Two-stage optimization operation method and device for wind, solar and hydropower generation system

Abstract

The present application discloses a two-stage optimization operation method and device for a wind, solar and hydroelectric power generation system, wherein the method includes: determining the joint optimization target of the wind, solar and hydroelectric complementary power generation system in the day-ahead and real-time two-stages; then determining the cascade hydropower optimization operation model according to the hydropower conversion relationship, hydropower output range, reservoir discharge flow restriction information, head constraint, reservoir capacity constraint, water balance constraint and / or the reservoir capacity constraint at the beginning and end of the scheduling period; based on the joint optimization target, determining the wind power and photovoltaic optimization operation model according to the prediction information; using the preset linearization strategy to convert the cascade hydropower optimization operation model and the wind power and photovoltaic optimization operation model into MILP problems respectively, and solving the MILP problem to obtain the optimal operation cost of the wind, solar and hydroelectric power generation system. Thus, the technical problem in the related technology that the existing power system scheduling model is difficult to adapt to the optimal scheduling of a multi-energy power system containing a high proportion of renewable energy due to the uncertainty of wind power and photovoltaic output is solved.

Description

Technical Field

[0001] The present application relates to the technical field of multi-energy complementary coordinated operation of power systems, and in particular to a two-stage optimized operation method and device for wind, solar and hydropower generation systems. Background Art

[0002] In recent years, with the rapid development of renewable energy power generation represented by wind power and photovoltaics, my country's power system has become increasingly complex and diversified. In a multi-energy power system with large-scale grid-connected renewable energy, the random fluctuations of wind and solar power increase the uncertainty of system operation, which brings serious problems to the safety and stability of power system operation. At the same time, due to the short-term uncertainty of factors such as wind power, photovoltaics, load, direct solar radiation and reservoir water inflow in the multi-energy power system, the day-ahead power generation plan cannot guide the actual operation of the multi-energy power system.

[0003] However, due to the uncertainty of wind power and photovoltaic output in related technologies, the existing power system dispatching model has been difficult to adapt to the optimal dispatching of multi-energy power systems containing a high proportion of renewable energy, and it is difficult to achieve the maximum utilization of renewable energy, which needs to be improved. Summary of the invention

[0004] The present application provides a two-stage optimization operation method and device for a wind, solar and hydropower generation system to solve the technical problem in the related technology that, due to the uncertainty of wind power and photovoltaic output, the existing power system scheduling model has become difficult to adapt to the optimization scheduling of a multi-energy power system containing a high proportion of renewable energy.

[0005] The first aspect of the present application provides a two-stage optimization operation method for a wind-solar-hydropower generation system, comprising the following steps: determining a joint optimization target for the day-ahead and real-time stages of the wind-solar-hydropower complementary power generation system; based on the joint optimization target, determining a cascade hydropower optimization operation model according to the hydropower conversion relationship, hydropower output range, reservoir discharge flow limit information, head constraints, reservoir capacity constraints, water balance constraints and / or reservoir capacity constraints at the beginning and end of the scheduling period; and based on the joint optimization target, determining a wind power and photovoltaic optimization operation model according to prediction information; using a preset linearization strategy to convert the cascade hydropower optimization operation model and the wind power and photovoltaic optimization operation models into MILP (Mixed Integer Linear Program) problems, respectively, and solving the MILP problem to obtain the optimal operation cost of the wind-solar-hydropower generation system.

[0006] Optionally, in one embodiment of the present application, the determination of the day-ahead and real-time two-stage joint optimization objectives of the wind-solar-hydro complementary power generation system includes: taking the minimization of the sum of each daily average cost as the optimization goal, generating an objective function based on the day-ahead cost and the real-time cost; and generating a power balance constraint based on the day-ahead constraint and the real-time constraint.

[0007] Optionally, in one embodiment of the present application, based on the joint optimization objective, the cascade hydropower optimization operation model is determined according to the hydropower conversion relationship, hydropower output range, reservoir discharge flow limitation information, head constraints, reservoir capacity constraints, water balance constraints and / or reservoir capacity constraints at the beginning and end of the scheduling period, including: determining the constraints of the wind-solar-hydropower generation system based on the hydraulic and power connections between the cascade hydropower stations.

[0008] Optionally, in one embodiment of the present application, the simplified formula of the wind power and photovoltaic optimization operation model is:

[0009]

[0010]

[0011]

[0012]

[0013] in, is the predicted output of wind farm k at time t, is the prediction error of wind farm k at time t, For the planned output of wind farm k at time t, is the abandoned wind power of wind farm k at time t; is the predicted output of PV plant j at time t, are the corresponding prediction error, planned output and abandoned optical power.

[0014] Optionally, in one embodiment of the present application, the preset linearization strategy is used to convert the cascade hydropower optimization operation model and the wind power and photovoltaic optimization operation models into MILP problems respectively, and the MILP problems are solved to obtain the optimal operation cost of the wind, solar and hydropower generation system, including: using a preset piecewise linearization formula to convert all or part of the nonlinear constraints into linear constraints, and solving them based on Gurobi.

