CN113822707B - Output decision method and device for electric power market, computer equipment and storage medium - Google Patents
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Abstract
The application relates to an output decision method, a device, computer equipment and a computer readable storage medium of an electric power market. The method comprises the following steps: acquiring the clearing price of the market in the day-ahead and the clearing price of the real-time market; the clearing price of the market in the day-ahead and the clearing price of the real-time market are input into an output decision model of the electric power market to perform medium-long term contract decomposition, so that an output distribution result of the new energy generator set and an output distribution result of the conventional energy generator set are generated; the output decision model of the electric power market is used for performing medium-long term contract decomposition on the new energy generator set and the conventional energy generator set. According to the output decision model of the electric power market, the output distribution of the new energy generator set and the output distribution of the conventional energy generator set are obtained according to the clearing price of the market in the day-ahead and the clearing price of the real-time market, so that a new energy generator manufacturer participates in bidding of the electric power market, and reliable execution of long-term transaction in the new energy is ensured.
Description
Technical Field
The present application relates to the field of power market technologies, and in particular, to a method and apparatus for determining output of a power market, a computer device, and a computer readable storage medium.
Background
At present, since fossil energy is increasingly exhausted and environmental problems are increasingly prominent, large-scale development and utilization of new energy are very important problems. With the continuous development of new energy power generation technology, new energy gradually participates in the competition of the electric power market.
However, the new energy generating set has larger volatility and randomness in the generating process, so that the new energy generating set does not have reliable power supply capability, and the new energy generating set is difficult to participate in medium-and-long-term power market transaction. At present, most researches focus on the field of medium-long-term contract decomposition for conventional energy generating sets, so that a medium-long-term contract decomposition method for the conventional energy generating sets is mature. However, when the long-term contract decomposition is performed on the hybrid generator set of the new energy and the conventional energy, the medium-and-long-term contract decomposition method of the conventional energy generator set cannot be directly used.
Therefore, there is a need to provide a medium-long term contract decomposition method for a hybrid power generation set of new energy and conventional energy, so that the long term contract decomposition of the new energy participates in spot market bidding, and reliable execution of medium-long term transaction is ensured.
Disclosure of Invention
In view of the foregoing, it is desirable to provide a method, apparatus, computer device, and computer-readable storage medium for determining output of an electric power market that enables new energy to participate in medium-to-long term transactions.
A method of power market output decision making, the method comprising:
Acquiring the clearing price of the market in the day-ahead and the clearing price of the real-time market;
the clearing price of the market in the day-ahead and the clearing price of the real-time market are input into an output decision model of the electric power market to perform medium-long term contract decomposition, so that an output distribution result of the new energy generator set and an output distribution result of the conventional energy generator set are generated; the output decision model of the electric power market is used for performing medium-long term contract decomposition on the new energy generator set and the conventional energy generator set.
In one embodiment, the process of generating the output decision model of the electric power market comprises the following steps:
Constructing an output decision double-layer model of an electric power market; the output decision double-layer model comprises an output decision upper layer model and an output decision lower layer model;
Converting the output decision double-layer model based on preset constraint conditions and KKT conditions to generate an output decision single-layer model;
and carrying out linearization treatment on the output decision single-layer model to obtain the output decision model of the electric power market.
In one embodiment, building an output decision bilayer model of an electric power market includes:
building an output decision upper model based on a first objective function and a first preset constraint condition; the first objective function is used for representing the income of new energy power generation manufacturers;
Constructing an output decision lower model based on a second objective function and a second preset constraint condition; the second objective function is used to characterize the overall social benefit.
In one embodiment, constructing the output decision upper model based on the first objective function and the first preset constraint condition includes:
constructing a first objective function based on the daily market gain and the real-time market gain of new energy power generation manufacturers;
Based on the contracted electric quantity constraint of the medium-and-long-term contract and the output constraint of the medium-and-long-term contract electric quantity in each period, a first preset constraint condition is constructed;
And constructing an output decision upper model based on the first objective function, the contracted electric quantity constraint of the medium-and-long-term contract and the output constraint of the medium-and-long-term contract electric quantity in each period.
In one embodiment, constructing the lower model of the output decision based on the second objective function and the second preset constraint condition includes:
Constructing a second objective function based on the purchase cost of electricity, the purchase cost of standby service and the penalty cost caused by load loss in the market in the day-ahead;
constructing a second preset constraint condition based on the power balance constraint, the real-time market standby adjustment quantity balance constraint, the conventional generator set output constraint, the conventional generator set standby constraint and the wind power generator set output constraint;
and constructing an output decision lower model based on the second objective function, the power balance constraint, the real-time market standby adjustment quantity balance constraint, the conventional generator set output constraint, the conventional generator set standby constraint and the wind power generator set output constraint.
In one embodiment, the method for generating the output decision single-layer model includes the steps of:
Performing equivalent replacement on a second objective function of the lower output decision model and a second preset constraint condition based on the KKT condition to generate an equivalent KKT condition of the lower output decision model;
And generating an output decision single-layer model according to the first objective function of the output decision upper-layer model, the first preset constraint condition and the equivalent KKT condition of the output decision lower-layer model.
In one embodiment, linearizing the output decision single-layer model to obtain an output decision model of the electric power market includes:
and linearizing the decision variables in the output decision single-layer model by adopting a binary unfolding method to obtain the output decision model of the electric power market.
An output decision device for an electric power market, the device comprising:
the parameter acquisition module is used for acquiring the clearing price of the market in the day-ahead and the clearing price of the real-time market;
The output decision module is used for inputting the clearing price of the market in the day and the clearing price of the real-time market into an output decision model of the electric power market to perform long-term contract decomposition, so as to generate an output distribution result of the new energy generator set and an output distribution result of the conventional energy generator set; the output decision model of the electric power market is used for performing medium-long term contract decomposition on the new energy generator set and the conventional energy generator set.
A computer device comprising a memory storing a computer program and a processor implementing the steps of the method as described above when the processor executes the computer program.
A computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of the method as above.
The method, the device, the computer equipment and the computer readable storage medium for deciding the output of the electric power market comprise the following steps: firstly, acquiring the clearing price of the market in the day-ahead and the clearing price of the real-time market; then, the clearing price of the market in the day and the clearing price of the real-time market are input into an output decision model of the electric power market to perform medium-long term contract decomposition, so that an output distribution result of the new energy generator set and an output distribution result of the conventional energy generator set are generated; the output decision model of the electric power market is used for performing medium-long term contract decomposition on the new energy generator set and the conventional energy generator set. According to the output decision model of the electric power market, the output distribution of the new energy generator set and the output distribution of the conventional energy generator set are obtained according to the clearing price of the market in the day-ahead and the clearing price of the real-time market, so that a new energy generator manufacturer participates in bidding of the electric power market, and reliable execution of long-term transaction in the new energy is ensured.
Drawings
FIG. 1 is an application environment diagram of an output decision method of an electric power market in one embodiment;
FIG. 2 is a flow chart of a method of power market output decision making in one embodiment;
FIG. 3 is a schematic diagram of a process for generating an output decision model of an electric power market in one embodiment;
FIG. 4 is a flow diagram of a two-layer model for building an output decision for an electric market in one embodiment;
FIG. 5 is a flow chart of a method for constructing an upper model of an output decision in one embodiment based on a first objective function and a first predetermined constraint;
FIG. 6 is a schematic flow chart of constructing an underlying model of an output decision based on a second objective function and a second preset constraint in one embodiment;
FIG. 7 is a schematic flow chart of a method for converting an output decision dual-layer model to generate an output decision single-layer model based on preset constraint conditions and KKT conditions in one embodiment;
FIG. 8 is a flow chart of a method for building an output decision model of an electric market in one embodiment;
FIG. 9 is a schematic diagram of the output of different wind power scenarios in one embodiment;
FIG. 10 is a graph showing the output distribution results of a new energy generator set and the output distribution results of a conventional energy generator set according to one embodiment;
FIG. 11 is a block diagram of an output decision device of an electric power market in one embodiment;
FIG. 12 is a block diagram of the output decision model building block in one embodiment;
fig. 13 is an internal structural view of a computer device in one embodiment.
