CN117314044A - Method for trading medium-and-long-term electric power markets of hydropower enrichment power grid with compatible excitation - Google Patents
Method for trading medium-and-long-term electric power markets of hydropower enrichment power grid with compatible excitation Download PDFInfo
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Abstract
The invention provides a medium-and-long-term electric power market trading method for a hydropower enrichment power grid compatible with excitation, which is characterized by comprising the following steps of: the method is characterized by adopting the purposes of maximum individual power generation benefit and minimum power grid side electricity purchasing cost; firstly, optimizing and dispatching the hydroelectric system by taking the minimum output of the hydroelectric system as the maximum target to obtain the guaranteed electric quantity; then, ensuring that the electric quantity is split into basic electric quantity, intra-provincial electric quantity and east-west electric quantity to participate in corresponding market transaction respectively, and adopting a reinforcement learning algorithm to carry out cyclic iterative solution; and finally, selecting different incoming water frequencies, and analyzing the power supply stability under different forms and different guaranteed electric quantities. The method has guiding significance for the problems of default, insufficient water resource utilization and the like caused by unreasonable competition of the cascade power stations, uncertain natural water supply and mismatching of the winning power quantity, and provides effective advice and direction for medium-long term transaction modes of the hydropower enrichment power grid.
Description
Technical Field
The invention belongs to the field of electric power market trading mechanisms, and particularly relates to a medium-and-long-term electric power market trading method considering incentive compatibility of a hydropower enrichment power grid.
Background
As a first electricity modification test point, the Yunnan electric market is actively explored and practically summarized for the last ten years, a market architecture mainly comprising medium-term and long-term transactions and assisted by short-term transactions is gradually formed, the market main body and the market electricity quantity are rapidly increased year by year, and the Yunnan achieves a leading exemplary effect in the aspect of clean energy electric market reform mainly comprising water and electricity. However, as the market reform continues to go deep, the existing transaction mechanisms encounter many difficulties, prominently manifested as: basically, the step upstream and downstream power stations of different owners are not communicated with each other and are respectively administrative when making a power generation plan and quotation strategy, so that the full utilization of water resources is not facilitated, and the phenomena of water discarding and default caused by mismatching of the standard power quantity and the power generation water quantity of the upstream and downstream power stations often occur; the random water supply causes the water and electricity to participate in market transaction with great risk; the large power station with strong regulation capability has natural advantages in electric power transaction, has larger market force and speaking right, is easy to form monopoly status, and the power station with poor regulation capability can only passively generate power according to natural conditions, thereby being unfavorable for realizing fair competition; the market competition is too strong, which is unfavorable for the main power generation body to withdraw investment, and the confidence of the power generation enterprises in investing new power sources is hit. Therefore, the exploration of a long-acting and practical medium-long-term electric power market trading method is important to guaranteeing the healthy development of the southwest hydropower enrichment electric power market in China.
In the recent development of foreign mature electric markets, only the Brazilian electric market is highly matched with the environment of the Yunnan electric market, and in-depth analysis of the Brazilian mode is of great reference significance for the reformation of the Yunnan electric market, so that domestic scholars discuss Brazilian electric market trading mechanisms in detail and summarize and analyze hydropower-dominant electric market mechanism design suggestions, and the invention tries to introduce the Brazilian mode into the middle and long-term electric market in the south of cloud so as to relieve the current difficulties in the hydropower enrichment electric market. At present, electric market research is relatively more developed aiming at hydropower, but literature for introducing foreign mature electric market mechanisms into domestic hydropower enrichment electric market is relatively rare, and the research is mainly in two aspects: (1) the adaptability, diversity and flexibility of Brazil electric power market mechanism are mainly summarized by biasing to macroscopic level and theoretical analysis, and suggestions such as water and electricity guarantee mechanism are provided by considering the combination of control and market in electric power reform in China. (2) Few documents, such as the construction of trade models in Brazil segments, are used in spot markets.
The above studies have several disadvantages: firstly, based on primary theoretical explanation, an actual model is not constructed to verify and analyze the problem; secondly, core operation mechanisms such as Brazil guaranteed capacity, electric quantity distribution and the like are less considered, 90% of electric quantity in actual hydropower markets in China is not considered to be completed through medium-long term transaction, and only 10% of electric quantity is considered to be completed through spot market transaction.
Disclosure of Invention
Along with the continuous expansion of the hydropower scale in southwest areas of China, hydropower takes the dominant role in medium-long term trading market, and the medium-long term trading method of the hydropower enrichment power grid with compatible excitation is provided by referencing the long term trading mode in Brazil power market. The method has significance for indicating the problems of water abandon, default and the like caused by mismatching of the standard electricity quantity and the generated water quantity in the step upstream and downstream power stations in the hydropower enrichment region of China, and has important reference significance for the reformation of the electric power market mainly comprising southwest hydropower of China.
In order to achieve the technical characteristics, the aim of the invention is realized in the following way: a medium-and-long-term electric power market trading method considering excitation compatibility of a hydropower enrichment power grid is characterized by comprising the following steps of: improving a Brazil electric power market model and adopting the aims of maximum individual power generation benefit and minimum grid side electricity purchasing cost; firstly, optimizing and dispatching the hydropower system by taking the minimum output of the hydropower system as the maximum target to obtain the guaranteed electric quantity of each hydropower station; then dividing the guaranteed electric quantity into basic electric quantity, intra-provincial electric quantity and western electric east-asia electric quantity, respectively participating in corresponding market transaction, and adopting a reinforcement learning algorithm to carry out cyclic iterative solution; and finally, selecting different incoming water frequencies, and analyzing the power supply stability under different forms.
A trade method for medium-long-term electric power market of a hydropower enrichment power grid compatible with excitation comprises the following specific operation steps:
step 1, calibrating a composition structure of the guaranteed electric quantity and repartitioning the guaranteed electric quantity:
taking a cascade hydropower station group as a research object, calibrating the guaranteed electric quantity of each hydropower station, and dividing the guaranteed electric quantity into three parts, namely basic electric quantity, inner-saving electric quantity and east-west electric quantity;
step 2, constructing a multi-level market electric quantity transaction model:
respectively constructing a basic market model, an intra-provincial market model and a western electric eastern market model, ensuring that the basic electric quantity, the intra-provincial electric quantity and the western electric eastern electric quantity in the electric quantity participate in basic market, intra-provincial market and western electric eastern market trading in sequence, and solving the trading electric quantity and the electric price through a reinforcement learning algorithm;
step 3, making a transaction electric quantity redistribution mechanism:
aiming at the problem that the actual power generation amount of the hydropower station is not matched with the bid-winning power amount in market transaction, a power distribution mechanism in a balance state, a power distribution mechanism in a surplus state and a power distribution mechanism in a loss state are respectively manufactured, transaction power is redistributed by using the mechanisms, and successful performance of contracts of members of the river basin cascade is ensured.
Preferably, the step 1 comprises the following detailed steps of:
taking a month as a time scale, taking a year as a period, carrying out optimal scheduling on a hydropower system by utilizing a minimum output maximum model, and defining the power generation capacity of each month of a hydropower station as the guaranteed power quantity of the month; splitting the guaranteed power quantity of the hydropower station into three parts: the power system comprises a basic power quantity, an intra-provincial power quantity and a western electric east power supply power quantity, wherein the basic power quantity is a winning power quantity directly purchased for guaranteeing basic income of each power station, and the power quantity is settled by adopting average electricity prices similar to the frequency of incoming water in the past year; then, each power station takes the electric quantity which does not exceed the residual guaranteed electric quantity as the intra-provincial electric quantity according to the self strategy, the intra-provincial market competition is put into, and the final intra-provincial bid-winning electric quantity of each power station is defined according to the intra-provincial load demand; and finally, deducting the residual electric quantity of the basic electric quantity and the bid-in-province bid-in electric quantity, and putting the total residual electric quantity into the western electric market competition.
