Peak-shaving electricity price compensation method for hydropower participating in water-wind-solar integrated system dispatch

Abstract

The present invention discloses a peak-shaving electricity price compensation method for hydropower participating in the scheduling of an integrated water, wind, and solar power system. The method comprises the following steps: S1: establishing a peak-shaving opportunity cost model for hydropower stations; S2: establishing a coordinated scheduling model for a water-wind-solar combined operation system under a peak-shaving electricity price compensation strategy based on the principle of prioritizing the consumption of new energy sources, and determining peak-shaving initiative constraints, reservoir water level constraints, reservoir storage capacity constraints, downstream flow constraints, output limit constraints, water balance constraints, ramp rate constraints, photovoltaic output limit constraints, wind power output limit constraints, and system power balance constraints; and S3: solving the model established in step S2 using an iterative solution method to obtain the paid peak-shaving range of the hydropower units. The method can quantify the opportunity cost of hydropower stations participating in peak-shaving cooperation, determine the maximum available peak-shaving depth of hydropower under different compensation prices, and evaluate the impact of different compensation prices on the economic and energy efficiency indicators of the units.

Description

Technical Field

[0001] The present invention relates to the field of electricity price and electricity economic technology, and in particular to a method for compensating for peak-shaving electricity prices when hydropower participates in the scheduling of a water-wind-solar integrated system. Background Art

[0002] In the integrated clean energy base, as the installed capacity of new energy increases, the peak-to-valley difference of the power grid gradually increases. Hydropower units have the advantages of fast load regulation rate and large regulation range. Their participation in peak and frequency regulation of renewable energy such as wind and solar power is considered to be an important technical means to absorb large-scale new energy.

[0003] For a long time, hydropower peak-shaving was considered cost-free and essentially free. There was no method to quantify the actual costs of hydropower peak-shaving, nor was there a reasonable compensation mechanism. In reality, when hydropower units frequently undertake peak-shaving duties, the total power generation of hydropower stations decreases, and overall profits decline without peak-shaving electricity price compensation. Furthermore, when hydropower units undertake peak-shaving duties, they operate outside their optimal operating ranges. During this period, the service life is reduced due to vibration, shaft wear, and other factors, increasing the fixed costs of the power station. Therefore, hydropower participation in peak-shaving carries costs, and requiring it to provide it free of charge is unreasonable. This could ultimately lead to a reluctance among hydropower stations to undertake peak-shaving duties, making it difficult to absorb wind and solar power. Hydropower stations operate in the electricity market based on economic incentives. However, due to the lack of a reasonable peak-shaving electricity price compensation strategy, under an administratively dominated market mechanism, insufficient incentives often occur, making it difficult to achieve "cost compensation and reasonable profits." Summary of the Invention

[0004] In response to the above-mentioned problems, the present invention aims to provide a peak-shaving electricity price compensation method for hydropower participating in the scheduling of integrated water, wind and solar systems, which can quantify the actual cost of hydropower stations participating in peak-shaving cooperation, determine the maximum available peak-shaving depth of hydropower under different compensation prices, and evaluate the impact of different compensation prices on the economic indicators and energy efficiency indicators of the units.

[0005] In order to achieve the above object, the technical solution adopted by the present invention is as follows:

[0006] The method for compensating for peak-shaving electricity prices when hydropower participates in the scheduling of a water-wind-solar integrated system is characterized by comprising the following steps:

[0007] S1: Establish a peak-shaving opportunity cost model for hydropower stations;

[0008] S2: Based on the principle of prioritizing the consumption of new energy, a coordinated dispatch model for the water-wind-solar joint operation system under the peak-shaving electricity price compensation strategy is established;

[0009] S3: Using an iterative solution method, the coordinated scheduling model of the hydropower-wind-solar joint operation system under the peak-shaving electricity price compensation strategy established in step S2 is solved to obtain the maximum paid peak-shaving depth of hydropower that meets the objective function.

[0010] Furthermore, the specific operation of step S1 includes the following steps:

[0011] S101: Use opportunity cost to measure the cost of peak load capacity contributed by hydropower unit i. Define the basic peak load coefficient of hydropower as α, then the basic peak load range of hydropower unit is [αP N , P N ]; where P N Indicates the rated installed capacity of hydropower;

[0012] S102: When the unit output When the peak load is less than 100%, the peak load cost is not taken into account;

[0013] S103: When the unit output When the peak load cost is taken into account and compensation is given, the amount of electricity on the grid during this period is W.

