Quantity reporting and quotation method and device considering distributed fault risk and adjustable capability scarcity

Abstract

The invention relates to a quantity and price reporting method considering distributed fault risk and adjustable capability scarcity. The method comprises the following steps: calculating the fault probability of distributed resources at the current moment; calculating the clearing power of the distributed resource m in the day-ahead electric energy market; a scarcity factor and the upper adjustable capacity, the lower adjustable capacity and the scarcity price of the distributed resources m in the intra-day stage are calculated; and constructing an intra-day electric energy and auxiliary service market model, and dynamically clearing the power of the resources. According to the method, the operation state of the distributed resources and the action mechanism of the fault characteristics are analyzed, the potential fault risk of the distributed resources is evaluated, dependence on the high-fault-probability resources is reduced, the resource scheduling structure is optimized, and the power supply reliability is improved; through distributed resource scarcity pricing and adjustable capacity evaluation based on marginal cost characteristics, market pricing can reflect the adjustable capacity and the opportunity cost of the resources, and the resources are guided to reasonably participate in different markets.

Description

Technical Field

[0001] This invention relates to the field of power market optimization and clearing technology, and in particular to a method and device for reporting quantity and pricing that takes into account the risk of distributed faults and the scarcity of adjustable capacity. Background Technology

[0002] With the continuous growth of global electricity demand and the rapid development of new energy technologies, my country's power system is undergoing profound changes. Distributed resources, as a flexible form of power supply, are of great significance in improving the cleanliness of the power grid and the diversity of energy supply. However, with the continuous connection of more and more resource equipment, the supply and demand balance of the power system has become more complex, especially in the face of the volatility of new energy generation and the risk of distributed resource failures, which threatens the system's security and stability.

[0003] The widespread application of distributed resources in the electricity and ancillary services market has made them an important means of improving power system flexibility. However, the fault characteristics and generation uncertainties of distributed resources can significantly affect their dispatch efficiency and responsiveness in the market, thereby impacting the system's economy and reliability. This is particularly true in distribution systems where faults are more frequent, increasing the risk of power outages for users. Existing electricity market clearing models are mostly based on the characteristics of traditional centralized power sources, neglecting the differentiated flexibility potential and fault characteristics of distributed resources during the regulation process. Therefore, how to achieve synergistic optimization of electricity and flexibility resources through market mechanisms while ensuring both economy and reliability has become a pressing issue for the current electricity market. Summary of the Invention

[0004] To address the issue of high power quantity reporting during the market clearing of distributed resources, which carries a high risk of failure and leads to a decrease in the operational reliability of the power distribution system, and ignores the differentiated flexibility potential and failure characteristics of distributed resources in the adjustment process, the primary objective of this invention is to provide a quantity reporting and pricing method that reduces dependence on high-failure-probability resources, optimizes the resource scheduling structure, improves power supply reliability, and enables market pricing to reflect the adjustability and opportunity cost of resources, taking into account the distributed failure risk and the scarcity of adjustability.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: a quotation method considering distributed failure risk and the scarcity of adjustability, the method comprising the following sequential steps:

[0006] (1) Calculate the load rate of the distributed resource based on its operating power at time t. and the probability of failure of distributed resources at the current moment ;

[0007] (2) Construct a day-ahead electricity market clearing model, minimize the objective function, and then calculate the clearing power of distributed resource m in the day-ahead electricity market. ;

[0008] (3) Based on the clearing power of distributed resource m in the day-ahead electricity market Calculate the scarcity factor that reflects the scarcity of distributed resource adjustment capabilities. And the adjustable capacity of distributed resource m during the intraday phase. Adjustable capacity and scarcity price ;

[0009] (4) Based on the adjustable capacity of distributed resource m during the intraday phase Adjustable capacity and scarcity price A daily electricity and ancillary services market model is constructed, with the optimization objective of minimizing the sum of electricity purchase cost and ancillary services market transaction cost. The power of resources is dynamically cleared to achieve the optimal reporting quantity of adjustable capacity.