Detailed Description
The present application will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present application more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
The output decision method of the power market provided by the application can be applied to an application environment shown in figure 1. As shown in fig. 1, the application environment includes an electronic device 120 and a server 140. The server 140 obtains the clearing price of the day-ahead market and the clearing price of the real-time market from the electronic device 120, and then the server 140 inputs the clearing price of the day-ahead market and the clearing price of the real-time market into an output decision model of the electric power market to perform middle-long term contract decomposition, so as to generate an output distribution result of the new energy generator set and an output distribution result of the conventional energy generator set; the output decision model of the electric power market is used for performing medium-long term contract decomposition on the new energy generator set and the conventional energy generator set. The electronic device 120 may be, but is not limited to, a personal computer, a notebook computer, a smart phone, a tablet computer, a portable wearable device, and the like.
In one embodiment, as shown in fig. 2, a power market output decision method is provided, which is applied to the electronic device 120 and the server 140, and includes steps 220 to 240.
S220, acquiring the clearing price of the market in the day-ahead and the clearing price of the real-time market.
Specifically, the price of the electricity market is that when the price is proper, the electricity generation side and the user side in the electricity market can balance the supply and demand of the electricity at the price level, that is, the demand in the electricity market is equal to the supply. The above-mentioned suitable price means a clear electricity price, and the formation of the clear electricity price is related to both supply on the power generation side and demand on the user side. At the price-out level, the amount of electricity that the user is willing to and can purchase is exactly equal to the amount of electricity that the power generation side is willing to and can supply. If the market price is higher than the discharged electricity price, the market supply electricity quantity is more than the required electricity quantity, and the supply is excessive. Competition between the power generation sides occurs, with the result that the electricity price is depressed, so that the amount of electricity that the user side is willing to purchase and can purchase is equal to the supply amount of the power generation side. Similarly, if the market price is lower than the discharged electricity price, the market is in short supply or excessive demand. Competition occurs between the user sides, and as a result, electricity prices are raised, so that demands are reduced, and markets tend to equalize. Furthermore, the operation of the current electric power market consists of daily scheduling and real-time scheduling, the effective utilization of power generation facilities and power transmission facilities in the area is realized by means of market adjustment, and the power demand of users is safely and reliably met with the lowest system operation cost. The daily market clearing amount is settled according to the daily market price, and the deviation amount between the real-time and the daily market clearing amount is settled according to the real-time market price.
Further, "day before" in the day before market refers to the day before the actual operation day of the power grid, and thus, the day before market refers to the electric energy trading market that occurs on this "day before". Through market in the daytime, the dispatching system of the power grid can optimize the dispatching plan of the operation days in time, and meanwhile, the power plant can adjust the power generation amount of the generator set according to the market trading result. For example, in the day-ahead market, market members are on 8 per day: 00-12: 00 submits the next day's bid schedule to the market operator. Market run at 12 per day: 00-14: 00 evaluates the bid schedule and selects the most efficient and economical mode of operation. At 14: 00-16: 00, the market operation unit notifies each member of the evaluation result, and 16:00 to day 8:00, the market operation part can also make certain adjustment according to the requirements of the system in aspects of economy, reliability and the like.
Further, the real-time market is effectively a balance market, so that a system dispatcher can balance the generated energy and the load amount of all periods in real-time operation and provide corresponding transaction prices for all parties participating in the transaction of the electric quantity, and quotations of market participants in the real-time market are submitted to a system dispatching operation mechanism after the off-the-shelf market is closed. Therefore, the real-time market is established for solving the system accident, network blocking and market settlement difficulty.
Further, the electronic device 120 stores the daily market price and the real-time market price, wherein the daily market price and the real-time market price of the electric power market are obtained by counting the electric power market data by human or computer equipment. Further, the server 140 obtains the price of the market in the past day and the price of the market in real time from the electronic device 120 through data transmission. The data transmission manner between the electronic device 120 and the server 140 is not limited to the manner of ethernet, bluetooth, radio station, wifi, etc., and the data transmission manner between the electronic device 120 and the server 140 is not limited in the present application.
S240, inputting the clearing prices of the market in the day and the clearing prices of the real-time market into an output decision model of the electric power market to perform medium-long term contract decomposition, so as to generate an output distribution result of the new energy generator set and an output distribution result of the conventional energy generator set; the output decision model of the electric power market is used for performing medium-long term contract decomposition on the new energy generator set and the conventional energy generator set.
In particular, for the power spot market, the time range includes the time period from the day before the real-time operation of the system to the real-time operation. The electric power spot market generally adopts a unified clearing mode, and market members voluntarily participate in reporting and physically communicate and settle the formed transaction plan. The spot market can form an optimized transaction plan which is suitable for the physical operation of the power system and shows the willingness of market members on a proper amount of advance, so that the full competition of electric quantity transaction is promoted by means of centralized clearing, and the efficient and optimized configuration of power resources is realized. Further, the medium-and-long-term contract decomposition is to decompose the electric quantity participating in the transaction to each period by signing the spread contract, so that a certain risk is avoided. Furthermore, the function of market price formation is brought into play in the spot market through medium-long term contract decomposition, short-term supply and demand relations and space-time values of electric power commodities can be truly reflected, and system security risks and financial risks of transactions are reduced. Further, long-and-medium-term transactions are characterized by longer transaction time granularity and longer time intervals between transaction time and delivery time.
The output force of the new energy generator set is distributed to the power generated by the new energy generator set in unit time, and the output force of the conventional energy generator set is distributed to the power generated by the conventional energy generator set in unit time. Further, the new energy generator set includes, but is not limited to, a wind power generator set, a hydro-electric generator set, a nuclear power generator set, and the like, and the conventional generator set includes, but is not limited to, a thermal power generator set.
Specifically, the output decision model of the electric power market comprehensively considers various factors such as a power supply structure, a load level, load characteristics, power generation characteristics and the like from the mechanism that new energy participates in the electric power market. The output decision model of the electric power market considers that independent system operators do not treat wind power differently in the market clearing process, only the bid-winning power of the wind power generator set is needed to be given out as the same as the thermal power, and the wind power dispatching is not needed to be responsible, so that the output of the output decision model of the electric power market is the optimal output strategy of a new energy generator manufacturer, and the output strategy comprises the bid-winning power of the new energy generator set, the output distribution of the new energy generator set and the output distribution of a conventional energy generator set.
Further, the daily market price and the real-time market price of the electric power market are obtained by counting the electric power market data by human or computer equipment, and the daily market price and the real-time market price are stored in the electronic device 120. The server 140 obtains the price of the market in the past and the price of the market in real time from the electronic device 120 through data transmission. The output decision model of the electric power market comprises an output decision upper layer model and an output decision lower layer model. The upper model of the output decision takes the maximization of the income of new energy power generation manufacturers as a target, and the lower model of the output decision takes the maximization of the total benefit of society as a target. The output decision upper model provides the output distribution result of the new energy power generator for the output decision lower model, so that the output decision lower model obtains the medium-long-term contract decomposition electric quantity of the new energy power generator. The output decision lower model transmits the obtained clearing price of the day-ahead market, the clearing price of the real-time market and the winning power of the new energy generator set to the output decision upper model, so that the output decision upper model obtains feedback conditions of the clearing price of the day-ahead market and the clearing price of the real-time market. Therefore, the output decision upper model and the output decision lower model are mutually influenced, and output distribution results of the new energy generator set and the conventional energy generator set are obtained according to the daily market price and the real-time market price.
Further, the server 140 inputs the acquired clearing prices of the day-ahead market and the clearing prices of the real-time market to the output decision model of the electric power market to perform long-term contract decomposition, so as to obtain the output distribution result of the new energy generator set and the output distribution result of the conventional energy generator set.
