Water-light storage multi-scene optimization control method and system

Abstract

The invention discloses a water-light-storage multi-scene optimization control method and system, and relates to the technical field of clean energy power generation, and the method comprises the specific steps: carrying out the scene generation and scene reduction based on photovoltaic historical operation data and meteorological data, and obtaining a photovoltaic output value; in the power smoothing scene, inputting the photovoltaic output value into a pre-constructed water-light-storage power smoothing model which meets the hydraulic constraint and the unit characteristics of the water-light-storage system and takes maximization of the income of the water-light-storage system as a target; solving a water-light storage power smoothing model; and under the auxiliary service scene, inputting the output condition of each unit, the capacity utilization condition of the electrochemical energy storage device and the daily demand of the power grid into a pre-established energy storage capacity distribution model to obtain an optimal capacity distribution scheme. Intermittent and fluctuating solar energy can be effectively converted into stable and reliable electric energy to be output, the capacity utilization rate of an energy storage unit is increased, the light abandoning phenomenon is greatly reduced, and the economic benefit of the system is improved.

Description

Technical Field

[0001] The present invention relates to the field of clean energy power generation technology, and more specifically to a multi-scenario optimization control method and system for hydro-photovoltaic storage. Background Art

[0002] With the increasing awareness of global climate change and environmental protection, the proposal of "dual carbon" marks a major step in the transformation of energy structure. New energy such as wind energy and solar energy have become an important part of the power system due to their clean and pollution-free characteristics. However, due to the intermittent and uncertain nature of solar energy, the phenomenon of "abandonment" has intensified, that is, during the peak period of power generation, the power grid cannot fully absorb the generated electricity and has to abandon the use of some renewable energy.

[0003] As a mature and efficient clean energy, hydropower has always played an important role in the power system. In recent years, with the continuous increase in wind and solar installed capacity, the regulation function of traditional hydropower stations has become particularly critical, but they are also facing the problem of overdraft of regulation capacity. In this context, by adding variable-speed reversible units to conventional hydropower stations to form hybrid pumped-storage power stations, as a hydropower energy storage, combined with electrochemical energy storage, it has become a feasible technical path. This combination can not only improve the power generation efficiency in the dry season and reduce the amount of water abandoned in the flood season, but also effectively promote the consumption of new energy, thereby improving the stability and reliability of the power grid.

[0004] The advantage of hydropower energy storage lies in its flexibility and regulation ability. It can store energy when there is excess solar power generation and release it when demand peaks or when there is insufficient renewable energy generation. This method not only helps to reduce the phenomenon of abandoned solar power, but also can smooth the volatility of renewable energy output to a certain extent, which is of great significance for building a more intelligent, efficient and green power grid system.

[0005] However, the existing joint optimization operation between hydropower energy storage and photovoltaics is still in its initial stage, and there is still a large technical gap in how to reasonably allocate stored energy and achieve optimal power smoothing. Therefore, it is an urgent problem for technicians in this field to study and develop an optimization control method and system that can adapt to various operation scenarios, promote the efficient use of new energy, and build a more intelligent, efficient and green power grid system. Summary of the invention

[0006] In view of this, the present invention provides a multi-scenario optimization control method and system for water-solar-storage to overcome the above-mentioned defects.

[0007] In order to achieve the above object, the present invention adopts the following technical solution:

[0008] A multi-scenario optimization control method for water-solar-storage, the specific steps are:

[0009] Based on the photovoltaic historical operation data and the meteorological data, scene generation and scene reduction are performed to obtain a photovoltaic output value;

[0010] In the power smoothing scenario, the photovoltaic output value is input into a pre-built hydro-solar-storage power smoothing model that satisfies hydraulic constraints and unit characteristics of the hydro-solar-storage system, with the goal of maximizing the benefits of the hydro-solar-storage system;

[0011] Solving the water-solar-storage power smoothing model to obtain the output of each unit in the water-solar-storage system and the capacity utilization of the electrochemical energy storage device;

[0012] In the auxiliary service scenario, the output of each of the units, the capacity utilization of the electrochemical energy storage device, and the daily demand of the power grid are input into a pre-established energy storage capacity allocation model to obtain the optimal capacity allocation plan.

[0013] Optionally, the water-photovoltaic-storage system includes: a photovoltaic generator set, a hydroelectric generator set, a pumped storage unit and an electrochemical energy storage device.

