Reliability assessment method for distribution networks in a market environment involving integrated energy parks

Abstract

This invention discloses a method for reliability assessment of distribution networks involving integrated energy parks in a market environment, belonging to the field of distribution network reliability assessment technology. The method includes: constructing constraints on multi-energy conversion equipment within the integrated energy park and quantity-price elasticity curves for end-user electricity in the integrated energy park; based on the constructed constraints on the multi-energy conversion equipment and the quantity-price elasticity curves for end-user electricity, changing the external electricity purchase price of the integrated energy park to obtain the integrated elastic quantity-price curve of the integrated energy park; constructing a two-state model of the probability distribution function of the electricity sales price and component failures according to the electricity sales price and the faults occurring in the distribution network, and generating typical distribution network operation scenarios; and simulating operation using an optimal load reduction model of the distribution network based on the electricity sales price and the electricity consumption of the integrated energy park, outputting evaluation indicators. The method described in this invention significantly improves the accuracy of the load model and achieves a complete and closed-loop quantitative reliability assessment.

Description

Technical Field

[0001] This invention relates to the field of distribution network reliability assessment technology, specifically a distribution network reliability assessment method involving integrated energy parks in a market environment. Background Technology

[0002] With the deepening of power system reform, the traditional power system operation mode is gradually shifting towards marketization. In a market environment, electricity prices fluctuate significantly with supply and demand, exhibiting high uncertainty and time-varying characteristics. This price volatility not only affects electricity users' consumption behavior but also poses new challenges to the operation and reliability assessment of distribution networks. Integrated energy parks, by integrating multiple energy forms such as electricity, gas, and heat, can dynamically adjust their energy consumption strategies based on external electricity and gas prices, resulting in highly elastic electricity demand and significantly impacting the load distribution and operating status of distribution networks. However, traditional distribution network reliability assessment methods are mostly based on fixed load models, failing to fully consider the impact of electricity price fluctuations on the user side, especially the flexible consumption behavior of integrated energy parks, leading to assessment results that deviate from the actual market environment. Therefore, there is an urgent need to propose a distribution network reliability assessment method that can consider electricity price fluctuations and the elastic response of integrated energy parks to improve the accuracy and adaptability of the assessment results. Summary of the Invention

[0003] In view of the above-mentioned problems, the present invention is proposed.

[0004] Therefore, the technical problem solved by this invention is that existing methods for assessing the reliability of power distribution networks have assumptions that are out of touch with the market environment, insufficient characterization of flexible resource response characteristics, and low accuracy of assessment results. It also addresses the issue of how to incorporate electricity price uncertainty and the flexible response characteristics of integrated energy parks into the assessment process.

[0005] To address the aforementioned technical problems, this invention provides the following technical solution: a method for reliability assessment of a distribution network involving integrated energy parks in a market environment, comprising: constructing constraints on multi-energy conversion equipment within the integrated energy park and quantity-price elasticity curves for end-user electricity in the integrated energy park; based on the constructed constraints on the multi-energy conversion equipment and the quantity-price elasticity curves for end-user electricity, changing the external electricity purchase price of the integrated energy park to obtain the integrated elastic quantity-price curve of the integrated energy park; constructing a two-state model of the probability distribution function of the external electricity purchase price and component failures according to the external electricity purchase price and the faults occurring in the distribution network, and generating typical distribution network operation scenarios; and simulating operation using an optimal load reduction model for the distribution network based on the external electricity purchase price and the electricity consumption of the integrated energy park, and outputting evaluation indicators.

[0006] As a preferred embodiment of the method for assessing the reliability of a distribution network involving an integrated energy park in the market environment described in this invention, the constraints of the multi-energy conversion equipment within the integrated energy park include the fact that the integrated energy park consists of internal multi-energy conversion equipment and terminal electricity, natural gas, and heat users. The multi-energy conversion equipment mainly includes gas turbine units, gas boilers, electric-to-gas equipment, electric heat pumps, and combined heat and power units.

