CN117728509A - Wind-solar-water scheduling strategy optimization method and system based on hydropower unit aggregation - Google Patents

Abstract

The invention provides a wind, light and water scheduling strategy optimization method and system based on hydropower unit aggregation, comprising the following steps: acquiring new energy historical operation data and data of each generator set of a wind, light, water and fire complementary power generation system; determining a corresponding optimization strategy coefficient according to the reservoir water storage capacity of the hydropower station, and solving a pre-constructed wind-solar-water coordinated operation optimization model based on the optimization strategy coefficient, new energy historical operation data and each generator set data to obtain an optimized output scheme of the wind-solar-water-fire complementary power generation system; the wind-light-water coordination operation optimization model is based on an adjustable hydropower unit polymerization operation optimization model, and is built with the maximum clean energy consumption of wind-light-water as a target; according to the method, the random fluctuation problem of wind-light-water resource power generation is solved through the optimization strategy coefficient, the solving accuracy of the wind-light-water coordinated operation optimization model is improved through the adjustable hydropower unit aggregate operation optimization model, and the medium-long-term optimization control with high accuracy is realized.

Description

Wind-solar-water scheduling strategy optimization method and system based on hydropower unit aggregation

Technical Field

The invention belongs to the technical field of new energy power generation, and particularly relates to a wind, light and water scheduling strategy optimization method and system based on hydropower unit aggregation.

Background

With the rapid development of clean energy power generation technology in recent years, clean energy mainly represented by water energy, wind energy and solar energy plays an increasingly important role in an electric power system. In hydropower enrichment areas, the problems of cooperative absorption of new energy and hydropower are increasingly prominent due to the influence of wind-solar resource random fluctuation, uncertainty of water inflow and other factors.

The problem of time sequence production simulation optimization of new energy and hydropower belongs to the problem of ultra-large scale mixed integer nonlinear programming under multi-time period coupling, and needs to consider various factors such as hydropower month/quarter adjustment capability, a thermal power unit combination mode, random fluctuation of new energy output and the like. In the existing research, a new energy and hydropower combined operation optimization model for short-term scheduling is complex, the new energy and hydropower combined operation optimization model is difficult to directly apply and solve under the condition that month and quarter are taken as optimization periods, the middle-long-term operation optimization method is usually optimized based on typical days, the random fluctuation of new energy output and water supply is not fully considered, and the accuracy of a solving result is low.

Disclosure of Invention

In order to overcome the defects in the prior art, the application of the invention provides a wind-solar-water scheduling strategy optimization method based on hydropower unit aggregation, which comprises the following steps:

acquiring new energy historical operation data and data of each generator set of a wind, light, water and fire complementary power generation system;

determining a corresponding optimization strategy coefficient according to the reservoir water storage capacity of the hydropower station, and solving a pre-constructed wind-solar-water coordinated operation optimization model based on the optimization strategy coefficient, new energy historical operation data and each generator set data to obtain an optimized output scheme of the wind-solar-water-fire complementary power generation system;

the wind-solar-water coordination operation optimization model is constructed based on an adjustable hydropower unit polymerization operation optimization model and aims at the maximum clean energy consumption of wind, solar and water, wherein the adjustable hydropower unit polymerization operation optimization model is constructed by a plurality of hydropower unit output constraints based on hydropower unit startup number polymerization.

Preferably, the optimization strategy coefficients include one or more of the following: a wind discarding punishment coefficient, a light discarding punishment coefficient and a water discarding punishment coefficient; the determining the corresponding optimization strategy coefficient according to the reservoir water storage capacity of the hydropower station comprises the following steps:

If the water storage capacity of the hydropower station bookstore is lower than the normal range, determining that the abandoned water punishment coefficient is larger than the abandoned wind punishment coefficient and the abandoned light punishment coefficient;

if the water storage amount of the hydropower station bookstore is higher than the normal range, determining that the abandoned water punishment coefficient is smaller than the abandoned wind punishment coefficient and the abandoned light punishment coefficient;

and if the water storage capacity of the hydropower station bookstore is in a normal range, determining that the abandoned wind punishment coefficient, the abandoned light punishment coefficient and the abandoned water punishment coefficient are all zero.

Preferably, the hydroelectric generating set output constraints include one or more of the following: the method comprises the following steps of unit aggregation output range constraint, unit aggregation output climbing constraint, unit aggregation running number range constraint and unit aggregation generating capacity range constraint.

Preferably, the expression of the aggregate force range constraint of the unit is as follows:

wherein: p (P) ^h (t) is the output of the h-class hydroelectric generating set at the t moment;p ^h the lower output limit of the h-class hydroelectric generating set is set;the upper output limit of the h-class hydroelectric generating set is set; s is S ^h (t) is the number of the h-class hydroelectric generating set started at the moment t;

the expression of the unit aggregate force climbing constraint is as follows:

wherein: delta ^h The climbing range of the h-class hydroelectric generating set is set; p (P) ^h (t+1) is the output of the h-class hydroelectric generating set at the time t+1; p (P) ^h (t) is the output of the h-class hydroelectric generating set at the t moment;p ^h the lower output limit of the h-class hydroelectric generating set is set; s is S ^h (t) is the number of the h-class hydroelectric generating set started at the moment t; s is S ^h (t+1) is the number of the h-class hydroelectric generating set starting-up at the moment t+1;

the expression of the unit polymerization operation number range constraint is as follows:

wherein:S ^h the minimum starting number of the h-class hydroelectric generating set is,the maximum starting number of the h-class hydroelectric generating set is set; s is S ^h (t) is the number of the h-class hydroelectric generating set started at the moment t;

the unit polymerization power generation amount range constraint expression is as follows:

wherein:the minimum generating capacity of the h-class hydroelectric generating set in the m period; p (P) ^h (t) is the output of the h-class hydroelectric generating set at the t moment; />And the maximum power generation amount of the h-class hydroelectric generating set in the m period is obtained.

Preferably, the constructing of the optimization model for the coordination operation of wind, light and water comprises the following steps:

constructing an objective function by taking the maximum clean energy consumption of wind, light and water as a target;

setting constraint conditions for the objective function based on the operation constraint of the adjustable hydroelectric generating set aggregate operation optimization model and the wind, light, water and fire complementary power generation system;

the constraints include one or more of the following: the system comprises a hydroelectric power generation system constraint condition, a photovoltaic power generation system constraint condition, a wind power generation system constraint condition, an operation constraint condition after a water-wind-solar complementary power generation system is connected with a power grid, a system rotation reserve capacity constraint, a regional load balance constraint, an inter-regional line transmission capacity constraint, a thermal power generation system operation constraint and a new energy power generation output constraint, wherein the thermal power generation system operation constraint comprises one or more of the following: thermal power unit optimizing power constraint, thermal power unit optimizing power climbing rate constraint, thermal power unit operation number constraint and thermal power start-stop logic constraint.

Preferably, the expression of the objective function is as follows:

wherein: c is an objective function; n is the total number of power grid partitions; t represents the total length of scheduling time; h is the classification number of the hydroelectric generating set; p (P) _w (t, n) wind power output of the grid partition n in a period t; p (P) _pv (t, n) is the photovoltaic power generation capacity of the grid partition n in a period t; p (P) _h (t, n) is the output of the h-th type aggregation hydroelectric generating set in the period t for the power grid partition n;the water discarding of the h-class hydroelectric generating set in the t period of the power grid partition n is shown; />Wind power generation of grid partition n in t period is abandoned,/->Photovoltaic power rejection at t period for grid partition n, ρ _w Punishment coefficients for the wind curtailment; ρ _pv Punishment coefficients for the light rejection; ρ _h Punishment coefficients for water reject.