[0014] Optionally, the process of scene generation and reduction is as follows:

[0015] Based on the photovoltaic historical operation data and the meteorological data, a Markov chain method is used to generate a scene to obtain a generated scene;

[0016] The generated scene is reduced by using an SBR algorithm based on Kantorovich distance to obtain a reduced scene.

[0017] Optionally, the water-photovoltaic storage system includes hydroelectric energy storage and electrochemical energy storage. Based on the ensemble empirical mode decomposition method, the photovoltaic power signal is decomposed into a low-frequency signal and a high-frequency signal. The high-frequency signal is inverted and given to the hydroelectric energy storage for smoothing, and the low-frequency signal is inverted and given to the electrochemical energy storage for smoothing.

[0018] Optionally, the expression of the water-solar storage power smoothing model is:

[0019]

[0020] In the formula, t is a variable representing the time; F cost is the total system revenue; C profit (t) is the smoothed return of the model in period t; u t (t) is the state variable of the unit in period t; C tur (t) is the power generation and pumping storage cost of the hydroelectric generator set in period t; C DG (t) is the operating cost of new energy in period t; C r is the investment cost of electrochemical energy storage; Ce (t) is the cost of smoothing PV power; T is the operation period.

[0021] Optionally, the constraints of the hydro-photovoltaic-storage power smoothing model include: photovoltaic output constraints; water balance constraints in the upper and lower reservoirs of the power station; reservoir size constraints; water level and reservoir capacity relationship constraints; power generation and pumping mutual exclusion constraints; water level fluctuation constraints; conventional hydropower generating unit output constraints; conventional hydropower generating unit power generation flow constraints; pumped storage unit output constraints; pumped storage unit power generation flow constraints; power generation head constraints; unit output characteristic relationship constraints; power station state switching constraints; unit start-up and shutdown state constraints; unit start-up and shutdown duration and maximum start-up and shutdown number constraints; unit output climbing constraints; battery charging and discharging power constraints; state of charge constraints and photovoltaic maximum power fluctuation access constraints.

[0022] Optionally, the expression of the energy storage capacity allocation model is:

[0023]

[0024] In the formula, F mar is the total revenue of the unit; C spo (t) is the peak-valley price difference income of the unit in the electricity market; C AGC (t) is the frequency regulation benefit of the unit; C res (t) is the reserve income of the unit.

[0025] Optionally, compared with the constraints of the hydro-solar-storage power smoothing model, the constraints of the energy storage capacity allocation model also include: frequency regulation constraints, backup capacity constraints; unit ramp operation constraints and electrochemical energy storage charging and discharging constraints.

[0026] Optionally, the steps of obtaining the optimal capacity allocation solution are:

[0027] Based on the output of each of the units, the remaining capacity, the power generation and the start / stop status of each unit at each moment are obtained;

[0028] Obtaining the daily demand of the power grid, and allocating the power generation, the frequency modulation and the standby capacity based on the relationship between the daily demand of the power grid and the sum of the remaining capacity of the unit and the available capacity of the electrochemical energy storage device, to obtain an optimal capacity allocation plan;

[0029] The allocation standards for the power generation, frequency regulation and reserve capacity are as follows:

[0030] If the daily demand of the power grid is greater than or equal to the sum of the remaining capacity of the unit and the available capacity of the electrochemical energy storage device, the power extraction, frequency regulation and spare capacity allocation are performed;

[0031] If the daily demand of the power grid is less than the sum of the remaining capacity of the unit and the available capacity of the electrochemical energy storage device, the power extraction distribution is performed.

[0032] A multi-scenario optimization control system for water-solar-storage includes:

[0033] Data collection module, used to collect photovoltaic historical operation data and meteorological data based on the water-solar-storage system;

[0034] A scenario generation module, used to generate and reduce scenarios based on the photovoltaic historical operation data and the meteorological data to obtain a photovoltaic output value;

[0035] A model building module is used to build a hydro-photovoltaic storage power smoothing model that meets hydraulic constraints and unit characteristics of the hydro-photovoltaic storage system in a power smoothing scenario, with the goal of maximizing the benefits of the hydro-photovoltaic storage system;

[0036] A calculation module, used to solve the water-solar-storage power smoothing model to obtain the output of each unit in the water-solar-storage system and the capacity utilization of the electrochemical energy storage device;

[0037] The solution generation module is used to input the output of each unit, the capacity utilization of the electrochemical energy storage device and the daily demand of the power grid into a pre-established energy storage capacity allocation model in an auxiliary service scenario to obtain an optimal capacity allocation solution.

