CN113822707A - Output decision method and device for power market, computer equipment and storage medium - Google Patents
Output decision method and device for power market, computer equipment and storage medium Download PDFInfo
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Abstract
The application relates to an output decision method, an output decision device, computer equipment and a computer readable storage medium of a power market. The method comprises the following steps: acquiring the clearing price of the current market and the clearing price of the real-time market; inputting the clearing price of the current market and the clearing price of the real-time market into an output decision model of the power market for medium and long term contract decomposition 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 carrying out medium and long term contract decomposition on the new energy generator set and the conventional energy generator set. The output decision model of the electric power market obtains the output distribution of the new energy generator set and the output distribution of the conventional energy generator set according to the clearing price of the day-ahead market and the clearing price of the real-time market, so that new energy power generation manufacturers participate in bidding of the electric power market, and reliable execution of long-term trade in the new energy is guaranteed.
Description
Technical Field
The present disclosure relates to the field of power market technologies, and in particular, to a method and an apparatus for deciding output of a power market, a computer device, and a computer-readable storage medium.
Background
At present, the large-scale development and utilization of new energy is a very important issue due to the increasing exhaustion of fossil energy and the outstanding environmental issues. With the continuous development of new energy power generation technology, new energy gradually participates in the competition of the power market.
However, the new energy power generation generator set does not have reliable power supply capacity due to large fluctuation and randomness in the power generation process, so that the new energy power generation generator set is difficult to participate in medium and long term power market transactions. At present, most research focuses on the field of medium-and-long term contract decomposition aiming at the conventional energy power generation generator set, so that the medium-and-long term contract decomposition method aiming at the conventional energy power generation generator set is mature. However, when a long-term contract decomposition is performed on a hybrid power generation generator set of new energy and conventional energy, a medium-and-long-term contract decomposition method of the conventional energy power generation generator set cannot be directly used.
Therefore, it is highly desirable to provide a medium-and-long term contract decomposition method for a hybrid power generation generator set of new energy and conventional energy, so that the long term contract decomposition of the new energy participates in spot market bidding, and the reliable execution of medium-and-long term trading is ensured.
Disclosure of Invention
In view of the above, there is a need to provide an output decision method, an output decision device, a computer device and a computer readable storage medium for enabling new energy to participate in the electric power market of medium and long term trading.
A method of force decision for an electricity market, the method comprising:
acquiring the clearing price of the current market and the clearing price of the real-time market;
inputting the clearing price of the current market and the clearing price of the real-time market into an output decision model of the power market for medium and long term contract decomposition 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 carrying out medium and 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 power market includes:
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;
converting the output decision double-layer model based on a preset constraint condition and a KKT condition to generate an output decision single-layer model;
and carrying out linearization processing on the output decision single-layer model to obtain the output decision model of the power market.
In one embodiment, constructing a power market contribution decision-making two-tier model comprises:
constructing an output decision upper layer model based on a first objective function and a first preset constraint condition; the first objective function is used for representing the income of a new energy power generation manufacturer;
constructing an output decision lower-layer model based on a second objective function and a second preset constraint condition; the second objective function is used to characterize the overall social welfare.
In one embodiment, constructing the output decision upper layer model based on the first objective function and the first predetermined constraint condition includes:
constructing a first objective function based on the day-ahead market income and the real-time market income of a new energy power generation manufacturer;
constructing a first preset constraint condition based on the agreed electric quantity constraint of the medium-long term contract and the output constraint of each time period of the medium-long term contract electric quantity;
and constructing an output decision upper layer model based on the first objective function, the agreed electric quantity constraint of the medium-long term contract and the output constraint of each period of the medium-long term contract electric quantity.
In one embodiment, constructing the output decision lower layer model based on the second objective function and the second predetermined constraint condition includes:
constructing a second objective function based on the electricity purchasing cost, the standby service purchasing cost and the penalty cost caused by load loss in the market at present;
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 layer model based on the second objective function, the power balance constraint, the real-time market reserve adjustment balance constraint, the conventional generator set output constraint, the conventional generator set reserve constraint and the wind power generator set output constraint.
In one embodiment, converting the contribution decision double-layer model based on the preset constraint condition and the KKT condition to generate a contribution decision single-layer model, includes:
performing equivalent replacement on a second target function and a second preset constraint condition of the output decision lower layer model based on the KKT condition 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 target 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 linearizing the output decision single-layer model to obtain the output decision model of the power market includes:
and (4) carrying out linear processing on the decision variables in the output decision single-layer model by adopting a binary expansion method to obtain the output decision model of the 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 day-ahead market 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 for medium and long term contract decomposition 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 carrying out medium and long term contract decomposition on the new energy generator set and the conventional energy generator set.
A computer device comprising a memory and a processor, the memory storing a computer program, the processor implementing the steps of the method as above when executing the computer program.
A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method as above.
The output decision method, the output decision device, the computer equipment and the computer readable storage medium of the power market comprise the following steps: firstly, acquiring the clearing price of the current market and the clearing price of the real-time market; then inputting the clearing price of the current market and the clearing price of the real-time market into an output decision model of the power market for medium and long term contract decomposition 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 carrying out medium and long term contract decomposition on the new energy generator set and the conventional energy generator set. The output decision model of the electric power market obtains the output distribution of the new energy generator set and the output distribution of the conventional energy generator set according to the clearing price of the day-ahead market and the clearing price of the real-time market, so that new energy power generation manufacturers participate in bidding of the electric power market, and reliable execution of long-term trade in the new energy is guaranteed.
Drawings
FIG. 1 is a diagram of an application environment of a method for power market contribution decision-making in one embodiment;
FIG. 2 is a schematic flow chart of an output decision method for the electricity market in one embodiment;
FIG. 3 is a schematic diagram of a process for generating an output decision model for an electricity market in one embodiment;
FIG. 4 is a schematic flow diagram of constructing a power market contribution decision-making two-tier model, under an embodiment;
FIG. 5 is a schematic flow chart illustrating a process of constructing an output decision upper layer model based on a first objective function and a first predetermined constraint condition according to an embodiment;
FIG. 6 is a schematic flow chart illustrating a process of constructing an output decision lower model based on a second objective function and a second predetermined constraint condition according to an embodiment;
FIG. 7 is a schematic flow chart illustrating the conversion of the output decision double-layer model into an output decision single-layer model based on the predetermined constraint condition and the KKT condition in one embodiment;
FIG. 8 is a schematic flow chart illustrating the process of constructing a power market contribution decision model in one embodiment;
FIG. 9 is a schematic diagram of the output of different wind power scenarios in one embodiment;
FIG. 10 is a schematic diagram of the distribution of the new energy genset output and the distribution of the conventional energy genset output in one embodiment;
FIG. 11 is a block diagram of an output decision device of the electric power market in one embodiment;
FIG. 12 is a block diagram of the structure of the contribution decision model building module in one embodiment;
FIG. 13 is a diagram illustrating an internal structure of a computer device according to an embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
The output decision method for the power market provided by the application can be applied to the application environment shown in fig. 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 power market for medium-long term contract decomposition 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 carrying out medium and 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 method for determining the output of the power market is provided, which is applied to the electronic device 120 and the server 140, and includes steps 220 to 240.
And S220, acquiring the clearing price of the day-ahead market and the clearing price of the real-time market.
