Flexible resource value quantitative evaluation method considering extreme weather guarantee supply

Abstract

The invention discloses a flexible resource value quantitative evaluation method considering extreme weather guarantee supply. The method comprises the following steps: 1) constructing an extreme weather-oriented power system extreme scene set; 2) based on the extreme scene set of the power system, constructing an emergency response scheduling model considering supply insurance of the extreme scene; 3) solving the emergency response scheduling model considering the supply insurance of the extreme scene, and obtaining an optimal load shedding result when the power system accesses or does not access the flexible resources in the extreme weather; and 4) calculating a flexibility resource value evaluation index based on the optimal load shedding result. The flexible resource value quantitative evaluation method can reasonably consider the effect of the flexible resources in the aspect of extreme weather guarantee supply in power grid planning, and provides an accurate and comprehensive flexible resource value quantitative result.

Description

Technical Field

[0001] The present invention relates to the field of power system planning and operation, and specifically to a method for quantitatively evaluating the value of flexibility resources considering power supply assurance in extreme weather. Background Art

[0002] As a key pillar of the national economy, ensuring a safe and reliable supply of electricity is fundamental to meeting people's needs for a better life and supporting stable social development. As the proportion of resource-dependent and environmentally constrained power sources such as wind and solar power increases, the impact of external disturbances such as extreme weather on the safe and stable operation of the power system is gradually increasing, and the contradictions in ensuring power supply are becoming increasingly prominent. The Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC) of the United Nations states that the rate of global warming has significantly accelerated over the past 50 years, and the instability of the climate system has intensified. The "China Climate Change Blue Book (2022)" also points out that my country is facing an increase in extreme events such as high temperatures and heavy rainfall, and faces a power supply crisis caused by extreme weather.

[0003] Flexibility resources, with their characteristics of rapid power regulation and spatiotemporal energy transfer, can effectively improve the system's resilience to various external disturbances, maintain safe and stable system operation, and ensure reliable power supply. To proactively respond to emergencies such as extreme weather or severe external disturbances, it is necessary to configure flexibility resources within the power system to provide sufficient flexible adjustment capabilities to cope with short-term and medium-term fluctuations in power output and load demand. However, due to high planning and operation costs, it is difficult to recoup the investment costs of flexibility resources through conventional operations, which has dampened enthusiasm for their development. In the long run, the flexibility needs of the power system will be difficult to meet under the conditions of a high proportion of renewable energy access. Against this backdrop, there is an urgent need to accurately and comprehensively assess the value of flexibility resources to provide a theoretical basis for the subsequent formulation of market regulatory mechanisms and subsidy policies for flexibility resources.

[0004] Domestic and international research teams have proposed numerous methods for assessing the value of flexibility resources, including simulation operations, flexible regulation characterization, and market analysis. The simulation operation method compares the daily operational simulation results of the power system with and without flexible resources, calculating the flexibility benefits provided by the flexible resources to the system under normal weather conditions as the flexibility resource value. The flexible regulation characterization method characterizes the flexible regulation capability domain of the power system under normal weather conditions with and without flexible resources, compares the characterization results, and calculates the corresponding flexible regulation index difference to characterize the flexible regulation value provided by the flexibility resources to the system. The market analysis method uses the additional market benefits provided by the flexibility resources to the system under normal weather conditions as the flexibility resource value from a market perspective. However, these methods only consider the flexibility value provided by flexibility resources to the system under normal weather conditions, but fail to accurately reveal the supply guarantee value provided by flexibility resources to the system under extreme weather conditions, affecting the comprehensiveness and accuracy of the final value assessment. Summary of the Invention

[0005] The present invention aims to provide a method for quantitatively assessing the value of flexible resources that take extreme weather into account, comprising the following steps:

[0006] 1) Construct a set of extreme scenarios for power systems facing extreme weather;

[0007] 2) Based on a set of extreme power system scenarios, an emergency response dispatch model that considers power supply under extreme scenarios is constructed;

[0008] 3) Solve the emergency response dispatch model considering the supply guarantee in extreme scenarios, and obtain the optimal load shedding results of the power system under extreme weather conditions with and without access to flexible resources.

[0009] 4) Based on the optimal load shedding results, calculate the flexibility resource value assessment index.

[0010] Furthermore, in step 1), the steps of constructing a set of extreme scenarios for the power system facing extreme weather include:

[0011] 1.1) Select the extreme event k to be evaluated;

[0012] 1.2) Construct the intensity time series variation curve Ω of extreme event k k ,Right now:

[0013]

[0014] Where: represents the intensity of extreme weather under type k in period t; represents the set of extreme weather duration periods under type k; the |·| operator represents the set cardinality operator, express The cardinality is T k , T k represents the duration of extreme event k.

[0015] 1.3) Calculate the system equipment failure probability, available capacity, and load demand time series changes under extreme event k through the brittleness function, efficiency function, and load-intensity correlation function, namely:

[0016]

[0017] Where, represents the real-time failure rate of device i; represents the brittleness function of device i under extreme weather type k; represents the performance function of the equipment under extreme weather type k; P i capacity (ω) represents the real-time available capacity of device i; Represents the device collection in the system; P D,t Indicates real-time load demand; Load k (ω,P D,Base ) represents the load-intensity correlation function under extreme weather type k; ω, P D,Base is the extreme event intensity and benchmark load;

[0018] 1.4) Intensity time series variation curve Ω based on extreme event k k , system equipment failure probability, available capacity and load demand time series changes, and construct a set of extreme power system scenarios through the sequential Monte Carlo simulation method

[0019] Furthermore, the emergency response dispatch model for power supply guarantee in extreme scenarios takes the minimum total load shedding loss of guaranteed load and non-guaranteed load as the goal and the power system operation safety constraints under extreme scenarios as the constraints.

[0020] Furthermore, the objective function of the emergency response scheduling model for ensuring supply in extreme scenarios is considered As shown below:

[0021]

[0022] Where: LS NL,s,t With LS CL,s,t are the load shedding amount of industrial and commercial load and the load shedding vector of residential load in period t respectively; C NL with C CLare the load shedding penalties for industrial and commercial loads and residential load shedding penalties for period t, respectively; e represents a column vector whose elements are all ones; is the set of scheduling periods under the extreme scenario s.