In the embodiment of the application, a power market output decision method is provided, and the price of the market in the day-ahead and the price of the market in the real-time are firstly obtained; then, the clearing price of the market in the day and the clearing price of the real-time market are input into an output decision model of the electric power market to perform medium-long term contract decomposition, so that an output distribution result of the new energy generator set and an output distribution result of the conventional energy generator set are generated; the output decision model of the electric power market is used for performing medium-long term contract decomposition on the new energy generator set and the conventional energy generator set. According to the output decision model of the electric power market, the output distribution of the new energy generator set and the output distribution of the conventional energy generator set are obtained according to the clearing price of the market in the day-ahead and the clearing price of the real-time market, so that a new energy generator manufacturer participates in bidding of the electric power market, and reliable execution of long-term transaction in the new energy is ensured.
In one embodiment, as shown in fig. 3, the process of generating the output decision model of the electric power market includes steps 320 to 360:
S320, constructing an output decision double-layer model of the electric power market; the output decision double-layer model comprises an output decision upper layer model and an output decision lower layer model;
Specifically, the output decision double-layer model of the electric power market adopts a stark-berg model, wherein the stark-berg model is a yield leading model, and the model reflects asymmetric competition among enterprises. Specifically, the stark-er model is a yield-leading model, and there is a distinction in the order of actions among vendors. The yield is determined according to the following sequence: the leader determines a yield that the follower vendor can observe and then determine his own yield based on the leader's yield. It should be noted that the leader knows well how the follower will act when deciding on its own production, which means that the leader can know the follower's reaction function. Thus, the leadership will naturally expect the effect of its own determined yield on the following manufacturer. It is in this regard that the yield determined by the leader will be a profit maximization yield constrained by the response function of the follower. In the stark-berg model, the leadership's decisions no longer require their own reaction functions.
Further, the output decision double-layer model of the electric power market adopts a leader-subordinate mode of a Stark-cell model, and comprises an output decision upper-layer model and an output decision lower-layer model. Specifically, the lower output decision model is a daily market and real-time market clearing model, and can be expanded to a daily electric power market and an electric power balance market. Furthermore, the lower output decision model takes social benefit maximization as an objective function, and the upper output decision model takes the benefit maximization of a new energy generator as an objective function. Specifically, the price of the market in the future and the price of the market in real time can be obtained through the lower model of the output decision. In addition, the lower model of the output decision can also obtain the winning power of the new energy generator set, the winning power of the conventional energy generator set and the standby power of the conventional energy generator set.
Further, the output decision upper model provides the output distribution result of the new energy generator to the output decision lower model, so that the output decision lower model obtains the quotation condition of the new energy generator, namely the middle-long term contract decomposition electric quantity. The output decision lower model obtains the middle-long-term contract decomposition electric quantity of the new energy generator set from the output decision upper model, and further, the output decision lower model transmits the obtained clearing price of the day-ahead market, the clearing price of the real-time market and the winning power of the new energy generator set to the output decision upper model, so that the output decision upper model obtains the clearing price feedback condition of the output distribution in the day-ahead market and the real-time market. Therefore, the output decision upper model and the output decision lower model are mutually influenced, so that the output distribution of the new energy generator set and the output distribution of the conventional energy generator set are obtained.
S340, converting the output decision double-layer model based on preset constraint conditions and KKT conditions to generate an output decision single-layer model.
Specifically, the output decision upper model provides output distribution of a new energy power generator to the output decision lower model, namely the middle-long term contract decomposition electric quantity. Further, the output decision lower model is based on output distribution of new energy power generation manufacturers transmitted by the output decision upper model, and the price of the market in the day-ahead, the price of the market in the real-time and the winning power of the new energy power generation set are obtained. Therefore, the variables of the output decision upper model are constants in the output decision lower model, and the output decision lower model is a linear model. Further, the lower model of the output decision is replaced by an equivalent optimality condition, so that the double-layer optimization model is converted into a single-layer mathematical programming problem (MATHEMATICAL PROGRAM WITH EQUILIBRIUM CONSTRAINTS, MPEC) with balanced constraint. Further, the construction of equivalent optimality conditions includes, but is not limited to, the Coulomb Take condition (KKT) and the dual transformation, as the present application is not limited thereto.
Optionally, the output decision upper layer model includes an objective function of the output decision upper layer model and a constraint condition of the output decision upper layer model, the output decision lower layer model includes an objective function of the output decision lower layer model and a constraint condition of the output decision lower layer model, and the objective function of the output decision lower layer model and the constraint condition are converted by adopting a KKT condition to generate an equivalent KKT condition of the output decision lower layer model. Further, the output decision single-layer model is formed based on the equivalent KKT condition of the output decision lower-layer model and the output decision upper-layer model. Specifically, the output decision single-layer model comprises an objective function of the output decision lower-layer model after KKT condition conversion, an equivalent KKT condition of the output decision lower-layer model after KKT condition conversion, an objective function of the output decision upper-layer model and constraint conditions of the output decision upper-layer model.
And S360, linearizing the output decision single-layer model to obtain an output decision model of the electric power market.
Specifically, although the output decision single-layer model is a single-layer model, some nonlinear terms exist in the output decision single-layer model, for example, when two decision variables are multiplied in the output decision single-layer model, the multiplied two decision variables are nonlinear. Therefore, if the output decision single-layer model with the nonlinear term is directly solved, the calculated amount is greatly increased, and the difficulty of searching the global optimal solution is increased. Therefore, linearization is needed to be performed on the output decision single-layer model, and the output decision single-layer model after linearization is an output decision model of the electric power market. The Mixed-INTERGER LINEAR Programming (MILP) model has the advantages of stability and easy optimization. Therefore, the output decision single-layer model is converted into a mixed positive integer linear programming model by linearizing the output decision single-layer model. In particular, a linear programming model refers to an objective function that is linear, and in addition, all constraints of the objective function are linear, and any real number can be taken by the objective function decision variables. But if there is a partial decision variable requirement in the linear programming problem that must be an integer, then the linear programming problem at this time is converted to a mixed integer linear programming problem. That is, for a hybrid positive integer linear programming model, its optimization conditions include not only conditional constraints but also integer constraints.
Further, the method for linearizing the output decision single-layer model includes, but is not limited to, complementation condition linearization and objective function linearization. Specifically, for the linearization of the complementarity conditions, it means that for the shape xy=0 complementarity constraint conditions, where x and y are lagrange multipliers and a continuous function, respectively, and x is greater than or equal to 0, M and u are used to linearize the shape xy=0 complementarity constraint conditions, where x is less than or equal to Mu, y is less than or equal to M (1-u), where M is a larger constant, and u is a variable from 0 to 1. Furthermore, for linearization of the objective function, the nonlinear term in the objective function of the output decision single-layer model is subjected to linearization processing by using a binary expansion method mainly aiming at the nonlinear term in the objective function of the output decision single-layer model.
Optionally, linearizing the nonlinear term in the objective function of the output decision single-layer model by a binary expansion method to obtain the objective function of the linearized output decision single-layer model. And the output decision model of the electric power market is formed together based on the objective function of the linearized output decision single-layer model, the equivalent KKT condition of the output decision lower-layer model and the constraint condition of the output decision upper-layer model.
In the embodiment of the application, a method for generating an output decision model of an electric power market is provided, and an output decision double-layer model of the electric power market is firstly constructed; the output decision double-layer model comprises an output decision upper layer model and an output decision lower layer model; then converting the output decision double-layer model based on preset constraint conditions and KKT conditions to generate an output decision single-layer model; and finally, linearizing the output decision single-layer model to obtain the output decision model of the electric power market. The output decision upper model provides the output distribution result of the new energy power generator for the output decision lower model, so that the output decision lower model obtains the medium-long-term contract decomposition electric quantity of the new energy power generator. The output decision lower model transmits the obtained clearing price of the day-ahead market, the clearing price of the real-time market and the winning power of the new energy generator set to the output decision upper model, so that the output decision upper model obtains feedback conditions of the clearing price of the day-ahead market and the clearing price of the real-time market. Therefore, the upper output decision model and the lower output decision model are mutually influenced, so that the more accurate output distribution result of the new energy generator set and the output distribution result of the conventional energy generator set are obtained.
In one embodiment, as shown in FIG. 4, an output decision bilayer model of the power marketplace is constructed, including steps 322 through 324.