[0038] It can be seen from the above technical solutions that, compared with the prior art, the present invention discloses a multi-scenario optimization control method and system for water, light and energy storage, which significantly improves the regulation capacity of the power system and the level of renewable energy consumption by making full use of the high flexibility and rapid response characteristics of water energy storage. Specifically, it can effectively convert intermittent and fluctuating solar energy into stable and reliable electric energy output, thereby greatly reducing the phenomenon of abandoned light due to the instability of new energy power generation. This not only improves the utilization rate of new energy power generation, but also enhances the stability and reliability of the power grid, provides technical support for achieving a higher proportion of clean energy access, and promotes the optimization of energy structure and the realization of green and low-carbon development goals. At the same time, the application of this technology also helps to reduce the operating costs of the power system and improve the overall economic and social benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0040] Figure 1 A schematic diagram of the method provided by the present invention;

[0041] Figure 2 A schematic diagram of the solution process using the improved Beluga optimization algorithm provided by the present invention. DETAILED DESCRIPTION

[0042] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0043] On the one hand, the present invention discloses a multi-scenario optimization control method for water-solar-storage, which takes into account both power smoothing and auxiliary service scenarios, such as Figure 1 As shown, the specific steps are:

[0044] S1. Generate and reduce scenarios based on historical photovoltaic operation data and meteorological data to obtain photovoltaic output values;

[0045] S2. In the power smoothing scenario, the PV output value is input into the pre-built unit characteristics that meet the hydraulic constraints and the hydro-PV-storage system, with the goal of maximizing the benefits of the hydro-PV-storage system;

[0046] S3, solving the hydro-photovoltaic storage power smoothing model to obtain the output of each unit in the hydro-photovoltaic storage system and the capacity utilization of the electrochemical energy storage device;

[0047] S4. In the auxiliary service scenario, the output of each unit, the capacity utilization of the electrochemical energy storage device and the daily demand of the power grid are input into the pre-established energy storage capacity allocation model to obtain the optimal capacity allocation plan.

[0048] In one embodiment, the water-photovoltaic-storage system includes: a photovoltaic power generation unit, a hydroelectric power generation unit, a pumped storage unit and an electrochemical energy storage device.

[0049] Furthermore, the devices applying the multi-scenario optimization control method of water, photovoltaic and storage mainly include: photovoltaic power generation units, hydroelectric power generation units, and pumped storage units. The upper reaches of the river serve as the upper reservoir of the pumped storage unit, and the lower reaches of the river serve as the lower reservoir of the pumped storage unit. The upper reservoir is replenished with water from natural runoff, and the water, photovoltaic and storage system flexibly interacts with the electrochemical energy storage device to smooth the photovoltaic power and assist the grid in frequency modulation, thereby reducing the amount of abandoned light.

[0050] In one embodiment, the photovoltaic historical operation data and meteorological data in S1 are based on data read from a SCADA system.

[0051] In one embodiment, the step of scene generation is: based on the historical photovoltaic operation data and meteorological data, a Markov chain method is used to generate the scene to obtain a generated scene.

[0052] Furthermore, the scene generation steps are:

[0053] According to the variation range of the prediction error value of the photovoltaic historical data, it is divided into n state intervals (n is the number of intervals), and then the photovoltaic prediction error value is converted into n corresponding discrete state quantities W1, W2, W3, ... W n If the photovoltaic prediction error state at the current moment is W i (1≤i<<n), then the photovoltaic prediction error state W at the next moment j The probability of (1≤j<<n) is P(x i+1 =W j |x i =W i )=P ij So for a photovoltaic data set with n prediction error states, the state transfer matrix P can be expressed as:

[0054]

[0055] Any prediction error state transition probability in its matrix can be obtained through statistics of historical data sets.

[0056] The cumulative state transfer matrix Q can be obtained by probabilistically summing the state transfer matrix P. P and Q are the basis for simulating the photovoltaic prediction error timing scenario.

[0057]

[0058] Where n is the number of divided intervals; i and j represent the counting subscripts at different times.

