermal power unit, P b is the unit fuel injection depth peak load value, P a It is the deep peak-shaving stable combustion load value of the unit without adding oil.

[0012] The second aspect of the present application provides a short-term joint operation device for multiple power sources taking into account deep peak-shaving of thermal power units, including: a confirmation module, which is used to determine the day-ahead optimization target of the multiple power source joint system in the day-ahead scheduling operation stage, taking into account the deep peak-shaving state of the thermal power units; a generation module, which is used to construct a day-ahead optimization scheduling model based on at least one decision variable, constraint condition and the day-ahead optimization target of the multiple power source power system, and generate the day-ahead optimization result corresponding to the day-ahead scheduling operation stage from the day-ahead optimization scheduling model; an operation module, which is used to roll-up the day-ahead optimization result based on a preset rolling optimization model and using the power output of the multiple power source power system in the day-ahead rolling optimization stage to obtain a target scheduling optimization result, so as to control the multiple power source power system to perform joint operation according to the target scheduling optimization result in the day-ahead rolling optimization stage.

[0013] Optionally, in one embodiment of the present application, the operation module includes: an acquisition unit, used to obtain the current power output of the multi-power supply power system in the current forecast control cycle of the intraday rolling optimization stage; a solution unit, used to solve the preset rolling optimization model based on the current power output and the historical operation scheduling optimization results of the previous forecast control cycle to obtain the target control instruction sequence of the current forecast control cycle; an update unit, used to update the historical operation scheduling optimization results based on the target control instruction sequence to obtain the target scheduling optimization result of the current forecast control cycle.

[0014] Optionally, in one embodiment of the present application, the constraints include: unit output constraints of thermal power units in the conventional peak-shaving stage; unit climbing constraints, minimum operating time constraints and shortest downtime constraints of thermal power units in the deep peak-shaving stage; output constraints and wind power abandonment constraints of wind power units; photovoltaic power abandonment constraints; power balance constraints, effective reserve capacity constraints, network static safety constraints and network N-5 safety constraints of the multi-power power system.

[0015] Optionally, in one embodiment of the present application, the day-ahead optimization objective is:

[0016]

[0017] Wherein, f is the output of the multi-power system, f 1is the operating cost of thermal power units in the conventional peak load regulation stage, f 2 is the start-up and shutdown cost of thermal power units in the conventional peak load regulation stage, f l is the loss cost of thermal power units in the deep peak regulation stage without oil injection, f t is the oil cost of thermal power units in the oil-intensive peak-shaving stage, f curt is the new energy consumption capacity of the multi-power system, P i,min is the minimum technical output of thermal power units, P i,max is the maximum output of the thermal power unit, P b is the unit fuel injection depth peak load value, P a It is the deep peak-shaving stable combustion load value of the unit without adding oil.

[0018] The third aspect of the present application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method for short-term joint operation of multiple power sources taking into account deep peak regulation of thermal power units as described in the above embodiment.

[0019] A fourth aspect of the present application provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the above-mentioned method for short-term joint operation of multiple power sources taking into account deep peak regulation of thermal power units.

[0020] A fifth aspect of the present application provides a computer program, which, when executed, implements the above-mentioned method for short-term joint operation of multiple power sources taking into account deep peak regulation of thermal power units.

[0021] The embodiment of the present application can dynamically correct the day-ahead dispatch result through intra-day rolling optimization, improve the accuracy and reliability of the dispatch plan arrangement of the multi-power joint system, and realize the coordinated optimization operation of the thermal power deep adjustment units in the short-term time scale. Thus, it solves the problem in the related technology that the existing optimization dispatch method does not conduct a comprehensive analysis of the peak-shaving and valley-filling effect of the energy storage power station and the response to the energy consumption requirements when determining the unit combination, resulting in reduced flexibility of the unit combination, making it difficult to maximize the efficiency of energy storage resources, and failing to fully consider the uncertainty of renewable energy forecasts and load forecasts in the power grid, resulting in insufficient or excessive configuration of spare capacity, reducing the stability and economy of the power grid, and at the same time, there is a neglect of the effectiveness of the configuration of the rotating reserve capacity and the effectiveness of the spare capacity in the N-1 safety verification mode, affecting the safe operation of the power grid.