[0023] Furthermore, the constraints of the emergency response scheduling model that considers supply guarantee in extreme scenarios include load shedding constraints, system operation constraints, and flexibility resource operation constraints.

[0024] Furthermore, the load shedding constraint is as follows:

[0025]

[0026] Where: R CL To ensure the supply load ratio, in the present invention, this coefficient is equal to the resident load ratio; P D,s,t is the load vector for period t under extreme weather scenario s.

[0027] Furthermore, the system operation constraints are as follows:

[0028]

[0029] Where: P G,s,t With P FR,s,t are the net output vectors of the original system’s traditional thermal power units and flexible resources in period t under extreme weather scenario s; A G With A FR are the association matrices of the traditional thermal power units and the flexibility resource nodes of the original system; F s,t is the branch power vector in period t under extreme weather scenario s; B and B F is the node admittance matrix and the branch admittance matrix; θ s,t is the phase angle vector of each node in time period t; ST F,s,t With ST G,s,t They are the fault state vectors of the transmission line i in period t and the traditional thermal power unit in the original system under the extreme weather scenario s; the element ST corresponding to the transmission line i in period t is F,s,t If it is 0, it indicates that the transmission line i is faulty during time period t; if it is 1, it indicates that the transmission line i is normal at this time; F max is the maximum power vector that can flow through the transmission line; and are the minimum and maximum output vectors of the traditional thermal power units in the original system during period t under extreme weather scenario s; R G is the maximum ramp power vector of the traditional thermal power unit in the original system; θ min and θ max are the lower limit and upper limit vectors of the node power phase angle respectively; is the set of scheduling periods under the extreme scenario s.

[0030] Further, the flexibility resource operation constraints are shown as follows

[0031]

[0032] where X s,t represents the flexibility resource related variable vector at time period t under extreme weather scenario s; S FR,s,t is the flexibility resource related state variable vector at time period t under extreme weather scenario s; Cap is the capacity vector of the flexibility resource; is the set of flexibility resource operation constraints at time period t under extreme weather scenario s.

[0033] Further, the flexibility resource value assessment indicators are shown as follows

[0034] V FR = V OPE (Cap) - V INV (Cap) + V EXT (Cap) (15)

[0035] where V OPE (Cap) represents the annualized operation revenue of the flexibility resource under normal weather condition at given capacity Cap; V INV (Cap) represents the annualized investment and fixed cost of the flexibility resource under normal weather condition at given capacity Cap; V EXT (Cap) represents the value of the flexibility resource under extreme weather condition at given capacity Cap.

[0036] Further, the annualized operation revenue V OPE (Cap) of the flexibility resource under normal weather condition at given capacity Cap, the annualized investment and fixed cost V INV (Cap) of the flexibility resource under normal weather condition at given capacity Cap, and the value V EXT (Cap) of the flexibility resource under extreme weather condition at given capacity Cap are shown as follows

[0037]

[0038] V EXT (Cap) = p EXT (W ECO (0) - W ECO (Cap)) (18)

[0039]

[0040] where C OPE and C INV represent the unit annualized operation revenue of the flexibility resource and the unit annualized investment and fixed cost of the flexibility resource under normal weather condition, respectively; pEXT Indicates the frequency of extreme weather events in a year; W ECO (0) and W ECO (Cap) represents the system cost of the power system before and after connecting to flexibility resources under extreme weather conditions; Represents a set of extreme scenarios The number of scenes included in .

[0041] It is worth noting that the present invention first constructs a set of extreme scenarios for the power system facing specific extreme weather, and reflects the correlation between the intensity of extreme events and the probability of equipment failure, performance and load demand through brittle functions, efficiency functions and load-intensity correlation functions, and reflects the impact of extreme weather on various components of the system. Based on this, the time series state curves of each component are generated through sequential Monte Carlo simulation, and finally a set of extreme scenarios for the power system is constructed; secondly, under the set of extreme scenarios, an emergency response model is used with the goal of minimizing the total load shedding loss of guaranteed load and non-guaranteed load in the system and the power system operation safety constraints under extreme scenarios as constraints to simulate the operation of the power system under extreme scenarios, and based on this, the optimal load shedding results of the power system under extreme weather when connected and not connected to flexibility resources are calculated as the data source for subsequent value quantification; finally, an indicator is designed to quantitatively evaluate the value of flexibility resources, and an indicator calculation method is proposed based on the substitution effect to realize the construction of a quantitative evaluation framework for the value of flexibility resources.

[0042] The technical effects of the present invention are undoubted, and the beneficial effects of the present invention are as follows:

[0043] 1) This paper proposes a method for constructing a set of extreme scenarios for power systems oriented towards specific extreme weather conditions. This method uses brittleness functions, utility functions, and load-intensity correlation functions to reflect the relationship between extreme event intensity and equipment failure probability, performance, and load demand, reflecting the impact of extreme weather on various system components. Sequential Monte Carlo simulations are then used to generate time-series state curves for each component, ultimately constructing a set of extreme scenarios for the power system.

[0044] 2) This paper proposes a method for constructing an emergency response scheduling model that considers power supply guarantees in extreme scenarios. Under a set of extreme scenarios, the emergency response model aims to minimize the total load shedding losses of both guaranteed and non-guaranteed loads in the system, and is constrained by the power system's operational safety constraints under extreme scenarios. This model can simulate the operation of the power system under extreme scenarios and, based on this, calculate the optimal load shedding results for the power system under extreme weather conditions, both with and without access to flexibility resources.

[0045] 3) This paper proposes a method for quantitatively assessing the value of flexibility resources. It designs indicators for quantifying the value of flexibility resources and proposes a calculation method based on the substitution effect, thereby constructing a framework for quantitatively assessing the value of flexibility resources. This method considers the role of flexibility resources in both normal and extreme weather conditions, providing accurate and comprehensive results for quantifying the value of flexibility resources.