Preferably, the step 2 of constructing the multi-level market electric quantity transaction model includes the following detailed steps:
according to the division structure of the guaranteed electric quantity, the three trade modes of the provincial market and the western electric east-delivery market share a basic market:
1) Basic market transaction mode: in the basic market, all power stations X% ensure that the electric quantity is directly purchased by a power grid at a monthly fixed electricity price, wherein the monthly fixed electricity price refers to an actual electricity price corresponding to similar years of historical contemporaneous water supply frequency, and the calculation method of the basic market electricity purchasing cost is shown in a formula (1):
Wherein t represents the current month number; m represents the hydropower station number; m represents the total number of hydropower stations;the electricity purchasing cost for purchasing basic electric quantity in the t month is expressed as follows: a meta-element; />The guaranteed electric quantity of the hydropower station m in the t month is expressed as follows: kWh;the average electricity price of the market at the t month corresponding to the similar year of the historical same-period incoming water frequency is expressed as follows: meta/kWh; x% represents the duty ratio of the basic electric quantity in the guaranteed electric quantity;
2) Intra-provincial market trading mode: after the basic electric quantity is purchased in the basic market, the residual in-provincial load demand is traded in the in-provincial market in an auction mode; the provincial market introduces a mixed two-segment auction combining electricity distribution and price determination, and the specific rules are as follows:
the first stage, "no bid for the measurement": assuming the highest initial price of the first stage, each generator only needs to submit the electric quantity for bidding at the price, if the supply is greater than the demand, the next round of auction is entered, the electric price is automatically reduced proportionally, the generator needs to reduce the bidding electric quantity in the next round of auction until the bidding total electric quantity meets the demand and does not exceed 1+Y% of the load demand, the first stage auction is ended, and the upper limit of the initial electric price of the second stage and the bid amount of each power station are determined at the same time, wherein Y% represents the exceeding part specified by the market;
Second stage, "bid no-reporting": the generator only needs to submit a quotation not higher than the quotation when the previous round is finished, all the generator bids form a supply curve from high to low according to the price, the final clear electricity price and the bid amount of each power station are determined, and the calculation method of the electricity purchasing cost of the intra-provincial market is shown in a formula (2):
wherein t represents the current month number; m represents the hydropower station number; m represents the total number of hydropower stations;the electricity purchasing cost for purchasing electricity in the province in the t month is expressed as follows: a meta-element; />The final electricity price of the market in the t month province is expressed as follows: kWh/yuan; />The intra-provincial load predicted at month t is expressed in units of: kWh; x% represents the duty ratio of the basic electric quantity in the guaranteed electric quantity;the guaranteed electric quantity of the hydropower station m in the t month is expressed as follows: kWh;
3) The western electric market transaction mode: the electricity suppliers are purchased according to the priority order, the remaining amount of the bid-unbiased guaranteed electricity is reserved, the priority order indicates that the electricity suppliers which are more active in the on-line in the provincial market can participate in the western electricity market with higher electricity price preferentially, and the specific method comprises the following steps: firstly, comparing the remaining guaranteed power quantity of the unmarked hydropower station with the self-guaranteed power quantity, and then, all the power stationsSequencing from small to large, priority of sequencing to participate in western electric market transaction, relative residual ratio of power station m ∈ >The calculation method of (2) is shown in the formula (3):
wherein t represents the current month number; m represents the hydropower station number;the relative remaining ratio of the bid-un-marked electric quantity of the power station m in the t month; />The electricity quantity of the hydropower station m is respectively expressed in the unit of: kWh; />The unit of the bid amount of the hydropower station m in the t-th month province in the market is: kWh; x% represents the duty ratio of the basic electric quantity in the guaranteed electric quantity;
the electricity purchase cost in the western electric market is calculated as follows:
wherein t represents the current month number;the electricity purchasing cost of the t month western electricity market is expressed in units of: a meta-element; />Representing predicted t-th lunar western electrical load in units of: kWh;
solving the intra-provincial and western electric market trading part by adopting reinforcement learning, wherein the essence of reinforcement learning is that an agent continuously interacts with an unknown environment to obtain a feedback updated strategy, so that a learning process of an optimal strategy is obtained, and a mathematical model is shown as a formula (5):
wherein n represents a learning round; step represents the strategy selected; q step n ]A factor value representing the reinforcement learning step strategy of the agent at the nth round; q step n+1 ]A factor value representing the reinforcement learning step strategy of the agent at the n+1th round; c (C) now Representing the current benefit; c (C) future Representing future benefits; alpha represents a learning rate; epsilon represents an attenuation factor for the importance of future benefits, and the specific steps are as follows:
Step 2.1: constructing a reinforcement learning environment:
state space: setting the corresponding value of the selected strategy in each round as the rewarding value of the round, otherwise, keeping other values unchanged, and taking the M multiplied by 3 matrix formed by the values as the strategy state of M power generators after the round is finished; action space: the bidding electric quantity is selected, so that modeling is facilitated, bidding strategies of all power generators are simplified into three strategies, namely reporting a small quantity of electric quantity, a medium quantity of electric quantity and a large quantity of electric quantity, and rewarding functions are respectively: current interest C now To market benefit in province, future benefit C future For the benefit of the western electric market, the sum of the two is the total prize of the round, and the Q step is indirectly caused due to the larger bid amount of certain power stations n ]Larger, it is inconvenient to observe the difference of different strategies of each generator, so that the actual electric quantity is replaced by the relative winning electric quantity obtained by dividing the actual winning electric quantity by the guaranteed electric quantity, wherein Q step n ]A factor value representing the reinforcement learning step strategy of the agent at the nth round;
step 2.2: to enable the generator to fully explore the unknown environment to prevent from sinking into local optima, a greedy strategy is adopted to select actions, expressed as:
wherein A represents an action space; p (P) epilson A permissible value representing a search probability range; eta represents a random number and the value range is 0,1 ]The method comprises the steps of carrying out a first treatment on the surface of the random (a) represents a randomly selected action in the action space a; argmax a∈A Q(s) represents the action of the current learning process with the largest return; selecting actions according to a greedy strategy and constructing an experience network; will explore the probability P epsilon The initial value is set to 0, so that the generator fully explores the environment, selects various strategies for game play and study, and gradually increases P according to fixed step length epsilon The value is up to 1, and all generator strategies select the action strategy with the largest return at the moment;
step 2.3: training network: updating Q step based on rewards obtained n ]Corresponding value, wherein Q [ step ] n ]A factor value representing the reinforcement learning step strategy of the agent at the nth round;
step 2.4: judging Q step n ]Whether or not to converge: the circulation is exited after convergence, the free market transaction is ended, and the total cost of electricity purchasing is obtained; otherwise, increasing the learning times and returning to step 2.2, wherein Q step n ]The factor value representing the agent in the n-th round of reinforcement learning step strategy.