[0059] In one embodiment, the scene reduction in S2 is implemented by an SBR algorithm based on Kantorovich distance, which can better preserve the distribution characteristics of the original scene. The algorithm steps are:

[0060] 1) Use the original scene set C i N i A set of retained scenes, C' i Indicates N' i Delete scene set; determine set C i The initial probability of the retained scene in 1 / N i , set C' i Initial probability of deletion scenario in 1 / N' i ;

[0061] 2) Each time, remove a scene s' that satisfies the following formula v Add the deletion scene set C' i middle;

[0062]

[0063] Where D k represents the Euclidean distance between scenes;

[0064] 3) Change the number of original scenes N i =N i -1 and the number of deleted scenes N' i =N' i -1, select the distance scene s' v Recent scenes v , the expression of the selection formula is:

[0065]

[0066] 4) Change and remove scenes' v Recent scenes v Probability To ensure that the scene set C is retained i The sum of all scene probabilities in the scene set is 1. Then update the probability of each scene in the deleted scene set to 1 / N' i ;

[0067] 5) Go to step 2) and repeat until the scene set C is retained. i Until the number of scenes in the set meets the set quantity requirements, take

[0068] Where i and j represent the number of scenes contained in the scene set.

[0069] In one embodiment, the hydro-photovoltaic energy storage system includes hydroelectric energy storage and electrochemical energy storage. Based on the ensemble empirical mode decomposition method, the original signal is decomposed into a low-frequency signal and a high-frequency signal. The high-frequency signal is inverted and given to the hydroelectric energy storage for smoothing, and the low-frequency signal is inverted and given to the electrochemical energy storage for smoothing.

[0070] Furthermore, the decomposed signal is called the intrinsic mode function component (IMF) of the original signal. The result of decomposing the original signal by the EEMD method is:

[0071]

[0072] Where X(t) is the original signal; IMF i (t) is the intrinsic modal component of the original signal after EEMD decomposition; r m(t) is the decomposition remainder; m is the number of natural mode components.

[0073] After the original signal is decomposed by EEMD, the IMF components with gradually decreasing frequencies can be obtained. According to the characteristics of the IMF components, a space-time filter based on the different periodic characteristic scales of the original signal is constructed.

[0074] The low-pass space-time filter can be expressed as:

[0075]

[0076] The high-pass space-time filter can be expressed as:

[0077]

[0078] Where, X If is the low-frequency signal of the original signal; X hf is the high-frequency signal of the original signal; k is the order of the space-time filter.

[0079] In one embodiment, in order to improve the smoothness of the photovoltaic curve, the maximum benefit of the model is taken as the objective function, and a penalty term is set to reduce the amount of abandoned light, and its expression is:

[0080]

[0081] In the formula, F cost is the total system revenue; C profit (t) is the smoothed return of the model in period t; u t (t) is the state variable of the unit in period t; C tur (t) is the power generation and pumping storage cost of the hydroelectric generator set in period t; C DG (t) is the operating cost of new energy in period t; C r is the investment cost of electrochemical energy storage; C e (t) is the cost of smoothing PV power; T is the operation period.

[0082] The smoothed return expression is:

[0083]

[0084] Where P Z (t) is the total smoothed photovoltaic power of the model in period t; s(t) is the smoothed price in period t.

[0085] The power generation and pumping cost of a hydroelectric generator unit in period t is composed of the start-up and shutdown cost of the unit and the pumping cost, and the calculation formula is:

[0086]

[0087] In the formula, Con / off The cost of starting and stopping the unit once; S o is the total number of starts and stops of the unit; S c P is the unit price of the unit’s generating power or pumped storage power; tur (t) is the generating power or pumping power of the hydroelectric generator set in period t.

[0088] The operating cost of new energy is composed of photovoltaic power generation cost and penalty cost of abandoned light, and the calculation formula is:

[0089] C DG (t) = C PV (t);

[0090] C PV (t) = C G,PV (P PV (t))+C p,PV (ΔP PV (t));

[0091] In the formula, C PV (t) are the photovoltaic power generation costs in period t; P PV (t) is the photovoltaic output power during period t; ΔP PV (t) is the abandoned optical power in period t; C G,PV (·) is the power generation cost of the photovoltaic unit; C p,PV (·)=ζ t ΔP PV (t) is the penalty cost of abandoning light, where ζ t is the penalty coefficient.

[0092] The cost calculation formula for electrochemical energy storage is:

[0093]

[0094] In the formula, E ess and P ess are the rated capacity and rated power of electrochemical energy storage, respectively; They are the investment cost per unit capacity and unit power of electrochemical energy storage respectively.

[0095] The expression for smoothed PV power cost is:

[0096]

[0097] In the formula, C x P is the price of electrochemical energy storage per unit charge and discharge capacity; bat (t) is the charging and discharging power at time t.