[0046] The present invention can be widely used in power system planning and scheduling, and can provide a flexibility resource value quantification assessment method that can reasonably consider the role of flexibility resources in ensuring power supply in extreme weather in power grid planning, and provide accurate and comprehensive flexibility resource value quantification results, which can be used to guide the market supervision mechanism and subsidy policy formulation for flexibility resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 This is a diagram illustrating the method proposed in the present invention using sequential Monte Carlo simulation to generate multiple equipment failure scenarios and ultimately construct a set of extreme scenarios for the power system;

[0048] Figure 2 This is a schematic diagram of the PJM-5 node system topology;

[0049] Figure 3 This is the time series intensity curve of a typical extreme high temperature event;

[0050] Figure 4 The timing curve of the maximum available capacity of the generator under extreme high temperature;

[0051] Figure 5 The load timing curves for normal and extreme scenarios;

[0052] Figure 6 Obtain solutions for emergency dispatch models under different configurations of flexible resources in extreme high temperatures;

[0053] Figure 7 Solve the results for calculating the value of flexibility resources under different capacity configurations during extreme high temperatures;

[0054] Figure 8 The results of the comprehensive value calculation of flexibility resources under extreme high temperatures with different capacity configurations and taking into account the supply value of extreme scenarios are solved. DETAILED DESCRIPTION

[0055] The present invention will be further described below with reference to the following examples, but it should not be understood that the scope of the present invention is limited to the following examples. Without departing from the above technical ideas of the present invention, various substitutions and modifications can be made according to common technical knowledge and customary means in the art, and all should be included in the scope of protection of the present invention.

[0056] Example 1:

[0057] See also Figures 1 to 8 , a quantitative assessment method for the value of flexible resources considering extreme weather supply guarantee includes the following steps:

[0058] 1) Construct a set of extreme scenarios for power systems facing extreme weather;

[0059] 2) Based on a set of extreme power system scenarios, an emergency response dispatch model that considers power supply under extreme scenarios is constructed;

[0060] 3) Solve the emergency response dispatch model considering the supply guarantee in extreme scenarios, and obtain the optimal load shedding results of the power system under extreme weather conditions with and without access to flexible resources.

[0061] 4) Based on the optimal load shedding results, calculate the flexibility resource value assessment index.

[0062] Example 2:

[0063] The method for quantitatively evaluating the value of flexible resources for ensuring power supply in extreme weather conditions has the same technical content as Example 1. Furthermore, in step 1), the step of constructing an extreme scenario set for the power system facing extreme weather conditions includes:

[0064] 1.1) Select the extreme event k to be evaluated;

[0065] 1.2) Construct the intensity time series variation curve Ω of extreme event k k ,Right now:

[0066]

[0067] Where: represents the intensity of extreme weather under type k in period t; represents the set of extreme weather duration periods under type k; the |·| operator represents the set cardinality operator, express The cardinality is T k , T k represents the duration of extreme event k.

[0068] 1.3) Calculate the system equipment failure probability, available capacity, and load demand time series changes under extreme event k through the brittleness function, efficiency function, and load-intensity correlation function, namely:

[0069]

[0070] Where, represents the real-time failure rate of device i; represents the brittleness function of device i under extreme weather type k; represents the performance function of the equipment under extreme weather type k; P i capacity (ω) represents the real-time available capacity of device i; Represents the device collection in the system; P D,t Indicates real-time load demand; Load k (ω,P D,Base ) represents the load-intensity correlation function under extreme weather type k; ω, P D,Base is the extreme event intensity and benchmark load;

[0071] 1.4) Intensity time series variation curve Ω based on extreme event k k , system equipment failure probability, available capacity and load demand time series changes, and construct a set of extreme power system scenarios through the sequential Monte Carlo simulation method

[0072] Example 3:

[0073] A method for quantitatively evaluating the value of flexible resources for ensuring power supply in extreme weather conditions has the same technical content as any one of Examples 1-2. Furthermore, an emergency response scheduling model for ensuring power supply in extreme scenarios takes minimization of the total load shedding loss of guaranteed load and non-guaranteed load as the goal, and the power system operation safety constraints under extreme scenarios as the constraints.

[0074] Example 4:

[0075] Considering the quantitative evaluation method of the flexibility resource value of extreme weather supply guarantee, the technical content is the same as any one of Examples 1-3, and further considering the objective function of the emergency response scheduling model for extreme scenario supply guarantee As shown below:

[0076]

[0077] Where: LS NL,s,t With LS CL,s,t are the load shedding amount of industrial and commercial load and the load shedding vector of residential load in period t respectively; C NL with C CL are the load shedding penalties for industrial and commercial loads and residential load shedding penalties for period t, respectively; e represents a column vector whose elements are all ones; is the set of scheduling periods under the extreme scenario s.

[0078] Example 5:

[0079] A quantitative evaluation method for the value of flexibility resources considering supply guarantee in extreme weather conditions has the same technical content as any one of Examples 1-4. Furthermore, the constraints of the emergency response scheduling model considering supply guarantee in extreme scenarios include load shedding constraints, system operation constraints, and flexibility resource operation constraints.

[0080] Example 6:

[0081] A method for quantitatively evaluating the value of flexible resources considering supply assurance in extreme weather conditions has the same technical content as any one of Examples 1-5. Furthermore, the load shedding constraints are as follows:

[0082]

[0083] Where: R CL To ensure the supply load ratio, in the present invention, this coefficient is equal to the resident load ratio; P D,s,t is the load vector for period t under extreme weather scenario s.