Specifically, the clearing price of the power market refers to that when the price is proper, the power generation side and the user side in the power market can achieve the balance of supply and demand of power at the price level, that is, the demand in the power market is equal to the supply. The above-mentioned appropriate price is to indicate the price of the clear electricity, and the price of the clear electricity is formed in association with both the supply on the power generation side and the demand on the user side. At the clearing price level, the amount of electricity that the user is willing and able to purchase is exactly the same as the amount of electricity that the power generation side is willing and able to supply. If the market price is higher than the clear electricity price, the market supply electric quantity is more than the demand electric 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 electricity on the power generation side. Similarly, if the market price is lower than the price of the clear electricity, the market has supply shortage or demand surplus. Competition between the user sides occurs, and as a result, the electricity price is raised, so that the demand is reduced, and the market tends to be balanced. Furthermore, the operation of the current power market consists of day-ahead scheduling and real-time scheduling, the effective utilization of power generation facilities and power transmission facilities in the region 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 amount of the day-ahead market offer is settled according to the day-ahead market price, and the deviation amount between the real-time and day-ahead market offers is settled according to the real-time market price.
Further, "day ahead" in the day-ahead market refers to the day before the actual operation day of the grid, and thus the day-ahead market refers to the electric energy trading market that occurs on this "day before". Through the market in the day ahead, the scheduling system of electric wire netting can in time optimize the scheduling plan of operation day, and the power generation plant also can be according to the generated energy of market trading result adjustment generating set simultaneously. For example, in the day-ahead market, market members are moving 8: 00-12: 00 submit the next day's bid to the market division. Market division 12: 00-14: 00 evaluate the bid plan and select the most efficient and economical mode of operation. At 14: 00-16: 00, the market operating unit notifies each member of the evaluation result, and in 16: 00 to day 8: 00, the market operation part can also make certain adjustment according to the requirements of system economy, reliability and the like.
Further, the real-time market is actually a balance market, so that the system dispatcher can balance the generated energy and the load in all periods of the real-time operation and provide corresponding trading prices for all parties participating in the electric quantity trading, and the quotes of the market participants in the real-time market are generally submitted to the system dispatching operation mechanism after the spot market is closed. Therefore, the real-time market is set up for solving the system accidents, network congestion and market settlement difficulties.
Further, the electronic device 120 stores therein the day-ahead market clearing price and the real-time market clearing price, wherein the day-ahead market clearing price and the real-time market clearing price of the electric power market are obtained by counting the electric power market data by a human or a computer device. Further, the server 140 obtains the clearing price of the market in the day before and the clearing price of the real-time market from the electronic device 120 through data transmission. The data transmission method between the electronic device 120 and the server 140 is not limited to ethernet, bluetooth, radio station, wifi, and the like, and the data transmission method between the electronic device 120 and the server 140 is not limited in the present application.
S240, 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 power market for medium and long term contract decomposition 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 carrying out medium and long term contract decomposition on the new energy generator set and the conventional energy generator set.
Specifically, for the electric power spot market, the time range includes the time between the previous day of the real-time operation of the system and the real-time operation. The electric power spot market generally adopts a unified clearing mode, market members voluntarily participate in declaration, and the formed trading plan is subjected to physical delivery and settlement. The spot market can form an optimized trading plan which is adaptive to the physical operation of the power system and reflects the willingness of market members in a proper amount of time in advance, so that the full competition of electric quantity trading is promoted by a centralized clearing means, and the efficient and optimized configuration of power resources is realized. Furthermore, medium-long term contract decomposition means that the electric quantity participating in transaction is not decomposed to each time interval by signing a differential contract, so that certain risk is avoided. Furthermore, the spot market can play the role of market price formation through medium-long term contract decomposition, the short-term supply-demand relation and the space-time value of the electric power commodity can be truly reflected, and the system security risk and the financial risk of transaction are reduced. Further, medium and long term trading is embodied in longer trading time granularity and longer time interval between trading time and delivery time.
The output of the new energy generator set is distributed as the power generated by the new energy generator set in unit time, and the output of the conventional energy generator set is distributed as 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 hydroelectric 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 power market starts from a mechanism that new energy participates in the power market, and comprehensively considers various factors such as a power supply structure, a load level, load characteristics, power generation characteristics and the like. The output decision model of the electric power market considers that an independent system operator does not treat wind power differently in the market clearing process, only needs to give the nominal power of the wind power generating set like thermal power without taking charge of wind power dispatching, and therefore the output of the output decision model of the electric power market is the optimal output strategy of a new energy power generation manufacturer, and specifically comprises the nominal power of the new energy power generating set, the output distribution of the new energy power generating set and the output distribution of a conventional energy power generating set.
Further, the day-ahead market clearing price and the real-time market clearing price of the electric power market, which are stored in the electronic device 120, are obtained by counting the electric power market data by a person or a computer device. The server 140 acquires the clearing price of the day-ahead market and the clearing price of the real-time market from the electronic device 120 through data transmission. The output decision model of the power market comprises an output decision upper layer model and an output decision lower layer model. The output decision upper layer model aims at maximizing the income of new energy power generation manufacturers, and the output decision lower layer model aims at maximizing the total social benefits. The output decision upper layer model provides an output distribution result of the new energy power generation manufacturer to the output decision lower layer model, so that the output decision lower layer model obtains medium and long term contract decomposition electric quantity of the new energy power generation manufacturer. And the output decision lower-layer 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-layer model, so that the output decision upper-layer model obtains the feedback condition of the output distribution of the clearing price of the day-ahead market and the clearing price of the real-time market. Therefore, the output decision upper layer model and the output decision lower layer 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 day-ahead market clearing price and the real-time market clearing price.
Further, the server 140 inputs the obtained clearing price of the day-ahead market and the clearing price of the real-time market into the output decision model of the electric power market for medium and long term contract decomposition, and further obtains an output distribution result of the new energy generator set and an output distribution result of the conventional energy generator set.
In the embodiment of the application, a power market output decision method is provided, and clearing prices of the day-ahead market and the real-time market are obtained at first; then inputting the clearing price of the current market and the clearing price of the real-time market into an output decision model of the power market for medium and long term contract decomposition 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 carrying out medium and long term contract decomposition on the new energy generator set and the conventional energy generator set. The output decision model of the electric power market obtains the output distribution of the new energy generator set and the output distribution of the conventional energy generator set according to the clearing price of the day-ahead market and the clearing price of the real-time market, so that new energy power generation manufacturers participate in bidding of the electric power market, and reliable execution of long-term trade in the new energy is guaranteed.
In one embodiment, as shown in fig. 3, the process of generating the output decision model of the electricity market includes steps 320 to 360:
s320, constructing an output decision double-layer model of the 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 power market adopts a starkeberg model, and the starkeberg model is a yield leading model and reflects asymmetric competition among enterprises. Specifically, the Stark-Berger model is a yield leader model, and the manufacturers have different action sequences. The yield was determined according to the following sequence: the lead manufacturer determines a yield, and the follower manufacturer can then observe this yield, and then determine his own yield based on the lead manufacturer's yield. It should be noted that the leading vendor, when deciding on its own production, has full knowledge of how the following vendor will act, which means that the leading vendor can know the response function of the following vendor. Thus, a leading vendor will naturally expect the effect of its own-determined yield on following vendors. It is in view of this effect that the yield decided by the leading vendor will be a profit-maximizing yield constrained by the response function of the following vendor. In the Starkeberg model, the leader's decision no longer requires its own reaction function.