[0007] As a preferred embodiment of the distribution network reliability assessment method involving integrated energy parks in the market environment described in this invention, the quantity-price elasticity curve of the terminal electricity users in the integrated energy park includes the following: the terminal electricity users in the integrated energy park change their electricity consumption according to external electricity price fluctuations, and the electricity consumption of the terminal electricity users is negatively correlated with the external electricity price. The constraint condition of the quantity-price elasticity curve of the terminal users is expressed as follows:

[0008] ,

[0009] ,

[0010] in, For end users Electricity consumption at all times The quantity-price elasticity curve for end users Duan Zai Electricity consumption at all times The quantity-price elasticity curve for end users The upper limit of electricity consumption for a given segment.

[0011] As a preferred embodiment of the method for assessing the reliability of a distribution network involving integrated energy parks in a market environment as described in this invention, the integrated energy park's comprehensive elastic quantity-price curve includes: establishing an optimal energy purchase model for the integrated energy park with the goal of minimizing its own energy consumption cost; obtaining the external electricity purchase volume of the integrated energy park under different external electricity purchase prices by changing the external electricity purchase price of the integrated energy park; and forming the integrated elastic quantity-price curve of the integrated energy park.

[0012] As a preferred embodiment of the distribution network reliability assessment method involving integrated energy parks in the market environment described in this invention, the integrated energy park's comprehensive elastic quantity-price curve includes the fact that the integrated energy park's own energy cost mainly consists of two parts: external electricity purchase cost and external gas purchase cost, expressed as follows:

[0013] ,

[0014] in, For the integrated energy park in The external electricity purchase price at any given time, For the integrated energy park in External power purchase at any time For the integrated energy park in The external gas purchase price at any given time, For the integrated energy park in The amount of gas purchased from outside at any given time.

[0015] As a preferred embodiment of the method for assessing the reliability of distribution networks in a market environment involving integrated energy parks as described in this invention, the generation of typical distribution network operation scenarios includes: considering the fluctuation of external electricity purchase prices in the distribution network under market conditions, constructing an external electricity purchase price probability distribution function; considering the random faults that may occur in the distribution network, modeling the internal components of the distribution network as a two-state model; forming a distribution network component operation state set and an external electricity purchase price set based on Monte Carlo sampling; and using the K-means clustering method to reduce the distribution network component operation state set and the external electricity purchase price set to generate typical distribution network operation scenarios.

[0016] As a preferred embodiment of the distribution network reliability assessment method involving integrated energy parks in a market environment as described in this invention, wherein: the external electricity purchase price probability distribution function includes, and can be regarded as a normal distribution, expressed as:

[0017] ,

[0018] in, For the integrated energy park in The external electricity purchase price at any given time, This represents the average external electricity purchase price. This represents the variance of the external electricity purchase price.

[0019] As a preferred embodiment of the method for assessing the reliability of a distribution network involving integrated energy parks in a market environment as described in this invention, the step of using the optimal load reduction model of the distribution network for operational simulation includes obtaining the electricity purchase volume of the integrated energy park based on the external electricity purchase price under different distribution network operation scenarios after reduction, combined with the constructed comprehensive elastic quantity-price curve, and obtaining the power load reduction amount under any scenario by solving the optimal load reduction model of the distribution network.

[0020] As a preferred embodiment of the distribution network reliability assessment method involving integrated energy parks in a market environment as described in this invention, the optimal load reduction model for the distribution network includes a method that minimizes the costs of electricity purchase and sale and load reduction for the distribution network within a time period, expressed as:

[0021] ,

[0022] in, To minimize the costs of electricity purchase and sale and load reduction over the specified time period, For time period, For the integrated energy park in The external electricity purchase price at any given time, For the distribution network in Always purchase electricity from the upper-level power grid. For the distribution network in The electricity price sold to the upstream power grid at all times is a known quantity. For the distribution network in The amount of electricity sold to the upper-level power grid at all times. To reduce the unit price of electricity load, For the distribution network in The amount of power load reduction at any given time.

[0023] As a preferred embodiment of the distribution network reliability assessment method involving integrated energy parks in a market environment as described in this invention, the optimal load reduction model of the distribution network includes solving the optimal load reduction model of the distribution network to obtain the power load reduction amount under any scenario, and outputting the power reliability evaluation index. The reliability evaluation index is expressed by the expected power load loss as follows:

[0024] ,

[0025] in, To anticipate power outage, For each scene The probability, For time period, For the scene Sub-distribution network in The amount of power load reduction at any given time.