[0010] Step (1) specifically refers to:

[0011] Calculate the load rate of distributed resources :

[0012] ;

[0013] In the formula: m∈{GT, WT, PV}, where GT, WT and PV are gas turbine, wind turbine and photovoltaic unit respectively; Let m be the rated capacity of the distributed resource.

[0014] Calculate the failure probability of the distributed resource at the current moment. :

[0015] ;

[0016] ;

[0017] In the formula: , , These represent the basic failure probability, the maximum failure probability under low load, and the maximum failure probability under heavy load for distributed resource m, respectively. , These are the maximum load rate threshold and minimum load rate threshold for distributed resource m, respectively. , Here, k represents the low-load threshold and heavy-load threshold for the distributed resource m to be in a healthy operating state, respectively. l,m and kh,m These are the parameters that vary under low-load and heavy-load conditions, respectively, and determine the rate at which the failure probability changes with the increase of the load rate.

[0018] Step (2) specifically refers to the objective function of the day-ahead electricity market clearing model as follows:

[0019] ;

[0020] In the formula: Let m be the number of distributed resources; T be the clearing period. Let be the energy trading clearing cost of distributed resource m at time t;

[0021] Clearing power of distributed resource m in the day-ahead electricity market The calculation formula is as follows:

[0022] ;

[0023] In the formula: The initial bid price for the electrical energy of distributed resources;

[0024] The current clearing power constraint is:

[0025] ;

[0026] In the formula: , These are the maximum power and minimum power of the distributed resource m, respectively.

[0027] The objective function and the clearing power constraint constitute the day-ahead electricity market clearing model;

[0028] Calculate the expected power loss of the power distribution system due to distributed resources participating in market transactions without considering the risk of failure. :

[0029] ;

[0030] In the formula: The larger the value, the greater the impact of resource failure on the system.

[0031] Step (3) specifically refers to: calculating the scarcity factor. :

[0032] ;

[0033] In the formula, k m The adjustment coefficient for controlling the increase of the scarcity factor m of distributed resources reflects the sensitivity to changes in current electrical energy. Let m be the maximum power of the distributed resource.

[0034] Based on scarcity factor Calculate scarcity price :

[0035] ;

[0036] In the formula: To control the sensitivity component of resource output adjustment to failure probability, a larger value causes the resource price to increase significantly when the failure probability is high. Represented as the system's marginal price; The initial bid price for the electrical energy of distributed resources;

[0037] Distributed resource m clearing winning bid electricity after calculating failure probability :

[0038] ;

[0039] In the formula: This indicates that as the probability of failure increases, the output of resources gradually decreases; The electrical energy power cleared in the market m days prior to the distribution of resources;

[0040] Calculate the adjustable capacity of distributed resource m during the intraday phase. Adjustable capacity :

[0041] ;

[0042] In the formula: This represents the maximum climbing capacity of the distributed resource m. Let m be the minimum power of the distributed resource.

[0043] Step (4) specifically refers to: the objective function of the intraday electricity and ancillary services market model is:

[0044] ;

[0045] Intraday clearing power constraint is:

[0046] ;

[0047] ;

[0048] In the formula: This represents the clearing power of the dynamically adjusted distributed resource m during time period t. Let m be the number of distributed resources; T be the clearing period. , These are the upward and downward auxiliary service clearing prices for distributed resource m during time period t, respectively. , These represent the upper and lower adjustable capacities of the distributed resource m during the intraday period in time period t. Let m be the maximum power of the distributed resource.

[0049] The intraday energy and ancillary services market model consists of the objective function and the intraday clearing power constraint.

[0050] Another object of the present invention is to provide an electronic device comprising:

[0051] Processor; and

[0052] A memory storing computer program instructions that, when executed by the processor, cause the processor to perform the quantity quotation method described above, which takes into account the risk of distributed failures and the scarcity of adjustability.

[0053] The present invention also provides a computer-readable storage medium having stored thereon computer program instructions, which, when executed by a processor, cause the processor to perform the quantity quotation method described above, taking into account the risk of distributed failures and the scarcity of adjustability.

