A method for evaluating reliability measurement of power system considering multiple types of transactions

Abstract

The application provides a kind of power system reliability measurement evaluation method considering multiple types of transactions, comprising: first, a system state sampling method considering the randomness of new energy output is proposed;Next, considering the characteristics of transaction subject sending and receiving, a power system stochastic state evaluation model considering inter-provincial medium and long-term and spot transaction strategy is established;Finally, based on the index convergence criterion, the mapping relationship between the power system stochastic state evaluation model and the reliability evaluation index is proposed based on the solution of the power system stochastic state evaluation model, and the quantitative calculation of the power system reliability evaluation index is realized.The application provides a thought for the evaluation of multiple types of transactions under the large-scale optimization configuration of source-grid-load-storage resources across provinces and regions, provides a universal transaction reliability measurement method for sending and receiving provinces, and has strong reference significance for the connection between the interactive transaction mechanism design and actual situation of source-grid-load-storage under the background of power marketization.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of electricity market technology, and in particular to a method for calculating and evaluating the reliability of power systems that takes into account multiple types of transactions. Background Technology

[0002] As my country's power market reform continues to develop, under a market-oriented environment, optimized clearing of transactions will gradually replace traditional economic dispatch, promote centralized supply and demand matching of power generation, grid, load and storage resources, guide the mutual assistance and complementarity of various types of resources through price signals, thereby promoting competition, improving efficiency, and achieving optimal allocation of various types of power generation, grid, load and storage resources.

[0003] However, existing centralized power trading markets face numerous challenges in the face of a rapidly changing electricity market environment. Regarding ensuring system reliability, current evaluation methods primarily focus on static market equilibrium and short-term trading matching. These methods often fail to comprehensively consider the dynamic operation and long-term stability of the power system. Particularly when considering the large-scale integration and market-based operation of renewable energy sources, existing evaluation methods merely focus on optimization objectives such as maximizing social welfare, resulting in an inability to effectively address the complexity and uncertainty of the power system. Consequently, the power supply conditions and reliability of each trading entity cannot be effectively guaranteed. Summary of the Invention

[0004] In view of this, embodiments of the present invention provide a power system reliability assessment method that takes into account multiple types of transactions. By introducing evaluation indicators and mathematical models for power system transactions, the method aims to quantitatively analyze the reliability evaluation of the power system under different operating conditions in a market-oriented environment, thereby providing theoretical support and reference for the feasibility study of transaction mechanism design and for improving the overall operating quality of the power system.

[0005] According to a first aspect of the present invention, a method for power system reliability assessment considering multiple types of transactions is provided, comprising: constructing a system state sampling method considering the randomness of new energy output, including: constructing a non-parametric new energy probabilistic output model based on historical new energy output data and kernel density estimation principles, and sampling the system state using Monte Carlo simulation; constructing a power system stochastic state assessment model considering inter-provincial medium- and long-term and spot trading strategies based on the transmission and reception characteristics of trading entities and stochastic production simulation optimization methods; constructing an index convergence criterion, and iteratively solving the power system stochastic state assessment model until convergence is achieved based on the index convergence criterion and the sampled system state to obtain the power system reliability state assessment result; and substituting the power system reliability state assessment result into the established mapping relationship between the power system stochastic state assessment model and the power system reliability assessment index to realize the quantitative calculation of the power system reliability assessment index.

[0006] In one implementation, the non-parametric new energy probabilistic output model is as follows:

[0007]

[0008] in, This indicates that in scenario m, the output of new energy vehicle k in province j at time t is... N represents the maximum output of new energy sources. k,j f is the probability variable corresponding to the output level of new energy k in province j. t (·) represents the mapping relationship between the output level of new energy sources and the probability at time t, where T is the combination of times involved in the evaluation period.

[0009] In another implementation, the power system stochastic state assessment model is:

[0010]

[0011] Where x is the power output vector of generating units within the province, y is the load shedding power vector, and z is the spot power transmission decision vector of the sending province, serving as the decision variables of the power system stochastic state assessment model. The objective function f(x,y,z) is set to minimize the load shedding loss of the system in each scenario. The equality constraint h(x,y,z) includes the power balance constraint within the province after considering the traded electricity volume, the spot traded electricity volume, and the load shedding electricity volume. The inequality constraint g(x,y,z) includes the load shedding loss of each province and the non-negative upper limit power of the spot power transmission of the sending province.

[0012] In another implementation, the variance coefficient is used to measure the convergence of the results after each iteration, in order to construct a convergence criterion for the index.

