Power market risk tracing method and system based on numerical perturbation

Abstract

The invention discloses an electric power market risk tracing method and system based on numerical perturbation. The method comprises the following steps: acquiring required net rack data, system operation prediction data and thermal power generating unit data; constructing a unit commitment optimization model considering new energy access; based on the unit combination optimization model considering the new energy access, constructing an economic dispatching optimization model considering the new energy access; solving the economic dispatching optimization model considering the new energy access to obtain a power market clearing result, and calculating power market key attention objects such as node marginal electricity price; calculating the sensitivity of each power market key concerned object when the power market key concerned object changes along with different influence factors; and analyzing and judging the relevance between each influence factor and the key concerned object of the electricity market based on the sensitivity data. According to the method, the relevance between the power market key concerned objects and the influence factors thereof is analyzed, so that the risk source of the power spot market is found, and a basis is provided for risk early warning and control of the power market.

Description

Technical Field

[0001] The present invention relates to the field of power market operation analysis, and in particular to a power market risk tracing method and system based on numerical perturbation. Background Art

[0002] The electricity market has established a market organization form that is closely integrated with the operation of the power system, which can fully restore the commodity attributes of electricity and truly play a role in price discovery and optimal resource allocation. As a supplement to the market-based power balance mechanism, electricity spot market transactions play a role in discovering prices, improving trading varieties, and forming full competition. In a mature and complete electricity market system, the spot market is a core link. Compared with direct transactions and medium- and long-term transactions between large users with strong planning attributes, the spot market can fully reflect the marginal power generation costs and power supply and demand in different time periods and locations, thereby better discovering electricity price signals.

[0003] However, the complex clearing mechanism and high-frequency trading characteristics of the spot market, as well as the diversified market behaviors and potential market loopholes of market players, make the spot market operation complex and changeable, and market risk tracing has also become a major challenge. At present, the pilot provinces for the construction of the national power market have not yet formed a set of systematic and effective technical means to conduct a comprehensive and integrated analysis of the operation of the spot market, so as to facilitate the power dispatching department to timely discover potential spot market operation risks. Therefore, it is necessary to conduct in-depth research on power market risk tracing technology in the process of power spot market construction and gradual improvement, grasp the market operation situation and risk level in real time, and timely discover the source of risks in the market operation process, so as to provide support for early warning of possible risk events and promote the efficient and stable development of the power market. Summary of the invention

[0004] The purpose of the present invention is to provide a risk tracing method and system considering the access of new energy under the current electricity spot market rules. The method can quickly and intuitively determine the degree of influence of various factors on the key focus objects of the electricity market.

[0005] To achieve the above object, the present invention adopts the following technical solution: In a first aspect, the present invention provides a method and system for tracing the risk of a power market based on numerical perturbation, including: obtaining required grid data, system operation prediction data, and thermal power unit data; Based on grid data, system operation prediction data, and thermal power unit data, a unit combination optimization model considering the access of new energy sources is constructed; Based on the unit commitment optimization model considering the access of renewable energy, an economic dispatch optimization model considering the access of renewable energy is constructed; Solve the economic dispatch optimization model considering the access of new energy, obtain the power market clearing results, and calculate the key focus of the power market such as the node marginal electricity price; Calculate the sensitivity of each power market focus object as it changes with different influencing factors; Based on sensitivity data analysis, the correlation between each influencing factor and the key focus of the power market is determined.

[0006] As a further improvement of the present invention, the grid data includes: the number of power system nodes, the number of lines, line impedance and maximum current carrying capacity, the node where the new energy collection station is located, the node where the thermal power unit is located, and the network topology; System operation prediction data includes: the total number of new energy output scenarios, the probability of each scenario occurring, and the load of each node in the system; The data of thermal power units include: the starting point of each output range of the thermal power unit, the end point of each output range, the energy price of each output range, the startup cost, the shutdown cost, the upper and lower limits of the output of the thermal power unit, the climbing ability, and the minimum start and stop time.

[0007] As a further improvement of the present invention, a unit commitment optimization model considering the access of new energy is constructed, including: The goal is to minimize the expected value of the total operating cost of the power system under all scenarios, taking into account the electric energy cost of the generator set and the start-up and shutdown costs caused by the start-up and shutdown of the unit; Construct constraints, including: system active power balance constraints, unit output equality constraints, unit output inequality constraints, unit ramp constraints, hot standby constraints, start-stop time constraints, start-up state constraints, start-stop cost constraints, and line flow constraints.

[0008] As a further improvement of the present invention, the unit commitment optimization model considering the access of new energy is as follows: The objective function aims to minimize the expected value of the total operating cost of the power system under all scenarios, mainly considering the electric energy cost of the generator and the start-up and shutdown costs caused by the start-up and shutdown of the unit.