S322, constructing an output decision upper model based on a first objective function and a first preset constraint condition; the first objective function is used for representing the benefits of new energy power generation manufacturers.
Specifically, the first objective function is an objective function of the output decision upper model, and the first preset constraint condition is a preset constraint condition of the output decision upper model. Further, the objective function of the upper model of the output decision is the income of the new energy generator, and the income of the new energy generator is settled on the basis of the differential contract authorized by the government. Specifically, there are two contractual forms in the medium and long term trading market: physical contracts and spread contracts. For the physical contract, how much electric quantity is signed by the physical contract, and how much electric quantity is required to be sent out by the new energy generator. However, the difference contract is not required to be physically executed, but only the difference between the current price of the spot market and the contract price is used for settlement, so that the difference contract is a financial hand bidding segment and only the settlement is influenced. The introduction of the spread contract to the electric power market has two effects, on one hand, the spread contract does not require physical execution in the spot market, so that the spread contract does not restrict the resource optimization configuration of the spot market; on the other hand, the spread contracts can effectively avoid that spot market quotations are too low or too high.
Further, the objective function of the output decision upper model comprises the benefits of new energy power generators in the market in the future and the benefits of the real-time market. Whereas the benefits of the market in the past include benefits of spread contracts and benefits of spot markets, the benefits of the real-time markets are benefits obtained by new energy power generators in the real-time markets, namely benefits obtained due to the uncertainty of wind power and balance fees required for payment are reduced.
Furthermore, the upper model of the output decision adopts the difference sum authorized by the government to be about the settlement basis of the income of the new energy generator, and takes the income of the new energy generator as an objective function. The output decision upper model provides output distribution of the new energy generator to the output decision lower model, so that the output decision lower model obtains quotation conditions of the new energy generator, namely the middle-long-term contract decomposed electric quantity. The output distribution of the new energy generator manufacturer comprises the winning power of the new energy generator set and the output distribution of the new energy generator set.
Further, the medium-and-long-term contract decomposition electric quantity is an electric quantity constraint on a bidding segment at a certain time, specifically, the medium-and-long-term contract decomposition electric quantity can be a daily electric quantity constraint or an electric quantity constraint of a plurality of hours, and the application is not limited to this. Optionally, the power decomposed by the medium-and-long-term contracts is a daily power constraint, that is, the power contracted by the medium-and-long-term contracts is the total power generated by the new energy generator set in the middle of the day, and the power decomposed by the medium-and-long-term contracts is the power generated by the new energy generator set at each time in the middle of the day. Thus, constraints of the force decision upper model include contracted power constraints of the medium-to-long term contracts and medium-to-long term contract decomposed power constraints.
S324, constructing an output decision lower model based on a second objective function and a second preset constraint condition; the second objective function is used to characterize the overall social benefit.
Specifically, the objective function of the underlying model of the output decision is that social benefit is maximized, i.e., scheduling cost is minimized. The scheduling cost includes the electricity purchase cost in the market in the day before, the purchase cost of the standby service, and the penalty cost caused by load shedding. Specifically, all of the electricity purchasing costs in the aforementioned day-ahead market include the cost of purchasing the new energy generator set for generating electricity and the cost of using the conventional energy generator set for generating electricity. Further, the new energy generator set may fail in the power generation process, such as a power generator failure, a line failure, etc., and when the new energy generator set fails and cannot generate power normally, in order to ensure that the generated energy can meet the user demand, some standby services need to be purchased, such as using a conventional generator set to generate power to meet the user power demand. Further, the penalty cost caused by the load loss refers to a line overload problem when the available capacity of the power generation system cannot meet the maximum load requirement of the system, so that related personnel can adopt some emergency dispatch problems to solve the load loss problem, and further, some cost increases.
Further, the low-level output decision model obtains the medium-long-term contract decomposed electric quantity of the new energy generator set from the upper-level output decision model, and further, the low-level output decision model transmits the obtained daily price of the market, the real-time price of the market and the winning power of the new energy generator set to the upper-level output decision model, so that the upper-level output decision model obtains the price feedback conditions of the output distribution in the daily market and the real-time market.
Further, constraint conditions of the lower model of the output decision include power constraint balance, reserve adjustment balance constraint in a real-time market, conventional generator set output constraint, conventional generator set reserve constraint and new energy generator set processing constraint. The power constraint balance indicates that the generated energy of the new energy generator set and the generated energy of the conventional generator set meet the load demand of the system. The reserve adjustment quantity balance constraint in the real-time market represents that the reserve quantity of the conventional generator set, the output and the load loss of the new energy generator set can be kept balanced, and the balance of the real-time market is reflected. The conventional generator set output constraint represents the winning power constraint of the conventional generator set, the conventional generator set standby constraint represents the constraint of the standby adjustment quantity of the conventional generator set, and the new energy generator set output constraint comprises the winning power constraint of the new energy generator set.
In the embodiment of the application, a method for constructing an output decision double-layer model of an electric power market is provided, wherein an output decision upper-layer model is constructed based on a first objective function and a first preset constraint condition; the first objective function is used for representing the income of new energy power generation manufacturers; then constructing an output decision lower model based on a second objective function and a second preset constraint condition; the second objective function is used to characterize the overall social benefit. The output decision upper model provides output distribution of the new energy generator to the output decision lower model, so that the output decision lower model obtains quotation conditions of the new energy generator, namely the middle-long-term contract decomposed electric quantity. The output decision lower model transmits the obtained clearing price of the day-ahead market, the clearing price of the real-time market and the winning power of the new energy generator set to the output decision upper model, so that the output decision upper model obtains the clearing price feedback condition of the output distribution in the day-ahead market and the real-time market. Therefore, the output decision upper model and the output decision lower model are mutually influenced, so that the output distribution of the new energy generator set and the output distribution of the conventional energy generator set are obtained, and the new energy is participated in medium-long term transaction of the electric power market.
In one embodiment, as shown in fig. 5, the output decision upper model is constructed based on the first objective function and the first preset constraint condition, and steps 420 to 460 are included.
S420, constructing a first objective function based on daily market gains and real-time market gains of new energy power generation manufacturers.
Specifically, a first objective function is constructed based on the daily market gain and the real-time market gain of a new energy generator, wherein the first objective function is an objective function of an output decision upper model. Specifically, the objective function of the output decision upper model includes the benefits of new energy power generators in the market in the future and the benefits of the real-time market. Further, the benefits of the new energy generator in the market before the day include the benefits of the spread contract and the benefits of the spot market, and the benefits of the new energy generator in the real-time market are the benefits of the new energy generator in the real-time market due to the uncertainty of the new energy, and the balance cost required to be paid is reduced. Furthermore, the new energy generator is a wind power generator, wherein the wind power generator uses wind power as energy for generating electricity. Thus, the objective function of the above-described force decision upper layer model can be expressed as:
Wherein R w represents the income of new energy power generation manufacturers, The contract price is a medium-long term contract price of the new energy generator set; lambda t and lambda st represent the price of the market before day and the price of the market in real time,/>Decomposing electric quantity for medium-long-term electricity consumption contract of new energy generator set at t moment,/>For the winning capacity of the new energy generator set at the time t,/>And/>And respectively representing the actual power generation amount and the wind curtailment power amount of the new energy generator set under a scene s, wherein the scene s is used for describing the uncertainty of wind power. In formula (1)/>Representing the income of the spot market of the new energy generator set at the time t,/>The income of the price contract of the time difference of the new energy generator set at the time t is expressed by the formula/>And representing the income of the new energy generator set in real time at the moment t under the scene s.
S440, constructing a first preset constraint condition based on the contracted electric quantity constraint of the medium-and-long-term contract and the bidding segment output constraint of each time of the electric quantity of the medium-and-long-term contract.
Specifically, a first preset constraint condition is built based on the contracted electric quantity constraint of the medium-and-long-term contract and the bidding section output constraint of each time of the medium-and-long-term contract electric quantity, and the first preset constraint condition is a constraint condition of an output decision upper model. Specifically, the contracted power constraints of the medium-to-long term contract may be expressed as:
Wherein P gm is the contracted electric quantity of the medium-and-long-term contract of the new energy generator set. The formula (2) represents constraint on the power decomposition capacity of the medium-and-long-term power consumption contract of the new energy generator set, namely the sum of the power decomposition capacities of the medium-and-long-term power consumption contract of the new energy generator set at the time t is not greater than the contracted power of the medium-and-long-term contract.