Further, the output decision double-layer model of the power market adopts a leader-slave form of a starkeberg model, and the output decision double-layer model comprises an output decision upper layer model and an output decision lower layer model. Specifically, the output decision-making lower-layer model is a day-ahead market and real-time market clearing model, and can also be expanded to a daily electric power market and an electric power balance market. Furthermore, the output decision lower layer model takes the maximization of social welfare as an objective function, and the output decision upper layer model takes the maximization of the income of a new energy power generation manufacturer as an objective function. Specifically, the clearing price of the day-ahead market and the clearing price of the real-time market can be obtained through the output decision lower-layer model. In addition, the output decision lower-layer model 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.
Furthermore, the output decision upper layer model provides an output distribution result of the new energy power generation manufacturer to the output decision lower layer model, so that the output decision lower layer model obtains the quotation condition of the new energy power generation manufacturer, namely the medium and long term contract decomposition electric quantity. And the output decision lower layer model obtains the medium and long term contract decomposed electric quantity of the new energy generator set from the output decision upper layer model, and further transmits the obtained clearing price of the day-ahead market, the clearing price of the real-time market and the normal bid power of the new energy generator set to the output decision upper layer model, so that the output decision upper layer 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 layer model and the output decision lower layer 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 S340, converting the output decision double-layer model based on the preset constraint condition and the KKT condition to generate an output decision single-layer model.
Specifically, the output decision upper layer model provides output distribution of new energy power generation manufacturers to the output decision lower layer model, namely, medium and long term contract decomposition electric quantity. Further, the output decision lower layer model obtains the 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 based on the output distribution of the new energy generator manufacturer transmitted by the output decision upper layer model. Therefore, the variables of the output decision upper layer model are constants in the output decision lower layer model, and the output decision lower layer model is a linear model. Further, the output decision lower layer model is replaced by an equivalent optimality condition, so that a double-layer optimization model is converted into a single-layer Mathematical programming problem with equilibrium constraint (MPEC). Further, the equivalent optimality condition is formed by, but not limited to, kuntake condition (KKT) and dual transformation, which is not limited in this application.
Optionally, the output decision upper layer model includes a target 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 a target function of the output decision lower layer model and a constraint condition of the output decision lower layer model, and the target function and the constraint condition of the output decision lower layer model are converted by using a KKT condition to generate an equivalent KKT condition of the output decision lower layer model. Furthermore, an output decision single-layer model is formed together 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 includes a target function of the output decision lower-layer model after the KKT condition conversion, an equivalent KKT condition of the output decision lower-layer model after the KKT condition conversion, a target function of the output decision upper-layer model, and a constraint condition of the output decision upper-layer model.
And S360, performing linearization processing on the output decision single-layer model to obtain the output decision model of the power market.
Specifically, although the contribution decision single-layer model is a single-layer model, some non-linear terms exist in the contribution decision single-layer model, for example, when two decision variables are multiplied in the contribution decision single-layer model, the multiplied two decision variables are represented as non-linearity. Therefore, if the output decision single-layer model with the non-linear term is directly solved, the calculation amount is greatly increased, and the difficulty of finding the global optimal solution is increased. Therefore, the output decision single-layer model needs to be linearized, and the output decision single-layer model after linearization is the output decision model of the power market. The Mixed-integer Linear Programming (MILP) model has the advantages of stability and easy optimization. Therefore, the output decision single-layer model is linearized and then converted into a mixed positive integer linear programming model. Specifically, the linear programming model means that the objective function is linear, all constraints of the objective function are linear, and the objective function decision variables can take any real number. However, if some of the decision variables in the linear programming problem require integers, then the linear programming problem transforms into a mixed integer linear programming problem. That is, for the mixed positive integer linear programming model, the optimization conditions include not only conditional constraints but also integer constraints.
Further, the output decision single-layer model is linearized by means including, but not limited to, complementary condition linearization and objective function linearization. Specifically, for the linearization of the complementarity condition, for a shape, such as xy ═ 0 complementarity constraint condition, where x and y are lagrange multipliers and a continuous function respectively, and x ≧ 0, M and u are used to linearize the shape, such as xy ═ 0 complementarity constraint condition, where the linearization process is performed with the complementarity constraint conditions of x ≦ Mu, y ≦ M (1-u), where M is a large constant, and u is a variable from 0 to 1. Further, for target function linearization, mainly aiming at the nonlinear term of the target function in the output decision single-layer model, the nonlinear term in the target function of the output decision single-layer model is linearized by using a binary expansion method.
Optionally, the nonlinear term in the objective function of the output decision single-layer model is linearized through a binary expansion method to obtain the linearized objective function of the output decision single-layer model. Based on the target function of the output decision single-layer model after linearization, the equivalent KKT condition of the output decision lower-layer model and the constraint condition of the output decision upper-layer model, the output decision model of the electric power market is formed together.
In the embodiment of the application, a method for generating an output decision model of an electric power market is provided, and the method comprises the steps of firstly 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; then, converting the output decision double-layer model based on a preset constraint condition and a KKT condition to generate an output decision single-layer model; and finally, carrying out linearization processing on the output decision single-layer model to obtain the output decision model of the power market. The output decision upper layer model provides an output distribution result of the new energy power generation manufacturer to the output decision lower layer model, so that the output decision lower layer model obtains medium and long term contract decomposition electric quantity of the new energy power generation manufacturer. And the output decision lower-layer 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-layer model, so that the output decision upper-layer model obtains the feedback condition of the output distribution of the clearing price of the day-ahead market and the clearing price of the real-time market. Therefore, the output decision upper layer model and the output decision lower layer 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, a decision-making double-layer model of the output of the power market is constructed, including steps 322 to 324.
S322, constructing an output decision upper layer model based on the first objective function and the first preset constraint condition; the first objective function is used for representing the income of a new energy power generation manufacturer.
Specifically, the first objective function is an objective function of the output decision upper layer model, and the first preset constraint condition is a preset constraint condition of the output decision upper layer model. Furthermore, the objective function of the output decision upper layer model is the income of the new energy power generation manufacturer, and the income of the new energy power generation manufacturer is settled on the basis of a price difference contract authorized by the government. Specifically, there are two contract forms in the medium and long term trading market: physical contracts and spread contracts. For the physical contract, what amount of electric quantity is signed by the physical contract, and what amount of electric quantity is to be sent by the new energy power generation manufacturer. The difference contract does not require physical execution, and only settles the difference between the electricity price of the spot market and the contract price, so the difference contract is a financial hand bidding section and only affects the settlement. The introduction of the spread contract into the electricity market has two effects, on one hand, the spread contract does not restrict the resource optimization configuration of the spot market because it does not require physical execution in the spot market; on the other hand, the spread contract can effectively avoid the over-low or over-high price quoted in the spot market.
Further, the objective function of the output decision upper layer model comprises the income of new energy power generation manufacturers in the day-ahead market and the income of the real-time market. The income of the market at present comprises the income of a spread contract and the income of a spot market, and the income of the real-time market is the income obtained by a new energy power generation manufacturer in the real-time market, namely the income obtained by overdue due to the uncertainty of wind power and the balance cost required to be paid by the minimum number of generations.