[0009]

[0010] In the formula, Z represents the set of scenarios, T represents the set of time periods, and G represents the set of all generators. represents the probability of scene z appearing, represents the bidding function of thermal power units, represents the quotation function of new energy units, represents the unit startup cost, represents the unit shutdown cost, represents the output variable of the thermal power unit at time t under scenario z, Represents the output variable of the new energy power station at time t under scenario z.

[0011] 1) System active power balance constraints

[0012] In the formula, S Represents the collection of all new energy power stations, N Represents the set of all nodes, Representation scene z Down t The output variables of new energy at every moment, Representation Node n exist t Load of time.

[0013] 2) Unit output equation constraints

[0014] In the formula, M represents the set of output segments of all thermal power units, Indicates the output of each section of the thermal power unit. Represents the state variable of the thermal power unit. This variable is a 0-1 variable. 0 means that the thermal power unit is in the shutdown state, and 1 means that the unit is in the startup state. Indicates the lower output limit of the unit.

[0015] 3) Unit output inequality constraints

[0016]

[0017]

[0018] In the formula, Indicates the unit g No. m The length of the output interval, Represents new energy units s Scenario z Down t The maximum available output at the moment due to actual wind and solar conditions. Represents new energy units s Scenario z Down t Actual effort at all times.

[0019] 4) Unit climbing constraints

[0020]

[0021] In the formula, Indicates the maximum ramp rate of the thermal power unit. Indicates the maximum shutdown ramp rate of thermal power units. Indicates the maximum ramp rate of thermal power units. Indicates the maximum startup ramp rate of the thermal power unit.

[0022] 5) Hot standby constraints

[0023] In the formula, represents the hot standby coefficient, Indicates time t of the total load.

[0024] 6) Start and stop time constraints

[0025]

[0026]

[0027] In the formula, Indicates the start and stop action of the thermal power unit. If it is 1, the unit is started at this moment. If it is -1, the unit is shut down at this moment. If it is 0, there is no start and stop action of the unit at this moment. Indicates the minimum continuous startup time of the unit. Indicates the minimum continuous shutdown time of the unit.

[0028] 7) Start-stop cost constraints

[0029]

[0030]

[0031]

[0032] In the formula, represents the hot start cost of thermal power units, Represents the shutdown cost of thermal power units.

[0033] 8) Line flow constraints

[0034] In the formula, is the power transfer distribution factor matrix, For line l The power transmission limit of Line A collection of lines.

[0035] As a further improvement of the present invention, an economic dispatch optimization model considering the access of new energy sources is constructed, including the following steps: When the thermal power units are operated according to the start-stop plan, the goal is to minimize the expected value of the total operating cost of the power system under all scenarios; Construct constraints, including: system active power balance constraints, unit output equality constraints, unit output inequality constraints, unit ramp constraints, hot standby constraints, and line flow constraints.

[0036] As a further improvement of the present invention, the economic dispatch optimization model considering the access of new energy is as follows: The objective function of economic dispatch is to minimize the expected value of the total operating cost of the power system under all scenarios when the start and stop plan of the thermal power unit is determined. Specifically,

[0037] In the formula, represents the probability of scene z appearing, represents the bidding function of thermal power units, represents the quotation function of new energy units, represents the output variable of the thermal power unit at time t under scenario z, Represents the output variable of the new energy power station at time t under scenario z.

[0038] The constraints of economic dispatch include: 1) System active power balance constraints

[0039] 2) Unit output equation constraints

[0040] In the formula, It represents the state parameter of the thermal power unit. 0 means that the thermal power unit is in the shutdown state, and 1 means that the unit is in the start state. This parameter is obtained by solving the unit combination optimization model.

[0041] 3) Unit output inequality constraints

[0042]

[0043]

[0044] 4) Unit climbing constraints

[0045]

[0046] 5) Hot standby constraints

[0047] 6) Line flow constraints .

[0048] As a further improvement of the present invention, obtaining the power market clearing result and calculating the key focus objects of the power market such as the node marginal electricity price include the following steps: According to the results of power market clearing, the Lagrange multipliers corresponding to the system active power balance constraint and line power flow constraint are obtained; Based on the Lagrange multipliers corresponding to the system active power balance constraints and line flow constraints, as well as the probabilities corresponding to each scenario, we obtain the node marginal electricity price under each scenario, the expected value of the node marginal electricity price, the expected value of the system marginal electricity price, the expected value of the node electricity price congestion component price, the expected value of the load-side average electricity price, the expected value of the load-side average congestion price, the expected value of the line average congestion price, the expected value of the market supply-demand ratio and other key objects of concern in the power market.

[0049] As a further improvement of the present invention, the key indicators of the power market include: The node marginal electricity price in each scenario is as follows:

[0050] In the formula, For the scene z Down t The Lagrange multiplier corresponding to the moment, and Respectively for scenes z Down t The Lagrange multipliers corresponding to the forward and reverse power flow constraints at each moment, The Lagrange multiplier and power transfer distribution factor matrix corresponding to the power flow constraint are represented by No. The inner product of the columns, Representation scene Probability of occurrence.