[0015] The second aspect of the present application provides a two-stage optimization operation device for a wind-solar-hydroelectric power generation system, including: a determination module, used to determine the joint optimization target of the wind-solar-hydroelectric complementary power generation system in the day-ahead and real-time stages; a constraint module, used to determine the cascade hydropower optimization operation model based on the joint optimization target, according to the hydropower conversion relationship, hydropower output range, reservoir discharge flow limit information, head constraint, reservoir capacity constraint, water balance constraint and / or the reservoir capacity constraint at the beginning and end of the scheduling period; and an optimization module, used to determine the wind power and photovoltaic optimization operation models based on the joint optimization target and according to the prediction information; a solution module, used to use a preset linearization strategy to convert the cascade hydropower optimization operation model and the wind power and photovoltaic optimization operation models into MILP problems respectively, and solve the MILP problem to obtain the optimal operation cost of the wind-solar-hydroelectric power generation system.

[0016] Optionally, in one embodiment of the present application, the determination module includes: a first generation unit, used to take the minimization of the sum of each daily average cost as the optimization goal, and generate an objective function based on the day-ahead cost and the real-time cost; a second generation unit, used to generate power balance constraints according to the day-ahead constraints and the real-time constraints.

[0017] Optionally, in one embodiment of the present application, the constraint module includes: a constraint unit, used to determine the constraint conditions of the wind-solar-hydropower generation system based on the hydraulic connection and power connection between the cascade hydropower stations.

[0018] Optionally, in one embodiment of the present application, the simplified formula of the wind power and photovoltaic optimization operation model is:

[0019]

[0020]

[0021]

[0022]

[0023] in, is the predicted output of wind farm k at time t, is the prediction error of wind farm k at time t, For the planned output of wind farm k at time t, is the abandoned wind power of wind farm k at time t; is the predicted output of PV plant j at time t, are the corresponding prediction error, planned output and abandoned optical power.

[0024] Optionally, in one embodiment of the present application, the solution module includes: a solution unit, used to convert all or part of the nonlinear constraints into linear constraints using a preset piecewise linearization formula, and solve them based on Gurobi.

[0025] 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 two-stage optimization operation method of the wind, solar and hydropower generation system as described in the above embodiment.

[0026] The fourth aspect of the present application provides a computer-readable storage medium, which stores a computer program. When the program is executed by a processor, it implements the above-mentioned two-stage optimization operation method of the wind-solar-hydropower generation system.

[0027] The embodiment of the present application can be based on the joint optimization objectives of the day-ahead and real-time two-stages of the wind-solar-hydro complementary power generation system, determine the cascade hydropower optimization operation model according to multiple constraints, and determine the wind power and photovoltaic optimization operation models according to the prediction information, so as to realize the conversion of the cascade hydropower optimization operation model and the wind power and photovoltaic optimization operation models into MILP problems respectively, and then realize the establishment and optimization of the wind-solar-hydro power generation system optimization scheduling model based on the uncertainty of wind power and photovoltaic output by obtaining the optimal operation cost of the wind-solar-hydro power generation system, so as to make full use of renewable energy and improve the reliability and economy of the power system. Thus, the technical problem in the related technology that the existing power system scheduling model has been difficult to adapt to the optimal scheduling of multi-energy power systems containing a high proportion of renewable energy due to the uncertainty of wind power and photovoltaic output is solved.

[0028] 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

[0029] 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:

[0030] Figure 1 A flow chart of a two-stage optimization operation method for a wind-solar-hydropower generation system provided according to an embodiment of the present application;

[0031] Figure 2 A flow chart of a two-stage optimization operation method for a wind-solar-water power generation system according to an embodiment of the present application;

[0032] Figure 3 A schematic diagram of the structure of a two-stage optimized operation device for a wind-solar-hydropower generation system provided according to an embodiment of the present application;

[0033] Figure 4 It is a schematic diagram of the structure of an electronic device provided according to an embodiment of the present application. DETAILED DESCRIPTION

[0034] 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.

[0035] The following describes the two-stage optimization operation method and device of the wind, solar and hydroelectric power generation system of the embodiment of the present application with reference to the accompanying drawings. In view of the technical problem that the existing power system dispatching model has been difficult to adapt to the optimal dispatching of a multi-energy power system with a high proportion of renewable energy due to the uncertainty of wind power and photovoltaic output in the related technology mentioned in the above background technology, the present application provides a two-stage optimization operation method for a wind, solar and hydroelectric power generation system, in which the cascade hydropower optimization operation model can be determined according to multiple constraints based on the joint optimization objectives of the wind, solar and hydroelectric complementary power generation system in the day-ahead and real-time stages, and the wind power and photovoltaic optimization operation models can be determined according to the prediction information, so as to realize the conversion of the cascade hydropower optimization operation model and the wind power and photovoltaic optimization operation model into MILP problems respectively, and then by obtaining the optimal operation cost of the wind, solar and hydroelectric power generation system, the establishment and optimization of the wind, solar and hydroelectric power generation system optimization dispatching model under the condition of wind power and photovoltaic output uncertainty can be realized, so that renewable energy can be utilized to the maximum extent and the reliability and economy of the power system can be improved. Thus, the technical problem that the existing power system dispatching model has been difficult to adapt to the optimal dispatching of a multi-energy power system with a high proportion of renewable energy due to the uncertainty of wind power and photovoltaic output in the related technology is solved.

