Water, wind, light and storage battery complementary scheduling method and device based on minimum abandoned electric quantity

Abstract

The invention discloses a water-wind-light-storage complementary scheduling method and device based on the minimum abandoned electric quantity, and aims to solve the problems of large abandoned electric quantity, insufficient scheduling flexibility and the like of an existing scheduling model. According to the model, wind power and photovoltaic prediction output curves and parameters of a hydropower station are comprehensively considered, minimization of abandoned electric quantity is taken as a target, power load constraint and hydropower station operation constraint are met at the same time, and coordinated and complementary operation of water, wind, light and storage is achieved. According to the specific technical scheme, data input, an objective function, constraint conditions, a solving method and the like are included. Through optimization scheduling, wind curtailment and light curtailment power are effectively reduced, and the utilization rate of renewable energy sources and the operation stability of a power grid are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power system dispatching, in particular to a water-wind-solar-storage complementary dispatching method and device based on minimum abandoned power. BACKGROUND

[0002] Currently, the penetration rate of renewable energy such as wind power and photovoltaic power in the power grid is rapidly increasing, but the output of renewable energy has significant intermittency and volatility, resulting in prominent wind curtailment and light curtailment problems. In particular, during the low load valley or when the power grid transmission is limited, the abandoned power further increases.

[0003] Traditional dispatching models are mostly based on a single scenario or deterministic prediction, and do not fully consider the spatiotemporal correlation between wind and light output and load demand. For example, the complementary characteristics of the peak night output of wind power and the daytime output of photovoltaic power, but the existing models are difficult to dynamically coordinate the linkage between the two and hydropower stations and pumped storage power stations, resulting in high wind curtailment rate. As an important regulating means, the operation efficiency of pumped storage power stations is affected by dynamic factors such as reservoir water level and amplitude constraints, and the existing models often ignore the real-time matching between pumped storage power stations and wind and light output, resulting in insufficient utilization of energy storage. Dynamic constraints such as reservoir flood control capacity and water level amplitude are not fully reflected in the dispatching model, resulting in deviations between the dispatching plan and the actual operation, and even causing safety hazards.

[0004] The existing technology has significant deficiencies in multi-scenario adaptability, energy storage coordination flexibility, and dynamic constraint modeling. SUMMARY

[0005] The present application aims to at least partially solve one of the technical problems in the related art.

[0006] To this end, the present application proposes a water-wind-solar-storage complementary dispatching method based on minimum abandoned power, which proposes a water-wind-solar-storage complementary dispatching model based on minimum abandoned power by fusing stochastic optimization and refined constraints, aiming to solve the core problems of high wind curtailment rate and poor dispatching robustness, and provide an efficient dispatching scheme for high-proportion renewable energy power grids.

[0007] Another object of the present application is to propose a water-wind-solar-storage complementary dispatching device based on minimum abandoned power.

[0008] A third object of the present application is to propose a computer device.

[0009] A fourth object of the present application is to propose a non-transitory computer readable storage medium.

[0010] To achieve the above-mentioned objects, the present application proposes a water-wind-solar-storage complementary dispatching method based on minimum abandoned power, comprising:

[0011] Obtain a representative wind, light output scene set and a representative load scene set of a clean energy base, and basic parameters of a hydropower station and a pumped storage power station;

[0012] Based on the obtained representative wind, light output scene set, representative load scene set and basic parameters of the hydropower station and the pumped storage power station, a complementary dispatching model of the wind, light, storage and clean energy base is constructed with the total abandoned power of the wind, light, storage and clean energy base as an objective function.

[0013] A load balance model of the clean energy grid system is constructed.

[0014] Based on the objective function and the load balance model, a water, wind, light, storage complementary dispatching model based on minimum abandoned power is constructed.

[0015] Constraint conditions of the water, wind, light, storage complementary dispatching model are constructed.

[0016] Based on the constraint conditions, the water, wind, light, storage complementary dispatching model based on minimum abandoned power is solved to obtain a water, wind, light, storage complementary dispatching strategy based on minimum abandoned power.

[0017] The water, wind, light, storage complementary dispatching method based on minimum abandoned power can further have the following additional technical features:

[0018] In an embodiment of the present application, the objective function is:

[0019]

[0020] Wherein, K is the number of possible grid load and water, wind, light, pumped storage output scenes, p k is the probability of the occurrence of the i th scene; T is the number of time periods of the dispatching period; is the wind power output of the t th time period in the i th scene; is the photovoltaic of the t th time period in the i th scene; is the hydropower output of the t th time period in the i th scene; is the power for pumped storage of the t th time period in the i th scene, and the maximum value is the installed capacity of the pumped storage; α i,t is the pumped storage efficiency of the t th time period in the i th scene; β i,t is the power generation efficiency of the t th time period in the i th scene; L i,t is the power transmission amount of the t th time period in the i th scene.

[0021] In an embodiment of the present application, the expression of the water, wind, light, storage complementary dispatching model based on minimum abandoned power is:

[0022]

[0023] In an embodiment of the present application, a representative wind, light output scene set and a representative load scene set of a clean energy base are obtained, including:

[0024] A historical wind power output sequence W = [ω1, ω2,..., ω T ], a historical photovoltaic output sequence P = [p1, p2,..., p T ], and a historical load sequence L = [l1, l2,..., l T ] of a power grid system are obtained.

[0025] The sequences are normalized output, and Min-Max standardization is adopted to eliminate dimensional differences:

[0026]

[0027] A multi-dimensional feature vector V t = [ω t , p t , l t , Δω t , Δp t , Δl t ] is constructed for each time, and the vector contains output values and adjacent period change rates at time t.

[0028] The data is divided into periods with a length of τ, and a period scene matrix is constructed, where N is the number of historical days.

[0029] Gaussian Copula is used to describe the nonlinear spatial correlation among wind, light, and load.