[0022] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0024] Figure 1 A flowchart of a method for short-term joint operation of multiple power sources taking into account deep peak regulation of thermal power units provided according to an embodiment of the present application;

[0025] Figure 2 A schematic diagram of an intraday rolling optimization process of a model predictive control algorithm according to an embodiment of the present application;

[0026] Figure 3 It is a structural schematic diagram of a multi-power short-term joint operation device considering deep peak regulation of thermal power units according to an embodiment of the present application;

[0027] Figure 4 Schematic diagram of the structure of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0028] Embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0029] The following describes the method and device for short-term joint operation of multiple power sources considering deep peak regulation of thermal power units in the embodiment of the present application with reference to the accompanying drawings. In view of the related technologies mentioned in the above background technology, the existing optimization scheduling method does not conduct a comprehensive analysis of the peak-shaving and valley-filling effect of the energy storage power station and the response to the energy consumption requirements when determining the unit combination, resulting in reduced flexibility of the unit combination, making it difficult to maximize the efficiency of energy storage resource utilization, and failing to fully consider the uncertainty of renewable energy forecasts and load forecasts in the power grid, resulting in insufficient or excessive configuration of spare capacity, reducing the stability and economy of the power grid, and at the same time, there is a problem of ignoring the effectiveness of the configuration of rotating spare capacity and the effectiveness of spare capacity in the N-1 safety verification mode, which affects the safe operation of the power grid. The present application provides a method for short-term joint operation of multiple power sources considering deep peak regulation of thermal power units, which dynamically corrects the day-ahead scheduling results through intra-day rolling optimization, improves the accuracy and reliability of the scheduling plan arrangement of the multi-power joint system, and can realize the coordinated optimization operation of thermal power deep regulation units on a short-term time scale. Thus, the problem in the related technology that the existing optimization scheduling method does not conduct a comprehensive analysis of the peak-shaving and valley-filling effect of the energy storage power station and the response to the energy consumption requirements when determining the unit combination, resulting in reduced flexibility of the unit combination and difficulty in maximizing the efficiency of energy storage resource utilization. It also fails to fully consider the uncertainty of renewable energy forecasts and load forecasts in the power grid, resulting in insufficient or excessive configuration of backup capacity, reducing the stability and economy of the power grid. At the same time, there is a neglect of the effectiveness of the configuration of the rotating reserve capacity and the effectiveness of the reserve capacity in the N-1 safety verification mode, affecting the safe operation of the power grid.

[0030] Specifically, Figure 1 A flow chart of a method for short-term joint operation of multiple power sources taking into account deep peak regulation of thermal power units provided in an embodiment of the present application.

[0031] like Figure 1 As shown, the multi-power short-term joint operation method considering deep peak regulation of thermal power units includes the following steps:

[0032] In step S101, during the day-ahead dispatching operation phase, the day-ahead optimization target of the multi-power source combined system is determined by taking into account the deep peak load regulation state of the thermal power units.

[0033] It is understandable that in the embodiment of the present application, the day-ahead optimization target in the day-ahead scheduling operation phase of the multi-power combined power generation system can be formulated from three aspects: the economy, safety, and renewable energy consumption of the multi-power power system. In addition, the penalty variables generated by the linearization processing of the constraints in the prediction model need to be included in the day-ahead optimization target.

[0034] Optionally, in one embodiment of the present application, the day-ahead optimization objective is:

[0035]

[0036] Where f is the output of the multi-power system, f 1 is the operating cost of thermal power units in the conventional peak load regulation stage, f 2 is the start-up and shutdown cost of thermal power units in the conventional peak load regulation stage, f l is the loss cost of thermal power units in the deep peak regulation stage without oil injection, f t is the oil cost of thermal power units in the oil-intensive peak-shaving stage, f curt is the new energy consumption capacity of the multi-power system, P i,min is the minimum technical output of thermal power units, P i,max is the maximum output of the thermal power unit, P b is the unit fuel injection depth peak load value, P a It is the deep peak-shaving stable combustion load value of the unit without adding oil.

[0037] In the actual implementation process, P i,min <P≤P i,max It can represent the conventional peak load regulation stage of thermal power units, P a <P≤P i,min It can represent the deep peak regulation stage of thermal power units without oil injection, P b <P≤P a When the thermal power units are deeply peak-shaving and the output is reduced to a certain extent, oil injection measures can be taken during the deep peak-shaving stage to maintain the normal operation of the unit boiler and water circulation through oil injection.