[0026] Another objective of this invention is to provide a distribution network reliability assessment system involving integrated energy parks in a market environment. This system can change the external electricity purchase price of the integrated energy park based on the constraints of the constructed multi-energy conversion equipment and the quantity-price elasticity curve of the end-user electricity user, thereby obtaining the comprehensive elastic quantity-price curve of the integrated energy park. This solves the problem that current distribution network reliability assessments do not adequately consider the market environment and the elastic response of the user side.

[0027] As a preferred embodiment of the distribution network reliability assessment system involving integrated energy parks in the market environment described in this invention, the system includes: an integrated energy park model construction module, an integrated elastic quantity-price curve construction module, a distribution network operation scenario generation module, and a reliability assessment simulation and calculation module. The integrated energy park model construction module is used to construct operating constraints for multi-energy conversion equipment within the integrated energy park based on its constituent elements, classify end-user electricity as reducible loads, and construct a quantity-price elasticity curve model between electricity consumption and external electricity prices. The integrated elastic quantity-price curve module is used to optimize the system by minimizing the energy cost of the integrated energy park itself. An optimal energy purchase model is constructed by changing the external electricity purchase price to solve for the optimal energy purchase model, forming a comprehensive elastic quantity-price curve. The distribution network operation scenario generation module is used to consider the fluctuation of the external electricity purchase price of the distribution network under the market environment, construct a probability distribution model, and model the internal components of the distribution network as a two-state model based on failure rate and repair time. Typical distribution network operation scenarios are generated by Monte Carlo method and K-means clustering algorithm. The reliability assessment simulation and calculation module is used to determine the amount of electricity purchased by the comprehensive energy park based on the external electricity purchase price and the elastic quantity-price curve under the typical operation scenario, construct the optimal load reduction model of the distribution network, and output the expected power load failure reliability evaluation index.

[0028] The beneficial effects of this invention are:

[0029] This invention provides a method for reliability assessment of distribution networks involving integrated energy parks in a market environment. It constructs constraints on multi-energy conversion equipment within the integrated energy park and quantity-price elasticity curves for end-user electricity consumption. This ensures the model accurately reflects the conversion capacity and operational boundaries of multi-energy coupling within the park, accurately characterizing user electricity consumption behavior response characteristics under market electricity price fluctuations. Based on the constructed constraints on multi-energy conversion equipment and the quantity-price elasticity curves for end-user electricity consumption, the external electricity purchase price of the integrated energy park is changed to obtain a comprehensive elastic quantity-price curve for the integrated energy park. This integrates the flexibility of multi-energy conversion within the park with the elastic response of end-users, quantifies the park's electricity purchase decisions under different market price signals, and provides accurate and dynamic load response model input. Based on the external electricity purchase price and faults occurring in the distribution network, a probability distribution function of the external electricity purchase price and component faults are constructed. The invention employs a two-state model to generate typical distribution network operation scenarios. It can simultaneously capture two types of uncertainties: market electricity price fluctuations and random physical equipment failures. It compresses a complex and high-level set of random operating states into a set of representative typical scenarios and corresponding probabilities, providing realistic, typical, and computationally feasible operating scenarios for subsequent implementation. Based on external electricity purchase prices and electricity consumption in integrated energy parks, it uses an optimal load reduction model for distribution networks to simulate operation, outputs evaluation indicators, quantitatively calculates the load reduction amount necessary to maintain the safe operation of the system under each scenario, and outputs the quantitative indicator of expected power load loss. This completes the calculation of multiple uncertainties from market environment, equipment status to user elastic response, forming a complete closed-loop calculation chain. The invention achieves better results in terms of accuracy in characterizing market environment and user-side elastic behavior, feasibility in handling and calculating multiple uncertainties, and quantitative assessment of system reliability. Attached Figure Description

[0030] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0031] Figure 1 This is an overall flowchart of a method for assessing the reliability of a distribution network in a market environment, including the participation of an integrated energy park, provided in Embodiment 1 of the present invention.

[0032] Figure 2 The diagram shows the structure of an integrated energy park, which is part of a method for assessing the reliability of a distribution network in a market environment, as provided in Embodiment 1 of the present invention.

