Hydropower station machine expansion capacity optimization method and device and computer equipment

Abstract

The invention relates to the technical field of reservoir dispatching, and discloses a hydropower station expansion capacity optimization method and device and computer device.The hydropower station expansion capacity optimization method comprises the steps that multiple hydropower station alternative expansion capacities are determined based on the capacity requirement of a power system and hydropower station engineering construction condition limitation; setting constraint conditions by taking the minimum total operation cost of the power system as a target, and constructing a power system operation model; aiming at the alternative expansion capacity of each hydropower station, carrying out optimization solution on the power system operation model to obtain power station operation process data corresponding to the alternative expansion capacity of each hydropower station; for each group of hydropower station expansion alternative schemes, calculating a plurality of performance indexes; and evaluating the multiple groups of hydropower station expansion alternative schemes based on the principal component indexes by utilizing a principal component analysis method, and selecting the hydropower station expansion alternative scheme with the highest evaluation value for expansion. The rationality of the capacity expansion scheme can be improved, and environmental development, safety and stability are facilitated.

Description

Technical Field

[0001] The present invention relates to the technical field of reservoir dispatching, and in particular to a method, a device and a computer device for optimizing the capacity expansion of a hydropower station. Background Art

[0002] Maximizing the absorption of renewable energy and developing optimal multi-energy complementary strategies based on the characteristics of renewable energy power generation output to achieve the dual carbon goals, and to support the large-scale, high-quality development of renewable energy, the functional positioning of conventional hydropower will shift from primarily focusing on electricity generation to a focus on both electricity generation and capacity regulation, thereby enhancing the flexibility of the power system. To promote this shift in hydropower positioning, expanding and increasing the capacity of existing hydropower will not only enhance hydropower regulation capabilities and water resource utilization, but also require minimal investment, deliver rapid results, and generate significant overall benefits.

[0003] Most of the existing research on hydropower expansion capacity optimization considers factors such as hydraulic structures, water diversion systems, and electrical equipment. By increasing the capacity of a single unit or the number of units, alternative expansion plans are formulated, and power installed capacity planning and production operation simulation are carried out. The expansion plan with the best economic efficiency is selected from the alternative plans, which is the optimal expansion capacity.

[0004] However, the current capacity expansion plans often only pursue the optimization of a few indicators such as economic benefits and investment costs, and ignore the balanced evaluation of indicators such as green and low-carbon, safety and stability, resulting in unreasonable expansion plans. Summary of the Invention

[0005] In view of this, the present invention provides a method, device and computer equipment for optimizing the capacity expansion of a hydropower station to improve the rationality of the capacity expansion plan, which is beneficial to environmental development and safety and stability.

[0006] In a first aspect, the present invention provides a method for optimizing the expansion capacity of a hydropower station, which comprises: determining a plurality of alternative expansion capacities of a hydropower station based on the capacity demand of the power system and the restrictions on the construction conditions of the hydropower station project; setting constraints and constructing a power system operation model with the goal of minimizing the total operating cost of the power system; optimizing and solving the power system operation model for each alternative expansion capacity of the hydropower station, obtaining the power station operation process data corresponding to each alternative expansion capacity of the hydropower station, and obtaining a plurality of groups of alternative expansion schemes for the hydropower station, each group of alternative expansion schemes for the hydropower station including the alternative expansion capacity of the hydropower station and the corresponding power station operation process data; calculating a plurality of performance indicators for each group of alternative expansion schemes for the hydropower station, the performance indicators including a plurality of economic indicators of the power station, a plurality of safety indicators of the power station and a plurality of low-carbon indicators of the power station; utilizing the principal component analysis method to screen the principal component indicators from the plurality of performance indicators, and evaluating the plurality of groups of alternative expansion schemes for the hydropower station based on the principal component indicators, and selecting the hydropower station expansion scheme with the highest evaluation value for the expansion of the hydropower station.

[0007] In this implementation, by considering the flexible adjustment needs of the power system and the construction conditions of the hydropower station project, multiple groups of alternative plans for hydropower station capacity expansion are formulated; with the goal of minimizing the total cost of system operation, a power system economic operation model is constructed to optimize the system operation process under different expansion plans; a multi-dimensional evaluation index system including economic indicators, safety indicators, and low-carbon indicators is constructed, and based on the obtained system operation process, the multi-dimensional evaluation index values ​​of different expansion plans are calculated; finally, a principal component analysis model is constructed, the principal components and weights are determined, the evaluation scores of different expansion plans are calculated, and the optimal expansion plan is obtained. This application comprehensively analyzes the impact of different hydropower expansion plans on the power system operation process, ensures the safe and economical operation of the system, and uses principal component analysis technology to achieve a comprehensive evaluation of hydropower expansion plans in the three dimensions of economy, safety, and low carbon. It can avoid the singleness and one-sidedness of traditional methods in capacity expansion optimization, improve the rationality of expansion plans, and is beneficial to environmental development and safety and stability.

[0008] In an optional embodiment, based on the capacity requirements of the power system and the restrictions on the construction conditions of the hydropower station project, multiple alternative expansion capacities of the hydropower station are determined, including: determining the soft constraints of the expansion capacity of the hydropower station in combination with the power balance and peak-shaving capacity requirements of the power system; determining the hard constraints of the expansion capacity of the hydropower station in combination with the upper limit of the construction capacity of the hydropower station project and the upper limit of the capacity of the transmission channel; and determining multiple alternative expansion capacities of the hydropower station allowed by the power system in combination with the soft constraints and the hard constraints.

[0009] In this implementation, the soft and hard constraints of the power system are comprehensively considered to determine the reasonable expansion capacity, providing reasonable options for subsequent expansion selection.

[0010] In an optional embodiment, with the goal of minimizing the total operating cost of the power system, constraints are set and a power system operation model is constructed, including: constructing an objective function with the minimum operating cost of the thermal power unit and the power load shedding cost; setting a first constraint condition based on the output of the thermal power unit, the output of the hydropower unit and the output of the new energy unit and the load demand of the power system; setting a second constraint condition based on the operation of the thermal power unit, setting a third constraint condition based on the operation of the hydropower unit, and setting a fourth constraint condition based on the operation of the new energy unit; and constructing a power system operation model in combination with the objective function and multiple constraints.

[0011] In this implementation method, hydropower expansion is considered in the overall power system, and an economic operation model of the power system including hydropower units, thermal power units, and new energy sources is constructed. The impact of different hydropower expansion plans on the operation process of the power system is comprehensively analyzed to ensure the safe and economical operation of the system.

[0012] In an optional embodiment, the objective function is:

[0013]

[0014] Where FC, FT, and FL are the total operating cost of the power system, the operating cost of the thermal power unit, and the power load shedding cost, respectively; i and t are the thermal power unit number and time period number, respectively; T is the number of operating time periods; NG is the number of thermal power units; a 1,i 、a 2,i 、a 3,i are the coal consumption coefficients of each order of thermal power unit i; PG i,t is the output of thermal power unit i in period t; C f is the price of coal; y i,t 、z i,t are the startup and shutdown operations of thermal power unit i in time period t, which are 0-1 integer variables; SUC i 、SDC i are the startup and shutdown costs of thermal power unit i; PL t is the power load shedding value in period t; C l is the load shedding penalty cost coefficient; Δt is the time period step.

