CN113032995B - Electric power market simulation optimization and operation method, system and simulation platform - Google Patents
Electric power market simulation optimization and operation method, system and simulation platform Download PDFInfo
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Abstract
The invention provides a method, a system and a simulation platform for simulation optimization and operation of an electric power market, wherein the optimization method comprises the following steps: based on various information sent by the electric power market, considering that the electricity vendors aim at minimum comprehensive cost, establishing an electricity vendor reporting model to obtain a load reporting curve of the electricity vendor; considering a generator to take the maximization of the generation income as a target, establishing a generator declaration model; simulating decision quotation of a generator by adopting an intelligent quotation algorithm to obtain a generator price declaration curve; according to a power generation output declared by a power generator and a power generation price declared curve of the power generator, a power selling company declared power quantity declared by a power selling company and a power selling company load declared curve, a multi-type power supply access simulation operation system is calculated, system balance, power grid safety and unit operation constraint conditions are considered, a safety constraint unit combination and a safety constraint economic dispatching model are established, and an optimal solution is obtained through a mixed integer programming algorithm.
Description
Technical Field
The invention belongs to the field of electric power markets, and particularly relates to a method, a system and a simulation platform for electric power market simulation optimization and operation.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
The vertical integrated operation mode of the power system has many defects, so that a competition mechanism is introduced into the power industry, and the power industry gradually markets.
The power market is the earliest reform in multiple countries in the west, and after years of development, perfection and practice, a power market basic theory is gradually formed, and abundant market construction experience is accumulated. The electric power market construction not only focuses on efficiency and competition mechanisms, but also considers development, risk distribution mechanisms and the like. The electric power market stably concerns the benefits of various market subjects, and no matter power generation enterprises, power selling enterprises, operators and the like pay attention to the operation conditions of the market, the electric power market has urgent needs of analyzing and evaluating the market. Therefore, the development of the simulation system of the electric power spot market has very important practical significance for promoting the further development of the reform of the electric power market in China.
The development of a power spot market simulation system is very concerned at home and abroad, but the existing research has a problem that the system is only suitable for a certain specific market mode, a database and a transaction platform can be continuously adjusted along with the increase of the market mode, and the workload is large and time is consumed. In addition, the power market rules are constantly changing, which puts higher demands on the development of the existing power market simulation systems. Firstly, consider that all districts all have characteristics separately in aspects such as power structure, rack structure, supply and demand level, electric power spot market construction probably has certain difference. And secondly, the characteristics of the power system such as full-amount guaranteed consumption of new energy and serious local network blockage cannot directly apply the construction experience of foreign related systems. And the construction of the technical support system of the electric power spot market in China is still in a groping stage, and the related reports of the system construction capable of realizing quick switching are less aiming at the multiple-change operation rules.
In summary, the establishment of the electric power spot market simulation system mainly needs to solve the following requirements: 1) aiming at different market modes and different transaction rules, the power market simulation system can be switched and adjusted quickly. 2) How to effectively simulate market quotation behaviors of members inside an electric power market; 3) and analyzing and evaluating the simulation test result. The processes of rapid analysis and processing of declared data, automatic clearing calculation, result display front end and the like are main measures for improving the use efficiency of the electric power spot market simulation system.
Disclosure of Invention
In order to solve the technical problems in the background art, the invention provides a method, a system and a simulation platform for electric power market simulation optimization and operation, which can perform repeatable parallel simulation trading tests aiming at different electric power market trading models in multiple directions and multiple angles through a spot market simulation system, thereby improving the efficiency of electric power market simulation. In addition, the invention adopts a layered architecture design, and comprises a platform support module, a data management module, a market application module, a market simulation module and an interface display module, wherein the modules are accessed in a service mode.
In order to achieve the purpose, the invention adopts the following technical scheme:
the invention provides a power market simulation optimization method in a first aspect.
A power market simulation optimization method comprises the following steps:
establishing an electricity vendor reporting model based on various information sent by an electricity market and considering that an electricity vendor aims at the minimum comprehensive cost to obtain a load reporting curve of the electricity vendor;
considering a generator to take the maximization of the generation income as a target, establishing a generator declaration model; simulating decision quotation of a generator by adopting an intelligent quotation algorithm to obtain a price declaration curve of the generator;
according to a power generation output declared by a power generator and a power generation price declared curve of the power generator, a power selling company declared power quantity declared by a power selling company and a power selling company load declared curve, a multi-type power supply access simulation operation system is calculated, a safety constraint unit combination and a safety constraint economic dispatching model are established by considering system balance, power grid safety and unit operation constraint conditions, and an optimal solution is obtained through a mixed integer programming algorithm.
A second aspect of the invention provides a power market simulation optimization system.
An electric power market simulation optimization system comprising:
a first module configured to: based on various information sent by the electric power market, considering that the electricity vendors aim at minimum comprehensive cost, establishing an electricity vendor reporting model to obtain a load reporting curve of the electricity vendor;
a second module configured to: considering a generator to take the maximization of the generation income as a target, establishing a generator declaration model; simulating decision quotation of a generator by adopting an intelligent quotation algorithm to obtain a generator price declaration curve;
a third module configured to: according to a power generation output declared by a power generator and a power generation price declared curve of the power generator, a power selling company declared electric quantity declared by a power selling company and a power selling company load declared curve, a multi-type power supply access simulation operation system is calculated, system balance, power grid safety and unit operation constraint conditions are considered, a safety constraint unit combination and a safety constraint economic dispatching model are established, and an optimal solution is obtained through a mixed integer programming algorithm.
A third aspect of the invention provides a power market simulation operation method.