[0036] Specifically, Figure 1 A flow chart of a two-stage optimization operation method for a wind-solar-hydropower generation system provided in an embodiment of the present application.

[0037] like Figure 1 As shown, the two-stage optimization operation method of the wind-solar-water power generation system includes the following steps:

[0038] In step S101, the joint optimization objectives of the wind-solar-hydro hybrid power generation system in the day-ahead and real-time stages are determined.

[0039] In the actual implementation process, due to the short-term uncertainty of factors such as wind power, photovoltaics, load, direct solar radiation and reservoir water inflow in the multi-energy power system, the formulated day-ahead power generation plan cannot guide the actual operation of the multi-energy power system. The embodiment of the present application can divide the model into two stages: day-ahead planning and real-time correction, and by determining the joint optimization objectives of the day-ahead and real-time stages of the wind, solar and water complementary power generation system, it is convenient to achieve the optimal scheduling of the multi-energy power system with a high proportion of renewable energy by establishing the corresponding model in the subsequent process.

[0040] Optionally, in one embodiment of the present application, the day-ahead and real-time two-stage joint optimization objectives of the wind-solar-hydro complementary power generation system are determined, including: taking the minimum sum of each daily average cost as the optimization goal, generating an objective function based on the day-ahead cost and the real-time cost; generating a power balance constraint based on the day-ahead constraint and the real-time constraint.

[0041] Specifically, the method for determining the day-ahead and real-time two-stage joint optimization target of the wind-solar-hydro hybrid power generation system in the embodiment of the present application may include:

[0042] (1) Objective function

[0043] In the embodiment of the present application, the minimum sum of the daily average costs can be used as the optimization goal, and the objective function f OPE The day-ahead cost f dh and real-time cost f rt Two parts:

[0044] minf OPE =f dh +f rt ,

[0045]

[0046]

[0047] Among them, the cost of the day before is f dh Includes water cost for hydropower and spare capacity cost for conventional units. Water is the unit cost of water resources, is the power generation flow of unit h in hydropower station c at time t, is the water abandoned from the reservoir at the corresponding moment; and is the positive and negative reserve capacity cost of the hydropower unit, and Reserve capacity for hydropower units reserved in the day ahead; real-time cost f rt Including the backup call cost, renewable energy curtailment cost and load loss penalty, π s is the probability of error scenario s, and The cost of calling the reserve of hydropower units in real time. and is the upper and lower reserve call amount of the hydropower unit in scenario s, The cost of power curtailment for new energy sources, and are the power limits for wind power and photovoltaic power, For load loss penalty, The amount of load loss.

[0048] (2) Power balance constraints

[0049] 1) Day-ahead constraints

[0050]

[0051] The embodiment of the present application should satisfy the power constraint at any time. is the load at time t predicted the day before. Apart from this, each power source meets its operation constraints.

[0052] In addition, in order to ensure that the reserved spare capacity of the conventional units can be dispatched in real time, the embodiment of the present application may impose the following constraints on the upper and lower spare of the hydropower units:

[0053]

[0054]

[0055]

[0056] 2) Real-time constraints

[0057]

[0058] The embodiment of the present application also satisfies the power balance constraint and the power supply operation constraint in real time. For the standby call, the following constraints can be satisfied:

[0059]

[0060]

[0061] In step S102, based on the joint optimization objective, the cascade hydropower optimization operation model is determined according to the hydropower conversion relationship, hydropower output range, reservoir discharge flow limit information, head constraint, storage capacity constraint, water balance constraint and / or storage capacity constraint at the beginning and end of the scheduling period.

[0062] Furthermore, based on the day-ahead and real-time two-stage joint optimization objectives of the wind-solar-hydro complementary power generation system, the embodiments of the present application can determine the cascade hydropower optimization operation model according to the hydropower conversion relationship, hydropower output range, reservoir discharge flow limitation information, head constraints, reservoir capacity constraints, water balance constraints and / or reservoir capacity constraints at the beginning and end of the scheduling period. Based on multiple constraints, the accuracy of the cascade hydropower optimization operation model and its applicability to the uncertainty of wind power and photovoltaic output are improved.

[0063] Optionally, in one embodiment of the present application, based on the joint optimization objective, the cascade hydropower optimization operation model is determined according to the hydropower conversion relationship, hydropower output range, reservoir discharge flow limitation information, head constraints, storage capacity constraints, water balance constraints and / or storage capacity constraints at the beginning and end of the scheduling period, including: determining the constraints of the wind, solar and hydropower generation system based on the hydraulic and power connections between the cascade hydropower stations.

[0064] In the actual implementation process, the constraints of the embodiment of the present application may be as follows:

[0065] (1) Hydropower conversion relationship

[0066] The output of a hydropower unit is a binary function of the generating head and generating flow, which is a nonlinear constraint:

[0067]

[0068] Among them, c is the hydropower station number, h is the hydropower unit number, is the active power output of unit h in hydropower station c at time t, is the corresponding power generation head, is the corresponding power generation flow, This is the power generation efficiency coefficient at this time.