[0013] In another implementation, the mapping relationship between the power system stochastic state assessment model and the power system reliability assessment index is as follows:

[0014] The expected power shortage index is calculated using the expected power generation capacity shortage or load reduction of the system within a given time period. The expected power shortage for each time period under scenario m can be measured by the following formula:

[0015]

[0016] The probability of insufficient power supply is measured by the probability that the available capacity of the power generation system cannot meet the maximum load demand under a specified system cycle. Specifically, the probability of insufficient power supply in each time period under scenario m can be measured by the following formula:

[0017]

[0018] in, This represents the total load shedding loss in system state m. Δt represents the total load loss of this province at time t under system state m, and Δt represents the length of the time interval set in the state sampling model of the sampled system state.

[0019] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0020] (1) This invention proposes a power system reliability assessment method for multi-type trading mechanism design, which can take into account the needs of various types of market participants for both supply guarantee level and power system operation reliability under the premise of market transaction economy, and provides theoretical support and operational guidance for the reliability and sustainable development of power system operation in market environment.

[0021] (2) This invention introduces quantitative evaluation indicators and mathematical models for the evaluation of multiple types of trading mechanisms. It can quantitatively analyze the reliability level of market participants in the results of multiple types of interactive transactions involving new energy sources, thereby providing new decision support tools for the design of trading mechanisms and the formulation of main strategies, which helps to improve the efficiency and reliability of the power market.

[0022] (3) This invention can help improve the design level of market mechanism under new energy trading, making market operation more adaptable and responsive, improving the matching of trading mode and the effectiveness of system scheduling. Practitioners can carry out relevant research work based on this. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0024] Figure 1 This is a flowchart illustrating the steps of a power system reliability calculation and evaluation method that takes into account multiple types of transactions according to the present invention.

[0025] Figure 2 To and Figure 1 The corresponding schematic diagram shows the specific process of power system reliability calculation and evaluation considering multiple types of transactions in this invention. Detailed Implementation

[0026] To provide a clearer understanding of the technical features, objectives, and effects of the embodiments of the present invention, specific implementation methods of the embodiments of the present invention will now be described with reference to the accompanying drawings.

[0027] In this document, “exemplary” means “serving as an example, illustration or description”, and any illustrations or implementations described herein as “exemplary” should not be construed as a more preferred or advantageous technical solution.

[0028] To enable those skilled in the art to better understand the technical solutions in the embodiments of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art should fall within the protection scope of the present invention.

[0029] The specific implementation of the embodiments of the present invention will be further described below with reference to the accompanying drawings.

[0030] See Figure 1 , Figure 2 This invention provides a method for power system reliability calculation and evaluation that takes into account multiple types of transactions, mainly including the following steps:

[0031] Step S1: Construct a system state sampling method that considers the randomness of new energy power output, including: constructing a non-parametric new energy probabilistic power output model based on historical new energy power output data and kernel density estimation principle, and sampling the system state using Monte Carlo simulation method;

[0032] It should be understood that constructing a non-parametric new energy probabilistic output model is to accurately model the uncertainty of the system state, and sampling the system state is to accurately provide the boundary conditions and state inputs for the system stochastic production simulation optimization model.

[0033] Step S2: Based on the characteristics of the trading entities and the stochastic production simulation optimization method, construct a stochastic state assessment model for the power system that takes into account the inter-provincial medium- and long-term and spot trading strategies.

[0034] Step S3: Construct the index convergence criterion. Based on the index convergence criterion and the sampled system state, iteratively solve the power system stochastic state assessment model until convergence, and obtain the power system reliability state assessment result.

[0035] Step S4: Substitute the power system reliability status assessment results into the established mapping relationship between the power system stochastic state assessment model and the power system reliability assessment indicators to realize the quantitative calculation of the power system reliability assessment indicators.

[0036] Optionally, the non-parametric new energy probabilistic output model is:

[0037]

[0038] in, This indicates that in scenario m, the output of new energy vehicle k in province j at time t is... N represents the maximum output of new energy sources. k,j f is the probability variable corresponding to the output level of new energy k in province j. t (·) represents the mapping relationship between the output level of new energy sources and the probability at time t, where T is the combination of times involved in the evaluation period.

[0039] Optionally, the power system stochastic state assessment model is:

[0040]

[0041] Where x is the power output vector of generating units within the province, y is the load shedding power vector, and z is the spot power transmission decision vector of the sending province, serving as the decision variables for the stochastic state assessment model of the power system.