Furthermore, the difference price authorized by the government is adopted by the output decision upper-level model to be the settlement basis of the income of the new energy power generation manufacturer, and the income of the new energy power generation manufacturer is used as a target function. The output decision upper layer model provides output distribution of new energy power generation manufacturers to the output decision lower layer model, so that the output decision lower layer model obtains the quotation condition of the new energy power generation manufacturers, namely medium and long term contract decomposition electric quantity. The output distribution of the new energy power generation manufacturer comprises the winning power of the new energy power generator set and the output distribution of the new energy power generator set.
Further, the medium-and-long-term contract decomposition electric quantity is an electric quantity constraint on a certain bidding period, specifically, the medium-and-long-term contract decomposition electric quantity may be a daily electric quantity constraint or an electric quantity constraint of several hours, and the application does not limit the electric quantity. Optionally, the medium-and-long-term contract decomposition electric quantity is a daily electric quantity constraint, that is, the agreed electric quantity of the medium-and-long-term contract is the total electric quantity generated by the new energy generator set in one day, and the medium-and-long-term contract decomposition electric quantity is the electric quantity generated by the new energy generator set at each time in one day. Therefore, the constraint conditions of the output decision upper layer model comprise the contracted electric quantity constraint of the medium-long term contract and the medium-long term contract decomposition electric quantity constraint.
S324, constructing an output decision lower layer model based on a second objective function and a second preset constraint condition; the second objective function is used to characterize the overall social welfare.
Specifically, the objective function of the output decision underlying model maximizes social welfare, i.e., the scheduling cost is minimized. The scheduling cost includes the electricity purchase cost in the market at the day-ahead, the purchase cost of the standby service, and the penalty cost caused by the loss of the load. Specifically, all the electricity purchase costs in the day-ahead market include the cost of purchasing electricity generated by a new energy generator set and the cost of generating electricity by a conventional energy generator set. Further, the new energy generator set may have a fault in the power generation process, such as a generator fault and a line fault, 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 power demand of the user. Further, the penalty cost caused by load loss refers to that when the available capacity of the power generation system cannot meet the maximum load requirement of the system, a line overload problem occurs, so that related personnel can solve the load loss problem by using some emergency scheduling problems, and further, some cost increase can be caused.
Further, the output decision lower layer model obtains medium and long term contract decomposed electric quantity of the new energy generator set from the output decision upper layer model, and further 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 layer model, so that the output decision upper layer model obtains the clearing price feedback condition of the output distribution in the day-ahead market and the real-time market.
Furthermore, the constraint conditions of the output decision lower layer model comprise power constraint balance, reserve adjustment balance constraint in a real-time market, output constraint of a conventional generator set, reserve constraint of the conventional generator set and processing constraint of the new energy generator set. 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 requirement of the system. The balance constraint of the standby adjustment quantity in the real-time market indicates that the balance among the standby quantity of the conventional generator set, the output of the new energy generator set and the load loss quantity can be kept, and the balance of the real-time market is reflected. The conventional generator set output constraint represents a normal power set winning power constraint, the conventional generator set standby constraint represents a conventional generator set standby regulating quantity constraint, and the new energy generator set output constraint comprises a new energy generator set winning power constraint.
In the embodiment of the application, a method for constructing an output decision double-layer model of an electric power market is provided, and 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 a new energy power generation manufacturer; then constructing an output decision lower-layer model based on a second objective function and a second preset constraint condition; the second objective function is used to characterize the overall social welfare. The output decision upper layer model provides output distribution of new energy power generation manufacturers to the output decision lower layer model, so that the output decision lower layer model obtains the quotation condition of the new energy power generation manufacturers, namely medium and long term contract decomposition electric quantity. And the output decision lower-layer 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-layer model, so that the output decision upper-layer model obtains the feedback condition of the clearing price of the output distribution in the day-ahead market and the real-time market. Therefore, the output decision upper layer model and the output decision lower layer 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 participates in the medium-term and long-term trading of the electric power market.
In one embodiment, as shown in fig. 5, the output decision upper layer model is constructed based on the first objective function and the first predetermined constraint, including steps 420 to 460.
And S420, constructing a first objective function based on the day-ahead market income and the real-time market income of the new energy power generation manufacturer.
Specifically, a first objective function is constructed based on the day-ahead market income and the real-time market income of a new energy power generation manufacturer, and the first objective function is an objective function of an output decision upper layer model. Specifically, the objective function of the output decision upper layer model comprises the income of new energy power generation manufacturers in the market at the day ahead and the income of the real-time market. Further, the income of the new energy power generation manufacturers in the market at present comprises the income of a spread contract and the income of a spot market, and the income of the new energy power generation manufacturers in the real-time market is the income obtained by the new energy power generation manufacturers in the real-time market due to the uncertainty of the new energy and the balance cost required to be paid by the small-rate power generation manufacturers. Further, the new energy power generation manufacturer is a wind power generator, wherein the wind power generator uses wind power as the power source for power generation. Therefore, the objective function of the above-mentioned output decision upper layer model can be expressed as:
wherein R iswRepresenting the income of new energy power generation manufacturers，
The price is contracted for the medium and long term of the new energy generator set; lambda [ alpha ]tAnd λstRepresenting the clearing price of the day-ahead market and the clearing price of the real-time market,
the electric quantity is decomposed for the power utilization contract of the new energy generating set in the middle and long term at the time t,
for the bid amount of the new energy generator set at the time t,
and
the method comprises the steps of respectively representing the actual power generation amount and the abandoned wind 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 the formula (1), the reaction mixture is,
the income of the spot market of the new energy generator set at the time t is shown,
representing the income of the new energy generator set with the price difference contract at the time t,
and the representation shows the income of the real-time market of the new energy generator set 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-long term contract and the output constraint of the bidding section of each time of the medium-long term contract electric quantity.
Specifically, a first preset constraint condition is constructed based on the contracted electric quantity constraint of the medium-long term contract and the output constraint of the bidding section at each time of the medium-long term contract electric quantity, and the first preset constraint condition is a constraint condition of the output decision upper layer model. Specifically, the contracted electric quantity constraint of the medium-and-long-term contract can be expressed as:
wherein, PgmThe method is the appointed electric quantity of the medium and long term contract of the new energy generator set. And (2) representing the constraint of decomposing the electric quantity by the medium-long term electricity contract of the new energy generator set, namely the sum of the decomposed electric quantities of the medium-long term electricity contract of the new energy generator set at the time t is not greater than the agreed electric quantity of the medium-long term contract.
Further, the output constraint of the bidding section at each time of the medium-long term contract electric quantity can be expressed as:
and (3) decomposing the upper and lower limit constraints of the electric quantity by the medium and long term electricity contract of the new energy generator set at the time t, namely decomposing the electric quantity by the medium and long term electricity contract of the new energy generator set at the time t does not exceed the winning electric quantity of the new energy generator set at the time t.
And S460, constructing an output decision upper layer model based on the first objective function, the agreed electric quantity constraint of the medium-long term contract and the output constraint of the bidding section of each time of the medium-long term contract electric quantity.
Specifically, the objective function of the output decision upper layer model and the constraint condition of the output decision upper layer model together form the output decision upper layer model. The output decision upper-layer model takes the income of a new energy power generation manufacturer as an objective function. Specifically, the objective function of the output decision upper model comprises the income of new energy power generation manufacturers in the market at the day before and the income of the real-time market, and in addition, the constraint conditions of the output decision upper model comprise the agreed electric quantity constraint of medium-long term contracts and the output constraint of the bidding sections of medium-long term contracts at different times. And constructing the output decision upper layer model together based on the target function of the output decision upper layer model, the agreed electric quantity constraint of the medium and long term contract and the output constraint of the competitive section of the medium and long term contract electric quantity at each time.