[0069] (2) Hydropower output range

[0070] Each hydropower unit and the reservoir as a whole can meet the output range constraints:

[0071]

[0072]

[0073] in, They are the upper and lower limits of the output of the unit and the reservoir respectively.

[0074] (3) Reservoir discharge flow limit

[0075] The outflow of the reservoir includes the power generation flow and the abandoned water flow, which can meet the flow range required by the reservoir:

[0076]

[0077] in, is the water discharge of reservoir c at time t, and The upper and lower limits of the reservoir discharge flow.

[0078] (4) Head constraint

[0079] Without considering the head loss, the generating head of the hydropower unit is the difference between the upstream water level and the tail water level. The construction and operation of each reservoir limits the maximum and minimum values ​​of its head:

[0080]

[0081]

[0082] in, and are the upstream water level and tail water level of reservoir c at time t, and are the upper and lower limits of the water head in reservoir c respectively.

[0083] There is a nonlinear relationship between the upstream water level and reservoir capacity, tailwater level and downstream flow of the reservoir:

[0084]

[0085]

[0086] Among them, V c,t is the storage capacity of reservoir c at time t.

[0087] (5) Storage capacity constraints

[0088]

[0089] Among them, V c,min and V c,max are the upper and lower limits of the adjustable storage capacity of reservoir c.

[0090] (6) Water balance constraints

[0091] The inflow, outflow and storage capacity changes of each reservoir must meet the water balance constraints. At the same time, due to the hydraulic connection between the upstream and downstream, the discharge flow of the upstream reservoir will flow into the downstream reservoir after a period of water flow lag:

[0092]

[0093] in, is the inflow of reservoir c at time t, For the upstream reservoir c-1 at t-τ c-1 The discharge flow at time, τc-1 is the water flow time lag from reservoir c-1 to reservoir c.

[0094] (7) Storage capacity constraints at the beginning and end of the dispatch period

[0095] For the storage capacity at the beginning and end of the scheduling period, the embodiment of the present application can meet the results given by the medium- and long-term optimization. At the same time, due to the random factors that may be encountered in the short-term scheduling problem, the storage capacity at the end of the scheduling period is allowed to fluctuate slightly within a certain range:

[0096]

[0097]

[0098]

[0099]

[0100] Among them, V c,t and V c,0 is the target storage capacity at the end of the dispatch period, and is the positive and negative deviation value of the storage capacity at the end of the scheduling period, and It is the upper limit of positive and negative deviation values.

[0101] In step S103, based on the joint optimization target, the wind power and photovoltaic optimization operation models are determined according to the prediction information.

[0102] Furthermore, based on the day-ahead and real-time two-stage joint optimization objectives of the wind, solar and hydropower complementary power generation system, the embodiments of the present application can determine the wind power and photovoltaic optimization operation models according to the prediction information, thereby improving the accuracy of the wind power and photovoltaic optimization operation models, as well as the applicability to the uncertainty of wind power and photovoltaic output.

[0103] Optionally, in one embodiment of the present application, a simplified formula of the wind power and photovoltaic optimization operation model is:

[0104]

[0105]

[0106]

[0107]

[0108] in, is the predicted output of wind farm k at time t, is the prediction error of wind farm k at time t, For the planned output of wind farm k at time t, is the abandoned wind power of wind farm k at time t; is the predicted output of PV plant j at time t, are the corresponding prediction error, planned output and abandoned optical power.

[0109] In the scheduling model, the embodiment of the present application can introduce prediction information to describe the uncertainty of wind power and photovoltaic power, and simplify the model into the form of energy blocks:

[0110]

[0111]

[0112]

[0113]

[0114] in, is the predicted output of wind farm k at time t, is the prediction error of wind farm k at time t, For the planned output of wind farm k at time t, is the abandoned wind power of wind farm k at time t; is the predicted output of PV plant j at time t, are the corresponding prediction error, planned output and abandoned optical power.

[0115] In step S104, the cascade hydropower optimization operation model and the wind power and photovoltaic optimization operation models are respectively converted into MILP problems using a preset linearization strategy, and the MILP problems are solved to obtain the optimal operation cost of the wind-solar-hydropower generation system.

[0116] It can be understood that solving the MILP problem can obtain an optimal solution with higher accuracy. The embodiment of the present application can use a preset linearization strategy to convert the cascade hydropower optimization operation model and the wind power and photovoltaic optimization operation models into MILP problems respectively, and solve them to obtain the optimal operating cost of the wind, solar and hydropower generation system. The embodiment of the present application can give the unit combination results and planned output curves of conventional units through day-ahead planning, describe the uncertainty in the power system with typical scenarios in the real-time stage, and make corrections based on the day-ahead results. Through integrated optimization, a day-ahead plan that takes into account various error scenarios that may actually occur can be given, and the optimal operating cost can be achieved.

[0117] It should be noted that the preset linearization strategy will be explained below.

[0118] Optionally, in one embodiment of the present application, a preset linearization strategy is used to convert the cascade hydropower optimization operation model and the wind power and photovoltaic optimization operation models into MILP problems, and the MILP problems are solved to obtain the optimal operating cost of the wind, solar and hydropower generation system, including: using a preset piecewise linearization formula to convert all or part of the nonlinear constraints into linear constraints, and solving them based on Gurobi.

