CN113379147B - Synchronous optimization method for remote hydropower contract and hydropower station group scheduling rule - Google Patents
Synchronous optimization method for remote hydropower contract and hydropower station group scheduling rule Download PDFInfo
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Abstract
The invention provides a synchronous optimization method of a long-term hydroelectric contract and a hydropower station group scheduling rule, which comprises the following steps: constructing a clearing model to simulate daily average spot price; acquiring a historical runoff sequence of the power station and taking the historical runoff sequence and the simulated spot-shipment electricity price as input parameters of a subsequent model; constructing a double-layer optimized dispatching model, optimizing the electric quantity of the hydropower contract by taking the data in the step 2 as input parameters of the double-layer optimized dispatching model and acquiring a deterministic dispatching track of the cascade reservoir group; determining the form of a scheduling rule on the basis of the optimal running track obtained in the step 3; after the form of the scheduling rule is determined, the scheduling rule parameters and the contract electric quantity of each month are synchronously optimized by using a parameter simulation optimization method. The invention realizes the synchronous optimization of the optimal operation track of the library group and the monthly contract electric quantity, and the model of the invention greatly reduces the calculation difficulty and dimension disaster of methods such as an exhaustion method and the like.
Description
Technical Field
The invention belongs to the technical field of hydropower management, and particularly relates to a synchronous optimization method of a long-term hydropower contract and a hydropower station group scheduling rule.
Background
With the fierce and vigorous reform of the power market, the formulation of a long-term hydropower contract has very important significance. However, because of the limited accuracy of runoff forecasting, not only the hydropower contract but also the guidance of reservoir dispatching rules are required in long-term dispatching. In this case, the utility company needs to determine the scheduling rules and the contract for the hydropower at the beginning of the year in synchronization.
Most of the existing researches focus on short-term unit cooperation of hydropower stations, and few researches mention the acquisition of long-term scheduling rules in the background of the power market. Under the background of electric power reform, long-term scheduling rules and contract management are particularly important for large hydropower merchants, especially hydropower station group managers.
The deterministic optimal scheduling model takes historical runoff data as input, a dynamic planning method is generally adopted to determine the optimal running track of the reservoir, and the scheduling rule can be extracted based on the optimal running track. However, because monthly contract electricity quantity needs to be optimized synchronously with a scheduling rule, a problem of dimension disaster is brought to the problem solving, and the traditional solving method cannot meet the problem solving requirement; furthermore, it is not known what changes may occur in the form of the scheduling rules in the context of the power market. Therefore, a method for synchronously optimizing the long-term hydropower contract and the hydropower station group dispatching rule needs to be researched to solve the problems.
Disclosure of Invention
The invention aims to provide a method for synchronously optimizing a remote hydropower contract and a hydropower station group dispatching rule aiming at the defects of the prior art.
In order to solve the technical problems, the invention adopts the following technical scheme:
a synchronous optimization method for a remote hydropower contract and a hydropower station group scheduling rule comprises the following steps:
step 1, forecasting different types of power supply and demand in provinces according to the installed power station and power load data of a research area, and constructing a clearing model to simulate daily average spot-shipment electricity price;
step 2, acquiring a power station historical runoff sequence and taking the power station historical runoff sequence and the simulated spot price as input parameters of a subsequent model;
step 3, constructing a double-layer optimized dispatching model, optimizing hydropower contract electric quantity by taking the data in the step 2 as input parameters of the double-layer optimized dispatching model, and acquiring a deterministic dispatching track of the cascade reservoir group;
step 4, determining the form of a scheduling rule on the basis of the optimal running track obtained in the step 3;
and 5, after the form of the scheduling rule is determined, synchronously optimizing the scheduling rule parameters and the contract electric quantity of each month by using a parameter simulation optimization method.