[0015] In an optional embodiment, a second constraint condition is set based on the operation of the thermal power unit; a third constraint condition is set based on the operation of the hydropower unit; and a fourth constraint condition is set based on the operation of the new energy unit, including: setting the output constraint of the thermal power unit based on the output upper limit and output lower limit of the thermal power unit; setting the climbing constraint of the thermal power unit based on the startup climbing rate limit, shutdown ramp rate limit, upward climbing rate limit and downward ramp rate limit of the thermal power unit; setting the operating state constraint of the thermal power unit based on the initial output and initial operating state of the thermal power unit; setting the water balance constraint based on the reservoir capacity of the hydropower station, the interval flow of the hydropower station, and the outflow flow of the hydropower station ; Set flow constraints based on the hydropower station's power generation flow, abandoned water flow, power generation flow of the generator unit, hydropower station outflow limit, and power generation flow limit of the generator unit; set storage capacity constraints based on the hydropower station's storage capacity limit, initial storage capacity, and final storage capacity; set head constraints based on the hydropower station's net head, head loss, dam water level, and tailwater level; set hydropower unit output constraints based on the hydropower unit's output limit; set hydropower unit basic characteristic curve constraints based on the hydropower station's tailwater level-discharge flow equation, water level-storage capacity equation, and head loss equation; set new energy unit operation constraints based on the lower limit of new energy utilization rate and the maximum output of new energy units.

[0016] In this implementation, a variety of constraints are proposed to ensure the safe and economical operation of the power system.

[0017] In an optional embodiment, the power station operation process data includes: the output of the thermal power unit during the operation of the thermal power unit, the output of the hydropower unit during the operation of the hydropower unit, the output of the new energy unit during the operation of the new energy unit, the outflow flow during the operation of the hydropower unit, and the power load shedding value.

[0018] In an optional embodiment, for each group of alternative expansion plans for hydropower stations, multiple performance indicators are calculated, including: calculating the expansion investment cost based on the alternative expansion capacity of the hydropower station and the unit capacity expansion cost as a first economic indicator; calculating the operating cost of the thermal power units in the power system as a second economic indicator; calculating the increased hydropower generation as a third economic indicator by combining the alternative expansion plans for the hydropower station and the corresponding hydropower unit output when no expansion is performed; calculating the electricity market revenue as a fourth economic indicator by combining the average electricity price; calculating the probability of power shortage as a first safety indicator based on the power load shedding value; calculating the power shortage expectation as a second safety indicator; calculating the available capacity margin of the power system as a third safety indicator based on the output of the thermal power units, the output of the hydropower units and the load demand of the power system; calculating the coal saving of the power system as a first low-carbon indicator based on the output of the thermal power units; calculating the carbon emission reduction of the power system as a second low-carbon indicator based on the output of the thermal power units; calculating the proportion of clean energy electricity as a third low-carbon indicator by combining the output of the hydropower units, the output of the new energy units, the power load shedding value and the load demand of the power system.

[0019] In this implementation method, a comprehensive evaluation of hydropower expansion plans in the three dimensions of economy, safety, and low carbon is achieved, including ten evaluation indicators: expansion investment cost, system operating cost, increased power generation, electricity market revenue, power shortage probability, power shortage expectation, available capacity margin, system coal saving, carbon emission reduction, and proportion of clean energy electricity. This can avoid the singleness and one-sidedness of traditional methods in optimizing expansion capacity and provide decision-making reference for decision makers.

[0020] In an optional embodiment, principal component analysis is used to screen principal component indicators from multiple performance indicators, including: standardizing the multiple performance indicators and constructing a covariance matrix of the performance indicators; calculating the eigenvalues ​​and eigenvectors of the covariance matrix, and sorting them according to the eigenvalues, and calculating the contribution rate and cumulative contribution rate of the multiple performance indicators based on the corresponding eigenvectors; selecting a preset number of performance indicators whose cumulative contribution rate exceeds a contribution rate threshold as principal component indicators, and calculating the weights of the principal component indicators; evaluating multiple groups of hydropower station expansion alternatives based on the principal component indicators, including: for each group of hydropower station expansion alternatives, using the principal component indicators and the corresponding weights for weighted summation to calculate the corresponding evaluation score.

[0021] In a second aspect, the present invention provides a device for optimizing the expansion capacity of a hydropower station, which includes: a selection module for determining a plurality of alternative expansion capacities of hydropower stations based on the capacity demand of the power system and the restrictions on the construction conditions of the hydropower station project; a construction module for setting power balance constraints and power station operation constraints with the goal of minimizing the total operating cost of the power system, and constructing a power system operation model; an optimization module for optimizing and solving the power system operation model for each alternative expansion capacity of the hydropower station, and obtaining power station operation process data corresponding to each alternative expansion capacity of the hydropower station. A plurality of groups of alternative plans for hydropower station expansion are obtained, each group of alternative plans for hydropower station expansion includes alternative expansion capacity of the hydropower station and corresponding operation process data of the power station; an index calculation module is used to calculate a plurality of performance indicators for each group of alternative plans for hydropower station expansion, and the performance indicators include a plurality of power station economic indicators, a plurality of power station safety indicators and a plurality of power station low-carbon indicators; a selection module is used to use the principal component analysis method to screen the principal component indicators from the plurality of performance indicators, and evaluate the plurality of groups of alternative plans for hydropower station expansion based on the principal component indicators, and select the alternative plan for hydropower station expansion with the highest evaluation value for the hydropower station expansion.

[0022] In a third aspect, the present invention provides a computer device comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the hydropower station capacity expansion optimization method according to the first aspect or any corresponding embodiment thereof by executing the computer instructions.

[0023] In a fourth aspect, the present invention provides a computer-readable storage medium having computer instructions stored thereon, the computer instructions being used to enable a computer to execute the hydropower station capacity expansion optimization method according to the first aspect or any corresponding embodiment thereof.

[0024] In a fifth aspect, the present invention provides a computer program product comprising computer instructions for causing a computer to execute the hydropower station capacity expansion optimization method according to the first aspect or any corresponding embodiment thereof. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0026] Figure 1 is a flow chart of a method for optimizing capacity expansion of a hydropower station according to an embodiment of the present invention;

[0027] Figure 2 is a flow chart of another method for optimizing capacity expansion of a hydropower station according to an embodiment of the present invention;

[0028] Figure 3 2 is a structural block diagram of a device for optimizing capacity expansion of a hydropower station according to an embodiment of the present invention;

[0029] Figure 4 Schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0030] To make the purpose, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making creative efforts shall fall within the scope of protection of the present invention.

[0031] The current expansion capacity plans often only pursue the optimization of a few indicators such as economic benefits and investment costs, ignoring the balanced evaluation of green, low-carbon, safety and stability indicators, resulting in unreasonable expansion plans. Therefore, this application proposes a hydropower station expansion capacity optimization method, which takes into account the flexible adjustment needs of the power system and the construction conditions of the hydropower station project, and formulates multiple groups of hydropower station expansion capacity alternative plans; with the goal of minimizing the total cost of system operation, constructs a power system economic operation model, and optimizes the system operation process under different expansion plans; constructs a multi-dimensional evaluation index system including economic indicators, safety indicators, and low-carbon indicators, and calculates the multi-dimensional evaluation index values ​​of different expansion plans based on the obtained system operation process; finally, constructs a principal component analysis model, determines the principal components and weights, calculates the evaluation scores of different expansion plans, and obtains the optimal expansion plan. It can improve the rationality of the expansion plan and is beneficial to environmental development and safety and stability.

[0032] According to an embodiment of the present invention, an embodiment of a method for optimizing the expansion capacity of a hydropower station is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0033] In this embodiment, a method for optimizing the capacity expansion of a hydropower station is provided. Figure 1 is a flow chart of a method for optimizing the capacity expansion of a hydropower station according to an embodiment of the present invention. It should be noted that if there are substantially the same results, this embodiment does not use Figure 1 The process sequence shown is limited. Figure 1 As shown, the process includes the following steps:

[0034] Step S101 : determining a plurality of alternative expansion capacities of hydropower stations based on the capacity requirements of the power system and the construction conditions of the hydropower station.

[0035] When the power system has a demand for capacity expansion, it is necessary to expand the capacity. Therefore, by analyzing the capacity demand of the power system, the soft constraint upper limit of the capacity expansion of the hydropower station can be determined.