[0033] Figure 3The comprehensive elasticity quantity-price curve of the integrated energy park is provided in Embodiment 1 of the present invention as a method for assessing the reliability of a distribution network involving an integrated energy park in a market environment. Detailed Implementation

[0034] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0035] Example 1, referring to Figures 1-3 As an embodiment of the present invention, a method for reliability assessment of a distribution network involving integrated energy parks in a market environment is provided, comprising:

[0036] S1: Construct the constraints of the multi-energy conversion equipment 100 inside the integrated energy park and the quantity-price elasticity curves of the end-user electricity in the integrated energy park.

[0037] Specifically, based on the main components of the integrated energy park in the distribution network, constraints are established for the multi-energy conversion equipment 100 within the integrated energy park.

[0038] The constraints of the multi-energy conversion equipment 100 within the integrated energy park include that the integrated energy park consists of the internal multi-energy conversion equipment 100 and the terminal electricity, natural gas, and heat users. The multi-energy conversion equipment 100 mainly includes gas turbine units, gas boilers, electric-to-gas equipment, electric heat pumps, and combined heat and power units.

[0039] Gas turbine units generate electricity by consuming natural gas, and their energy relationship can be expressed as follows:

[0040] ,

[0041] in, For gas turbine units exist Active power at any given time For gas turbine units Rated power.

[0042] Gas-fired boilers generate heat energy by consuming natural gas, and the energy relationship can be expressed as follows:

[0043] ,

[0044] in, Gas-fired boiler exist Active power at any given time Gas-fired boiler Rated power.

[0045] Electric-to-gas (EPC) equipment produces natural gas by consuming electrical energy; its energy relationship is expressed as follows:

[0046] ,

[0047] in, For electro-gas conversion equipment exist Active power at any given time For electro-gas conversion equipment Rated power.

[0048] The relationship between electric heat pumps is expressed as follows:

[0049] ,

[0050] in, For heat pumps exist Active power at any given time For heat pumps Rated power.

[0051] The relationship between combined heat and power (CHP) units is expressed as follows:

[0052] ,

[0053] ,

[0054] ,

[0055] in, For combined heat and power units exist Power generation at any given moment For combined heat and power units Rated power generation capacity For combined heat and power units exist Heating power at any time For combined heat and power units Customized thermal power, For combined heat and power units The energy conversion correlation coefficient is a fixed value.

[0056] The composition of an integrated energy park is as follows Figure 2As shown, it consists of a multi-energy conversion device 100 and an end user 200. The multi-energy conversion device 100 consists of a gas turbine unit, a gas boiler, a combined heat and power unit, an electric-to-gas conversion device, and an electric heat pump. The end user 200 consists of an electricity user, a natural gas user, and a heat user. The left side shows the external input of gas and electricity. The purchased energy is processed and distributed by the internal multi-energy conversion device 100 and then output at the end user 200 to meet the specific needs of the end user 200 for electricity, gas, and heat.

[0057] It should be noted that the quantity-price elasticity curve of end-user electricity in the integrated energy park includes the fact that end-user electricity consumption changes according to external electricity price fluctuations. Therefore, end-user electricity users can be considered as load-reducible. Generally, the electricity consumption of end-user electricity users is negatively correlated with the external electricity price. This relationship can be modeled as a downward-sloping step curve, i.e., the quantity-price elasticity curve of end-user 200. The constraints of the quantity-price elasticity curve of end-user 200 are expressed as follows:

[0058] ,

[0059] ,

[0060] in, For end users 200 Electricity consumption at all times For end users, the 200th quantity-price elasticity curve Duan Zai Electricity consumption at all times For end users, the 200th quantity-price elasticity curve The upper limit of electricity consumption for a given segment.

[0061] It should also be noted that by constructing the constraints of multi-energy conversion equipment 100 and the quantity-price elasticity curves of end users 200 within the integrated energy park, multi-energy flow collaborative optimization scheduling is achieved, solving the technical problems of coordinated operation of multiple energy conversion and demand response, improving energy utilization efficiency, reducing operating costs, and enhancing system flexibility and reliability.

[0062] S2: Based on the constraints of the constructed multi-energy conversion device 100 and the quantity-price elasticity curve of the end-user electricity, the external electricity purchase price of the integrated energy park is changed to obtain the integrated elasticity quantity-price curve of the integrated energy park.

[0063] Specifically, based on the constraints of various multi-energy conversion devices 100 within the integrated energy park and the quantity-price elasticity curves of end-user electricity in the integrated energy park, an optimal energy purchase model for the integrated energy park is established.