[0054] As can be seen from the above technical solution, the beneficial effects of the present invention are as follows: First, by analyzing the operating status and fault characteristics of distributed resources and assessing their potential fault risks, the present invention reduces dependence on high-fault-probability resources, optimizes the resource scheduling structure, and improves power supply reliability. Second, based on the marginal cost characteristics of distributed resource scarcity pricing and adjustable capacity assessment, the present invention enables market pricing to reflect the adjustability of resources and their opportunity costs, guiding resources to participate rationally in different markets. Third, by proposing a joint optimization and clearing strategy for the day-ahead energy market and the intraday ancillary service market, the present invention effectively compensates for the opportunity costs of distributed resources caused by fault risks and reserved flexibility, further improving the system's economy and operational stability. Attached Figure Description

[0055] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0056] like Figure 1 As shown, a pricing method for quantity quotation that considers distributed failure risk and the scarcity of adjustability includes the following sequential steps:

[0057] (1) Calculate the load rate of the distributed resource based on its operating power at time t. and the probability of failure of distributed resources at the current moment ;

[0058] (2) Construct a day-ahead electricity market clearing model, minimize the objective function, and then calculate the clearing power of distributed resource m in the day-ahead electricity market. ;

[0059] (3) Based on the clearing power of distributed resource m in the day-ahead electricity market Calculate the scarcity factor that reflects the scarcity of distributed resource adjustment capabilities. And the adjustable capacity of distributed resource m during the intraday phase. Adjustable capacity and scarcity price ;

[0060] (4) Based on the adjustable capacity of distributed resource m during the intraday phase Adjustable capacity and scarcity price A daily electricity and ancillary services market model is constructed, with the optimization objective of minimizing the sum of electricity purchase cost and ancillary services market transaction cost. The power of resources is dynamically cleared to achieve the optimal reporting quantity of adjustable capacity.

[0061] Step (1) specifically refers to:

[0062] Calculate the load rate of distributed resources :

[0063] ;

[0064] In the formula: m∈{GT, WT, PV}, where GT, WT and PV are gas turbine, wind turbine and photovoltaic unit respectively; Let m be the rated capacity of the distributed resource.

[0065] Calculate the failure probability of the distributed resource at the current moment. :

[0066] ;

[0067] ;

[0068] In the formula: , , These represent the basic failure probability, the maximum failure probability under low load, and the maximum failure probability under heavy load for distributed resource m, respectively. , These are the maximum load rate threshold and minimum load rate threshold for distributed resource m, respectively. , Here, k represents the low-load threshold and heavy-load threshold for the distributed resource m to be in a healthy operating state, respectively. l,m and k h,m These are the parameters that vary under low-load and heavy-load conditions, respectively, and determine the rate at which the failure probability changes with the increase of the load rate.

[0069] Step (2) specifically refers to the objective function of the day-ahead electricity market clearing model as follows:

[0070] ;

[0071] In the formula: Let m be the number of distributed resources; T be the clearing period. Let be the energy trading clearing cost of distributed resource m at time t;

[0072] Clearing power of distributed resource m in the day-ahead electricity market The calculation formula is as follows:

[0073] ;

[0074] In the formula: The initial bid price for the electrical energy of distributed resources;

[0075] The current clearing power constraint is:

[0076] ;

[0077] In the formula: , These are the maximum power and minimum power of the distributed resource m, respectively.

[0078] The objective function and the clearing power constraint constitute the day-ahead electricity market clearing model;

[0079] Calculate the expected power loss of the power distribution system due to distributed resources participating in market transactions without considering the risk of failure. :

[0080] ;

[0081] In the formula: The larger the value, the greater the impact of resource failure on the system.

[0082] Step (3) specifically refers to: calculating the scarcity factor. :

[0083] ;

[0084] In the formula, k mThe adjustment coefficient for controlling the increase of the scarcity factor m of distributed resources reflects the sensitivity to changes in current electrical energy. Let m be the maximum power of the distributed resource.