[0030] Time weight α and space weight β are introduced, and the distance between scenes is defined:

[0031]

[0032] Where C is the Copula correlation coefficient matrix, and ||·||F is the Frobenius norm. F

[0033] The elbow rule is used to determine the number of clusters K, and K typical scenes and corresponding probabilities are output.

[0034] In an embodiment of the present application, for a power grid system with fixed load, the load of the power grid system is determined, including:

[0035] The photovoltaic installed capacity, typical meteorological year photovoltaic average output process, wind power installed capacity, typical meteorological year wind power average output process, hydropower station installed capacity, number of units, and single unit capacity are obtained.

[0036] ​The annual photovoltaic power generation curve and the annual wind power generation curve are calculated, the photovoltaic power generation curve is calculated by the photovoltaic installed capacity L p , the peak sunshine hours T p , and the system efficiency η p , P = L p × T p × η p ; the wind turbine power generation W = L w × P w × η w in each time period is calculated by the average wind power process of the typical meteorological year, wherein L w is the wind power installed capacity, P w refers to the size of the wind power resource, and η w refers to the ratio of the actual power generation to the theoretical power generation of the wind turbine in a certain time period, and then the active wind annual power generation curve is obtained.

[0037] The annual power transmission channel power is calculated:

[0038]

[0039] wherein S d,i is the smaller value of the photovoltaic + wind power average output GF d,i of the i-th hour of the d-th day in a year and the rated capacity K of the power transmission channel;

[0040] The annual hydropower station power generation is calculated:

[0041]

[0042] wherein C d,i is the hydropower station power generation of the i-th hour of the d-th day, which is the available value of K-S d,i on the power generation curve;

[0043] The wind and light storage power of the pumped storage power station is:

[0044]

[0045] wherein B d,i is the pumped storage storage power of the i-th hour of the d-th day, which is the available value of GF d,i -K on the pumping curve;

[0046] Based on the water balance calculation, the water consumption of the hydropower station per year = the pumped storage water pumping amount per year + the upstream water inflow:

[0047]

[0048] wherein α d,i (h) is the water consumption required for generating unit power; and β d,i(h) is the pumped water volume per kilowatt-hour; Q is the water volume of the reservoir planned for power generation;

[0049] In the process of solving the constraint model, the following constraints are considered:

[0050] Water level constraint: the water level of the reservoir meets the basic requirements of reservoir regulation, and the water level is between the flood control high water level and the flood control limit water level. The power generation capacity of the hydropower station or the storage capacity of the pumped storage power station needs to be calculated to consume or increase the reservoir capacity. The corresponding water level is calculated through the water level-storage capacity curve of the reservoir;

[0051] Maximum amplitude constraint of hydropower: |C d,i -C d,i+1 |≤N h,ramp and |C d+1,24 -C d,1 |≤N h,ramp , where N h,ramp is the maximum amplitude of the unit time of the hydropower station;

[0052] Maximum amplitude constraint of pumped storage: |B d,i -B d,i+1 |≤N p,ramp and |B d+1,24 -B d,1 |≤N h,ramp , where N h,ramp is the maximum amplitude of the unit time of the pumped storage power station;

[0053] Reservoir water level-flow constraint:

[0054]

[0055] Q min ≤Q (d,i) ≤Q max

[0056] In the formula, Z (d,i) is the water level of the reservoir at the beginning of the t period of the i day; and are the upper and lower limits of the water level of the reservoir in the d day, respectively; Q min and Q max are the upper and lower limits of the reservoir outflow, respectively, Q (d,i) = α d,i (h) × C d,i - β d,i (h) × B d,i ;

[0057] By selecting the data of a typical year, equations (1), (2), (3), and (4) are solved simultaneously, considering the constraint conditions, to finally determine the on-grid power of the water, wind, light, and clean energy base M.

[0058] In one embodiment of the present application, the constraint conditions of the water and wind landscape complementary scheduling model are constructed, and the corresponding constraint conditions include:

[0059] The pumped storage energy storage condition constraint is constructed, and the pumped storage energy storage condition constraint is:

[0060]

[0061] Wherein, wherein is the wind power output of scenario i in period t; is the photovoltaic of scenario i in period t; is the hydropower output of scenario i in period t; L i,t is the power transmission amount of the clean energy base connected to the power grid in scenario i in period t, is the maximum capacity of the pumped storage power station for pumped storage in scenario i in period t;

[0062] The reservoir water balance constraint is constructed, and the water balance constraint is:

[0063] V (i,t+1) = V (i,t) +(QI (i,t) -Q (i,t) )×Δ t

[0064] Wherein, V (i,t) and V (i,t+1) are the reservoir capacities at the beginning and the end of period t in scenario i; QI (i,t) is the average inflow of the reservoir in scenario i in period t, Q (i,t) is the average outflow of the reservoir in scenario i in period t;

[0065] The available water quantity constraint of the reservoir is constructed, and the available water quantity constraint is:

[0066]

[0067] Wherein, Q (i,t) is the average outflow of the reservoir in scenario i in period t; W available(i) represents the total water discharged by the reservoir in scenario i in the scheduling period; K is the number of scenarios of possible grid load and water, wind, light, and pumped storage output scenarios;

[0068] The reservoir water level and flow constraint is constructed, and the water level and flow constraint is:

[0069]

[0070] In the formula, Z (i,t) is the water level of the reservoir at the beginning of period t in scenario i; and are the upper and lower limits of the water level of the reservoir in scenario i, respectively, Q min and Q max are the upper and lower limits of the discharge of the reservoir, respectively;

[0071] a water energy utilization constraint is constructed, and the water energy utilization constraint is:

[0072]

[0073] wherein, and H (i,t) are the output, the power generation discharge and the water head of the hydropower station in scenario i at time period t, respectively; K (i) represents the output coefficient of the generator set of the hydropower station;

[0074] a maximum variation constraint of hydropower is constructed, and the maximum variation constraint of hydropower is:

[0075]

[0076] wherein is the maximum variation per unit time of power generation of the hydropower station, is the output of the hydropower in scenario i at time period t, is the output of the hydropower in scenario i at time period t+1;