[0051] The expected value of the node marginal electricity price is as follows:

[0052] The expected value of the system marginal electricity price is as follows:

[0053] The expected value of the average electricity price on the load side is as follows:

[0054] The expected value of the node electricity price blocking component price is as follows:

[0055] The expected value of the average congestion price on the load side is as follows:

[0056] The expected value of the average line congestion price is as follows:

[0057] In the formula, and Representation scene z Down t Timeline The Lagrange multipliers corresponding to the reverse power flow constraint and the forward power flow constraint are: represents the power transfer distribution factor matrix No. Line List, Representation Node From this definition, we can see that the average line congestion price reflects the load caused by the line. The average of the congestion prices of each node caused by congestion.

[0058] As a further improvement of the present invention, the sensitivity of each key focus object in the power market when it changes with different influencing factors is calculated: Take the obtained data as the reference level, and appropriately change the influencing factors of node load, total system load, line transmission capacity, and maximum dispatchable power of new energy on this basis; When each influencing factor is at different levels, the corresponding values ​​of each key focus object of the power market are repeatedly calculated; According to the results of the key focus objects of each power market changing with the influencing factors, numerical differentiation is performed on them to calculate the sensitivity of the key focus objects of each power market when the influencing factors are at different levels.

[0059] In a second aspect, the present invention provides a power market risk tracing system based on numerical perturbation, comprising: Data acquisition module, used to obtain required grid data, system operation prediction data, and thermal power unit data; The first building module is used to build a unit combination optimization model considering the access of new energy based on grid data, system operation prediction data, and thermal power unit data; The second building module is used to build an economic dispatch optimization model considering the access of new energy based on the unit commitment optimization model considering the access of new energy; The first calculation module is used to construct and solve the economic dispatch optimization model considering the access of new energy, obtain the power market clearing results, and calculate the key focus of the power market such as the node marginal electricity price; The second calculation module is used to calculate the sensitivity of each key focus object of the power market when it changes with different influencing factors; The analysis and judgment module is used to analyze and judge the relevance of various influencing factors and key focus objects in the power market based on sensitivity data.

[0060] The beneficial effects of the present invention are: The method of the present invention adopts a method based on multi-scenario random optimization to deal with the randomness and volatility brought by the output of new energy, thereby constructing a safety-constrained unit combination optimization model considering wind power and photovoltaic power generation, a safety-constrained economic dispatch optimization model and a node marginal electricity price calculation model, and successfully establishing a power spot market clearing model including new energy power stations and thermal power units. Then, the sensitivity of the power market's key focus objects such as the expected value of the load side average electricity price, the expected value of the load side average congestion price, and the expected value of the line average congestion price when changing with the load level, new energy output and other influencing factors is used to analyze the correlation between the power market's key focus objects and their influencing factors, thereby helping to discover the source of risks in the power spot market and providing a basis for early warning and control of power market risks. For example, when the load level and new energy output change, the fluctuations of the load side average electricity price and congestion price can help predict the potential risks of the power market. Based on this, participants in the power market can adopt corresponding strategies for risk early warning and management to ensure the stable operation and reliability of the market. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 It is a flow chart of the method for evaluating power system transmission congestion under the access of new energy sources described in the present invention.

[0062] Figure 2 This is a schematic diagram of the piecewise linearization of fuel costs for thermal power units.

[0063] Figure 3 Schematic diagram of the average electricity price sensitivity curve on the load side when the total system load changes, where (a) is the average electricity price sensitivity curve from time 7 to time 12 and (b) is the average electricity price sensitivity curve from time 13 to time 18.

[0064] Figure 4 Schematic diagram of the average congestion price sensitivity curve on the load side when the total system load changes. Among them, (a) is the average congestion price sensitivity curve from time 7 to time 12, and (b) is the average congestion price sensitivity curve from time 13 to time 18.

[0065] The present invention is further described in detail below in conjunction with the accompanying drawings. DETAILED DESCRIPTION

[0066] In order to enable those skilled in the art to better understand the technical solutions in the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0067] The first aspect of the present invention is to provide a method for tracing the risk of the power market based on numerical perturbation, comprising the following steps: S1, obtain the required grid data, system operation prediction data, and thermal power unit data; S2, based on grid data, system operation prediction data, and thermal power unit data, build a unit combination optimization model that takes into account the access of new energy sources; S3, based on the unit commitment optimization model considering the access of renewable energy, construct an economic dispatch optimization model considering the access of renewable energy; S4, solve the economic dispatch optimization model considering the access of new energy, obtain the power market clearing results, calculate the key focus of the power market such as the node marginal electricity price; S5, calculate the sensitivity of each key focus object in the power market as it changes with different influencing factors; S6, based on sensitivity data analysis, determine the relevance of each influencing factor to the key focus objects of the power market.