Preferably, the expression of the system rotation reserve capacity constraint is:

wherein: p (P) _j,max (t, n) is the upper limit of the output force of the j-th type thermal power unit in the t period in the power grid partition n; p (P) _j,min (t, n) is the lower output limit of the j-th type thermal power unit in the t period in the power grid partition n; p (P) _w (t, n) wind power output of the grid partition n in a period t; p (P) _pv (t, n) is the photovoltaic power generation capacity of the grid partition n in a period t; p (P) _h，max (t, n) is the upper output limit of the h-class hydroelectric generating set in the t period in the power grid partition n; p (P) _h，min (t, n) is the lower output limit of the h-class hydroelectric generating set in the t period in the power grid partition n; s is S _j (t, n) is the number of thermal power start-up units of the j-th type unit in the t period in the power grid partition n; s is S _h Group h in grid section n during time tThe number of hydropower stations is counted; p (P) _l (t, n) then represents the electrical load of grid partition n for the t-th period; p (P) _re The positive rotation set for the system is reserved; n (N) _re Negative rotation set for the system is reserved; n is the total number of power grid partitions; j is the unit classification number of the thermal power;

the expression of the regional load balance constraint is as follows:

wherein: p (P) _j (t, n) is the power generation power of the j-th type thermal power unit in the t-th period of the power grid partition n; s is S _j (t, n) is the number of thermal power start-up units of the jth group of units in the t period in the power grid partition n; p (P) _h (t, n) is the hydroelectric generating set output of the power grid partition n in the h class of the period t; s is S _h (t, n) is the number of the startup of the hydroelectric generating set in the h class of the period t; p (P) _w (t, n) wind power output of the grid partition n in a period t; p (P) _pv (t, n) is the photovoltaic power generation capacity of the grid partition n in a period t; l (L) _i (t) is the transmission power of the ith transmission line in the t-th period; p (P) _l (t, n) then represents the electrical load of grid partition n for the t-th period; h is the classified number of the hydroelectric generating set; j is the unit classification number of the thermal power;

the expression of the inter-area line transmission capacity constraint is as follows:

-L _i,max ≤L _i (t)≤L _i,max

wherein: l (L) _i,max Is the limit of the transmission capacity of the ith transmission line; l (L) _i (t) is the transmission power of the ith transmission line in the t-th period; the expression of the new energy power generation force constraint is as follows:

wherein:the theoretical output of wind power at the time t is calculated for the power grid partition n; p (P) _w (t, n) wind power output of the grid partition n in a period t; />The theoretical output of photovoltaic power generation at the time t is given to the grid partition n; p (P) _pv And (t, n) is the photovoltaic power generation capacity of the power grid partition n in the period t.

Preferably, the new energy history operation data includes one or more of the following: wind power history data, photovoltaic history data, and load history data; each genset data includes one or more of the following: the capacity of each generator set, the type of each generator set, the output limit data of each generator set, the power of the power grid outgoing link and the power grid topology; the optimized output scheme includes one or more of the following: the starting mode of each unit, the time sequence output curve of each unit and the actual output of new energy.

Based on the same conception, the invention also provides a wind-light-water scheduling strategy optimization system based on the aggregation of the hydroelectric generating sets, which comprises the following steps:

and a data acquisition module: acquiring new energy historical operation data and data of each generator set of a wind, light, water and fire complementary power generation system;

And (3) an optimization solving module: determining a corresponding optimization strategy coefficient according to the reservoir water storage capacity of the hydropower station, and solving a pre-constructed wind-solar-water coordinated operation optimization model based on the optimization strategy coefficient, new energy historical operation data and each generator set data to obtain an optimized output scheme of the wind-solar-water-fire complementary power generation system;

the wind-solar-water coordination operation optimization model is constructed based on an adjustable hydropower unit polymerization operation optimization model and aims at the maximum clean energy consumption of wind, solar and water, wherein the adjustable hydropower unit polymerization operation optimization model is constructed by a plurality of hydropower unit output constraints based on hydropower unit startup number polymerization.

Preferably, the optimization strategy coefficients include one or more of the following: a wind discarding punishment coefficient, a light discarding punishment coefficient and a water discarding punishment coefficient; the optimization solving module determines a corresponding optimization strategy coefficient according to the reservoir water storage capacity of the hydropower station, and comprises the following steps:

if the water storage capacity of the hydropower station bookstore is lower than the normal range, determining that the abandoned water punishment coefficient is larger than the abandoned wind punishment coefficient and the abandoned light punishment coefficient;

if the water storage amount of the hydropower station bookstore is higher than the normal range, determining that the abandoned water punishment coefficient is smaller than the abandoned wind punishment coefficient and the abandoned light punishment coefficient;

And if the water storage capacity of the hydropower station bookstore is in a normal range, determining that the abandoned wind punishment coefficient, the abandoned light punishment coefficient and the abandoned water punishment coefficient are all zero.

Preferably, the hydroelectric generating set output constraints include one or more of the following: the method comprises the following steps of unit aggregation output range constraint, unit aggregation output climbing constraint, unit aggregation running number range constraint and unit aggregation generating capacity range constraint.

Preferably, the expression of the aggregate force range constraint of the unit is as follows:

wherein: p (P) ^h (t) is the output of the h-class hydroelectric generating set at the t moment;p ^h the lower output limit of the h-class hydroelectric generating set is set;the upper output limit of the h-class hydroelectric generating set is set; s is S ^h (t) is the number of the h-class hydroelectric generating set started at the moment t;

the expression of the unit aggregate force climbing constraint is as follows:

wherein: delta ^h The climbing range of the h-class hydroelectric generating set is set; p (P) ^h (t+1) is the output of the h-class hydroelectric generating set at the time t+1; p (P) ^h (t) is the output of the h-class hydroelectric generating set at the t moment;p ^h the lower output limit of the h-class hydroelectric generating set is set; s is S ^h (t) is the number of the h-class hydroelectric generating set started at the moment t; s is S ^h (t+1) is the number of the h-class hydroelectric generating set starting-up at the moment t+1;

the expression of the unit polymerization operation number range constraint is as follows:

wherein:S ^h the minimum starting number of the h-class hydroelectric generating set is, The maximum starting number of the h-class hydroelectric generating set is set; s is S ^h (t) is the number of the h-class hydroelectric generating set started at the moment t;

the unit polymerization power generation amount range constraint expression is as follows:

wherein:the minimum generating capacity of the h-class hydroelectric generating set in the m period; p (P) ^h (t) is the output of the h-class hydroelectric generating set at the t moment; />And the maximum power generation amount of the h-class hydroelectric generating set in the m period is obtained.

Preferably, the construction of the wind-light-water coordination operation optimization model in the optimization solving module comprises the following steps:

constructing an objective function by taking the maximum clean energy consumption of wind, light and water as a target;

setting constraint conditions for the objective function based on the operation constraint of the adjustable hydroelectric generating set aggregate operation optimization model and the wind, light, water and fire complementary power generation system;

the constraints include one or more of the following: the system comprises a hydroelectric power generation system constraint condition, a photovoltaic power generation system constraint condition, a wind power generation system constraint condition, an operation constraint condition after a water-wind-solar complementary power generation system is connected with a power grid, a system rotation reserve capacity constraint, a regional load balance constraint, an inter-regional line transmission capacity constraint, a thermal power generation system operation constraint and a new energy power generation output constraint, wherein the thermal power generation system operation constraint comprises one or more of the following: thermal power unit optimizing power constraint, thermal power unit optimizing power climbing rate constraint, thermal power unit operation number constraint and thermal power start-stop logic constraint.

Preferably, the expression of the objective function is as follows:

wherein: c is an objective function; n is the total number of power grid partitions; t represents the total length of scheduling time; h is the classification number of the hydroelectric generating set; p (P) _w (t, n) wind power output of the grid partition n in a period t; p (P) _pv (t, n) is the photovoltaic power generation capacity of the grid partition n in a period t; p (P) _h (t, n) is the output of the h-th type aggregation hydroelectric generating set in the period t for the power grid partition n;the water discarding of the h-class hydroelectric generating set in the t period of the power grid partition n is shown; />Wind power generation of grid partition n in t period is abandoned,/->Photovoltaic power rejection at t period for grid partition n, ρ _w Punishment coefficients for the wind curtailment; ρ _pv Punishment coefficients for the light rejection; ρ _h Punishment coefficients for water reject.