[0084] Example 7:

[0085] A method for quantitatively evaluating the value of flexible resources considering extreme weather supply guarantees is similar in technical content to any one of Examples 1-6. Furthermore, the system operation constraints are as follows:

[0086]

[0087]

[0088] Where: P G,s,t With P FR,s,t are the net output vectors of the original system’s traditional thermal power units and flexible resources in period t under extreme weather scenario s; A G With A FR are the association matrices of the traditional thermal power units and the flexibility resource nodes of the original system; F s,t is the branch power vector in period t under extreme weather scenario s; B and B F is the node admittance matrix and the branch admittance matrix; θ s,t is the phase angle vector of each node in time period t; ST F,s,t With ST G,s,t They are the fault state vectors of the transmission line i in period t and the traditional thermal power unit in the original system under the extreme weather scenario s; the element ST corresponding to the transmission line i in period t is F,s,t If it is 0, it indicates that the transmission line i is faulty during time period t; if it is 1, it indicates that the transmission line i is normal at this time; F max is the maximum power vector that can flow through the transmission line; and are the minimum and maximum output vectors of the traditional thermal power units in the original system during period t under extreme weather scenario s; R Gis the maximum ramp power vector of the traditional thermal power unit in the original system; θ min and θ max are the lower limit and upper limit vectors of the node power phase angle respectively; is the set of scheduling periods under the extreme scenario s.

[0089] Example 8:

[0090] Considering the quantitative evaluation method of flexibility resource value for extreme weather supply guarantee, the technical content is the same as any one of Examples 1-7. Furthermore, the flexibility resource operation constraints are as follows

[0091]

[0092] Where: X s,t represents the variable vector related to the operation constraints of flexibility resources in time period t under extreme weather scenario s; S FR,s,t is the state variable vector of flexibility resources in period t under extreme weather scenario s; Cap is the capacity vector of flexibility resources; is the set of flexible resource operation constraints in time period t under extreme scenario s.

[0093] Example 9:

[0094] The method for quantitatively evaluating the value of flexibility resources considering extreme weather supply guarantee has the same technical content as any one of Examples 1-8. Furthermore, the flexibility resource value evaluation index is as follows:

[0095] V FR =V OPE (Cap)-V INV (Cap)+V EXT (Cap) (15)

[0096] Where: V OPE (Cap) represents the annual operating income of the flexibility resource under normal weather conditions at a given capacity Cap; V INV (Cap) represents the annual investment and fixed cost of flexible resources under a given capacity Cap; V EXT (Cap) represents the value of flexibility resources under extreme weather conditions at a given capacity Cap.

[0097] Example 10:

[0098] Considering the quantitative evaluation method of the flexibility resource value under extreme weather conditions, the technical content is the same as any one of Examples 1-9. Furthermore, the annualized operating income V of the flexibility resource under normal weather conditions at a given capacity Cap is OPE (Cap), annual investment and fixed cost V of flexible resources under a given capacity Cap INV(Cap), the value of flexibility resources under extreme weather conditions at a given capacity Cap V EXT (Cap) is as follows:

[0099]

[0100] V EXT (Cap) = p EXT (W ECO (0)-W ECO (Cap)) (18)

[0101]

[0102] Where: C OPE with C INV They represent the annualized operating income per unit of flexibility resources and the annualized investment and fixed cost per unit of flexibility resources under normal weather conditions; p EXT Indicates the frequency of extreme weather events in a year; W ECO (0) and W ECO (Cap) represents the system cost of the power system before and after connecting to flexibility resources under extreme weather conditions; Represents a set of extreme scenarios The number of scenes included in .

[0103] Example 11:

[0104] Considering the quantitative assessment method of the flexibility resource value of extreme weather supply guarantee, the steps include:

[0105] 1. Method for constructing a set of extreme scenarios for power systems in specific extreme weather conditions

[0106] 1.1 Modeling the impact of extreme weather on system components

[0107] During the evaluation process, it is necessary to consider the impact of extreme events on various components of the entire power system (such as source: generator, network: transmission line, load: load, etc.). For some system equipment such as generators and transmission lines, this paper considers the impact of extreme events on these equipment by introducing brittleness and efficiency functions:

[0108]

[0109]

[0110] Where: represents the brittleness function of device i under extreme weather type k, which is a function of the intensity of the extreme event ω, and the output is the real-time failure rate of device i represents the utility function of the device under extreme weather type k, which is also a function of the extreme weather intensity ω. The output is the real-time available capacity P of device i i capacity (ω), such as the upper limit of generator output and the maximum transmission power of transmission lines; Represents a collection of devices in the system.

[0111] For load, the present invention considers the impact of extreme weather on load by introducing a load-intensity correlation function:

[0112] P D =Load k (ω,P D,Base ) (3)

[0113] Where: Load k (ω,P D,Base ) represents the load-intensity correlation function under extreme weather type k, which is a function of the extreme event intensity ω and the benchmark load P D,Base The output is the real-time load demand P under the extreme event k with intensity ω. D .

[0114] The meaning of extreme weather intensity ω varies for different extreme events. To more intuitively and clearly describe the intensity of different types of extreme weather, this project follows the description method of extreme weather intensity in the field of "power system resilience," using key parameters that can intuitively reflect the intensity of extreme weather to represent extreme weather intensity. (For example, for several common extreme events, such as extreme high temperatures, temperature can be used as the intensity parameter, and for extreme hurricanes, wind speed can be used as the intensity parameter.) The specific meaning and expression of the brittleness function, efficiency function, and load-intensity correlation function also depend on the specific extreme weather.

[0115] This completes the modeling of the impact of extreme weather on various system components, expressing equipment failure rate, equipment performance, and load as functions of extreme event-related parameters. These functions can be obtained through empirical statistics, historical data, fitting, and formulating empirical functions.

[0116] 1.2 Construction of power system extreme scenario collection

[0117] The present invention first models extreme event scenarios. In the modeling of extreme event scenarios, the present invention assumes that extreme events will gradually evolve over time, that is, their event intensity ω will change over time. Although it is assumed that the system is exposed to the same weather conditions at any given time, by modeling weather events as stagnation events, the complexity of the modeling process is reduced because regional weather aspects are not considered. This is a valid assumption for distribution systems covering small geographical areas, which are usually exposed to the same weather at any given time. However, it is not suitable for the study of the resilience of transmission systems covering large geographical areas, which limits the evaluation method and may lead to an overestimation or overly pessimistic evaluation of system resilience. Therefore, it is more practical to consider extreme event scenarios with temporal changes, which also makes the evaluation method proposed in the present invention more universal.

