Power system reliability measurement and evaluation method considering multi-type transactions

Abstract

The invention provides a power system reliability measurement and evaluation method considering multi-type transactions. The method comprises the following steps: firstly, providing a system state sampling method considering new energy output randomness; then, considering the sending and receiving characteristics of a transaction subject, and establishing a power system stochastic state evaluation model considering an inter-provincial medium-and-long-term and spot transaction strategy; and finally, constructing a solving result with the power system random state evaluation model based on the index convergence criterion, and proposing a mapping relation between the power system random state evaluation model and the reliability evaluation index, thereby realizing quantitative calculation of the power system reliability evaluation index. According to the method, a thought is provided for evaluation of multi-type transactions under cross-provincial wide-range optimization configuration of source network load storage resources, a universal transaction reliability measurement and calculation method is provided for transmitting and receiving end provinces, and the method has relatively strong reference significance for connection of source network load storage interactive transaction mechanism design and actual conditions under the power marketization background.

Description

Technical Field

[0001] The embodiments of the present invention relate to the technical field of power market, and in particular to a method for calculating and evaluating the reliability of a power system taking into account multiple types of transactions. Background Art

[0002] In a market-oriented environment, transaction optimization and clearing will gradually replace traditional economic dispatch, promote the centralized supply and demand matching of source, grid, load and storage resources, and guide the mutual assistance and complementarity of various types of resources through price signals, thereby promoting competition, improving efficiency and achieving the optimal allocation of various types of resources such as source, grid, load and storage.

[0003] However, the existing centralized trading market faces many challenges in the face of the rapidly changing power market environment. In terms of ensuring the reliability of system operation, the existing trading mechanism evaluation methods mainly focus on static market equilibrium and short-term transaction matching. These methods often fail to fully consider the dynamic operation and long-term stability of the power system. Especially when considering the large-scale integration and market-oriented operation of new energy, the existing trading mechanism evaluation methods only focus on the optimization goals of transactions such as maximizing social welfare, which makes it impossible to effectively deal with the complexity and uncertainty of the power system, and the power supply conditions and reliability of each trading entity cannot be effectively guaranteed. Summary of the invention

[0004] In view of this, an embodiment of the present invention provides a method for measuring and evaluating the reliability of an electric power system taking into account multiple types of transactions. By introducing evaluation indicators and mathematical models of electric power system transactions, it aims to quantitatively analyze the reliability evaluation of the electric power system under different operating conditions in a market environment, thereby providing theoretical support and reference for the feasibility study of the design of the transaction mechanism and improving the overall operation quality of the electric power system.

[0005] According to a first aspect of an embodiment of the present invention, a method for measuring and evaluating the reliability of a power system taking into account multiple types of transactions is provided, including: constructing a system state sampling method that takes into account the randomness of new energy output, including: constructing a non-parametric new energy probabilistic output model based on historical new energy output data and the principle of kernel density estimation, and adopting a Monte Carlo simulation method to sample system states; constructing a random state evaluation model for a power system that takes into account medium- and long-term inter-provincial and spot trading strategies based on the sending and receiving characteristics of trading entities and a random production simulation optimization method; constructing an indicator convergence criterion, and iteratively solving the random state evaluation model for a power system according to the indicator convergence criterion and the sampled system states until convergence, to obtain an evaluation result of the reliability state of the power system; substituting the evaluation result of the reliability state of the power system into the mapping relationship between the established random state evaluation model for the power system and the reliability evaluation index of the power system, to realize quantitative calculation of the reliability evaluation index of the power system.

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

[0007]

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

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

[0010]

[0011] Among them, x is the power vector of the unit output in the province, y is the load shedding power vector, and z is the spot delivery decision vector of the sending province. As the decision variable of the random state assessment model of the power system, the objective function f(x, y, z) is set to minimize the load loss of the system in various scenarios. The equality constraint h(x, y, z) includes the power balance constraint within the province after considering the trading power, spot trading power, and load shedding power. The inequality constraint g(x, y, z) includes the load loss amount of each province and the non-negative upper limit power of the spot delivery in the sending province.

[0012] In another implementation, the variance coefficient is used to measure the convergence of the results after each iteration to construct an indicator convergence criterion.