[0014] W=∫P t H d t

[0015] According to the water consumption rate μ under the corresponding working conditions t , convert the online power W into water consumption V:

[0016] V=∫μ t P t H d t

[0017] Calculate the water head and the basic peak load lower bound αP at time t N The corresponding water consumption rate μ0 converts the water consumption V into the minimum power generation W0 when no downward peak regulation is performed:

[0018]

[0019] At this time, W0-W is the opportunity power for paid peak load regulation, and the unit power generation profit of the hydropower unit is g H -L H , then the peak load opportunity cost C is

[0020] C=(g H -L H )(W0-W)

[0021] Where g H and L H They represent the benchmark on-grid electricity price and levelized cost per kilowatt-hour of hydropower respectively.

[0022] Furthermore, the method for calculating the water consumption rate in step S103 includes the following steps:

[0023] Step 1: Calculate the efficiency of the turbine generator set based on the actual efficiency test of the turbine generator set using the flow meter flow measurement method

[0024]

[0025] in,

[0026]

[0027] Where, P T is the turbine output power; P h is the turbine input power; ρ is the water density; Q is the turbine flow; g is the local gravity acceleration; H is the turbine working head; P g is the generator power; ηg is the generator efficiency;

[0028] Step 2: Construct an Elman neural network model, use the data of the real turbine generator unit efficiency test as the input of the model, extend the turbine unit efficiency data, and use the Elman neural network model to calculate the water consumption rate. The calculation formula of the comprehensive water consumption rate of the hydropower station is:

[0029]

[0030] Where μ is the comprehensive water consumption rate, unit is m 3 / (kWh); E is the total power generation, unit is kW; V is the total water volume for power generation, unit is m 3 ; N is the power output of the power station, unit is kW; Q is the power flow, unit is m 3 / s; T is the calculation period; C is the unit conversion constant (C = 3600 / 9.81); H j is the net generating head, in m; η is the efficiency of the turbine generator set.

[0031] Furthermore, the specific operation of step S2 includes the following steps:

[0032] S201: Based on the principle of priority consumption of new energy, a peak-shaving compensation optimization scheduling model for the water-wind-solar joint operation system is established. The hydropower output is adjusted every hour, and the maximum amount of renewable energy on-grid power under different compensation electricity prices is taken as the objective function. The objective function of the coordinated scheduling model of the water-wind-solar joint operation system under the peak-shaving electricity price compensation strategy is:

[0033]

[0034] Where, is the peak load compensation price of hydropower, unit is / kWh; Pt W 、P t S is the output of wind farm and photovoltaic farm at time t, in MW; N is the total number of intervals in the time range; Δt is the time interval;

[0035] S202: Determine constraints, which include peak-shaving initiative constraints, reservoir water level constraints, reservoir storage capacity constraints, downstream flow constraints, output limit constraints, water balance constraints, ramp rate constraints, photovoltaic output limit constraints, wind power output limit constraints, and system power balance constraints.

[0036] Furthermore, the specific operation of step S3 includes the following steps:

[0037] S301: The peak-shaving compensation optimization scheduling model for the hydro-wind-solar combined operation system established in step S201 is divided into two parts: scheduling strategy solution and peak-shaving initiative verification. In the scheduling strategy solution part, the peak-shaving initiative constraint is ignored, and the paid peak-shaving capacity constraint is added to obtain a coordinated scheduling model for the hydro-wind-solar integrated system that does not consider peak-shaving initiative.

[0038] S302: Using the range of the paid peak-shaving power of the hydropower unit within a scheduling cycle as the iteration variable, the iterative algorithm is used to solve the model obtained in step S301. During the iteration process, the paid peak-shaving capacity of the hydropower unit is The feasible interval is By iterating Gradually reduce it until the algorithm converges to the maximum paid peak-shaving capacity of hydropower that meets the peak-shaving initiative; Provide paid peak-shaving capacity for hydropower units. They are respectively the upper and lower limits of the paid peak-shaving power of the hydropower units in a scheduling cycle.

[0039] Furthermore, the specific operation of step S302 includes the following steps:

[0040] S3021: Initialization,

[0041] S3022: Solve the model obtained in step S301 to obtain the current optimized scheduling strategy; the upper and lower limits of the paid peak load capacity in the model obtained in step S301 are set as in, are the upper and lower limits of the paid peak-shaving power of hydropower units in a dispatching cycle respectively;

[0042] S3023: Under the current peak load regulation strategy, calculate the profit partial derivatives of hydropower, wind power, and photovoltaic power, and update and upper and lower limits of paid peak load regulation capacity If the peak load initiative constraint is not satisfied, Updated to If the peak load initiative constraint is satisfied, Updated to

[0043] S3024: Check whether the paid peak-shaving range of the hydropower unit has converged. If so, end the algorithm; otherwise, return to step S3022 to continue iteration.