Further, in step 1, the method for constructing the clearing model to simulate the daily average spot electricity price includes:
first, the requirements to establish different time periods are as follows:
in the formula: d i Is the power demand of the i-th period, D est Is a Gaussian distributed time interval estimation demand, SP i Is the spot price in time i, SP 0 Is the threshold value of electricity prices, elsa is the elastic coefficient of electricity prices;
residual demand RD i Supplied by fossil power, as follows:
in the formula:
and &>
Supplying wind energy, light energy and hydroelectric energy in the spot market at the i-time period;
the spot price is determined by matching the intersection of the remaining demand with the supply cost function curve, the equation:
in the formula:
for the total thermal power supply, RD is the remaining demand.
Further, the double-layer optimized scheduling model in the step 3 is of a double-layer structure, and the outer layer searches the optimal contract electric quantity of each month by adopting an intelligent algorithm; the inner layer takes the contract electric quantity fed back by the outer layer as input, a successive approximation dynamic programming algorithm is adopted to obtain the optimal running track and the maximum benefit of the reservoir group under the contract situation fed back by each initially determined outer layer, and the maximum benefit is fed back to the outer layer.
Further, the maximum benefits include three benefits of power generation income, power generation guarantee rate and contract satisfaction rate.
Further, the power generation profit comes from the day-ahead power market and the forward contract market, and the calculation formula of the power generation profit is as follows:
wherein: m represents the total years of deterministic optimization; mon k,i Represents the total number of days at month k of year i; Δ t represents the time step (day), N p,j Representing the power generation of p hydropower stations in time period j, SP j Representing a simulated day-ahead electricity price. CV of k Contract electricity quantity representing k months; and CP k The contract electricity price represents the contract electricity price signed by the month k, and the contract electricity price is assumed to be the average value of the grid electricity prices of the water buffalo, the river-separating rock and the high dam bar bank unit in the section.
Further, the determining the form of the scheduling rule in step 4 includes:
firstly, drawing up an aggregation rule form, namely determining a relation between a total power generation decision of a reservoir group and what exists;
and then drawing a decomposition rule form, namely determining the relation between decision variables such as the power generation capacity or the discharge capacity of each reservoir and the like, and the purpose of the step is to determine how to distribute the total power generation capacity to each reservoir.
Further, in the step 5, parameter optimization is performed by taking the linear relation parameters of the aggregation rule and the decomposition rule and the contract electric quantity of each month as variables to be optimized and taking the power generation income, the power generation guarantee rate and the contract satisfaction rate as targets.
Compared with the prior art, the invention has the beneficial effects that:
1. the invention adopts a double-layer deterministic optimization scheduling model, realizes the synchronous optimization of the optimal operation track of the library group and monthly contract electric quantity, and greatly reduces the calculation difficulty and dimension disaster of methods such as an exhaustion method and the like by the model;
2. the method utilizes the aggregation decomposition technology, plans a scheduling rule form after combining the electric power market according to the optimal running track, and the obtained aggregation rule is different from the traditional form and plans the scheduling rules under different spot-shipment electricity price situations;
3. compared with the conventional dispatching mode, the matched dispatching rule provided by the invention can obviously improve the economic benefit of hydropower suppliers compared with the long-term hydropower contract.
Drawings
FIG. 1 is a flow chart of a synchronization optimization method according to an embodiment of the present invention;
FIG. 2 is a diagram of a power market clearing simulation at the present day in accordance with an embodiment of the present invention;
fig. 3 shows an optimal operation track and a matching contract electric quantity provided by the embodiment of the present invention, (a) a total energy storage operation track interval that is optimal for many years in a cascade reservoir group, and (b) a relationship between an optimal electric power generation interval in different months and a matching contract electric quantity;
fig. 4 shows the fitting effect of the scheduling rule provided in the embodiment of the present invention (for example, month 5), (a) the fitting effect of the scheduling rule when the electricity price is 0.1 to 0.4 yuan, (b) the fitting effect of the scheduling rule when the electricity price is 0.4 to 0.7 yuan, (c) the fitting effect of the scheduling rule when the electricity price is 0.7 to 1.0 yuan;
fig. 5 is a comparison graph of the optimal benefit, the benefit of the optimized scheduling rule, and the benefit of the conventional scheduling provided by the embodiment of the present invention (month 5 is taken as an example).