Preferably, the detailed steps of the transaction electricity redistribution mechanism in the step 3 are as follows:
according to the situation that the actual generated energy is equal to, greater than and smaller than the guaranteed electric quantity, three different distribution modes of balance, surplus and deficiency are set, and positive improvement strategies are provided for an electric quantity distribution mechanism in a deficiency state, and are respectively introduced as follows:
1) Distribution of electric quantity in equilibrium state:
the balance state indicates that the actual generated electricity of the alliance is exactly equal to the sum of the guaranteed electricity of all power stations in the alliance, at the moment, only internal adjustment is needed between the watercourses, the hydropower stations with excess electricity are sold to hydropower stations with the excess electricity to reach balance at a lower price, and at the moment, the actual electricity of all hydropower stations is exactly equal to the guaranteed electricity of all hydropower stations;
2) Distribution of electric quantity in surplus state:
the surplus state is used for indicating that the total electricity generation quantity of the alliance is larger than the sum of the electricity quantity of the alliance guarantee; after the internal adjustment of the alliance mentioned in the last step is completed, surplus electric quantity still exists for part of hydropower stations, and the surplus electric quantity is not distributed in a uniform distribution mode any more and is sold as private electric quantity to a watershed with an absence or participates in a subsequent spot market;
3) Distribution of electric quantity in loss state:
the loss state indicates that the sum of the actual generated energy of the alliance is smaller than the sum of the guaranteed electric energy of the alliance power stations, namely the loss is considered, and according to the market rule, all hydropower stations in the basin purchase electric quantity filling deficiency to the basin in the surplus state so as to guarantee normal performance of contracts, and the concrete operation is as follows:
step 3.1: the guaranteed electric quantity of the hydropower station m is recalculated, the actual electric energy generation amounts of all hydropower stations in the alliance are collected together, and the new guaranteed electric quantity of the hydropower station m is obtained by distributing the actual electric energy according to the proportion of the original guaranteed electric quantity of each hydropower station m:
Wherein t represents the current month number; m represents the hydropower station number; m represents the total number of hydropower stations;the guaranteed electric quantity of the hydropower station m in the t month is expressed as follows: kWh; />The unit of the guaranteed electric quantity for new verification in the t month of the hydropower station m is as follows: kWh; />The actual power generation capacity of the hydropower station m in the t month is expressed as follows: kWh;
step 3.2: calculating the difference value between the original guaranteed electric quantity and the new guaranteed electric quantity of the hydropower station m;
wherein t represents the current month orderA number; m represents the hydropower station number;the guaranteed electric quantity of the hydropower station m in the t month is expressed as follows: kWh; />The unit of the newly-approved guaranteed electric quantity for the t month of the hydropower station m is as follows: kWh; />The difference value between the original guaranteed electric quantity and the new guaranteed electric quantity of the hydropower station m in the t month is represented by the following units: kWh;
step 3.3: calculating the difference value between the new guaranteed electric quantity and the actual generated energy of the hydropower station m, and if the hydropower station itself finishes the self-guaranteed electric quantity, reducing punishment specific gravity, wherein the difference value is 0;
wherein t represents the current month number; m represents the hydropower station number;the difference value between the newly guaranteed electricity quantity and the actual electricity generation quantity of the hydropower station m in the t month is expressed as follows: kWh; />The unit of the guaranteed electric quantity for new verification in the t month of the hydropower station m is as follows: kWh; />The actual power generation capacity of the hydropower station m in the t month is expressed as follows: kWh;
Step 3.4: and calculating the shortage electric quantity needed to be born by the hydropower station m:
wherein t represents the current month number; m represents the hydropower station number;the relative electricity shortage required to be born in the t month of the hydropower station m is represented as follows: kWh; />The difference value between the new guaranteed electric quantity and the actual electric energy of the hydropower station m in the t month is expressed as follows: kWh; />The difference value between the original guaranteed electric quantity and the new guaranteed electric quantity of the hydropower station m in the t month is represented by the following units: kWh;
step 3.5: the actual electricity shortage amount needed to be born by the hydropower station m is distributed:
wherein t represents the current month number; m represents the hydropower station number; m represents the total number of hydropower stations;the actual electricity shortage amount which the hydropower station m needs to bear in the t month is shown as follows: kWh; />The guaranteed electric quantity of the hydropower station m in the t month is expressed as follows: kWh; />The actual power generation capacity of the hydropower station m in the t month is expressed as follows: kWh; />The relative electricity shortage required to be born in the t month of the hydropower station m is represented as follows: kWh.
The invention has the following beneficial effects:
1. the invention independently promotes market competition while improving the benefits of the generator, effectively reduces the electricity clearing price in the province, and realizes the excitation compatibility of the overall benefit and the individual benefit.
2. The method can resist the risk of water abandoning and default caused by mismatching of the power bid amount and the actual power generation amount in the river basin cascade hydroelectric power when the coming water is withered to a certain extent, and further ensures the power supply stability of the whole system.
3. The invention effectively relieves the risk of water abandon and default caused by mismatching of the standard electricity quantity and the generated water quantity in the current step upstream and downstream power stations, the electricity quantity redistribution mechanism can also effectively overcome the transaction risk caused by random water supply, and the large power station with strong regulation capability has the problem of natural monopoly status on the electric power transaction, thereby having important reference significance for the reformation of the electric power market mainly in southwest of China.
Drawings
The invention is further described below with reference to the drawings and examples.
Fig. 1 is a diagram of a guaranteed power architecture.
FIG. 2 is a drawing of a two-round auction mechanism within a province.
FIG. 3 is a flow chart of the overall transaction of the present invention.
Fig. 4 is a schematic diagram of the power redistribution mechanism in various states.
Fig. 5 is a drainage basin topology.
Fig. 6 is a graph of the present invention's in-circuit power rates and actual power rates at different X%.
FIG. 7 is a graph of various types of electricity purchasing costs of the present invention at different X%.
Fig. 8 is a plot of the market invention revenue and actual revenue for the generator (X% = 0.3).
Fig. 9 shows the ratio of the shortage of electricity in each basin at different frequencies.
Fig. 10 shows the overall power shortage ratio at different frequencies.
FIG. 11 shows the overall water rejection of the hydropower station at different water input frequencies.
Detailed Description
The invention is further described below with reference to the drawings and examples.
As a first electricity modification test point, the Yunnan electric market is actively explored and practically summarized for the last ten years, a market architecture mainly comprising medium-term and long-term transactions and assisted by short-term transactions is gradually formed, the market main body and the market electricity quantity are rapidly increased year by year, and the Yunnan achieves a leading exemplary effect in the aspect of clean energy electric market reform mainly comprising water and electricity. However, as the market reform continues to go deep, the existing transaction mechanisms encounter many difficulties, prominently manifested as: basically, the step upstream and downstream power stations of different owners are not communicated with each other and are respectively administrative when making a power generation plan and quotation strategy, so that the full utilization of water resources is not facilitated, and the phenomena of water discarding and default caused by mismatching of the standard power quantity and the power generation water quantity of the upstream and downstream power stations often occur; the random water supply causes the water and electricity to participate in market transaction with great risk; the large power station with strong regulation capability has natural advantages in electric power transaction, has larger market force and speaking right, is easy to form monopoly status, and the power station with poor regulation capability can only passively generate power according to natural conditions, thereby being unfavorable for realizing fair competition; the market competition is too strong, which is unfavorable for the main power generation body to withdraw investment, and the confidence of the power generation enterprises in investing new power sources is hit. Therefore, the exploration of a long-acting and practical medium-long-term electric power market trading method is important to guaranteeing the healthy development of the southwest hydropower enrichment electric power market in China.