[0044] The beneficial effects of the present invention are:

[0045] 1. The peak-shaving electricity price compensation method for hydropower participating in the water-wind-solar integrated system scheduling in this invention clarifies the mutual influence between the peak-shaving benchmark, opportunity cost and efficiency loss of hydropower units, and uses this as a criterion to derive a quantification method for the peak-shaving opportunity cost of hydropower units under all operating conditions.

[0046] 2. The peak-shaving electricity price compensation method for hydropower participating in the water-wind-solar integrated system scheduling in the present invention has formulated a peak-shaving electricity price compensation strategy suitable for hydropower participating in the water-wind-solar integrated system scheduling. It can quantify the actual cost of hydropower stations participating in peak-shaving cooperation, determine the maximum available peak-shaving depth of hydropower under different compensation prices, and evaluate the impact of different compensation prices on the economic indicators and energy efficiency indicators of the units; while ensuring the absorption of wind and solar power, it does not affect the enthusiasm of hydropower to participate in the peak-shaving of the power system, and solves the problem that the single electricity energy market cannot recover the flexibility peak-shaving cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 It is the Elman network structure in the present invention.

[0048] Figure 2 This is the relationship curve between wind speed and wind power output in the present invention.

[0049] Figure 3 This is a flow chart of the iterative solution method of the coordinated scheduling model of the integrated water-wind-solar system under the peak-shaving electricity price compensation strategy in the present invention.

[0050] Figure 4 The real machine efficiency and the Elman neural network predict the full-condition turbine efficiency and characteristic head corresponding water consumption rate in the simulation experiment of the present invention.

[0051] Figure 5 This is the opportunity cost result of hydropower peak regulation within a scheduling cycle in the simulation experiment of the present invention.

[0052] Figure 6 This is the power output process result of the integrated water-wind-solar system under the peak-shaving compensation strategy in the simulation experiment of the present invention.

[0053] Figure 7These are the profit changes of hydropower stations and wind and solar power stations under different peak-shaving benchmarks in the simulation experiment of the present invention.

[0054] Figure 8 The results of the hydropower, wind and solar power grid-connected electricity and alliance profits under different compensation prices in the simulation experiment of the present invention. DETAILED DESCRIPTION

[0055] In order to enable those skilled in the art to better understand the technical solution of the present invention, the technical solution of the present invention is further described below in conjunction with the accompanying drawings and embodiments.

[0056] The peak-shaving electricity price compensation method for hydropower participating in the water-wind-solar integrated system dispatching includes the following steps:

[0057] S1: Constructing the actual cost model of peak regulation of hydropower stations;

[0058] The cost of peak load regulation capacity contributed by hydropower unit i can be measured by “opportunity cost”, and the basic peak load regulation coefficient of hydropower is defined as α, that is, [αP N , P N ] is the basic peak regulation interval of the hydropower unit, where P N Indicates the rated installed capacity of hydropower.

[0059] When the unit output According to the above discussion, the peak load cost should not be taken into account.

[0060] When the unit output When the unit working condition tends to be unfavorable, the water consumption rate increases. At this time, the peak load cost should be taken into account and compensated. The online power W in this period is

[0061] W=∫P t H d t (1)

[0062] According to the water consumption rate μ under this working condition t Convert the online power W at this time into water consumption V:

[0063] V=∫μ t P t Hd t (2)

[0064] Calculate the water head and the basic peak load lower bound αP at time t N The corresponding water consumption rate μ0 converts the water consumption V into the minimum power generation W0 when no downward peak regulation is performed:

[0065]

[0066] At this time, W0-W is the opportunity power for paid peak load regulation, and the unit power generation profit of the hydropower unit is gH -L H , then the peak load opportunity cost C is

[0067] C=(g H -L H )(W0-W) (4)

[0068] Where g H and L H They represent the benchmark on-grid electricity price and levelized cost per kilowatt-hour of hydropower respectively.