[0068] The core of the power market risk tracing method based on numerical perturbation proposed in the present invention is to trace and analyze the impact of various factors in the power market on key market indicators through a series of data processing and model construction. In the model construction stage, the unit combination optimization model of new energy access is considered. This model aims to determine how to optimally arrange the start and stop plans of various units to minimize the total cost while meeting the security constraints of the power grid. The access of new energy makes the model more complicated because the output of new energy is uncertain and intermittent. Economic dispatch optimization model considering the access of new energy: On the basis of the unit combination, the output plan of each unit is further determined to minimize the operating cost while meeting the load demand. The access of new energy also needs to consider the uncertainty of its output.

[0069] Furthermore, in the solution and optimization stage, by solving the economic dispatch optimization model, the clearing results of the power market are obtained, including key indicators such as the marginal electricity price of each node. These indicators reflect the supply and demand relationship and price formation mechanism of the power market. Sensitivity analysis and risk tracing calculate the sensitivity of the key focus objects of each power market (such as node marginal electricity price) when it changes with different influencing factors (such as new energy output, load changes, unit failures, etc.). Sensitivity reflects the degree of influence of factor changes on key indicators. Correlation judgment, based on sensitivity data analysis, can judge the correlation between each influencing factor and the key focus objects of the power market. This helps to identify which factors have a significant impact on the price and stability of the power market, thereby providing a basis for power market supervision and risk management.

[0070] This method constructs an optimization model for the power system considering the access of new energy sources and uses sensitivity analysis technology to effectively trace the risks in the power market. The core of this method is to use numerical perturbations (i.e. small changes) to observe the changes in key market indicators, thereby revealing the intrinsic connection between various factors and market indicators. This method not only helps to understand the complex dynamics of the power market, but also provides a scientific basis for policy formulation, risk management and market supervision of the power market.

[0071] The present invention will be further described below in conjunction with the accompanying drawings, but the content of the present invention is not limited thereto.

[0072] See also Figure 1 , a risk tracing method considering the access of new energy under the current electricity spot market rules, the method comprises the following steps: Step 1: When applying the model proposed in the present invention, it is necessary to first obtain the required grid data, system operation prediction data, and thermal power unit data from relevant departments.

[0073] Grid data: number of power system nodes, number of lines, line impedance and maximum current carrying capacity, nodes where new energy collection stations are located, nodes where thermal power units are located, network topology, etc.

[0074] System operation prediction data: total number of new energy output scenarios, probability of occurrence of each scenario, load on each node of the system, etc.

[0075] Thermal power unit data: starting point of each output range of the thermal power unit, end point of each output range, energy price of each output range, startup cost, shutdown cost, upper and lower limits of output of the thermal power unit, climbing ability, minimum start and stop time and other parameters.

[0076] Step 2: Construct a unit combination optimization model considering the access of new energy, including constructing objective functions and constraints.

[0077] The objective function aims to minimize the expected value of the total operating cost of the power system under all scenarios, mainly considering the electric energy cost of the generator and the start-up and shutdown costs caused by the start-up and shutdown of the unit. Where Z represents the set of scenarios, T represents the set of time periods, and G represents the set of all generators. represents the probability of scene z appearing, represents the bidding function of thermal power units, represents the quotation function of new energy units, represents the unit startup cost, represents the unit shutdown cost, represents the output variable of the thermal power unit at time t under scenario z, Represents the output variable of the new energy power station at time t under scenario z. Because the unit start-stop plan obtained by unit combination needs to be used for the solution of subsequent economic dispatch and node marginal electricity prices and other key objects of the power market, the unit combination optimization model needs to solve a definite unit start-stop plan, so the start-stop plans of all scenarios should be consistent, so that the start-stop costs of each scenario are consistent.

[0078] (1) To facilitate market clearing, the current spot market rules require that the electricity price of thermal power units be a monotonically increasing power generation price curve throughout the day, with a maximum of ten segments, and three to ten segments can be freely selected for declaration; each segment needs to declare the starting point (MW) of the output range, the end point (MW) of the output range, and the energy price (yuan / MWh) of the range, such as Figure 2 The specific piecewise linearization method is determined by each power plant in accordance with the spot market rules. The following formula is an example of a 2-segment quotation function for a unit, where and are the first-stage electricity price and the second-stage electricity price of unit g, respectively, and , and are the upper and lower limits of the output of unit g, Indicates the length of the first output range of unit g.

[0079] (2) Since new energy power stations do not need to use fossil energy to generate electricity, the cost of electricity is extremely low. The actual electricity price of new energy power stations is related to the electricity price policies of large-scale wind power and photovoltaic power in each province. It can be roughly considered that the settlement is based on the historical comprehensive electricity price of new energy, that is, the electricity price of new energy power stations is a constant value.