Preferably, the expression of the system rotation reserve capacity constraint is:

wherein: p (P) _j,max (t, n) is the upper limit of the output force of the j-th type thermal power unit in the t period in the power grid partition n; p (P) _j,min (t, n) is the lower output limit of the j-th type thermal power unit in the t period in the power grid partition n; p (P) _w (t, n) wind power output of the grid partition n in a period t; p (P) _pv (t, n) is the photovoltaic power generation capacity of the grid partition n in a period t; p (P) _h，max (t, n) is the upper output limit of the h-class hydroelectric generating set in the t period in the power grid partition n; p (P) _h，min (t, n) is the lower output limit of the h-class hydroelectric generating set in the t period in the power grid partition n; s is S _j (t, n) is the number of thermal power start-up units of the j-th type unit in the t period in the power grid partition n; s is S _h (t, n) the hydropower start-up number of the h-type unit in the t period in the power grid partition n; p (P) _l (t, n) then represents the electrical load of grid partition n for the t-th period; p (P) _re The positive rotation set for the system is reserved; n (N) _re Negative rotation set for the system is reserved; n is the total number of power grid partitions; j is the unit classification number of the thermal power;

the expression of the regional load balance constraint is as follows:

wherein: p (P) _j (t, n) is the power generation power of the j-th type thermal power unit in the t-th period of the power grid partition n; s is S _j (t, n) is the number of thermal power start-up units of the jth group of units in the t period in the power grid partition n; p (P) _h (t, n) is the hydroelectric generating set output of the power grid partition n in the h class of the period t; s is S _h (t, n) is the number of the startup of the hydroelectric generating set in the h class of the period t; p (P) _w (t, n) wind power output of the grid partition n in a period t; p (P) _pv (t, n) is the photovoltaic power generation capacity of the grid partition n in a period t; l (L) _i (t) is the transmission power of the ith transmission line in the t-th period; p (P) _l (t, n) then represents the electrical load of grid partition n for the t-th period; h is the classified number of the hydroelectric generating set; j is the unit classification number of the thermal power;

the expression of the inter-area line transmission capacity constraint is as follows:

-L _i,max ≤L _i (t)≤L _i,max

wherein: l (L) _i,max Is the limit of the transmission capacity of the ith transmission line; l (L) _i (t) is the transmission power of the ith transmission line in the t-th period; the expression of the new energy power generation force constraint is as follows:

wherein:the theoretical output of wind power at the time t is calculated for the power grid partition n; p (P) _w (t, n) wind power output of the grid partition n in a period t; />The theoretical output of photovoltaic power generation at the time t is given to the grid partition n; p (P) _pv And (t, n) is the photovoltaic power generation capacity of the power grid partition n in the period t.

Preferably, the new energy history operation data in the data acquisition module includes one or more of the following: wind power history data, photovoltaic history data, and load history data; each generating set data in the data acquisition module comprises one or more of the following: the capacity of each generator set, the type of each generator set, the output limit data of each generator set, the power of the power grid outgoing link and the power grid topology; the optimized output scheme in the optimized solving module comprises one or more of the following: the starting mode of each unit, the time sequence output curve of each unit and the actual output of new energy.

Compared with the closest prior art, the invention has the following beneficial effects:

the invention provides a wind, light and water scheduling strategy optimization method and system based on hydropower unit aggregation, comprising the following steps: acquiring new energy historical operation data and data of each generator set of a wind, light, water and fire complementary power generation system; determining a corresponding optimization strategy coefficient according to the reservoir water storage capacity of the hydropower station, and solving a pre-constructed wind-solar-water coordinated operation optimization model based on the optimization strategy coefficient, new energy historical operation data and each generator set data to obtain an optimized output scheme of the wind-solar-water-fire complementary power generation system; the wind-light-water coordination operation optimization model is based on an adjustable hydropower unit polymerization operation optimization model, and is built with the maximum clean energy consumption of wind-light-water as a target; according to the method, the random fluctuation problem of wind-light-water resource power generation is solved through the optimization strategy coefficient, the solving accuracy of the wind-light-water coordinated operation optimization model is improved through the adjustable hydropower unit aggregate operation optimization model, and the medium-long-term optimization control with high accuracy is realized.

Drawings

FIG. 1 is a schematic flow chart of a wind-solar-water scheduling strategy optimization method based on hydropower unit aggregation;

fig. 2 is a schematic structural diagram of a wind-light-water scheduling strategy optimization system based on hydropower unit aggregation.

Detailed Description

The following describes the embodiments of the present application in further detail with reference to the drawings.

Example 1:

the invention provides a wind-solar-water scheduling strategy optimization method based on hydropower unit aggregation, a specific flow diagram is shown in fig. 1, and the method comprises the following steps:

step 1: acquiring new energy historical operation data and data of each generator set of a wind, light, water and fire complementary power generation system;

step 2: determining a corresponding optimization strategy coefficient according to the reservoir water storage capacity of the hydropower station, and solving a pre-constructed wind-solar-water coordinated operation optimization model based on the optimization strategy coefficient, new energy historical operation data and each generator set data to obtain an optimized output scheme of the wind-solar-water-fire complementary power generation system;

the wind-solar-water coordination operation optimization model is constructed based on an adjustable hydropower unit polymerization operation optimization model and aims at the maximum clean energy consumption of wind, solar and water, wherein the adjustable hydropower unit polymerization operation optimization model is constructed by a plurality of hydropower unit output constraints based on hydropower unit startup number polymerization.

Before step 1, a wind-light-water coordination operation optimization model needs to be built. The wind-light-water coordination operation optimization model is based on an adjustable hydropower unit polymerization operation optimization model, and is built with the maximum clean energy consumption of wind-light-water as a target.

Specifically, the adjustable hydropower unit polymerization operation optimization model is constructed by a plurality of hydropower unit output constraints based on hydropower unit startup number polymerization, and specifically comprises one or more of the following: the method comprises the following steps of unit aggregation output range constraint, unit aggregation output climbing constraint, unit aggregation running number range constraint and unit aggregation generating capacity range constraint.

(1) And (3) unit aggregate output range constraint: the output of the hydroelectric generating set cannot be regulated limitlessly, and the hydroelectric generating set needs to operate within a certain output range.

Wherein: p (P) ^h (t) is the output of the h-class hydroelectric generating set at the moment t,p ^h is the lower output limit of the h-class hydroelectric generating set,the upper output limit of the h-class hydroelectric generating set is S ^h And (t) is the starting number of the h-th hydroelectric generating set at the moment t, wherein h and t are variables, h represents the type of the hydroelectric generating set, and t represents the time.

(2) And (3) unit aggregate force climbing constraint: the output adjustment rate of the hydroelectric generating set is limited, the output is required to be adjusted gradually within a certain time, and the problems of damage to the generating set and the quality of a power grid caused by abrupt change are avoided. Therefore, in the aggregation operation, the unit aggregation output climbing needs to be restrained so as to ensure stable adjustment of the output.

Wherein: delta ^h The climbing range of the h-class hydroelectric generating set is set; p (P) ^h (t+1) is the output of the h-class hydroelectric generating set at the time t+1; p (P) ^h (t) is the output of the h-class hydroelectric generating set at the t moment; s is S ^h (t) is the number of the h-class hydroelectric generating set started at the moment t; s is S ^h (t+1) is the number of the h-class hydroelectric generating set starting-up at the moment t+1;p ^h the lower output limit of the h-class hydroelectric generating set is set. Wherein h and t are variables, h represents the type of hydroelectric generating set, and t represents time.

(3) The range constraint of the aggregate operation number of the units: the number of the hydroelectric generating sets is large, polymerization operation needs to be carried out within a certain range, and the influence on the generating efficiency and the economy caused by the excessive or insufficient number of the generating sets is avoided. Therefore, in the polymerization operation, the range of the number of the polymerization operations of the unit needs to be constrained to ensure the power generation efficiency and the economy.

Wherein:S ^h the minimum starting number of the h-class hydroelectric generating set is,the maximum starting number of the h-class hydroelectric generating set is set; s is S ^h And (t) is the starting number of the h-th hydroelectric generating set at the moment t, wherein h and t are variables, h represents the type of the hydroelectric generating set, and t represents the time.