In this embodiment of the present application, constructing an output decision upper layer model based on a first objective function and a first preset constraint condition includes: constructing a first objective function based on the day-ahead market income and the real-time market income of a new energy power generation manufacturer; constructing a first preset constraint condition based on the agreed electric quantity constraint of the medium-long term contract and the output constraint of the bidding section at each time of the medium-long term contract electric quantity; and constructing an output decision upper layer model based on the first objective function, the agreed electric quantity constraint of the medium-long term contract and the output constraint of each time bidding section of the medium-long term contract electric quantity. The output decision upper-layer model aims at maximizing the self income of new energy power generation manufacturers, so that the new energy power generation manufacturers can maximize the self benefits and arrange the output distribution of the new energy power generation sets in all time periods.
In one embodiment, as shown in fig. 6, the output decision lower layer model is constructed based on the second objective function and the second predetermined constraint, which includes steps 520 to 560.
S520, constructing a second objective function based on the electricity purchase cost, the standby service purchase cost and the penalty cost caused by load loss in the market at the day before.
Specifically, a second objective function is constructed based on the electricity purchase cost, the standby service purchase cost and the penalty cost caused by load loss in the market at the day before, and the second objective function is an objective function of the output decision lower-layer model. Specifically, the output decision lower-layer model takes the total social benefit, namely the scheduling cost, as an objective function. The objective function of the output decision underlying model comprises the electricity purchasing cost in the market at the day-ahead, the purchasing cost of the standby service and the penalty cost caused by load loss. Further, the new energy power generation manufacturer is a wind power generator. Therefore, the objective function of the output decision underlying model can be expressed as:
wherein, CgjbtRepresenting the quote, P, of the b-th bidding leg of a conventional generator set j at t in the day-ahead marketgjbtIndicating the bid amount, delta, of a conventional genset j in the day-ahead market during the b-th bidding period at time tsRepresenting the probability of occurrence of a wind power scene s;
represents the upper standby adjustment amount of the conventional generator set j at the time t under the scene s,
representing the lower standby adjustment of the conventional genset j at time t under scenario s,
represents the standby price of the standby generator on the conventional generator set j,
The reserve price for the standby under the conventional genset j,
representing the amount of lost load at time t under system scenario s. Further, in the above-mentioned case,
and the electricity purchasing cost at the time t in the market before the day is represented, wherein the electricity purchasing cost of the conventional generator set at the time t and the electricity purchasing cost of the new energy generator set are included.
Representing the cost of purchasing backup service at time t in a wind scenario s,
and (4) representing the penalty cost caused by the load loss at the moment t under the wind power scene s.
And S540, constructing a second preset constraint condition based on the power balance constraint, the real-time market reserve adjustment balance constraint, the conventional generator set output constraint, the conventional generator set reserve constraint and the wind power generator set output constraint.
Specifically, a second preset constraint condition is constructed based on the power balance constraint, the real-time market reserve adjustment balance constraint, the conventional generator set output constraint, the conventional generator set reserve constraint and the wind power generator set output constraint, and the second preset constraint condition is a constraint condition of an output decision lower layer model. Wherein the power balance constraint may be expressed as:
wherein, PltRepresenting the load demand of the system at time t. And (5) representing that the sum of the successful bid power of the new energy generator set at the time t and the successful bid power of the conventional generator set j at the time t is required to meet the load requirement of the system at the time t.
Further, the real-time market reserve adjustment balance constraint may be expressed as:
wherein the content of the first and second substances,
representing the amount of system workload loss in a wind power scenario s,
represents the air curtailment quantity of the system under the wind power scene s, lambdastA lagrange multiplier representing a real-time market reserve adjustment balance constraint,
showing the system air curtailment amount under the wind power scene s
The lagrange multiplier of the lower bound is,
system air volume abandonment under wind power scene s
The lagrange multiplier of the upper bound is,
representing the system load loss under the wind power scene s
The lagrange multiplier of the lower bound is,
representing the system load loss under the wind power scene s
Lagrange multiplier of upper bound. And (6) representing that the balance is kept among the upper spare adjustment amount of the conventional generator set j at the time t, the lower spare adjustment amount of the conventional generator set j at the time t, the winning power of the new energy generator set and the system load loss amount. And the expression (7) shows that the air abandoning amount of the new energy generator set at the time t is not higher than the actual power generation amount of the new energy generator set at the time t. Equation (8) indicates that the load loss of the system at time t is not higher than the load demand of the system at time t.
Further, the conventional genset output constraint may be expressed as:
wherein the content of the first and second substances,
represents the maximum value of the power of the b-th bidding section of the conventional generator set j at the time t, PgjminRepresents the minimum value of the output of the conventional generator set j at the moment t, PgjmaxThe maximum value of the output of the conventional generator set j at the moment t;
a lagrange multiplier representing the power cap of the b-th bidding segment of the conventional genset j at time t,
a lagrange multiplier representing the lower power limit of the b-th bidding period of the conventional generator set j at the time t,
a lagrange multiplier representing the lower power limit of the conventional generator set j at time t,
and the Lagrange multiplier represents the upper limit of the standard power of the conventional generator set j at the time t. Equation (9) represents the power P of the b-th bidding period of the conventional generator set j at the time tgjbtThe upper and lower limits of (1), equation (10), represent the power P of the conventional generator set j at time tgjtThe upper and lower limits of (2).
Further, the conventional genset standby constraint may be expressed as:
wherein the content of the first and second substances,
represents the maximum value of the upper backup adjustment amount of the conventional genset j,
represents the maximum value of the lower backup adjustment amount of the conventional genset j,
indicating upper standby capacity of conventional genset j
The lagrange multiplier of the lower bound is,
indicating upper standby capacity of conventional genset j
The lagrange multiplier of the upper bound is,
indicating lower standby capacity of conventional genset j
The lagrange multiplier of the lower bound is,
lower standby adjustment for conventional genset j
The lagrange multiplier of the upper bound is,
represents the normal power P of the conventional generator set j at the time tgjtLower standby regulating quantity of conventional generator set j
The lagrange multiplier of the lower bound of the difference,
represents the normal power P of the conventional generator set j at the time tgjtUpper standby regulating variable with conventional generator set j
The lagrange multipliers of the upper result limit are added. Equation (11) represents the upper standby adjustment for conventional genset j
The lower and upper limits of (1), equation (12) represents the lower standby adjustment for conventional genset j
The formula (13) represents the normal power P of the conventional generator set j at the time tgjtLower standby regulating quantity of conventional generator set j
The lower limit of the difference is represented by the formula (14) which represents the medium-winning power of the conventional generator set j at the time t and the upper standby regulating quantity of the conventional generator set j
Upper limit of addition result.