[0038] Specifically, the day-ahead optimization target can be formulated from the aspects of the economy, safety and renewable energy consumption of the multi-power system. Among them, the economy of the operation of the multi-power system mainly includes the operating costs and start-up and shutdown costs of conventional thermal power units, which can be specifically expressed as:

[0039]

[0040] In the formula, Indicates the start / stop status of the unit, which is a 0-1 variable; P i t is the output of conventional thermal power unit i in period t; N G is the number of conventional units; N T is the number of time periods; F i t (P i t ) is the time period t, the conventional thermal power unit i is P i t The cost of continuous output for a period of time is piecewise linearized into the form shown in equations (1) and (2), F i,lis the cost coefficient of conventional thermal power unit i in the lth segment, NL is the number of segments, is the output of the lth segment of conventional thermal power unit i in time period t. The start-up and shutdown costs of conventional thermal power units are:

[0041]

[0042] The startup cost of a conventional unit varies with the continuous downtime before startup, and can generally be expressed as an exponential function of the downtime. The specific expression is:

[0043]

[0044] In the formula, S 0i , S 1i , τ i are the startup cost coefficients of conventional thermal power units, The continuous downtime of unit i at time t. Formula (6) contains a large number of nonlinear links. It is linearized by linearization method and approximately expressed as a step function, which represents the startup cost of conventional thermal power unit i in time period t in intervals. Specifically, it is:

[0045]

[0046] Where J is the number of intervals divided by the nonlinear function, ND j is the longest downtime value corresponding to interval j, ND 0 =0, K j is the corresponding startup cost when the continuous downtime falls within interval j. The more intervals the startup cost function is divided into and the fewer time periods in each interval, the closer the linearized step function is to the original exponential function.

[0047] The shutdown cost of conventional thermal power units is usually set to a constant value, as shown in formula (7):

[0048]

[0049] In the formula, is the shutdown cost constant of conventional thermal power unit i. After linearization, the shutdown cost of conventional thermal power unit is shown in formula (8):

[0050]

[0051] When the thermal power unit is in the deep peak regulation stage without oil injection, the rotor parts are subjected to alternating stress, resulting in fatigue loss and creep loss of the unit, which shortens the service life of the unit and increases additional operating costs. The commonly used method for calculating the loss cost of thermal power units is to roughly calculate it based on the Manson-Coffin formula:

[0052]

[0053] In the formula, λ is the operation influence coefficient of the thermal power unit; Cunit is the purchase cost of the thermal power unit; Nf(P) is the number of rotor cracking cycles, which can be determined by the rotor low-cycle fatigue curve.

[0054] Oil injection measures should be taken to maintain the normal operation of the unit boiler and water circulation. At this time, the oil injection cost is:

[0055] f t =Q oil S oil , (10)

[0056] In the formula, Q oil is the fuel consumption of the unit during stable combustion; S oil Oil price for the season.

[0057] The economical new energy absorption capacity of the multi-power system is represented by the sum of the abandoned wind power and abandoned photovoltaic power in the dispatching period, which is expressed as:

[0058]

[0059] in, is the wind power abandoned by wind farm w in period t, N W is the total number of wind farms, is the abandoned light power of photovoltaic power station s in time period t, N s is the total number of photovoltaic power plants, and Δt is the duration of each period. 1 、M 2 The penalty coefficients for abandoning wind and solar power are corresponding respectively. The more wind and solar power are abandoned, the weaker the absorption capacity of renewable energy will be.

[0060] The comprehensiveness of the operation of a multi-power system is reflected in the differentiation of the day-ahead optimization targets of thermal power units at different peak load stages, as follows:

[0061]

[0062] Among them, P i,min is the minimum technical output of thermal power units, P i,max is the maximum output of the thermal power unit, P b is the unit fuel injection depth peak load value, P a It is the deep peak-shaving stable combustion load value of the unit without adding oil.

[0063] In step S102, a day-ahead optimization scheduling model is constructed based on at least one decision variable, constraint conditions and day-ahead optimization objectives of the multi-power supply power system, and the day-ahead optimization scheduling model generates a day-ahead optimization result corresponding to the day-ahead scheduling operation phase.

[0064] It can be understood that in the embodiments of the present application, the decision variables of the multi-power supply power system may include but are not limited to: the start and stop state variables, output plan and effective rotating reserve capacity of the thermal power units in each time period, the output plan of the wind farm in each time period, the output plan of the photovoltaic power station in each time period, the operating condition state variables of the energy storage power station in each time period, the output plan and effective rotating reserve capacity.