[0077] a maximum variation constraint of pumped storage is constructed, and the maximum variation constraint of pumped storage is:

[0078]

[0079] wherein is the maximum variation per unit time of pumped storage of the pumped storage power station, is the power for pumping of the pumped storage power station in scenario i at time period t, is the power for pumping of the pumped storage power station in scenario i at time period t+1;

[0080] for the reservoir with the flood control task, a dynamic flood control storage constraint also needs to be considered, and the dynamic flood control storage constraint is:

[0081]

[0082] wherein, V (j,t) represents the initial storage of the reservoir with the flood control task in scenario j at time period t; represents the storage corresponding to the normal storage level of the reservoir in scenario j; V t flood represents the flood control storage to be reserved by the reservoir at time period t; J is the total number of scenarios with the flood control task;

[0083] Based on the constraint condition, the water, wind, light, and storage complementary scheduling model based on the minimum abandoned power is solved, and the optimal output plans of the wind power, photovoltaic, hydropower, and pumped storage power station in each period are obtained, so that the water, wind, light, and storage complementary scheduling based on the minimum abandoned power is realized.

[0084] In an embodiment of the present application, solving the water, wind, light, and storage complementary scheduling model comprises:

[0085] Based on the decision variables of the water, wind, light, and storage complementary scheduling model, the parameters in all representative scenarios are determined, the wind power output in each scenario is initialized according to the generated K typical scenarios and the probability distribution thereof, the photovoltaic output the load demand L i,t , the pumped storage efficiency alpha i,t and beta i,t , and the initial reservoir capacity of the reservoir, the water level-storage capacity curve, and the flood control constraint parameters;

[0086] A linearized random optimization model is constructed: a multi-scenario random optimization model is constructed with the minimum expected abandoned power as the target, and the constraints include power balance, pumped storage energy storage limit, water balance, water level / flow dynamic constraint, and amplitude limit;

[0087] All scenario variables are solved by using random linear programming, and are directly solved by using a commercial optimization solver;

[0088] The optimal output plans and the abandoned power in each scenario are extracted, the weighted expected abandoned power is calculated, the feasibility of the water level, the reservoir capacity, and the amplitude constraint is verified, and the time-division wind, light, and storage output allocation, the pumped storage power station mode switching strategy, and the reservoir water level control scheme are output;

[0089] Random disturbance scenarios are generated by Monte Carlo simulation to verify the stability of the model under the prediction error, and the pumped storage efficiency parameters are optimized;

[0090] The solution of the water, wind, light, and storage complementary scheduling model is output.

[0091] To achieve the above purpose, another aspect of the present application provides a water, wind, light, and storage complementary scheduling device based on the minimum abandoned power, comprising:

[0092] An information acquisition module is configured to acquire a representative wind and light output scenario set and a representative load scenario set of a clean energy base, and basic parameters of a hydropower station and a pumped storage power station;

[0093] A parameter acquisition module is configured to, based on the obtained representative wind and light output scenario set, the representative load scenario set, and the basic parameters of the hydropower station and the pumped storage power station, construct a complementary scheduling model of the water, wind, light, and storage clean energy project with the minimum total abandoned power of the wind, light, and storage clean energy base as an objective function;

[0094] A model construction module is configured to construct a load balance model of the clean energy power grid system; and a water, wind, light and storage complementary dispatching model based on minimum abandoned power is constructed based on the target function and the load balance model.

[0095] A constraint construction module is configured to construct a constraint condition of the water, wind, light and storage complementary dispatching model.

[0096] A solution module is configured to solve the water, wind, light and storage complementary dispatching model to obtain a water, wind, light and storage complementary dispatching method based on minimum abandoned power and minimum abandoned power.

[0097] The water, wind, light and storage complementary dispatching method and device based on minimum abandoned power provided by the embodiment of the application obtain a representative wind and light output scene set and a representative load scene set of a clean energy base, and basic parameters of a hydropower station and a pumped storage power station.

[0098] Based on the obtained representative wind and light output scene set, representative load scene set and basic parameters of the hydropower station and pumped storage power station, a complementary dispatching model of the water, wind, light and storage clean energy project is constructed with minimum total abandoned power of the water, wind, light and storage clean energy base as a target function; a load balance model of the power grid system is constructed; a constraint condition of the water, wind, light and storage complementary dispatching model is constructed; and the water, wind, light and storage complementary dispatching model is solved to obtain a water, wind, light and storage complementary dispatching strategy based on minimum abandoned power and minimum abandoned power. The application solves the core problems of high abandoned power rate and poor dispatching robustness, and provides an efficient dispatching scheme for a high-proportion renewable energy power grid.

[0099] To achieve the above object, the third aspect of the present application provides a computer device, comprising: a processor and a memory; wherein the processor runs a program corresponding to an executable program code stored in the memory by reading the executable program code, so as to implement the method according to the first aspect of the present application.

[0100] To achieve the above object, the fourth aspect of the present application provides a non-transitory computer readable storage medium, which stores a computer program, and the program is executed by a processor to implement the method according to the first aspect of the present application.

[0101] Additional aspects and advantages of the application will be made apparent by the following description and the accompanying drawings. BRIEF DESCRIPTION OF DRAWINGS

[0102] The above and / or additional aspects and advantages of the application will become apparent and be readily understood by considering the following detailed description, including the accompanying drawings, in which:

[0103] Figure 1is a flow chart of a water-wind-solar-storage complementary scheduling method based on minimum abandoned power according to an embodiment of the present application;

[0104] Figure 2 is another flow chart of a water-wind-solar-storage complementary scheduling method based on minimum abandoned power according to an embodiment of the present application;

[0105] Figure 3 is a structural diagram of a water-wind-solar-storage complementary scheduling device based on minimum abandoned power according to an embodiment of the present application;

[0106] Figure 4 is a computer device according to an embodiment of the present application. DETAILED DESCRIPTION

[0107] It should be noted that the embodiments in the present application and the features in the embodiments can be combined with each other without conflict. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0108] In order for those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should belong to the scope of protection of the present application.