[0080] The start-up and stop process of wind turbines is relatively simple, and is mainly automatically controlled by wind speed changes. Wind turbines do not have complicated preheating and cooling processes, so the energy consumption and equipment wear during their start-up and stop processes are relatively small. The start-up and stop process of photovoltaic power generation systems is even simpler, and is basically directly determined by sunshine conditions. The rise and fall of the sun automatically triggers the start and stop of the photovoltaic system. There are no mechanical moving parts, and there is no obvious start-up and stop cost. Therefore, the start-up and stop costs of the units mainly consider the start-up and stop costs of thermal power units.

[0081] The constraints of unit commitment include: 1) System active power balance constraints According to the power balance requirements of the system at each moment, we have: (3) In the formula, S Represents the collection of all new energy power stations, N Represents the set of all nodes, Representation scene z Down t The variable of renewable energy output at the moment. This constraint represents the scenario z Down t At all times, the output of all generators is equal to the total load of all nodes, without considering network losses.

[0082] 2) Unit output equation constraints (4) In the formula, M represents the set of output segments of all thermal power units, Indicates the output of each section of the thermal power unit. Represents the state variable of the thermal power unit. This variable is a 0-1 variable. 0 means that the thermal power unit is in the shutdown state, and 1 means that the unit is in the startup state. Indicates the lower limit of the unit's output. This constraint indicates that the thermal power unit g In the scene z time t If it is in the on state, the total output is the sum of the outputs of each output range plus the lower limit of the unit output.

[0083] 3) Unit output inequality constraints (5) (6) (7) In the formula, Indicates the unit g No. m The length of the output interval, Represents new energy units s Scenarioz Down t The maximum available output at the moment due to actual wind and solar conditions. Represents new energy units s Scenario z Down t The actual output at the moment. Constraint (4) indicates that when the thermal power unit is in the on state, the output should be between the upper and lower limits of the unit output; when the unit is in the off state, the output should be 0. Constraint (5) indicates that the output of each section of the thermal power unit should be between 0 and the length of the section. Constraint (6) indicates that the output of each section of the new energy unit should be between 0 and the scene z Down t Maximum available output at any time.

[0084] 4) Unit climbing constraints (8) (9) In the formula, Indicates the maximum ramp rate of the thermal power unit. Indicates the maximum shutdown ramp rate of thermal power units. Indicates the maximum ramp rate of thermal power units. represents the maximum startup ramp rate of the thermal power unit. Constraint (7) indicates that when the thermal power unit is in the scenario z of t When the unit is in the on state at the moment, the output decrease compared with the previous moment should be less than the maximum down-slope rate; when the unit is in the off state, the output decrease compared with the previous moment should be less than the maximum off-slope rate. Constraint (8) indicates that when the unit is in t When the -1 moment is in the on state, the output increased at the next moment should be less than the maximum ramp-up rate; when it is in the off state, the output increased at the next moment should be less than the maximum startup ramp-up rate.

[0085] 5) Hot standby constraints (10) In the formula, represents the hot standby coefficient, Indicates time t The total load of t The remaining available capacity of all started thermal power units should be greater than the required reserve capacity.

[0086] 6) Start and stop time constraints (11) (12) (13) In the formula, Indicates the start and stop action of the thermal power unit. If it is 1, the unit is started at this moment. If it is -1, the unit is shut down at this moment. If it is 0, there is no start and stop action of the unit at this moment. Indicates the minimum continuous startup time of the unit. represents the minimum continuous shutdown time of the unit. Constraint (10) means that if the moment t After the unit is started, The unit must be in the on state during the time. t For other cases, this constraint is relaxed. Constraint (11) means that if the moment t If the unit is shut down, the The unit must be in shutdown state within the specified time. If it is any other case, this constraint is relaxed.

[0087] 7) Start-stop cost constraints (14) (15) (16) (17) In the formula, represents the startup cost of thermal power units, represents the shutdown cost of thermal power units. Constraint (15) represents the shutdown cost of thermal power units. g At the moment t If activated, , then the startup cost is greater than or equal to the startup cost. Because the objective function requires the minimum cost, the startup cost is ultimately equal to the startup cost. If it is other cases, is 0 or -1, then the intersection of this constraint and constraint (13) is still constraint (13). Constraint (16) indicates that the unit g At the moment t If the machine stops, , then the shutdown cost is greater than or equal to the shutdown cost of the unit. Because the objective function requires the minimum cost, the shutdown cost is ultimately equal to the shutdown cost. If it is other cases, If it is 0 or -1, the intersection of this constraint and constraint (14) is still constraint (14).

[0088] 8) Line flow constraints (18) In the formula, is the power transfer distribution factor matrix, For line l The power transmission limit of Line is a set of lines. This constraint indicates that the transmission power on each line must not exceed the transmission limit of the line.

[0089] Step 3: Construct an economic dispatch optimization model considering the access of new energy sources, including constructing the optimization objective function and constraints.

[0090] The objective function of economic dispatch is to minimize the expected value of the total operating cost of the power system under all scenarios when the thermal power units are operated according to the start-stop plan determined in step 3, as shown in formula (18). When the start-stop plan of the unit is determined, the start-stop cost is determined accordingly and is a constant, so this item can be ignored.