(4) Unit polymerization power generation range constraint: the generating capacity of the hydroelectric generating set needs to meet certain range requirements, and in the aggregation operation, the range of the generating capacity of the generating set needs to be restrained so as to meet the requirements of power grid load.

In the method, in the process of the invention,the minimum generating capacity of the h-class hydroelectric generating set in the m period；P ^h (t) is the output of the h-class hydroelectric generating set at the t moment; />And the maximum power generation amount of the h-type hydroelectric generating set in the m period is obtained, wherein h and m are variables, h represents the type of the hydroelectric generating set, and m represents the time in a specified range.

The adjustable hydroelectric generating set aggregate operation optimization model constructed by the method realizes simplification of the running variable and constraint of the hydroelectric generating set by introducing the integer variable representing the number of the integral running to replace the 0-1 variable of the running state of each set, and simultaneously can adjust the hydroelectric output.

And then, constructing a wind-light-water coordination operation optimization model. Specifically, the method fully considers the constraint condition of the hydroelectric power generation system, the constraint condition of the photovoltaic power generation system, the constraint condition of the wind power generation system and the operation constraint condition of the water-wind-solar complementary system after being connected to the power grid, aims at the maximum clean energy consumption, optimizes the power balance of the whole grid in a time-interval mode, realizes better complementation on the natural characteristics and technical characteristics of wind power photovoltaic and water power, mainly utilizes the characteristics of energy storage characteristics and flexible start and stop of the hydropower station to stabilize the drastic fluctuation of wind power photovoltaic, and improves the influence of the fluctuation of wind power photovoltaic on the power system and reduces the wind power rejection amount by the combined operation of the water power and the wind power, and on the other hand, can also make up the defect of the seasonal characteristics of the water power to a certain extent by utilizing the complementation between the wind power and the wind power. The objective function and constraints of the model are as follows:

(1) Objective function: the total-network clean energy generating capacity is the largest, different weight coefficients are respectively set for wind, light and water generating capacity, and different operation optimization strategies of wind, light and water simultaneous priority consumption, new energy priority consumption and hydropower priority consumption are realized by adjusting the weight coefficients, so that the calculation demands of different scenes are met.

Wherein: c is an objective function; n is the total number of power grid partitions; t represents the total length of scheduling time; h is the classification number of the hydroelectric generating set; p (P) _w (t, n) wind power output of the grid partition n in a period t; p (P) _pv (t, n) is the photovoltaic power generation capacity of the grid partition n in a period t; p (P) _h (t, n) aggregating hydropower station output for a power grid partition n in a period t class h;the water discarding of the h hydropower station in the t period of the power grid partition n is represented; />Wind power generation of grid partition n in t period is abandoned,/->Photovoltaic power rejection at t period for grid partition n, ρ _w Punishment coefficients for the wind curtailment; ρ _pv Punishment coefficients for the light rejection; ρ _h Punishment coefficients for water reject.

The wind abandoning penalty coefficient ρ in the present application _w Light rejection penalty coefficient ρ _pv And a water reject penalty coefficient ρ _h In order to optimize the strategy coefficient, the value of the strategy coefficient reflects the priority of wind power, photovoltaic power generation and hydropower. In the embodiment of the present disclosure, taking a dead water period, a rich water period and a flat water period as examples, a value strategy for optimizing a strategy coefficient is described:

In the dead water period, the reservoir of the hydropower station has lower water storage capacity, smaller water flow and relatively weaker hydroelectric power generation capacity, and the hydropower station should preferentially consume the hydropower at the moment so as to utilize the hydroelectric power generation capacity to the greatest extent, reduce the water storage capacity of the reservoir and reserve a water source for the subsequent seasons. At this point ρ _h Far greater than ρ _w And ρ _pv 。

In the water-rich period, the reservoir of the hydropower station has higher water storage capacity, larger water flow and relatively stronger hydropower generation capacity, and new energy should be preferentially consumed at the moment so as to maximally utilize the new energy generation capacity and reduce the dependence of hydropower generation on the reservoir. At this point ρ _h Much smaller than ρ _w And ρ _pv 。

In the water leveling period, the water storage capacity and the water flow of the reservoir are at normal levels, and the water-electricity generating capacity is relatively stable. In this case, an equivalent digestion strategy can be adopted, that is, the hydropower and the new energy are generated again, and digestion is performed according to the power generation capacity. At this point ρ _h 、ρ _w And ρ _pv All 0.

In the application of the invention, different water-wind-solar complementary operation optimization strategies are realized by introducing punishment coefficients into the objective function, and different priority consumption strategies are adopted to maximize the energy utilization benefit according to seasonal changes and energy supply and demand conditions. The priority of wind power, photovoltaic power generation and hydropower is mainly reflected in different weight coefficients in front of wind power, photovoltaic power generation and hydropower waste in each season and each moment in an optimization target, so that random fluctuation of wind, light and water clean energy is fully considered, the accuracy of an optimization treatment scheme is improved, and meanwhile, the practicability of medium-long period optimization is also considered for setting of an optimization strategy coefficient.

(2) Constraints, in particular, include one or more of the following: the system comprises a hydroelectric power generation system constraint condition, a photovoltaic power generation system constraint condition, a wind power generation system constraint condition, an operation constraint condition after a water-wind-solar complementary power generation system is connected with a power grid, a system rotation reserve capacity constraint, a regional load balance constraint, an inter-regional line transmission capacity constraint, a thermal power generation system operation constraint and a new energy power generation output constraint, wherein the thermal power generation system operation constraint comprises one or more of the following: thermal power unit optimizing power constraint, thermal power unit optimizing power climbing rate constraint, thermal power unit operation number constraint and thermal power start-stop logic constraint.

1) The expression of the above system rotational reserve capacity constraint is as follows:

wherein: p (P) _re The positive rotation set for the system is reserved; n (N) _re Negative set for systemRotating for standby; p (P) _j,max (t, n) is the upper output limit of the j-th thermal power unit in the power grid partition n in the t period; p (P) _j,min (t, n) is the lower output limit of the j-th thermal power unit in the power grid partition n in the t period; p (P) _h，max (t, n) is the upper output limit of the h-class hydroelectric generating set in the t period in the power grid partition n; p (P) _h,min (t, n) is the lower output limit of the h-class hydroelectric generating set in the t period in the power grid partition n; s is S _j (t, n) is the number of thermal power start-up units of the j-th type unit in the t period in the power grid partition n; s is S _h (t, n) the hydropower start-up number of the h-type unit in the t period in the power grid partition n; p (P) _l (t, n) then represents the electrical load of grid partition n for the t-th period; n is the total number of power grid partitions; j is the unit classification number of the thermal power.

2) The above expression for the zone load balancing constraint is as follows:

wherein: p (P) _j (t, n) is the power generation power of the j-th type thermal power unit in the t-th period of the power grid partition n; s is S _j (t, n) is the number of thermal power start-up units of the jth group of units in the t period in the power grid partition n; p (P) _h (t, n) is the hydroelectric generating set output of the power grid partition n in the h class of the period t; s is S _h (t, n) is the number of the startup of the hydroelectric generating set in the h class of the period t; p (P) _w (t, n) wind power output of the grid partition n in a period t; p (P) _pv (t, n) is the photovoltaic power generation capacity of the grid partition n in a period t; l (L) _i (t) is the transmission power of the ith transmission line in the t-th period; p (P) _l (t, n) then represents the electrical load of grid partition n for the t-th period; h is the classified number of the hydroelectric generating set; j is the unit classification number of the thermal power.

3) The expression of the inter-area line transmission capacity constraint is as follows:

-L _i,max ≤L _i (t)≤L _i,max (8)

wherein: l (L) _i,max Is the limit of the transmission capacity of the ith transmission line; l (L) _i And (t) is the transmission power of the ith transmission line in the t-th period.