[0119] As a possible implementation method, the embodiment of the present application can convert some nonlinear constraints existing in the hydropower model (such as the functional relationship between the hydropower conversion gate, the upstream water level-reservoir capacity, and the tailwater level-downstream flow) into linear constraints using a piecewise linearization method, linearize the model, and enable it to be quickly solved by the commercial software Gurobi.

[0120] Taking the linearization of hydropower conversion relationship as an example, since hydropower power is and the power generation flow And the power generation head Related binary functions, therefore, the embodiments of the present application can be respectively and Linearize the power generation flow It is divided into E segment, and the range of water head change is divided into F segment. When the water head size H falls in the i-th segment [H i-1 ,H i ], use the average water head of this section As the calculated value of the water head, The piecewise linearization method is used to express the output P of the hydropower unit. HY and power generation flow Q Gen The functional relationship between them.

[0121] The specific linearization formula can be shown as follows:

[0122]

[0123]

[0124]

[0125]

[0126]

[0127]

[0128]

[0129]

[0130]

[0131]

[0132]

[0133] Among them, x f To represent the water head H HY Is it a 0-1 variable in segment f? is the water head H HY In the value of segment f, if H HY In paragraph f, on the contrary, and are the minimum and maximum values ​​of the fth segment within the head variation range, respectively; is the water head H HY The equivalent mean value when it is in segment f. f,e To indicate the water head of the hydropower unit Power generation flow under Is it a 0-1 variable in segment e? For power generation flow In the value of section e, if the hydropower unit is at head If the power generation flow is in the e segment, then and on the contrary, and Q f,fd,e-1 and Q f,fd,e The power generation flow Minimum and maximum values ​​within the range of variation, segment e. e,f and β e,f Water head Output of water generator set and power generation flow The slope and intercept of the e-th segment in the piecewise linearization function of .

[0134] The piecewise linearization method used between the upstream water level-reservoir capacity and the tail water level-downstream flow is the same as above, which will not be repeated here. The embodiment of the present application can be piecewise linearized, and the optimization model can be described by a mixed integer linearization model, and finally quickly solved based on Gurobi.

[0135] Combination Figure 2 As shown, the working principle of the two-stage optimization operation method of the wind-solar-hydropower generation system of the embodiment of the present application is explained in detail by using an embodiment.

[0136] like Figure 2 As shown, the embodiment of the present application may include the following steps:

[0137] Step S201: Determine the day-ahead and real-time two-stage joint optimization target of the wind-solar-hydroelectric complementary power generation system. Specifically, the method for determining the day-ahead and real-time two-stage joint optimization target of the wind-solar-hydroelectric complementary power generation system in the embodiment of the present application may include:

[0138] (1) Objective function

[0139] In the embodiment of the present application, the minimum sum of the daily average costs can be used as the optimization goal, and the objective function f OPE The day-ahead cost f dh and real-time cost f rt Two parts:

[0140] minf OPE =f dh +f rt ,

[0141]

[0142]

[0143] Among them, the cost of the day before is f dh Includes water cost for hydropower and spare capacity cost for conventional units. Water is the unit cost of water resources, is the power generation flow of unit h in hydropower station c at time t, is the water abandoned from the reservoir at the corresponding moment; and is the positive and negative reserve capacity cost of the hydropower unit, and Reserve capacity for hydropower units reserved in the day ahead; real-time cost f rt Including the backup call cost, renewable energy curtailment cost and load loss penalty, π s is the probability of error scenario s, and The cost of calling the reserve of hydropower units in real time. and is the upper and lower reserve call amount of the hydropower unit in scenario s, The cost of power curtailment for new energy sources, and are the power limits for wind power and photovoltaic power, For load loss penalty, The amount of load loss.

[0144] (2) Power balance constraints

[0145] 1) Day-ahead constraints

[0146]

[0147] The embodiment of the present application should satisfy the power constraint at any time. is the load at time t predicted the day before. Apart from this, each power source meets its operation constraints.

[0148] In addition, in order to ensure that the reserved spare capacity of the conventional units can be dispatched in real time, the embodiment of the present application may impose the following constraints on the upper and lower spare of the hydropower units:

[0149]

[0150]

[0151]

[0152] 3) Real-time constraints

[0153]

[0154] The embodiment of the present application also satisfies the power balance constraint and the power supply operation constraint in real time. For the standby call, the following constraints can be satisfied:

[0155]

[0156]

[0157] Step S202: Determine the optimal operation model of cascade hydropower; comprehensively consider the hydraulic connection and power connection between cascade hydropower stations, and determine the constraints of the wind-solar-water complementary power generation system. In the actual implementation process, the constraints of the embodiment of the present application can be as follows:

[0158] (1) Hydropower conversion relationship

[0159] The output of a hydropower unit is a binary function of the generating head and generating flow, which is a nonlinear constraint:

[0160]

[0161] Among them, c is the hydropower station number, h is the hydropower unit number, is the active power output of unit h in hydropower station c at time t, is the corresponding power generation head, is the corresponding power generation flow, This is the power generation efficiency coefficient at this time.