An electric power market simulation operation method comprises the following steps:
step (1): acquiring various data of a power generator and a power seller by utilizing the existing architecture foundation;
step (2): obtaining an optimization result by the power market simulation optimization method based on the first aspect based on the data provided in the step (1);
and (3): according to the main data provided in the step (2), realizing the main business function of the electric power spot market according to the requirement, and evaluating the market clearing data and the result thereof;
and (4): developing market simulation drilling through network model parameters, various clearing data and market rules;
and (5): displaying various graphs, curves and various data analysis;
and (6): generating a power dispatching strategy based on various graphs, curves and various data; and providing data support for the operation of the power market based on the power scheduling strategy.
A fourth aspect of the invention provides an electric power market simulation run system.
An electric power market simulation run system comprising:
a platform support module configured to: the method comprises the steps that various data of a power generator and a power seller are obtained by utilizing an existing architecture foundation, and basic service is provided for a data management module;
a data management module configured to: obtaining an optimization result by the power market simulation optimization method based on the first aspect based on data provided by a database in the platform support module;
a marketplace application module configured to: based on the main data provided by the data management module, the main business function of the electric power spot market is realized according to the requirement, and the market clearing data and the result thereof are evaluated;
a market simulation module configured to: developing market simulation drilling through network model parameters, various clearing data and market rules;
an interface presentation module configured to: displaying various graphs, curves and various data analysis;
a scheduling module configured to: generating a power dispatching strategy based on various graphs, curves and various data; and providing data support for the operation of the power market based on the power scheduling strategy.
A fifth aspect of the invention provides a computer-readable storage medium.
A computer-readable storage medium, on which a computer program is stored, which program, when being executed by a processor, carries out the steps of a power market simulation optimization method according to the first aspect and/or carries out the steps of a power market simulation run method according to the third aspect.
A sixth aspect of the invention provides a computer apparatus.
A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor executes the program to implement the steps of a power market simulation optimization method according to the first aspect and/or to perform the steps of a power market simulation operation method according to the third aspect.
A seventh aspect of the invention provides a simulation platform for power market simulation.
A simulation platform for electric power market simulation, comprising an electric power market simulation optimization system based on the second aspect and/or an electric power market simulation operation system based on the fourth aspect.
Compared with the prior art, the invention has the beneficial effects that:
the invention adopts an efficient, reliable and expandable architecture system design, meets the characteristics of multiple types of users, large data interaction amount and the like in the electric power spot market, realizes simple and safe access to systems for various market members, and is convenient for market main bodies such as power generation enterprises, power selling enterprises and the like to carry out information interaction. The system adopts a layered architecture design and comprises a platform module, a data management module, a market application module, a market simulation module and an interface display module. The modules are accessed in a service mode, and can be quickly switched and adjusted under different modes and different rules; in the face of problems such as serious network blockage, a strong model and algorithm can be used for fast calculation and analysis, and clear results are obtained; through constantly optimizing interactive performance, improving show new ability, strengthening the experiment managerial ability, let the user have a good experience sense, can carry out the simulation experiment effectively, directly perceivedly.
Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, are included to provide a further understanding of the invention, and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description, serve to explain the invention and not to limit the invention.
Fig. 1 is a flowchart of reporting of an electricity selling enterprise according to an embodiment of the present invention;
FIG. 2 is a flow chart of a power generation enterprise declaration described in an embodiment of the invention;
FIG. 3 is a schematic diagram of an intelligent agent quotation based on Q-learning algorithm according to an embodiment of the present invention;
FIG. 4 is a diagram illustrating an intelligent agent quotation according to an embodiment of the present invention;
FIG. 5 is a graph of a day ahead line power flow during example verification in accordance with an embodiment of the present invention;
FIG. 6 is a graph of a real-time line power flow during verification of an embodiment of the present invention;
FIG. 7 is a graph illustrating a real-time output curve of a unit under example verification in an embodiment of the present invention;
FIG. 8 is a graph of the output of the day-ahead node in the example verification in accordance with an embodiment of the present invention;
FIG. 9 is a graph of node electricity prices for example verification in an embodiment of the present invention;
FIG. 10 is a graph of the day-ahead electricity prices for nodes in an example verification in an embodiment of the present invention;
FIG. 11 is a graph of node real-time electricity prices during example verification in an embodiment of the present invention;
FIG. 12 is a schematic diagram of a logical model of the system according to an embodiment of the present invention;
FIG. 13 is a flow chart of a system according to an embodiment of the present invention;
FIG. 14 is a system architecture diagram according to an embodiment of the present invention;
FIG. 15 is a diagram illustrating a simulation deduction of the transaction process of the system according to the embodiment of the present invention;
FIG. 16 is a flow diagram of a system transaction declaration policy in accordance with an embodiment of the present invention;
FIG. 17 is a flow chart of a power market simulation optimization method according to an embodiment of the present invention;
fig. 18 is a flowchart of a power market simulation operation method according to an embodiment of the present invention.
Detailed Description
The invention is further described with reference to the following figures and examples.
It is to be understood that the following detailed description is exemplary and is intended to provide further explanation of the invention as claimed. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments in accordance with the invention. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
The existing research has a problem that the existing research is only suitable for a specific market mode, and the database and the trading platform are continuously adjusted along with the increase of the market mode, so that the workload is large and the time is consumed. In addition, the power market rules are constantly changing, which puts higher demands on the development of the existing power market simulation systems. In order to solve the above problems, the present invention provides various embodiments.
Example one
The embodiment provides a power market simulation optimization method.
As shown in fig. 17, a power market simulation optimization method includes:
establishing an electricity vendor reporting model based on various information sent by an electricity market and considering that an electricity vendor aims at the minimum comprehensive cost to obtain a load reporting curve of the electricity vendor;
considering a generator to take the maximization of the generation income as a target, establishing a generator declaration model; simulating decision quotation of a generator by adopting an intelligent quotation algorithm to obtain a price declaration curve of the generator;
according to a power generation output declared by a power generator and a power generation price declared curve of the power generator, a power selling company declared power quantity declared by a power selling company and a power selling company load declared curve, a multi-type power supply access simulation operation system is calculated, a safety constraint unit combination and a safety constraint economic dispatching model are established by considering system balance, power grid safety and unit operation constraint conditions, and an optimal solution is obtained through a mixed integer programming algorithm.