[0064] The comprehensive elasticity quantity-price curve of the integrated energy park includes: establishing an optimal energy purchase model for the integrated energy park with the goal of minimizing its own energy consumption cost; obtaining the external electricity purchase volume of the integrated energy park under different external electricity purchase prices by changing the external electricity purchase price of the integrated energy park; and forming the comprehensive elasticity quantity-price curve of the integrated energy park.

[0065] It should be noted that the comprehensive elasticity quantity-price curve of the integrated energy park includes the fact that the integrated energy park's own energy costs mainly consist of two parts: external electricity purchase costs and external gas purchase costs, expressed as follows:

[0066] ,

[0067] in, For the integrated energy park in The external electricity purchase price at any given time, For the integrated energy park in External power purchase at any time For the integrated energy park in The external gas purchase price at any given time, For the integrated energy park in The amount of gas purchased from outside at any given time.

[0068] The constraints of the optimal energy purchase model for the integrated energy park include the constraints of the constructed multi-energy conversion equipment 100, the constraints of the quantity-price elasticity curve of the end users 200, and the energy balance constraints. The specific energy balance constraints are as follows:

[0069] The power balance constraint is expressed as:

[0070] ,

[0071] in, For the integrated energy park in External power purchase at any time For end users 200 Electricity consumption at all times For electro-gas conversion equipment exist Active power at any given time For heat pumps exist Active power at any given time For gas turbine units exist Active power at any given time For combined heat and power units exist Power generation at any given moment.

[0072] The natural gas balance constraint is expressed as:

[0073] ,

[0074] in, For the integrated energy park in External power purchase at any time For end users 200 Gas consumption at any time Gas-fired boiler exist Active power at any given time For gas turbine units Energy conversion efficiency, For gas turbine units exist Active power at any given time For electro-gas conversion equipment Energy conversion efficiency, For electro-gas conversion equipment exist Active power at any given moment.

[0075] Thermodynamic equilibrium constraints are expressed as:

[0076] ,

[0077] in, For end users 200 Constant use of heat, For heat pumps Energy conversion efficiency, For heat pumps exist Active power at any given time Gas-fired boiler Energy conversion efficiency, Gas-fired boiler exist Active power at any given time For combined heat and power units exist Power generation at any given moment.

[0078] It should also be noted that, based on the optimal energy purchase model for integrated energy parks, given the external electricity and gas purchase prices, the external electricity and gas purchase volumes for the integrated energy park can be obtained. Assuming the external gas purchase price remains constant over a period of time, by continuously changing the external electricity purchase price and solving the optimal energy purchase model for integrated energy parks, a set of external electricity purchase volumes under different external electricity purchase prices is obtained, such as... Figure 3 As shown, this set of external electricity purchase prices is regarded as the horizontal axis, and the corresponding external electricity purchase volume is regarded as the vertical axis. Connecting them forms the comprehensive elasticity quantity-price curve of the integrated energy park.

[0079] First, we define an optimization operator for externally purchased electricity. This operator aims to describe the amount of externally purchased electricity that minimizes the objective function of the optimal energy purchase model for the integrated energy park, and is expressed as:

[0080] ,

[0081] in, An optimized operator for externally purchased electricity. The objective function of the optimal energy purchase model is denoted as .

[0082] Then, assuming that the external gas purchase price remains constant during the optimization period, it is expressed as:

[0083] ,

[0084] in, For a moment External gas purchase price, It is a fixed parameter.

[0085] Subsequently, all times within the given optimization period External electricity purchase price restrictions, for each Solve the given and The optimal energy purchase model for integrated energy parks yields the calculation results of the corresponding external energy purchase optimization operator, as shown below:

[0086] ,

[0087] ,

[0088] in, For a moment The minimum external electricity purchase price, For a moment The maximum value of the external electricity purchase price.

[0089] Based on this, obtain any time point The trajectory of the optimal solution of the external power purchase optimization operator is the comprehensive elastic quantity-price curve.

[0090] It should also be noted that by constructing an optimal energy purchase model, based on multi-energy conversion constraints, user demand elasticity, and energy balance constraints, and by changing the external electricity price, a comprehensive elastic quantity-price curve is obtained. This solves the technical problem that the overall response behavior of the multi-energy coupled system to the external electricity price is difficult to accurately characterize, enabling a quantitative assessment of the potential for aggregated energy consumption in the park, and improving the overall economy and operational flexibility of the energy system.