[0091] (19) The constraints of economic dispatch include: 1) System active power balance constraints (20) 2) Unit output equation constraints (twenty one) In the formula, It represents the state parameter of the thermal power unit. 0 means that the thermal power unit is in the shutdown state, and 1 means that the unit is in the start state. This parameter is obtained by solving the unit combination optimization model.

[0092] 3) Unit output inequality constraints (twenty two) (twenty three) (twenty four) 4) Unit climbing constraints (25) (26) 5) Hot standby constraints (27) 6) Line flow constraints (28) Step 4: Solve the safety-constrained unit commitment according to step 2 and solve the safety-constrained economic dispatch according to step 3 to obtain the power market clearing result, calculate the key focus objects of the power market such as the node marginal electricity price, and the definition and calculation formula of each key focus object are as follows: The node marginal electricity price in each scenario is given by formula (28): (29) In the formula, For the scene z Down t The Lagrange multiplier corresponding to the time constraint (19) is, and Respectively for scenes z Down t The Lagrange multipliers corresponding to the forward flow constraint and the reverse flow constraint are: The Lagrange multiplier and power transfer distribution factor matrix corresponding to the power flow constraint are represented by No. The inner product of the columns, Representation scene Probability of occurrence.

[0093] The expected value of the node marginal electricity price is as follows: (30) The expected value of the system marginal electricity price is as follows: (31) The expected value of the average electricity price on the load side is as follows: (32) The expected value of the node electricity price blocking component price is as follows: (33) The expected value of the average congestion price on the load side is as follows: (34) The expected value of the average line congestion price is as follows: (35) In the formula, and Representation scene z Down t Time constraint (27) The Lagrange multipliers corresponding to the reverse power flow constraint and the forward power flow constraint are: represents the power transfer distribution factor matrix No. Line List, Representation Node From this definition, we can see that the average line congestion price reflects the load caused by the line. The average of the congestion prices of each node caused by congestion.

[0094] Step 5: Calculate the sensitivity of each power market focus object to changes in different influencing factors: Step 5.1: Take the data obtained in step 1 as the reference level, and on this basis appropriately change influencing factors such as node load, total system load, line transmission capacity, and maximum dispatchable power of new energy.

[0095] Step 5.2: When the influencing factors are at different levels, repeat steps 2, 3 and 4 to achieve power market clearing and calculate the corresponding values ​​of the key focus objects in the power market.

[0096] Step 5.3: Based on the results of the changes in the key focus objects of each power market with the influencing factors obtained in step 5.2, use formula (36) to calculate the sensitivity of the key focus objects of each power market when each influencing factor is at different levels. As the focus of the power market, For a certain influencing factor, is the step size of the influencing factor change, and the step size is 1% of the reference level of the factor.

[0097] (36) Step 6: Feedback the sensitivity data obtained in step 5 to relevant personnel through a computer to analyze and determine the relevance of each influencing factor to the key focus of the power market.

[0098] In order to make the purpose, technical scheme and advantages of the embodiments of the present invention clearer, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. The components of the embodiments of the present invention described and shown in the drawings here can usually be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0099] A 129-node test system is used to verify the feasibility and effectiveness of the proposed power market risk tracing method and system based on numerical perturbation. There are 89 thermal power units, 26 new energy stations, 129 nodes and 162 lines in the system. The number of new energy output scenarios is 5. Using the power market risk tracing method described in the present invention, the sensitivity curves of the average electricity price on the load side and the average congestion price on the load side when the total load of the system changes can be obtained as follows: Figure 3 and Figure 4 As shown, it can be used as a basis for decision-making on risk warning, prevention and control in the power market.

[0100] Depend on Figure 3It can be seen that in the time period from 11:00 to 17:00, the sensitivity of the average electricity price on the load side is significantly greater than at other times. When the load level is low, roughly below 95% of the reference level, the sensitivity will also be greater. This reflects that when new energy is generated in large quantities or the load level is relatively low, that is, when the penetration rate of new energy is high, the electricity price will also be more volatile, and a small increase in the load cannot effectively weaken this volatility, and may even aggravate the fluctuation of electricity prices. Therefore, we should focus on the period from 11:00 to 17:00 and the load level is low, in which case the risk is greater. In particular, at 12:00 and when the load level is around 85% of the reference level, the average electricity price on the load side is most sensitive, and the risk of electricity price fluctuations is high.

[0101] Depend on Figure 4 It can be seen that the average load-side congestion price sensitivity is large during the time period from 11:00 to 16:00, while the average load-side congestion price sensitivity in other time periods is very small, close to 0. This shows that transmission congestion mainly occurs from 11:00 to 16:00. When the load level reaches 108% of the reference level and above, the sensitivity of the average load-side congestion price is relatively small, which means that when the load is large, the impact of line congestion on prices will not change significantly. This is mainly because the penetration rate of new energy is low at this time, and even if the congestion of new energy transmission channels is alleviated, it is still not enough to have a significant impact on the average node marginal electricity price on the load side of the entire system. When the load level is small and between 11:00 and 16:00, it can be found that the sensitivity of the average load-side congestion price is large, which means that timely measures to alleviate the congestion of new energy transmission channels when this happens can effectively resolve the risk of transmission congestion, thereby reducing the average node marginal electricity price on the load side of the entire system.