[0065] Optionally, in one embodiment of the present application, the constraints include: unit output constraints of thermal power units in the conventional peak-shaving stage; unit climbing constraints, minimum operating time constraints and shortest downtime constraints of thermal power units in the deep peak-shaving stage; output constraints and wind power abandonment constraints of wind power units; photovoltaic power abandonment constraints; power balance constraints, effective reserve capacity constraints, network static safety constraints and network N-1 safety constraints of multi-power power systems.

[0066] In the actual implementation process, the constraints can be divided into generator set operation constraints and power system operation constraints. Among them, the generator set operation constraints include the operation constraints of thermal power units, wind power units and photovoltaic units. For thermal power units, their operation constraints include: unit output constraint formula (13), unit ramp constraint formula (14), shortest operation time constraint formula (15) and shortest downtime constraint formula (16). Specifically, for conventional peak load units:

[0067]

[0068] For deep peak load units:

[0069]

[0070] In the formula, is the start and stop state variable of the unit, is the start / stop status of unit i before the start of the scheduling cycle, P i min is the minimum output of the unit, P i max is the maximum output of the unit, The effective positive spinning reserve capacity provided for the unit, The effective negative spinning reserve capacity provided to the unit, is the piecewise linear output of the unit, r i uis the maximum upward climbing rate of the unit, r i d is the maximum downward climbing rate of the unit, T i on is the shortest running time of the unit, T i off is the shortest downtime of the unit, T i on,0 is the continuous operation time of the unit before the start of the scheduling cycle, T i off,0 is the continuous downtime of the unit before the start of the scheduling cycle, N T is the number of time periods in the scheduling cycle.

[0071] The operating constraints of wind power include: the output constraints of each wind farm and the wind power abandonment constraints. Since the conversion process from wind speed to wind power is not considered, the wind power ramp rate constraints are not considered. Specifically:

[0072]

[0073] In the formula, is the output plan of wind farm k in its period t, is the wind power abandonment plan of wind farm k in period t, The day-ahead wind power forecast for wind farm k in its time period t.

[0074] Similar to wind turbine operation constraints, photovoltaic constraints include abandoned power constraints, etc., as follows:

[0075]

[0076] In the formula, is the output plan of photovoltaic power station k in its time period t, is the wind power abandonment plan of photovoltaic power station k in its period t, The day-ahead PV power forecast for PV plant k in its time period t.

[0077] The power system operation constraints include power balance constraints, effective reserve capacity constraints, network static security constraints, and network N-1 security constraints. For the power balance constraint, the network loss is set to a fixed percentage of the total load r loss , the power balance constraint of the system is expressed as:

[0078]

[0079] In the formula, is the load demand of load node i in period t; N load is the total number of load nodes.

[0080] For the effective reserve capacity constraint, for safety reasons, during the day-ahead optimization scheduling phase, due to the inaccuracy of wind power forecast, photovoltaic power forecast and load forecast, the system itself needs to reserve a certain amount of reserve capacity at a certain confidence level. The effective reserve capacity constraint of the system is as follows:

[0081]

[0082] In the formula, ASR up and ASR down In order to achieve a certain confidence level, the positive and negative spinning reserve capacity required by the system is considered after considering the uncertainties of wind power forecast, photovoltaic power forecast and load forecast.

[0083] For the network static security constraints, the cross-sectional power flow calculation method based on the DC power flow power transfer distribution factor is used, and the spinning reserve capacity of the generator set is included in the static security constraints of the power grid to ensure the effectiveness of the reserve capacity. The network static security constraints are as follows:

[0084]

[0085] Where Gg is the generator output power transfer distribution factor matrix of the generator group to the line, Gl is the load demand power transfer distribution factor matrix of the load to the line; R up is the column vector of the effective positive spinning reserve capacity provided by the generator sets, R down The column vector of the effective negative spinning reserve provided by the generator sets; w load is the column vector of the load demand proportion of the system load node; R up,all is the total effective positive spinning reserve provided by the system, R down,all The total effective negative spinning reserve provided to the system is; P l,max is the maximum transmission power of line l.