4) The expression of the optimized power constraint of the thermal power generating unit is as follows:

0≤ΔP _j (t,n)≤[P _j,max (t,n)-P _j,min (t,n)]·S _j (t,n) (9)

P _j (t,n)＝P _j,min (t,n)·S _j (t,n)+ΔP _j (t,n) (10)

wherein: ΔP _j (t, n) is the optimized power of the j-th thermal power unit in the region n in the period t; p (P) _j,max (t, n) is the upper limit of the output force of the j-th type thermal power unit in the t-th period in the power grid partition n; p (P) _j,min (t, n) is the lower output limit of the j-th type thermal power unit in the t-th period in the power grid partition n; s is S _j (t, n) is the number of thermal power start-up units of the jth group of units in the t period in the power grid partition n; p (P) _j And (t, n) is the power generation power of the j-th type thermal power generating unit in the t-th period of the power grid partition n.

5) The expression of the optimized power ramp rate constraint of the thermal power generating unit is as follows:

P _j (t+1,n)-P _j (t,n)≤ΔP _j,up (n) (11)

P _j (t,n)-P _j (t+1,n)≤ΔP _j,down (n) (12)

wherein: p (P) _j The (t+1, n) is the power generation power of the j-th type thermal power unit in the t+1 time period of the power grid partition n; p (P) _j (t, n) is the power generation power of the j-th type thermal power unit in the t-th period of the power grid partition n; ΔP _j，up (n) the maximum climbing rate of the jth unit in the power grid partition n; ΔP _j，down And (n) the maximum downward slope rate of the jth unit in the power grid partition n.

6) The expression of the thermal power generating unit operation number constraint is as follows:

0≤S _j (t,n)≤S _j.max (n) (13)

wherein: s is S _j (t, n) is the number of thermal power start-up units of the jth group of units in the t period in the power grid partition n; s is S _j.max And (n) is the total number of the j-th type units in the power grid partition n.

7) The thermal power on-off logic constraint expression is as follows:

0≤Y(t)+Z(t)≤1 (16)

wherein: a start instruction for a period t of the Y (t) system; z (t) is a shutdown instruction of a system t period; s is S _j (t, n) is the number of thermal power start-up units of the jth group of units in the t period in the power grid partition n; s is S _j (t-1, n) is the number of thermal power start-up units of the j-th type units in the t-1 period in the power grid partition n;and the total number of the j-th type units in the power grid partition n.

8) The expression of the new energy power generation force constraint is as follows:

wherein:the theoretical output of wind power at the time t is calculated for the power grid partition n; p (P) _w (t, n) wind power output of the grid partition n in a period t; />The theoretical output of photovoltaic power generation at the time t is given to the grid partition n; p (P) _pv And (t, n) is the photovoltaic power generation capacity of the power grid partition n in the period t.

In the embodiment of the disclosure, the above-mentioned adjustable hydropower unit aggregate operation optimization model is used as a constraint condition of a hydropower system, and then the constructed wind-solar-water coordinated operation optimization model is used, so that the solving output scheme of the optimization model is more accurate.

In step 1, specifically, new energy historical operation data and each generator set data of a wind, light, water and fire complementary power generation system are obtained, and in the embodiment of the disclosure, the method includes: collecting and arranging wind power historical data, photovoltaic historical data and load historical data with the time resolution of 15-60 minutes in an actual provincial power grid for a certain time length; and generating set information of various power supplies such as thermal power, hydropower, wind power, photovoltaic power and the like, wherein the set information comprises one or more of the following: unit capacity, unit type, limit output data of the unit, power of a power grid outgoing link, power grid topology and the like.

And step 2, determining a corresponding optimization strategy coefficient according to the reservoir water storage capacity of the hydropower station, and calling a Cplex solver to solve based on the optimization strategy coefficient, the new energy historical operation data and the data of each generator set, so that the power supply deficiency and electricity abandoning conditions of the power grid at each moment, namely an optimized output scheme of the wind, light, water and fire complementary power generation system, can be obtained.

According to the method, the optimization strategy coefficient determined based on the reservoir water storage capacity and the adjustable hydropower unit aggregate operation optimization model are used for solving the wind-light-water coordinated operation optimization model which is built in advance, wherein the optimization strategy coefficient solves the problem of random fluctuation of wind-light-water resource power generation, the adjustable hydropower unit aggregate operation optimization model improves the solving accuracy of the wind-light-water coordinated operation optimization model, and medium-long-term optimization control with high accuracy is achieved.

Example 2:

based on the same inventive concept, the invention further provides a wind-solar-water scheduling strategy optimization system based on the hydropower unit aggregation. The system structure is shown in fig. 2, and comprises:

and a data acquisition module: acquiring new energy historical operation data and data of each generator set of a wind, light, water and fire complementary power generation system;

And (3) an optimization solving module: determining a corresponding optimization strategy coefficient according to the reservoir water storage capacity of the hydropower station, and solving a pre-constructed wind-solar-water coordinated operation optimization model based on the optimization strategy coefficient, new energy historical operation data and each generator set data to obtain an optimized output scheme of the wind-solar-water-fire complementary power generation system;

the wind-solar-water coordination operation optimization model is constructed based on an adjustable hydropower unit polymerization operation optimization model and aims at the maximum clean energy consumption of wind, solar and water, wherein the adjustable hydropower unit polymerization operation optimization model is constructed by a plurality of hydropower unit output constraints based on hydropower unit startup number polymerization.

Preferably, the optimization strategy coefficients include one or more of the following: a wind discarding punishment coefficient, a light discarding punishment coefficient and a water discarding punishment coefficient; the optimization solving module determines a corresponding optimization strategy coefficient according to the reservoir water storage capacity of the hydropower station, and comprises the following steps:

if the water storage capacity of the hydropower station bookstore is lower than the normal range, determining that the abandoned water punishment coefficient is larger than the abandoned wind punishment coefficient and the abandoned light punishment coefficient;

if the water storage amount of the hydropower station bookstore is higher than the normal range, determining that the abandoned water punishment coefficient is smaller than the abandoned wind punishment coefficient and the abandoned light punishment coefficient;

And if the water storage capacity of the hydropower station bookstore is in a normal range, determining that the abandoned wind punishment coefficient, the abandoned light punishment coefficient and the abandoned water punishment coefficient are all zero.

Preferably, the hydroelectric generating set output constraints include one or more of the following: the method comprises the following steps of unit aggregation output range constraint, unit aggregation output climbing constraint, unit aggregation running number range constraint and unit aggregation generating capacity range constraint.

Preferably, the expression of the aggregate force range constraint of the unit is as follows:

/>

wherein: p (P) ^h (t) is the output of the h-class hydroelectric generating set at the t moment;p ^h the lower output limit of the h-class hydroelectric generating set is set;the upper output limit of the h-class hydroelectric generating set is set; s is S ^h (t) is the number of the h-class hydroelectric generating set started at the moment t;

the expression of the unit aggregate force climbing constraint is as follows:

wherein: delta ^h The climbing range of the h-class hydroelectric generating set is set; p (P) ^h (t+1) is the output of the h-class hydroelectric generating set at the time t+1; p (P) ^h (t) is the output of the h-class hydroelectric generating set at the t moment;p ^h the lower output limit of the h-class hydroelectric generating set is set; s is S ^h (t) is the number of the h-class hydroelectric generating set started at the moment t; s is S ^h (t+1) is the number of the h-class hydroelectric generating set starting-up at the moment t+1;

the expression of the unit polymerization operation number range constraint is as follows:

wherein: s is S ^h The minimum starting number of the h-class hydroelectric generating set is, The maximum starting number of the h-class hydroelectric generating set is set; s is S ^h (t) is the number of the h-class hydroelectric generating set started at the moment t;

the unit polymerization power generation amount range constraint expression is as follows:

wherein:the minimum generating capacity of the h-class hydroelectric generating set in the m period; p (P) ^h (t) is the output of the h-class hydroelectric generating set at the t moment; />And the maximum power generation amount of the h-class hydroelectric generating set in the m period is obtained.