[0069] In the present invention, the water consumption rate is evaluated by a method based on the efficiency test of a real hydropower station and the Elman neural network. Specifically:

[0070] The comprehensive water consumption rate refers to the amount of water consumed by a hydropower station for each unit of electricity it generates. It is an important indicator used to measure the economic operation of a hydropower station. The calculation formula for the comprehensive water consumption rate of a hydropower station is:

[0071]

[0072] Where μ is the comprehensive water consumption rate, unit is m 3 / (kWh); E is the total power generation, unit is kW; V is the total water volume for power generation, unit is m 3 ; N is the power output of the power station, unit is kW; Q is the power flow, unit is m 3 / s; T is the calculation period; C is the unit conversion constant (C = 3600 / 9.81); H j is the net generating head, in m; η is the efficiency of the turbine generator set.

[0073] It can be seen from formula (5) that obtaining the unit efficiency under any working condition is the key to solving the water consumption rate at any peak-shaving depth. In general, in order to obtain the efficiency under any working condition, a prototype efficiency test of the full-head section of the hydro-generator set is first carried out to obtain the measured turbine efficiency curve under each head, draw the comprehensive characteristic curve of the turbine operation, and then obtain the efficiency at different peak-shaving depths based on this curve. Manual calculation and curve drawing not only require a large workload, but also have poor accuracy and adaptability. Based on the efficiency test of the real machine in the full-head section, the present invention utilizes the characteristics of the Elman neural network that can approximate any nonlinear mapping with arbitrary accuracy and the advantage of modeling by learning historical data. By training the prototype efficiency test data under limited head, the efficiency of any required operating condition can be easily and quickly calculated.

[0074] More specifically, the actual efficiency test of a hydro-turbine generator set (flow measurement with a flow meter) involves measuring the flow velocity at each point of the flow section using a flow meter, then integrating the flow rate Q along the cross-sectional area to calculate the absolute efficiency of the turbine. The turbine set efficiency calculation formula is:

[0075]

[0076] in,

[0077]

[0078] Where, P T is the turbine output power; P h is the turbine input power; ρ is the water density; Q is the turbine flow; g is the local gravity acceleration; H is the turbine working head; P g is the generator power; η g is the generator efficiency;

[0079] The working water head H of the turbine working condition adopts the unit energy difference between the volute inlet section and the draft tube outlet section, and the calculation formula is:

[0080]

[0081] Where Z1-Z2+(P1-P2) / ρg is the hydrostatic head, which is measured using a differential pressure sensor. The differential pressure sensor was calibrated with a Druck DPI610 portable pressure calibrator before and after the test. is the dynamic head, which is calculated from the flow rate Q, the cross-sectional areas S1 and S2 of the flow channel inlet and tailwater outlet.

[0082] The turbine flow rate Q is measured using the velocity-area method of a velocity meter. After obtaining the cross-sectional velocity using the velocity meter, the velocity distribution curve is first checked to determine if there are any suspicious measured values. The velocity in the sidewall area is calculated using the extrapolation method based on the velocity distribution pattern. The extrapolation formula is:

[0083] V y =V a ×(y / a) 1 / m (9)

[0084] Where V y is the flow velocity at a distance "y" from the wall; V a is the measured velocity at the near-wall measuring point; a is the distance from the near-wall velocity meter to the pipe wall; m is a coefficient related to the wall roughness and flow conditions, and is determined according to the relevant provisions in Appendix E of ISO 3354, usually m = 7.

[0085] In order to measure the flow rate of the flow section, a steel pipe bracket is arranged along the diameter of the flow section, and a flow meter is arranged on the bracket. The flow rate is calculated using the numerical integration method:

[0086]

[0087] Where Q iThe flow rate is obtained by numerically integrating the velocity measured by the flow meter on the bracket radius, where i represents the bracket radius. Due to the uneven distribution of velocity, there are differences between the flow rates calculated based on the velocity distribution of each radius. Therefore, the final calculated flow rate should be the arithmetic mean of the flow rates of n radii:

[0088]

[0089] Where n is the number of radii where the velocity meter is placed during the actual machine test. If two diameter brackets are placed, there are 4 radii, so n is 4.

[0090] To correct for the crowding effect, if the propeller flowmeter bracket uses a Φ60 steel pipe, and the ratio of the bracket's frontal area to the pipe's cross-sectional area is less than 6%, the actual flow rate can be calculated using the flow correction coefficient K formula as specified in the regulations:

[0091] Q=(1-K)Q c (12)

[0092] Where,

[0093] K=0.12S+0.03S C 13)

[0094] S C =πZd 2 / 4A 14)

[0095] Among them, S C is the displacement coefficient of the flowmeter; Z is the number of flowmeters; d is the rotating diameter of the flowmeter blade; A is the measuring cross-sectional area.