[0162] (2) Hydropower output range

[0163] Each hydropower unit and the reservoir as a whole can meet the output range constraints:

[0164]

[0165]

[0166] in, They are the upper and lower limits of the output of the unit and the reservoir respectively.

[0167] (3) Reservoir discharge flow limit

[0168] The outflow of the reservoir includes the power generation flow and the abandoned water flow, which can meet the flow range required by the reservoir:

[0169]

[0170] in, is the water discharge of reservoir c at time t, and The upper and lower limits of the reservoir discharge flow.

[0171] (4) Head constraint

[0172] Without considering the head loss, the generating head of the hydropower unit is the difference between the upstream water level and the tail water level. The construction and operation of each reservoir limits the maximum and minimum values ​​of its head:

[0173]

[0174]

[0175] in, and are the upstream water level and tail water level of reservoir c at time t, and are the upper and lower limits of the water head in reservoir c respectively.

[0176] There is a nonlinear relationship between the upstream water level and reservoir capacity, tailwater level and downstream flow of the reservoir:

[0177]

[0178]

[0179] Among them, V c,t is the storage capacity of reservoir c at time t.

[0180] (5) Storage capacity constraints

[0181]

[0182] Among them, V c,min and V c,max are the upper and lower limits of the adjustable storage capacity of reservoir c.

[0183] (6) Water balance constraints

[0184] The inflow, outflow and storage capacity changes of each reservoir must meet the water balance constraints. At the same time, due to the hydraulic connection between the upstream and downstream, the discharge flow of the upstream reservoir will flow into the downstream reservoir after a period of water flow lag:

[0185]

[0186] in, is the inflow of reservoir c at time t, For the upstream reservoir c-1 at t-τ c-1 The discharge flow at time, τ c-1 is the water flow time lag from reservoir c-1 to reservoir c.

[0187] (7) Storage capacity constraints at the beginning and end of the dispatch period

[0188] For the storage capacity at the beginning and end of the scheduling period, the embodiment of the present application can meet the results given by the medium- and long-term optimization. At the same time, due to the random factors that may be encountered in the short-term scheduling problem, the storage capacity at the end of the scheduling period is allowed to fluctuate slightly within a certain range:

[0189]

[0190]

[0191]

[0192]

[0193] Among them, V c,t and V c,0 is the target storage capacity at the end of the dispatch period, and is the positive and negative deviation value of the storage capacity at the end of the scheduling period, and It is the upper limit of positive and negative deviation values.

[0194] Step S203: Determine the optimal operation model of wind power and photovoltaic power. In the scheduling model, the embodiment of the present application can introduce prediction information to describe the uncertainty of wind power and photovoltaic power, and simplify the model into the form of energy blocks:

[0195]

[0196]

[0197]

[0198]

[0199] in, is the predicted output of wind farm k at time t, is the prediction error of wind farm k at time t, For the planned output of wind farm k at time t, is the abandoned wind power of wind farm k at time t; is the predicted output of PV plant j at time t, are the corresponding prediction error, planned output and abandoned optical power.

[0200] Step S204: Use the linearization strategy to convert the optimization model into a MILP problem, and determine the corresponding solution algorithm. As a possible implementation method, the embodiment of the present application can convert some nonlinear constraints in the hydropower model (such as the functional relationship between the hydropower conversion gate, the upstream water level-reservoir capacity, and the tailwater level-downstream flow) into linear constraints using a piecewise linearization method, linearize the model, and enable it to be quickly solved by the commercial software Gurobi.

[0201] Taking the linearization of hydropower conversion relationship as an example, since hydropower power is and the power generation flow And the power generation head Related binary functions, therefore, the embodiments of the present application can be respectively and Linearize the power generation flow It is divided into E segment, and the range of water head change is divided into F segment. When the water head size H falls in the i-th segment [H i-1 ,H i ], use the average water head of this section As the calculated value of the water head, The piecewise linearization method is used to express the output P of the hydropower unit. HY and power generation flow Q Gen The functional relationship between them.

[0202] The specific linearization formula can be shown as follows:

[0203]

[0204]

[0205]

[0206]

[0207]

[0208]

[0209]

[0210]

[0211]

[0212]

[0213]

[0214] Among them, x f To represent the water head H HY Is it a 0-1 variable in segment f? is the water head H HY In the value of segment f, if H HY In paragraph f, on the contrary, and are the minimum and maximum values ​​of the fth segment within the head variation range, respectively; is the water head H HY The equivalent mean value when it is in segment f. f,e To indicate the water head of the hydropower unit Power generation flow under Is it a 0-1 variable in segment e? For power generation flow In the value of section e, if the hydropower unit is at head If the power generation flow is in the e segment, then and on the contrary, and Q f,fd,e-1 and Q f,fd,e The power generation flow Minimum and maximum values ​​within the range of variation, segment e. e,f and β e,f Water head Output of water generator set and power generation flow The slope and intercept of the e-th segment in the piecewise linearization function of .

[0215] The piecewise linearization method used between the upstream water level-reservoir capacity and the tail water level-downstream flow is the same as above, which will not be repeated here. The embodiment of the present application can be piecewise linearized, and the optimization model can be described by a mixed integer linearization model, and finally quickly solved based on Gurobi.