[0102] The invention discloses a method for tracing the risk of the power market based on numerical perturbation, and obtains the required basic data from relevant departments. A method based on multi-scenario random optimization is adopted to deal with the randomness and volatility brought by the output of new energy sources, thereby constructing a safety-constrained unit combination optimization model considering wind power and photovoltaic power generation, a safety-constrained economic dispatch optimization model and a node marginal electricity price calculation model, and successfully establishing a power spot market clearing model including new energy power stations and thermal power units. The obtained data is used to solve the power spot market clearing model. Then, the expected value of the average electricity price on the load side, the expected value of the average congestion price on the load side, the expected value of the average congestion price on the line, and other power market focus objects are calculated. By changing the influencing factors such as load level and new energy output, the sensitivity of the power market focus objects when changing with each influencing factor is obtained, and the correlation between the power market focus objects and their influencing factors is analyzed, so as to help discover the risk sources of the power spot market and provide a basis for the early warning and control of power market risks.

[0103] In a second aspect, the present invention provides a device for predicting the probability of a day-ahead electricity price based on deep learning, comprising: Data acquisition module, used to obtain required grid data, system operation prediction data, and thermal power unit data; The first building module is used to build a unit combination optimization model considering the access of new energy based on grid data, system operation prediction data, and thermal power unit data; The second building module is used to build an economic dispatch optimization model considering the access of new energy based on the unit commitment optimization model considering the access of new energy; The first calculation module is used to construct and solve the economic dispatch optimization model considering the access of new energy, obtain the power market clearing results, and calculate the key focus of the power market such as the node marginal electricity price; The second calculation module is used to calculate the sensitivity of each key focus object of the power market when it changes with different influencing factors; The analysis and judgment module is used to analyze and judge the relevance of various influencing factors and key focus objects in the power market based on sensitivity data.

[0104] The third aspect of the present invention is to provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements a method for tracing the risk of an electricity market based on numerical perturbation when executing the computer program.

[0105] A fourth aspect of the present invention is to provide a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, a method for tracing the risk of the electricity market based on numerical perturbation is implemented.

[0106] A fourth aspect of the present invention is to provide a computer program product, which includes computer instructions, characterized in that the computer instructions instruct the computer to execute a method for tracing the risk of the electricity market based on numerical perturbations.

[0107] The above contents are only for explaining the technical idea of ​​the present invention and cannot be used to limit the protection scope of the present invention. Any changes made on the basis of the technical solution in accordance with the technical idea proposed by the present invention shall fall within the protection scope of the claims of the present invention.

[0108] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0109] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0110] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0111] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0112] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for tracing the source of power market risks based on numerical perturbation, characterized in that: include: Obtain the required grid data, system operation prediction data, and thermal power unit data; Based on grid data, system operation prediction data, and thermal power unit data, a unit combination optimization model considering the access of new energy sources is constructed; Based on the unit commitment optimization model considering the access of renewable energy, an economic dispatch optimization model considering the access of renewable energy is constructed; Solve the economic dispatch optimization model considering the access of new energy, obtain the power market clearing results, and calculate the key focus of the power market such as the node marginal electricity price; Calculate the sensitivity of each power market focus object as it changes with different influencing factors; Based on sensitivity data analysis, the correlation between each influencing factor and the key focus of the power market is determined.

2. A method for tracing the source of power market risks based on numerical perturbation according to claim 1, characterized in that: The grid data includes: the number of power system nodes, the number of lines, line impedance and maximum current carrying capacity, the node where the new energy collection station is located, the node where the thermal power unit is located, and the network topology; System operation prediction data includes: the total number of new energy output scenarios, the probability of each scenario occurring, and the load of each node in the system; The data of thermal power units include: the starting point of each output range of the thermal power unit, the end point of each output range, the energy price of each output range, the startup cost, the shutdown cost, the upper and lower limits of the output of the thermal power unit, the climbing ability, and the minimum start and stop time.

3. A method for tracing the source of power market risks based on numerical perturbation according to claim 1, characterized in that: Construct a unit combination optimization model considering the access of new energy, including: The goal is to minimize the expected value of the total operating cost of the power system under all scenarios, taking into account the electric energy cost of the generator set and the start-up and shutdown costs caused by the start-up and shutdown of the unit; Construct constraints, including: system active power balance constraints, unit output equality constraints, unit output inequality constraints, unit ramp constraints, hot standby constraints, start-stop time constraints, start-up state constraints, start-stop cost constraints, and line flow constraints.