[0096] The specific method for predicting the continuous efficiency of turbines based on Elman neural network is as follows: Elman neural network is generally divided into four layers: input layer, hidden layer, receiving layer and output layer. Figure 1 As shown in Figure 1, the connections between the input, hidden, and output layers are similar to those of a feedforward network. The units in the input layer serve only as signal transmission, while the units in the output layer act as linear weights. The transfer function of the hidden layer units can be linear or nonlinear. The successor layer, also known as the state layer, can be considered a one-step delay operator that memorizes the output value of the hidden layer units at the previous moment and returns it to the network input.

[0097] Attach Figure 1 For example, the nonlinear state space expression of the Elman network is

[0098] y(k)=g(w 3 x(k))

[0099] x(k)=f(w 1 x c (k)+w 2(u(k-1))

[0100] x c (k) = x(k-1) (15)

[0101] Where y(k) is the m-dimensional output node vector; x(k) is the n-dimensional intermediate layer node unit vector; u is the r-dimensional input vector; x c is the n-dimensional feedback state vector; w 3 is the connection weight from the middle layer to the output layer; w 2 is the connection weight from the input layer to the middle layer; w 1 is the connection weight from the receiving layer to the middle layer; g(*) is the transfer function of the output neuron, which is a linear combination of the output of the middle layer; f(*) is the transfer function of the middle layer neuron, and the S function is often used.

[0102] Elman neural network also uses BP algorithm to modify weights, and the learning index function uses the error square sum function.

[0103]

[0104] Where: Enter the target vector.

[0105] Using actual turbine efficiency data from limited operating conditions during real-machine testing as input to the Elman neural network, the efficiency of turbine units under any operating condition can be calculated. The Elman neural network is a predictive algorithm used to extend efficiency data. Furthermore, the water consumption rate is calculated using the formula for calculating the comprehensive water consumption rate of the hydropower station.

[0106] Furthermore, step S2: establishing a coordinated dispatch model of the water-wind-solar joint operation system under the peak-shaving electricity price compensation strategy.

[0107] Specifically, based on the principle of priority consumption of new energy, a coordinated dispatch model of the water-wind-solar joint operation system under the peak-shaving electricity price compensation strategy is established. The hydropower output is adjusted every hour, and the maximum amount of renewable energy on-grid power under different compensation prices is taken as the objective function. The objective function of the coordinated dispatch model of the water-wind-solar joint operation system under the peak-shaving electricity price compensation strategy is:

[0108]

[0109] Where, is the peak load compensation price of hydropower, unit is / kWh; P t W 、P t S is the output of the wind farm and photovoltaic farm at time t, in MW; N is the total number of intervals in the time range; Δt is the time interval.

[0110] The present invention takes into account the peak-shaving initiative constraints, the operation-related constraints of hydropower, wind power, and photovoltaic power generation units, and the power balance constraints of the joint operation system.

[0111] 1) Active peak load regulation constraints

[0112] Based on equations (1) to (4), the costs of downward peak regulation of hydropower units at different depths are obtained. Combining the peak regulation compensation income of hydropower units and the peak regulation expenditure of wind and solar power, the benefits of each entity in the integrated system within time t are obtained:

[0113]

[0114] Where, F H 、F W 、F S They represent the income of hydropower, wind power and photovoltaic power within time t; g S Represents the benchmark photovoltaic grid-connected electricity price; L S represents the normalized cost of electricity for the optical level, j h represents the peak load-shaving capacity contributed by hydropower, j s and j w They represent the newly added grid-connected space for photovoltaic and wind power respectively, and the size depends on the bidding in the peak-shaving ancillary service market.

[0115] When hydropower does not conduct downward peak regulation, the revenue of each entity within time t is

[0116]

[0117] Where, F H，basic 、F W，basic 、F S，basic Represent the income of hydropower, wind power and photovoltaic power within time t, Pt W，basic 、P t W ，basic 、P t W，bastic They represent the output of hydropower, wind farm and photovoltaic farm in period t, MW respectively.

[0118] Taking the basic peak-shaving lower limit income as the benchmark, it is obvious that only when hydropower units can benefit from paid peak-shaving will they tend to further increase the depth of peak-shaving and participate in peak-shaving. Moreover, if wind and solar power stations further accept the peak-shaving services of hydropower stations to increase the amount of wind and solar power grid-connected electricity, the profits of wind and solar power stations must also increase. The mathematical expression is:

[0119]

[0120] 2) Reservoir water level constraints

[0121]

[0122] Where Z minH 、Z maxH 、Z t H are the lower limit, upper limit and water level of the hydropower station at time t respectively.