As a further embodiment, the intelligent quotation algorithm comprises:
constructing a cost function according to the power generation cost of a power generator, making a decision variable for each section of price in the cost function, and constructing a quotation function;
considering the constraint condition of the strategy coefficient, discretizing the strategy coefficient at equal distance to obtain a corresponding bidding function;
adopting a Q-learning algorithm to construct a decision model, and determining a learning method of an optimal strategy by using the decision model;
and simulating generator decision quotation according to the learning method of the optimal strategy.
As a further embodiment, the various types of information issued by the electricity marketplace include: and (3) integrating the electricity price, the day-ahead node electricity price, the real-time node electricity price, the historical electricity purchasing cost of an electricity seller and the prediction data published in real time by the electricity market at each stage of the electricity market.
In the day-ahead market, each market member acquires day-ahead market transaction related information, such as day-ahead predicted load, unit operation, power grid operation conditions and the like. The generator declares the day-ahead market electricity price (representing the price of unit electric energy when the unit operates in different output areas) through the technical support system, and the electricity seller declares the day-ahead market electricity quantity through the transaction system. The electric power market calculates the market trade clearance before the day by calling a Safety Constraint Unit Combination (SCUC) program, and determines the dispatching output of each generator set and the marginal electricity price of a node before the day; and finally, the market publishes information such as the daily market clearing load, the electricity price and the like to each participant.
In the real-time market, the electric power market calls an economic dispatching model (SCED) considering safety constraints to clear the real-time market according to the actual load of a user and the daily quoted price of the sealed generator set, and determines the information of the actual power generation plan, the real-time savings, the marginal electricity price of the real-time nodes and the like of each generator set. Meanwhile, the electricity vendor settles the account according to the deviation between the actual load of the user and the load declared in the day ahead (based on a certain assumption that the electricity vendor does not proxy the rapid generator set, and the difference part can only be bought or sold from the real-time market), and for the load of the part, the deviation is predicted, and the deviation electric quantity is actually processed according to a certain assessment standard and method.
The quotation modes comprise power generation side quotation, user side no quotation, power generation amount quotation and user side quotation; the number of the days of birth and death includes 96 points and 24 points; the declaration rule comprises the number of the capacity sections of the unit, the limit value proportion of the number of the quotation sections, and upper and lower limits of the declaration price. The generator trading mode is shown in FIG. 1; the electricity vendor transaction pattern is shown in figure 2.
The intelligent quotation algorithm is an intelligent entity which continuously acquires knowledge from the environment and obtains the maximum benefit by improving the self ability, namely, the quotation generation process of the intelligent agent is a machine learning process. Taking the reinforcement learning algorithm as an example, the iterative update process is as follows:
constructing a cost function according to the power generation cost of a power generation enterprise:
in the formula: q. q.sGiGenerating capacity owned by a power generation enterprise agent i;
capacity and electricity price of the s section are respectively; n is the total number of segments of the function. C is to bei(qGi) Making decision variable according to price of each function segment, and adopting AiAnd constructing an offer function. f. ofbid(qGi)＝Ai·Ci(qGi)
The formula represents that the agent does not change the electricity quantity of each section of the cost function, and only changes the electricity price of each section.
The policy coefficients satisfy the following constraints: a. thei,min≤Ai≤Ai,max
Ai,min，Ai,maxThe upper limit and the lower limit of the strategy coefficient; for the convenience of research, strategy coefficients are discretized by M parts at equal distance, so a strategy set D of a power generator iiIs composed of
Wherein the content of the first and second substances,
the corresponding bidding function is
The calculation is performed using the Q-learning algorithm. The implementation process is shown in fig. 3 and fig. 4. The Q-learning algorithm achieves the strategy of overall return maximization by maintaining the Q value of a function over an action-environment space. Considering the environment as a discrete state space, the extended decision problem using Markov chain transfer models and accumulated returns of this particular specification is called the Markov decision process. In general, the Markov decision process can be represented by a 4-tuple < X, S, R, T >, where X is the set of environmental states; s is a behavior set of the agent; r(s) is a return function, and T is a transfer function.
The key to using Q-learning algorithm to construct decision model is to construct proper state X-action S space and report back. The learning method for determining the optimal strategy of the agent in the environment state is characterized in that the agent obtains feedback information (return) in the interaction with the environment and updates a utility function Q on a space reflecting the state X-action S, and the updating formula is as follows:
wherein x represents an environmental state; x is the number oftThe environmental state at the moment t; s represents the agent's behavior policy; stBehavior strategy at the moment t; r istIs the return at the time t; y is the state s at time ttAfter the action b is executed, a new state environment is reached; qt(x, s) is the long-term expected yield after the action s is executed in the state x at the time t; beta is the learning rate; eta is the discount factor. In order to guarantee the availability and the flexibility of the algorithm, an SOA mode is adopted for the deployment of the algorithm.
The clearing calculation is carried out on the reported data of the power generation and power selling merchants on the power spot market simulation system, and the day-ahead power quantity and power price, the real-time power quantity and power price, basic data of power generation enterprises (day-ahead power quantity, day-ahead settlement, real-time power quantity, real-time settlement, medium-and-long-term power quantity, power price and income, power generation income and unit profit and the like) and basic data of power selling enterprises (reported power quantity, day-ahead settlement, real-time power quantity and settlement, medium-and-long-term power quantity, power price and income, power purchasing expense and cost and the like) are obtained.