[0118] Assume that in the “Evaluation of Power System Resilience Considering Supply Guarantee Indicators”, a certain extreme weather type k is considered, then its time series intensity curve Ω k The definition is as follows:

[0119]

[0120] Where: represents the intensity of extreme weather under type k in period t; represents the set of extreme weather duration periods under type k; the |·| operator represents the set cardinality operator, express The cardinality is T k , T k Represents the duration of the extreme event k. Without loss of generality, in the present invention, a typical time series intensity curve is used to represent the time series intensity curve of a type of event.

[0121] For a certain extreme weather type k, obtain its typical time series intensity curve Ω k Then, the system equipment failure probability, available capacity, and load demand time series changes under such events can be calculated through the brittleness function, efficiency function, and load-intensity correlation function:

[0122]

[0123] Since the failure of equipment during extreme events is random, and the probability is the system equipment failure probability Therefore, in this invention, based on the framework of the multi-scenario method, sequential Monte Carlo simulation is used to generate multiple equipment failure scenarios and finally construct an extreme scenario set of the power system to reflect the randomness of equipment failures, such as Figure 1 shown.

[0124] In summary, the present application models extreme event scenarios, generates device timing state curves through sequential Monte Carlo simulation, and constructs a power system extreme scenario set accordingly. The scenario set provides boundary conditions and scenario basis for "power system resilience assessment considering power supply guarantee indicators". The overall method is summarized as follows: 1) select the extreme event k (extreme high temperature, extreme hurricane, etc.) that needs to be evaluated, and select the key parameters associated with the intensity according to the specific extreme event; 2) according to experience statistics, historical data, etc. Method, construct the intensity time-varying curve of the extreme event k ; 3) according to k and sequential Monte Carlo simulation method, construct a power system extreme scenario set

[0125] 2, emergency response scheduling model considering extreme scenario power supply

[0126] The present application takes the minimum total load loss of the system supply load and non-supply load as the target, and the operation safety constraint of the power system under the extreme scenario as the constraint condition, and establishes the following emergency response scheduling model.

[0127] 2.1, objective function

[0128]

[0129] In the formula: LS NL,s,t and LS CL,s,t are the commercial and industrial load shedding amount and the resident load shedding vector at time period t; C NL and C CL are the commercial and industrial load shedding amount penalty and the resident load shedding amount penalty at time period t; e represents an element column vector with all elements being one; is the scheduling time period set under the extreme scenario s.

[0130] 2.2, constraint condition

[0131] 1) load shedding constraint

[0132]

[0133] In the formula: R CL is the supply load proportion coefficient, which is equal to the resident load proportion coefficient in the present application; P D,s,t is the load vector at time period t under the extreme weather scenario s.

[0134] 2) system operation constraint

[0135]

[0136] In the formula: P G,s,t and P FR,s,tare the net output vectors of the original system’s traditional thermal power units and flexible resources in period t under extreme weather scenario s; A G With A FR are the association matrices of the traditional thermal power units and the flexibility resource nodes of the original system; F s,t is the branch power vector in period t under extreme weather scenario s; B and B F is the node admittance matrix and the branch admittance matrix; θ t is the phase angle vector of each node in time period t; ST F,s,t With ST G,s,t They are the fault state vectors of the transmission line and the traditional thermal power unit in the original system under the extreme weather scenario s and period t respectively (for ST F,s,t The connotation is that when the transmission line i in period t corresponds to the element ST F,s,t If it is 0, it means that the transmission line i is faulty during time period t, and if it is 1, it means that the transmission line i is normal at this time; ST G,s,t The connotation and ST F,s,t Similar); F max is the maximum power vector that can flow through the transmission line; and are the minimum and maximum output vectors of the traditional thermal power units in the original system during period t under extreme weather scenario s; R G is the maximum ramp power vector of the traditional thermal power unit in the original system; θ min and θ max are the lower limit and upper limit vectors of the node power phase angle respectively; is the set of scheduling periods under the extreme scenario s.

[0137] In the above model, Equation (11) represents the node net power injection constraint; Equations (12) and (13) represent the power flow constraints; Equations (14) and (15) represent the operating constraints of the traditional thermal power generator units in the original system: output upper and lower limit constraints and ramp constraints; Equation (16) represents the node power phase angle constraint.

[0138] 3) Flexibility resource operation constraints

[0139]

[0140] Where: X s,t represents the variable vector related to the operation constraints of flexibility resources in time period t under extreme weather scenario s, which is composed of vector P FR,s,t and S FR,s,t Composition, of which S FR,s,t is the state variable vector of flexibility resources in period t under extreme weather scenario s. The specific connotation and form of this variable are determined by the specific flexibility resources. Cap is the capacity vector of flexibility resources. If Cap = 0, it means that no flexibility resources are connected to the system. It is the set of operating constraints of the flexibility resources in time period t under extreme scenario s. The specific connotation and form of this constraint set are determined by the specific flexibility resources and their capacity Cap.

[0141] After completing the above modeling, the optimal load shedding results of the power system under extreme weather conditions with and without access to flexible resources are calculated based on the model. Serves as a data source for subsequent value quantification.

[0142] 3. Quantitative assessment of flexibility resource value

[0143] The quantitative indicators of the project value are defined as follows:

[0144] V FR =V OPE (Cap)-V INV (Cap)+V EXT (Cap) (18)

[0145] Where: V OPE (Cap) represents the annual operating income of the flexibility resource under normal weather conditions at a given capacity Cap; V INV (Cap) represents the annual investment and fixed cost of flexible resources under a given capacity Cap; V EXT (Cap) represents the value of flexibility resources under extreme weather conditions at a given capacity Cap. The above three indicators are calculated as follows:

[0146] Calculation method for annualized operating income, annualized investment, and fixed costs under normal weather conditions

[0147]

[0148] Where: C OPE with C INV They represent the annualized operating income per unit of flexibility resources under normal weather conditions and the annualized investment and fixed cost per unit of flexibility resources.