4. A method for tracing the source of power market risks based on numerical perturbation according to claim 1 or 3, characterized in that: The unit combination optimization model considering the access of new energy is as follows: The objective function aims to minimize the expected value of the total operating cost of the power system under all scenarios, mainly considering the electric energy cost of the generator and the start-up and shutdown costs caused by the start-up and shutdown of the unit. In the formula, Z represents the set of scenarios, T represents the set of time periods, and G represents the set of all generators. represents the probability of scene z appearing, represents the bidding function of thermal power units, represents the quotation function of new energy units, represents the unit startup cost, represents the unit shutdown cost, represents the output variable of the thermal power unit at time t under scenario z, Represents the output variable of the new energy power station at time t under scenario z.

5. The method for tracing the risk of the power market based on numerical perturbation according to claim 1, characterized in that: Constructing an economic dispatch optimization model considering the access of new energy sources includes the following steps: When the thermal power units are operated according to the start-stop plan, the goal is to minimize the expected value of the total operating cost of the power system under all scenarios; Construct constraints, including: system active power balance constraints, unit output equality constraints, unit output inequality constraints, unit ramp constraints, hot standby constraints, and line flow constraints.

6. A method for tracing the risk of power market based on numerical perturbation according to claim 1 or 5, characterized in that: The economic dispatch optimization model considering the access of new energy is as follows: The objective function of economic dispatch is to minimize the expected value of the total operating cost of the power system under all scenarios when the start and stop plan of the thermal power unit is determined. Specifically, In the formula, represents the probability of scene z appearing, represents the bidding function of thermal power units, represents the quotation function of new energy units, represents the output variable of the thermal power unit at time t under scenario z, Represents the output variable of the new energy power station at time t under scenario z.

7. A method for tracing the source of power market risks based on numerical perturbation according to claim 1, characterized in that: Obtaining the power market clearing results and calculating the key power market targets such as node marginal electricity prices include the following steps: According to the results of power market clearing, the Lagrange multipliers corresponding to the system active power balance constraint and line power flow constraint are obtained; Based on the Lagrange multipliers corresponding to the system active power balance constraints and line flow constraints, as well as the probabilities corresponding to each scenario, we obtain the node marginal electricity price under each scenario, the expected value of the node marginal electricity price, the expected value of the system marginal electricity price, the expected value of the node electricity price congestion component price, the expected value of the load-side average electricity price, the expected value of the load-side average congestion price, the expected value of the line average congestion price, the expected value of the market supply-demand ratio and other key objects of concern in the power market.

8. A method for tracing the source of power market risks based on numerical perturbation according to claim 1 or 7, characterized in that: Key indicators of the electricity market include: The node marginal electricity price in each scenario is as follows: In the formula, For the scene z Down t The Lagrange multiplier corresponding to the moment, and Respectively for scenes z Down t The Lagrange multipliers corresponding to the forward and reverse power flow constraints at each moment, The Lagrange multiplier and power transfer distribution factor matrix corresponding to the power flow constraint are represented by No. The inner product of the columns, Representation scene Probability of occurrence; The expected value of the node marginal electricity price is as follows: The expected value of the system marginal electricity price is as follows: The expected value of the average electricity price on the load side is as follows: The expected value of the node electricity price blocking component price is as follows: The expected value of the average congestion price on the load side is as follows: The expected value of the average line congestion price is as follows: Where N represents the set of all system nodes, and Representation scene z Down t Timeline The Lagrange multipliers corresponding to the reverse power flow constraint and the forward power flow constraint are: represents the power transfer distribution factor matrix No. Line List, Representation Node of load.

9. The method for tracing the source of power market risk based on numerical perturbation according to claim 1, characterized in that: Calculate the sensitivity of each power market focus object as different influencing factors change: Take the obtained data as the reference level, and on this basis appropriately change the influencing factors of node load, total system load, line transmission capacity, and maximum dispatchable power of new energy; When each influencing factor is at different levels, the corresponding values ​​of each key focus object of the power market are repeatedly calculated; According to the results of the key focus objects of each power market changing with the influencing factors, numerical differentiation is performed on them to calculate the sensitivity of the key focus objects of each power market when the influencing factors are at different levels.

10. A power market risk tracing system based on numerical perturbation, characterized in that: include: Data acquisition module, used to obtain required grid data, system operation prediction data, and thermal power unit data; The first building module is used to build a unit combination optimization model considering the access of new energy based on grid data, system operation prediction data, and thermal power unit data; The second building module is used to build an economic dispatch optimization model considering the access of new energy based on the unit commitment optimization model considering the access of new energy; The first calculation module is used to construct and solve the economic dispatch optimization model considering the access of new energy, obtain the power market clearing results, and calculate the key focus of the power market such as the node marginal electricity price; The second calculation module is used to calculate the sensitivity of each key focus object of the power market when it changes with different influencing factors; The analysis and judgment module is used to analyze and judge the relevance of various influencing factors and key focus objects in the power market based on sensitivity data.

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