[0098] In one embodiment, the constraints of the hydro-photovoltaic-storage power smoothing model include: photovoltaic output constraints; water volume balance constraints above and below the power station reservoirs; reservoir size constraints; water level and reservoir capacity relationship constraints; power generation and pumping mutual exclusion constraints; water level fluctuation constraints; conventional hydroelectric generator unit output constraints; conventional hydroelectric generator unit power generation flow constraints; pumped storage unit output constraints; pumped storage unit power generation flow constraints; power generation head constraints; unit output characteristic relationship constraints; power station state switching constraints; unit start-up and shutdown state constraints; unit start-up and shutdown duration; maximum start-up and shutdown number constraints; unit output climbing constraints; battery charging and discharging power constraints; state of charge constraints and photovoltaic maximum power fluctuation access constraints.

[0099] Furthermore, the parameters in the hydro-photovoltaic storage power smoothing model satisfy the following constraints:

[0100] 1) Photovoltaic output constraints:

[0101]

[0102] Where P pv (t) are the photovoltaic output power at time t; are the maximum output power of photovoltaic power.

[0103] 2) Constraints on water balance between upper and lower reservoirs of power stations:

[0104]

[0105] In the formula, are the storage capacities of the upper and lower storages at time t respectively; are the inflow flows of the upper and lower reservoirs at time t respectively; is the power generation flow of the four units at time t; is the pumping flow of the two pumped storage units at time t; Q e is the ecological flow of the power station; Δt is the step length of the calculation period.

[0106] 3) Storage capacity constraints:

[0107]

[0108] In the formula, are the minimum storage capacities of the upper and lower storages, respectively; are the maximum capacities of the upper and lower reservoirs, are the storage capacity values ​​of the upper and lower storages at time t respectively; is the initial storage capacity of the upper and lower storages.

[0109] 4) Constraints on water level and storage capacity:

[0110]

[0111] In the formula, are the water levels of the upper and lower reservoirs at time t respectively; up (·),f down (·) are the relationship curves between the upper and lower reservoir capacities and water levels, respectively.

[0112] 5) Mutual exclusion constraints for power generation and pumping:

[0113]

[0114] In the formula, They are respectively the power generation power and pumping power of the power station at time t; the power generation and pumping states are mutually exclusive.

[0115] 6) Water level fluctuation constraints:

[0116]

[0117] Where ε is the water level difference between the upper reservoir at the beginning and end of the smoothing period; ω is the water level difference between adjacent smoothing periods.

[0118] 7) Output constraints of conventional hydroelectric generating units:

[0119]

[0120] In the formula, i is a counting variable, representing the number of hydropower units; is the start-up and shutdown state variable of the i-th hydroelectric generator unit in period t, 1 means the unit is started, and 0 means the unit is shut down; are the minimum and maximum output values ​​of the i-th hydroelectric generator set respectively; is the output value of the i-th hydroelectric generator set at time t; N g is the number of hydroelectric generating units.

[0121] 8) Power generation flow constraints of conventional hydroelectric generators:

[0122]

[0123] In the formula, are the minimum and maximum values ​​of the power generation flow of the i-th hydroelectric generator set respectively; is the power generation flow of the i-th hydroelectric generator unit at time t.

[0124] 9) Output constraints of pumped storage units:

[0125]

[0126] In the formula, j is a counting variable, representing the number of pumped storage units. The start and stop state variables of the jth pumped storage unit in period t; are the minimum and maximum output values ​​of the jth pumped storage unit respectively; is the output value of the jth pumped storage unit at time t; N p is the number of pumped storage units.

[0127] 10) Constraints on power generation flow of pumped storage units:

[0128]

[0129] In the formula, are the minimum and maximum values ​​of the pumped storage flow of the jth pumped storage unit respectively; It represents the pumped storage flow of the jth pumped storage unit at time t.

[0130] 11) Power generation head constraints:

[0131]

[0132] In the formula, H i,t represents the generating head of the i-th unit at time t.

[0133] 12) Unit output characteristic relationship constraints:

[0134]

[0135] In the formula, f i,nhq (·),f j,nhq (·) are the relationship functions between the output of conventional units and pumped storage units and the flow rate and water head, respectively.

[0136] 13) Power station state switching constraints:

[0137]

[0138] In order to increase the operating life of the unit, the unit must be shut down for a period of time when switching between extraction states.

[0139] 14) Unit start and stop status constraints:

[0140]

[0141] In the formula, is the startup operation variable of unit i in period t; It means that unit i was started from time t-1 to time t, otherwise is the shutdown operation variable of unit i in period t, It means that unit i was shut down from time t-1 to time t, otherwise

[0142] 15) Unit start-up and shutdown duration and maximum start-up and shutdown times constraints:

[0143]

[0144] In the formula, They are the minimum start and stop duration of the unit respectively; is the upper limit of the number of startup times of the unit within the smoothing cycle; λ is the counting subscript.