[0091] S3: Based on the external electricity purchase price and the faults that occur in the distribution network, construct the probability distribution function of the external electricity purchase price and the two-state model of the component fault, and generate typical distribution network operation scenarios.

[0092] Specifically, generating typical distribution network operation scenarios involves considering the fluctuations in external electricity purchase prices in the distribution network under market conditions, constructing a probability distribution function for external electricity purchase prices, and considering the random faults that may occur in the distribution network. The internal components of the distribution network are modeled as two-state models, and a set of operating states and external electricity purchase prices for distribution network components are formed based on Monte Carlo sampling. Finally, K-means clustering is used to reduce the set of operating states and external electricity purchase prices of distribution network components to generate typical distribution network operation scenarios.

[0093] The probability distribution function of external electricity purchase price includes, and can be regarded as a normal distribution, expressed as:

[0094] ,

[0095] in, For the integrated energy park in The external electricity purchase price at any given time, This represents the average external electricity purchase price. The variance of the external electricity purchase price. All data were obtained from historical sample data.

[0096] Any component within a power distribution network can be considered to meet the failure rate requirement. The average repair time is In a two-state model, the sampling run duration and sampling repair time can be regarded as exponential distributions.

[0097] The two-state model is represented as:

[0098] ,

[0099] ,

[0100] in, For internal components of the power distribution network The two-state model, For components Availability This refers to the operating status of the component. Component failure The component is functioning normally. The average repair time, This refers to the failure rate.

[0101] The sampling run time is expressed as:

[0102] ,

[0103] in, For the duration of the sampling run, This refers to the failure rate.

[0104] Sampling repair time is expressed as:

[0105] ,

[0106] in, For sampling repair time, This represents the average repair time.

[0107] Based on Monte Carlo sampling, random sampling is performed on the probability distribution function of the external electricity purchase price and the exponential distribution function of the sampling operation time and sampling repair time of the internal components of the distribution network to simulate... The system then uses a set of operating states of distribution network components and a set of external electricity purchase prices to generate scenario reduction data using the K-means clustering method. A typical power distribution network operation scenario, each scenario The probability is .

[0108] It should also be noted that by integrating the normal distribution model of external electricity purchase price with the two-state model of components based on failure rate and repair time, and by using Monte Carlo sampling and K-means clustering to generate and reduce scenarios, the problem of simultaneously considering the dual uncertainty of observed price fluctuations and random failures is solved. This generates a set of typical operating scenarios with probabilistic representativeness, providing key inputs for the collaborative optimization of the distribution network under complex uncertainties in the park and reducing the complexity of stochastic programming calculations.

[0109] S4: Based on the external electricity purchase price and the electricity consumption of the integrated energy park, the optimal load reduction model of the distribution network is used to conduct operation simulation and output evaluation indicators.

[0110] Specifically, the operation simulation using the optimal load reduction model of the distribution network includes obtaining the electricity purchase price of the integrated energy park based on the external electricity purchase price under different distribution network operation scenarios after reduction, combined with the constructed comprehensive elastic quantity-price curve, simulating the system operation under various scenarios using the optimal load reduction model of the distribution network, calculating the reliability evaluation index based on the simulation results, and obtaining the power load reduction amount under any scenario by solving the optimal load reduction model of the distribution network.

[0111] The optimal load shedding model for a distribution network includes a method to minimize the costs of electricity purchase and sale and load shedding within a given time period, expressed as:

[0112] ,

[0113] in, To minimize the costs of electricity purchase and sale and load reduction over the specified time period, For time period, For the integrated energy park in The external electricity purchase price at any given time, For the distribution network in Always purchase electricity from the upper-level power grid. For the distribution network in The electricity price sold to the upstream power grid at all times is a known quantity. For the distribution network in The amount of electricity sold to the upper-level power grid at all times. To reduce the unit price of electricity load, For the distribution network in The amount of power load reduction at any given time.

[0114] It should be noted that the optimal load reduction model for the distribution network satisfies power balance constraints, load reduction constraints, generator output constraints, and branch power flow constraints.