And S560, constructing an output decision lower layer model based on the second objective function, the power balance constraint, the real-time market reserve adjustment 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 output decision lower layer model and the constraint condition of the output decision lower layer model together form the output decision lower layer model. The output decision lower-layer model takes the total social welfare as an objective function. Specifically, the objective function of the output decision underlying model comprises the electricity purchase cost of the market at the day-ahead, the purchase cost of the standby service and the penalty cost caused by load loss. In addition, the constraint conditions of the output decision lower layer model comprise power balance constraint, real-time market reserve adjustment balance constraint, conventional generator set output constraint, conventional generator set reserve constraint and wind power generator set output constraint. And constructing an output decision lower layer model based on the objective function, the power balance constraint, the real-time market reserve adjustment balance constraint, the conventional generator set output constraint, the conventional generator set reserve constraint and the wind power generator set output constraint of the output decision lower layer model.
In this embodiment of the present application, constructing an output decision lower layer model based on a second objective function and a second preset constraint condition includes: constructing a second objective function based on the electricity purchasing cost, the standby service purchasing cost and the penalty cost caused by load loss in the market at present; 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 layer model based on the second objective function, the power balance constraint, the real-time market reserve adjustment balance constraint, the conventional generator set output constraint, the conventional generator set reserve constraint and the wind power generator set output constraint. The output decision lower-layer model aims at maximizing the total social benefits, and simultaneously meets the requirements that new energy power generation manufacturers can maximize benefits and maximize the total social benefits, so that output distribution of the new energy power generation set at each time interval is arranged.
In one embodiment, as shown in fig. 7, the contribution decision double-layer model is transformed based on the preset constraint condition and the KKT condition to generate a contribution decision single-layer model, which includes steps 342 to 344.
And S342, performing equivalent replacement on the second target function and the second preset constraint condition of the output decision lower layer model based on the KKT condition to generate an equivalent KKT condition of the output decision lower layer model.
Specifically, the output decision upper layer model provides output distribution of new energy power generation manufacturers to the output decision lower layer model, namely, medium and long term contract decomposition electric quantity. Further, the output decision lower layer model obtains the 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 based on the output distribution result of the new energy generator manufacturer transmitted by the output decision upper layer model. Therefore, the variables of the output decision upper layer model are constants in the output decision lower layer model, and the output decision lower layer model is a linear model. Therefore, the output decision lower layer model can be replaced by an equivalent optimality condition, so that the output decision double-layer model is converted into a single-layer mathematical programming problem with balance constraint.
Further, a Lagrangian function of the output decision lower layer model is constructed based on a target function of the output decision lower layer model and a constraint condition of the output decision lower layer model. Specifically, a lagrangian function of the output decision lower layer model is constructed according to an objective function of the output decision lower layer model shown in formula (4), a power balance constraint of the output decision lower layer model shown in formula (5), real-time market reserve adjustment balance constraints shown in formulas (6) to (8), conventional generator set output constraints shown in formulas (9) to (10), conventional generator set reserve constraints shown in formulas (11) to (14), and wind turbine generator set output constraints shown in formulas (15) to (16), where the lagrangian function is specifically expressed as:
wherein L represents the lagrangian function of the underlying model of the contribution decision.Further, the decision variables of the output decision lower layer model in the formula (17) are subjected to partial derivation to obtain a first-order KKT condition. Specifically, the decision variable of the output decision lower layer model is the winning power of the new energy generator set at the time t
Normal power P of generator set j at time tgjtAnd the upper standby regulating quantity of the conventional generator set j at the time t
Lower standby regulating quantity of conventional generator set j at time t
Loss of load of system
Air volume is abandoned to new forms of energy generating set at moment t
Further, the partial derivation is performed on the decision variables in the output decision lower layer model in the formula (17) to obtain a first-order KKT condition, which can be expressed as:
further, a first-order KKT condition is obtained by performing partial derivation on the decision variables of the contribution decision underlying model in the formula (17), and the first-order KKT condition is the formulas (18) to (41). Therefore, the target function of the output decision lower model and the constraint condition of the output decision lower model are equivalently replaced based on the KKT condition to generate an equivalent KKT condition of the output decision lower model, and the equivalent KKT condition of the output decision lower model is expressed by the formulas (18) - (41).
And S344, generating an output decision single-layer model according to the first target 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, an output decision single-layer model is formed together according to a target function of an output decision upper-layer model shown in formula (1), agreed electric quantity constraints of medium-long term contracts shown in formula (2), output constraints of medium-long term contracts in all periods shown in formula (3), and equivalent KKT conditions of output decision lower-layer models shown in formulas (18) - (41). Further, the objective function of the output decision single-layer model is an objective function of the output decision upper-layer model shown in formula (1), and the constraint conditions of the output decision single-layer model are the constraint conditions of the output decision upper-layer model shown in formulas (2) to (3) and the equivalent KKT conditions of the output decision lower-layer model shown in formulas (18) to (41).
In this embodiment of the present application, converting the output decision double-layer model based on the preset constraint condition and the KKT condition to generate an output decision single-layer model, including: performing equivalent replacement on a second target function and a second preset constraint condition of the output decision lower layer model based on the KKT condition 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 target 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. By performing equivalent optimality condition replacement on the output decision lower layer model, the double-layer optimization model is converted into a single-layer mathematical programming problem with balance constraint, the output decision double-layer model is simplified, and the calculation complexity is further reduced. Therefore, the contribution decision single-layer model is easier to optimize parameters than the contribution decision double-layer model.
In one embodiment, the linearizing the output decision single-layer model to obtain the output decision model of the power market includes:
and (4) carrying out linear processing on the decision variables in the output decision single-layer model by adopting a binary expansion method to obtain the output decision model of the power market.
Specifically, the output decision single-layer model is linearized by a method of linearizing a target function of the output decision single-layer model. The target function linearization mainly aims at the nonlinear term in the target function in the output decision single-layer model, and the nonlinear term is linearized by using a binary expansion method, wherein the nonlinear term is embodied in a form of multiplying two decision variables. Specifically, the objective function of the output decision single-layer model is an 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 linearized based on a binary expansion method, and the objective function of the output decision single-layer model after linearization can be expressed as:
further, the nonlinear term of the objective function of the output decision single-layer model is linearized based on a binary expansion method, and the objective function of the output decision single-layer model after linearization is obtained. And obtaining the output decision model of the power market based on the target function of the output decision single-layer model after the linearization processing and the constraint condition of the original output decision single-layer model. Specifically, the output decision model constraint conditions of the power market are the constraint conditions of the output decision upper layer models of equations (2) to (3) and the equivalent KKT conditions of the output decision lower layer models shown in equations (18) to (41).
In the embodiment of the present application, the performing linearization on the output decision single-layer model to obtain the output decision model of the power market includes: and (4) carrying out linear processing on the decision variables in the output decision single-layer model by adopting a binary expansion method to obtain the output decision model of the power market. The output decision model of the power market obtained after the output decision single-layer model is subjected to linearization processing is small in calculated amount, and the difficulty in finding the global optimal solution is greatly reduced. Therefore, the output decision model of the power market is easier to find a global optimal solution than the output decision single-layer model.
In a specific embodiment, as shown in fig. 7, a method for constructing a contribution decision model of a power market is provided, which includes steps 601 to 613.
S601, acquiring the clearing price of the market in the day ahead;
s602, acquiring a clearing price of a real-time market;
and S603, 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 power market for medium and long term contract decomposition to generate an output distribution result of the new energy generator set and an output distribution result of the conventional energy generator set.
The method for constructing the output decision model of the power market includes steps 604 to 613.