[0042] Furthermore, in the aforementioned stochastic state assessment model of the power system, the objective function f(x,y,z) is set to minimize the load shedding loss of the system in each scenario. For the sending-end province, when the load shedding amount is 0, the objective function simultaneously maximizes the province's medium- and long-term power transmission, and maximizes the available spot power output when the medium- and long-term power transmission reaches its upper limit. The equality constraint h(x,y,z) includes the power balance constraint within the province after considering the traded electricity volume, the spot traded electricity volume, and the load shedding electricity volume; while the inequality constraint g(x,y,z) includes the load shedding loss amount of each province, the non-negativity of the upper limit power of spot power transmission (only for sending-end provinces); the maximum spot power purchase capacity of the receiving-end province does not exceed the upper limit that the sending-end province can bear; it also includes the output constraint of conventional units, the power transmission capacity constraint of inter-provincial tie lines; and the unit ramp-up power constraint.

[0043] Optionally, the variance coefficient can be used to measure the convergence of the results after each iteration in order to construct an index convergence criterion.

[0044] Optionally, the mapping relationship between the power system stochastic state assessment model and the power system reliability assessment index is as follows:

[0045] The expected power shortage index is calculated using the expected power generation capacity shortage or load reduction of the system within a given time period. The expected power shortage for each time period under scenario m can be measured by the following formula:

[0046]

[0047] The probability of insufficient power supply is measured by the probability that the available capacity of the power generation system cannot meet the maximum load demand under a specified system cycle. Specifically, the probability of insufficient power supply in each time period under scenario m can be measured by the following formula:

[0048]

[0049] in, This represents the total load shedding loss in system state m. Δt represents the total load loss of this province at time t under system state m, and Δt represents the length of the time interval set in the state sampling model of the sampled system state.

[0050] In summary, the beneficial effects of this invention are as follows:

[0051] (1) This invention proposes a power system reliability assessment method for multi-type trading mechanism design, which can take into account the needs of various types of market participants for both supply guarantee level and power system operation reliability under the premise of market transaction economy, and provides theoretical support and operational guidance for the reliability and sustainable development of power system operation in market environment.

[0052] (2) This invention introduces quantitative evaluation indicators and mathematical models for the evaluation of multiple types of trading mechanisms. It can quantitatively analyze the reliability level of market participants in the results of multiple types of interactive transactions involving new energy sources, thereby providing new decision support tools for the design of trading mechanisms and the formulation of main strategies, which helps to improve the efficiency and reliability of the power market.

[0053] (3) This invention can help improve the design level of market mechanism under new energy trading, making market operation more adaptable and responsive, improving the matching of trading mode and the effectiveness of system scheduling. Practitioners can carry out relevant research work based on this.

[0054] Specifically, the solution of the present invention is further described with reference to the following examples:

[0055] To improve the efficiency and reliability of various trading markets, in-depth evaluation and optimization are necessary. Research should be conducted on power system reliability assessment under medium- and long-term, spot trading boundaries. This will provide new decision support tools for power market participants, policymakers, and operators, promoting the stable development and sustainable operation of the power market. Therefore, this invention provides a power system reliability assessment method that considers various trading types. This method relies on Monte Carlo Simulation (MCS) and a DC power flow-based optimization calculation method for power system load shedding levels. Monte Carlo simulation has proven to be an efficient method for calculating power system reliability indicators. With a sufficient sample size, reliability indicators can be statistically estimated by analyzing sample conditions. Regardless of the tools used, the MCS method typically includes the following three basic steps:

[0056] First, establish a predictive model and identify the dependent variable that needs to be predicted and the independent variables (also known as input variables, risk variables, or predictor variables) that drive the prediction.

[0057] Secondly, the probability distribution of the independent variables needs to be specified. This process usually relies on historical data or the analyst's subjective judgment, and is accomplished by defining a series of possible values ​​and assigning appropriate probability weights to each value.

[0058] Finally, the simulation is run repeatedly to generate random values ​​for the independent variables until a sufficient number of results are collected. These results can constitute a representative sample of almost an infinite number of possible combinations.

[0059] Therefore, this invention combines the MCS principle and addresses the problem of power system index calculation by performing the following steps in sequence: (1) system state sampling; (2) system minimum load shedding level assessment; (3) reliability index calculation.