[0086] For the network N-1 security constraint, while considering the grid N-1 security constraint, the effective reserve capacity of the grid in the N-1 state is included in the consideration range of the N-1 state, so that the system still has a certain effective rotating reserve capacity in the N-1 state, leaving a certain margin for the unexpected fluctuation of net load caused by uncertain factors in the grid (such as: uncertainty in wind power forecast and load forecast). The network N-1 security constraint is as follows:

[0087]

[0088] In the formula, is the generator output power transfer distribution factor matrix after N-1 faults occur in line c, is the load demand power transfer distribution factor matrix after N-1 faults occur in line c, and is the effective positive spinning reserve capacity and negative spinning reserve capacity of the generator set after N-1 faults occur on line c, and is the total effective positive spinning reserve capacity and negative spinning reserve capacity of all generator sets after N-1 faults occur in line c. D is the disconnection transfer distribution factor matrix of the fault line set to the network line, and its element d l,c is the power transfer distribution factor for line l when line c has N-1 faults, D c is the cth column of the D matrix, Gg c is the cth row of Gg, Gl c is the cth row of Gl.

[0089] In step S103, in the intra-day rolling optimization stage, based on the preset rolling optimization model, the power output of the multi-power system is used to roll-up the optimization results of the previous day to obtain the target scheduling optimization results, so as to control the multi-power system to perform joint operation according to the target scheduling optimization results in the intra-day rolling optimization stage.

[0090] It should be noted that the preset rolling optimization model can be set by those skilled in the art according to actual conditions and is not specifically limited here.

[0091] It can be understood that in the embodiments of the present application, the preset rolling optimization model can adopt the model predictive control MPC (Model Predictive Control) algorithm structure, take the unit start and stop plan scheduled on the day before as the boundary condition, update the latest ultra-short-term prediction results of renewable energy, and use the actual operating status of the power supply as feedback. The daily output plan is gradually updated through an intra-day rolling method, thereby controlling the joint operation of the multi-power power system according to the target scheduling optimization results updated at each step.

[0092] Optionally, in one embodiment of the present application, in the intraday rolling optimization stage, based on a preset rolling optimization model, the power output of the multi-power power system is used to roll-up the day-ahead optimization results, including: in the current forecast control cycle of the intraday rolling optimization stage, obtaining the current power output of the multi-power power system; solving the preset rolling optimization model based on the current power output and the historical operation scheduling optimization results of the previous forecast control cycle to obtain a target control instruction sequence for the current forecast control cycle; updating the historical operation scheduling optimization results based on the target control instruction sequence to obtain a target scheduling optimization result for the current forecast control cycle.

[0093] In the actual execution process, the MPC strategy mainly includes: ① At the current time k and the current state x(k), based on the pre-set prediction model, predict the future state of the system at a certain time step, while considering the current and future constraints, and obtain the control instruction sequence at the future k+1, k+2,…, k+M moments by solving the optimization problem; ② Apply the first value of the control instruction sequence to the control system; ③ At time k+1, update the state to x(k+1), and repeat the above steps.

[0094] As can be seen from the above, the main function of the MPC algorithm is a continuous alternation process of rolling local optimization and rolling implementation control. Figure 2 As shown in the figure, it is a schematic diagram of the intraday rolling optimization process of the model predictive control algorithm. The scheduling time range is set to 4 hours and the accuracy is 15 minutes. In each predictive control cycle, only the actual scheduling instructions at the next 15 minutes point are updated. The intraday optimization is achieved through continuous rolling. Specifically:

[0095] Step S201: Start.

[0096] Among them, the intraday rolling optimization of the model predictive control algorithm was started.

[0097] Step S202: Input the latest renewable energy and load forecast results.

[0098] Among them, the historical operation scheduling optimization results of the previous predictive control cycle are input.

[0099] Step S203: Measure the current power output: P 0 (k).

[0100] Therein, the current power output of the multi-power power system is measured.

[0101] Step S204: Establish an active output prediction model, P(k+t|k)=P 0 (k)+Δu(k+t∣k),t=1,2,L,N T .

[0102] Among them, the prediction model in MPC is required to be able to predict the future response of the system based on the historical information and future input of the controlled object. Specifically for intraday optimization problems, the active output increment of each power source is used as the control vector, and the active output of each power source predicted at the future time k+t at time k is used as the output variable.

[0103] Step S205: Solve the optimization model.

[0104] Among them, the day-ahead optimization goal of intraday scheduling is to adjust the day-ahead scheduling plan based on the latest forecast information. In order to ensure the overall economy of the full-day scheduling results, there are restrictions on the adjustment range of the unit output in intraday scheduling. The difference between the output of the energy storage device at each moment and the day-ahead scheduling result must be controlled within a certain range of differences, while reducing wind and solar power abandonment.