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

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

[0015]

[0016] The probability that the available capacity of the power generation system cannot meet the maximum load demand under the specified period of the system is used to measure the power shortage probability index. The power shortage probability of the system in each period under scenario m can be measured by the following formula:

[0017]

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

[0019] Compared with the prior art, the present invention has the following beneficial effects:

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

[0021] (2) The present invention introduces quantitative evaluation indicators and mathematical models for the evaluation of multi-type trading mechanisms, which can quantitatively analyze the reliability level of multi-type interactive trading results of market players taking into account new energy, thereby providing a new decision support tool for trading mechanism design and subject strategy formulation, which helps to improve the efficiency and reliability of the electricity market.

[0022] (3) The present invention can help improve the level of market-oriented mechanism design under new energy trading, make market-oriented operations more adaptable and responsive, improve the matching of trading models and the effectiveness of system scheduling, and actual engineering personnel can carry out relevant research work based on this. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of 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 ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0024] Figure 1 A flowchart of the steps of a method for calculating and evaluating the reliability of a power system taking into account multiple types of transactions according to the present invention;

[0025] Figure 2 For Figure 1 Corresponding to the specific flow chart of the present invention for calculating and evaluating the reliability of the power system taking into account multiple types of transactions. DETAILED DESCRIPTION

[0026] In order to have a clearer understanding of the technical features, purposes and effects of the embodiments of the present invention, the specific implementation of the embodiments of the present invention is now described with reference to the accompanying drawings.

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

[0028] In order 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 in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in the field based on the embodiments in the embodiments of the present invention should fall within the scope of protection of the embodiments of the present invention.

[0029] The specific implementation of the embodiment of the present invention is further described below in conjunction with the accompanying drawings of the embodiment of the present invention.

[0030] See also Figure 1 , Figure 2 The present invention provides a method for calculating and evaluating the reliability of a power system taking into account multiple types of transactions, which mainly includes the following steps:

[0031] Step S1, 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 new energy historical output data and kernel density estimation principle, and adopting Monte Carlo simulation method to sample system state;

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

[0033] Step S2: Based on the sending and receiving characteristics of the trading entities and the random production simulation optimization method, a random state assessment model of the power system is constructed taking into account the medium- and long-term inter-provincial and spot trading strategies;

[0034] Step S3, constructing an indicator convergence criterion, and iteratively solving the power system random state assessment model according to the indicator convergence criterion and the sampled system state until convergence, to obtain a power system reliability state assessment result;

[0035] Step S4: Substitute the power system reliability status assessment result into the established mapping relationship between the power system random status assessment model and the power system reliability assessment index to achieve quantitative calculation of the power system reliability assessment index.

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

[0037]

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

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

[0040]

[0041] Among them, x is the power vector of the unit output in the province, y is the load shedding power vector, and z is the spot delivery decision vector of the sending province, which serve as the decision variables of the random state assessment model of the power system.

[0042] In addition, in the random state assessment model of the power system, the objective function f(x,y,z) is set to minimize the load loss of the system in each scenario. For the sending province, when the load loss is 0, the objective function simultaneously makes the medium- and long-term transmission volume of the province as large as possible, and when the medium- and long-term transmission volume reaches the upper limit, the spot output available for trading is as large as possible. The equality constraint h(x,y,z) includes the power balance constraint within the province after considering the trading power, spot trading power, and load shedding power; while the inequality constraint g(x,y,z) includes the load loss of each province, the non-negative upper limit power of spot transmission (only in the sending province); the maximum spot purchase capacity of the receiving province does not exceed the upper limit that the sending province can bear; it also includes the output constraint of conventional units, the power transmission capacity constraint of inter-provincial interconnection lines; and the unit climbing power constraint.

[0043] Optionally, the variance coefficient is used to measure the convergence of the results after each iteration to construct an indicator convergence criterion.

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

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

[0046]

[0047] The probability that the available capacity of the power generation system cannot meet the maximum load demand under the specified period of the system is used to measure the power shortage probability index. The power shortage probability of the system in each period under scenario m can be measured by the following formula:

[0048]

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

[0050] In summary, the beneficial effects of the present invention are:

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

[0052] (2) The present invention introduces quantitative evaluation indicators and mathematical models for the evaluation of multi-type trading mechanisms, which can quantitatively analyze the reliability level of multi-type interactive trading results of market players taking into account new energy, thereby providing a new decision support tool for trading mechanism design and subject strategy formulation, which helps to improve the efficiency and reliability of the electricity market.

[0053] (3) The present invention can help improve the level of market-oriented mechanism design under new energy trading, make market-oriented operations more adaptable and responsive, improve the matching of trading models and the effectiveness of system scheduling, and actual engineering personnel can carry out relevant research work based on this.