The technical problem to be solved by the invention is to provide a long-term electric power market trading method in a hydropower enrichment power grid with compatible excitation, which adopts the aim of maximum individual power generation benefit and minimum grid side electricity purchasing cost, and firstly, optimizes and schedules the hydropower system with the aim of maximum minimum hydropower system output to obtain guaranteed electric quantity; then, ensuring that the electric quantity is split into basic electric quantity, intra-provincial electric quantity and east-west electric quantity to participate in corresponding market transaction respectively, and adopting a reinforcement learning algorithm to carry out cyclic iterative solution; finally, different incoming water frequencies are selected, different forms are analyzed, and the power supply stability under different guaranteed electric quantity is realized according to the following steps 1-3:
step 1, calibrating a composition structure of the guaranteed electric quantity and repartitioning the guaranteed electric quantity: taking a cascade hydropower station group of the Yunnan electric network jurisdiction of China as a research object, calibrating the guaranteed electric quantity of each hydropower station, and dividing the guaranteed electric quantity into three parts, namely basic electric quantity, inner-provincial electric quantity and east-west electric quantity;
step 2, constructing a multi-level market electric quantity transaction model: respectively constructing a basic market model, an intra-provincial market model and a western electric eastern market model, ensuring that the basic electric quantity, the intra-provincial electric quantity and the western electric eastern electric quantity in the electric quantity participate in basic market, intra-provincial market and western electric eastern market trading in sequence, and solving the trading electric quantity and the electric price through a reinforcement learning algorithm;
Step 3, making a transaction electric quantity redistribution mechanism: aiming at the problem that the actual generated energy of the hydropower station is not matched with the bid-winning electric quantity in market transaction, an electric quantity distribution mechanism in a balance state, an electric quantity distribution mechanism in a surplus state and an electric quantity distribution mechanism in a loss state are respectively manufactured, transaction electric quantity is redistributed by utilizing the mechanisms, and successful performance of contracts of members of the river basin cascade is ensured;
therefore, the invention provides a long-term electric power market trading method in a hydropower enrichment power grid with compatible excitation, which aims to promote full competition in the market while pursuing the maximum individual benefit by a generator and further reduce the power grid electricity purchasing cost so as to realize the compatible excitation, therefore, the objective functions of the generator and the power grid participating in the competition in the market are as follows:
1) Power generator layer: the individual power generation benefit is the largest:
wherein t represents the current month number; m represents the hydropower station number; t represents the total number of calculated segments; j (J) m Representing the total income of the power station m, wherein the unit is: a meta-element;the basic income of the power station m in the t month is expressed as follows: a meta-element; />The generating income of the power station m participating in the provincial market in the t month is expressed as follows: a meta-element; />The generating income of the western electric east delivery market of the power station m month t is expressed by the following units: a meta-element; / >And the electricity redistribution income of the power station m in t month is expressed as follows: if the actual power generation capacity of the power station is smaller than the guaranteed power, the benefit is a negative number, otherwise, the benefit is a positive number;
2) Grid level: the total electricity purchasing cost is the smallest:
wherein t represents the current month number; t represents the total number of calculated segments; c represents the total electricity purchasing cost of the power grid, and the unit is: a meta-element;the electricity purchasing cost of the basic electric quantity of the power grid at the t month is represented by the following units: a meta-element; />The electricity purchasing cost for representing the electricity quantity in the power grid is as follows: a meta-element; />The electricity purchasing cost for representing the electricity transmission quantity of the western electric east of the power grid in t month is as follows: a meta-element; the following constraints need to be met:
(1) Load supply and demand balance constraint:
wherein t represents the current month number; m represents the hydropower station number; m represents the total number of hydropower stations;the output of the hydropower station m at the t month is expressed as follows: kWh; />The total output of other types of power supplies (thermal power, wind power, photovoltaic power and hydropower stations which do not participate in optimization calculation) in the t month is expressed as follows: kWh; />The unit of the predicted t month provincial internal load demand is: kWh; />The unit for predicting the load demand of western electric east delivery in the t month is as follows: kWh.
(2) And (3) constraint of total power in a power station:
wherein t represents the current month number; m represents the hydropower station number; The guaranteed electric quantity of the hydropower station m in the t month is shown as follows: kWh; />The actual total winning bid amount of the hydropower station m in the t month is shown as follows: kWh; />The unit of the marked electricity quantity of the hydropower station m in the basic electricity quantity of the t month is: kWh, ->The unit of the marked electricity quantity of the hydropower station m in the provincial market of the t month is: kWh, ->The marked electricity quantity of the hydropower station m in the western electric market of the t month is shown as follows: the kWh constraint is used for guaranteeing the power supply safety, so that the standard electric quantity in the power station is limited within the guaranteed electric quantity, and the fact that excessive electric quantity is fictionally reported by the power station but is difficult to fulfill a contract is prevented.
(3) The basic electric quantity duty ratio constraint:
wherein t represents the current month numberThe method comprises the steps of carrying out a first treatment on the surface of the m represents the hydropower station number; m represents the total number of hydropower stations;the guaranteed electric quantity of the hydropower station m in the t month is shown as follows: kWh; />The unit of the load in the province and the load demand of the eastern business predicted in the t month is: kWh; x% represents the duty cycle of the base charge in the guaranteed charge, which constraint is to ensure that the basic market acquired charge does not exceed the total load demand in the province.
(4) Upper and lower limit constraints for intra-provincial market quotes:
wherein t represents the current month number;the final clear electricity price in the province of the t month is shown as follows: meta/kWh; The upper limit of the intra-provincial quotation for month t is expressed in units of: meta/kWh; />Lower limit of bid in province expressed in the month t is: meta/kWh.
Further, the step 1 comprises the following detailed steps of:
as shown in fig. 1, because the overall regulation capability of the Yunnan hydropower station is weak and the transaction mode taking a month as a period in the current Yunnan electric power market is active, the invention takes the month as a time scale and takes the year as a period, and utilizes the minimum output maximum mode to optimally regulate the Yunnan hydropower system, and the monthly power generation capacity of the hydropower station is defined as the guaranteed power quantity of the month. Splitting the guaranteed power quantity of the hydropower station into three parts: basic electric quantity, provincial electric quantity and western electric east power transmission electric quantity, wherein the basic electric quantity isThe winning electricity quantity directly purchased by each power station obtaining basic income is ensured, and the electricity quantity is settled by adopting average electricity prices with similar incoming water frequency in the past year; then, each power station takes the electric quantity which does not exceed (1-X%) of the guaranteed electric quantity as the intra-provincial electric quantity, and inputs the intra-provincial market competition, and the final intra-provincial bid-winning electric quantity of each power station is defined according to the intra-provincial load demand; finally, deducting the basic electric quantity and the winning electric quantity (1-X) -A m The percentage) of the guaranteed electric quantity is fully put into the western electric market competition, wherein X% is the proportion of the basic electric quantity to the guaranteed electric quantity; a is that m % is the proportion of the winning power in the province to the guaranteed power.
Further, the detailed steps of constructing the multi-level market electric quantity transaction model in the step 2 are as follows:
three trade modes of trade market are shared according to the division structure of the guaranteed electric quantity:
1) Basic market transaction mode: in the basic market, all power stations X% ensure that the electric quantity is directly purchased by a power grid at a monthly fixed electricity price, wherein the monthly fixed electricity price refers to an actual electricity price corresponding to a similar year of a historical contemporaneous water supply frequency, and the corresponding basic market electricity purchasing cost is shown in a formula (1):
wherein t represents the current month; m represents the hydropower station number; m represents the total number of hydropower stations;the electricity purchase cost representing the basic electricity purchased at the t month is given in units of: a meta-element; />The electricity quantity of the hydropower station m at the t month is respectively shown, and the unit is: kWh; />The average electricity price of the market at the t month corresponding to the similar year of the historical same-period incoming water frequency is expressed as follows: meta/kWh; x% represents the occupation of the basic electric quantity in the guaranteed electric quantityRatio.
2) Intra-provincial market trading mode: after the electricity quantity of the basic market is purchased, the residual in-provincial load demand is traded in the in-provincial market in an auction mode. The provincial market introduces a hybrid two-segment auction combining "electricity distribution" and "price determination", the specific rules are shown in fig. 2:
In the first stage, "no bid for the amount". Assuming the initial price at which the first stage is highest, each generator only needs to submit the amount of electricity bid at that price. If the supply and demand are greater than the supply and demand to enter the next round of auction, the price of electricity is automatically reduced proportionally, the generator needs to reduce the bidding electric quantity in the next round of auction until the nth round of bidding total electric quantity meets the demand and does not exceed (1+Y%) times the load demand, the auction of the first stage is ended, and the upper limit price of the initial price of electricity of the second stage is determined nst And the amount of winning in each power station, E1, E2, E3, E4 in fig. 2 represent the amount of winning in the first stage of the power stations 1,2,3,4, respectively, and Y% represents a prescribed proportion of exceeding.