[0145] 16) Unit output climbing constraints:

[0146]

[0147] In the formula, They are the maximum allowed ramp rates for conventional units and pumped storage units respectively.

[0148] 17) Battery charging and discharging power constraints:

[0149] 0≤P abs , P relea ≤P max ;

[0150] Where P abs , P relea are the charging and discharging power of the electrochemical energy storage station, P max It is the maximum charge and discharge power allowed by electrochemical energy storage.

[0151] 18) Charge state constraints:

[0152]

[0153] In the formula, E ess (t) is the capacity of the energy storage at time t; μ abs , μ relea It is the charging and discharging efficiency of the battery.

[0154] 19) Grid access constraints for photovoltaic maximum power fluctuations:

[0155]

[0156] Where δ is the photovoltaic power fluctuation rate; max[P obj (t)], min[P obj (t)] represent the target power P in any 10 minutes. obj (t) maximum and minimum values; is the rated power of the photovoltaic power station.

[0157] In one embodiment, the hydro-photovoltaic storage power smoothing model is a high-dimensional, nonlinear model with complex constraints, which is transformed into a mixed-integer linear programming (MILP) problem through linear transformation and solved using the commercial solver Gurobi.

[0158] The relationship between the unit output and the flow rate and water head is nonlinear. The triangular weight method is used to linearize it, and the flow rate and water level are divided into n1-1 and n2-1 intervals, which can be expressed as follows:

[0159]

[0160] In the formula, Q n ,H n are the endpoint values ​​of each interval of flow and water level respectively. Any Q,h value in the range of n to n+1 can be expressed by the following formula:

[0161]

[0162] Where a0, a1, b0, b1 are all positive real numbers.

[0163] The relationship between the unit output, flow rate and water head can be expressed as follows:

[0164]

[0165] In the formula, p ij is the intermediate variable; ij is the combination coefficient. Therefore, the output power of the unit can be expressed as the variable λ ij and the known quantity p ij The expression is converted from a nonlinear expression to a linear expression.

[0166] In one embodiment, water energy storage and electrochemical energy storage are combined and regarded as an independent entity, and the remaining capacity is used to participate in various markets to obtain benefits. The objective function is to maximize the economic benefits of the unit, including the peak-valley price difference benefits of the electricity market, auxiliary market frequency regulation, and standby benefits. A water-solar storage capacity allocation model is constructed, and its expression is:

[0167]

[0168] In the formula, F mar is the total revenue of the unit; C spo (t) is the peak-valley price difference income of the unit in the electricity market; C AGC (t) is the frequency regulation benefit of the unit; C res (t) is the reserve income of the unit.

[0169] Among them, the calculation method of peak-to-valley spread profit is as follows:

[0170]

[0171] In the formula, s g (t),s p (t) are the electricity prices at the time of power generation and pumping, respectively.

[0172] The calculation method of frequency modulation benefit is as follows:

[0173]

[0174] In the formula, s cap is the frequency regulation capacity price; P cap (t) is the frequency modulation capacity at time t.

[0175] The calculation of standby income is as follows:

[0176]

[0177] In the formula, s res is the reserve capacity price; P res (t) is the spare capacity of the unit for power generation or water pumping.

[0178] Optionally, compared with the constraints of the hydro-solar-storage power smoothing model, the constraints of the energy storage capacity allocation model also include: frequency regulation constraints; backup capacity constraints; unit ramp operation constraints and electrochemical energy storage charging and discharging constraints.

[0179] Furthermore, the frequency modulation constraint is:

[0180]

[0181] Where, σ is the maximum ratio of the unit frequency regulation capacity to the rated power; P max(t) is the maximum output allowed by the unit at time t; For frequency modulation capacity requirements.

[0182] The spare capacity constraint is:

[0183]

[0184] In the formula, τ is the maximum ratio of the unit's spare capacity to the rated power; The requirement for spare capacity.

[0185] Unit climbing operation constraints:

[0186]

[0187] In the formula, are the outputs of conventional unit i and pumped storage unit j at time t in the first stage respectively.

[0188] Electrochemical energy storage charging and discharging constraints:

[0189]

[0190] In the formula, and are the minimum and maximum capacity allowed for electrochemical energy storage; E' ess (t) is the available capacity of the electrochemical energy storage device at time t.