[0054] Specifically, the scheme of the present invention is further described according to the following examples:

[0055] In order to improve the efficiency and reliability of multi-type trading markets, in-depth evaluation and optimization must be carried out, and research on the reliability measurement and evaluation of power systems under the boundaries of medium- and long-term spot transactions must be carried out, so as to provide new decision-making support tools for power market participants, policy makers and operators, and promote the stable development and sustainable operation of the power market. To this end, the present invention provides a method for measuring and evaluating the reliability of power systems taking into account multi-type transactions, which relies on Monte Carlo simulation (MCS) and a DC power flow-based power system load shedding level optimization calculation method. Among them, Monte Carlo simulation has been proven to be an efficient method for calculating power system reliability indicators. In the case of a sufficient number of samples, the reliability indicators can be statistically estimated by analyzing the sample status. Regardless of the tool used, the MCS method generally includes the following three basic steps:

[0056] First, a prediction model is established to identify the dependent variable to be predicted and the independent variables that drive the prediction (also called input variables, risk variables, or predictor variables).

[0057] Second, the probability distribution of the independent variable needs to be specified. This process usually relies on historical data or the analyst's subjective judgment, and is done by defining a range 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 outcomes have been collected that constitute a representative sample of the nearly infinite number of possible combinations.

[0059] To this end, the present invention combines the MCS principle and performs the following steps in succession for the problem of power system index calculation: (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 renewable energy output

[0061] According to the output characteristics of new energy units, the output of new energy units is estimated through kernel density to form a probability density function, which is 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 rules, such as the power generation of photovoltaics at night is approximately zero, and there are certain differences in output characteristics between time periods, a corresponding model is established based on the output characteristics of new energy in different time periods, expressed as:

[0062]

[0063] For the new energy probabilistic output model in formula (1), the Monte Carlo simulation method is used to generate its output state scenario, which is expressed as {s 1，t ,…,s m，t ,…,s M，t In addition, the sampling state of the system in the mth scene and time t is the joint distribution function of each component, which can be expressed as Then the probability of the mth scenario occurring in the system can be approximately expressed as follows:

[0064]

[0065] In the formula, n(s m，t ) is the frequency of occurrence of the mth scene in the M states sampled by the system.

[0066] (2) A random state assessment model for power systems taking into account inter-provincial medium- and long-term and spot trading strategies

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

[0068] For the sampling state of the system in each period under the mth scenario, the total load loss can be calculated based on the production simulation of each province based on the DC optimal power flow. For example, for a receiving province j, under scenario m and time t, the present invention takes the minimum load shedding as the goal and establishes its random state assessment model of the power system based on the random production simulation principle, which is expressed as:

[0069] minΔD j，m，t , t=1,...,T (3)

[0070]

[0071] ΔD j，m，t ≥0,t=1,...,T (5)

[0072]

[0073]

[0074] Where, ΔD j，m， t is the load loss at time t in a receiving province j in scenario m within the cross-region and cross-province trading area. represents the output of conventional unit i in province j at time t in scenario m; It represents the output of renewable energy i in province j at time t in scenario m, which is determined by the sampling results of (2); represents the output of thermal power unit i purchased by province j in the spot market at time t in scenario m; In scenario m, the output of unit i at time t corresponding to the electricity purchased by province j in province n in the inter-provincial medium- and long-term transactions; Θ j is the set of conventional units in the province; Γ j It is the set of new energy generating units in the province after deducting the new energy generating units sent out; Λ j-n is the set of thermal power units purchased by province j from province n in inter-provincial spot transactions; K j-n D is the set of new energy units purchased by province j from province n in the medium- and long-term inter-provincial transactions; j，t is the load of province j at time t; represents the minimum output of conventional unit i in province j during operation; represents the maximum output of conventional unit i in province j during operation, which is usually determined by installed capacity; represents the maximum output that each unit can deliver in the spot market in province n, which is determined by the production simulation results of the sending province in the following text; represents the maximum transmission capacity of the inter-provincial connection line l; H l，n represents the power transfer distribution factor of inter-provincial tie line l to province n; Represents the ramp capacity of unit i in province j.

[0075] In the proposed random state assessment model of the power system, the objective function (3) means that the system has the minimum load loss in each scenario. The constraint (4) means the power balance constraint within the province after considering the trading power, spot trading power and load shedding power. The constraint (5) indicates that the load loss in each province is negative. The constraint (6) indicates the maximum spot power 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 interconnection line does not exceed its maximum transmission capacity. The constraint (9) indicates that the ramp power of each unit does not exceed its maximum ramp capacity.