[0036] When expanding a hydropower station, hydraulic structures such as the diversion tunnel, powerhouse, and spillway must be appropriately renovated to meet constraints such as unit installation and safe operation. Different hydropower stations have different foundations, and the civil engineering and electromechanical facilities that require renovation also vary. The capacity of a hydropower station expansion must be determined based on meeting construction requirements. Therefore, analyzing the construction conditions of a hydropower station can determine the hard upper limit of the capacity expansion.

[0037] Under the premise of meeting the capacity requirements and engineering construction conditions, the expansion capacity range is determined to obtain a variety of alternative expansion capacity plans for hydropower stations.

[0038] Exemplarily, when the soft constraint upper limit of the expansion capacity of the hydropower station is a, and the hard constraint upper limit of the expansion capacity of the hydropower station is b, the expansion capacity upper limit is the minimum value of a and b.

[0039] As can be understood, the number of expansion units is an integer, and the corresponding expansion capacity is fixed. The upper limit of the expansion capacity is determined based on the upper limit of the expansion capacity. A positive integer between the upper limit and 0 is used as the candidate number of expansion units. The candidate number of expansion units includes the upper limit. Furthermore, the candidate expansion capacity corresponding to the candidate number of expansion units is determined based on the unit expansion capacity.

[0040] For example, when the upper limit of the number of expansion units that can be satisfied by the upper limit of the expansion capacity is 3, the number of alternative expansion units is 1, 2, and 3. Assuming that the unit expansion capacity is s, the alternative expansion capacities are s, 2s, and 3s.

[0041] Step S102 : Taking the minimization of the total operating cost of the power system as the goal, setting constraints and constructing a power system operation model.

[0042] Since the thermal power units in the power system need to burn coal during operation, the total operating cost of the power system includes the operating cost of the thermal power units.

[0043] Among them, the constraints include the balance constraints between the output of each unit and the load demand of the power system, the operation constraints of thermal power units, the operation constraints of hydropower units, and the operation constraints of new energy units.

[0044] The power system operation model is constructed by combining the objective function of minimizing the total cost and corresponding multiple constraints.

[0045] Step S103 , optimizing and solving the power system operation model for each alternative expansion capacity of the hydropower station, obtaining the power station operation process data corresponding to each alternative expansion capacity of the hydropower station, and obtaining multiple groups of alternative expansion plans for the hydropower station.

[0046] By adopting JAVA programming language modeling and calling CPLEX optimization solver, the operation process of the power system under different alternative expansion capacity is optimized for each alternative expansion capacity, and the operation process data is obtained.

[0047] In one implementation, the power station operation process data includes the output of the thermal power units during the operation of the thermal power units, the output of the hydropower units during the operation of the hydropower units, the output of the new energy units during the operation of the new energy units, the outflow flow during the operation of the hydropower units, and the power load shedding value.

[0048] The potential expansion capacities and corresponding plant operation data are combined into a set of potential expansion plans, resulting in multiple sets of potential expansion plans. The plant operation data can reflect the operational status of the potential expansion capacities and, in subsequent steps, assist in analyzing each set of potential expansion plans and selecting the optimal one.

[0049] Step S104 : calculating a plurality of performance indicators for each group of hydropower station expansion alternatives.

[0050] Among them, the performance indicators include a variety of power plant economic indicators, a variety of power plant safety indicators and a variety of power plant low-carbon indicators.

[0051] Combined with the alternative expansion capacity of the hydropower station in the alternative expansion plan and the corresponding power station operation process data, various performance indicators are calculated to construct a multi-dimensional evaluation index system.

[0052] Step S105 , using principal component analysis to select principal component indicators from multiple performance indicators, and evaluating multiple groups of hydropower station expansion alternatives based on the principal component indicators, and selecting the hydropower station expansion alternative with the highest evaluation value to expand the hydropower station.

[0053] Based on the evaluation index values ​​of each expansion scheme obtained in step S104, the principal component analysis method is used to screen the principal component indicators with the largest initial impact from multiple evaluation index values, and an evaluation matrix is ​​constructed to calculate the covariance matrix and its eigenvalues ​​and eigenvectors corresponding to the evaluation matrix. The principal components and weights affecting the scheme evaluation are determined by sorting the eigenvalues, and the evaluation scores of different expansion schemes are calculated. The optimal expansion scheme is obtained from the multiple hydropower station expansion alternatives according to the evaluation scores, among which the optimal expansion scheme has the highest evaluation value.

[0054] The hydropower station capacity expansion optimization method provided in this embodiment formulates multiple groups of hydropower station capacity expansion alternative plans by considering the flexible adjustment needs of the power system and the construction conditions of the hydropower station project; takes the minimum total cost of system operation as the goal, constructs a power system economic operation model, and optimizes the system operation process under different expansion plans; constructs a multi-dimensional evaluation index system including economic indicators, safety indicators, and low-carbon indicators, and calculates the multi-dimensional evaluation index values ​​of different expansion plans based on the obtained system operation process; finally, constructs a principal component analysis model, determines the principal components and weights, calculates the evaluation scores of different expansion plans, and obtains the optimal expansion plan. This application comprehensively analyzes the impact of different hydropower expansion plans on the power system operation process, ensures the safe and economical operation of the system, and uses principal component analysis technology to achieve a comprehensive evaluation of hydropower expansion plans in the three dimensions of economy, safety, and low carbon. It can avoid the singleness and one-sidedness of traditional methods in capacity expansion optimization, improve the rationality of expansion plans, and is beneficial to environmental development and safety and stability.

[0055] In this embodiment, a method for optimizing the capacity expansion of a hydropower station is provided. Figure 2 is a flow chart of another method for optimizing the capacity expansion of a hydropower station according to an embodiment of the present invention. It should be noted that if there are substantially the same results, this embodiment does not use Figure 2 The process sequence shown is limited. Figure 2 As shown, the process includes the following steps:

[0056] Step S201 : determining a plurality of alternative expansion capacities of hydropower stations based on the capacity requirements of the power system and the construction conditions of the hydropower station.

[0057] Specifically, the above step S201 includes:

[0058] Step S2011 , determining soft constraints for capacity expansion of the hydropower station in combination with the power balance and peak-shaving capacity demand of the power system.

[0059] Collect historical operating data of the power system where the hydropower station is located. The historical operating data includes the total load of the power system and the total output of renewable energy. Calculate the net load of the power system after deducting the output of renewable energy, as shown in the following formula:

[0060]

[0061] Where t is the time period number; are the net load of the power system, the total load of the power system, and the total output of renewable energy (MW) in time period t.

[0062] Based on the net load of the power system Calculate the system power balance during the period of maximum net load of the power system, as shown in the following formula:

[0063]

[0064] Where: N th 、N hp are the available capacities (MW) of thermal power and hydropower in the system after deducting blocked capacity, which are obtained by adding the available capacities of each thermal power unit and hydropower unit; Indicates the maximum net load (MW), The system power profit or loss value (MW) during the period of maximum net load.

[0065] The peak load capacity demand of the power system is expressed as follows:

[0066]

[0067] Where: Indicates the minimum net load (MW), are the regulating capacity (MW) of thermal power and hydropower in the system, which are obtained by adding the adjustable capacity of each thermal power unit and hydropower unit; is the system peak-shaving capacity profit or loss value (MW).

[0068] Considering the power balance and peak load demand of the power system, the soft constraint condition for the expansion capacity of the hydropower station can be expressed as:

[0069]

[0070] Where: N kj is the expanded capacity of the hydropower station (MW).

[0071] Step S2012: determine the hard constraint conditions for the expansion capacity of the hydropower station by combining the upper limit of the hydropower station construction capacity and the upper limit of the transmission channel capacity.