[0085] Based on scarcity factor Calculate scarcity price :

[0086] ;

[0087] In the formula: To control the sensitivity component of resource output adjustment to failure probability, a larger value causes the resource price to increase significantly when the failure probability is high. Represented as the system's marginal price; The initial bid price for the electrical energy of distributed resources;

[0088] Distributed resource m clearing winning bid electricity after calculating failure probability :

[0089] ;

[0090] In the formula: This indicates that as the probability of failure increases, the output of resources gradually decreases; The electrical energy power cleared in the market m days prior to the distribution of resources;

[0091] In the ancillary services market, the ramp-up capability of resources is determined by their current operating status and remaining adjustable margin. Resources operating below full capacity have the flexibility to be increased, responding to positive system adjustment demands by increasing output; while resources that have won bids can be reduced to mitigate system overload risks by decreasing output. Considering fault characteristics, the power adjustment margin of distributed resources needs to be adjusted.

[0092] Calculate the adjustable capacity of distributed resource m during the intraday phase. Adjustable capacity :

[0093] ;

[0094] In the formula: This represents the maximum climbing capacity of the distributed resource m. Let m be the minimum power of the distributed resource.

[0095] Step (4) specifically refers to: the objective function of the intraday electricity and ancillary services market model is:

[0096] ;

[0097] During the intraday scheduling phase, the planned output of various resources is dynamically adjusted based on the previous day's assessment results. A joint optimization and clearing mechanism for intraday electrical energy and ancillary services markets, taking into account the risk of failure, is proposed to achieve dynamic optimization of resource reporting strategies and avoid high reporting behavior of high-failure resources.

[0098] Intraday clearing power constraint is:

[0099] ;

[0100] ;

[0101] In the formula: This represents the clearing power of the dynamically adjusted distributed resource m during time period t. Let m be the number of distributed resources; T be the clearing period. , These are the upward and downward auxiliary service clearing prices for distributed resource m during time period t, respectively. , These represent the upper and lower adjustable capacities of the distributed resource m during the intraday period in time period t. Let m be the maximum power of the distributed resource.

[0102] The intraday energy and ancillary services market model consists of the objective function and the intraday clearing power constraint.

[0103] In summary, this invention analyzes the operational status and fault characteristics of distributed resources, assesses their potential fault risks, reduces reliance on high-failure-probability resources, optimizes resource scheduling structures, and improves power supply reliability. Based on marginal cost characteristics, this invention's distributed resource scarcity pricing and adjustable capacity assessment enable market pricing to reflect resource adjustability and opportunity costs, guiding resources to participate rationally in different markets. Furthermore, by proposing a joint optimization and clearing strategy for the day-ahead energy market and the intraday ancillary service market, this invention effectively compensates for the opportunity costs arising from the fault risks and reserved flexibility of distributed resources, further enhancing system economy and operational stability.

[0104] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention. The scope of protection claimed by the appended claims and their equivalents is defined.

Claims

1. A quantity quotation method considering distributed failure risk and the scarcity of adjustability, characterized in that: The method includes the following steps in sequence: (1) Calculate the load rate of the distributed resource based on its operating power at time t. and the probability of failure of distributed resources at the current moment ; (2) Construct a day-ahead electricity market clearing model, minimize the objective function, and then calculate the clearing power of distributed resource m in the day-ahead electricity market. ; (3) Based on the clearing power of distributed resource m in the day-ahead electricity market Calculate the scarcity factor that reflects the scarcity of distributed resource adjustment capabilities. And the adjustable capacity of distributed resource m during the intraday phase. Adjustable capacity and scarcity price ; (4) Based on the adjustable capacity of distributed resource m during the intraday phase Adjustable capacity and scarcity price A daily electricity and ancillary services market model is constructed, with the optimization objective of minimizing the sum of electricity purchase cost and ancillary services market transaction cost. The power of resources is dynamically cleared to achieve the optimal reporting quantity of adjustable capacity.