[0216] According to the two-stage optimization operation method of the wind-solar-hydroelectric power generation system proposed in the embodiment of the present application, the cascade hydropower optimization operation model can be determined according to multiple constraints based on the joint optimization objectives of the wind-solar-hydroelectric complementary power generation system in the day-ahead and real-time stages, and the wind power and photovoltaic optimization operation models can be determined according to the prediction information, so as to realize the conversion of the cascade hydropower optimization operation model and the wind power and photovoltaic optimization operation models into MILP problems respectively, and then realize the establishment and optimization of the wind-solar-hydroelectric power generation system optimization scheduling model based on the uncertainty of wind power and photovoltaic output by obtaining the optimal operation cost of the wind-solar-hydroelectric power generation system, so as to maximize the use of renewable energy and improve the reliability and economy of the power system. Thus, the technical problem in the related technology that the existing power system scheduling model has been difficult to adapt to the optimization scheduling of multi-energy power systems containing a high proportion of renewable energy due to the uncertainty of wind power and photovoltaic output is solved.

[0217] Next, a two-stage optimized operation device for a wind-solar-hydropower generation system proposed according to an embodiment of the present application will be described with reference to the accompanying drawings.

[0218] Figure 3 It is a block diagram of a two-stage optimized operation device of a wind-solar-hydropower generation system according to an embodiment of the present application.

[0219] like Figure 3 As shown, the two-stage optimization operation device 10 of the wind-solar-hydroelectric power generation system includes: a determination module 100, a constraint module 300, an optimization module 300 and a solution module 400.

[0220] Specifically, the determination module 100 is used to determine the joint optimization objectives of the wind-solar-hydro hybrid power generation system in the day-ahead and real-time stages.

[0221] The constraint module 200 is used to determine the cascade hydropower optimization operation model based on the joint optimization target, according to the hydropower conversion relationship, hydropower output range, reservoir discharge flow restriction information, head constraint, reservoir capacity constraint, water balance constraint and / or the reservoir capacity constraint at the beginning and end of the scheduling period.

[0222] The optimization module 300 is used to determine the wind power and photovoltaic optimization operation model based on the joint optimization target and the prediction information.

[0223] The solution module 400 is used to convert the cascade hydropower optimization operation model and the wind power and photovoltaic optimization operation models into MILP problems using a preset linearization strategy, and solve the MILP problem to obtain the optimal operation cost of the wind, solar and hydropower generation system.

[0224] Optionally, in one embodiment of the present application, the determination module 100 includes: a first generation unit and a second generation unit.

[0225] The first generating unit is used to take the minimum sum of each daily average cost as the optimization goal and generate the objective function based on the day-ahead cost and the real-time cost.

[0226] The second generating unit is used to generate the power balance constraint according to the day-ahead constraint and the real-time constraint.

[0227] Optionally, in one embodiment of the present application, the constraint module 200 includes: a constraint unit.

[0228] Among them, the constraint unit is used to determine the constraint conditions of the wind-solar-water power generation system based on the hydraulic and power connections between the cascade hydropower stations.

[0229] Optionally, in one embodiment of the present application, a simplified formula of the wind power and photovoltaic optimization operation model is:

[0230]

[0231]

[0232]

[0233]

[0234] in, is the predicted output of wind farm k at time t, is the prediction error of wind farm k at time t, For the planned output of wind farm k at time t, is the abandoned wind power of wind farm k at time t; is the predicted output of PV plant j at time t, are the corresponding prediction error, planned output and abandoned optical power.

[0235] Optionally, in one embodiment of the present application, the solution module 400 includes: a solution unit.

[0236] The solving unit is used to convert all or part of the nonlinear constraints into linear constraints using a preset piecewise linearization formula, and solve them based on Gurobi.

[0237] It should be noted that the above explanation of the embodiment of the two-stage optimal operation method of the wind-solar-hydropower generation system is also applicable to the two-stage optimal operation device of the wind-solar-hydropower generation system of this embodiment, and will not be repeated here.

[0238] According to the two-stage optimization operation device of the wind, solar and hydroelectric power generation system proposed in the embodiment of the present application, it is possible to determine the cascade hydropower optimization operation model based on multiple constraints based on the joint optimization objectives of the wind, solar and hydroelectric complementary power generation system in the day-ahead and real-time stages, and determine the wind power and photovoltaic optimization operation models based on the prediction information, thereby realizing the conversion of the cascade hydropower optimization operation model and the wind power and photovoltaic optimization operation models into MILP problems respectively, and then by obtaining the optimal operation cost of the wind, solar and hydroelectric power generation system, the establishment and optimization of the wind, solar and hydroelectric power generation system optimization scheduling model based on the uncertainty of wind power and photovoltaic output is realized, so that renewable energy can be utilized to the maximum extent and the reliability and economy of the power system can be improved. Thus, the technical problem in the related technology that the existing power system scheduling model has been difficult to adapt to the optimization scheduling of multi-energy power systems containing a high proportion of renewable energy due to the uncertainty of wind power and photovoltaic output is solved.