[0060] (1) System state sampling method considering the randomness of new energy output

[0061] Based on the output characteristics of new energy units, the output of these units is estimated using kernel density to form a probability density function, which is then summarized as a probability output curve, describing the probability at a certain output level. Considering that new energy sources such as photovoltaics have certain output patterns, such as the near-zero nighttime power generation of photovoltaics and the differences in output characteristics between time periods, a corresponding model is established based on the time-of-day output characteristics of new energy sources, expressed as:

[0062]

[0063] For the new energy probabilistic power output model in equation (1), the Monte Carlo simulation method is used to generate its power output state scenario, which is represented as {s 1，t , ..., s m，t , ..., s M，t Furthermore, the sampled state of the system in the m-th scene at time t is the joint distribution function of all components, which can be expressed as: The probability of the m-th scenario occurring in the system can be approximately expressed by the following formula:

[0064]

[0065] In the formula, n(s) m，t Let be the frequency of the m-th scene among the M states sampled by the system.

[0066] (2) Stochastic state assessment model of power system taking into account inter-provincial medium- and long-term and spot trading strategies

[0067] 1) Stochastic state assessment model of power system in receiving provinces

[0068] For the sampled states of the system in scenario m at each time period, the total load loss can be calculated based on the production simulation of optimal DC power flow for each province. For example, for a receiving-end province j, at times m and t in scenario m, this invention establishes a stochastic state assessment model of the power system based on the principle of stochastic production simulation, with the goal of minimizing load shedding, as follows:

[0069] minΔD j,m,t , t=1,...,T (3)

[0070]

[0071] ΔD j,m,t ≥0, t=1,...,T (5)

[0072]

[0073]

[0074] In the formula, ΔD j，m，t For a cross-regional and cross-provincial transaction area, in scenario m, the load loss at time t is... This represents the output of conventional generator unit i in province j at time t in scenario m; This indicates that in scenario m, the output of new energy i in province j at time t is determined by the sampling results of (2); This represents the output of thermal power unit i purchased by province j in the spot market at time t in scenario m; This represents the output of generator unit i at time t, corresponding to the electricity purchased by province j in province n during inter-provincial medium- and long-term transactions; Θ j This refers to the collection of conventional generating units in the province; Γ j The set of renewable energy generating units in the province, excluding those used for power transmission to other regions; Λ j-n Let K be the set of thermal power units purchased by province j from province n in inter-provincial spot trading; j-n D represents the set of new energy generating units purchased by province j from province n in inter-provincial medium- and long-term transactions. j，t For province j, the load at time t; This represents the minimum output of conventional generating unit i in province j during operation; This represents the maximum output of conventional generating unit i in province j during operation, which is usually determined by the installed capacity. This represents the maximum output that each unit in province n can deliver in the spot market, which is determined using the production simulation results of the sending province in the following text. H represents the maximum transmission capacity of the inter-provincial link line l; l，n This represents the power transfer distribution factor of inter-provincial link line l to province n. This represents the ramp-up capacity of unit i in province j.

[0075] In the proposed stochastic state assessment model of the power system, the objective function (3) means that the load loss of the system is minimized in each scenario. The constraint (4) means that the power balance constraint within the province is considered after considering the transaction volume, spot transaction volume, and load shedding volume. The constraint (5) indicates that the load loss of each province is negative. The constraint (6) indicates the maximum spot purchase capacity of the province. The constraint (7) is the output constraint of the conventional units. The constraint (8) indicates that the power of the inter-provincial tie line does not exceed its maximum transmission capacity. The constraint (9) indicates that the ramping power of each unit does not exceed its maximum ramping capacity.

[0076] 2) Stochastic state assessment model of power system in sending provinces

[0077] For a certain sending-end province n, at times m and t, this invention establishes a power system stochastic state assessment model based on the principle of stochastic production simulation, namely, a power system load shedding assessment model, which takes into account both load shedding targets and transaction economics. This model is expressed as:

[0078]

[0079] ΔD n,m,t ≥0,t=1,...,T (12)

[0080]

[0081] In the formula, M is a sufficiently large constant, while the medium- and long-term power transmission volume is... The quantity is determined based on the clearing results and the trading strategies of the sending provinces. Specifically, when the sending provinces consider prioritizing fulfilling their external transmission curves more critical than fulfilling their own load, After decomposing the medium- and long-term electricity trading curve, the resulting electricity curve is fully transmitted; however, when the sending province believes that priority should be given to meeting its own load, Take the smaller value between the decomposition curve and the actual renewable energy output at the sending end.