[0072] The expansion capacity of the hydropower station needs to be determined based on the conditions of the project construction, and the installed capacity of the hydropower station after expansion cannot exceed the maximum transmission capacity of the interconnection line to avoid section obstruction. Therefore, considering the transmission channel capacity and the limitations of the project construction conditions, the hard constraint of the expansion capacity of the hydropower station can be expressed as:

[0073]

[0074] Where: are the upper limit of hydropower expansion capacity (MW) corresponding to the construction conditions and transmission channel capacity; N line 、N cap They are the capacity of the transmission channel and the current installed capacity of the hydropower station (MW).

[0075] Step S2013 , combining the soft constraint conditions and the hard constraint conditions to determine the alternative expansion capacities of various hydropower stations allowed by the power system.

[0076] Combining equations (4) and (5), the allowable range of alternative expansion capacity of hydropower stations in the power system is:

[0077]

[0078] Where: It is the upper limit (MW) of hydropower station expansion capacity after comprehensive consideration of soft and hard constraints.

[0079] Taking the single unit capacity N of the hydropower station in operation as an example single As the capacity step, multiple expansion plans are formulated by increasing the number of units one by one, as shown below:

[0080]

[0081] Where: s and S represent the number of expansion units and the maximum allowed number of expansion units, respectively, both of which are integers; ξ s It represents the total expansion capacity (MW) of expansion plan s.

[0082] Step S202 : With the goal of minimizing the total operating cost of the power system, power balance constraints and power station operation constraints are set to construct a power system operation model.

[0083] Specifically, the above step S201 includes:

[0084] Step S2021: construct an objective function with the minimum operation cost of the thermal power unit and the power load shedding cost.

[0085] The operating cost of thermal power units includes coal burning cost and shutdown and startup loss cost. The constructed objective function is:

[0086]

[0087] Where FC, FT, and FL are the total operating cost of the power system, the operating cost of the thermal power unit, and the power load shedding cost (yuan), respectively; i and t are the thermal power unit number and time period number, respectively; T is the number of operating time periods; NG is the number of thermal power units; a 1,i (t / MW2h), a 2,i (t / MWh), a 3,i (t / h) are the coal consumption coefficients of each order of thermal power unit i; PG i,t is the output of thermal power unit i in period t (MW); C f is the coal price (yuan / t); y i,t 、z i,tare the startup and shutdown operations of thermal power unit i in time period t, which are 0-1 integer variables; SUC i 、SDC i are respectively the startup and shutdown costs of thermal power unit i (yuan); PL t is the power load shedding value in period t (MW); C l is the load shedding penalty cost coefficient (yuan / MWh); Δt is the time period step (h).

[0088] Step S2022: setting a first constraint condition based on the output of the thermal power unit, the output of the hydropower unit, the output of the new energy unit, and the load demand of the power system.

[0089] Among them, the first constraint is the power balance constraint, which is expressed as:

[0090]

[0091] Where: k and m are the hydropower station number and hydropower unit number respectively; NH is the number of hydropower stations; NU is the number of hydropower stations; k is the number of units in hydropower station k; PH k,m,t is the output of unit m in hydropower station k in time period t (MW); PW t PL t are the output and load shedding value of the new energy unit in time period t (MW); D t is the system load demand (MW) during period t.

[0092] Step S2023, setting a second constraint condition based on the operation of the thermal power unit; setting a third constraint condition based on the operation of the hydropower unit; and setting a fourth constraint condition based on the operation of the new energy unit.

[0093] Specifically, the second constraint condition is the thermal power unit operation constraint, and setting the thermal power unit operation constraint includes:

[0094] Step a1: setting output constraints of the thermal power generation units based on the output upper limit and output lower limit of the thermal power generation units.

[0095]

[0096] Where: are the lower and upper output limits of thermal power unit i (MW); u i,t is the operating status of unit i in time period t, which is a 0-1 integer variable.

[0097] Step a2: setting the thermal power unit ramping constraint based on the startup ramping rate limit, shutdown ramping rate limit, upward ramping rate limit, and downward ramping rate limit of the thermal power unit.

[0098]

[0099] PG i,t -PG i,t-1 ≤RU i u i,t-1 +SU i y i,t (12)

[0100] PG i,t-1 -PG i,t ≤RD i u i,t +SD i z i,t (13)

[0101] Where: SU i , SD i They are respectively the startup ramp rate limit and shutdown ramp rate limit of thermal power unit i (MW / h); RU i , RD i They are the upward ramp rate limit and downward ramp rate limit (MW / h) of thermal power unit i under normal operating conditions.

[0102] Step a3: setting operating state constraints of the thermal power unit based on the initial output and initial operating state of the thermal power unit.

[0103]

[0104] y i,t -z i,t =u i,t -u i,t-1 (15)

[0105] y i,t +z i,t ≤1 (16)

[0106]

[0107] Where: PG i,beg is the initial output of thermal power unit i (MW); u i,beg is the initial operating state of thermal power unit i; TA i TB i are the minimum continuous grid-connected period and off-grid period of thermal power unit i (h).

[0108] Specifically, the third constraint condition is the hydropower unit operation constraint, and setting the hydropower unit operation constraint includes:

[0109] Step b1: setting water balance constraints based on the reservoir capacity of the hydropower station, the interval flow of the hydropower station, and the outflow flow of the hydropower station.

[0110]

[0111] Where: VS k,t is the storage capacity of hydropower station k in time period t (m3); B k,t 、R k,t are the interval flow and outflow flow of hydropower station k in time period t (m3 / s); IU k is the set of power stations directly upstream of hydropower station k; kh is the number of the power station directly upstream of power station k; τ kh,k is the water flow delay time from hydropower station kh to hydropower station k (h).

[0112] Step b2: setting flow constraints based on the power generation flow of the hydropower station, the abandoned water flow, the power generation flow of the generator set, the outflow limit of the hydropower station, and the power generation flow limit of the generator set.

[0113]

[0114] Where: R' k,t , R” k , t are the power generation flow and abandoned water flow of hydropower station k in time period t (m3 / s); R' k,m,t is the power generation flow of unit m in hydropower station k in time period t (m3 / s); are the minimum and maximum discharge limits of hydropower station k (m3 / s); (R' k,m ) min 、(R' k,m ) max are the minimum power flow limit and maximum power flow limit of unit m in hydropower station k (m3 / s); u k,m,t is the operating status of unit m in hydropower station k in time period t, which is a 0-1 integer variable.

[0115] Step b3: setting storage capacity constraints based on the storage capacity limit, initial storage capacity, and final storage capacity of the hydropower station.

[0116]

[0117] Where: are the minimum storage capacity limit and maximum storage capacity limit of hydropower station k (m3); VS k,beg VS k,end are the initial storage capacity and final storage capacity of hydropower station k (m3).

[0118] Step b4: setting head constraints based on the net head, head loss, water level above the dam, and tailwater level of the hydropower station.

[0119]

[0120] Where: H k,t、 are the net head and head loss (m) of hydropower station k in period t; ZF k,t 、ZT k,t is the water level above the dam and tailwater level of hydropower station k in time period t (m).

[0121] Step b5: setting the output constraint of the hydropower generating unit based on the output limit of the hydropower generating unit.

[0122]

[0123] Where: are the lower and upper output limits (MW) of unit m in hydropower station k; is the power generation equation of unit m in hydropower station k.

[0124] Step b6: setting the basic characteristic curve constraints of the hydropower unit based on the tailwater level-discharge flow equation, water level-storage capacity equation and head loss equation of the hydropower station.

[0125]

[0126] Where: is the tailwater level-discharge equation of hydropower station k; is the water level-storage capacity equation of hydropower station k; is the head loss equation for hydropower station k.

[0127] Specifically, the fourth constraint condition is the new energy unit operation constraint, and setting the new energy unit operation constraint includes:

[0128] The operation constraints of new energy units are set based on the lower limit of new energy utilization rate and the maximum output of new energy units.