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

[0077] For a certain sending province n, at the scene m and time t, the present invention takes into account both the load shedding target and the transaction economy, and establishes a random state assessment model of the power system based on the random production simulation principle, namely, a load shedding assessment model of the power system, which is expressed as:

[0078]

[0079] ΔD n，m，t ≥0,t=1,...,T (12)

[0080]

[0081] Where M is a sufficiently large constant, and the medium- and long-term external power generation It is determined based on the clearing results and the trading strategy of the sending province. When the sending province believes that it is more important to give priority to satisfying the external transmission curve than to satisfying the load of the province, After decomposing the medium- and long-term transaction power curve, the obtained power curve is sent out in full; and when the sending province believes that the load of the province should be met first, 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's load loss in each scenario is minimized, and when the load loss is 0, the province's medium- and long-term transmission volume is as large as possible, and when the medium- and long-term transmission volume reaches the upper limit, the spot output available for trading is as large as possible; the constraint (11) means the power balance constraint within the province after considering the trading power, spot trading power, and load shedding power; constraint (12) indicates that the province's load loss cannot be negative; similarly, constraint (13) indicates that the province's spot transmission upper limit power cannot be negative; constraint (14) is the upper and lower limit constraints on the output of conventional units after considering the spot output; constraint (15) indicates that the province's medium- and long-term transmission volume does not exceed the upper limit of its externally transmitted renewable energy power generation; constraint (16) indicates that after considering the spot output, the ramp power of each unit does not exceed its maximum ramp capacity.

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

[0084] 1) Construction of indicator convergence criteria

[0085] The calculation results of the random state assessment model of the power system mentioned above rely on random state sampling of the power system, and it is necessary to build a convergence criterion to stably and quantitatively calculate the reliability index of the power system. In order to judge the convergence and calculation accuracy of the solution results of the above model, 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 as follows:

[0086]

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

[0088] 2) Calculation of power system reliability index

[0089] The reliability of a power system refers to the ability of the system to operate normally under given conditions, including the continuity and stability of power supply. During operation, the power system may encounter various faults, such as unit failures and transmission line failures, which may lead to power outages or insufficient power supply. Representative evaluation indicators include the expected value of power shortage and the probability of power shortage.

[0090] The traditional definition of Expected Energy Not Supplied (EENS) is: the expected amount of electricity that a system can expect to consume in a given period of time due to a shortage of generating capacity or a reduction in load power. This indicator can indicate the degree of power shortage in the power system and can comprehensively assess the reliability of the power system. By calculating EENS, system operators can quantify and estimate the possible power shortage in the power system.

[0091] For EENS, the reliability of the system in each period under scenario m can be measured by the following formula:

[0092]

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

[0094] The probability of insufficient power, also known as LOLP or load loss probability, is traditionally defined as the probability that the available capacity of the power generation system cannot meet the maximum load demand under the specified period of the system. LOLP is closely related to the reliability of the power system (including redundancy and safety), directly and truly reflects the supply and demand situation of the power market, and also quantifies the risk of insufficient system capacity. The larger the LOLP, the more likely the power generator is to exercise market power; when LOLP is close to 0, it indicates that the power supply is sufficient and the power generation market is close to a fully competitive market.

[0095] For LOLP, the reliability of the system at each time period under scenario m can be measured by the following formula:

[0096]

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

[0098] In summary, the beneficial effects of the present invention are:

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

[0100] (2) The present invention introduces quantitative evaluation indicators and mathematical models for the evaluation of multi-type trading mechanisms, which can quantitatively analyze the reliability level of multi-type interactive trading results of market players taking into account new energy, thereby providing a new decision support tool for trading mechanism design and subject strategy formulation, which helps to improve the efficiency and reliability of the electricity market.

[0101] (3) The present invention can help improve the level of market-oriented mechanism design under new energy trading, make market-oriented operations more adaptable and responsive, improve the matching of trading models and the effectiveness of system scheduling, and actual engineering personnel can carry out relevant research work based on this.

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

[0103] Computer readable storage medium can be a tangible device that can hold and store instructions used by an instruction execution device. Computer readable storage medium can be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination thereof. More specific examples (non-exhaustive list) of computer readable storage medium include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, for example, a punch card or a convex structure in a groove on which instructions are stored, and any suitable combination thereof. The computer readable storage medium used here is not interpreted as a transient signal itself, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagated by a waveguide or other transmission medium (for example, a light pulse by an optical fiber cable), or an electrical signal transmitted by a wire.