[0109] The water-wind-solar-storage complementary scheduling method, device, equipment and storage medium based on minimum abandoned power according to the embodiments of the present application will be described below with reference to the accompanying drawings.

[0110] Figure 1 is a flow chart of a water-wind-solar-storage complementary scheduling method based on minimum abandoned power according to an embodiment of the present application, as shown in Figure 1 and Figure 2 , the method includes but is not limited to the following steps:

[0111] Step S110, obtaining a representative wind and light output scene set and a representative load scene set of a clean energy base, and basic parameters of a hydropower station and a pumped storage power station.

[0112] In some examples, only the historical output sequence of the power grid system, the compliance sequence can be obtained, in which case, the step of constructing a representative scene set is:

[0113] The historical wind power output sequence W of the power grid system is obtained = [ω1, ω2,..., ω T ], the historical photovoltaic output sequence P = [p1, p2,..., p T ], and the historical load sequence L = [l1, l2,..., lT ];

[0114] The above sequence is normalized to output, using Min-Max standardization to eliminate dimensional differences:

[0115]

[0116] For each time, construct a multi-dimensional feature vector V t = [ω t , p t , l t , Δω t , Δp t , Δl t ], which contains the output value at time t and the change rate of adjacent periods;

[0117] Divide the data into periods of length τ (such as 24 hours), and construct a period scene matrix where N is the number of historical days;

[0118] Use Gaussian Copula to describe the nonlinear spatial correlation between wind, light, and load;

[0119] Introduce time weight α and space weight β, and define the distance between scenes:

[0120]

[0121] where C is the Copula correlation coefficient matrix, and ||·||F is the Frobenius norm; F

[0122] Determine the number of clusters K by the elbow rule, and output K typical scenes and corresponding probabilities.

[0123] In some examples, it is also necessary to determine the grid system load, the steps are:

[0124] Obtain the photovoltaic installed capacity, the average photovoltaic output process of the typical meteorological year, the wind power installed capacity, the average wind power output process of the typical meteorological year, the hydropower station installed capacity, the number of units and the capacity of a single unit.

[0125] Calculate the annual photovoltaic power generation curve and the annual wind power generation curve. The photovoltaic power generation curve can be calculated by the photovoltaic installed capacity L p , the peak sunshine hours T p , and the system efficiency η p , P = L p × T p × η p ; the wind turbine power generation W = L w × P w is calculated by the average wind power output process of the typical meteorological year.X w where L w is the installed capacity of wind power, wind energy P w refers to the size of wind resources, the power generation coefficient η w refers to the ratio of the actual power generation of the wind turbine generator set to the theoretical power generation in a certain time, and further refers to the active wind annual power generation curve.

[0126] Calculate the annual power transmission capacity:

[0127]

[0128] where S d,i is the average output of photovoltaic + wind power on the dth day of the year and the ith hour GF d,i and the smaller value of the rated capacity K of the power transmission channel;

[0129] Calculate the annual power generation of the hydropower station:

[0130]

[0131] where C d,i is the power generation of the hydropower station on the dth day and the ith hour, which is K-S d,i the available value on the power generation curve;

[0132] Pumped storage power station wind light storage power:

[0133]

[0134] where B d,i is the pumped storage power of the dth day and the ith hour, which is GF d,i K available value on the pumping curve;

[0135] Based on the water balance calculation, that is, the water consumption of the hydropower station per year = pumped storage per year + upstream water inflow (water planned for power generation),

[0136]

[0137] where, α d,i (h) is the water required to generate one unit of electricity, which is related to water level, generator power, blade opening, etc. β d,i (h) is the pumped water volume per kilowatt-hour, which is related to water level, pumping power, etc. Q is the water planned for power generation in the reservoir.

[0138] In the process of solving the constraint model, the following constraints are considered:

[0139] Water level constraint: the water level of the reservoir meets the basic reservoir scheduling requirements, the water level is between the flood control high water level and the flood control limit water level, the power generation capacity or the pumped storage power storage capacity of the hydropower station needs to be calculated to consume or increase the reservoir capacity, and the corresponding water level is calculated through the water level-storage capacity curve of the reservoir.

[0140] Hydropower maximum amplitude constraint: |C d,i -C d,i+1 |≤N h,ramp and |C d+1,24 -C d,1 |≤N h,ramp , wherein N h,ramp is the maximum amplitude of the hydropower station per unit time.

[0141] Pumped storage maximum amplitude constraint: |B d,i -B d,i+1 |≤N p,ramp and |B d+1,24 -B d,1 |≤N h,ramp , wherein N h,ramp is the maximum amplitude of the pumped storage power station per unit time.

[0142] Reservoir water level flow constraint:

[0143]

[0144] Q min ≤Q (d,i) ≤Q max

[0145] In the formula, Z (d,i) is the water level of the reservoir at the beginning of the t period on the i day; and are the upper and lower limits of the water level of the reservoir on the d day, since the water level of the reservoir corresponds to the reservoir capacity one by one, the reservoir capacity constraint is equivalent to the water level constraint, and it is not necessary to consider repeatedly; Q min and Q max are the upper and lower limits of the reservoir outflow, Q (d,i) =α d,i (h)×C d,i -β d,i (h)×B d,i .

[0146] By selecting the data of a typical year, simultaneously solving equations (1), (2), (3), and (4), considering the constraint conditions, the online power of the water, wind, light, and clean energy base M can be finally determined.

[0147] Step S120, based on the obtained representative wind, light output scene set, representative load scene set and basic parameters of the hydropower station and pumped storage power station, a total abandoned power quantity minimum target function of the water, wind, light and storage clean energy base is constructed.