[0129]

[0130] Where: τ ne is the lower limit of new energy utilization rate; PW t obs is the maximum power generation capacity (MW) of the new energy units in the power system during time period t.

[0131] Step S2024: construct a power system operation model by combining the objective function and multiple constraints.

[0132] Step S203 , optimizing and solving the power system operation model for each alternative expansion capacity of the hydropower station, obtaining the power station operation process data corresponding to each alternative expansion capacity of the hydropower station, and obtaining multiple groups of alternative expansion plans for the hydropower station.

[0133] Based on the economic operation model of the power system constructed by equations (8)-(27), different expansion schemes ξ are optimized with a year as the cycle and an hour as the time period. s The power system operation process under (0≤s≤S), including the thermal power unit output PG during the thermal power unit output process i,t , the output PH of the hydropower unit during the hydropower unit output process k,m,t , the output PW of new energy units during the new energy output process t , outflow flow R during the operation of hydropower units k,t , the power load shedding value PL during the power load shedding process t wait.

[0134] In one implementation, the present application uses the JAVA programming language to model and invokes the CPLEX optimization solver to solve the model. Other methods that can solve the model can also be used, and are not specifically listed here.

[0135] Step S204 : calculating a plurality of performance indicators for each group of hydropower station expansion alternatives.

[0136] Among them, the performance indicators include a variety of power plant economic indicators, a variety of power plant safety indicators and a variety of power plant low-carbon indicators.

[0137] Specifically, the above step S204 includes:

[0138] Step S2041: Calculate the economic performance index of the power plant.

[0139] The economic indicators of power plants take into account the cost of expansion, the operating costs of hydropower units, and the revenue from power generation. Specifically, they include:

[0140] Step d1 : calculating the expansion investment cost based on the alternative expansion capacity of the hydropower station and the unit capacity expansion cost as a first economic indicator.

[0141] The investment cost for capacity expansion is equal to the product of the capacity expansion of the power station and the unit capacity expansion cost, which can be calculated as follows:

[0142] η s,1 =sN single C cap (28)

[0143] Where: η s,1 is the first economic index, which represents the expansion investment cost index value (yuan) of expansion plan s, that is; C cap is the unit capacity expansion cost (yuan / MW).

[0144] Step d2: Calculate the operating cost of the thermal power units in the power system as a second economic indicator.

[0145] η s,2 =FT s (29)

[0146] Where: η s,2 is the second economic index, which is the power system operation cost index value of expansion plan s (yuan); FT s It represents the operating cost of thermal power units in the power system corresponding to the expansion plan s (yuan).

[0147] Step d3, calculating the increased hydropower generation as the third economic indicator by combining the alternative expansion plan of the hydropower station and the corresponding hydropower unit output when the expansion is not carried out.

[0148] The additional power generation refers to the additional power generation resulting from the improved water energy utilization rate of the hydropower station after the expansion, and is calculated as follows:

[0149]

[0150] Where: η s,3 is the third economic index, which is the hydropower generation index value (MWh) of expansion plan s; s,k,m,t is the output (MW) of unit m in hydropower station k corresponding to expansion plan s in time period t; PH 0,k,m,t is the output (MW) of unit m in hydropower station k during time period t when there is no capacity expansion.

[0151] Step d4, calculating the electricity market revenue in combination with the average electricity price as the fourth economic indicator.

[0152] The electricity market revenue refers to the revenue gained by a hydropower station from participating in the electricity market, which is calculated as follows:

[0153]

[0154] Where: η s,4 is the fourth economic index, which is the power market revenue index value (yuan) of expansion plan s; mh is the month number; PR mh is the average on-grid electricity price in month mh (yuan / MWh); α mh , β mh They are the starting and ending time period numbers corresponding to the mh month respectively.

[0155] Step S2042: Calculate the power plant safety index.

[0156] The power station safety indicators take into account the load shedding state and the capacity shortage state. Specifically, they include:

[0157] Step e1: Calculate the power shortage probability based on the power load shedding value as a first safety indicator.

[0158] The power shortage probability indicates the probability that the available capacity of the power generation system cannot meet the system load demand, resulting in load shedding. It is calculated as follows:

[0159]

[0160] Where: η s,5 is the first safety indicator, and is the power shortage probability index value of expansion plan s; PL s,t is the load shedding value (MW) of the system corresponding to the expansion plan s in time period t; LOL s,t is the load shedding state of the system corresponding to the expansion plan s in time period t, which is a 0-1 integer variable, LOL s,t =1 means the system has load shedding during period t, otherwise LOL t =0.

[0161] Step e2: Calculate the power shortage expectation as the second safety indicator.

[0162] The power shortage expectation represents the expected value of the power supply energy shortage caused by the insufficient available capacity of the power generation system. It is calculated as follows:

[0163]

[0164] Where: η s,6 is the second safety indicator, which is the expected power shortage indicator value (MWh) of expansion plan s.

[0165] Step e3, calculating the available capacity margin of the power system based on the output of the thermal power units, the output of the hydropower units and the load demand of the power system as a third safety indicator.

[0166] Available capacity margin represents the minimum difference between the available generating capacity of hydropower and thermal power and the load demand, and is calculated as follows:

[0167]

[0168] Where: η s,7 is the third safety indicator, and is the available capacity margin indicator value (MW) of expansion plan s; u s,i,t is the operating status of thermal power unit i in time period t in expansion plan s; u s,k,m,t is the operating status of unit m in power station k in expansion plan s during time period t.

[0169] Step S2043: Calculate the low-carbon index of the power plant.

[0170] Among them, the low-carbon index of power plants takes into account the use of carbon and clean energy. Specifically, it includes:

[0171] Step f1, calculating the coal saving amount of the power system based on the output of the thermal power units as a first low-carbon indicator.

[0172] The coal saving of the power system refers to the reduction in system coal consumption after hydropower expansion compared to without expansion, and is calculated as follows:

[0173]

[0174] Where: η s,8 is the first low-carbon index, is the system coal saving index value (t) of expansion plan s; PG s,i,t is the output (MW) of thermal power unit i in time period t in scheme s; Coal(s) is the total coal consumption of the system in scheme s (t).

[0175] Step f2: Calculate the carbon emission reduction of the power system based on the output of the thermal power units as the second low-carbon indicator.

[0176] Carbon emission reduction refers to the reduction in carbon emissions generated by the operation of the hydropower system after the expansion of hydropower capacity compared to without the expansion, and is calculated as follows:

[0177]

[0178] Where: η s,9 is the second low-carbon indicator, which is the carbon emission reduction index value (t) of expansion plan s; δ is the carbon emission coefficient per unit coal-fired power (tCO2 / MWh).

[0179] In step f3, the clean energy electricity ratio is calculated based on the output of the hydropower units, the output of the new energy units, the power load shedding value, and the load demand of the power system as the third low-carbon indicator.

[0180] The clean energy electricity ratio refers to the ratio of clean energy power generation to the total power generation of the system, which is calculated as follows:

[0181]

[0182] Where: η s,10 is the third low-carbon index, which is the clean energy electricity proportion index value of expansion plan s; PW s,t is the output of renewable energy in expansion plan s during period t (MW).

[0183] Step S205 , using principal component analysis to select principal component indicators from multiple performance indicators, and evaluating multiple groups of hydropower station expansion alternatives based on the principal component indicators, and selecting the hydropower station expansion alternative with the highest evaluation value to expand the hydropower station.

[0184] Based on the various performance indicators in step S204, the evaluation value of each expansion solution is calculated respectively, and an evaluation matrix η is constructed as shown below:

[0185]

[0186] Where: η sj is the evaluation value of the jth performance indicator in the sth expansion plan, j=10.

[0187] Multiple performance indicators are standardized and the covariance matrix of the performance indicators is constructed.