[0123] 3) Reservoir capacity constraints

[0124] V minH ≤V t H ≤V maxH (twenty two)

[0125] Where V minH 、V maxH 、V t H are the lower limit, upper limit and storage capacity of the hydropower station water level at time t respectively.

[0126] 4) Downflow flow restriction

[0127]

[0128] Where Q minH , Q maxH , Q t H are the lower limit, upper limit and discharge volume of the reservoir at time t respectively.

[0129] 5) Output limit constraints

[0130] P minH ≤P t H ≤P maxH (twenty four)

[0131] Where, P minH 、P maxH 、P t H They are the lower limit, upper limit and output of the hydropower station at time t respectively.

[0132] 6) Water balance constraints

[0133]

[0134] Where V t H 、 are the initial and final water storage capacities of the hydropower station during period t; Q t rk , Q t fd , Q t qs They are the inflow flow, power generation flow and abandoned water flow of the hydropower station during period t.

[0135] 7) Climbing rate constraint

[0136]

[0137] Where V t up 、V t down They are the maximum increase and decrease output rates of the hydropower units, MW / h.

[0138] 8) Photovoltaic output limit constraints

[0139] 0≤P t S ≤P maxS (27)

[0140] in,

[0141]

[0142] Where, P maxS is the maximum predicted output of the photovoltaic field, MW; is the photovoltaic output at time t, MW; IC PV Installing photovoltaic power plants; R stc are the solar radiation intensity at time t and under standard conditions respectively; is the temperature coefficient of the photovoltaic panel; T t 、T stc are the temperature at time t and under standard conditions respectively.

[0143] 9) Wind power output restrictions

[0144] 0≤P t w ≤P max w (29)

[0145] Among them, the fan output P t w It is related to wind speed, blade speed and its structural parameters. The relationship curve between wind speed and output is shown in the attached figure. Figure 2 As shown, the formula for calculating the output is:

[0146]

[0147] in,

[0148]

[0149] Where, P maxw is the maximum predicted output of the wind farm; P t W is the wind power output at time t; ρair is the air density; A wind is the swept area; V t is the wind speed passing through the i-th wind turbine at the t-th moment; V cut-in and V cut-out are the cut-in and cut-out wind speeds of the fan respectively; V rated is the rated wind speed of the fan; C p is the power coefficient; λ1 is the intermediate variable; λ is the tip speed ratio; β is the pitch angle; ω r is the rated speed of the fan; R is the radius of the fan rotor.

[0150] When the wind speed is less than the cut-in wind speed, the wind turbine does not generate electricity; when the wind speed is greater than the cut-in wind speed and less than the cut-out wind speed, the wind turbine generates electricity in maximum power tracking mode; when the wind speed is greater than the cut-out wind speed, the wind turbine maintains rated power operation.

[0151] 10) System power balance constraints

[0152] P t L =P t H +P t S +P t W +P t SH -P t AB (32)

[0153] Where: P t L is the system load at time t, MW; P t H 、P t S 、P t W are the hydropower, photovoltaic and wind power power at time t, MW; P t SH 、P t AB are the power shortage and curtailment power at time t, in MW respectively.

[0154] Furthermore, step S3: solving the coordinated scheduling model of the water-wind-solar combined operation system under the peak-shaving electricity price compensation strategy established in step S2.

[0155] Specifically, the coordinated dispatch model of the water-wind-solar joint operation system under the peak electricity price compensation strategy is a nonlinear two-layer model. For the inner layer problem, can be considered as a given parameter. This invention proposes an iterative solution algorithm. The solution of Equation (17) is divided into two parts: the scheduling strategy solution and the peak-shaving initiative verification. In the scheduling strategy solution part, the peak-shaving initiative constraint is ignored, and the paid peak-shaving capacity constraint is added. The coordinated scheduling model for the integrated water-wind-solar system without considering the peak-shaving initiative is obtained as shown in Equation (33).

[0156]

[0157] Where, Provide paid peak-shaving capacity for hydropower units. They are respectively the upper and lower limits of the paid peak-shaving power of the hydropower units in a scheduling cycle.

[0158] Taking the range of paid peak load regulation power of hydropower units within a dispatching cycle as the iterative variable, the iterative algorithm is designed as shown in the attached figure. Figure 3 During the iteration process, the paid peak load capacity of the hydropower unit The feasible interval is By iterating Gradually reduce it until the algorithm converges to the maximum paid peak-shaving capacity of hydropower that meets the peak-shaving initiative.