The electric power spot market clearing module is mainly used for carrying out resource optimization configuration, taking into account a multi-type power access system according to the situations that power generation enterprises declare power generation output and quotation curves, power selling enterprises declare electric quantity and the like, establishing a safety constraint unit combination and a safety constraint economic dispatching model by considering constraint conditions such as system balance, power grid safety, unit operation and the like, and obtaining an optimal solution through a mixed integer planning algorithm.
The unit combination problem is a high-dimension, discrete, non-convex and non-linear optimization problem. On the basis of the existing research results, a new mathematical model of the unit combination problem is established, and the existing solving algorithm is given. The electricity purchasing cost is minimized in the model as an objective function, and system constraints, unit constraints, environmental constraints brought under a new environment of an electric power market, network safety constraints and the like are considered. The system constraints comprise system active power balance constraints, system rotation standby constraints and the like, and the unit constraints comprise maximum and minimum output constraints of a generator, minimum operation and stop time constraints of a unit, climbing rate constraints of the unit and the like.
The objective function of the unit combination problem can be expressed as:
the constraint conditions of the unit combination problem are expressed as follows:
(1) and (4) system constraint:
I. system active power balance constraints
In the formula: t is 1,2,., T (the same applies below).
System rotation standby constraint
(2) Unit restraint:
I. maximum and minimum output constraints for a generator
Pimin(t)≤Pi(t)≤Pimax(t) (3-4)
In the formula: 1,2,., N (the same applies below).
Minimum run and downtime constraints for a unit
[Yi on(t-1)-Ti on]·[Ui(t-1)-Ui(t)]≥0 (3-5)
[Yi off(t-1)-Ti off]·[Ui(t)-Ui(t-1)]≥0 (3-6)
Unit ramp rate constraint
Pi(t)-Pi(t-1)≤RURi (3-7)
Pi(t-1)-Pi(t)≤RDRi (3-8)
(3) And (3) environmental constraint:
exhaust gas (SO) discharged by power generation enterprises2NOx, etc.) should meet the environmental protection requirements, i.e., emissions permit should not be exceeded. Then there are:
(4) network security constraints:
I. system reactive power balance and generator reactive power upper and lower limit restraint
Qimin(t)≤Qi(t)·Ui(t)≤Qimax(t) (3-11)
System voltage constraints
Vmin≤V≤Vmax (3-12)
For solving the unit combination problem, a Lagrangian Relax (LR) method is an ideal method. The LR method has the basic ideas: for system constraints related to each unit capable of being started and stopped, the system constraints are put into a main problem, and for unit constraints related to only a single unit capable of being started and stopped, the system constraints are put into a sub problem to be considered, and solution is carried out iteratively between the main problem and the sub problem, so that the optimal solution of the main problem can be obtained. The LR method is convenient to process various constraints by introducing a Lagrange multiplier, and the calculated amount of the LR method linearly increases along with the increase of the system scale, so that the method is suitable for solving the actual large-scale mixed integer programming problem. The LR method can effectively solve the problem of large-scale system optimization. And (3) relaxing formulas (3-2) and (3-3) to obtain Lagrange relaxation problem:
in the formula: λ (t), μ (t) are the t-period lagrange multipliers. The vector form is:
λ＝[λ(1),λ(2),...,λ(T)]T
μ＝[μ(1),μ(2),...,μ(T)]T
the equation (3-13) is written expanded as:
L＝L1+L2 (3-14)
wherein the former part L1The generator set is related and changes along with the change of the output of the generator set; the latter part L2Associated only with μ (t). Therefore, the LR method can form a two-layer optimization algorithm by applying dual theory, as follows:
the bottom layer problem is used for solving the optimization problem of a single unit:
wherein, i is 1, 2.
The upper layer problem optimizes the lagrange multiplier:
wherein, mu (T) is more than or equal to 0, T is 0, 1.
The lagrangian function value is optimized for a given λ, μ for the underlying problem.
The selection of the initial value and the adjustment of the Lagrangian multiplier both have direct influence on the convergence characteristics of the LR method. The selection of the proper initial value can reduce the iteration times and converge as early as possible, thereby reducing the calculation time. Therefore, the average coal consumption of each unit of the system is sequenced, then the load is distributed according to the principle of the equivalent consumption micro-increment rate, and the obtained equivalent consumption micro-increment rate is used as an initial value of a Lagrange multiplier corresponding to the power balance constraint.
The economic dispatching means that under the condition of ensuring the safe operation of the power system and the qualified quality of the electric energy, the energy and the equipment are effectively utilized, so that the operation cost of the power system is minimized. The primary task is to ensure the coordination of electrical safety and economic benefits. The electric energy is transmitted in a power grid according to a certain rule, and meanwhile, the power generation and the power consumption are instantly balanced, so that a problem appears in one link, the normal operation of the whole power grid can be caused, and the premise of the introduction of the power market is to accord with the objective rules and ensure the safe and stable operation of the system. In consideration of randomness characteristics of uncertainty problems and influences of randomness characteristics on system operation, a robust economic dispatching optimization model can be constructed on the basis of a robust optimization theory, randomness factors are introduced into the model through opportunity constraints, distribution of the randomness factors under random disturbance conditions is researched by taking the uncertainty factors as targets, limiting conditions meeting certain robustness are introduced into the constraints, and corresponding robust solutions are obtained through decision optimization. When the constraint condition is satisfied within the confidence interval, the robust economic dispatching optimization model can realize equivalent expression of the randomness problem in the economic dispatching, and the decision is prevented from being conservative. In a word, based on a decision idea of a robust optimization economic dispatching model based on the robust optimization theory and the optimization under the worst condition, an uncertainty factor set under the system operation environment is expanded, and the power system is ensured to safely and stably operate within a certain disturbance range.
The objective function of the robust economic dispatching optimization model is minimized for the electricity purchasing cost, and the objective function is expressed as follows:
wherein, F is the total electricity purchasing cost of the power consumer; pi(t) is the output of the unit i in the time period t; fi[Pi(t)]The output of the unit is Pi(t) the corresponding quote.