[0148] The target function is:

[0149]

[0150] Wherein, K is the number of possible grid load and water, wind, light, pumped storage output scene scenes, p k is the probability of the occurrence of the ith scene; T is the number of time periods of the scheduling period; is the wind power output of the t time period in the scene i; is the photovoltaic of the t time period in the scene i; is the hydropower output of the t time period in the scene i; is the power for pumped storage power station pumping in the t time period in the scene i, the maximum value is the installed capacity of pumped storage; α i,t is the pumped storage power station pumped storage efficiency in the t time period in the scene i, that is, the conversion rate of converting electric energy into potential energy, which is related to the water level of the upstream and downstream of the reservoir; β i,t is the pumped storage power station power generation efficiency in the t time period in the scene i, that is, the conversion rate of converting the potential energy of water into electric energy, which is related to the water level of the upstream and downstream of the reservoir, and the average value of the power generation efficiency under the water level difference is taken; L i,t is the power transmission quantity of the clean energy base connected to the grid in the t time period in the scene i.

[0151] Step S130, a load balance model of the clean energy grid system is constructed.

[0152] The power load balance model of the clean energy grid system is: Wherein is the wind power output of the t time period in the scene i; is the photovoltaic of the t time period in the scene i; is the hydropower output of the t time period in the scene i; L i,t is the power transmission quantity of the clean energy base connected to the grid in the t time period in the scene i.

[0153] Step S140, based on the target function and the power load balance model, a water, wind, light and storage complementary dispatching model based on minimum abandoned power is constructed.

[0154] The water, wind, light and storage complementary dispatching model based on minimum abandoned power is expressed as:

[0155]

[0156] Step S150, the constraint condition of the water, wind, light and storage complementary dispatching model is constructed.

[0157] The pumped storage energy storage condition constraints are constructed as follows:

[0158]

[0159] Among them, For wind power output during time period t in scenario i; For photovoltaic data during time period t in scenario i; L represents the hydropower output during time period t in scenario i; i,t For the clean energy base connected to the grid during time period t in scenario i, t represents the maximum capacity of the pumped storage power station used for pumped storage in scenario i during time period t, which is affected by upstream water level, generator installed capacity, and generator operating status.

[0160] Construct water balance constraints for the reservoir, wherein the water constraints are:

[0161] V (i,t+1) =V (i,t) +(QI (i,t) -Q (i,t) )×Δ t

[0162] Among them, V (i,t) and V (i,t+1) These represent the reservoir capacity at the beginning and end of time period t in scenario i, respectively; QI (i,t) Let Q be the average inflow rate of the reservoir during time period t in scenario i. (i,t) Let be the average outflow from the reservoir during time period t in scenario i.

[0163] Construct a water availability constraint for the reservoir, wherein the water availability constraint is:

[0164]

[0165] Among them, Q (i,t) W represents the average discharge flow from the reservoir during time period t in scenario i; available(i) This represents the total amount of water released from the reservoir during the scheduling period in scenario i; K is the number of possible grid load and water, wind, solar, and pumped storage power output scenarios.

[0166] Construct a reservoir water level-discharge constraint, wherein the water level-discharge constraint is:

[0167]

[0168] In the formula, Z (i,t) Let be the water level of the reservoir at the beginning of time period t in scenario i; and are the upper and lower limits of the water level of the reservoir in scenario i, since the water level of the reservoir is in one-to-one correspondence with the reservoir capacity, the reservoir capacity constraint is equivalent to the water level constraint, and thus it is not necessary to consider it repeatedly; Q min and Q max are the upper and lower limits of the discharge of the reservoir.

[0169] The water energy utilization constraint is constructed, and the water energy utilization constraint is:

[0170]

[0171] wherein, and H (i,t) are the output, the power generation discharge and the water head of the hydropower station in scenario i at time period t; K (i) represents the output coefficient of the generator set of the hydropower station.

[0172] The maximum amplitude constraint of the hydropower is constructed, and the maximum amplitude constraint of the hydropower is:

[0173]

[0174] wherein is the maximum amplitude of the unit time of the power generation of the hydropower station, is the hydropower output in scenario i at time period t, is the hydropower output in scenario i at time period t+1.

[0175] The maximum amplitude constraint of the pumped storage is constructed, and the maximum amplitude constraint of the pumped storage is:

[0176]

[0177] wherein is the maximum amplitude of the unit time of the pumped storage of the pumped storage power station, is the power for pumping of the pumped storage power station in scenario i at time period t, is the power for pumping of the pumped storage power station in scenario i at time period t+1.

[0178] For the reservoir with the flood control task, the dynamic flood control capacity constraint also needs to be considered, and the dynamic flood control capacity constraint is:

[0179]

[0180] wherein, V (j,t) represents the initial reservoir capacity of the reservoir with the flood control task in scenario j at time period t; represents the reservoir capacity corresponding to the normal storage level of the pumped storage reservoir in scenario j; V t flood represents the flood control capacity to be reserved by the reservoir at time period t; J is the total number of scenarios with the flood control task.

[0181] Step S160, the water wind light storage complementary scheduling model is solved, and the water wind light storage complementary optimal strategy based on the minimum abandoned power is obtained. The solving steps are as follows:

[0182] Step 1: all scene variables are solved by using a random linear programming (SLP), and a commercial optimization solver (CPLEX or Gurobi) is directly solved.

[0183] Step 2: the optimal output plan and the abandoned power of each scene are extracted, the weighted expected abandoned power is calculated, the feasibility of the water level, the storage capacity and the amplitude constraints is verified, and the time-sharing wind light storage output distribution, the pumped storage power station mode switching strategy and the reservoir water level control scheme are output.

[0184] Step 3: random disturbance scenes are generated by Monte Carlo simulation to verify the stability of the model under the prediction error.

[0185] Step 4: the solution of the wind light pumped storage complementary scheduling model is output.

[0186] The water wind light storage complementary scheduling method based on the minimum abandoned power provided by the embodiment of the application, by fusing random optimization and refined constraints, a water wind light storage complementary scheduling model based on the minimum abandoned power is proposed, aiming to solve the core problems of high abandoned power rate and poor scheduling robustness, and to provide an efficient scheduling scheme for a high proportion of renewable energy power grid.