[0105] Step S206: Update the active power output plan at time k+1.

[0106] Among them, based on the solution of the optimization model, the active power output plan at time k+1 is obtained, and the system output plan is updated.

[0107] Step S207: Determine whether the optimization cycle is completed.

[0108] If the optimization cycle is completed, the process proceeds to step S209 , otherwise, the process proceeds to step S208 .

[0109] Step S208: let k=k+1.

[0110] In which, let k = k + 1, and continue to roll the model predictive control algorithm within the day. Due to the limited prediction accuracy of wind power, photovoltaic and load, there is a deviation between the open-loop MPC control results and the actual unit output, so correction is required, and the current actual active output value of the system is used as the initial value of the new round of rolling optimization scheduling to form a closed-loop control, overcome the uncertainty of the system, and make the active output command obtained in the new round of optimization more accurate.

[0111] Step S209: End.

[0112] Among them, the intraday rolling optimization of the model predictive control algorithm is completed.

[0113] According to the method for short-term joint operation of multiple power sources taking into account deep peak regulation of thermal power units proposed in the embodiment of the present application, the day-ahead scheduling result can be dynamically corrected through intra-day rolling optimization, thereby improving the accuracy and reliability of the scheduling plan arrangement of the multiple power source joint system, and realizing the coordinated optimization operation of thermal power deep regulation units in a short-term time scale. Thus, the problem in the related art that the existing optimization scheduling method does not conduct a comprehensive analysis of the peak-shaving and valley-filling effect of the energy storage power station and the response to the energy consumption requirements when determining the unit combination is solved, resulting in reduced flexibility of the unit combination, making it difficult to maximize the efficiency of energy storage resources, and failing to fully consider the uncertainty of renewable energy forecasts and load forecasts in the power grid, resulting in insufficient or excessive configuration of spare capacity, reducing the stability and economy of the power grid, and at the same time, there is a neglect of the effectiveness of the configuration of the rotating reserve capacity and the effectiveness of the spare capacity in the N-1 safety verification mode, affecting the safe operation of the power grid.

[0114] Next, a short-term joint operation device of multiple power sources taking into account deep peak regulation of thermal power units proposed in accordance with an embodiment of the present application will be described with reference to the accompanying drawings.

[0115] Figure 3 It is a structural schematic diagram of a multi-power short-term joint operation device considering deep peak regulation of thermal power units in an embodiment of the present application.

[0116] like Figure 3 As shown, the short-term joint operation device 10 for multiple power sources taking into account deep peak regulation of thermal power units includes: a confirmation module 100, a generation module 200 and an operation module 300.

[0117] The confirmation module 100 is used to determine the day-ahead optimization target of the multi-power supply joint system by considering the deep peak-shaving state of the thermal power units during the day-ahead scheduling operation stage.

[0118] The generation module 200 is used to construct a day-ahead optimization scheduling model based on at least one decision variable, constraint conditions and day-ahead optimization objectives of the multi-power supply power system, and generate a day-ahead optimization result corresponding to the day-ahead scheduling operation stage from the day-ahead optimization scheduling model.

[0119] The operation module 300 is used to roll over the day-ahead optimization results based on a preset rolling optimization model and the power output of the multi-power system during the intra-day rolling optimization phase to obtain a target scheduling optimization result, so as to control the multi-power system to perform joint operation according to the target scheduling optimization result during the intra-day rolling optimization phase.

[0120] Optionally, in one embodiment of the present application, the operation module 300 includes: an updating unit, a solving unit and an acquiring unit.

[0121] Among them, the acquisition unit is used to obtain the current power output of the multi-power power system under the current prediction control cycle in the intraday rolling optimization stage.

[0122] The solving unit is used to solve the preset rolling optimization model based on the current power output and the historical operation scheduling optimization results of the previous predictive control cycle to obtain the target control instruction sequence of the current predictive control cycle.

[0123] The updating unit is used to update the historical operation scheduling optimization results based on the target control instruction sequence to obtain the target scheduling optimization results of the current predictive control cycle.