And expressing uncertain factors by adopting normal distribution with symmetrical deviation, and performing probabilistic modeling and solving on an objective function or a constraint condition. And adjusting robust parameters in the robust economic dispatching optimization model to control the conservative degree of dispatching decisions, and contracting the value range of uncertainty parameters to realize compromise between the robustness and the economy of a dispatching strategy. The robust economic dispatching optimization model can effectively reflect the situation that how to make dispatching decision when the power system faces complex uncertainty problem so as to ensure the safety and the economy of the system. The model also has important reference value for the renewable energy sources to access the power grid and participate in economic dispatch in market competition environments.
According to the situations that power generation enterprises declare power generation output and quotation curves, power selling enterprises declare electric quantity and the like, the multi-type power access system is calculated, constraint conditions such as system balance, power grid safety and unit operation are considered, a safety constraint unit combination and a safety constraint economic dispatching model are established, and then an optimal solution is obtained through a mixed integer planning algorithm. The clearing results are shown below:
table 1 shows basic data of a power generation enterprise, including information of day-ahead electric quantity, day-ahead settlement, real-time electric quantity, real-time settlement, medium-and long-term electric quantity, electricity price and income, power generation income, unit profit and the like; table 2 basic data of the electricity selling enterprises include reported electric quantity, day-ahead settlement, real-time electric quantity and settlement, medium and long-term electric quantity, electricity price and income, electricity purchasing expense and cost and the like.
Table 1: basic data of power generation enterprise
Table 2: basic data of electricity selling enterprise
Fig. 5 is a day-ahead line flow curve and fig. 6 is a real-time line flow curve. The power flow distribution condition of each line can be known through the power flow curve, and whether the power flow exceeds the limit or not is judged. It can be seen that line 3 has a very small current share. In addition, the power flow distribution of line 1 and line 2 is large between 9:00 and 13: 00. Fig. 7 shows the unit real-time output curve. As can be seen from the figure, the output of each genset is similar. When the load demand is larger, the system optimizes the resource allocation and improves the power generation output. Fig. 8 shows a day-ahead nodal output curve. As can be seen from the figure, the output quantity of the nodes between 9:00-12:00 and 18:00-22:00 is large, namely the load requirement of the system is large in the period of time. Fig. 9 is a node electricity price curve, fig. 10 is a node day-ahead electricity price curve, and fig. 11 is a real-time node electricity price curve. As can be seen from fig. 9 and 7, as the load demand increases, the average declared power rates of the entire market increase, and the marginal power rates of the market also increase. In the case where the power supply is relatively sufficient, the electricity rate increase speed is relatively smooth. When the power supply is short, the electricity price increases rapidly, and the market tends to be unstable.
Example two
The embodiment provides an electric power market simulation optimization system.
An electric power market simulation optimization system comprising:
a first module configured to: based on various information sent by the electric power market, considering that the electricity vendors aim at minimum comprehensive cost, establishing an electricity vendor reporting model to obtain a load reporting curve of the electricity vendor;
a second module configured to: considering a generator to take the maximization of the generation income as a target, establishing a generator declaration model; simulating decision quotation of a generator by adopting an intelligent quotation algorithm to obtain a generator price declaration curve;
a third module configured to: according to a power generation output declared by a power generator and a power generation price declared curve of the power generator, a power selling company declared electric quantity declared by a power selling company and a power selling company load declared curve, a multi-type power supply access simulation operation system is calculated, system balance, power grid safety and unit operation constraint conditions are considered, a safety constraint unit combination and a safety constraint economic dispatching model are established, and an optimal solution is obtained through a mixed integer programming algorithm.
It should be noted here that the first module, the second module, and the third module are the same as the example and the application scenario implemented in the first embodiment, but are not limited to the disclosure of the first embodiment. It should be noted that the modules described above as part of a system may be implemented in a computer system such as a set of computer-executable instructions.
EXAMPLE III
As shown in fig. 18, the present embodiment provides an electric power market simulation operation method.
An electric power market simulation operation method comprises the following steps:
step (1): acquiring various data of a power generator and a power seller by utilizing the existing architecture foundation;
step (2): based on the data provided in the step (1), obtaining an optimization result through the power market simulation optimization method based on the first embodiment;
and (3): according to the main data provided in the step (2), realizing the main business function of the electric power spot market according to the requirement, and evaluating the market clearing data and the result thereof;
and (4): developing market simulation drilling through network model parameters, various clearing data and market rules;
and (5): displaying various graphs, curves and various data analysis;
and (6): generating a power dispatching strategy based on various graphs, curves and various data; and providing data support for the operation of the power market based on the power scheduling strategy.
The invention can effectively simulate the data reporting process of various market members such as power generation enterprises, power selling enterprises and the like, and the key point is the market bidding behavior, which comprises the following steps:
acquiring various kinds of information issued by an electric power market; the various information released by the power market comprises information such as comprehensive electricity price, day-ahead node electricity price, real-time node electricity price, historical electricity purchasing cost of electricity vendors, prediction data released by the power market in real time and the like;
based on various information released by the electric power market, an electricity vendor establishes an electricity vendor reporting model with the aim of minimizing the comprehensive electricity purchasing cost to obtain an electricity vendor load reporting curve;
the method comprises the following steps that a generator establishes a generator declaration model with the aim of maximizing the generation income; and simulating decision quotation of the power generator by adopting an intelligent agent algorithm to obtain a power price declaration curve of the power generator.
The quotation modes comprise power generation side quotation, user side no quotation, power generation amount quotation and user side quotation; the number of the days of birth and death includes 96 points and 24 points; the declaration rule comprises the number of the capacity sections of the unit, the limit value proportion of the number of the quotation sections, and upper and lower limits of the declaration price.