[0159] The solution algorithm includes the following four main steps.

[0160] Step 1: Initialize, let

[0161] Step 2: Solve the model of formula (33) to obtain the current optimal dispatching strategy; the upper and lower limits of the paid peak load capacity in the model shown in formula (33) are set as

[0162] Step 3: Under the current peak load regulation strategy, calculate the profit partial derivatives of hydropower, wind power, and photovoltaic power, and update and upper and lower limits of paid peak load regulation capacity If the peak load initiative constraint is not satisfied, Updated to If the peak load initiative constraint is satisfied, Updated to

[0163] Step 4: Check whether the paid peak regulation range of the hydropower unit converges. If so, end the algorithm; otherwise, return to step 2 to continue iteration.

[0164] Simulation experiment:

[0165] Attachment Figure 4 In order to use the method of the present invention to obtain the real machine efficiency of a certain place and the Elman neural network to predict the full-condition turbine efficiency and the corresponding water consumption rate of the characteristic head,

[0166] Attachment Figure 5 The chart shows how the opportunity cost of hydropower peak shaving varies with output and head when α = 70%. This example illustrates this. At the same head, as output increases, the peak shaving opportunity cost for different peak shaving coefficients consistently shows a trend of first increasing and then decreasing. This is because the opportunity cost of hydropower peak shaving is related to both output and water consumption. Under conditions with maximum water consumption, the unit output is low, and the peak shaving opportunity cost does not reach its maximum value. At the same output, as head increases, the peak shaving opportunity cost for different peak shaving coefficients consistently shows a decreasing trend, consistent with the relationship between water consumption and head.

[0167] As attached Figure 6 As shown, hydropower effectively improves the output characteristics of wind and solar power through flexible regulation. The planned power transmission curve for bundled transmission presents a stepped shape, meeting the requirements for power transmission stability. In addition, this scheduling method can meet the power grid's transmission guarantee rate requirements.

[0168] Attachment Figure 7 Taking a dispatch cycle as an example, the paper focuses on the profit changes of hydropower plants and wind and solar power plants as the peak load regulation depth increases when the peak load regulation coefficient α = 70%.

[0169] Attachment Figure 8 Figure 3. Grid-connected hydropower, wind power, and solar power generation, as well as alliance profits, at different compensation prices. As compensation prices increase, hydropower profits gradually increase, while wind power profits initially increase and then decrease, while peak-shaving costs gradually increase. The alliance achieves its maximum profit at a compensation price of 0.17 yuan / kWh.

[0170] The basic principles, main features, and advantages of the present invention are shown and described above. Those skilled in the art should understand that the present invention is not limited to the foregoing embodiments. The foregoing embodiments and descriptions are merely illustrative of the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of protection claimed in the present invention is defined by the appended claims and their equivalents.

Claims

1. A peak-shaving electricity price compensation method for hydropower participating in the scheduling of a water-wind-solar integrated system, characterized in that: The following steps are included: S1: Establish a peak-shaving opportunity cost model for hydropower stations; S2: Based on the principle of giving priority to the consumption of new energy, a coordinated dispatch model for the water-wind-solar joint operation system under the peak-shaving electricity price compensation strategy is established; S3: Using an iterative solution method, solve the coordinated scheduling model of the hydropower-wind-solar combined operation system under the peak-shaving electricity price compensation strategy established in step S2 to obtain the maximum paid peak-shaving depth of hydropower that meets the objective function; The specific operation of step S1 includes the following steps: S101: Use opportunity cost to measure the cost of peak load capacity contributed by hydropower unit i. Define the basic peak load coefficient of hydropower as α, then the basic peak load range of hydropower unit is [αP N , P N ]; where P N Indicates the rated installed capacity of hydropower; S102: When the unit output When the peak load is less than 100%, the peak load cost is not taken into account; S103: When the unit output When the peak load cost is taken into account and compensation is given, the amount of electricity on the grid during this period is W. W=∫P t H dt According to the water consumption rate μ under the corresponding working conditions t , convert the online power W into water consumption V: V=∫μ t P t H dt Calculate the water head and the basic peak load lower bound αP at time t N The corresponding water consumption rate μ0 converts the water consumption V into the minimum power generation W0 when no downward peak regulation is performed: At this time, W0-W is the opportunity power for paid peak load regulation, and the unit power generation profit of the hydropower unit is g H -L H , then the peak load opportunity cost C is C=(g H -L H )(W0-W) Where g H and L H They represent the benchmark on-grid electricity price and levelized cost per kilowatt-hour of hydropower respectively.