[0187] In order to realize the above-mentioned embodiment, as Figure 3 shown, the embodiment also provides a water wind light storage complementary scheduling device based on the minimum abandoned power, which comprises:

[0188] An information acquisition module 201 is configured to acquire a representative wind and light output scene set and a representative load scene set of a clean energy base, and basic parameters of a hydropower station and a pumped storage power station;

[0189] A parameter acquisition module 202 is configured to, based on the obtained representative wind and light output scene set, the representative load scene set and the basic parameters of the hydropower station and the pumped storage power station, take the minimum total abandoned power of the wind light storage clean energy base as an objective function, and construct a complementary scheduling model of the water wind light storage clean energy project;

[0190] A model construction module 203 is configured to construct a load balance model of a clean energy power grid system, construct a water wind light storage complementary scheduling model based on the minimum abandoned power based on the objective function and the load balance model, and construct a constraint condition of the water wind light storage complementary scheduling model;

[0191] A solving module 204 is configured to solve the water wind light storage complementary scheduling model, and obtain a water wind light storage complementary scheduling method based on the minimum abandoned power and the minimum abandoned power.

[0192] Specifically, the modules of the device are described as follows:

[0193] The information acquisition module 201 is configured to acquire and store information required for model calculation, including historical wind power output sequence, historical photovoltaic output sequence, historical load sequence, and basic parameters of hydropower stations such as reservoir capacity, available water volume, water energy utilization efficiency, and maximum amplitude of hydropower.

[0194] The parameter acquisition module 202 is configured to determine parameters in all representative scenarios based on decision variables of the wind-photovoltaic-pumped storage complementary dispatching model acquired and stored by the information acquisition module 201. K typical scenarios and their probability distributions generated according to the average output processes of photovoltaic and wind power in a typical meteorological year are used to initialize the wind power output, photovoltaic output, load demand, pumped storage efficiency, and initial reservoir capacity, water level-capacity curve, and flood control constraint parameters in each scenario.

[0195] The model construction module 203 is configured to construct a linearized random optimization model. Based on the representative wind and light output scenario set, the representative load scenario set, and the basic parameters of hydropower stations and pumped storage power stations, the model construction module 203 is configured to construct an objective function with the minimum expected power abandonment as the target, and construct a load balancing model of a clean energy power grid system, pumped storage energy storage condition constraints, reservoir water balance constraints, reservoir available water volume constraints, reservoir water level-flow constraints, water energy utilization constraints, maximum amplitude constraints of hydropower, maximum amplitude constraints of pumped storage, dynamic flood control capacity constraints, and finally form a wind-photovoltaic-pumped storage complementary dispatching model based on the minimum power abandonment.

[0196] The parameter acquisition module 202 is configured to determine parameters in all representative scenarios based on decision variables of the wind-photovoltaic-pumped storage complementary dispatching model acquired and stored by the information acquisition module 201. K typical scenarios and their probability distributions generated according to the average output processes of photovoltaic and wind power in a typical meteorological year are used to initialize the wind power output, photovoltaic output, load demand, pumped storage efficiency, and initial reservoir capacity, water level-capacity curve, and flood control constraint parameters in each scenario.

[0197] The model construction module 203 is configured to construct a linearized random optimization model. Based on the representative wind, light output scene set and representative load scene set and basic parameters of the hydropower station and pumped storage power station, the module constructs an objective function with the minimum expected power abandonment as the target, and is further configured to construct a load balancing model of the clean energy power grid system, pumped storage energy storage condition constraints, reservoir water balance constraints, reservoir available water constraints, reservoir water level and flow constraints, water energy utilization constraints, maximum amplitude constraints of hydropower and pumped storage, dynamic flood control storage capacity constraints, and finally form a water, wind, light and storage complementary scheduling model based on the minimum power abandonment.

[0198] The solving module 204 jointly solves all scene variables by using a stochastic linear programming (SLP), and directly solves by using a commercial optimization solver (CPLEX or Gurobi). The optimal output plan and power abandonment of each scene are extracted, the weighted expected power abandonment is calculated, the feasibility of the water level, reservoir capacity and amplitude constraints is verified, the time-division wind, light and storage output distribution, the mode switching strategy of the hydropower station and pumped storage power station and the reservoir water level control scheme are output. The random disturbance scene is generated by Monte Carlo simulation, and the stability of the model under the prediction error is verified. Finally, the solution of the water, wind, light and storage complementary scheduling model is output.

[0199] According to the water, wind, light and storage complementary scheduling device based on the minimum power abandonment provided by the embodiment of the application, by fusing random optimization and refined constraints, a water, wind, light and storage complementary scheduling model based on the minimum power abandonment is proposed, so as to solve the core problems of high power abandonment rate and poor scheduling robustness, and provide an efficient scheduling scheme for a high-proportion renewable energy power grid.

[0200] In order to realize the method of the above-mentioned embodiment, the application further provides a computer device. Figure 4 As shown in the figure, the computer device 600 comprises a memory 601 and a processor 602. The processor 602 runs a program corresponding to an executable program code stored in the memory 601 by reading the executable program code, so as to realize each step of the method described above.

[0201] In order to realize the above-mentioned embodiment, the application further provides a non-transitory computer readable storage medium, which stores a computer program. When the program is executed by a processor, the method described in the above-mentioned embodiment is realized.

[0202] In the description of the present specification, the description of the terms "one embodiment", "some embodiments", "an example", "a specific example", or "some examples" and the like means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In the present specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Also, the specific features, structures, materials or characteristics described can be combined in any appropriate manner in any one or more embodiments or examples. Furthermore, the person skilled in the art can combine and combine the different embodiments or examples described in the present specification and the features of the different embodiments or examples, without contradiction.

[0203] In addition, the terms "first", "second", are used only for the purpose of description, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the technical features indicated. Therefore, the features defined with "first", "second" can explicitly or implicitly include at least one of the features. In the description of the present application, the meaning of "a plurality of" is at least two, for example, two, three, etc., unless otherwise explicitly specifically limited.