A unit combination optimization algorithm considering safety constraints; in a certain scheduling period, the load balance is satisfied, certain boundary conditions and standby requirements are met, and the start-stop state of the unit is reasonably distributed, so that the power generation cost is minimized; the unit combination is an optimization problem, which means that under certain constraint conditions, the reasonable value of the controllable quantity is determined, so that the set objective function is optimized.
An economic scheduling algorithm that takes into account security constraints; on the premise of ensuring safe operation of electric power and qualified quality of electric energy, constraint conditions such as system power balance, reserve capacity, network safety and the like are met, and loads are reasonably distributed among generator sets, so that the operation cost of the system is minimum;
the electric power spot market clearing module is mainly used for carrying out resource optimization configuration, taking into account a multi-type power access system according to the situations that power generation enterprises declare power generation output and quotation curves, power selling enterprises declare electric quantity and the like, establishing a safety constraint unit combination and a safety constraint economic dispatching model by considering constraint conditions such as system balance, power grid safety, unit operation and the like, and obtaining an optimal solution through a mixed integer planning algorithm.
Example four
The embodiment provides an electric power market simulation operation system.
An electric power market simulation run system comprising:
a platform support module configured to: the method comprises the steps that various data of a power generator and a power seller are obtained by utilizing an existing architecture foundation, and basic service is provided for a data management module;
a data management module configured to: based on data provided by a database in the platform supporting module, obtaining an optimization result through the electric power market simulation optimization method based on the first embodiment;
a marketplace application module configured to: based on the main data provided by the data management module, the main business function of the electric power spot market is realized according to the requirement, and the market clearing data and the result thereof are evaluated;
a market simulation module configured to: developing market simulation drilling through network model parameters, various clearing data and market rules;
an interface presentation module configured to: displaying various graphs, curves and various data analysis;
a scheduling module configured to: generating a power dispatching strategy based on various graphs, curves and various data; and providing data support for the operation of the power market based on the power scheduling strategy.
As a further implementation mode, the optimization result is subjected to N-1 check and sensitivity check through system security evaluation in the data management module.
The electric power spot market simulation system consists of three layers of B-S (browser-server) and C-S (customer service-server) mixed structures consisting of a client program/Web browser, an application server and a database server. The system logic model is shown in fig. 12. The system logically comprises a plurality of markets which trade at the same time, and allows registered legal users to select any market to trade, all operations of the users are carried out through a customer service end program and a Web browser, and an independent database provides data.
Fig. 13 shows a flow chart of the system. The system calculates the time-sharing unit combination and the time-sharing node electricity price according to basic data declared by power generation and power selling enterprises and by combining power grid model parameters, power system constraint conditions, load data, market rules and the like, and further obtains clearing results of all market members.
The system adopts an efficient, reliable and extensible architecture system design, meets the characteristics of multiple types of users, large data interaction amount and the like in the electric power spot market, realizes simple and safe access to the system by each market member, and facilitates information interaction of market main bodies such as power generation enterprises, power selling enterprises and the like. The system adopts a layered architecture design and comprises a platform module, a data management module, a market application module, a market simulation module and an interface display module. Modules are accessed in a service mode. See fig. 14 for a specific implementation.
A platform support module. The module comprises a database system, a data processing library, a software support platform and other basic platforms. Various basic platforms in the module can utilize the existing architecture base to provide basic service for the upper layer module.
And a data management module. The module carries out optimization calculation through a Security Constraint Unit Combination (SCUC) and a Security Constraint Economic Dispatch (SCED) core algorithm based on data provided by a database in the platform support module to obtain an optimization result; and performing N-1 verification, sensitivity verification and the like on the result through system safety evaluation in the module.
And (5) a market application module. The module realizes the main business function of the electric power spot market according to the requirement based on the main data provided by the data management module, and comprises a day-ahead market, a real-time market, an auxiliary service market and the like. Meanwhile, the module can also realize the clearing settlement of the market and the evaluation of the result.
And a market simulation module. And carrying out market simulation drilling through network model parameters, various clearing data and market rules to match the trade.
And an interface display module. Market members can visually see various charts, curves and various data analyses from the simulation platform, such as power generation analysis, power selling analysis, market clearing analysis, power price analysis and the like.
The spot market simulation system provides a stable and reliable running environment for market members participating in clearing, and can monitor and store data in real time in the process of simulating trading. The platform human-computer interaction interface is simple in design and rapid in response, accurate extraction of key data of market operation is achieved through various visual display technologies, and the operation condition of the spot market is displayed in an all-round mode. The flexibility of the simulation system is realized by establishing the flexibility of an architecture system, the flexibility of market scene change and the like.
The method includes the steps of. With the access of various types of power generation sources and the participation of large and small users, the scale of the power market is gradually enlarged, and the data interaction among power generation enterprises, power selling enterprises, power trading and dispatching mechanisms, monitoring mechanisms and the like becomes more and more complex. Therefore, in order to ensure safe and stable operation of the power grid, higher requirements are put forward on the reliability, timeliness and the like of data. The system adopts a relatively mature modeling technology and an efficient solving method to ensure that a more accurate numerical value is obtained, solves the problem of big data of power grid operation and market operation through distributed database storage, ensures that interactive data among market members are more reliable, and further ensures that the system is reliably operated.
And the expansion is easy. The system is supported by a plurality of module frameworks, micro-service processing can be carried out on some modules, namely, basic functions are integrated and packaged according to the business requirements of the spot market, the system can be called in real time in response to the continuous change of the market mode, different business functions can be called conveniently and flexibly, and the running efficiency of the system is improved. The system can also realize the integrated operation of business such as trading, settlement and the like in the market at the day and in real time, and realize the effective connection of market operation and power grid operation, market trading and system scheduling and the like.