[0188] Specifically, various performance indicators are classified into indicators. The larger the indicator value, the better it is as a very large indicator, and the smaller the indicator value, the better it is as a very small indicator.

[0189] Among them, increased power generation, electricity market revenue, available capacity margin, system coal saving, carbon emission reduction, and the proportion of clean energy electricity are extremely large indicators; expansion investment costs, system operating costs, power shortage probability, and power shortage expectations are extremely small indicators.

[0190] Perform the same trend transformation on the data elements in the evaluation matrix, keep the extremely large index value unchanged, take the inverse of the extremely small index value, and transform it into the extremely large index, as shown in the following formula:

[0191]

[0192] Furthermore, the matrix after trending is normalized, as shown in the following formula:

[0193]

[0194] Where: κ j are the standard value and standard deviation of the jth column in the evaluation matrix η, respectively.

[0195] Finally, calculate the covariance matrix R of the evaluation matrix = (r sj ) J×J , as shown below:

[0196] r sj =cov(η s ,η j ) (41)

[0197] Where: η s ,η j are the column vectors of the sth and jth columns in the evaluation matrix η respectively; cov(η s ,η j ) is η s ,η j The covariance between .

[0198] Furthermore, the eigenvalues ​​and eigenvectors of the covariance matrix are calculated and sorted according to the eigenvalues, and the contribution rates and cumulative contribution rates of various performance indicators are calculated based on the corresponding eigenvectors.

[0199] Specifically, solve the characteristic equation of the covariance matrix R and calculate the eigenvalue. The characteristic equation is as follows:

[0200] |R-λE|=0 (42)

[0201] Where: E is the unit matrix; λ is the eigenvalue vector; 0 is the zero vector.

[0202] Sort the eigenvalues ​​in descending order, and the sorted eigenvalue vector is expressed as:

[0203] λ={λ1,L,λ tz ,L,λ TZ} (43)

[0204] Where: tz, TZ are the eigenvalue number and the number of eigenvalues ​​respectively. Each eigenvalue satisfies: λ1≥λ2≥L≥λ TZ .

[0205] For each eigenvalue λ tz , find the corresponding eigenvector v by solving the following equation tz :

[0206] (R-λ tz E)v tz =0 (44)

[0207] Where: v tz is the eigenvalue λ tz The corresponding eigenvector.

[0208] Based on the obtained eigenvalues, the contribution rate and cumulative contribution rate of each component are calculated:

[0209]

[0210] Where: w tz is the contribution rate of the tzth component; γ ts is the cumulative contribution rate of the first ts components.

[0211] Furthermore, a preset number of performance indicators whose cumulative contribution rates exceed a contribution rate threshold are selected as principal component indicators, and the weights of the principal component indicators are calculated.

[0212] Specifically, according to γ ts ≥0.9, that is, the first TS components corresponding to the eigenvalues ​​with cumulative contribution rates exceeding 90% are taken as the principal component indicators, and the projection matrix V of the evaluation matrix η is constructed:

[0213] V=η×[v1,v2,L,v TS ] (46)

[0214] The weight of each principal component is calculated as follows:

[0215]

[0216] Furthermore, for each group of hydropower station expansion alternatives, the principal component index and the corresponding weight are weighted and summed to calculate the corresponding evaluation score.

[0217] Specifically, the comprehensive evaluation scores of each expansion plan are as follows:

[0218]

[0219] Where: PC s is the final evaluation score of the expansion plan s; V s,tz is the element in the sth row and tzth column of the projection matrix V.

[0220] The expansion plan with the highest score is the optimal hydropower expansion plan.

[0221] The hydropower station capacity expansion optimization method provided in this embodiment has the following advantages over traditional methods:

[0222] 1) Consider hydropower capacity expansion in the overall power system, build an economic operation model for the power system that includes hydropower units, thermal power units, and new energy sources, and comprehensively analyze the impact of different hydropower capacity expansion plans on the power system operation process to ensure safe and economic operation of the system.

[0223] 2) Using principal component analysis technology, a comprehensive evaluation of hydropower expansion plans in the three dimensions of economy, safety, and low carbon was achieved. The evaluation included ten evaluation indicators: expansion investment cost, system operating cost, increased power generation, electricity market revenue, power shortage probability, power shortage expectation, available capacity margin, system coal saving, carbon emission reduction, and the proportion of clean energy electricity. This can avoid the singleness and one-sidedness of traditional methods in optimizing expansion capacity and provide decision-makers with a reference for decision-making.

[0224] This embodiment also provides a device for optimizing the capacity expansion of a hydropower station. This device is used to implement the above-mentioned embodiments and preferred implementations, and details already described will not be repeated. As used below, the term "module" may refer to a combination of software and / or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, implementation using hardware, or a combination of software and hardware, is also possible and contemplated.

[0225] This embodiment provides a device for optimizing the capacity expansion of a hydropower station. Figure 3As shown, including:

[0226] The alternative module 301 is used for determining the alternative expansion capacities of various hydropower stations based on the capacity requirements of the power system and the restrictions on the construction conditions of the hydropower station project.

[0227] The construction module 302 is used to set constraints and construct a power system operation model with the goal of minimizing the total operation cost of the power system.

[0228] The optimization module 303 is used to optimize and solve the power system operation model for each alternative expansion capacity of the hydropower station, obtain the power station operation process data corresponding to each alternative expansion capacity of the hydropower station, and obtain multiple groups of alternative expansion plans for the hydropower station, each group of alternative expansion plans for the hydropower station including the alternative expansion capacity of the hydropower station and the corresponding power station operation process data.

[0229] The indicator calculation module 304 is used to calculate multiple performance indicators for each group of hydropower station expansion alternatives, where the performance indicators include multiple power station economic indicators, multiple power station safety indicators, and multiple power station low-carbon indicators.

[0230] The selection module 305 is used to use the principal component analysis method to screen the principal component indicators from multiple performance indicators, and evaluate multiple groups of hydropower station expansion alternatives based on the principal component indicators, and select the hydropower station expansion alternative with the highest evaluation value to expand the hydropower station.

[0231] In some optional implementations, the alternative module 301 includes:

[0232] The first determining unit is used to determine the soft constraint conditions for the expansion capacity of the hydropower station in combination with the power balance and peak-shaving capacity demand of the power system.

[0233] The second determining unit is used to determine the hard constraint conditions for the expansion capacity of the hydropower station by combining the upper limit of the hydropower station project construction capacity and the upper limit of the transmission channel capacity.

[0234] The third determining unit is used to determine the alternative expansion capacities of multiple hydropower stations allowed by the power system by combining the soft constraint conditions and the hard constraint conditions.

[0235] In some optional implementations, the building block 302 includes:

[0236] The first construction unit is used to construct an objective function with the operation cost of the thermal power unit and the power load shedding cost as the minimum.

[0237] The first setting unit is used to set a first constraint condition based on the output of the thermal power unit, the output of the hydropower unit and the output of the new energy unit and the load demand of the power system.

[0238] The second setting unit is used to set the second constraint condition based on the operation of the thermal power unit; set the third constraint condition based on the operation of the hydropower unit; and set the fourth constraint condition based on the operation of the new energy unit.

[0239] The second construction unit is used to construct a power system operation model by combining the objective function and multiple constraints.

[0240] In some optional embodiments, the first building block includes:

[0241] The first construction subunit is used to construct the objective function:

[0242]

[0243] Where FC, FT, and FL are the total operating cost of the power system, the operating cost of the thermal power unit, and the power load shedding cost, respectively; i and t are the thermal power unit number and time period number, respectively; T is the number of operating time periods; NG is the number of thermal power units; a 1,i 、a 2,i 、a 3,i are the coal consumption coefficients of each order of thermal power unit i; PG i,t is the output of thermal power unit i in period t; C f is the price of coal; y i,t 、z i,t are the startup and shutdown operations of thermal power unit i in time period t, which are 0-1 integer variables; SUC i 、SDC i are the startup and shutdown costs of thermal power unit i; PL t is the power load shedding value in period t; C l is the load shedding penalty cost coefficient; Δt is the time period step.