Further, the bid-segment-out-force constraint at each time of the medium-and-long-term contract power can be expressed as:
And (3) upper and lower limit constraint of the middle-long-term electricity consumption contract decomposition electric quantity of the new energy generator set at the time t is that the middle-long-term electricity consumption contract decomposition electric quantity of the new energy generator set at the time t cannot exceed the winning electric quantity of the new energy generator set at the time t.
S460, constructing an output decision upper model based on the first objective function, the contracted electric quantity constraint of the medium-long-term contract and the output constraint of each time bidding segment of the electric quantity of the medium-long-term contract.
Specifically, the objective function of the output decision upper model and the constraint condition of the output decision upper model jointly form the output decision upper model. The output decision upper layer model takes the income of a new energy generator as an objective function. Specifically, the objective function of the upper output decision model includes the income of new energy power generators in the market in the day-ahead and the income of real-time markets, and in addition, the constraint conditions of the upper output decision model include the constraint of the contracted electric quantity of the medium-and-long-term contract and the constraint of the bidding segment of each time of the electric quantity of the medium-and-long-term contract. And constructing the output decision upper model based on an objective function of the output decision upper model, the contracted electric quantity constraint of the medium-long-term contract and the bidding segment output constraint of each time of the medium-long-term contract electric quantity.
In the embodiment of the application, the construction of the output decision upper model based on the first objective function and the first preset constraint condition comprises the following steps: constructing a first objective function based on the daily market gain and the real-time market gain of new energy power generation manufacturers; constructing a first preset constraint condition based on the contracted electric quantity constraint of the medium-and-long-term contract and the bidding segment output constraint of each time of the electric quantity of the medium-and-long-term contract; and constructing an output decision upper model based on the first objective function, the contracted electric quantity constraint of the medium-and-long-term contract and the output constraint of each time bidding segment of the electric quantity of the medium-and-long-term contract. The output decision upper model aims at maximizing the self-income of the new energy generator, so that the new energy generator can realize the self-benefit maximization and arrange the output distribution of the new energy generator set in each period.
In one embodiment, as shown in fig. 6, the lower model of the output decision is constructed based on the second objective function and the second preset constraint condition, including steps 520 to 560.
S520, constructing a second objective function based on the purchase cost of electricity, the purchase cost of standby service and the penalty cost caused by load loss in the market in the day-ahead.
Specifically, a second objective function is constructed based on the purchase cost of electricity, the purchase cost of standby service and the penalty cost caused by load loss in the market in the day-ahead, wherein the second objective function is an objective function of the lower model of the output decision. Specifically, the lower model of the output decision takes the total social benefit as an objective function, wherein the total social benefit is the scheduling cost. The objective function of the lower model of the output decision comprises the electricity purchasing cost in the market in the future, the purchasing cost of standby service and the punishment cost caused by load loss. Furthermore, the new energy power generator is a wind power generator. Thus, the objective function of the above-described force decision underlying model can be expressed as:
Wherein, C gjbt represents the bid price of the b-th bidding segment of the conventional generator set j at t in the day-ahead market, P gjbt represents the bid amount of the b-th bidding segment of the conventional generator set j at t in the day-ahead market, and delta s represents the occurrence probability of the wind power scene s; Representing the upper standby adjustment quantity of a conventional generator set j at the moment t under the scene S,/> Representing the lower standby adjustment quantity of a conventional generator set j at the moment t under the scene S,/>Representing reserve price for reserve on conventional genset j,/>Spare price for spare under conventional genset j,/>The amount of load loss at time t in the system scene S is shown. Further,/>The electricity purchase cost at the time t in the market in the day before is represented, wherein the electricity purchase cost of the conventional generator set at the time t and the electricity purchase cost of the new energy generator set are included. /(I)Representing the cost of purchasing backup service at time t in wind scene s,/>And (3) representing penalty cost caused by load loss at time t in the wind power scene s.
S540, constructing a second preset constraint condition based on the power balance constraint, the real-time market standby adjustment quantity balance constraint, the conventional generator set output constraint, the conventional generator set standby constraint and the wind power generator set output constraint.
Specifically, a second preset constraint condition is built based on the power balance constraint, the real-time market standby adjustment quantity balance constraint, the conventional generator set output constraint, the conventional generator set standby constraint and the wind power generator set output constraint, and the second preset constraint condition is a constraint condition of an output decision lower model. Wherein the power balance constraint can be expressed as:
Wherein P lt represents the load demand of the system at time t. The formula (5) shows that the sum of the winning power of the new energy generator set at the time t and the winning power of the conventional generator set j at the time t is required to meet the load demand of the system at the time t.
Further, the real-time market reserve adjustment balance constraint can be expressed as:
Wherein, Representing the amount of load loss of the system in a wind scene s,/>Represents the air discarding quantity of the system under the wind power scene s, lambda st represents Lagrange multiplier of real-time market standby adjustment quantity balance constraint,/>Represents the system air discarding quantity/>, under the wind power scene sLagrangian multiplier of lower limit,/>System air discarding quantity/>, under wind power scene sLagrangian multiplier of upper limit,/>Representing the system load loss/>, under a wind power scene sLagrangian multiplier of lower limit,/>Representing the system load loss/>, under a wind power scene sThe Lagrangian multiplier of the upper limit. Equation (6) indicates that the balance among the upper standby adjustment amount of the conventional generator set j at the time t, the lower standby adjustment amount of the conventional generator set j at the time t, the winning power of the new energy generator set and the load loss of the system is to be maintained. Equation (7) indicates that the air rejection rate of the new energy generator set at the time t is not higher than the actual power generation rate of the new energy generator set at the time t. Equation (8) indicates that the system's load loss at time t must not be higher than the system's load demand at time t.
Further, the conventional genset output constraint may be expressed as:
Wherein, The maximum value of the power of the b bidding segment of the conventional generator set j at the t moment, the minimum value of the output force of the conventional generator set j at the t moment and the maximum value of the output force of the conventional generator set j at the t moment are represented by P gjmin, and the maximum value of the output force of the conventional generator set j at the t moment is represented by P gjmax; /(I)Lagrangian multiplier,/>, representing the upper power limit of the b-th bidding segment of a conventional generator set j at time tLagrangian multiplier,/>, representing the lower power limit of the b-th bidding segment of a conventional generator set j at time tLagrangian multiplier representing lower power limit of conventional generator set j in t moment,/>The Lagrangian multiplier that represents the upper power limit of the conventional genset j at time t. Equation (9) represents the upper and lower limits of the b-th bid-segment power P gjbt of the conventional generator set j at time t, and equation (10) represents the upper and lower limits of the bid-segment power P gjt of the conventional generator set j at time t.
Further, conventional genset backup constraints may be expressed as:
Wherein, Maximum value representing the upper standby adjustment quantity of a conventional generator set j,/>Maximum value representing the next standby adjustment quantity of a conventional generator set j,/>Representing the upper standby adjustment quantity/>, of a conventional generator set jLagrangian multiplier of lower limit,/>Representing the upper standby adjustment quantity/>, of a conventional generator set jLagrangian multiplier of upper limit,/>Representing the next reserve adjustment amount/>, of a conventional genset jLagrangian multiplier of lower limit,/>Lower standby adjustment quantity/>, of conventional generator set jLagrangian multiplier of upper limit,/>Represents the nominal power P gjt of the conventional generator set j at the time t and the lower standby adjustment quantity/>, of the conventional generator set jLagrangian multiplier with lower difference limit,/>Represents the nominal power P gjt of the conventional generator set j at the time t and the upper standby adjustment quantity/>, of the conventional generator set jThe Lagrangian multiplier of the upper limit of the result is added. Equation (11) represents the upper standby adjustment amount/>, of the conventional generator set jThe upper and lower limits of (2) and (12) represent the lower standby adjustment amount/>, of the conventional generator set jThe upper limit and the lower limit of the power (13) of the normal generator set j at the time t represent the winning power P gjt and the lower standby adjustment quantity of the normal generator set jThe lower limit of the difference value, the formula (14) represents the winning power of the conventional generator set j at the time t and the upper standby adjustment quantity/>, of the conventional generator set jThe upper limit of the addition result.