[0124] Optionally, in one embodiment of the present application, the constraints include: unit output constraints of thermal power units in the conventional peak-shaving stage; unit climbing constraints, minimum operating time constraints and shortest downtime constraints of thermal power units in the deep peak-shaving stage; output constraints and wind power abandonment constraints of wind power units; photovoltaic power abandonment constraints; power balance constraints, effective reserve capacity constraints, network static safety constraints and network N-5 safety constraints of multi-power power systems.

[0125] Optionally, in one embodiment of the present application, the day-ahead optimization objective is:

[0126]

[0127] Where f is the output of the multi-power system, f 1 is the operating cost of thermal power units in the conventional peak load regulation stage, f 2 is the start-up and shutdown cost of thermal power units in the conventional peak load regulation stage, f l is the loss cost of thermal power units in the deep peak regulation stage without oil injection, f t is the oil cost of thermal power units in the oil-intensive peak-shaving stage, f curt is the new energy consumption capacity of the multi-power system, P i,min is the minimum technical output of thermal power units, P i,max is the maximum output of the thermal power unit, P b is the unit fuel injection depth peak load value, P a It is the deep peak-shaving stable combustion load value of the unit without adding oil.

[0128] It should be noted that the aforementioned explanation of the embodiment of the method for short-term joint operation of multiple power sources considering deep peak regulation of thermal power units is also applicable to the device for short-term joint operation of multiple power sources considering deep peak regulation of thermal power units in this embodiment, and will not be repeated here.

[0129] According to the multi-power short-term joint operation device considering the deep peak regulation of thermal power units proposed in the embodiment of the present application, the day-ahead scheduling result can be dynamically corrected through intra-day rolling optimization, which improves the accuracy and reliability of the scheduling plan arrangement of the multi-power joint system, and can achieve coordinated optimization operation of thermal power deep regulation units in a short-term time scale. Thus, the problem in the related technology that the existing optimization scheduling method does not conduct a comprehensive analysis of the peak-shaving and valley-filling effect of the energy storage power station and the response to the energy consumption requirements when determining the unit combination is solved, resulting in reduced flexibility of the unit combination, making it difficult to maximize the efficiency of energy storage resources, and failing to fully consider the uncertainty of renewable energy forecasts and load forecasts in the power grid, resulting in insufficient or excessive configuration of spare capacity, reducing the stability and economy of the power grid, and at the same time, there is a neglect of the effectiveness of the configuration of the rotating reserve capacity and the effectiveness of the spare capacity in the N-1 safety verification mode, affecting the safe operation of the power grid.

[0130] Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. The electronic device may include:

[0131] Memory 401 , processor 402 , and a computer program stored in the memory 401 and executable on the processor 402 .

[0132] When the processor 402 executes the program, the short-term joint operation method of multiple power sources considering the deep peak regulation of thermal power units provided in the above embodiment is implemented.

[0133] Furthermore, the electronic device further comprises:

[0134] The communication interface 403 is used for communication between the memory 401 and the processor 402 .

[0135] The memory 401 is used to store computer programs that can be executed on the processor 402 .

[0136] The memory 401 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0137] If the memory 401, the processor 402 and the communication interface 403 are implemented independently, the communication interface 403, the memory 401 and the processor 402 can be connected to each other through a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 4 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0138] Optionally, in a specific implementation, if the memory 401, the processor 402 and the communication interface 403 are integrated on a chip, the memory 401, the processor 402 and the communication interface 403 can communicate with each other through an internal interface.

[0139] The processor 402 may be a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application.

[0140] This embodiment also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned method for short-term joint operation of multiple power sources taking into account deep peak regulation of thermal power units.

[0141] This embodiment also provides a computer program, which, when executed, implements the above-mentioned method for short-term joint operation of multiple power sources taking into account deep peak regulation of thermal power units.

[0142] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0143] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0144] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or N executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0145] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purpose of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or N wirings (electronic devices), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways as necessary and then storing it in a computer memory.

[0146] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiment, the N steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0147] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0148] In addition, each functional unit in each embodiment of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0149] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present application. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A method for short-term joint operation of multiple power sources considering deep peak regulation of thermal power units, characterized in that: The following steps are involved: In the day-ahead dispatching operation stage, the deep peak load regulation state of thermal power units is taken into consideration to determine the day-ahead optimization target of the multi-power combined system; Building a day-ahead optimization scheduling model based on at least one decision variable, constraint conditions and the day-ahead optimization target of the multi-power supply power system, and generating a day-ahead optimization result corresponding to the day-ahead scheduling operation phase by the day-ahead optimization scheduling model; During the intra-day rolling optimization stage, based on a preset rolling optimization model, the day-ahead optimization result is rolled over using the power output of the multi-power system to obtain a target scheduling optimization result, so as to control the multi-power system to perform joint operation according to the target scheduling optimization result during the intra-day rolling optimization stage.