[0149] The value of flexibility resources under extreme weather conditions

[0150] The value of flexibility resources under extreme weather conditions at a given capacity Cap is calculated based on the optimal load shedding results of the power system under extreme weather conditions with and without the flexibility resources connected, as shown below:

[0151] V EXT (Cap) = p EXT (W ECO (0)-W ECO (Cap)) (21)

[0152] Where: p EXTIndicates the frequency of extreme weather events in a year; W ECO (0) and W ECO (Cap) represents the system cost of the power system before and after the integration of flexibility resources in extreme weather conditions, and is calculated as follows:

[0153]

[0154] Where: Represents a set of extreme scenarios The number of scenes included in .

[0155] Example 12:

[0156] The verification of the quantitative assessment method of the flexibility resource value considering extreme weather supply guarantee is as follows:

[0157] (1) Constructing a set of extreme scenarios for power systems targeting specific extreme weather conditions

[0158] First, the extreme weather type that needs to be evaluated is selected, and the key parameters associated with its intensity are selected according to the specific extreme weather type; secondly, based on empirical statistics, historical data and other methods, a time-series curve of extreme weather intensity is constructed; finally, the sequential Monte Carlo simulation method is used to construct a set of extreme scenarios for the power system.

[0159] (2) Construct an emergency response scheduling model that considers supply assurance in extreme scenarios

[0160] For each extreme scenario in the extreme scenario set, the objective function is to minimize the total load shedding loss of the guaranteed load and non-guaranteed load in the system (Equation (8)). The load shedding constraint (Equations (9)-(10)), the node net power injection constraint (Equation (11)), the power flow constraint (Equations (12)-(13)), the upper and lower limit constraints of the output of the traditional thermal power generating units in the original system (Equation (14)), the ramp constraint of the traditional thermal power generating units in the original system (Equation (15)), the node power phase angle constraint (Equation (16)), and the flexibility resource operation constraint (Equation (17)) are used as constraints. An emergency response scheduling model considering the supply guarantee of extreme scenarios is established.

[0161] (3) Solve the emergency response scheduling model with and without access to flexible resources

[0162] Based on the construction of an emergency response dispatch model that takes into account the supply guarantee in extreme scenarios, the optimal load shedding results of the power system under extreme weather conditions when connected and not connected to flexibility resources are calculated as the data source for subsequent value quantification.

[0163] (4) Quantitative evaluation of the value of flexibility resources

[0164] Based on the calculation method of quantitative indicators of flexibility resource value, the value of flexibility resources is quantitatively calculated according to the optimal load shedding results of the power system when it is connected and not connected to flexibility resources under extreme weather conditions.

[0165] Specific simulation results

[0166] We intend to use the PJM 5-node system to demonstrate the proposed method and conduct a quantitative evaluation of the value of flexibility resources under extreme high temperature events. The flexibility resource selected for evaluation is energy storage. The system topology and information are as follows: Figure 2 As shown in Table 1-3.

[0167] Table 1 Modified PJM-5 node system load base

[0168]

[0169] Table 2 PJM-5 node system traditional firepower unit information

[0170]

[0171] Table 3 Modified PJM-5 node system branch information

[0172]

[0173]

[0174] 1) Construction of extreme scene collection

[0175] For extreme high temperature events, the temperature is selected as the intensity parameter ω. First, the time series intensity curve of this type of extreme event is constructed. The temperature curve of an extreme high temperature event that lasted for one week in a prefecture-level city in China is selected as the typical time series intensity curve, such as Figure 3 shown.

[0176] In obtaining the time series intensity curve Ω k Next, we further constructed scenarios for the impact of extreme weather on the status of various system components. First, consider thermal generators. Extreme high temperatures reduce the available capacity of thermal generator units because they affect their cooling systems. Therefore, we can describe the impact of extreme high temperatures on thermal generator units using a utility function. In this project, the utility function for extreme high temperature events is modeled as follows:

[0177]

[0178] Where: γ HW is the degradation rate of the maximum available capacity of the generator to extreme high temperatures; is the available capacity reduction threshold. The correlation coefficients of the efficiency function under extreme high temperature events are shown in Table 4.

[0179] Table 4 Correlation coefficients of the effectiveness function under extreme high temperature events

[0180]

[0181] From the expression of the efficiency function under extreme high temperature events, we can see that it is actually a function of the intensity of the extreme event. k After that, by substituting it into the efficiency function, we can obtain the timing curve of the maximum available capacity of the generator under extreme high temperature, as shown in Figure 4 shown.

[0182] The second most common factor is transmission lines. In extremely hot weather, there's a significant correlation between rising temperatures and the failure rate of transmission lines. Generally speaking, as temperatures rise, the failure rate of transmission lines increases. The main reasons for this increase include equipment temperature increases affecting its mechanical properties, accelerated degradation of insulation materials, and overloaded operation. Therefore, in this project, the failure rate of transmission lines is set to a relatively high constant to account for the impact of extreme heat on transmission lines:

[0183]

[0184] Where: is a constant, which is set to 0.03 times / day in this project. The above formula shows that the brittleness function of transmission lines under extreme high temperatures is actually a constant.

[0185] Finally, regarding load, extreme high temperatures have a significant impact on electricity load, primarily through increased use of air conditioners and cooling equipment, leading to a rapid increase in electricity demand. When the temperature exceeds a certain threshold (e.g., 30°C or 35°C), electricity demand from various users shows a significant upward trend. In this project, a load-intensity correlation function is used to describe the relationship between electricity demand and extreme temperatures. The function is modeled as follows:

[0186]

[0187] Where: are the load factors in the normal scenario and the extreme high temperature scenario, respectively. The value range of these coefficients is [0,1], which is equivalent to the per-unit value of the load in the period t; P D,Base is the load base value at each node. The specific numerical information is shown in Table 1; is the load vector of each node in the system at time period t under the extreme scenario. The correlation coefficient of the load-intensity correlation function is shown in Table 5.

[0188] Table 5 Correlation coefficient of load-intensity correlation function

[0189]

[0190] From the expression of the load-intensity correlation function, it can be seen that it is actually a function of the intensity of extreme events and the load time series coefficient under normal scenarios. Take the typical summer day load curve as the load time series coefficient curve under normal scenarios and compare it with the time series intensity curve Ω k By introducing the load-intensity correlation function, we can obtain the time series curve of the system load under extreme high temperature. The load time series curves under normal and extreme scenarios are as follows: Figure 5 shown.