[0191] The improved Beluga optimization algorithm is used to solve the water-solar storage capacity allocation model. Figure 2 As shown, the improved process is as follows:

[0192] The chaotic sequence is used to replace the random variables that obey the standard probability distribution to improve the quality of the initial population. The Logistic-Tent chaotic mapping is applied to the White Whale optimization algorithm, and r chaotic sequences are generated using the Logistic-Tent mapping:

[0193]

[0194] Where r is the number of chaotic sequences; h is the control parameter; mod is the modulo operator.

[0195] Then transform the chaotic sequence into the initial solution X:

[0196] X=x(μ b -l b )+l b ;

[0197] Opposite number X O The approximate opposite number X' is:

[0198]

[0199] Where x is the system variable; μ b and l b are the upper and lower limits of the variable; c is the midpoint of the upper and lower limits of the variable; rand(c,X O ) is uniformly distributed in (c,X O )’s random number.

[0200] In one embodiment, the steps for obtaining the optimal capacity allocation solution are:

[0201] Based on the output of each unit, obtain the remaining capacity, power generation and start / stop status of each unit at each moment;

[0202] The daily demand of the power grid is obtained, and based on the relationship between the daily demand of the power grid and the sum of the remaining capacity of the unit and the available capacity of the electrochemical energy storage device, the power generation, frequency regulation and spare capacity allocation are allocated to obtain the optimal capacity allocation plan.

[0203] In one embodiment, the allocation criteria for power generation, frequency regulation and reserve capacity are:

[0204] If the daily demand of the power grid is greater than or equal to the sum of the remaining capacity of the unit and the available capacity of the electrochemical energy storage device, the power generation, frequency regulation and spare capacity allocation will be carried out;

[0205] If the daily demand of the power grid is less than the sum of the remaining capacity of the unit and the available capacity of the electrochemical energy storage device, the power generation will be allocated.

[0206] On the other hand, this embodiment also discloses a multi-scenario optimization control system for water, light and energy storage, including:

[0207] Data collection module, used to collect photovoltaic historical operation data and meteorological data based on the water-solar-storage system;

[0208] The scenario generation module is used to generate and reduce scenarios based on the historical operation data of photovoltaic power plants and meteorological data to obtain photovoltaic output values;

[0209] The model building module is used to build a hydro-solar-storage power smoothing model that meets hydraulic constraints and the unit characteristics of the hydro-solar-storage system in the power smoothing scenario, with the goal of maximizing the benefits of the hydro-solar-storage system;

[0210] The calculation module is used to solve the hydro-photovoltaic-storage power smoothing model to obtain the output of each unit in the hydro-photovoltaic-storage system and the capacity utilization of the electrochemical energy storage device;

[0211] The solution generation module is used to input the output of each unit, the capacity utilization of the electrochemical energy storage device and the daily demand of the power grid into a pre-established energy storage capacity allocation model in the auxiliary service scenario to obtain the optimal capacity allocation solution.

[0212] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0213] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A multi-scenario optimization control method for water-solar-storage, characterized in that: The specific steps are: Based on the photovoltaic historical operation data and the meteorological data, scene generation and scene reduction are performed to obtain a photovoltaic output value; In the power smoothing scenario, the photovoltaic output value is input into a pre-built hydro-solar-storage power smoothing model that satisfies hydraulic constraints and unit characteristics of the hydro-solar-storage system, with the goal of maximizing the benefits of the hydro-solar-storage system; Solving the water-solar-storage power smoothing model to obtain the output of each unit in the water-solar-storage system and the capacity utilization of the electrochemical energy storage device; In the auxiliary service scenario, the output of each of the units, the capacity utilization of the electrochemical energy storage device, and the daily demand of the power grid are input into a pre-established energy storage capacity allocation model to obtain the optimal capacity allocation plan.

2. According to claim 1, a multi-scenario optimization control method for water-solar-storage is characterized in that: The water-photovoltaic-storage system comprises: a photovoltaic power generation unit, a hydroelectric power generation unit, a pumped storage unit and an electrochemical energy storage device.

3. According to claim 1, a multi-scenario optimization control method for water-solar-storage is characterized in that: The process of scene generation and reduction is as follows: Based on the photovoltaic historical operation data and the meteorological data, a Markov chain method is used to generate a scene to obtain a generated scene; The generated scene is reduced by using an SBR algorithm based on Kantorovich distance to obtain a reduced scene.