2. The method according to claim 1, characterized in that In the intraday rolling optimization stage, based on a preset rolling optimization model, the power output of the multi-power power system is used to roll-update the day-ahead optimization result, including: Under the current forecast control cycle of the intraday rolling optimization phase, obtaining the current power output of the multi-power power system; Solving the preset rolling optimization model based on the current power output and the historical operation scheduling optimization results of the previous prediction control cycle to obtain the target control instruction sequence of the current prediction control cycle; The historical operation scheduling optimization result is updated based on the target control instruction sequence to obtain the target scheduling optimization result of the current predictive control cycle.

3. The method according to claim 1, characterized in that The constraints include: unit output constraints of thermal power units in the conventional peak-shaving stage; unit climbing constraints, minimum operating time constraints and shortest downtime constraints of thermal power units in the deep peak-shaving stage; output constraints and wind power abandonment constraints of wind power units; photovoltaic power abandonment constraints; power balance constraints, effective reserve capacity constraints, network static safety constraints and network N-1 safety constraints of the multi-power power system.

4. The method according to claim 1, characterized in that The day-ahead optimization objective is: Wherein, f is the output of the multi-power system, f1 is the operating cost of the thermal power unit in the conventional peak-shaving stage, f2 is the start-stop cost of the thermal power unit in the conventional peak-shaving stage, and f l is the loss cost of thermal power units in the deep peak regulation stage without oil injection, f t is the oil cost of thermal power units in the oil-intensive peak-shaving stage, f curt is the new energy consumption capacity of the multi-power system, P i,min The minimum technical output of thermal power units. P i,max is the maximum output of the thermal power unit, P b is the unit fuel injection depth peak load value, P a It is the deep peak-shaving stable combustion load value of the unit without adding oil.

5. A short-term joint operation device for multiple power sources considering deep peak regulation of thermal power units, characterized in that: include: The confirmation module is used to determine the day-ahead optimization target of the multi-power supply joint system by considering the deep peak-shaving state of the thermal power units during the day-ahead scheduling operation stage; A generation module, configured to construct a day-ahead optimization scheduling model based on at least one decision variable of the multi-power supply power system, a constraint condition and the day-ahead optimization target, and generate a day-ahead optimization result corresponding to the day-ahead scheduling operation phase from the day-ahead optimization scheduling model; An operation module is used to roll over the day-ahead optimization result based on a preset rolling optimization model and the power output of the multi-power system during the intra-day rolling optimization phase to obtain a target scheduling optimization result, so as to control the multi-power system to perform joint operation according to the target scheduling optimization result during the intra-day rolling optimization phase.

6. The device according to claim 5, characterized in that The operation module includes: An acquisition unit, configured to acquire a current power output of the multi-power power system in a current forecast control cycle of the intraday rolling optimization phase; A solving unit, configured to solve the preset rolling optimization model based on the current power output and the historical operation scheduling optimization result of the previous prediction control cycle, and obtain a target control instruction sequence of the current prediction control cycle; An updating unit is used to update the historical operation scheduling optimization result based on the target control instruction sequence to obtain the target scheduling optimization result of the current predictive control cycle.

7. The device according to claim 5, characterized in that The constraints include: unit output constraints of thermal power units in the conventional peak-shaving stage; unit climbing constraints, minimum operating time constraints and shortest downtime constraints of thermal power units in the deep peak-shaving stage; output constraints and wind power abandonment constraints of wind power units; photovoltaic power abandonment constraints; power balance constraints, effective reserve capacity constraints, network static safety constraints and network N-5 safety constraints of the multi-power power system.

8. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method for short-term joint operation of multiple power sources taking into account deep peak regulation of thermal power units as described in any one of claims 1 to 4.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: The program is executed by a processor to implement the method for short-term joint operation of multiple power sources taking into account deep peak regulation of thermal power units as described in any one of claims 1 to 4.

10. A computer program product, comprising a computer program, characterized in that The computer program is executed to implement the method for short-term joint operation of multiple power sources taking into account deep peak regulation of thermal power units as described in any one of claims 1 to 4.

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