The second stage, "bid no report". The generator only needs to submit the final quotation which is not higher than the last round. All the generator bids form a supply curve from high to low according to the price, and the final electricity clearing price and the bid amount of each power station are determined. The electricity purchasing cost of the provincial market is shown as a formula (2):
wherein t represents the current month; m represents the hydropower station number; m represents the total number of hydropower stations;the electricity purchase cost for purchasing the electricity in the province in the t month is expressed as follows: a meta-element; / >The final electricity price of the market in the t month province is expressed as follows: kWh/yuan; />The intra-provincial load predicted at month t is expressed in units of: kWh; x% represents the duty ratio of the basic electric quantity in the guaranteed electric quantity;the electricity quantity of the hydropower station m at the t month is respectively shown, and the unit is: kWh.
3) The western electric market transaction mode: the electricity suppliers are purchased according to the priority order, the remaining amount of the bid-unbiased guaranteed electricity is reserved, the priority order indicates that the electricity suppliers which are more active in the on-line in the provincial market can participate in the western electricity market with higher electricity price preferentially, and the specific thinking is as follows: firstly, comparing the remaining guaranteed power quantity of the unmarked hydropower station with the self-guaranteed power quantity, and then, all the power stationsSequencing from small to large, priority of sequencing to participate in western electric market transaction, relative residual ratio of power station m ∈>The calculation method of (2) is as shown in the formula (3).
Wherein t represents the current month number; m represents the hydropower station number;the relative remaining ratio of the bid-un-marked electric quantity of the power station m in the t month; />The electricity quantity of the hydropower station m is respectively expressed in the unit of: kWh; />The unit of the bid amount of the hydropower station m in the t-th month province in the market is: kWh; x% represents the duty ratio of the base charge amount in the guaranteed charge amount.
The electricity purchase cost in the western electric market is calculated as follows:
Wherein t represents the current month number;the electricity purchasing cost of the t month western electricity market is expressed in units of: a meta-element; />Representing predicted t-th lunar western electrical load in units of: kWh.
The invention adopts reinforcement learning to solve the trade parts of provincial and western electric markets. The essence of reinforcement learning is that an agent continuously interacts with an unknown environment to obtain feedback and update a strategy, so that the learning process of an optimal strategy is obtained. The mathematical model is shown in formula (5):
Q[step n+1 ]＝(1-α)Q[step n ]+α(C now +εC future ) (5)
wherein n represents a learning round; step represents the strategy selected; q step n ]A factor value representing the reinforcement learning step strategy of the agent at the nth round; q step n+1 ]A factor value representing the reinforcement learning step strategy of the agent at the n+1th round; c (C) now Representing the current benefit; c (C) future Representing future benefits; alpha represents a learning rate; epsilon represents the attenuation factor that pays attention to future benefits. The specific steps of the invention are as follows:
step 2.1: and constructing a reinforcement learning environment.
State space: setting the corresponding value of the selected strategy in each round as the rewarding value of the round, otherwise, keeping other values unchanged, and taking the M multiplied by 3 matrix formed by the values as the strategy states of M power generators after the round is finished. Action space: the bid amount is selected. In order to facilitate modeling, bidding strategies of each generator are simplified into three strategies, namely reporting a small amount of electricity, a medium amount of electricity and a large amount of electricity. Bonus function: current interest C now To market benefit in province, future benefit C future For revenue in the western electric market, the sum of the two is the overall prize for this round. Because of larger bid-winning power in some power stationsQ step is caused by n ]Larger, it is inconvenient to observe the difference of different strategies of each generator, so that the actual electric quantity is replaced by the relative winning electric quantity obtained by dividing the actual winning electric quantity by the guaranteed electric quantity, wherein Q step n ]The factor value representing the agent in the n-th round of reinforcement learning step strategy.
Step 2.2: in order for a generator to fully explore the unknown environment to prevent from sinking into local optimum, the invention adopts a greedy strategy to select actions, which can be expressed as:
wherein A represents an action space; p (P) epilson A permissible value representing a search probability range; eta represents a random number and the value range is 0,1]The method comprises the steps of carrying out a first treatment on the surface of the random (a) represents a randomly selected action in the action space a; argmax a∈A Q(s) represents the action of the current learning process in which the return is the greatest. An action is selected and an empirical network is constructed according to a greedy policy. The invention will explore probability P epsilon The initial value is set to 0, so that the generator fully explores the environment, selects various strategies for game play and study, and gradually increases P according to fixed step length epsilon And the value is up to 1, and all the generator strategies select the action strategy with the largest return.
Step 2.3: the network is trained. Updating Q step based on rewards obtained n ]Corresponding value, wherein Q [ step ] n ]The factor value representing the agent in the n-th round of reinforcement learning step strategy.
Step 2.4: judging Q step n ]Whether or not to converge. The circulation is exited after convergence, the free market transaction is ended, and the total cost of electricity purchasing is obtained; otherwise, increasing the learning times and returning to step 2.2, wherein Q step n ]The factor value representing the agent in the n-th round of reinforcement learning step strategy.
Further, the detailed steps of the transaction electric quantity redistribution mechanism in the step 3 are as follows:
according to the situation that the actual generated energy is equal to, greater than and smaller than the guaranteed electric quantity, three different distribution modes of balance, surplus and deficit are set, and positive improvement strategies are provided for an electric quantity distribution mechanism in a deficit state, and are described below.
1) Distribution of electric quantity in equilibrium state:
as shown in fig. 4 (a), the equilibrium state indicates that the total amount of electricity generated by the coalition is exactly equal to the sum of the guaranteed amounts of electricity for all the power stations in the coalition. Only internal adjustment is needed at this time, and the hydropower station with excess electric quantity is sold to the hydropower station which does not reach the guaranteed electric quantity at a lower price so as to reach balance, and the actual electric quantity of all hydropower stations is exactly equal to the guaranteed electric quantity.
2) Distribution of electric quantity in surplus state:
as shown in fig. 4 (b), the surplus state is a state indicating that the total amount of electricity generated by the federation is greater than the sum of the amount of electricity guaranteed by the federation. After the internal adjustment mentioned in the previous step is completed, surplus electricity still exists for part of hydropower stations, and the surplus electricity is not distributed in a uniform manner, but is sold as private electricity to a watershed with an absence or participates in the subsequent spot market.
3) Distribution of electric quantity in loss state:
as shown in fig. 4 (c), the loss state indicates that the sum of the actual power generation amounts of the alliance is smaller than the sum of the guaranteed power amounts of the alliance power stations, namely, the loss is regarded as the loss, and according to the market rule, all the hydropower stations in the watershed purchase electric quantity to fill the deficiency to ensure normal performance of the contract, and the specific operation is as follows:
the loss state indicates that the sum of the actual generated energy of the alliance is smaller than the sum of the guaranteed electric energy of the alliance power stations, namely the loss is considered, and according to the market rule, all hydropower stations in the basin purchase electric quantity filling deficiency to the basin in the surplus state so as to guarantee normal performance of contracts, and the concrete operation is as follows:
step 3.1: and recalculating the guaranteed electric quantity of the hydropower station m. And (3) collecting the actual power generation amounts of all the hydropower stations in the alliance together, and distributing the actual power generation amounts according to the proportion of the original guaranteed power to obtain the new guaranteed power of the hydropower station m.
Wherein t represents the current month number; m represents the hydropower station number; m represents the total number of hydropower stations;the guaranteed electric quantity of the hydropower station m in the t month is expressed as follows: kWh; />The unit of the guaranteed electric quantity for new verification in the t month of the hydropower station m is as follows: kWh; />The actual power generation capacity of the hydropower station m in the t month is expressed as follows: kWh.
Step 3.2: and calculating the difference value between the original guaranteed electric quantity and the new guaranteed electric quantity of the hydropower station m.
Wherein t represents the current month number; m represents the hydropower station number;the guaranteed electric quantity of the hydropower station m in the t month is expressed as follows: kWh; />The unit of the newly-approved guaranteed electric quantity for the t month of the hydropower station m is as follows: kWh; />The difference value between the original guaranteed electric quantity and the new guaranteed electric quantity of the hydropower station m in the t month is represented by the following units: kWh.