[0244] In some optional implementations, the second setting unit includes:

[0245] The first setting subunit is used to set the output constraint of the thermal power group based on the output upper limit and the output lower limit of the thermal power group.

[0246] The second setting subunit is used to set the thermal power unit climbing constraint based on the startup climbing rate limit, shutdown descending rate limit, upward climbing rate limit and downward descending rate limit of the thermal power unit.

[0247] The third setting subunit is used to set the operating state constraint of the thermal power unit based on the initial output and initial operating state of the thermal power unit.

[0248] The fourth setting subunit is used to set water balance constraints based on the storage capacity of the hydropower station, the interval flow of the hydropower station, and the outflow flow of the hydropower station.

[0249] The fifth setting subunit is used to set flow constraints based on the power generation flow of the hydropower station, the abandoned water flow, the power generation flow of the generator set, the outflow flow limit of the hydropower station, and the power generation flow limit of the generator set.

[0250] The sixth setting subunit is used to set storage capacity constraints based on the storage capacity limit, initial storage capacity and final storage capacity of the hydropower station.

[0251] The seventh setting subunit is used to set the head constraint based on the net head, head loss, water level above the dam, and tailwater level of the hydropower station.

[0252] The eighth setting subunit is configured to set the output constraint of the hydropower generating unit based on the output limit of the hydropower generating unit.

[0253] The ninth setting subunit is used to set the basic characteristic curve constraints of the hydropower unit based on the tailwater level-discharge flow equation, water level-storage capacity equation and head loss equation of the hydropower station.

[0254] The tenth setting subunit is used to set the operation constraints of the new energy unit based on the lower limit of the new energy utilization rate and the maximum output of the new energy unit.

[0255] In some optional implementations, the indicator calculation module 304 includes:

[0256] The first indicator calculation unit is used to calculate the expansion investment cost based on the alternative expansion capacity of the hydropower station and the unit capacity expansion cost as the first economic indicator.

[0257] The second indicator calculation unit is used to calculate the operating cost of the thermal power units in the power system as a second economic indicator.

[0258] The third indicator calculation unit is used to calculate the increased hydropower generation as the third economic indicator by combining the alternative expansion plan of the hydropower station and the corresponding hydropower unit output when the expansion is not carried out.

[0259] The fourth indicator calculation unit is used to calculate the electricity market revenue in combination with the average electricity price as the fourth economic indicator.

[0260] The fifth indicator calculation unit is used to calculate the power shortage probability based on the power load shedding value as the first safety indicator.

[0261] The sixth indicator calculation unit is used to calculate the power shortage expectation as the second safety indicator.

[0262] The seventh indicator calculation unit is used to calculate the available capacity margin of the power system based on the output of the thermal power unit, the output of the hydropower unit and the load demand of the power system as the third safety indicator.

[0263] The eighth indicator calculation unit is used to calculate the coal saving amount of the power system based on the output of the thermal power units as the first low-carbon indicator.

[0264] The ninth indicator calculation unit is used to calculate the carbon emission reduction of the power system based on the output of the thermal power units as the second low-carbon indicator.

[0265] The tenth indicator calculation unit is used to calculate the proportion of clean energy electricity as the third low-carbon indicator by combining the output of hydropower units, the output of new energy units, the power load shedding value and the load demand of the power system.

[0266] In some optional implementations, the selection module 305 includes:

[0267] The first calculation unit is used to perform standardization processing on multiple performance indicators and construct a covariance matrix of the performance indicators.

[0268] The second calculation unit is used to calculate the eigenvalues ​​and eigenvectors of the covariance matrix, sort them according to the eigenvalues, and calculate the contribution rates and cumulative contribution rates of multiple performance indicators based on the corresponding eigenvectors.

[0269] The third calculation unit is used to select a preset number of performance indicators whose cumulative contribution rates exceed a contribution rate threshold as principal component indicators, and calculate the weights of the principal component indicators.

[0270] The fourth calculation unit is used to calculate the corresponding evaluation score for each group of hydropower station expansion alternative plans by using the principal component index and the corresponding weighted sum.

[0271] The further functional description of each of the above modules and units is the same as that of the above corresponding embodiments and will not be repeated here.

[0272] The hydropower station capacity expansion optimization device in this embodiment is presented in the form of a functional unit, where the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above functions.

[0273] The embodiment of the present invention also provides a computer device having the above Figure 3 The hydropower station capacity expansion optimization device shown.

[0274] See also Figure 4 , Figure 4 is a structural diagram of a computer device provided by an optional embodiment of the present invention, such as Figure 4As shown, the computer device includes: one or more processors 10, memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components utilize different buses to communicate with each other and can be installed on a common mainboard or installed in other ways as needed. The processor can process the instructions executed in the computer device, including instructions stored in the memory or on the memory to display the graphical information of the GUI on an external input / output device (such as, a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Equally, multiple computer devices can be connected, and each device provides part of the necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 4 A processor 10 is taken as an example.

[0275] The processor 10 may be a central processing unit, a network processor, or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit, a programmable logic device, or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic, or any combination thereof.

[0276] The memory 20 stores instructions that can be executed by at least one processor 10, so as to enable at least one processor 10 to execute the method shown in the above embodiment.

[0277] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and application programs required for at least one function; the data storage area may store data created based on the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely located relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0278] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0279] The computer device further includes an input device 30 and an output device 40. The processor 10, the memory 20, the input device 30 and the output device 40 may be connected via a bus or other means. Figure 4 The bus connection is taken as an example.

[0280] The input device 30 can receive input digital or character information and generate key signal input related to user settings and function control of the computer device, such as a touch screen, a keypad, a mouse, a trackpad, a touch pad, an indicator stick, one or more mouse buttons, a trackball, a joystick, etc. The output device 40 can include a display device, an auxiliary lighting device (e.g., an LED), and a tactile feedback device (e.g., a vibration motor). The above-mentioned display device includes but is not limited to a liquid crystal display, a light emitting diode, a display, and a plasma display. In some optional embodiments, the display device can be a touch screen.

[0281] The embodiment of the present invention also provides a computer-readable storage medium. The above-mentioned method according to the embodiment of the present invention can be implemented in hardware, firmware, or implemented as a computer code that can be recorded in a storage medium, or implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and downloaded through a network and will be stored in a local storage medium, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state drive, etc.; further, the storage medium can also include a combination of the above-mentioned types of memory. It can be understood that a computer, a processor, a microprocessor controller or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor or hardware, the method shown in the above embodiment is implemented.

[0282] A portion of the present invention may be applied as a computer program product, such as a computer program instruction, which, when executed by a computer, can call or provide the method and / or technical solution according to the present invention through the operation of the computer. Those skilled in the art should understand that the form in which the computer program instruction exists in a computer-readable medium includes, but is not limited to, a source file, an executable file, an installation package file, etc. Accordingly, the way in which the computer program instruction is executed by the computer includes, but is not limited to: the computer directly executes the instruction, or the computer compiles the instruction and then executes the corresponding compiled program, or the computer reads and executes the instruction, or the computer reads and installs the instruction and then executes the corresponding installed program. Here, the computer-readable medium may be any available computer-readable storage medium or communication medium that can be accessed by the computer.