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

[0105] The computer program instructions for performing the operation of the present 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" language or similar programming languages. Computer-readable program instructions may be executed entirely on a user's computer, partially on a user's computer, as an independent software package, partially on a user's computer, partially on a remote computer, or entirely on a remote computer or server. In the case of 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., using an Internet service provider to connect via the Internet). In some embodiments, an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), may be personalized by utilizing the state information of the computer-readable program instructions, and the electronic circuit may execute the computer-readable program instructions, thereby realizing various aspects of the present invention.

[0106] Various aspects of the present invention are described herein with reference to the flow charts and / or block diagrams of the methods, devices (systems) and computer program products according to embodiments of the present invention. It should be understood that each box of the flow chart and / or block diagram and the combination of each box in the flow chart and / or block diagram 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 device, thereby producing a machine, so that when these instructions are executed by the processor of the computer or other programmable data processing device, a device that implements the functions / actions specified in one or more boxes in the flowchart and / or block diagram is generated. These computer-readable program instructions can also be stored in a computer-readable storage medium, and these instructions cause the computer, programmable data processing device, and / or other equipment to work in a specific manner, so that the computer-readable medium storing the instructions includes a manufactured product, which includes instructions for implementing various aspects of the functions / actions specified in one or more boxes in 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 so that a series of operating steps are 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 implement the functions / actions specified in one or more boxes in the flowchart and / or block diagram.

[0109] The flow charts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the systems, methods and computer program products according to multiple embodiments of the present invention. In this regard, each box in the flow chart or block diagram can represent a part of a module, a program segment or an instruction, and a part of the module, a program segment or an instruction contains one or more executable instructions for realizing the specified logical function. In some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented by a dedicated hardware-based system that performs the specified function or action, or can be implemented by a combination of dedicated hardware and computer instructions. It is well known to those skilled in the art that it is equivalent to implement it by hardware, implement it by software, and implement it by combining software and hardware.

[0110] Embodiments of the present invention have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The selection of terms used herein is intended to best explain the principles of the embodiments, practical applications, or technical improvements in the marketplace, or to enable other persons of ordinary skill in the art to understand the embodiments disclosed herein. The scope of the present invention is defined by the appended claims.

Claims

1. A method for calculating and evaluating the reliability of a power system taking into account multiple types of transactions, characterized in that: include: Construct a system state sampling method that takes into account the randomness of new energy output, including: construct a non-parametric new energy probabilistic output model based on new energy historical output data and kernel density estimation principles, and use Monte Carlo simulation method to sample system states; Based on the characteristics of transaction entities and random production simulation optimization methods, a random state assessment model of the power system is constructed, taking into account the medium- and long-term inter-provincial and spot trading strategies. Construct an indicator convergence criterion, and iteratively solve the random state assessment model of the power system according to the indicator convergence criterion and the sampled system state until convergence, and obtain the power system reliability state assessment result; The reliability status assessment results of the power system are substituted into the mapping relationship between the established random status assessment model of the power system and the reliability assessment index of the power system to realize the quantitative calculation of the reliability assessment index of the power system.

2. The method according to claim 1, characterized in that The non-parametric new energy probabilistic output model is: in, represents the output of new energy k in province j at time t in scenario m, Represents the maximum value of the new energy output, N k,j is the probability variable corresponding to the output level of new energy k in province j, f t (·) is the mapping relationship between the output level of renewable energy and probability at time t, and T is the time combination involved in the evaluation period.

3. The method according to claim 2, characterized in that The random state assessment model of the power system is: Among them, x is the power vector of the unit output in the province, y is the load shedding power vector, and z is the spot delivery decision vector of the sending province. As the decision variable of the random state assessment model of the power system, the objective function f(x, y, z) is set to minimize the load loss of the system in various scenarios. The equality constraint h(x, y, z) includes the power balance constraint within the province after considering the trading power, spot trading power, and load shedding power. The inequality constraint g(x, y, z) includes the load loss amount of each province and the non-negative upper limit power of the spot delivery in the sending province.

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

5. The method according to claim 1, characterized in that The mapping relationship between the power system random state assessment model and the power system reliability assessment index is: The expected power shortage index is calculated by using the expected power of the system due to power generation capacity shortage or load power reduction within a given time. The power shortage expectation of the system in each period under scenario m can be measured by the following formula: The probability that the available capacity of the power generation system cannot meet the maximum load demand under the specified period of the system is used to measure the power shortage probability index. The power shortage probability of the system in each period under scenario m can be measured by the following formula: in, It represents the total load loss of the system in state m, It represents the total load loss of this province at time t under the system state m, and Δt represents the setting length of the time interval in the state sampling model of the sampling system state.

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