Detailed Description
The technical solutions in the embodiments of the present invention will be described clearly and completely with reference to the following embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the embodiments and features of the embodiments may be combined with each other without conflict.
The present invention is further illustrated by the following examples, which are not to be construed as limiting the invention.
As shown in fig. 1, the invention discloses a synchronous optimization method of a remote hydropower contract and a hydropower station group dispatching rule, which comprises the following steps:
step 1, forecasting different types of power supply and demand in provinces according to the installed power station and power load data of a research area, and constructing a clearing model to simulate daily average spot-shipment electricity price;
in the embodiment, different types of power supply and demand in the province are predicted according to installation and power load data of all power stations in the Hubei province, and a clearing model is constructed to simulate the daily average spot price of electricity in the Hubei province; this step simulates the spot shipment clear electricity prices (in days as the period length) for the long sequence in Hubei province (2008-2019). The model assumes that the power market is a completely competitive power market and cannot be used, and all power generators bid according to the marginal cost; and assumes that the transmission capacity constraints and network loss functions in the grid are not taken into account.
In order to minimize the marginal operation cost of the system, wind, light and hydropower supply with lower marginal cost is arranged preferentially, and then orderly supply of thermal power generating units is arranged. Wind power, photovoltaic power and hydroelectric power are approximately regarded as zero cost (subsidy bidding), the power generation cost mainly comes from thermal power, and the clearing price of the whole power market is the cost of marginal supply of thermal power generating units. Fig. 2 is a diagram showing a simulation method of how spot electricity prices are cleared.
The requirements for the different periods are as follows:
in the formula: d i Is the power demand of the ith period; d est Is a time period estimation demand subject to a gaussian distribution; SP i Is the spot price for time period i; SP 0 Is a threshold value of electricity prices; elsa is the elastic coefficient of electricity prices.
Residual demand RD i Supplied by fossil power, as follows:
in the formula:
and &>
The method is used for supplying wind energy, light energy and hydroelectric energy in the spot market at the i-period (namely bidding electric quantity). />
The spot price is determined by matching the intersection of the remaining demand with the thermal power supply cost function curve, the equation being as follows:
in the formula:
for the total thermal power supply, RD is the remaining demand.
Step 2, acquiring a historical runoff sequence and taking the historical runoff sequence and the spot electricity price simulated in the step 1 as input parameters of a deterministic optimization scheduling model;
and 3, constructing a double-layer optimized scheduling model, optimizing hydropower contract electric quantity and acquiring a deterministic scheduling track of the Qingjiang step reservoir group. Specifically, the model is of a double-layer structure, the outer layer adopts an intelligent algorithm (NSGA-II) to search the optimal contract electric quantity in each month, specifically, a plurality of contract electric quantity scenes are preliminarily constructed, and then a set of contract electric quantity with the maximum benefit is selected in the scenes; the inner layer takes the contract electric quantity fed back by the outer layer as input, a successive approximation dynamic programming algorithm (DPSA) is adopted to search the optimal running track and the maximum benefit of the reservoir group under the contract situation fed back by the outer layer, wherein the maximum benefit comprises three benefits of power generation income, power generation guarantee rate and contract satisfaction rate, and the maximum benefit is fed back to the outer layer.
In the step, the optimization targets (the inner layer and the outer layer both comprise the following three items) of the double-layer model comprise three items of power generation income (economic benefit), power generation guarantee rate (the proportion of the number of guaranteed output time segments required by a power grid to the total number of time segments required by a hydropower provider) and contract satisfaction rate (the proportion of the number of time segments to the total time segment required by the power grid to the power provider) which are respectively equal to the power generation amount of the double-layer model; the power generation income comes from the day-ahead power market and the long-term contract market, and the calculation formula of the power generation income is as follows:
wherein: m represents the total years of deterministic optimization; mon k,i Represents the total number of days at month k of year i; Δ t represents a time step (day), N p,j Representing the power generation of p hydropower stations in time period j, SP j Representing a simulated day-ahead electricity price. CV is a function of k Contract electricity quantity representing k months; and CP k And the contract electricity price represents the contract electricity price signed in k month, and in the section, the contract electricity price is assumed to be the average value of the network electricity prices of the water belock, the river isolation rock and the high dam continent unit.