Preferably, the construction of the wind-light-water coordination operation optimization model in the optimization solving module comprises the following steps:

constructing an objective function by taking the maximum clean energy consumption of wind, light and water as a target;

setting constraint conditions for the objective function based on the operation constraint of the adjustable hydroelectric generating set aggregate operation optimization model and the wind, light, water and fire complementary power generation system;

the constraints include one or more of the following: the system comprises a hydroelectric power generation system constraint condition, a photovoltaic power generation system constraint condition, a wind power generation system constraint condition, an operation constraint condition after a water-wind-solar complementary power generation system is connected with a power grid, a system rotation reserve capacity constraint, a regional load balance constraint, an inter-regional line transmission capacity constraint, a thermal power generation system operation constraint and a new energy power generation output constraint, wherein the thermal power generation system operation constraint comprises one or more of the following: thermal power unit optimizing power constraint, thermal power unit optimizing power climbing rate constraint, thermal power unit operation number constraint and thermal power start-stop logic constraint.

Preferably, the expression of the objective function is as follows:

wherein: c is an objective function; n is the total number of power grid partitions; t represents the total length of scheduling time; h is the classification number of the hydroelectric generating set; p (P) _w (t, n) wind power output of the grid partition n in a period t; p (P) _pv (t, n) is the photovoltaic power generation capacity of the grid partition n in a period t; p (P) _h (t, n) is the output of the h-th type aggregation hydroelectric generating set in the period t for the power grid partition n;the water discarding of the h-class hydroelectric generating set in the t period of the power grid partition n is shown; />Wind power generation of grid partition n in t period is abandoned,/->Photovoltaic power rejection at t period for grid partition n, ρ _w Punishment coefficients for the wind curtailment; ρ _pv Punishment coefficients for the light rejection; ρ _h Punishment coefficients for water reject.

Preferably, the expression of the system rotation reserve capacity constraint is:

wherein: p (P) _j,max (t, n) is the upper limit of the output force of the j-th type thermal power unit in the t period in the power grid partition n; p (P) _j,min (t, n) is the lower output limit of the j-th type thermal power unit in the t period in the power grid partition n; p (P) _w (t, n) wind power output of the grid partition n in a period t; p (P) _pv (t, n) is the photovoltaic power generation capacity of the grid partition n in a period t; p (P) _h，max (t, n) is the upper output limit of the h-class hydroelectric generating set in the t period in the power grid partition n; p (P) _h，min (t, n) is the lower output limit of the h-class hydroelectric generating set in the t period in the power grid partition n; s is S _j (t, n) is the number of thermal power start-up units of the j-th type unit in the t period in the power grid partition n; s is S _h (t, n) the hydropower start-up number of the h-type unit in the t period in the power grid partition n; p (P) _l (t, n) then represents the electrical load of grid partition n for the t-th period; p (P) _re The positive rotation set for the system is reserved; n (N) _re Negative rotation set for the system is reserved; n is the total number of power grid partitions; j is the unit classification number of the thermal power;

the expression of the regional load balance constraint is as follows:

wherein: p (P) _j (t, n) is the power generation power of the j-th type thermal power unit in the t-th period of the power grid partition n; s is S _j (t, n) is the number of thermal power start-up units of the jth group of units in the t period in the power grid partition n; p (P) _h (t, n) is the grid partition nThe output of the hydroelectric generating set in the h class of the period t; s is S _h (t, n) is the number of the startup of the hydroelectric generating set in the h class of the period t; p (P) _w (t, n) wind power output of the grid partition n in a period t; p (P) _pv (t, n) is the photovoltaic power generation capacity of the grid partition n in a period t; l (L) _i (t) is the transmission power of the ith transmission line in the t-th period; p (P) _l (t, n) then represents the electrical load of grid partition n for the t-th period; h is the classified number of the hydroelectric generating set; j is the unit classification number of the thermal power;

the expression of the inter-area line transmission capacity constraint is as follows:

-L _i,max ≤L _i (t)≤L _i,max

wherein: l (L) _i,max Is the limit of the transmission capacity of the ith transmission line; l (L) _i (t) is the transmission power of the ith transmission line in the t-th period;

the expression of the new energy power generation force constraint is as follows:

wherein: p (P) _w ^* (t, n) is the theoretical output of wind power at the moment t of the grid partition n; p (P) _w (t, n) wind power output of the grid partition n in a period t;the theoretical output of photovoltaic power generation at the time t is given to the grid partition n; p (P) _pv And (t, n) is the photovoltaic power generation capacity of the power grid partition n in the period t.

Preferably, the new energy history operation data in the data acquisition module includes one or more of the following: wind power history data, photovoltaic history data, and load history data; each generating set data in the data acquisition module comprises one or more of the following: the capacity of each generator set, the type of each generator set, the output limit data of each generator set, the power of the power grid outgoing link and the power grid topology; the optimized output scheme in the optimized solving module comprises one or more of the following: the starting mode of each unit, the time sequence output curve of each unit and the actual output of new energy.

According to the method, the data acquisition module and the optimization solving module are used for solving a pre-constructed wind-light-water coordinated operation optimization model based on an optimization strategy coefficient determined by reservoir water storage and an adjustable hydropower unit aggregate operation optimization model, so that an optimization system capable of solving the problem of random fluctuation of wind-light-water resource power generation and realizing medium-long term optimization control with high accuracy for a wind-light-water-fire comprehensive power generation system is provided.

Example 3:

based on the same inventive concept, the present application also provides a computer device, which comprises a processor and a memory, wherein the memory is used for storing a computer program, the computer program comprises program instructions, and the processor is used for executing the program instructions stored in the computer storage medium. The processor may be a central processing unit (Central Processing Unit, CPU), but may also be other general purpose processor, digital signal processor (Digital Signal Processor, DSP), application specific integrated circuit (Application SpecificIntegrated Circuit, ASIC), off-the-shelf Programmable gate array (FPGA) or other Programmable logic device, discrete gate or transistor logic device, discrete hardware components, etc., which are a computing core and a control core of the terminal adapted to implement one or more instructions, in particular to load and execute one or more instructions in a computer storage medium to implement the corresponding method flow or corresponding functions, to implement the steps of a hydropower-unit-aggregation-based wind and light scheduling policy optimization method in the above embodiments.

Example 4:

based on the same inventive concept, the present application also provides a storage medium, in particular, a computer readable storage medium (Memory), which is a Memory device in a computer device, for storing programs and data. It is understood that the computer readable storage medium herein may include both built-in storage media in a computer device and extended storage media supported by the computer device. The computer-readable storage medium provides a storage space storing an operating system of the terminal. Also stored in the memory space are one or more instructions, which may be one or more computer programs (including program code), adapted to be loaded and executed by the processor. The computer readable storage medium herein may be a high-speed RAM memory or a non-volatile memory (non-volatile memory), such as at least one magnetic disk memory. One or more instructions stored in a computer-readable storage medium may be loaded and executed by a processor to implement the steps of a wind-solar-water scheduling policy optimization method based on hydropower unit aggregation in the above embodiments.

It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

It should be noted that the foregoing embodiments are merely for illustrating the technical solution of the present application and not for limiting the scope of protection of the present application, and although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those skilled in the art that various changes, modifications or equivalents may be made to the specific embodiments of the application after reading the present application, and these changes, modifications or equivalents are within the scope of protection of the claims appended hereto.

Claims (16)

1. A wind-light-water scheduling strategy optimization method based on hydropower unit aggregation is characterized by comprising the following steps:

acquiring new energy historical operation data and data of each generator set of a wind, light, water and fire complementary power generation system;

determining a corresponding optimization strategy coefficient according to the reservoir water storage capacity of the hydropower station, and solving a pre-constructed wind-solar-water coordinated operation optimization model based on the optimization strategy coefficient, new energy historical operation data and each generator set data to obtain an optimized output scheme of the wind-solar-water-fire complementary power generation system;

the wind-solar-water coordination operation optimization model is constructed based on an adjustable hydropower unit polymerization operation optimization model and aims at the maximum clean energy consumption of wind, solar and water, wherein the adjustable hydropower unit polymerization operation optimization model is constructed by a plurality of hydropower unit output constraints based on hydropower unit startup number polymerization.