S604, constructing a first objective function based on the day-ahead market income and the real-time market income of a new energy power generation manufacturer;
s605, constructing a first preset constraint condition based on the agreed electric quantity constraint of the medium-long term contract and the output constraint of the medium-long term contract electric quantity in each period;
s606, constructing an output decision upper layer model based on the first objective function, the agreed electric quantity constraint of the medium-long term contract and the output constraint of each time period of the medium-long term contract electric quantity.
S607, constructing a second objective function based on the electricity purchasing cost, the standby service purchasing cost and the penalty cost caused by load loss in the market at the day before;
s608, constructing a second preset constraint condition based on the power balance constraint, the real-time market reserve adjustment balance constraint, the conventional generator set output constraint, the conventional generator set reserve constraint and the wind power generator set output constraint;
and S609, constructing an output decision lower layer model based on the second objective function, the power balance constraint, the real-time market reserve adjustment balance constraint, the conventional generator set output constraint, the conventional generator set reserve constraint and the wind power generator set output constraint.
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 the second target function and the second preset constraint condition of the output decision lower layer model based on the KKT condition to generate an equivalent KKT condition of the output decision lower layer model.
And S612, generating an output decision single-layer model according to the first target 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, performing linear processing on the decision variables in the output decision single-layer model by adopting a binary expansion method to obtain the output decision model of the power market.
In order to verify the feasibility of the output decision model of the electricity market, a specific embodiment is described below.
The new energy generator set adopts a wind power generator set, and the conventional energy generator set adopts a conventional thermal generator set. Wherein, wind power generating set is 1 group, and conventional thermal power generating set is 3 groups. The quotation mode of adoption is 5 segmentation quotations, and wherein, last reserve quotation is 1.1 times of conventional thermal power generating set highest quotation, and lower reserve quotation is 0.9 times of conventional thermal power generating set lowest quotation, and the penalty cost expense that the system loses load and arouses is $ 400. 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
The method comprises the following steps of adopting a multi-scene technology, randomly sampling a wind power time sequence by a Latin hypercube sampling method, obtaining a multi-scene time sequence curve of wind power, and describing the uncertainty of the wind power:
considering 10% of predicted output of the new energy generator set as a standard deviation, performing 3000 times of random sampling by using a Latin hypercube sampling method to obtain 3000 wind power output scenes, reducing redundant and irrelevant information in the scenes by using an Euclidean distance method, and finally obtaining 7 wind power scenes to describe the uncertain output of the wind power, wherein the result is shown in FIG. 9 and describes 7 wind power scenes to describe the uncertain output of the wind power.
Suppose that the medium-long term contract electric quantity of the new energy generating set is 2000MW, and the contract electric price of the medium-long term price-difference contract is $ 56. And (3) combining the table 1 and the table 2 and the preset parameter values, and inputting the known quantity into an output decision model of the power market. Fig. 10 shows the output allocation result of the new energy generator set and the output allocation result of the conventional energy generator set in an embodiment. As shown in fig. 10, the wind power generator in the medium-and-long-term contract tends to arrange the output of the contract electric quantity at a time when the difference from the spot market price is large, that is, at a time when the spot market price is the lowest, so that the electric quantity distribution of the wind power generator can be changed by signing the differential contract price.
The average decomposition method is used for comparing with the output decision model of the power market provided by the application, and table 3 shows the social benefit comparison result obtained by using the output decision model of the power market of the average decomposition method. As shown in table 3, the total social benefit obtained by using the output decision model of the power market is significantly increased, and it can be seen that the medium-and-long-term contract decomposition method obtained by the output decision model of the power market provided by the present application has greater total social benefit than the average decomposition method.
TABLE 3
It should be understood that although the various steps in the flow charts of fig. 2-8 are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least some of the steps in fig. 2-8 may include multiple steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, which are not necessarily performed in sequence, but may be performed in turn or alternately with other steps or at least some of the other steps.
In one embodiment, as shown in fig. 11, there is provided an output decision device 700 for an electric power market, including:
and a parameter obtaining module 720, configured to obtain the clearing price of the day-ahead market and the clearing price of the real-time market.
The output decision module 740 is used for inputting the clearing price of the day-ahead market and the clearing price of the real-time market into the output decision model of the power market for medium-long term contract decomposition 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 carrying out medium and 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 power market further includes an output decision model building module 760, where the output decision model building module 760 includes:
an output decision double-layer model building unit 762 configured to build an output decision double-layer model of the power market; the output decision double-layer model comprises an output decision upper-layer model and an output decision lower-layer model;
the contribution decision single-layer model conversion unit 764 is configured to convert the contribution decision double-layer model based on a preset constraint condition and a KKT condition to generate a contribution decision single-layer model;
and the linear processing unit 766 is used for performing linear processing on the output decision single-layer model to obtain an output decision model of the power market.
In one embodiment, the output decision double-layer model building unit 762 is further configured to build an output decision upper-layer model based on the first objective function and the first preset constraint condition; the first objective function is used for representing the income of a new energy power generation manufacturer; constructing an output decision lower-layer model based on a second objective function and a second preset constraint condition; the second objective function is used to characterize the overall social welfare.
In one embodiment, the output decision single-layer model conversion unit 764 is further configured to perform equivalent replacement on the second target 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 target 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 on the decision variables in the output decision single-layer model by using a binary expansion method, so as to obtain the output decision model of the power market.
The division of each module in the output decision device of the power market is only used for illustration, and in other embodiments, the output decision device of the power market may be divided into different modules as needed to complete all or part of the functions of the output decision device of the power market.
In one embodiment, FIG. 13 is a diagram illustrating an internal structure of a computer device in one embodiment. As shown in fig. 13, the computer device may be a server, which includes a processor, a memory, and a 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 comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, 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 network connection, and the internal memory provides an environment for running an operating system and a computer program in the nonvolatile storage medium. The computer program can be executed by a processor for implementing the output decision method of the power market provided by the above embodiments.
The implementation of each module in the output decision device of the power market provided in the embodiment of the present application may be in the form of a computer program. The computer program may be run on a computer device or a server. The program modules constituting the computer program may be stored on a memory of the computer device or the server. Which when executed by a processor, performs the steps of the method described in the embodiments of the present 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 the output decision method for the power market.
A computer program product comprising instructions which, when run on a computer, cause the computer to perform a method of output decision for an electricity market.
Any reference to memory, storage, database, or other medium used by embodiments of the present application may include non-volatile and/or volatile memory. Suitable non-volatile 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), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and bus dynamic RAM (RDRAM).
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. A method for power market contribution decision-making, the method comprising:
acquiring the clearing price of the current market and the clearing price of the real-time market;
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 power market for medium and long term contract decomposition to generate an output distribution result of the new energy generator set and an output distribution result of the conventional energy generator set; and the output decision model of the electric power market is used for carrying out medium and long term contract decomposition on the new energy generator set and the conventional energy generator set.
2. The method of claim 1, wherein the generating of the output decision model of the electricity market comprises:
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;
converting the output decision double-layer model based on a preset constraint condition and a KKT condition to generate an output decision single-layer model;
and carrying out linearization processing on the output decision single-layer model to obtain the output decision model of the electric power market.
3. The method of claim 2, wherein constructing a contribution decision bi-level model for the electricity market comprises:
constructing the output decision upper layer model based on a first objective function and a first preset constraint condition; the first objective function is used for representing the income of a new energy power generation manufacturer;
constructing the output decision lower layer model based on a second objective function and a second preset constraint condition; the second objective function is used to characterize the overall social welfare.