Specifically, in the optimization model, the calculation of three target benefit values is closely related to the scheduling track and the formulated contract electric quantity, and the model greatly reduces the calculation difficulty and dimension disaster of methods such as an exhaustion method and the like. The specific benefit values of the three objectives of the optimal solution obtained in this subsection are compared with the conventional benefits in table 1.
TABLE 1 comparison of the benefits of the optimal versus conventional solutions
It can be seen that under the premise of ensuring that the contract satisfaction rate is not reduced, the power generation benefit and the power generation guarantee rate of the optimal scheme are improved compared with those of the conventional scheme.
Fig. 3 shows the variation locus (converted from the optimal scheduling locus of the reservoir group) of the contract electric quantity optimized in each month and the total stored energy of the reservoir group system. (a) The graph is a multi-year optimal total energy storage operation track interval of the cascade reservoir group, and (b) represents a relation graph of optimal power generation intervals and matched contract electric quantity in different months. The energy storage period/water storage period of the reservoir group of Qingjiang cascade occurs from 7 months to 9 months; the collapse period occurs from 10 months to 3 months. As shown in fig. 3 (b), the hydropower generation amount per month can satisfy the contractual target. Specifically, the contract electric quantity signed by the flood season is more than that of the flood season, because more water comes from the flood season step hydropower station group, and in addition, the current price is mostly lower than the contract price in the flood season, and more electric power needs to be sold in the long-term contract market, so that the maximization of the hydropower income is realized. Therefore, the optimization result is in line with the actual situation.
And 4, determining the form of the scheduling rule on the basis of the optimal running track obtained in the step 3. In the fitting of the scheduling rules, RMSE minimum and certainty coefficients R are used 2 The maximum is taken as a fitting target, independent variable and dependent variable parameters of the dispatching rule are preferably selected, and the form of the dispatching rule is further determined (namely, how to determine the total generating capacity of the reservoir group and the distributed generating capacity of each reservoir according to the current states of the reservoir group and each reservoir, including the stored water quantity, the stored energy and other parameters). Generally, different independent variable and dependent variable schemes are adopted to fit the best scheme set of effect parameters as the optimal scheduling rule form scheme, and no determined threshold value standard exists.
(1) And (4) drawing up an aggregation rule form, namely determining a relation between the total power generation decision of the reservoir group and the total power generation decision.
Firstly, the stored energy and the stored water quantity of the reservoir group in each time interval are obtained in an integral mode to be used as one of standby variables, the total generated energy is used as a dependent variable, and fitting effect comparison is carried out (assuming that all the stored water quantity of the reservoir group in the time interval is discharged).
Finally, the scheduling rule effect of the aggregation system in each month is best, wherein the stored energy and the spot price of electricity in different time periods in the same month are used as independent variables, and the total generated energy is used as a dependent variable. Taking the fitting effect of the scheduling rules of the month of May and the month of September as an example, the reliability of the rule form is shown (namely, the stored energy and spot electricity prices are used as independent variables, and the total generated energy is used as a dependent variable), and the result is shown in FIG. 4, wherein the fitting rule R of each month 2 Are all greater than 0.4.
As shown in fig. 4, a linear relationship in which there is a positive correlation between the stored energy and the total output of the reservoir group in the same price zone (this figure is shown by taking a 5-month fitting effect as an example), (a) shows a fitting effect of the scheduling rule when the electricity price is 0.1 to 0.4 yuan, (b) shows a fitting effect of the scheduling rule when the electricity price is 0.4 to 0.7 yuan, and (c) shows a fitting effect of the scheduling rule when the electricity price is 0.7 to 1.0 yuan. The rule form is different from the traditional rule, and shows that after the spot-rate electricity price is segmented, a remarkable piecewise linear relation exists between the total generated energy and the available electric energy. Therefore, after the power market is developed, the scheduling rule form of the hydropower company is changed, and the spot price of electricity should be used as one of independent variables.