Step 3.3: and calculating the difference value between the newly ensured electric quantity and the actual electric quantity of the hydropower station m. If the power station itself finishes self-guaranteeing electric quantity, the punishment proportion is reduced, and the difference value is 0.
In the method, in the process of the invention,t represents the current month number; m represents the hydropower station number;the difference value between the new guaranteed electric quantity and the actual electric energy of the hydropower station m in the t month is expressed as follows: kWh; />The unit of the newly-approved guaranteed electric quantity for the t month of the hydropower station m is as follows: kWh; / >The actual power generation capacity of the hydropower station m in the t month is expressed as follows: kWh.
Step 3.4: and calculating the shortage electric quantity which needs to be born by the hydropower station m.
Wherein t represents the current month number; m represents the hydropower station number;the relative electricity shortage required to be born in the t month of the hydropower station m is represented as follows: kWh->The difference value between the new guaranteed electric quantity and the actual electric energy of the hydropower station m in the t month is expressed as follows: kWh; />The difference value between the original guaranteed electric quantity and the new guaranteed electric quantity of the hydropower station m in the t month is represented by the following units: kWh.
Step 3.5: the actual shortage of electricity that the hydropower station m needs to bear is allocated.
Wherein t represents the current month number; m represents the hydropower station number; m represents the total number of hydropower stations;the actual electricity shortage amount required to be born by the hydropower station m in the t month is shown as follows: kWh; />The guaranteed electric quantity of the hydropower station m in the t month is shown as follows: kWh; />The real electricity generation amount of the hydropower station m at the t month is expressed as follows: kWh; />The relative shortage electric quantity which the hydropower station m needs to bear in the t month is represented as follows: kWh.
The overall transaction flow is finally shown in fig. 3.
Example 2:
the 5-river basin 52-step hydropower station group in Yunnan province is taken as a research object (accounting for about 80 percent of the installed capacity of the whole-grid water regulating electric installation), the topological structure of the river basin is shown in figure 5, wherein the installed capacities of the water electric installation respectively account for 21 percent, 29 percent and 50 percent in annual regulation, quaternary regulation and daily regulation, and the overall water electric installation regulation capacity is weaker. According to the frequency of the water supply, taking the actual electricity prices of similar years of the average market electricity prices of the Yunnan power grid month in 2017-2022 as the acquisition electricity prices of the basic market; the upper limit of the auction electricity price of the first stage of the intra-provincial market refers to the highest water and electricity price of 0.313 yuan/kWh specified by Yunnan provincial, and the lower limit of the electricity price refers to the lowest practical value of 0.1 yuan/kWh in recent years; three bidding strategies of the generator in the provincial market are respectively set as a small reporting amount (30%), a medium reporting amount (60%) and a large reporting amount (90%) of residual guaranteed electric quantity; the electricity price and the electricity generator report amount of each round of auction in the first stage of the provincial market are reduced by 0.9 times of the previous round; exploring probability value P in reinforcement learning epsilon Taking 0.8.
The power supply stability under the different forms and the incoming water frequency and the economic benefit of the market of the generator are respectively analyzed in detail as follows:
first, the economic benefit analysis of the market of the generator: in order to further verify whether the transaction method of the invention accords with the excitation compatibility principle, the economic benefit situation is analyzed from two different levels of a power grid and a generator respectively, and because of uncertain parameters of a Gaussian item in the method, the operation result of each time has a difference within a certain range, the converged electricity discharge price takes the average value of the last groups as the electricity discharge price of an intra-provincial market, fig. 6 is the range of different basic electric quantity occupation ratios X% (the comparison process of the intra-provincial electricity discharge price, fig. 7 is the relationship curve of the basic market, the intra-provincial market and the total electricity purchase cost under the different basic electric quantity occupation ratios X%, and fig. 8 is the graph of the current gain and the actual gain of the generator market when the electricity purchase cost is optimal (X% =0.3), and the result shows that 1) the power grid level (the intra-provincial electricity purchase cost). The invention can effectively reduce the electricity price of the intra-provincial market, and the more the basic electric quantity proportion X% is, the smaller the intra-provincial electricity clearing price is. As shown in fig. 6, when the basic electricity ratio X% gradually increases from 0 to 0.4, the annual electricity price shows an overall decreasing trend, and when X% =0, 0.1, 0.2, 0.3, and 0.4, the average decreasing amplitude of the other electricity price curves is 6.8%, 16.7%, 17.9%, 20.2%, and 25.7% respectively, compared with the red-line electricity price curves, mainly because as X% increases, the direct purchase electricity quantity of the basic market increases, the in-province load participating in the market auction and the guaranteed electricity quantity of the generator correspondingly decrease, but because the load demand participating in the in-province market decreases more than the guaranteed electricity quantity reduced by the generator, the competition is more intense, and the price of the in-province market is further reduced is promoted. (2) As the basic electric quantity proportion X% increases, the total electricity purchasing cost tends to decrease first and then increase. As shown in fig. 7, when X% = 0.3 is done, the total electricity purchase cost reaches a minimum of 51.7 yuan, and the main reason is that the lower the in-province electricity clearing price is not meant to be the smaller the total electricity purchase cost, the more electricity is purchased in the province when X% is larger, and the larger the basic market electricity purchase cost is. 2) Power producer level (overall power generation efficiency). As can be seen from fig. 6, the electricity prices under different basic electricity ratios are generally lower than the actual electricity prices, which indicates that the total in-province profits of the generator are reduced compared with the actual profits, and fig. 7 also verifies that the total in-province profits of all the generators are 12 months when X% = 0.3, wherein the total in-province profits, the western electricity profits and the total profits are 51.70 yuan, 102.80 yuan and 154.33 yuan respectively, and the actual in-province profits of all the generators are 12 months on the left side of the coordinate axis, wherein the total in-province profits, the western electricity profits and the total in-province profits of all the generators are 61.90 yuan, 84.65 yuan and 146.55 yuan respectively, and it can be seen that the total in-province of the generator is 10.2 yuan less than the actual in-province profits, but the total in-province profits of the generator are 18.4 yuan higher than the actual in the invention, so that the total in-province profits of the generator is still increased by 8.0 yuan, which helps to further excite the generator to participate in market entums, thereby achieving the purpose of fully utilizing water resources.
Then, power stability analysis at different forms and incoming water frequencies:
the power supply stability and the water discard reduction condition of the electric quantity redistribution mechanism under different water supply frequencies are analyzed, wherein the guaranteed electric quantity is obtained by optimizing and dispatching a Yunnan hydropower system by utilizing a minimum output maximum model according to 2018 actual water supply (taking the electric quantity generated by each hydropower station in each month as the guaranteed electric quantity), the power supply stability is represented by the ratio of the shortage electric quantity, and the ratio calculation formula is as follows:
wherein: t represents the current month number; m represents the hydropower station number; m represents the total number of hydropower stations;indicating the ratio of the shortage electric quantity in the t month; />The actual power generation capacity of the power station m in the t month is expressed as follows: kWh; />The guaranteed electric quantity of the power station m in the t month is expressed as follows: kWh.
First, the hydropower station alliance (small alliance) of each river basin is analyzed, and the electricity shortage proportion conditions of different river basins are analyzed. As shown in fig. 9, through the internal adjustment of the river basin, the electric quantity redistribution mechanism can resist a certain uncertainty of the incoming water, and the better the adjustment capability, the more can resist the risk of the deviation of the incoming water. When the incoming water frequency is 10% -30%, the five watercourses can finish electric quantity adjustment through the inside of the watercourses, and the shortage electric quantity is not required to be purchased from other watercourses; when the water comes out gradually (more than 30%), the other watershed has the shortage electric quantity at different degrees except for the watershed C with fewer power stations and the annual regulation capacity and the watershed E with a plurality of annual regulation power stations; when the incoming water frequency reaches 60% or above, the shortage of electricity exists in all the watercourses each month due to the sharp reduction of the incoming water and the limited adjustment capability of the power station.