S560, constructing an output decision lower model based on the second objective function, the power balance constraint, the real-time market reserve adjustment quantity balance constraint, the conventional generator set output constraint, the conventional generator set reserve constraint and the wind power generator set output constraint.
Specifically, the objective function of the lower output decision model and the constraint condition of the lower output decision model jointly form the lower output decision model. The lower model of the output decision takes the total welfare of the society as an objective function. Specifically, the objective function of the lower model of the output decision includes the electricity purchase cost of the market in the future, the purchase cost of the standby service, and the penalty cost caused by load loss. In addition, constraint conditions of the lower model of the output decision comprise power balance constraint, real-time market standby adjustment quantity balance constraint, conventional generator set output constraint, conventional generator set standby constraint and wind power generator set output constraint. And constructing the output decision lower model based on an objective function, a power balance constraint, a real-time market standby adjustment quantity balance constraint, a conventional generator set output constraint, a conventional generator set standby constraint and a wind power generator set output constraint of the output decision lower model.
In the embodiment of the application, the construction of the lower model of the output decision based on the second objective function and the second preset constraint condition comprises the following steps: constructing a second objective function based on the purchase cost of electricity, the purchase cost of standby service and the penalty cost caused by load loss in the market in the day-ahead; constructing a second preset constraint condition based on the power balance constraint, the real-time market standby adjustment quantity balance constraint, the conventional generator set output constraint, the conventional generator set standby constraint and the wind power generator set output constraint; and constructing an output decision lower model based on the second objective function, the power balance constraint, the real-time market standby adjustment quantity balance constraint, the conventional generator set output constraint, the conventional generator set standby constraint and the wind power generator set output constraint. The lower model of the output decision aims at maximizing the total benefit of the society, and simultaneously meets the requirements that new energy power generation manufacturers can maximize the benefit of themselves and maximize the total benefit of the society, so that the output distribution of the new energy power generation sets in each period is arranged.
In one embodiment, as shown in fig. 7, the output decision dual-layer model is converted based on the preset constraint condition and the KKT condition to generate the output decision single-layer model, which includes steps 342 to 344.
S342, performing equivalent replacement on a second objective function of the lower output decision model and a second preset constraint condition based on the KKT condition, and generating an equivalent KKT condition of the lower output decision model.
Specifically, the output decision upper model provides output distribution of a new energy power generator to the output decision lower model, namely the middle-long term contract decomposition electric quantity. Further, the output decision lower model obtains the price of the market in the day-ahead, the price of the market in the real-time market and the winning power of the new energy generator set based on the output distribution result of the new energy generator transmitted by the output decision upper model. Therefore, the variables of the output decision upper model are constants in the output decision lower model, and the output decision lower model is a linear model. Therefore, the lower model of the output decision can be replaced by an equivalent optimality condition, so that the double-layer model of the output decision is converted into a single-layer mathematical programming problem with balanced constraint.
Further, firstly, a Lagrange function of the lower output decision model is constructed based on an objective function of the lower output decision model and constraint conditions of the lower output decision model. Specifically, a lagrangian function of the output decision lower model is constructed according to an objective function of the output decision lower model shown in the formula (4), a power balance constraint of the output decision lower model shown in the formula (5), a real-time market standby adjustment quantity balance constraint shown in the formulas (6) - (8), a conventional generator set output constraint shown in the formulas (9) - (10), a conventional generator set standby constraint shown in the formulas (11) - (14) and a wind power generator set output constraint shown in the formulas (15) - (16), wherein the lagrangian function specifically comprises the following steps:
where L represents the lagrangian function of the underlying model of the output decision. Further, the decision variables of the lower-layer model of the output decision in the formula (17) are subjected to bias derivation to obtain a first-order KKT condition. Specifically, the decision variable of the lower-layer model of the output decision is the winning power of the new energy generator set at the time t Winning power P gjt of conventional generator set j at time t, and upper standby adjustment quantity/>, of conventional generator set j at time tSpare regulating variable/> of conventional generator set j at t momentLoad loss of system/>New energy generator set discards air quantity/>, at t momentFurther, the first order KKT condition is obtained by performing bias derivation on the decision variable in the lower layer model of the yielding decision in the formula (17), and the first order KKT condition can be expressed as:
/>
Further, the first order KKT condition is obtained by deviating the decision variable of the lower model of the output decision in the formula (17), and the first order KKT condition is represented by the formulas (18) - (41). Therefore, the objective function of the lower output decision model and the constraint condition of the lower output decision model are equivalently replaced based on the KKT condition, so that the equivalent KKT condition of the lower output decision model is generated, and the equivalent KKT condition of the lower output decision model is expressed by formulas (18) - (41).
And S344, generating an output decision single-layer model according to the first objective function of the output decision upper-layer model, the first preset constraint condition and the equivalent KKT condition of the output decision lower-layer model.
Specifically, the output decision single-layer model is formed by the objective function of the output decision upper-layer model shown in the formula (1), the contracted electric quantity constraint of the middle-long-term contract shown in the formula (2), the output constraint of the middle-long-term contract electric quantity in each period shown in the formula (3) and the equivalent KKT condition of the output decision lower-layer model shown in the formulas (18) - (41). Further, the objective function of the output decision single-layer model is the objective function of the output decision upper-layer model shown in the formula (1), and the constraint conditions of the output decision single-layer model are the constraint conditions of the output decision upper-layer models shown in the formulas (2) - (3) and the equivalent KKT conditions of the output decision lower-layer models shown in the formulas (18) - (41).
In the embodiment of the application, the output decision double-layer model is converted based on the preset constraint condition and the KKT condition to generate the output decision single-layer model, which comprises the following steps: performing equivalent replacement on a second objective function of the lower output decision model and a second preset constraint condition based on the KKT condition to generate an equivalent KKT condition of the lower output decision model; and generating an output decision single-layer model according to the first objective function of the output decision upper-layer model, the first preset constraint condition and the equivalent KKT condition of the output decision lower-layer model. And the equivalent optimality condition replacement is carried out on the lower model of the output decision, so that the double-layer optimization model is converted into a single-layer mathematical programming problem with balanced constraint, the output decision double-layer model is simplified, and the calculation complexity is further reduced. Therefore, the output decision single-layer model is easier to optimize parameters than the output decision double-layer model.
In one embodiment, linearizing the output decision single-layer model to obtain an output decision model of the electric power market includes:
and linearizing the decision variables in the output decision single-layer model by adopting a binary unfolding method to obtain the output decision model of the electric power market.
Specifically, the linearization processing mode of the output decision single-layer model is to linearize an objective function of the output decision single-layer model. The objective function linearization is mainly aimed at a nonlinear term in an objective function in an output decision single-layer model, and the nonlinear term is subjected to linearization treatment by using a binary expansion method, and is embodied in a form of multiplying two decision variables. Specifically, the objective function of the output decision single-layer model is the objective function of the output decision single-layer model. Therefore, the nonlinear term of the objective function of the output decision single-layer model is based on a binary expansion method to carry out linearization, and the objective function of the output decision single-layer model after linearization can be expressed as:
Further, linearization processing is carried out on a nonlinear term of the objective function of the output decision single-layer model based on a binary expansion method, so that the objective function of the output decision single-layer model after linearization processing is obtained. And obtaining the output decision model of the electric power market based on the objective function of the output decision single-layer model after linearization treatment and the constraint condition of the original output decision single-layer model. Specifically, the constraint conditions of the output decision model of the electric power market are the constraint conditions of the output decision upper layer models of the formulas (2) - (3) and the equivalent KKT conditions of the output decision lower layer models shown in the formulas (18) - (41).