The spot market simulation system can realize that multiple market members carry out quotation decision through different trading rules in multiple trading modes, namely, business integration operation such as trading, settlement and the like in markets such as day-ahead, real-time and auxiliary services is realized. By simulating different types of market main bodies to make declaration decisions according to the grasped market information and carrying out optimization calculation by the system according to the stored data, clear results are obtained and displayed at the front end, so that the platform is more clear and ordered to build.
As shown in fig. 15, each plant station corresponds to a market simulation platform, and a reporting decision is made by logging in a system through a dedicated ID. The declaration data is stored in a database, and system calling is facilitated. The power grid simulation is to change constraint parameters by performing simulation operation (such as changing unit parameters) on power grid equipment; establishing a model through a core algorithm module and obtaining a clearing result through a solver; selecting different bidding mechanisms and transaction modes through market rule simulation to obtain corresponding clearing results; the output and quotation curves, the load requirements, the output result analysis and other flows corresponding to each market are obtained through the simulation operation of the spot market, and finally, the information is displayed on the electric power spot trading platform in all directions in the forms of graphs, tables and characters, and different market members can see self declaration data, output data and analysis results through exclusive subsystems.
The system adopts a layered architecture design, modules can be decoupled, and the competitiveness of electricity selling enterprises can be improved.
EXAMPLE five
A computer-readable storage medium, on which a computer program is stored, which when executed by a processor implements the steps of a power market simulation optimization method as described in the first embodiment and/or performs the steps of a power market simulation operation method as described in the third embodiment.
EXAMPLE six
A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor executes the program to implement the steps of a power market simulation optimization method according to one of the above embodiments and/or to perform the steps of a power market simulation operation method according to the third embodiment.
EXAMPLE seven
The embodiment provides a simulation platform for electric power market simulation.
A simulation platform for electric power market simulation comprises an electric power market simulation optimization system based on the second embodiment and/or an electric power market simulation operation system based on the fourth embodiment.
In summary, the present invention establishes an electric power spot market simulation system based on the electric power spot market trading principle in a certain area. The system adopts an efficient, reliable and extensible architecture system design, meets the characteristics of multiple types of users, large data interaction amount and the like in the electric power spot market, realizes simple and safe access to the system by each market member, and facilitates information interaction of market main bodies such as power generation enterprises, power selling enterprises and the like. The system adopts a layered architecture design and comprises a platform module, a data management module, a market application module, a market simulation module and an interface display module. Modules are accessed in a service mode. The system analyzes the electric quantity and benefits of the power generation enterprises by combining the quotation curves of the power generation enterprises and the unit operation parameters; combining the reported electric quantity of each electricity selling enterprise and the real-time comprehensive electricity price curve to carry out batch zero-income analysis on the electricity selling enterprises; and according to the clearing result analysis, carrying out settlement analysis on each member in the market to obtain the clearing settlement result of each member in the market. In addition, the system also carries out comparison and analysis on the output power price of the power generation enterprise and the load power price of the power selling enterprise so as to visually compare the income conditions of all market members.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of a hardware embodiment, a software embodiment, or an embodiment combining software and hardware aspects. Rather, the invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, optical storage, and the like) having computer-usable program code embodied in the medium.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program that can be stored in a computer-readable storage medium and includes processes of the embodiments of the methods described above when the computer program is executed. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (9)
1. A power market simulation optimization method is characterized by comprising the following steps:
based on various information sent by the electric power market, considering that the electricity vendors aim at minimum comprehensive cost, establishing an electricity vendor reporting model to obtain a load reporting curve of the electricity vendor;
considering a generator to take the maximization of the generation income as a target, establishing a generator declaration model; simulating decision quotation of a generator by adopting an intelligent quotation algorithm to obtain a generator price declaration curve;
establishing a safety constraint unit combination and a safety constraint economic dispatching model according to a power generation output declared by a power generator and a power generation price declaration curve of the power generator, a power selling company declared power quantity declared by a power selling company and a power selling company load declaration curve, and a multi-type power supply access simulation operation system, considering system balance, power grid safety and unit operation constraint conditions, and obtaining an optimal solution through a mixed integer programming algorithm;
the establishing of the safety constraint unit combination specifically comprises the following steps: establishing a new mathematical model of the unit combination problem, wherein the new mathematical model of the unit combination problem takes the electricity purchasing cost minimization as an objective function, and the consideration comprises the following steps: system constraints, unit constraints, environmental constraints brought under the new environment of the power market and network security constraints, wherein the system constraints comprise: the system active power balance constraint and the system rotation standby constraint, the unit constraint comprises: the method comprises the following steps of generator maximum and minimum output constraints, unit minimum operation and downtime constraints and unit climbing rate constraints, wherein the network safety constraints comprise: system reactive power balance, generator reactive power upper and lower limit constraint and system voltage constraint;
the objective function of the safety-constrained economic dispatch model is expressed as follows:
wherein, F is the total electricity purchasing cost of the power consumer; pi(t) is the output of the unit i in the time period t; fi[Pi(t)]The output of the unit is Pi(t) the corresponding quote;
the objective function of the new mathematical model of the unit combination problem is as follows:
the active power balance constraint of the system is as follows:
the system rotation standby constraints are:
the maximum and minimum output constraints of the generator are as follows:
Pimin(t)≤Pi(t)≤Pimax(t)
the minimum operation and stop time constraint of the unit is as follows:
[Yi on(t-1)-Ti on]·[Ui(t-1)-Ui(t)]≥0
[Yi off(t-1)-Ti off]·[Ui(t)-Ui(t-1)]≥0
the unit climbing rate constraint is as follows:
Pi(t)-Pi(t-1)≤RURi
Pi(t-1)-Pi(t)≤RDRi
the environmental constraints are:
the system reactive power balance and the reactive power upper and lower limit constraints of the generator are as follows:
Qimin(t)≤Qi(t)·Ui(t)≤Qimax(t)
the system voltage constraints are:
Vmin≤V≤Vmax
wherein, i is 1, 2.. times.n;
the various types of information sent by the electric power market comprise: and (3) integrating the electricity price, the day-ahead node electricity price, the real-time node electricity price, the historical electricity purchasing cost of an electricity seller and the prediction data published by the electricity market in real time at each stage of the electricity market.