2. The quantity quotation method considering distributed failure risk and the scarcity of adjustability according to claim 1, characterized in that: Step (1) specifically refers to: Calculate the load rate of distributed resources : ； In the formula: m∈{GT, WT, PV}, where GT, WT and PV are gas turbine, wind turbine and photovoltaic unit respectively; Let m be the rated capacity of the distributed resource. Calculate the failure probability of the distributed resource at the current moment. : ； ； In the formula: , , These represent the basic failure probability, the maximum failure probability under low load, and the maximum failure probability under heavy load for distributed resource m, respectively. , These are the maximum load rate threshold and minimum load rate threshold for distributed resource m, respectively. , Here, k represents the low-load threshold and heavy-load threshold for the distributed resource m to be in a healthy operating state, respectively. l,m and k h,m These are the parameters that vary under low-load and heavy-load conditions, respectively, and determine the rate at which the failure probability changes with the increase of the load rate.

3. The quantity quotation method considering distributed failure risk and the scarcity of adjustability according to claim 1, characterized in that: Step (2) specifically refers to the objective function of the day-ahead electricity market clearing model as follows: ； In the formula: Let m be the number of distributed resources; T be the clearing period. Let be the energy trading clearing cost of distributed resource m at time t; Clearing power of distributed resource m in the day-ahead electricity market The calculation formula is as follows: ； In the formula: The initial bid price for the electrical energy of distributed resources; The current clearing power constraint is: ； In the formula: , These are the maximum power and minimum power of the distributed resource m, respectively. The objective function and the clearing power constraint constitute the day-ahead electricity market clearing model; Calculate the expected power loss of the power distribution system due to distributed resources participating in market transactions without considering the risk of failure. : ； In the formula: The larger the value, the greater the impact of resource failure on the system.

4. The quantity quotation method considering distributed failure risk and the scarcity of adjustability according to claim 1, characterized in that: Step (3) specifically refers to: calculating the scarcity factor. : ； In the formula, k m The adjustment coefficient for controlling the increase of the scarcity factor m of distributed resources reflects the sensitivity to changes in current electrical energy. Let m be the maximum power of the distributed resource. Based on scarcity factor Calculate scarcity price : ； In the formula: To control the sensitivity component of resource output adjustment to failure probability, a larger value causes the resource price to increase significantly when the failure probability is high. Represented as the system's marginal price; The initial bid price for the electrical energy of distributed resources; Distributed resource m clearing winning bid electricity after calculating failure probability : ； In the formula: This indicates that as the probability of failure increases, the output of resources gradually decreases; The electrical energy power cleared in the market m days prior to the distribution of resources; Calculate the adjustable capacity of distributed resource m during the intraday phase. Adjustable capacity : ； In the formula: This represents the maximum climbing capacity of the distributed resource m. Let m be the minimum power of the distributed resource.

5. The quantity quotation method considering distributed failure risk and the scarcity of adjustability according to claim 1, characterized in that: Step (4) specifically refers to: the objective function of the intraday electricity and ancillary services market model is: ； Intraday clearing power constraint is: ； ； In the formula: This represents the clearing power of the dynamically adjusted distributed resource m during time period t. Let m be the number of distributed resources; T be the clearing period. , These are the upward and downward auxiliary service clearing prices for distributed resource m during time period t, respectively. , These represent the upper and lower adjustable capacities of the distributed resource m during the intraday period in time period t. Let m be the maximum power of the distributed resource. The intraday energy and ancillary services market model consists of the objective function and the intraday clearing power constraint.

6. An electronic device, comprising: processor; as well as A memory storing computer program instructions that, when executed by the processor, cause the processor to perform a quotation method that takes into account the risk of distributed failures and the scarcity of adjustability as described in any one of claims 1-5.

7. A computer-readable storage medium having stored thereon computer program instructions, which, when executed by a processor, cause the processor to perform a quotation method considering distributed failure risk and the scarcity of adjustability as described in any one of claims 1-5.

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