(2) And (3) drawing up a decomposition rule form, namely determining the relation between decision variables such as the respective generated energy or discharge rate of each reservoir and the like, wherein the step aims to determine how to distribute the total generated energy to each reservoir.
Firstly, respectively selecting water distribution bealock outflow, water distribution bealock output, water storage capacity at the end of a water distribution bealock time period, river rock outflow, river rock output and water storage capacity at the end of a river rock time period as 6 groups of alternative dependent variables, and comparing the fitting effects of 3 × 6=18 different decomposition rule forms, wherein the total generated energy of a reservoir group, the initial water storage capacity and inflow capacity of the water distribution bealock time period, the initial water storage capacity and inflow capacity of the river rock time period are taken as 3 groups of alternative independent variables.
Finally, when the total generated energy is smaller than 650MW, a better fitting relation exists between the water storage capacity at the end of the water buffet period, the initial water storage capacity at the initial water buffet period and the inflow capacity at the initial water buffet period (R in each month) 2 >0.5 Determining the puerto release flow and the generated energy of the water blanket according to the water balance; the power generation capacity of the river separation rock and the high dam continent is iterated by dataCalculated (in this subsection it is assumed that the dam reservoir level is constant, i.e. dam inflow = outflow). In addition, when the total generated energy is larger than or equal to 650MW, the fitting effect of the relationship between the water buffet output and the total output of the library group time period and the primary energy storage amount of the water buffet time period is good (R in each month) 2 >0.5)。
Finally, the form of the decomposition rule is: when the total generated energy of the reservoir group is less than 650MW, determining the contribution of the water belock (binary linear relationship) according to the initial water storage amount of the water belock in the time period and the inflow amount of the water belock in the time period, and further determining the contribution of the rest two reservoirs; and when the total generated energy is larger than or equal to 650MW, determining the water buffet output (binary linear relation) according to the total output of the reservoir group and the initial energy storage amount of the water buffet in the period of time, and further determining the output of the other two reservoirs.
And 5, after the form of the dispatching rule is determined, synchronously optimizing the dispatching rule parameters and the contract electric quantity of each month by using a parameter simulation optimization method, specifically, taking linear relation parameters (including slope, intercept and the like of the linear relation) of the aggregation rule and the decomposition rule and the contract electric quantity of each month as variables to be optimized, wherein the optimization target is the same as that in the step 3, and comprises three items of power generation income (economic benefit), power generation guarantee rate (the proportion of the guaranteed output time period number to the total time period required by a power grid when the electric power generation of a hydropower provider is greater than the guaranteed output time period number to the total time period required by the power grid) and contract satisfaction rate (the proportion of the contract time period number to the total time period when the electric power generation of the hydropower provider is greater than the agreed amount), and optimizing the parameters (namely, the slope, the intercept and the like of the linear relation and the contract electric quantity of each month) by adopting NSGA-II. Fig. 5 shows the optimal benefit, the benefit of fitting the scheduling rule, the optimized scheduling rule, and the benefit of the regular scheduling rule, taking may as an example. FIG. 5 shows that the benefit of optimizing the rule scheme is only second to the optimal benefit; whereas the conventional scheme is the least effective.
While the invention has been described with reference to a preferred embodiment, it will be understood by those skilled in the art that various changes in form and detail may be made therein without departing from the spirit and scope of the invention.