2. The method of claim 1, wherein the optimization strategy coefficients comprise one or more of: a wind discarding punishment coefficient, a light discarding punishment coefficient and a water discarding punishment coefficient; the determining the corresponding optimization strategy coefficient according to the reservoir water storage capacity of the hydropower station comprises the following steps:

If the water storage capacity of the hydropower station bookstore is lower than the normal range, determining that the abandoned water punishment coefficient is larger than the abandoned wind punishment coefficient and the abandoned light punishment coefficient;

if the water storage amount of the hydropower station bookstore is higher than the normal range, determining that the abandoned water punishment coefficient is smaller than the abandoned wind punishment coefficient and the abandoned light punishment coefficient;

and if the water storage capacity of the hydropower station bookstore is in a normal range, determining that the abandoned wind punishment coefficient, the abandoned light punishment coefficient and the abandoned water punishment coefficient are all zero.

3. The method of claim 1, wherein the hydroelectric generating set output constraints comprise one or more of: the method comprises the following steps of unit aggregation output range constraint, unit aggregation output climbing constraint, unit aggregation running number range constraint and unit aggregation generating capacity range constraint.

4. A method according to claim 3, wherein the aggregate force range constraint of the aggregate set is expressed as follows:

wherein: p (P) ^h (t) is the output of the h-class hydroelectric generating set at the t moment;p ^h the lower output limit of the h-class hydroelectric generating set is set;the upper output limit of the h-class hydroelectric generating set is set; s is S ^h (t) is the number of the h-class hydroelectric generating set started at the moment t;

the expression of the unit aggregate force climbing constraint is as follows:

wherein: delta ^h The climbing range of the h-class hydroelectric generating set is set; p (P) ^h (t+1) is the output of the h-class hydroelectric generating set at the time t+1; p (P) ^h (t) is the output of the h-class hydroelectric generating set at the t moment; p is p ^h The lower output limit of the h-class hydroelectric generating set is set; s is S ^h (t) is the number of the h-class hydroelectric generating set started at the moment t; s is S ^h (t+1) is the number of the h-class hydroelectric generating set starting-up at the moment t+1;

the expression of the unit polymerization operation number range constraint is as follows:

wherein: s is S ^h The minimum starting number of the h-class hydroelectric generating set is,the maximum starting number of the h-class hydroelectric generating set is set; s is S ^h (t) is the number of the h-class hydroelectric generating set started at the moment t;

the unit polymerization power generation amount range constraint expression is as follows:

wherein:the minimum generating capacity of the h-class hydroelectric generating set in the m period; p (P) ^h (t) is the output of the h-class hydroelectric generating set at the t moment;and the maximum power generation amount of the h-class hydroelectric generating set in the m period is obtained.

5. The method according to claim 1, wherein the constructing of the wind-solar-water coordination operation optimization model comprises:

constructing an objective function by taking the maximum clean energy consumption of wind, light and water as a target;

setting constraint conditions for the objective function based on the operation constraint of the adjustable hydroelectric generating set aggregate operation optimization model and the wind, light, water and fire complementary power generation system;

The constraints include one or more of the following: the system comprises a hydroelectric power generation system constraint condition, a photovoltaic power generation system constraint condition, a wind power generation system constraint condition, an operation constraint condition after a water-wind-solar complementary power generation system is connected with a power grid, a system rotation reserve capacity constraint, a regional load balance constraint, an inter-regional line transmission capacity constraint, a thermal power generation system operation constraint and a new energy power generation output constraint, wherein the thermal power generation system operation constraint comprises one or more of the following: thermal power unit optimizing power constraint, thermal power unit optimizing power climbing rate constraint, thermal power unit operation number constraint and thermal power start-stop logic constraint.

6. The method of claim 5, wherein the expression of the objective function is as follows:

wherein: c is an objective function; n is the total number of power grid partitions; t represents the total length of scheduling time; h is the classification number of the hydroelectric generating set; p (P) _w (t, n) wind power output of the grid partition n in a period t; p (P) _pv (t, n) is the photovoltaic power generation capacity of the grid partition n in a period t; p (P) _h (t, n) is the output of the h-th type aggregation hydroelectric generating set in the period t for the power grid partition n;the water discarding of the h-class hydroelectric generating set in the t period of the power grid partition n is shown; / >Wind power generation of grid partition n in t period is abandoned,/->Photovoltaic power rejection at t period for grid partition n, ρ _w Punishment coefficients for the wind curtailment; ρ _p ^v Punishment coefficients for the light rejection; ρ _h Punishment coefficients for water reject.

7. The method of claim 5, wherein the expression of the system rotational reserve capacity constraint is:

wherein: p (P) _j,max (t, n) is the upper limit of the output force of the j-th type thermal power unit in the t period in the power grid partition n; p (P) _j,min (t, n) is the lower output limit of the j-th type thermal power unit in the t period in the power grid partition n; p (P) _w (t, n) wind power output of the grid partition n in a period t; p (P) _pv (t, n) is the photovoltaic power generation capacity of the grid partition n in a period t; p (P) _h，max (t, n) is the upper output limit of the h-class hydroelectric generating set in the t period in the power grid partition n; p (P) _h，min (t, n) is the lower output limit of the h-class hydroelectric generating set in the t period in the power grid partition n; s is S _j (t, n) is the number of thermal power start-up units of the j-th type unit in the t period in the power grid partition n; s is S _h (t, n) the hydropower start-up number of the h-type unit in the t period in the power grid partition n; p (P) _l (t, n) then tableShowing the power load of grid partition n t-th period; p (P) _re The positive rotation set for the system is reserved; n (N) _re Negative rotation set for the system is reserved; n is the total number of power grid partitions; j is the unit classification number of the thermal power;

The expression of the regional load balance constraint is as follows:

wherein: p (P) _j (t, n) is the power generation power of the j-th type thermal power unit in the t-th period of the power grid partition n; s is S _j (t, n) is the number of thermal power start-up units of the jth group of units in the t period in the power grid partition n; p (P) _h (t, n) is the hydroelectric generating set output of the power grid partition n in the h class of the period t; s is S _h (t, n) is the number of the startup of the hydroelectric generating set in the h class of the period t; p (P) _w (t, n) wind power output of the grid partition n in a period t; p (P) _pv (t, n) is the photovoltaic power generation capacity of the grid partition n in a period t; l (L) _i (t) is the transmission power of the ith transmission line in the t-th period; p (P) _l (t, n) then represents the electrical load of grid partition n for the t-th period; h is the classified number of the hydroelectric generating set; j is the unit classification number of the thermal power;

the expression of the inter-area line transmission capacity constraint is as follows:

-L _i,max ≤L _i (t)≤L _i,max

wherein: l (L) _i,max Is the limit of the transmission capacity of the ith transmission line; l (L) _i (t) is the transmission power of the ith transmission line in the t-th period;

the expression of the new energy power generation force constraint is as follows:

wherein:the theoretical output of wind power at the time t is calculated for the power grid partition n; p (P) _w (t, n) wind power output of the grid partition n in a period t; />The theoretical output of photovoltaic power generation at the time t is given to the grid partition n; p (P) _pv And (t, n) is the photovoltaic power generation capacity of the power grid partition n in the period t.

8. The method of claim 1, wherein the new energy historical operating data comprises one or more of: wind power history data, photovoltaic history data, and load history data; each genset data includes one or more of the following: the capacity of each generator set, the type of each generator set, the output limit data of each generator set, the power of the power grid outgoing link and the power grid topology; the optimized output scheme includes one or more of the following: the starting mode of each unit, the time sequence output curve of each unit and the actual output of new energy.

9. Wind-light-water scheduling strategy optimization system based on hydropower unit aggregation is characterized by comprising:

and a data acquisition module: acquiring new energy historical operation data and data of each generator set of a wind, light, water and fire complementary power generation system;

and (3) an optimization solving module: determining a corresponding optimization strategy coefficient according to the reservoir water storage capacity of the hydropower station, and solving a pre-constructed wind-solar-water coordinated operation optimization model based on the optimization strategy coefficient, new energy historical operation data and each generator set data to obtain an optimized output scheme of the wind-solar-water-fire complementary power generation system;

The wind-solar-water coordination operation optimization model is constructed based on an adjustable hydropower unit polymerization operation optimization model and aims at the maximum clean energy consumption of wind, solar and water, wherein the adjustable hydropower unit polymerization operation optimization model is constructed by a plurality of hydropower unit output constraints based on hydropower unit startup number polymerization.