4. The method of claim 3, wherein constructing the imposed decision upper-level model based on the first objective function and the first predetermined constraint comprises:
constructing the first objective function based on the day-ahead market income and the real-time market income of the new energy power generation manufacturer;
constructing the first preset constraint condition based on the agreed electric quantity constraint of the medium-long term contract and the output constraint of each time period of the medium-long term contract electric quantity;
and constructing the output decision upper layer model based on the first objective function, the agreed electric quantity constraint of the medium-long term contract and the output constraint of each period of the medium-long term contract electric quantity.
5. The method of claim 3, wherein said constructing the output decision lower layer model based on the second objective function and the second predetermined constraint comprises:
constructing the second objective function based on the electricity purchasing cost, the standby service purchasing cost and the penalty cost caused by load loss in the market at present;
constructing a second preset constraint condition based on a power balance constraint, a real-time market reserve adjustment balance constraint, a conventional generator set output constraint, a conventional generator set reserve constraint and a wind power generator set output constraint;
and constructing the output decision lower layer model based on the second objective function, the power balance constraint, the real-time market reserve adjustment balance constraint, the conventional generator set output constraint, the conventional generator set reserve constraint and the wind power generator set output constraint.
6. The method of claim 2, wherein converting the contribution decision double-layer model based on preset constraints and KKT conditions to generate a contribution decision single-layer model comprises:
performing equivalent replacement on a second target function and a second preset constraint condition of the output decision lower layer model based on a KKT condition to generate an equivalent KKT condition of the output decision lower layer model;
and generating the output decision single-layer model according to the first target 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.
7. The method of claim 2, wherein said linearizing said contribution decision-plane model to obtain said contribution decision-plane model for said electricity market comprises:
and carrying out linear processing on the decision variables in the output decision single-layer model by adopting a binary expansion method to obtain the output decision model of the electric power market.
8. 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 day-ahead market 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 for medium and long term contract decomposition to generate an output distribution result of the new energy generator set and an output distribution result of the conventional energy generator set; and the output decision model of the electric power market is used for carrying out medium and long term contract decomposition on the new energy generator set and the conventional energy generator set.
9. A computer device comprising a memory and a processor, the memory storing a computer program, wherein the processor implements the steps of the method of any one of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 7.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116362828A (en) * | 2023-03-06 | 2023-06-30 | 中能智新科技产业发展有限公司 | Thermal power generating unit quotation decision method based on similarity scene and particle swarm algorithm |
Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106998072A (en) * | 2017-05-15 | 2017-08-01 | 国网江苏省电力公司电力科学研究院 | A kind of mixed energy storage system capacity configuration optimizing method for optimizing operation towards power distribution network |
| WO2018059096A1 (en) * | 2016-09-30 | 2018-04-05 | 国电南瑞科技股份有限公司 | Combined decision method for power generation plans of multiple power sources, and storage medium |
| CN109301876A (en) * | 2018-09-04 | 2019-02-01 | 清华大学 | A kind of electric power ahead market of constraint condition elastification goes out clearing method |
| CN110021965A (en) * | 2019-03-20 | 2019-07-16 | 南方电网科学研究院有限责任公司 | A method of wind-powered electricity generation bidding strategy is formulated based on prediction error |
| CN110310173A (en) * | 2019-06-11 | 2019-10-08 | 上海电力学院 | A kind of renewable energy participate in long-term electricity transaction power energy allocation method |
| CN111598719A (en) * | 2020-04-09 | 2020-08-28 | 云南电网有限责任公司 | New energy seller transaction method and system for spot power market |
| CN111738497A (en) * | 2020-06-03 | 2020-10-02 | 杭州电子科技大学 | Virtual power plant double-layer optimization scheduling method considering demand side response |
| CN112116476A (en) * | 2020-09-23 | 2020-12-22 | 中国农业大学 | Comprehensive energy system simulation method considering wind power and carbon transaction mechanism |
| CN112288491A (en) * | 2020-11-04 | 2021-01-29 | 国网湖南省电力有限公司 | Water-fire-electricity generator set same-station clearing method and system in electric power spot market |
| CN112418643A (en) * | 2020-11-18 | 2021-02-26 | 国网四川省电力公司经济技术研究院 | Network source collaborative planning method for multi-target market under wind power integration |
| CN112686730A (en) * | 2021-01-04 | 2021-04-20 | 国网甘肃省电力公司 | New energy participation spot market and green certificate market trading decision method and system |
| CN112952908A (en) * | 2021-03-29 | 2021-06-11 | 四川大学 | Multi-cooperation micro-grid main body distributed coordination transaction method |
-
2021
- 2021-09-10 CN CN202111062933.XA patent/CN113822707B/en active Active
Patent Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2018059096A1 (en) * | 2016-09-30 | 2018-04-05 | 国电南瑞科技股份有限公司 | Combined decision method for power generation plans of multiple power sources, and storage medium |
| CN106998072A (en) * | 2017-05-15 | 2017-08-01 | 国网江苏省电力公司电力科学研究院 | A kind of mixed energy storage system capacity configuration optimizing method for optimizing operation towards power distribution network |
| CN109301876A (en) * | 2018-09-04 | 2019-02-01 | 清华大学 | A kind of electric power ahead market of constraint condition elastification goes out clearing method |
| CN110021965A (en) * | 2019-03-20 | 2019-07-16 | 南方电网科学研究院有限责任公司 | A method of wind-powered electricity generation bidding strategy is formulated based on prediction error |
| CN110310173A (en) * | 2019-06-11 | 2019-10-08 | 上海电力学院 | A kind of renewable energy participate in long-term electricity transaction power energy allocation method |
| CN111598719A (en) * | 2020-04-09 | 2020-08-28 | 云南电网有限责任公司 | New energy seller transaction method and system for spot power market |
| CN111738497A (en) * | 2020-06-03 | 2020-10-02 | 杭州电子科技大学 | Virtual power plant double-layer optimization scheduling method considering demand side response |
| CN112116476A (en) * | 2020-09-23 | 2020-12-22 | 中国农业大学 | Comprehensive energy system simulation method considering wind power and carbon transaction mechanism |
| CN112288491A (en) * | 2020-11-04 | 2021-01-29 | 国网湖南省电力有限公司 | Water-fire-electricity generator set same-station clearing method and system in electric power spot market |
| CN112418643A (en) * | 2020-11-18 | 2021-02-26 | 国网四川省电力公司经济技术研究院 | Network source collaborative planning method for multi-target market under wind power integration |
| CN112686730A (en) * | 2021-01-04 | 2021-04-20 | 国网甘肃省电力公司 | New energy participation spot market and green certificate market trading decision method and system |
| CN112952908A (en) * | 2021-03-29 | 2021-06-11 | 四川大学 | Multi-cooperation micro-grid main body distributed coordination transaction method |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116362828A (en) * | 2023-03-06 | 2023-06-30 | 中能智新科技产业发展有限公司 | Thermal power generating unit quotation decision method based on similarity scene and particle swarm algorithm |
| CN116362828B (en) * | 2023-03-06 | 2023-10-20 | 中能智新科技产业发展有限公司 | Thermal power generating unit quotation decision method based on similarity scene and particle swarm algorithm |
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