[0239] 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:

[0240] Memory 401 , processor 402 , and a computer program stored in the memory 401 and executable on the processor 402 .

[0241] When the processor 402 executes the program, the two-stage optimization operation method of the wind-solar-hydropower generation system provided in the above embodiment is implemented.

[0242] Furthermore, the electronic device further comprises:

[0243] The communication interface 403 is used for communication between the memory 401 and the processor 402 .

[0244] The memory 401 is used to store computer programs that can be executed on the processor 402 .

[0245] 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.

[0246] 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 (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.

[0247] 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.

[0248] 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.

[0249] This embodiment also provides a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, the two-stage optimization operation method of the wind-solar-hydropower generation system is implemented as described above.

[0250] 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.

[0251] 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.

[0252] 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.

[0253] 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.

[0254] 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.

[0255] 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.

[0256] 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.

[0257] 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 two-stage optimization operation method for a wind, solar and hydropower generation system, characterized in that: The following steps are involved: Determine the joint optimization objectives of the wind-solar-hydro hybrid power generation system in the day-ahead and real-time stages; Based on the joint optimization objective, determining the cascade hydropower optimization operation model according to the hydropower conversion relationship, hydropower output range, reservoir discharge flow restriction information, head constraint, reservoir capacity constraint, water balance constraint and / or the initial and final reservoir capacity constraints of the dispatching period; and Based on the joint optimization objective, determining the wind power and photovoltaic optimization operation model according to the prediction information; The cascade hydropower optimization operation model and the wind power and photovoltaic optimization operation models are respectively converted into MILP problems by using a preset linearization strategy, and the MILP problems are solved to obtain the optimal operation cost of the wind-solar-hydropower generation system; Wherein, the cascade hydropower optimization operation model is determined based on the joint optimization target according to the hydropower conversion relationship, hydropower output range, reservoir discharge flow restriction information, head constraint, reservoir capacity constraint, water balance constraint and / or the beginning and end reservoir capacity constraint of the scheduling period, including: determining the constraint conditions of the wind-solar-water power generation system based on the hydraulic connection and power connection between the cascade hydropower stations; Among them, the simplified formula of the wind power and photovoltaic optimization operation model is: in, is the predicted output of wind farm k at time t, is the prediction error of wind farm k at time t, For the planned output of wind farm k at time t, is the abandoned wind power of wind farm k at time t; is the predicted output of PV plant j at time t, are the corresponding prediction error, planned output and abandoned optical power.

2. The method according to claim 1, characterized in that The determination of the day-ahead and real-time two-stage joint optimization objectives of the wind-solar-hydroelectric complementary power generation system includes: The minimum sum of each daily average cost is taken as the optimization goal, and the objective function is generated based on the day-ahead cost and the real-time cost; Generate power balance constraints based on day-ahead constraints and real-time constraints.

3. The method according to claim 1, characterized in that The method uses a preset linearization strategy to convert the cascade hydropower optimization operation model and the wind power and photovoltaic optimization operation models into MILP problems, and solves the MILP problems to obtain the optimal operation cost of the wind-solar-hydropower generation system, including: Use the preset piecewise linearization formula to convert all or part of the nonlinear constraints into linear constraints, and solve them based on Gurobi.

4. A two-stage optimized operation device for a wind, solar and hydroelectric power generation system, characterized in that: include: A determination module is used to determine the joint optimization objectives of the wind-solar-water hybrid power generation system in the day-ahead and real-time stages; A constraint module is used to determine the cascade hydropower optimization operation model based on the joint optimization objective, according to the hydropower conversion relationship, hydropower output range, reservoir discharge flow restriction information, head constraint, reservoir capacity constraint, water balance constraint and / or the initial and final reservoir capacity constraints of the scheduling period; as well as An optimization module, used to determine the wind power and photovoltaic optimization operation model based on the joint optimization target and the prediction information; A solution module, used to convert the cascade hydropower optimization operation model and the wind power and photovoltaic optimization operation models into MILP problems respectively by using a preset linearization strategy, and solve the MILP problems to obtain the optimal operation cost of the wind-solar-hydropower generation system; Wherein, the constraint module comprises: a constraint unit, which is used to determine the constraint conditions of the wind-solar-water power generation system based on the hydraulic connection and power connection between the cascade hydropower stations; Among them, the simplified formula of the wind power and photovoltaic optimization operation model is: in, is the predicted output of wind farm k at time t, is the prediction error of wind farm k at time t, For the planned output of wind farm k at time t, is the abandoned wind power of wind farm k at time t; is the predicted output of PV plant j at time t, are the corresponding prediction error, planned output and abandoned optical power.

5. The device according to claim 4, characterized in that The determination module comprises: A first generating unit is used to take the minimum sum of each daily average cost as the optimization goal and generate an objective function based on the day-ahead cost and the real-time cost; The second generating unit is used to generate the power balance constraint according to the day-ahead constraint and the real-time constraint.

6. 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 two-stage optimization operation method for a wind-solar-hydropower generation system as described in any one of claims 1 to 3.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: The program is executed by a processor to implement the two-stage optimization operation method of a wind-solar-water power generation system as described in any one of claims 1-3.

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