10. The system of claim 9, wherein the optimization strategy coefficients comprise one or more of: a wind discarding punishment coefficient, a light discarding punishment coefficient and a water discarding punishment coefficient; the optimization solving module determines a corresponding optimization strategy coefficient according to the reservoir water storage capacity of the hydropower station, and comprises the following steps:

if the water storage capacity of the hydropower station bookstore is lower than the normal range, determining that the abandoned water punishment coefficient is larger than the abandoned wind punishment coefficient and the abandoned light punishment coefficient;

if the water storage amount of the hydropower station bookstore is higher than the normal range, determining that the abandoned water punishment coefficient is smaller than the abandoned wind punishment coefficient and the abandoned light punishment coefficient;

and if the water storage capacity of the hydropower station bookstore is in a normal range, determining that the abandoned wind punishment coefficient, the abandoned light punishment coefficient and the abandoned water punishment coefficient are all zero.

11. The system of claim 9, wherein the hydroelectric generating set output constraints comprise one or more of: the method comprises the following steps of unit aggregation output range constraint, unit aggregation output climbing constraint, unit aggregation running number range constraint and unit aggregation generating capacity range constraint.

12. The system of claim 11, wherein the aggregate force range constraint of the aggregate set is expressed as follows:

wherein: p (P) ^h (t) is the output of the h-class hydroelectric generating set at the t moment; ^h

p is the lower output limit of the h-class hydroelectric generating set;the upper output limit of the h-class hydroelectric generating set is set; s is S ^h (t) is the number of the h-class hydroelectric generating set started at the moment t;

the expression of the unit aggregate force climbing constraint is as follows:

wherein: delta ^h The climbing range of the h-class hydroelectric generating set is set; p (P) ^h (t+1) is the output of the h-class hydroelectric generating set at the time t+1; p (P) ^h (t) is the output of the h-class hydroelectric generating set at the t moment; p is p ^h The lower output limit of the h-class hydroelectric generating set is set; s is S ^h (t) is the number of the h-class hydroelectric generating set started at the moment t; s is S ^h (t+1) is the number of the h-class hydroelectric generating set starting-up at the moment t+1;

the expression of the unit polymerization operation number range constraint is as follows:

wherein: s is S ^h S is the minimum starting number of the h-class hydroelectric generating set ^h The maximum starting number of the h-class hydroelectric generating set is set; s is S ^h (t) is the number of the h-class hydroelectric generating set started at the moment t;

the unit polymerization power generation amount range constraint expression is as follows:

wherein:the minimum generating capacity of the h-class hydroelectric generating set in the m period; p (P) ^h (t) is the output of the h-class hydroelectric generating set at the t moment;and the maximum power generation amount of the h-class hydroelectric generating set in the m period is obtained.

13. The system of claim 9, wherein the construction of the optimization model for the coordinated operation of the wind, the light and the water in the optimization solving module comprises:

constructing an objective function by taking the maximum clean energy consumption of wind, light and water as a target;

setting constraint conditions for the objective function based on the operation constraint of the adjustable hydroelectric generating set aggregate operation optimization model and the wind, light, water and fire complementary power generation system;

the constraints include one or more of the following: the system comprises a hydroelectric power generation system constraint condition, a photovoltaic power generation system constraint condition, a wind power generation system constraint condition, an operation constraint condition after a water-wind-solar complementary power generation system is connected with a power grid, a system rotation reserve capacity constraint, a regional load balance constraint, an inter-regional line transmission capacity constraint, a thermal power generation system operation constraint and a new energy power generation output constraint, wherein the thermal power generation system operation constraint comprises one or more of the following: thermal power unit optimizing power constraint, thermal power unit optimizing power climbing rate constraint, thermal power unit operation number constraint and thermal power start-stop logic constraint.

14. The system of claim 13, wherein the expression of the objective function is as follows:

Wherein: c is an objective function; n is the total number of power grid partitions; t represents the total length of scheduling time; h is the classification number of the hydroelectric generating set; p (P) _w (t, n) wind power output of the grid partition n in a period t; p (P) _pv (t, n) is the photovoltaic power generation capacity of the grid partition n in a period t; p (P) _h (t, n) is the output of the h-th type aggregation hydroelectric generating set in the period t for the power grid partition n;the water discarding of the h-class hydroelectric generating set in the t period of the power grid partition n is shown; />Wind power generation of grid partition n in t period is abandoned,/->Photovoltaic power rejection at t period for grid partition n, ρ _w Punishment coefficients for the wind curtailment; ρ _pv Punishment coefficients for the light rejection; ρ _h Punishment coefficients for water reject.

15. The system of claim 13, wherein the expression for the system rotation reserve capacity constraint is:

wherein: p (P) _j,max (t, n) is the upper limit of the output force of the j-th type thermal power unit in the t period in the power grid partition n; p (P) _j,min (t, n) is the lower output limit of the j-th type thermal power unit in the t period in the power grid partition n; p (P) _w (t, n) wind power output of the grid partition n in a period t; p (P) _pv (t, n) is the photovoltaic power generation capacity of the grid partition n in a period t; p (P) _h，max (t, n) is the upper output limit of the h-class hydroelectric generating set in the t period in the power grid partition n; p (P) _h，min (t, n) is the lower output limit of the h-class hydroelectric generating set in the t period in the power grid partition n; s is S _j (t, n) is the number of thermal power start-up units of the j-th type unit in the t period in the power grid partition n; s is S _h (t, n) the hydropower start-up number of the h-type unit in the t period in the power grid partition n; p (P) _l (t, n) then represents the electrical load of grid partition n for the t-th period; p (P) _re The positive rotation set for the system is reserved; n (N) _re Negative rotation set for the system is reserved; n is the total number of power grid partitions; j is the unit classification number of the thermal power;

the expression of the regional load balance constraint is as follows:

wherein: p (P) _j (t, n) is the power generation power of the j-th type thermal power unit in the t-th period of the power grid partition n; s is S _j (t, n) is the number of thermal power start-up units of the jth group of units in the t period in the power grid partition n; p (P) _h (t, n) is the hydroelectric generating set output of the power grid partition n in the h class of the period t; s is S _h (t, n) is the number of the startup of the hydroelectric generating set in the h class of the period t; p (P) _w (t, n) wind power output of the grid partition n in a period t; p (P) _pv (t, n) is the photovoltaic power generation capacity of the grid partition n in a period t; l (L) _i (t) is the transmission power of the ith transmission line in the t-th period; p (P) _l (t, n) then represents the electrical load of grid partition n for the t-th period; h is the classified number of the hydroelectric generating set; j is the unit classification number of the thermal power;

the expression of the inter-area line transmission capacity constraint is as follows:

-L _i,max ≤L _i (t)≤L _i,max

wherein: l (L) _i,max Is the limit of the transmission capacity of the ith transmission line; l (L) _i (t) is the transmission power of the ith transmission line in the t-th period;

the expression of the new energy power generation force constraint is as follows:

wherein:the theoretical output of wind power at the time t is calculated for the power grid partition n; p (P) _w (t, n) wind power output of the grid partition n in a period t; />The theoretical output of photovoltaic power generation at the time t is given to the grid partition n; p (P) _pv And (t, n) is the photovoltaic power generation capacity of the power grid partition n in the period t.

16. The system of claim 9, wherein the new energy historical operating data in the data acquisition module comprises one or more of: wind power history data, photovoltaic history data, and load history data; each generating set data in the data acquisition module comprises one or more of the following: the capacity of each generator set, the type of each generator set, the output limit data of each generator set, the power of the power grid outgoing link and the power grid topology; the optimized output scheme in the optimized solving module comprises one or more of the following: the starting mode of each unit, the time sequence output curve of each unit and the actual output of new energy.