[0082] The objective function (10) means that the system minimizes the load loss in each scenario, maximizes the medium- and long-term power transmission of the province when the load loss is 0, and maximizes the available spot power output when the medium- and long-term power transmission reaches the upper limit. The constraint (11) means that the power balance constraint within the province is considered after considering the trading volume, spot trading volume, and load shedding volume. The constraint (12) means that the load loss of the province cannot be negative. Similarly, the constraint (13) means that the upper limit power of the province's spot power transmission cannot be negative. The constraint (14) means that the upper and lower limits of the output of conventional units are considered after considering the spot power output. The constraint (15) means that the medium- and long-term power transmission of the province does not exceed the upper limit of its external new energy power generation. The constraint (16) means that the ramping power of each unit does not exceed its maximum ramping capacity after considering the spot power output.

[0083] (3) Quantitative calculation model for power system reliability assessment indicators

[0084] 1) Construction of indicator convergence criteria

[0085] The calculation results of the aforementioned power system stochastic state assessment model rely on the sampling of stochastic states in the power system. Therefore, a convergence criterion needs to be constructed to stably quantify the power system reliability indicators. To determine the convergence and computational accuracy of the model's solution, the variance coefficient is used to measure the convergence of the results after each iteration. Taking the Loss of Load Possibility (LOLP) as an example, the calculation method is described below:

[0086]

[0087] In the formula, V represents the expected value estimate of the function; V(·) represents the variance of the function; It represents the variance estimator of the function.

[0088] 2) Calculation of power system reliability indicators

[0089] Power system reliability refers to the system's ability to operate normally under given conditions, including the continuity and stability of power supply. During operation, power systems may encounter various faults, such as generator failures and transmission line faults, which can lead to power outages or insufficient power supply. Representative assessment indicators include the expected value of insufficient power supply and the probability of insufficient power supply.

[0090] The traditional definition of Expected Energy Not Supplied (EENS) is the expected amount of electricity a system will generate within a given timeframe due to a shortage of generating capacity or a reduction in load. This indicator reflects the degree of power supply shortage in a power system and provides a comprehensive assessment of its reliability. By calculating EENS, system operators can quantitatively estimate the potential for power supply shortages.

[0091] For EENS, the system reliability at each time point in scenario m can be measured by the following formula:

[0092]

[0093] In the formula, This represents the total load shedding loss in system state m. The total load loss of this province at time t represents the state m of the system. It is determined by the optimization results determined by formulas (3)-(9) or (10)-(16). Δt represents the length of the time interval set in the aforementioned state sampling model.

[0094] The probability of insufficient power supply, also known as the Loss of Load (LOLP) or load shedding probability, is traditionally defined as the probability that the available capacity of a power generation system cannot meet the maximum load demand under a specified period. LOLP is closely related to the reliability of the power system (including adequacy and safety), directly and realistically reflecting the supply and demand situation in the electricity market, and also quantifying the risk of insufficient system capacity. A higher LOLP indicates that power generators are more likely to exercise market power; when LOLP is close to 0, it indicates that the power supply is abundant and the power generation market is close to a perfectly competitive market.

[0095] For LOLP, the reliability of the system at different time periods in scenario m can be measured by the following formula:

[0096]

[0097] When evaluating reliability indicators, the above calculation process is applicable to both the receiving and sending provinces.

[0098] In summary, the beneficial effects of this invention are as follows:

[0099] (1) This invention proposes a power system reliability assessment method for multi-type trading mechanism design, which can take into account the needs of various types of market participants for both supply guarantee level and power system operation reliability under the premise of market transaction economy, and provides theoretical support and operational guidance for the reliability and sustainable development of power system operation in market environment.

[0100] (2) This invention introduces quantitative evaluation indicators and mathematical models for the evaluation of multiple types of trading mechanisms. It can quantitatively analyze the reliability level of market participants in the results of multiple types of interactive transactions involving new energy sources, thereby providing new decision support tools for the design of trading mechanisms and the formulation of main strategies, which helps to improve the efficiency and reliability of the power market.

[0101] (3) This invention can help improve the design level of market mechanism under new energy trading, making market operation more adaptable and responsive, improving the matching of trading mode and the effectiveness of system scheduling. Practitioners can carry out relevant research work based on this.

[0102] It should be noted that the present invention can be a method, system, apparatus, and / or computer program product. A computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for causing a processor to implement various aspects of the present invention.