In the embodiment of the application, linearization processing is carried out on the output decision single-layer model to obtain the output decision model of the electric power market, which comprises the following steps: and linearizing the decision variables in the output decision single-layer model by adopting a binary unfolding method to obtain the output decision model of the electric power market. And the output decision model of the power market obtained after linearization treatment of the output decision single-layer model has small calculated amount, and the difficulty of searching the global optimal solution is greatly reduced. Therefore, the output decision model of the electric power market is easier to find a globally optimal solution than the output decision single-layer model.
In a specific embodiment, as shown in fig. 7, a method for constructing an output decision model of an electric power market is provided, including steps 601 to 613.
S601, acquiring a clearing price of a market in the future;
s602, acquiring a clear price of a real-time market;
And S603, inputting the clearing prices of the market in the day and the clearing prices of the real-time market into an output decision model of the electric power market to perform medium-long term contract decomposition, and generating an output distribution result of the new energy generator set and an output distribution result of the conventional energy generator set.
Wherein, the power market output decision model is constructed, including steps 604 to 613.
S604, constructing a first objective function based on daily market benefits and real-time market benefits of new energy power generation manufacturers;
s605, constructing a first preset constraint condition based on the contracted electric quantity constraint of the medium-and-long-term contract and the output constraint of the medium-and-long-term contract electric quantity in each period;
s606, constructing an output decision upper model based on the first objective function, the contracted electric quantity constraint of the medium-and-long-term contract and the output constraint of the medium-and-long-term contract electric quantity in each period.
S607, constructing a second objective function based on the purchase cost of electricity, the purchase cost of standby service and the penalty cost caused by load loss in the market in the day-ahead;
s608, constructing a second preset constraint condition based on the power balance constraint, the real-time market standby adjustment quantity balance constraint, the conventional generator set output constraint, the conventional generator set standby constraint and the wind power generator set output constraint;
S609, constructing an output decision lower model based on a second objective function, a power balance constraint, a real-time market reserve adjustment quantity balance constraint, a conventional generator set output constraint, a conventional generator set reserve constraint and a wind power generator set output constraint.
And S610, constructing an output decision double-layer model of the electric power market based on the output decision upper layer model and the output decision lower layer model.
S611, performing equivalent replacement on a second objective function of the lower output decision model and a second preset constraint condition based on the KKT condition, and generating an equivalent KKT condition of the lower output decision model.
S612, generating an output decision single-layer model according to the first objective function of the output decision upper-layer model, the first preset constraint condition and the equivalent KKT condition of the output decision lower-layer model.
And S613, linearizing decision variables in the output decision single-layer model by adopting a binary unfolding method to obtain an output decision model of the electric power market.
To verify the feasibility of the output decision model of the above-described power market, a specific embodiment is described below.
The new energy generator set is assumed to be a wind power generator set, and the conventional energy generator set is assumed to be a conventional thermal generator set. The wind power generator sets are 1 group, and the conventional thermal generator sets are 3 groups. The adopted quotation mode is 5-section quotation, wherein the upper standby quotation is 1.1 times of the highest quotation of the conventional thermal generator set, the lower standby quotation is 0.9 times of the lowest quotation of the conventional thermal generator set, and the punishment cost caused by system load loss is 400 dollars. Basic information of the new energy generator set and the conventional energy generator set is shown in table 1, and quotation information of the conventional energy generator set is shown in table 2.
TABLE 1
TABLE 2
Randomly sampling a wind power time sequence by a Latin hypercube sampling method by adopting a multi-scene technology, obtaining a multi-scene time sequence curve of wind power, and describing the uncertainty of the wind power:
taking 10% of predicted output of a new energy generator set as standard deviation, randomly sampling 3000 times by using a Latin hypercube sampling method to obtain 3000 wind power output scenes, reducing redundant and irrelevant information existing in the scenes by using an Euclidean distance method, and finally obtaining 7 wind power scenes to describe the uncertainty output of wind power, wherein the result is shown in fig. 9, and describing 7 wind power scenes to describe the uncertainty output of wind power.
Assume that the middle-long term contract electric quantity of the new energy generator set is 2000MW, and the contracted electric price of the middle-long term spread contract is 56 dollars. In combination with tables 1 and 2 and the preset parameter values described above, known quantities are input into the output decision model of the electric market. As shown in fig. 10, the output distribution result of the new energy generator set and the output distribution result of the conventional energy generator set in an embodiment are shown. As shown in fig. 10, wind power generators of medium-and-long-term contracts tend to arrange the output of the contract power at a time when the difference from the spot market price is large, that is, the output is arranged in a period where the spot market price is the lowest, and therefore, the power distribution situation of the wind power generator can be changed by signing the differential contract price of the time sharing.
Comparing the average division method with the output decision model of the electric power market provided by the application, and table 3 shows social benefit comparison results obtained by using the output decision model of the electric power market of the average division method. As shown in Table 3, the total social benefit obtained by using the output decision model of the electric power market is obviously increased, and the middle-long-term contract decomposition method obtained by using the output decision model of the electric power market provided by the application has larger total social benefit than that obtained by using the average decomposition method.
TABLE 3 Table 3
It should be understood that, although the steps in the flowcharts of fig. 2-8 are shown in order as indicated by the arrows, these steps are not necessarily performed in order as indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in fig. 2-8 may include multiple steps or stages that are not necessarily performed at the same time, but may be performed at different times, nor does the order in which the steps or stages are performed necessarily performed in sequence, but may be performed alternately or alternately with at least a portion of the steps or stages in other steps or other steps.
In one embodiment, as shown in FIG. 11, there is provided an output decision device 700 for an electric power market, comprising:
the parameter obtaining module 720 is configured to obtain the price of the market in the day-ahead market and the price of the market in real time.
The output decision module 740 is used for inputting the clearing price of the market in the day and the clearing price of the real-time market into an output decision model of the electric power market to perform long-term contract decomposition, so as to generate an output distribution result of the new energy generator set and an output distribution result of the conventional energy generator set; the output decision model of the electric power market is used for performing medium-long term contract decomposition on the new energy generator set and the conventional energy generator set.
In one embodiment, as shown in fig. 12, the output decision device 700 of the electric power market further includes an output decision model building module 760, where the output decision model building module 760 includes:
The output decision double-layer model construction unit 762 is used for constructing an output decision double-layer model of the electric power market; the output decision double-layer model comprises an output decision upper layer model and an output decision lower layer model;
The output decision single-layer model conversion unit 764 is used for converting the output decision double-layer model based on preset constraint conditions and KKT conditions to generate an output decision single-layer model;
and the linear processing unit 766 is used for carrying out linearization processing on the output decision single-layer model to obtain an output decision model of the electric power market.
In one embodiment, the output decision two-layer model building unit 762 is further configured to build an output decision upper layer model based on the first objective function and a first preset constraint condition; the first objective function is used for representing the income of new energy power generation manufacturers; constructing an output decision lower model based on a second objective function and a second preset constraint condition; the second objective function is used to characterize the overall social benefit.
In one embodiment, the output decision single-layer model conversion unit 764 is further configured to perform equivalent replacement on the second objective function and the second preset constraint condition of the output decision lower layer model based on the KKT condition, so as to generate an equivalent KKT condition of the output decision lower layer model; and generating an output decision single-layer model according to the first objective function of the output decision upper-layer model, the first preset constraint condition and the equivalent KKT condition of the output decision lower-layer model.
In one embodiment, the linear processing unit 766 is further configured to perform linearization processing on the decision variables in the output decision single-layer model by adopting a binary expansion method, so as to obtain an output decision model of the electric power market.
The division of the modules in the output decision device of the electric power market is only used for illustration, and in other embodiments, the output decision device of the electric power market can be divided into different modules according to the needs, so as to complete all or part of functions of the output decision device of the electric power market.
In one embodiment, FIG. 13 is a schematic diagram of the internal architecture of a computer device in one embodiment. As shown in fig. 13, the computer device may be a server, the computer device including a processor, memory, and network interface connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The database of the computer device is used for storing audio data, the network interface of the computer device is used for communicating with an external terminal through a network connection, and the internal memory provides an environment for the operation of an operating system and a computer program in a nonvolatile storage medium. The computer program is executable by the processor for implementing the power market output decision method provided by the above embodiments.