And secondly, analyzing the ratio of the whole electricity shortage quantity of the hydroelectric system when all hydropower stations in the whole river basin are in alliance (big alliance). Fig. 10 shows the overall electricity shortage condition of the hydropower system in the large league, and it can be seen that the overall electricity shortage occurs when the incoming water frequency reaches 50%, and the water shortage occurs in the watershed when the incoming water frequency reaches 40% in fig. 9, which indicates that the electric quantity redistribution mechanism is performed again between the five watersheds to resist the more withered incoming water. And analyzing the water-abandoning reduction condition, taking a 6 month water-enlarging period as an example, and taking fig. 11 as an overall water-abandoning condition of hydropower stations under different water-supply frequencies as a example, it can be seen that under the electric quantity redistribution mechanism, the power station with strong regulation capability is penalized for avoiding the occurrence of the shortage of other power stations in the drainage basin, the water resource is fully utilized to actively generate electricity to supplement the shortage electric quantity of the drainage basin, and the water-abandoning is obviously reduced while the electric quantity shortage rate is reduced.
Claims (5)
1. A medium-and-long-term electric power market trading method considering excitation compatibility of a hydropower enrichment power grid is characterized by comprising the following steps of: improving a Brazil electric power market model and adopting the aims of maximum individual power generation benefit and minimum grid side electricity purchasing cost; firstly, optimizing and dispatching the hydropower system by taking the minimum output of the hydropower system as the maximum target to obtain the guaranteed electric quantity of each hydropower station; then dividing the guaranteed electric quantity into basic electric quantity, intra-provincial electric quantity and western electric east-asia electric quantity, respectively participating in corresponding market transaction, and adopting a reinforcement learning algorithm to carry out cyclic iterative solution; and finally, selecting different incoming water frequencies, and analyzing the power supply stability under different forms.
2. The method for trading the medium-long-term electric power market in the hydropower enrichment power grid compatible with excitation according to claim 1, which is characterized by comprising the following specific operation steps:
step 1, calibrating a composition structure of the guaranteed electric quantity and repartitioning the guaranteed electric quantity:
taking a cascade hydropower station group as a research object, calibrating the guaranteed electric quantity of each hydropower station, and dividing the guaranteed electric quantity into three parts, namely basic electric quantity, inner-saving electric quantity and east-west electric quantity;
step 2, constructing a multi-level market electric quantity transaction model:
respectively constructing a basic market model, an intra-provincial market model and a western electric eastern market model, ensuring that the basic electric quantity, the intra-provincial electric quantity and the western electric eastern electric quantity in the electric quantity participate in basic market, intra-provincial market and western electric eastern market trading in sequence, and solving the trading electric quantity and the electric price through a reinforcement learning algorithm;
step 3, making a transaction electric quantity redistribution mechanism:
aiming at the problem that the actual power generation amount of the hydropower station is not matched with the bid-winning power amount in market transaction, a power distribution mechanism in a balance state, a power distribution mechanism in a surplus state and a power distribution mechanism in a loss state are respectively manufactured, transaction power is redistributed by using the mechanisms, and successful performance of contracts of members of the river basin cascade is ensured.
3. The method for trading the medium-term and long-term electric power market in the hydropower enrichment power grid compatible with excitation according to claim 2, wherein the detailed steps of calibrating the guaranteed electric quantity and repartitioning the composition structure of the guaranteed electric quantity in the step 1 are as follows:
taking a month as a time scale, taking a year as a period, carrying out optimal scheduling on a hydropower system by utilizing a minimum output maximum model, and defining the power generation capacity of each month of a hydropower station as the guaranteed power quantity of the month; splitting the guaranteed power quantity of the hydropower station into three parts: the power system comprises a basic power quantity, an intra-provincial power quantity and a western electric east power supply power quantity, wherein the basic power quantity is a winning power quantity directly purchased for guaranteeing basic income of each power station, and the power quantity is settled by adopting average electricity prices similar to the frequency of incoming water in the past year; then, each power station takes the electric quantity which does not exceed the residual guaranteed electric quantity as the intra-provincial electric quantity according to the self strategy, the intra-provincial market competition is put into, and the final intra-provincial bid-winning electric quantity of each power station is defined according to the intra-provincial load demand; and finally, deducting the residual electric quantity of the basic electric quantity and the bid-in-province bid-in electric quantity, and putting the total residual electric quantity into the western electric market competition.
4. The method for trading the medium-term and long-term electric power markets of the hydropower enrichment power grid compatible with excitation according to claim 2, wherein the detailed steps of constructing the multi-level market electric power trading model in the step 2 are as follows:
According to the division structure of the guaranteed electric quantity, the three trade modes of the provincial market and the western electric east-delivery market share a basic market:
1) Basic market transaction mode: in the basic market, all power stations X% ensure that the electric quantity is directly purchased by a power grid at a monthly fixed electricity price, wherein the monthly fixed electricity price refers to an actual electricity price corresponding to similar years of historical contemporaneous water supply frequency, and the calculation method of the basic market electricity purchasing cost is shown in a formula (1):
wherein t represents the current month number; m represents the hydropower station number; m represents the total number of hydropower stations;the electricity purchasing cost for purchasing basic electric quantity in the t month is expressed as follows: a meta-element; />The guaranteed electric quantity of the hydropower station m in the t month is expressed as follows: kWh; />The average electricity price of the market at the t month corresponding to the similar year of the historical same-period incoming water frequency is expressed as follows: meta/kWh; x% represents the duty ratio of the basic electric quantity in the guaranteed electric quantity;
2) Intra-provincial market trading mode: after the basic electric quantity is purchased in the basic market, the residual in-provincial load demand is traded in the in-provincial market in an auction mode; the provincial market introduces a mixed two-segment auction combining electricity distribution and price determination, and the specific rules are as follows:
the first stage, "no bid for the measurement": assuming the highest initial price of the first stage, each generator only needs to submit the electric quantity for bidding at the price, if the supply is greater than the demand, the next round of auction is entered, the electric price is automatically reduced proportionally, the generator needs to reduce the bidding electric quantity in the next round of auction until the bidding total electric quantity meets the demand and does not exceed 1+Y% of the load demand, the first stage auction is ended, and the upper limit of the initial electric price of the second stage and the bid amount of each power station are determined at the same time, wherein Y% represents the exceeding part specified by the market;
Second stage, "bid no-reporting": the generator only needs to submit a quotation not higher than the quotation when the previous round is finished, all the generator bids form a supply curve from high to low according to the price, the final clear electricity price and the bid amount of each power station are determined, and the calculation method of the electricity purchasing cost of the intra-provincial market is shown in a formula (2):
wherein t represents the current month number; m represents the hydropower station number; m represents the total number of hydropower stations;the electricity purchasing cost for purchasing electricity in the province in the t month is expressed as follows: a meta-element; />The final electricity price of the market in the t month province is expressed as follows: kWh/yuan;intra-provincial load representing t-th month predictionThe bits are: kWh; x% represents the duty ratio of the basic electric quantity in the guaranteed electric quantity; />The guaranteed electric quantity of the hydropower station m in the t month is expressed as follows: kWh;
3) The western electric market transaction mode: the electricity suppliers are purchased according to the priority order, the remaining amount of the bid-unbiased guaranteed electricity is reserved, the priority order indicates that the electricity suppliers which are more active in the on-line in the provincial market can participate in the western electricity market with higher electricity price preferentially, and the specific method comprises the following steps: firstly, comparing the remaining guaranteed power quantity of the unmarked hydropower station with the self-guaranteed power quantity, and then, all the power stationsSequencing from small to large, priority of sequencing to participate in western electric market transaction, relative residual ratio of power station m ∈ >The calculation method of (2) is shown in the formula (3):