[0103] Computer-readable storage media can be tangible devices capable of holding and storing instructions for use by an instruction execution device. Computer-readable storage media can be, for example, but not limited to, electrical storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any suitable combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital multifunction disc (DVD), memory sticks, floppy disks, mechanical encoding devices, such as punch cards or recessed protrusions storing instructions thereon, and any suitable combination thereof. The computer-readable storage media used herein are not to be construed as transient signals themselves, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., light pulses through fiber optic cables), or electrical signals transmitted through wires.

[0104] The computer-readable program instructions described herein can be downloaded from computer-readable storage media to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to the computer-readable storage media in the respective computing / processing device.

[0105] The computer program instructions used to perform the operations of this invention may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, Python, etc., and conventional procedural programming languages ​​such as "C" or similar languages. The computer-readable program instructions may be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), is personalized by utilizing state information from the computer-readable program instructions. This electronic circuitry can execute the computer-readable program instructions to implement various aspects of the invention.

[0106] Various aspects of the present invention are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-readable program instructions.

[0107] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that, when executed by the processor of the computer or other programmable data processing apparatus, they create means for implementing the functions / actions specified in one or more blocks of the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium that causes a computer, programmable data processing apparatus, and / or other device to operate in a particular manner; thus, the computer-readable medium storing the instructions comprises an article of manufacture that includes instructions for implementing aspects of the functions / actions specified in one or more blocks of the flowchart and / or block diagram.

[0108] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to perform the functions / actions specified in one or more boxes of a flowchart and / or block diagram.

[0109] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of an instruction containing one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions. It will be known to those skilled in the art that implementation in hardware, implementation in software, and implementation using a combination of software and hardware are equivalent.

[0110] The various embodiments of the present invention have been described above. These descriptions are exemplary and not exhaustive, and are not limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen to best explain the principles, practical application, or technical improvements to the embodiments in the market, or to enable others skilled in the art to understand the embodiments disclosed herein. The scope of the invention is defined by the appended claims.

Claims

1. A method for calculating and evaluating the reliability of a power system that takes into account multiple types of transactions, characterized in that, include: A system state sampling method that considers the stochasticity of new energy power output is constructed, including: constructing a non-parametric new energy probabilistic power output model based on historical new energy power output data and kernel density estimation principle, and sampling the system state using Monte Carlo simulation method; Based on the characteristics of the trading entities and the stochastic production simulation optimization method, a stochastic state assessment model for the power system that takes into account the inter-provincial medium- and long-term and spot trading strategies is constructed. Convergence criteria for indicators are constructed. Based on the convergence criteria and the sampled system state, the stochastic state assessment model of the power system is iteratively solved until convergence, and the power system reliability state assessment result is obtained. The results of power system reliability status assessment are substituted into the established mapping relationship between the power system stochastic status assessment model and the power system reliability assessment index to realize the quantitative calculation of the power system reliability assessment index. The non-parametric new energy probabilistic output model is as follows: in, This indicates that in scenario m, the output of new energy vehicle k in province j at time t is... N represents the maximum output of new energy sources. k,j f is the probability variable corresponding to the output level of new energy k in province j. t (·) represents the mapping relationship between the output level of new energy sources and probability at time t, where T is the combination of times involved in the evaluation period; The stochastic state assessment model for the power system is as follows: Where x is the power output vector of generating units within the province, y is the load shedding power vector, and z is the spot power transmission decision vector of the sending province, serving as the decision variables of the power system stochastic state assessment model. The objective function f(x,y,z) is set to minimize the load shedding loss of the system in each scenario. The equality constraint h(x,y,z) includes the power balance constraint within the province after considering the traded electricity, spot traded electricity, and load shedding electricity. The inequality constraint g(x,y,z) includes the load shedding loss of each province and the non-negative upper limit power of the spot power transmission of the sending province.

2. The method according to claim 1, characterized in that, The variance coefficient is used to measure the convergence of the results after each iteration, so as to construct the index convergence criterion.

3. The method according to claim 1, characterized in that, The mapping relationship between the power system stochastic state assessment model and the power system reliability assessment index is as follows: The expected power shortage index is calculated using the expected power generation capacity shortage or load reduction of the system within a given time period. The expected power shortage for each time period under scenario m is measured by the following formula: The probability of insufficient power supply is measured by the probability that the available capacity of the power generation system cannot meet the maximum load demand under the specified system cycle. Specifically, the probability of insufficient power supply in each time period under scenario m is measured by the following formula: in, This represents the total load loss in system state m. Δt represents the total load loss of this province at time t under system state m, and Δt represents the length of the time interval set in the state sampling model of the sampled system state.

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