Claims

1. A minimum abandoned power-based water, wind, light, and storage complementary scheduling method, characterized in that, The method comprises the following steps: Obtain a representative wind, light output scene set and a representative load scene set of a clean energy base, and basic parameters of a hydropower station and a pumped storage power station; Based on the obtained representative wind, light output scene set, representative load scene set and basic parameters of the hydropower station and pumped storage power station, a complementary dispatching model of the water, wind, light and storage clean energy project is constructed with the minimum total abandoned power of the wind, light and storage clean energy base as an objective function; A load balance model of the clean energy grid system is constructed; Based on the objective function and the load balance model, a water, wind, light and storage complementary dispatching model based on the minimum abandoned power is constructed; Constraint conditions of the water, wind, light and storage complementary dispatching model are constructed; Based on the constraint conditions, the water, wind, light and storage complementary dispatching model based on the minimum abandoned power is solved to obtain a water, wind, light and storage complementary dispatching strategy based on the minimum abandoned power.

2. The method of claim 1, wherein, The objective function is: Wherein, K is the number of grid load and water, wind, light, pumped storage output scenarios that may exist, p k is the probability of the i-th scenario; T is the number of time periods of the scheduling period; is the wind power output of the t time period in scenario i; is the photovoltaic of the t time period in scenario i; is the hydropower output of the t time period in scenario i; is the electricity amount for pumping in the pumped storage power station in the t time period in scenario i, the maximum value is the installed capacity of the pumped storage; α i,t is the pumped storage efficiency of the pumped storage power station in the t time period in scenario i; β i,t is the power generation efficiency of the pumped storage power station in the t time period in scenario i; L i,t is the power transmission amount of the clean energy base connected to the grid in the t time period in scenario i.

3. The method of claim 1, wherein, The expression of the water, wind, light and storage complementary dispatching model based on the minimum abandoned power is:

4. The method of claim 1, wherein, Obtaining a representative wind, light output scene set and a representative load scene set of a clean energy base comprises: Obtain the historical wind power output sequence W = [ω1, ω2,..., ω T ], the historical photovoltaic output sequence P = [p1, p2,..., p T ], and the historical load sequence L = [l1, l2,..., l T ]. Normalizing the output of the sequence by using Min-Max standardization to eliminate dimensional differences: For each time instant a multi-dimensional feature vector V is constructed t = [ω t , p t , l t , Δω t , Δp t , Δl t ], the vector contains the output value at time t and the rate of change in the adjacent time interval; The data is divided into time periods of length τ, and a time period context matrix is constructed where N is the number of historical days; Using Gaussian Copula to describe the nonlinear spatial correlation among wind, light and load; Introducing time weight α and space weight β to define the distance between scenes: where C is a Copula correlation coefficient matrix, || · || F is the Frobenius norm; Determining the number of clusters K by the elbow rule, and outputting K typical scenes and corresponding probabilities.

5. The method of claim 1, wherein, For a grid system with fixed load, the grid system load is determined, comprising: Obtaining the installed capacity of photovoltaic, the average output process of photovoltaic in a typical meteorological year, the installed capacity of wind power, the average output process of wind power in a typical meteorological year, the installed capacity of hydropower station, the number of units and the capacity of single unit; The annual photovoltaic power generation curve and the annual wind power generation curve are calculated, the photovoltaic power generation curve is calculated by photovoltaic installed capacity L p , peak sunshine hours T p and system efficiency η p , P=L p ×T p ×η p ; the wind turbine power generation in each period is calculated by the average output process of wind power in a typical meteorological year, W=L w ×P w ×η w , wherein L w is the wind power installed capacity, wind energy P w refers to the size of wind power resources, the power generation coefficient η w refers to the ratio of the actual power generation of the wind turbine to the theoretical power generation in a certain period of time, and then the active wind annual power generation curve is obtained. Calculating the annual transmission capacity of the power transmission channel: where S d,i is the average power output of the photovoltaic + wind power plant GF d,i is the smaller value of the power output of the photovoltaic + wind power plant GF and the rated capacity K of the transmission channel. Calculating the annual power generation of the hydropower station: where C d,i is the power generation of the hydropower station on the dth day and ith hour, which is K-S d,i the acceptable value on the power generation curve; The wind, light and storage power of the pumped storage power station: where B d,i is the pumped storage energy storage at day d and hour i, which is GF d,i - K is a value that can be taken on the pumping curve; Based on water balance calculation, the water consumption of the hydropower station per year = the pumped storage per year + the upstream water inflow: where α d,i (h) is the amount of water required per unit of electricity generated; β d,i (h) is the amount of water pumped per unit of electricity; Q is the amount of water planned to be used for electricity generation; In the process of solving the constraint model, the following constraints are considered: Water level constraint: the water level of the reservoir meets the basic requirements of reservoir dispatching, and the water level is between the flood control high water level and the flood control limit water level. The reservoir capacity that needs to be consumed or increased is calculated by the power generation of the hydropower station or the storage capacity of the pumped storage power station. The corresponding water level is calculated by the water level-storage capacity curve of the reservoir; Water and electricity maximum amplitude constraint: |C d,i -C d,i+1 |≤N h,ramp and |C d+1,24 -C d,1 |≤N h,ramp , wherein N h,ramp is the maximum amplitude of the unit time of the hydropower station power generation. Pumped storage maximum amplitude constraint: |B d,i -B d,i+1 |≤N p,ramp and |B d+1,24 -B d,1 |≤N h,ramp where N h,ramp is the maximum amplitude of pumped storage per unit time of the pumped storage power station. Reservoir water level-flow constraint: Q min ≤Q (d,i) ≤Q max wherein Z (d,i) is the water level of the reservoir at the beginning of the time period t on day i; and are the upper and lower limits of the water level of the reservoir on day d, respectively; Q min and Q max are the upper and lower limits of the outflow of the reservoir, respectively, Q (d,i) = a d,i (h) x C d,i - b d,i (h) x B d,i ; By selecting data of a typical year, simultaneously solving equations (1), (2), (3) and (4), considering the constraint conditions, the on-grid power of the water, wind, light and storage clean energy base M is finally determined.