4. The multi-scenario optimization control method for water-solar-storage according to claim 2 is characterized in that: The water-photovoltaic storage system includes hydroelectric energy storage and electrochemical energy storage. Based on the ensemble empirical mode decomposition method, the photovoltaic power signal is decomposed into a low-frequency signal and a high-frequency signal. The high-frequency signal is inverted and given to the hydroelectric energy storage for smoothing, and the low-frequency signal is inverted and given to the electrochemical energy storage for smoothing.

5. The multi-scenario optimization control method for water-solar-storage according to claim 1 is characterized in that: The expression of the water-solar storage power smoothing model is: In the formula, t is a variable representing the time; F cost is the total system revenue; C profit (t) is the smoothed return of the model in period t; u t (t) is the state variable of the unit in period t; C tur (t) is the power generation and pumping storage cost of the hydroelectric generator set in period t; C DG (t) is the operating cost of new energy in period t; C r is the investment cost of electrochemical energy storage; C e (t) is the cost of smoothing PV power; T is the operation period.

6. The multi-scenario optimization control method for water-solar-storage according to claim 1 is characterized in that: The constraints of the hydro-photovoltaic-storage power smoothing model include: photovoltaic output constraint; water volume balance constraint of the upper and lower reservoirs of the power station; reservoir size constraint; water level and reservoir capacity relationship constraint; power generation and pumping mutual exclusion constraint; water level fluctuation constraint; conventional hydropower generating unit output constraint; conventional hydropower generating unit power generation flow constraint; pumped storage unit output constraint; pumped storage unit power generation flow constraint; power generation head constraint; unit output characteristic relationship constraint; power station state switching constraint; unit start-up and shutdown state constraint; unit start-up and shutdown duration and maximum start-up and shutdown times constraint; unit output climbing constraint; battery charging and discharging power constraint; state of charge constraint and photovoltaic maximum power fluctuation access constraint.

7. The multi-scenario optimization control method for water-solar-storage according to claim 1 is characterized in that: The expression of the energy storage capacity allocation model is: In the formula, F mar is the total revenue of the unit; C spo (t) is the peak-valley price difference income of the unit in the electricity market; C AGC (t) is the frequency regulation benefit of the unit; C res (t) is the reserve income of the unit.

8. The multi-scenario optimization control method for water-solar-storage according to claim 7 is characterized in that: Compared with the constraints of the hydro-solar-storage power smoothing model, the constraints of the energy storage capacity allocation model also include: frequency regulation constraints; backup capacity constraints; unit ramp operation constraints and electrochemical energy storage charging and discharging constraints.

9. The multi-scenario optimization control method for water-solar-storage according to claim 2 is characterized in that: The steps for obtaining the optimal capacity allocation solution are: Based on the output of each of the units, the remaining capacity, the power generation and the start / stop status of each unit at each moment are obtained; Obtaining the daily demand of the power grid, and allocating the power generation, the frequency modulation and the standby capacity based on the relationship between the daily demand of the power grid and the sum of the remaining capacity of the unit and the available capacity of the electrochemical energy storage device, to obtain an optimal capacity allocation plan; The allocation standards for the power generation, frequency regulation and reserve capacity are as follows: If the daily demand of the power grid is greater than or equal to the sum of the remaining capacity of the unit and the available capacity of the electrochemical energy storage device, the power extraction, frequency regulation and spare capacity allocation are performed; If the daily demand of the power grid is less than the sum of the remaining capacity of the unit and the available capacity of the electrochemical energy storage device, the power extraction distribution is performed.

10. A multi-scenario optimization control system for water, light and energy storage, characterized in that: include: Data collection module, used to collect photovoltaic historical operation data and meteorological data based on the water-solar-storage system; A scenario generation module, used to generate and reduce scenarios based on the photovoltaic historical operation data and the meteorological data to obtain a photovoltaic output value; A model building module is used to build a hydro-photovoltaic storage power smoothing model that meets hydraulic constraints and unit characteristics of the hydro-photovoltaic storage system in a power smoothing scenario, with the goal of maximizing the benefits of the hydro-photovoltaic storage system; A calculation module, used to solve the water-solar-storage power smoothing model to obtain the output of each unit in the water-solar-storage system and the capacity utilization of the electrochemical energy storage device; The solution generation module is used to input the output of each unit, the capacity utilization of the electrochemical energy storage device and the daily demand of the power grid into a pre-established energy storage capacity allocation model in an auxiliary service scenario to obtain an optimal capacity allocation solution.

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