[0283] Although the embodiments of the present invention have been described with reference to the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention. Such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A method for optimizing the capacity expansion of a hydropower station, characterized in that: The method comprises: Determine the capacity expansion options for various hydropower stations based on the power system's capacity requirements and the constraints on hydropower station construction conditions; Taking the minimization of the total operating cost of the power system as the goal, setting constraints and constructing a power system operation model; For each alternative expansion capacity of a hydropower station, the power system operation model is optimized and solved to obtain power station operation process data corresponding to each alternative expansion capacity of the hydropower station, thereby obtaining multiple groups of alternative expansion plans for the hydropower station, each group of the alternative expansion plans for the hydropower station including the alternative expansion capacity of the hydropower station and the corresponding power station operation process data; For each set of hydropower station expansion alternatives, multiple performance indicators are calculated, including multiple power station economic indicators, multiple power station safety indicators, and multiple power station low-carbon indicators; The principal component analysis method is used to screen the principal component indicators from the multiple performance indicators, and multiple groups of hydropower station expansion alternatives are evaluated based on the principal component indicators, and the hydropower station expansion alternative with the highest evaluation value is selected to expand the hydropower station.

2. The hydropower station capacity expansion optimization method according to claim 1, characterized in that: Based on the capacity requirements of the power system and the construction conditions of the hydropower station, various alternative expansion capacities of the hydropower station are determined, including: Determining soft constraints on the capacity expansion of the hydropower station in combination with the power balance and peak-shaving capacity requirements of the power system; Determine the hard constraint conditions for the expansion capacity of the hydropower station by combining the upper limit of the hydropower station construction capacity and the upper limit of the transmission channel capacity; The soft constraint conditions and the hard constraint conditions are combined to determine a plurality of alternative expansion capacities of the hydropower station allowed by the power system.

3. The hydropower station capacity expansion optimization method according to claim 1, characterized in that: The method of minimizing the total operating cost of the power system, setting constraints, and constructing a power system operation model includes: The objective function is constructed by minimizing the operating cost of thermal power units and the power load shedding cost; The first constraint condition is set based on the output of thermal power units, hydropower units, and new energy units and the load demand of the power system; The second constraint condition is set based on the operation of thermal power units; the third constraint condition is set based on the operation of hydropower units; and the fourth constraint condition is set based on the operation of new energy units. The power system operation model is constructed by combining the objective function and multiple constraints.

4. The hydropower station capacity expansion optimization method according to claim 3, characterized in that: The objective function is: Wherein, FC, FT, and FL are the total operating cost of the power system, the operating cost of the thermal power unit, and the power load shedding cost, respectively; i and t are the thermal power unit number and time period number, respectively; T is the number of operating hours; NG is the number of thermal power units; a 1,i 、a 2,i 、a 3,i are the coal consumption coefficients of each order of thermal power unit i; PG i,t is the output of thermal power unit i in period t; C f is the price of coal; y i,t 、z i,t are the startup and shutdown operations of thermal power unit i in time period t, which are 0-1 integer variables; SUC i 、SDC i are the startup and shutdown costs of thermal power unit i; PL t is the power load shedding value in period t; C l is the load shedding penalty cost coefficient; Δt is the time period step.

5. The method for optimizing the capacity expansion of a hydropower station according to claim 3, characterized in that: The second constraint condition is set based on the operation of the thermal power unit; Setting the third constraint condition based on the operation of the hydropower unit; The fourth constraint condition is set based on the operation of new energy units, including: Set output constraints for thermal power units based on their upper and lower output limits; Set thermal power unit ramp constraints based on the startup ramp rate limit, shutdown ramp rate limit, upward ramp rate limit, and downward ramp rate limit of the thermal power unit; Setting the operating state constraints of the thermal power units based on the initial output and initial operating state of the thermal power units; Set water balance constraints based on the hydropower station's storage capacity, hydropower station's interval flow, and hydropower station's outflow flow; Set flow constraints based on the hydropower station's generated flow, abandoned water flow, generated flow of the generator set, hydropower station's outflow limit, and generated flow limit of the generator set; Set storage capacity constraints based on the hydropower station's storage capacity limit, initial storage capacity, and final storage capacity; Set head constraints based on the hydropower station's net head, head loss, dam water level, and tailwater level; Set the output constraints of hydropower units based on their output limits; Set the basic characteristic curve constraints of the hydropower unit based on the tailwater level-discharge flow equation, water level-storage capacity equation and head loss equation of the hydropower station; The operation constraints of new energy units are set based on the lower limit of new energy utilization rate and the maximum output of new energy units.

6. The method for optimizing the capacity expansion of a hydropower station according to any one of claims 1 to 5, characterized in that: The power plant operation process data includes: The output of thermal power units during the operation of thermal power units, the output of hydropower units during the operation of hydropower units, the output of new energy units during the operation of new energy units, the outflow flow during the operation of hydropower units, and the power load shedding value.

7. The method for optimizing the capacity expansion of a hydropower station according to claim 6, characterized in that: For each set of hydropower station expansion alternatives, multiple performance indicators are calculated, including: Calculating the expansion investment cost based on the alternative expansion capacity and the unit capacity expansion cost of the hydropower station as a first economic indicator; Calculating the operating cost of the thermal power units in the power system as a second economic indicator; Calculate the additional hydropower generation based on the alternative expansion plan of the hydropower station and the corresponding output of the hydropower units when no expansion is performed, as the third economic indicator; Calculate the electricity market revenue in combination with the average electricity price as the fourth economic indicator; Calculating a power shortage probability based on the power load shedding value as a first safety indicator; Calculate the power shortage expectation as a second safety indicator; Calculating the available capacity margin of the power system based on the output of the thermal power unit, the output of the hydropower unit, and the load demand of the power system as a third safety indicator; Calculating the coal saving of the power system based on the output of the thermal power unit as a first low-carbon indicator; Calculating the carbon emission reduction of the power system based on the output of the thermal power units as a second low-carbon indicator; The proportion of clean energy electricity is calculated based on the output of the hydropower unit, the output of the new energy unit, the power load cut value and the load demand of the power system as the third low-carbon indicator.

8. The method for optimizing the capacity expansion of a hydropower station according to claim 1, characterized in that: The principal component analysis method is used to select the principal component index from the multiple performance indexes, including: Standardizing the multiple performance indicators and constructing a covariance matrix of the performance indicators; Calculating the eigenvalues ​​and eigenvectors of the covariance matrix, sorting them according to the eigenvalues, and calculating the contribution rates and cumulative contribution rates of the multiple performance indicators based on the corresponding eigenvectors; Selecting a preset number of performance indicators whose cumulative contribution rates exceed a contribution rate threshold as the principal component indicators, and calculating the weights of the principal component indicators; The evaluating of multiple groups of hydropower station expansion alternatives based on the principal component index includes: For each group of hydropower station expansion alternatives, the principal component indicators and the corresponding weights are weighted and summed to calculate the corresponding evaluation score.

9. A device for optimizing the capacity expansion of a hydropower station, characterized in that: The device comprises: The alternative module is used to determine the alternative expansion capacity of various hydropower stations based on the capacity requirements of the power system and the construction conditions of the hydropower station; A construction module is used to set power balance constraints and power station operation constraints with the goal of minimizing the total operating cost of the power system, and to construct a power system operation model; an optimization module for optimizing and solving the power system operation model for each alternative expansion capacity of a hydropower station, obtaining power station operation process data corresponding to each alternative expansion capacity of the hydropower station, and obtaining multiple groups of alternative expansion plans for the hydropower station, each group of the alternative expansion plans for the hydropower station including the alternative expansion capacity of the hydropower station and the corresponding power station operation process data; An indicator calculation module is used to calculate multiple performance indicators for each group of hydropower station expansion alternatives, wherein the performance indicators include multiple power station economic indicators, multiple power station safety indicators, and multiple power station low-carbon indicators; The selection module is used to use the principal component analysis method to screen the principal component indicators from the multiple performance indicators, and evaluate multiple groups of hydropower station expansion alternatives based on the principal component indicators, and select the hydropower station expansion alternative with the highest evaluation value to expand the hydropower station.

10. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the hydropower station capacity expansion optimization method according to any one of claims 1 to 8 by executing the computer instructions.

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