[0191] In this paper, we consider the potential for failures in power transmission lines during extreme heat events. We also assume that, in an extreme event, a device will not be damaged again after a failure and repair. This means that each device will fail at most once during an extreme event. The average device failure repair time during extreme heat events is set to 10 hours. Based on the equipment failure rate time series curve in the power system and the above assumptions, we can perform sequential Monte Carlo sampling on the device's time series failure states, ultimately constructing a scenario for the system's transmission line failure time series states during extreme heat events:

[0192]

[0193] At this point, the complete set of extreme high temperature scenarios has been constructed. The next step will be to evaluate the value of flexibility resources at different capacities under different extreme scenarios based on this data.

[0194] 2) Flexibility Resource Value Assessment

[0195] According to the method proposed in the present invention, different scenarios and flexible resource systems with different configurations are connected to simulate operations. Different flexible resource configuration schemes are set, as shown in Table 6.

[0196] Table 6 Different flexibility resource allocation schemes

[0197]

[0198] After determining the flexibility resource allocation scheme, solve the emergency response scheduling model considering supply guarantee under different scenarios and configurations, and set the load shedding penalty C for industrial and commercial loads. NL The cost of ensuring supply of residential load is C1￥ / kWh. CL is 3￥ / kWh, the solution is as follows Figure 6 shown.

[0199] right Figure 6The resilience evaluation results considering the supply guarantee index are analyzed as follows: 1) for extreme high-temperature weather, the influence of the event on the system load supply shows a persistent and repeated trend. This is because one of the main reasons for the load supply failure in the extreme high-temperature event is the increase in load caused by excessively high temperature and the reduction in capacity of thermal power generators, and the air temperature shows a relatively regular time-varying fluctuation trend, that is, the air temperature is higher in the middle of the day and falls in the evening. Therefore, under the extreme high-temperature weather, the load supply ratio curve shows a trough around 14 o'clock (the highest point of the air temperature of the day), and especially in the period 86 (the highest point of the air temperature of the entire extreme high-temperature event), the load supply may decrease sharply. Therefore, under the extreme high-temperature event, the load supply capacity of the system shows a relatively regular time-varying fluctuation and repetition trend. As the extreme high-temperature event proceeds, the air temperature falls, and therefore the system's ability to supply load can be maintained at a high level from the fifth day of the event. It should be noted that another trough of the load supply ratio curve under the extreme high-temperature weather exists near 19-21 o'clock of the day, because this period is the peak load period, and the extreme high temperature further aggravates the peak load phenomenon, resulting in insufficient load supply; 2) for the support ability of the flexible resource to supply guarantee under extreme weather, it can be seen from Figure 6 that after the increase of the flexible resource, the load supply ratio curve of the system in the extreme scenario shows an overall upward trend, and the more resources are accessed, the higher the curve moves, indicating that the energy storage has the ability to support the system to supply guarantee and can effectively alleviate the situation of large load shedding under extreme conditions.

[0200] Further calculate the value of flexible resource under extreme weather conditions. By comparing the cost before and after the access of flexible resource, the reduction of system cost after the access of flexible resource is calculated, and the value quantification evaluation is completed. First, solve the emergency response dispatching model of the system before and after the access of flexible resource under different scenarios, according to the system cost under different flexible resource configuration schemes and the original system configuration under different extreme scenarios, the value V EXT (Cap) of the flexible resource under extreme weather conditions is calculated. Figure 7

[0201] After the calculation of the value of flexible resource under extreme weather conditions, the next step is to calculate the total value of flexible resource. The regular benefit of flexible resource is reflected in the operating cost saved by the system, so the regular economic benefit C OPE of per unit capacity of flexible resource can be calculated through market research, statistical analysis of system historical operation data, etc. And the planning cost C INV ​, can be determined by statistically analyzing historical system operation and planning parameters. In this project, the parameters related to the conventional economic benefits and planning cost calculation of different flexibility resources are shown in Table 8.

[0202] Table 8 Parameter settings related to conventional economic benefits and planning cost calculations for different flexibility resources

[0203]

[0204] Based on the parameters in the table above and the value of flexibility resources in extreme scenarios under different capacity configurations, the value of flexibility resources V that takes into account the supply guarantee in extreme scenarios can be calculated. FR Assuming the frequency of extreme high temperature is 0.05 times / year, the calculation results are as follows: Figure 8 shown.

[0205] The connotation of the value of flexibility resources that guarantee supply in extreme scenarios is the long-term net benefit of the operation of flexibility resources. When this value is greater than 0, it means that the long-term operation income of the resource can cover the installation and fixed costs and can achieve long-term profitability; otherwise, it means that the operation of flexibility resources will face continuous losses and is not economically feasible for planned operation. Figure 6 As shown in the figure, the load supply capacity of the system in extreme scenarios shows an upward trend with the increase of flexibility resources. However, blindly planning and commissioning flexibility resources without considering the economic cost will cause the power system to face huge economic losses.

[0206] The results of the demonstration demonstrate that the proposed method accurately reflects the improvement in the system's energy supply capacity brought by flexible resources in extreme weather conditions, revealing their value in ensuring energy supply in extreme scenarios. It also comprehensively evaluates the value of flexible resources under both normal and extreme weather conditions, providing theoretical support for subsequent investment attraction, fiscal allocation, and the formulation of flexible resource subsidy policies for public energy services.

Claims

1. Considering the quantitative evaluation method of resource value of flexibility in ensuring supply in extreme weather, the characteristics are: The following steps are involved: 1) Construct a set of extreme scenarios for power systems facing extreme weather; 2) Based on a set of extreme power system scenarios, an emergency response dispatch model that considers power supply under extreme scenarios is constructed; 3) Solve the emergency response dispatch model considering the supply guarantee in extreme scenarios, and obtain the optimal load shedding results of the power system with and without access to flexible resources under extreme weather conditions 4) Based on the optimal load shedding results, calculate the flexibility resource value assessment index.