The implementation of each module in the output decision device of the electric power market provided by the embodiment of the application can be in the form of a computer program. The computer program may run on a computer device or a server. Program modules of the computer program may be stored in the memory of the computer device or server. Which when executed by a processor, performs the steps of the method described in the embodiments of the application.
The embodiment of the application also provides a computer readable storage medium. One or more non-transitory computer-readable storage media containing computer-executable instructions that, when executed by one or more processors, cause the processors to perform the steps of a power market output decision method.
A computer program product containing instructions that, when run on a computer, cause the computer to perform a method of power market output decision making.
Any reference to memory, storage, database, or other medium used by embodiments of the application may include non-volatile and/or volatile memory. Suitable nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM), which acts as external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link (SYNCHLINK) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples illustrate only a few embodiments of the application, which are described in detail and are not to be construed as limiting the scope of the application. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the application, which are all within the scope of the application. Accordingly, the scope of protection of the present application is to be determined by the appended claims.
Claims (10)
1. A method of making an output decision for an electric market, the method comprising:
Acquiring the clearing price of the market in the day-ahead and the clearing price of the real-time market;
The clearing price of the day-ahead market and the clearing price of the real-time market are input into an output decision model of an electric power market to conduct middle-long-term contract decomposition, and an output distribution result of a new energy generator set and an output distribution result of a conventional energy generator set are generated; the output decision model of the electric power market is used for performing medium-long term contract decomposition on the new energy generator set and the conventional energy generator set; the medium-and-long-term contract decomposition is used for decomposing the electric quantity participating in the transaction to each period so as to reflect the short-term supply and demand relationship of the electric power commodities;
the generation process of the output decision model of the electric power market comprises the following steps:
constructing an output decision double-layer model of the electric power market; the output decision double-layer model comprises an output decision upper layer model and an output decision lower layer model;
the objective function of the output decision upper model is as follows:
The objective function of the lower model of the output decision is as follows:
Wherein R w represents the income of new energy power generation manufacturers, The contract price is a medium-long term contract price of the new energy generator set; lambda t and lambda st represent the price of the market before day and the price of the market in real time,/>Decomposing electric quantity for medium-long-term electricity consumption contract of new energy generator set at t moment,/>For the winning capacity of the new energy generator set at the time t,/>And/>Respectively representing the actual power generation amount and the wind curtailment power amount of the new energy generator set under a scene s, wherein the scene s is used for describing the uncertainty of wind power; c gjbt represents the bid amount of the b-th bidding segment of the conventional generator set j at t in the day-ahead market, P gjbt represents the bid amount of the b-th bidding segment of the conventional generator set j at t in the day-ahead market, and delta s represents the occurrence probability of a wind power scene s; /(I)Representing the upper standby adjustment quantity of a conventional generator set j at the moment t under the scene S,/>Representing the lower standby adjustment quantity of a conventional generator set j at the moment t under the scene S,/>Representing reserve price for reserve on conventional genset j,/>Spare price for spare under conventional genset j,/>Representing the load loss amount at time t under the system scene S;
Converting the output decision double-layer model based on preset constraint conditions and KKT conditions to generate an output decision single-layer model;
And carrying out linearization treatment on the output decision single-layer model to obtain the output decision model of the electric power market.
2. The method of claim 1, wherein said constructing an output decision bilayer model of the power marketplace comprises:
Constructing the output decision upper model based on a first objective function and a first preset constraint condition; the first objective function is used for representing the income of new energy power generation manufacturers;
Constructing the lower-layer model of the output decision based on a second objective function and a second preset constraint condition; the second objective function is used to characterize the overall social benefit.
3. The method of claim 2, wherein constructing the output decision upper model based on a first objective function and a first preset constraint comprises:
constructing the first objective function based on the daily market gain and the real-time market gain of the new energy power generator;
Constructing the first preset constraint condition based on the contracted electric quantity constraint of the medium-and-long-term contract and the output constraint of the medium-and-long-term contract electric quantity in each period;
and constructing the output decision upper model based on the first objective function, the contracted electric quantity constraint of the medium-and-long-term contract and the output constraint of the medium-and-long-term contract electric quantity in each period.
4. The method of claim 2, wherein constructing the lower model of the output decision based on a second objective function and a second preset constraint comprises:
constructing the second objective function based on the purchase cost of electricity, the purchase cost of standby service and the penalty cost caused by load loss in the market in the day-ahead;
Constructing a second preset constraint condition based on a power balance constraint, a real-time market standby adjustment quantity balance constraint, a conventional generator set output constraint, a conventional generator set standby constraint and a wind power generator set output constraint;
And constructing the output decision lower model based on the second objective function, the power balance constraint, the real-time market standby adjustment quantity balance constraint, the conventional generator set output constraint, the conventional generator set standby constraint and the wind power generator set output constraint.
5. The method of claim 1, wherein the transforming the output decision bilayer model based on preset constraints and KKT conditions to generate an output decision monolayer model comprises:
Performing equivalent replacement on the second objective function of the lower-level output decision model and a second preset constraint condition based on the KKT condition to generate an equivalent KKT condition of the lower-level output decision model;
And generating the output decision single-layer model according to the first objective function of the output decision upper-layer model, a first preset constraint condition and an equivalent KKT condition of the output decision lower-layer model.
6. The method of claim 1, wherein the linearizing the output decision single-layer model to obtain the output decision model of the electric power market comprises:
And linearizing decision variables in the output decision single-layer model by adopting a binary unfolding method to obtain the output decision model of the electric power market.
7. An output decision device for an electric power market, the device comprising:
the parameter acquisition module is used for acquiring the clearing price of the market in the day-ahead and the clearing price of the real-time market;
the output decision module is used for inputting the clearing price of the day-ahead market and the clearing price of the real-time market into an output decision model of the electric power market to perform long-term contract decomposition, so as to generate an output distribution result of the new energy generator set and an output distribution result of the conventional energy generator set; the output decision model of the electric power market is used for performing medium-long term contract decomposition on the new energy generator set and the conventional energy generator set; the medium-and-long-term contract decomposition is used for decomposing the electric quantity participating in the transaction to each period so as to reflect the short-term supply and demand relationship of the electric power commodities;
The output decision model building module is used for building an output decision double-layer model of the electric power market; the output decision double-layer model comprises an output decision upper layer model and an output decision lower layer model; the objective function of the output decision upper model is as follows:
The objective function of the lower model of the output decision is as follows:
Wherein R w represents the income of new energy power generation manufacturers, The contract price is a medium-long term contract price of the new energy generator set; lambda t and lambda st represent the price of the market before day and the price of the market in real time,/>Decomposing electric quantity for medium-long-term electricity consumption contract of new energy generator set at t moment,/>For the winning capacity of the new energy generator set at the time t,/>And/>Respectively representing the actual power generation amount and the wind curtailment power amount of the new energy generator set under a scene s, wherein the scene s is used for describing the uncertainty of wind power; c gjbt represents the bid amount of the b-th bidding segment of the conventional generator set j at t in the day-ahead market, P gjbt represents the bid amount of the b-th bidding segment of the conventional generator set j at t in the day-ahead market, and delta s represents the occurrence probability of a wind power scene s; /(I)Representing the upper standby adjustment quantity of a conventional generator set j at the moment t under the scene S,/>Representing the lower standby adjustment quantity of a conventional generator set j at the moment t under the scene S,/>Representing reserve price for reserve on conventional genset j,/>Spare price for spare under conventional genset j,/>Representing the load loss amount at time t under the system scene S;
Converting the output decision double-layer model based on preset constraint conditions and KKT conditions to generate an output decision single-layer model;
And carrying out linearization treatment on the output decision single-layer model to obtain the output decision model of the electric power market.
8. The apparatus of claim 7, wherein the output decision model building module is further configured to build an output decision upper model based on a first objective function and a first preset constraint; the first objective function is used for representing the income of new energy power generation manufacturers; constructing an output decision lower model based on a second objective function and a second preset constraint condition; the second objective function is used to characterize the overall social benefit.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1 to 6 when the computer program is executed.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 6.
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