2. The peak-shaving electricity price compensation method for hydropower participating in the water-wind-solar integrated system scheduling according to claim 1 is characterized in that: The method for calculating the water consumption rate in step S103 includes the following steps: Step 1: Calculate the efficiency of the turbine generator set based on the actual efficiency test of the turbine generator set using the flow meter flow measurement method in, Where, P T is the turbine output power; P h is the turbine input power; ρ is the water density; Q is the turbine flow; g is the local gravity acceleration; H is the turbine working head; P g is the generator power; η g is the generator efficiency; Step 2: Construct an Elman neural network model, use the data of the real turbine generator unit efficiency test as the input of the model, extend the turbine unit efficiency data, and use the Elman neural network model to calculate the water consumption rate. The calculation formula of the comprehensive water consumption rate of the hydropower station is: Where μ is the comprehensive water consumption rate, unit is m 3 / (kWh); E is the total power generation, unit is kW; V is the total water volume for power generation, unit is m 3 ; N is the power output of the power station, unit is kW; Q is the power flow, unit is m 3 / s; T is the calculation period; C is the unit conversion constant, C = 3600 / 9.81; H j is the net generating head, in m; η is the efficiency of the turbine generator set.

3. The peak-shaving electricity price compensation method for hydropower participating in the water-wind-solar integrated system scheduling according to claim 2 is characterized in that: The specific operation of step S2 includes the following steps: S201: Based on the principle of priority consumption of new energy, a peak-shaving compensation optimization scheduling model for the water-wind-solar joint operation system is established. The hydropower output is adjusted every hour, and the maximum amount of renewable energy on-grid power under different compensation electricity prices is taken as the objective function. The objective function of the coordinated scheduling model of the water-wind-solar joint operation system under the peak-shaving electricity price compensation strategy is: Where, The peak load compensation price for hydropower is RMB / kWh; is the output of wind farm and photovoltaic farm at time t, in MW; N is the total number of intervals in the time range; Δt is the time interval; S202: Determine constraints, which include peak-shaving initiative constraints, reservoir water level constraints, reservoir storage capacity constraints, downstream flow constraints, output limit constraints, water balance constraints, ramp rate constraints, photovoltaic output limit constraints, wind power output limit constraints, and system power balance constraints.

4. The peak-shaving electricity price compensation method for hydropower participating in the water-wind-solar integrated system scheduling according to claim 3 is characterized in that: The specific operation of step S3 includes the following steps: S301: The peak-shaving compensation optimization scheduling model for the hydro-wind-solar combined operation system established in step S201 is divided into two parts: scheduling strategy solution and peak-shaving initiative verification. In the scheduling strategy solution part, the peak-shaving initiative constraint is ignored, and the paid peak-shaving capacity constraint is added to obtain a coordinated scheduling model for the hydro-wind-solar integrated system that does not consider peak-shaving initiative. S302: Using the range of the paid peak-shaving power of the hydropower unit within a scheduling cycle as the iteration variable, the iterative algorithm is used to solve the model obtained in step S301. During the iteration process, the paid peak-shaving capacity of the hydropower unit is The feasible interval is By iterating Gradually reduce it until the algorithm converges to the maximum paid peak-shaving capacity of hydropower that meets the peak-shaving initiative; Provide paid peak-shaving capacity for hydropower units. They are respectively the upper and lower limits of the paid peak-shaving power of the hydropower units in a scheduling cycle.

5. The peak-shaving electricity price compensation method for hydropower participating in the water-wind-solar integrated system scheduling according to claim 4 is characterized in that: The specific operation of step S302 includes the following steps: S3021: Initialization, S3022: Solve the model obtained in step S301 to obtain the current optimized scheduling strategy; the upper and lower limits of the paid peak load capacity in the model obtained in step S301 are set as in, are the upper and lower limits of the paid peak-shaving power of hydropower units in a dispatching cycle respectively; S3023: Under the current peak load regulation strategy, calculate the profit partial derivatives of hydropower, wind power, and photovoltaic power, and update and upper and lower limits of paid peak load regulation capacity If the peak load initiative constraint is not satisfied, Updated to If the peak load initiative constraint is satisfied, Updated to S3024: Check whether the paid peak-shaving range of the hydropower unit has converged. If so, end the algorithm; otherwise, return to step S3022 to continue iteration.

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