2. The method for quantitatively evaluating the value of flexible resources considering extreme weather supply according to claim 1 is characterized in that: In step 1), the steps of constructing a set of extreme scenarios for power systems facing extreme weather conditions include: 1.1) Select the extreme event k to be evaluated; 1.2) Construct the intensity time series variation curve Ω of extreme event k k ,Right now: Where: represents the intensity of extreme weather under type k in period t; represents the set of extreme weather duration periods under type k; the |·| operator represents the set cardinality operator, express The cardinality is T k , T k represents the duration of extreme event k. 1.3) Calculate the system equipment failure probability, available capacity, and load demand time series changes under extreme event k through the brittleness function, efficiency function, and load-intensity correlation function, namely: Where, represents the real-time failure rate of device i; represents the brittleness function of device i under extreme weather type k; represents the effectiveness function of the equipment under extreme weather type k; represents the real-time available capacity of device i; I represents the set of devices in the system; P D,t Indicates real-time load demand; Load k (ω,P D,Base ) represents the load-intensity correlation function under extreme weather type k; ω, P D,Base is the extreme event intensity and benchmark load; 1.4) Intensity time series variation curve Ω based on extreme event k k , system equipment failure probability, available capacity and load demand time series changes, and construct a set of extreme power system scenarios through the sequential Monte Carlo simulation method 3. The method for quantitatively evaluating the value of flexible resources considering extreme weather supply according to claim 1 is characterized in that: The emergency response dispatch model considering power supply in extreme scenarios aims to minimize the total load shedding loss of guaranteed load and non-guaranteed load, and takes the power system operation safety constraints under extreme scenarios as constraints.

4. The method for quantitatively evaluating the value of flexible resources considering extreme weather supply guarantee according to claim 1 is characterized in that: Objective function of the emergency response scheduling model considering supply guarantee in extreme scenarios As shown below: Where: LS NL,s,t With LS CL,s,t are the load shedding amount of industrial and commercial load and the load shedding vector of residential load in period t respectively; C NL with C CL are the load shedding penalties for industrial and commercial loads and residential load shedding penalties for period t, respectively; e represents a column vector whose elements are all ones; is the set of scheduling periods under the extreme scenario s.

5. The method for quantitatively evaluating the value of flexible resources considering extreme weather supply guarantee according to claim 1 is characterized in that: The constraints of the emergency response scheduling model that considers supply assurance in extreme scenarios include load shedding constraints, system operation constraints, and flexibility resource operation constraints.

6. The method for quantitatively evaluating the value of flexible resources considering extreme weather supply according to claim 5 is characterized in that: The load shedding constraints are as follows: Where: R CL To ensure the supply load ratio, in the present invention, this coefficient is equal to the resident load ratio; P D,s,t is the load vector for period t under extreme weather scenario s.

7. The method for quantitatively evaluating the value of flexible resources considering extreme weather supply guarantee according to claim 5 is characterized in that: The system operation constraints are as follows: Where: P G,s,t With P FR,s,t are the net output vectors of the original system’s traditional thermal power units and flexible resources in period t under extreme weather scenario s; A G With A FR are the association matrices of the traditional thermal power units and the flexibility resource nodes of the original system; F s,t is the branch power vector in period t under extreme weather scenario s; B and B F is the node admittance matrix and the branch admittance matrix; θ s,t is the phase angle vector of each node in time period t; ST F,s,t With ST G,s,t They are the fault state vectors of the transmission line i in period t and the traditional thermal power unit in the original system under the extreme weather scenario s; the element ST corresponding to the transmission line i in period t is F,s,t If it is 0, it indicates that the transmission line i is faulty during time period t; if it is 1, it indicates that the transmission line i is normal at this time; F max is the maximum power vector that can flow through the transmission line; and are the minimum and maximum output vectors of the traditional thermal power units in the original system during period t under extreme weather scenario s; R G is the maximum ramp power vector of the traditional thermal power unit in the original system; θ min and θ max are the lower limit and upper limit vectors of the node power phase angle respectively; is the set of scheduling periods under the extreme scenario s.

8. The method for quantitatively evaluating the value of flexible resources considering extreme weather supply according to claim 5 is characterized in that: The flexibility resource operation constraints are as follows Where: X s,t represents the variable vector related to the operation constraints of flexibility resources in time period t under extreme weather scenario s; S FR,s,t is the state variable vector of flexibility resources in period t under extreme weather scenario s; Cap is the capacity vector of flexibility resources; is the set of flexible resource operation constraints in time period t under extreme scenario s.

9. The method for quantitatively evaluating the value of flexible resources considering extreme weather supply guarantee according to claim 1 is characterized in that: The evaluation indicators of flexibility resource value are as follows: In FR =V OPE (Cap)-V INV (Cap)+V EXT (Cap) (15) Where: V OPE (Cap) represents the annual operating income of the flexibility resource under normal weather conditions at a given capacity Cap; V INV (Cap) represents the annual investment and fixed cost of flexible resources under a given capacity Cap; V EXT (Cap) represents the value of flexibility resources under extreme weather conditions at a given capacity Cap.

10. The method for quantitatively evaluating the value of flexible resources considering extreme weather supply according to claim 9 is characterized in that: Annual operating income V of flexible resources under normal weather conditions at a given capacity Cap OPE (Cap), annual investment and fixed cost V of flexible resources under a given capacity Cap INV (Cap), the value of flexibility resources under extreme weather conditions at a given capacity Cap V EXT (Cap) is as follows: V EXT (Cap)=p EXT (W ECO (0)-W ECO (Cap)) (18) Where: C OPE with C INV They represent the annualized operating income per unit of flexibility resources and the annualized investment and fixed cost per unit of flexibility resources under normal weather conditions; p EXT Indicates the frequency of extreme weather events in a year; W ECO (0) and W ECO (Cap) represents the system cost of the power system before and after connecting to flexibility resources under extreme weather conditions; Represents a set of extreme scenarios The number of scenes included in .

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