wherein t represents the current month number; m represents the hydropower station number;the relative remaining ratio of the bid-un-marked electric quantity of the power station m in the t month; />The electricity quantity of the hydropower station m is respectively expressed in the unit of: kWh; />The unit of the bid amount of the hydropower station m in the t-th month province in the market is: kWh; x% represents the duty ratio of the basic electric quantity in the guaranteed electric quantity;
the electricity purchase cost in the western electric market is calculated as follows:
wherein t represents the current month number;the electricity purchasing cost of the t month western electricity market is expressed in units of: a meta-element; />Representing predicted t-th lunar western electrical load in units of: kWh;
solving the intra-provincial and western electric market trading part by adopting reinforcement learning, wherein the essence of reinforcement learning is that an agent continuously interacts with an unknown environment to obtain a feedback updated strategy, so that a learning process of an optimal strategy is obtained, and a mathematical model is shown as a formula (5):
Q[step n+1 ]＝(1-α)Q[step n ]+α(C now +εC future ) (5)
wherein n represents a learning round; step represents the strategy selected; q step n ]A factor value representing the reinforcement learning step strategy of the agent at the nth round; q step n+1 ]A factor value representing the reinforcement learning step strategy of the agent at the n+1th round; c (C) now Representing the current benefit; c (C) future Representing future benefits; alpha represents a learning rate; epsilon represents an attenuation factor for the importance of future benefits, and the specific steps are as follows:
Step 2.1: constructing a reinforcement learning environment:
state space: setting the corresponding value of the selected strategy in each round as the rewarding value of the round, otherwise, keeping other values unchanged, and taking the M multiplied by 3 matrix formed by the values as the strategy state of M power generators after the round is finished; action space: the bidding electric quantity is selected, so that the bidding strategies of all power generators are simplified into three strategies for reporting a small amount of electricity, a medium amount of electricity and a large amount of electricity for modeling convenienceQuantity, reward function: current interest C now To market benefit in province, future benefit C future For the benefit of the western electric market, the sum of the two is the total prize of the round, and the Q step is indirectly caused due to the larger bid amount of certain power stations n ]Larger, it is inconvenient to observe the difference of different strategies of each generator, so that the actual electric quantity is replaced by the relative winning electric quantity obtained by dividing the actual winning electric quantity by the guaranteed electric quantity, wherein Q step n ]A factor value representing the reinforcement learning step strategy of the agent at the nth round;
step 2.2: to enable the generator to fully explore the unknown environment to prevent from sinking into local optima, a greedy strategy is adopted to select actions, expressed as:
wherein A represents an action space; p (P) epilson A permissible value representing a search probability range; eta represents a random number and the value range is 0,1 ]The method comprises the steps of carrying out a first treatment on the surface of the random (a) represents a randomly selected action in the action space a; argmax a∈A Q(s) represents the action of the current learning process with the largest return; selecting actions according to a greedy strategy and constructing an experience network; will explore the probability P epsilon The initial value is set to 0, so that the generator fully explores the environment, selects various strategies for game play and study, and gradually increases P according to fixed step length epsilon The value is up to 1, and all generator strategies select the action strategy with the largest return at the moment;
step 2.3: training network: updating Q step based on rewards obtained n ]Corresponding value, wherein Q [ step ] n ]A factor value representing the reinforcement learning step strategy of the agent at the nth round;
step 2.4: judging Q step n ]Whether or not to converge: the circulation is exited after convergence, the free market transaction is ended, and the total cost of electricity purchasing is obtained; otherwise, increasing the learning times and returning to step 2.2, wherein Q step n ]The factor value representing the agent in the n-th round of reinforcement learning step strategy.
5. The method for trading the medium-term and long-term electric power market in the hydropower enrichment power grid compatible with excitation according to claim 2, wherein the detailed steps of making the trade electric quantity redistribution mechanism in the step 3 are as follows:
according to the situation that the actual generated energy is equal to, greater than and smaller than the guaranteed electric quantity, three different distribution modes of balance, surplus and deficiency are set, and positive improvement strategies are provided for an electric quantity distribution mechanism in a deficiency state, and are respectively introduced as follows:
1) Distribution of electric quantity in equilibrium state:
the balance state indicates that the actual generated electricity of the alliance is exactly equal to the sum of the guaranteed electricity of all power stations in the alliance, at the moment, only internal adjustment is needed between the watercourses, the hydropower stations with excess electricity are sold to hydropower stations with the excess electricity to reach balance at a lower price, and at the moment, the actual electricity of all hydropower stations is exactly equal to the guaranteed electricity of all hydropower stations;
2) Distribution of electric quantity in surplus state:
the surplus state is used for indicating that the total electricity generation quantity of the alliance is larger than the sum of the electricity quantity of the alliance guarantee; after the internal adjustment of the alliance mentioned in the last step is completed, surplus electric quantity still exists for part of hydropower stations, and the surplus electric quantity is not distributed in a uniform distribution mode any more and is sold as private electric quantity to a watershed with an absence or participates in a subsequent spot market;
3) Distribution of electric quantity in loss state:
the loss state indicates that the sum of the actual generated energy of the alliance is smaller than the sum of the guaranteed electric energy of the alliance power stations, namely the loss is considered, and according to the market rule, all hydropower stations in the basin purchase electric quantity filling deficiency to the basin in the surplus state so as to guarantee normal performance of contracts, and the concrete operation is as follows:
step 3.1: the guaranteed electric quantity of the hydropower station m is recalculated, the actual electric energy generation amounts of all hydropower stations in the alliance are collected together, and the new guaranteed electric quantity of the hydropower station m is obtained by distributing the actual electric energy according to the proportion of the original guaranteed electric quantity of each hydropower station m:
Wherein t represents the current month number; m represents the hydropower station number; m represents the total number of hydropower stations;the guaranteed electric quantity of the hydropower station m in the t month is expressed as follows: kWh; />The unit of the guaranteed electric quantity for new verification in the t month of the hydropower station m is as follows: kWh; />The actual power generation capacity of the hydropower station m in the t month is expressed as follows: kWh;
step 3.2: calculating the difference value between the original guaranteed electric quantity and the new guaranteed electric quantity of the hydropower station m;
wherein t represents the current month number; m represents the hydropower station number;the guaranteed electric quantity of the hydropower station m in the t month is expressed as follows: kWh; />The unit of the newly-approved guaranteed electric quantity for the t month of the hydropower station m is as follows: kWh; />The difference value between the original guaranteed electric quantity and the new guaranteed electric quantity of the hydropower station m in the t month is represented by the following units: kWh;
step 3.3: calculating the difference value between the new guaranteed electric quantity and the actual generated energy of the hydropower station m, and if the hydropower station itself finishes the self-guaranteed electric quantity, reducing punishment specific gravity, wherein the difference value is 0;
wherein t represents the current month number; m represents the hydropower station number;the difference value between the newly guaranteed electricity quantity and the actual electricity generation quantity of the hydropower station m in the t month is expressed as follows: kWh; />The unit of the guaranteed electric quantity for new verification in the t month of the hydropower station m is as follows: kWh; />The actual power generation capacity of the hydropower station m in the t month is expressed as follows: kWh;
Step 3.4: and calculating the shortage electric quantity needed to be born by the hydropower station m:
wherein t represents the current month number; m represents the hydropower station number;the relative electricity shortage required to be born in the t month of the hydropower station m is represented as follows: kWh; />The difference value between the new guaranteed electric quantity and the actual electric energy of the hydropower station m in the t month is expressed as follows: kWh; />Representing the mth month of hydropower station mThe unit of the difference between the original guaranteed electric quantity and the new guaranteed electric quantity is: kWh;
step 3.5: the actual electricity shortage amount needed to be born by the hydropower station m is distributed:
wherein t represents the current month number; m represents the hydropower station number; m represents the total number of hydropower stations;the actual electricity shortage amount which the hydropower station m needs to bear in the t month is shown as follows: kWh; />The guaranteed electric quantity of the hydropower station m in the t month is expressed as follows: kWh; />The actual power generation capacity of the hydropower station m in the t month is expressed as follows: kWh; />The relative electricity shortage required to be born in the t month of the hydropower station m is represented as follows: kWh. />
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