6. The method of claim 1, wherein, The constraint conditions of the water, wind, light and storage complementary dispatching model include: The pumped storage energy storage condition constraint is: wherein, wherein is the wind power output in scenario i at time period t; is the photovoltaic power output in scenario i at time period t; is the hydro power output in scenario i at time period t; i,t is the transmission capacity of the clean energy base connected to the grid in scenario i at time period t, is the maximum capacity of pumped storage power station for pumped storage in scenario i at time period t; The reservoir water balance constraint is: V (i,t+1) = V (i,t) + (QI (i,t) - Q (i,t) ) x Δ t where V (i,t) and V (i,t+1) are the initial and final reservoir storage volumes at time t for scenario i; Qi (i,t) is the average inflow to the reservoir during time t for scenario i, and Q (i,t) is the average outflow from the reservoir during time t for scenario i. The available water capacity constraint is: wherein Q (i,t) is the average discharge of the reservoir in scenario i in time period t; W available(i) represents the total water volume discharged by the reservoir in scenario i in the scheduling period; K is the number of scenarios of possible grid load and water, wind, light, and pumped storage output scenarios; The reservoir water level-flow constraint is: wherein Z (i,t) is the water level of the reservoir at the beginning of time period t in scenario i; and are the upper and lower limits of the water level of the reservoir in scenario i, respectively, Q min and Q max are the upper and lower limits of the outflow of the reservoir, respectively. The water energy utilization constraint is: wherein, and H (i,t) P, Q, and H are the power output, the flow rate of the power generation, and the water head of the hydropower station at time period t in scenario i, respectively; K (i) represents the power output coefficient of the generator set of the hydropower station. The maximum amplitude constraint of hydropower is: wherein is the maximum amplitude of the unit time of the hydropower station, is the hydropower output of the t period in scenario i, is the hydropower output of the t+1 period in scenario i; The maximum amplitude constraint of pumped storage is: wherein is the maximum variation per unit of time of the pumped storage for the pumped storage power plant, is the electrical quantity used for pumping in the pumped storage power plant in the time period t for the scenario i, is the electrical quantity used for pumping in the pumped storage power plant in the time period t+1 for the scenario i; For the reservoir with flood control task, dynamic flood control storage capacity constraint also needs to be considered, which is: wherein, V (j,t) Vj(t) represents the initial reservoir capacity of the reservoir undertaking flood control tasks at time period t in scenario j; V j max Vj(t) represents the initial reservoir capacity of the reservoir undertaking flood control tasks at time period t in scenario j; V t flood Vj(t) represents the initial reservoir capacity of the reservoir undertaking flood control tasks at time period t in scenario j; V t flood Vj(t) represents the initial reservoir capacity of the reservoir undertaking flood control tasks at time period t in scenario j; V t flood Vj(t) represents the initial reservoir capacity of the reservoir undertaking flood control tasks at time period t in scenario j; V t flood Vj(t) represents the initial reservoir capacity of the reservoir undertaking flood control tasks at time Based on the constraint condition, the water-wind-solar-storage complementary scheduling model based on minimum abandoned power is solved, and the optimal output plan of wind power, photovoltaic power, hydropower and pumped storage power station in each period is obtained, realizing the water-wind-solar-storage complementary scheduling based on minimum abandoned power.

7. The method of claim 1, wherein, Solving the water-wind-solar-storage complementary scheduling model includes: Determine all representative scenarios parameters based on the decision variables of water-wind landscape complementary scheduling model; initialize the wind power output of each scenario according to the generated K typical scenarios and their probability distribution Photovoltaic output Load demand L i,t , Pumped storage efficiency α i,t and β i,t , and the initial reservoir capacity, water level-storage capacity curve and flood control constraint parameters; A linearized stochastic optimization model is constructed: a multi-scenario stochastic optimization model is constructed to minimize the expected abandoned power, and the constraints include power balance, pumped storage energy storage limit, water balance, dynamic water level / flow constraint and amplitude limit; All scene variables are solved by random linear programming, and the solution is directly solved by a commercial optimization solver; Extract the optimal output plan and abandoned power of each scene, calculate the weighted expected abandoned power, verify the feasibility of water level, storage capacity and amplitude constraint, and output the time-sharing wind-solar-storage output distribution, pumped storage station mode switching strategy and reservoir water level control scheme; Through Monte Carlo simulation, random disturbance scenes are generated to verify the stability of the model under prediction error, and the pumped storage efficiency parameters are optimized; The solution of the water-wind-solar-storage complementary scheduling model is output.

8. A minimum abandoned power-based water, wind, light, and storage complementary scheduling device, characterized in that, It includes: An information acquisition module is configured to acquire representative wind and light output scene sets and representative load scene sets of a clean energy base, and basic parameters of hydropower stations and pumped storage power stations; A parameter acquisition module is configured to construct a complementary scheduling model of a water-wind-solar-storage clean energy project based on the obtained representative wind and light output scene sets, representative load scene sets, and basic parameters of hydropower stations and pumped storage power stations, with the minimum total abandoned power of the wind-solar-storage clean energy base as the objective function; A model construction module is configured to construct a load balance model of a clean energy grid system, construct a water-wind-solar-storage complementary scheduling model based on the minimum abandoned power based on the objective function and the load balance model, and construct constraint conditions of the water-wind-solar-storage complementary scheduling model; A solving module is configured to solve the water-wind-solar-storage complementary scheduling model to obtain a water-wind-solar-storage complementary scheduling method based on the minimum abandoned power and the minimum abandoned power.

9. A computer device, comprising: It includes a processor and a memory; The processor runs a program corresponding to the executable program code in the memory by reading the executable program code, to implement the method of any one of claims 1-7.

10. A non-transitory computer-readable storage medium having stored thereon a computer program, characterized in that, The program is executed by the processor to implement the method of any one of claims 1-7.

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