Claims (6)
1. A synchronous optimization method for a remote hydropower contract and a hydropower station group scheduling rule is characterized by comprising the following steps:
step 1, forecasting different types of power supply and demand in provinces according to the installed power station and power load data of a research area, and constructing a clearing model to simulate daily average spot-shipment electricity price;
step 2, acquiring a power station historical runoff sequence and taking the power station historical runoff sequence and the simulated spot electricity price as input parameters of a subsequent model;
step 3, constructing a double-layer optimized dispatching model, optimizing hydropower contract electric quantity by taking the data in the step 2 as input parameters of the double-layer optimized dispatching model, and acquiring a deterministic dispatching track of the cascade reservoir group;
step 4, determining the form of a scheduling rule on the basis of the optimal running track obtained in the step 3;
step 5, after the form of the scheduling rule is determined, synchronously optimizing the scheduling rule parameters and the contract electric quantity of each month by using a parameter simulation optimization method;
wherein, the determining the form of the scheduling rule in step 4 includes:
firstly, a polymerization rule form is drawn up, namely the stored energy and spot price of a reservoir group are used as independent variables, and the total generated energy is used as a dependent variable;
drawing a decomposition rule form, namely determining the output of a certain reservoir in the reservoir group according to the initial water storage capacity and the time interval inflow rate of the time interval and further determining the output of the other reservoirs when the total generated energy of the reservoir group is less than 650 MW; and when the total generated energy is larger than or equal to 650MW, determining the output of a certain reservoir in the reservoir group according to the total output of the reservoir group and the initial energy storage amount in the time period so as to determine the outputs of the other reservoirs.
2. The synchronous optimization method of the long-term hydropower contract and the hydropower station group dispatching rule according to claim 1, wherein in the step 1, the method for constructing the clearing model to simulate the daily average spot electricity price comprises the following steps:
first, the requirements to establish the different periods are as follows:
in the formula: d i Is the power demand of the i-th period, D est Is a Gaussian-distributed time interval estimation demand, SP i Is the spot price in time period i, SP 0 Is the threshold value of electricity prices, elsa is the elastic coefficient of electricity prices;
residual demand RD i Supplied by fossil power, as follows:
in the formula: e i w 、E i s And E i s Supplying wind energy, light energy and hydroelectric energy in the spot market at the i-time period;
the spot price is determined by matching the intersection of the remaining demand with the supply cost function curve, the equation:
in the formula: e i t (SP i ) For the total thermal power supply, RD is the remaining demand.
3. The synchronous optimization method of the long-term hydropower contract and hydropower station group dispatching rule according to claim 1, wherein the double-layer optimized dispatching model in the step 3 is of a double-layer structure, and the optimal contract electric quantity of each month is searched by adopting an intelligent algorithm on the outer layer; the inner layer takes the contract electric quantity fed back by the outer layer as input, a successive approximation dynamic programming algorithm is adopted to obtain the optimal running track and the maximum benefit of the reservoir group under the contract situation fed back by each initially determined outer layer, and the maximum benefit is fed back to the outer layer.
4. The method for synchronously optimizing the remote hydropower contract and the hydropower station group dispatching rule according to claim 3, wherein the maximum benefit comprises three benefits of power generation income, power generation guarantee rate and contract satisfaction rate.
5. The synchronous optimization method of the remote hydropower contract and hydropower station group dispatching rule according to claim 4, wherein the power generation profit comes from a day-ahead power market and a remote contract market, and a calculation formula of the power generation profit is as follows:
wherein: m represents the total years of deterministic optimization; mon k,i Represents the total number of days of the k month of the i year; Δ t represents the time step (day), N p,j Representing the power generation of p hydropower stations in time period j, SP j Representing the simulated day-ahead electricity price, CV k Contract electricity quantity representing k months; and CP k The contract electricity price represents the contract electricity price signed by the month k, and the contract electricity price is assumed to be the average value of the grid electricity prices of the water buffalo, the river-separating rock and the high dam bar bank unit in the section.
6. The synchronous optimization method of the long-term hydropower contract and the hydropower station group dispatching rule according to claim 1, wherein in the step 5, parameter optimization is performed by taking linear relation parameters of the aggregation rule and the decomposition rule and monthly contract electric quantity as variables to be optimized and taking three items of power generation income, power generation guarantee rate and contract satisfaction rate as targets.
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