2. The electric power market simulation optimization method according to claim 1, wherein the intelligent price quotation algorithm comprises:
constructing a cost function according to the power generation cost of a power generator, and constructing a quotation function by taking each section of price in the cost function as a decision variable;
considering the constraint condition of the strategy coefficient, discretizing the strategy coefficient at equal distance to obtain a corresponding bidding function;
adopting a Q-learning algorithm to construct a decision model, and determining a learning method of an optimal strategy by using the decision model;
and simulating generator decision quotation according to the learning method of the optimal strategy.
3. An electric power market simulation optimization system, comprising:
a first module configured to: based on various information sent by the electric power market, considering that the electricity vendors aim at minimum comprehensive cost, establishing an electricity vendor reporting model to obtain a load reporting curve of the electricity vendor;
a second module configured to: considering a generator to take the maximization of the generation income as a target, establishing a generator declaration model; simulating decision quotation of a generator by adopting an intelligent quotation algorithm to obtain a generator price declaration curve;
a third module configured to: establishing a safety constraint unit combination and a safety constraint economic dispatching model according to a power generation output declared by a power generator and a power generation price declaration curve of the power generator, a power selling company declared power quantity declared by a power selling company and a power selling company load declaration curve, and a multi-type power supply access simulation operation system, considering system balance, power grid safety and unit operation constraint conditions, and obtaining an optimal solution through a mixed integer programming algorithm;
the establishing of the safety constraint unit combination specifically comprises the following steps: establishing a new mathematical model of the unit combination problem, wherein the new mathematical model of the unit combination problem takes the electricity purchasing cost minimization as an objective function, and the consideration comprises the following steps: system constraints, unit constraints, environmental constraints brought under the new environment of the power market and network security constraints, wherein the system constraints comprise: the system active power balance constraint and the system rotation standby constraint, the unit constraint comprises: the method comprises the following steps of generator maximum and minimum output constraints, unit minimum operation and downtime constraints and unit climbing rate constraints, wherein the network safety constraints comprise: system reactive power balance, generator reactive power upper and lower limit constraint and system voltage constraint;
the objective function of the safety-constrained economic dispatch model is expressed as follows:
wherein, F is the total electricity purchasing cost of the power consumer; pi(t) is the output of the unit i in the time period t; fi[Pi(t)]The output of the unit is Pi(t) the corresponding quote;
the objective function of the new mathematical model of the unit combination problem is as follows:
the active power balance constraint of the system is as follows:
the system rotation standby constraints are:
the maximum and minimum output constraints of the generator are as follows:
Pimin(t)≤Pi(t)≤Pimax(t)
the minimum operation and stop time constraint of the unit is as follows:
[Yi on(t-1)-Ti on]·[Ui(t-1)-Ui(t)]≥0
[Yi off(t-1)-Ti off]·[Ui(t)-Ui(t-1)]≥0
the unit climbing rate constraint is as follows:
Pi(t)-Pi(t-1)≤RURi
Pi(t-1)-Pi(t)≤RDRi
the environmental constraints are:
the system reactive power balance and the reactive power upper and lower limit constraints of the generator are as follows:
Qimin(t)≤Qi(t)·Ui(t)≤Qimax(t)
the system voltage constraints are:
Vmin≤V≤Vmax
wherein, i is 1, 2.. times.n;
the various types of information sent by the electric power market comprise: and (3) integrating the electricity price, the day-ahead node electricity price, the real-time node electricity price, the historical electricity purchasing cost of an electricity seller and the prediction data published by the electricity market in real time at each stage of the electricity market.
4. A power market simulation operation method is characterized by comprising the following steps:
step (1): acquiring data of a power generator and a power seller by utilizing an existing architecture foundation;
step (2): obtaining an optimization result by a power market simulation optimization method based on any one of claims 1-2 based on the data provided in step (1);
and (3): according to the data provided in the step (2), realizing the business function of the electric power spot market according to the requirement, and evaluating the market clearing data and the result thereof;
and (4): developing market simulation drilling through network model parameters, clearing data and market rules;
and (5): displaying graphs, curves and data analysis;
and (6): generating a power dispatching strategy based on the chart, the curve and the data; and providing data support for the operation of the power market based on the power scheduling strategy.
5. An electric power market simulation operation system, comprising:
a platform support module configured to: acquiring data of a power generator and a power seller by using the existing architecture foundation, and providing basic service for the data management module;
a data management module configured to: obtaining an optimization result by a power market simulation optimization method based on any one of claims 1-2 based on data provided by a database in the platform support module;
a marketplace application module configured to: based on the data provided by the data management module, realizing the business function of the electric power spot market according to the requirement, and evaluating the market clearing data and the result thereof;
a market simulation module configured to: developing market simulation drilling through network model parameters, clearing data and market rules;
an interface presentation module configured to: displaying graphs, curves and data analysis;
a scheduling module configured to: generating a power dispatching strategy based on the chart, the curve and the data; and providing data support for the operation of the power market based on the power scheduling strategy.
6. The electric power market simulation operation system according to claim 5, wherein the optimization result is subjected to N-1 check and sensitivity check through system safety evaluation in the data management module.
7. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of a power market simulation optimization method according to any one of claims 1-2 and/or carries out the steps of a power market simulation run method according to claim 4.
8. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program performs the steps of a power market simulation optimization method according to any one of claims 1-2 and/or performs the steps of a power market simulation run method according to claim 4.
9. A simulation platform for electric power market simulation, characterized by comprising an electric power market simulation optimization system according to claim 3 and/or an electric power market simulation operation system according to any one of claims 5 to 6.
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