[0115] Power balance constraints for nodes connected to the upstream power grid The power balance constraint is expressed as:

[0116] ,

[0117] ,

[0118] ,

[0119] in, For the line The reciprocal of reactance, For nodes exist Phase angle at time, For nodes The generator on Active power at any given time For nodes superior Power load at any given time For nodes The integrated energy park Real-time electricity purchase volume For nodes Electricity load reduction To represent nodes exist Always purchase electricity from the upper-level power grid. To represent nodes exist The amount of electricity sold to the upper-level power grid at all times. For the distribution network in Always purchase electricity from the upper-level power grid. For the distribution network in The electricity sold to the upper-level power grid is constantly being transferred.

[0120] For other nodes Its power balance constraint is expressed as:

[0121] ,

[0122] in, For the line The reciprocal of reactance, For nodes exist Phase angle at time, For nodes The generator on Active power at any given time For nodes superior Power load at any given time For nodes The integrated energy park Real-time electricity purchase volume For nodes The amount of electricity load reduction.

[0123] The load reduction constraint is expressed as:

[0124] ,

[0125] ,

[0126] in, This represents the amount of electricity load reduction.

[0127] The generator output constraint is expressed as:

[0128] ,

[0129] in, For nodes The rated power of the generator on the machine.

[0130] Branch flow constraints are represented as:

[0131] ,

[0132] ,

[0133] in, For connecting nodes On the side road The constant flow of electricity For nodes exist Phase angle at time, For connecting nodes The branch road's flow restriction.

[0134] By solving the optimal load reduction model of the distribution network, any scenario can be obtained. The amount of power load reduction is used to calculate power reliability evaluation indicators.

[0135] The reliability evaluation index is represented by the expected power loss (EENS), which is expressed as:

[0136] ,

[0137] in, To anticipate power outage, For each scene The probability, For the scene Sub-distribution network in The amount of power load reduction at any given time.

[0138] The smaller the expected power loss value, the stronger the reliability of the distribution network.

[0139] It should also be noted that by integrating the comprehensive elasticity quantity-price curve with typical operating scenarios and constructing an optimal load reduction model for simulation, the problem of coordinating and optimizing economic dispatch and load management under the dual uncertainties of the market and physical conditions, and ensuring power supply reliability, is solved. This enables a coordinated quantitative assessment of the operating risks of the distribution network and the response capabilities of the park, optimizes the economic benefits of system operation, and accurately quantifies reliability.

[0140] Example 2, an embodiment of the present invention, provides a distribution network reliability assessment system with the participation of integrated energy parks in a market environment, including an integrated energy park model construction module, an integrated elastic quantity-price curve construction module, a distribution network operation scenario generation module, and a reliability assessment simulation and calculation module.

[0141] Among them, the integrated energy park model construction module is used to construct the operating constraints of 100 multi-energy conversion equipment inside the park based on the constituent elements of the integrated energy park of the distribution network, divide the end power users into loads that can be reduced, and construct the quantity-price elasticity curve model between electricity consumption and external electricity price.

[0142] The integrated elastic quantity-price curve construction module is used to construct an optimal energy purchase model with the goal of minimizing the energy consumption cost of the integrated energy park itself. The optimal energy purchase model is solved by changing the external electricity purchase price, and an integrated elastic quantity-price curve is formed.

[0143] The distribution network operation scenario generation module is used to consider the fluctuation of external electricity purchase price in the market environment, construct a probability distribution model, model the internal components of the distribution network as a two-state model based on failure rate and repair time, and generate typical distribution network operation scenarios through Monte Carlo method and K-means clustering algorithm.

[0144] The reliability assessment simulation and calculation module is used to determine the amount of electricity purchased by the integrated energy park based on the external electricity purchase price and the elastic quantity-price curve under typical operating scenarios, construct the optimal load reduction model of the distribution network, and output the expected power outage reliability evaluation index.

[0145] This embodiment also provides a computer device, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements the method for assessing the reliability of a power distribution network involving integrated energy parks in a market environment as proposed in the above embodiment.

[0146] This embodiment also provides a computer-readable storage medium storing a computer program thereon. When the computer program is executed by a processor, it implements the method for assessing the reliability of a distribution network in a market environment involving integrated energy parks, as proposed in the above embodiment.

[0147] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0148] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-including system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0149] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0150] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0151] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for reliability assessment of distribution networks involving integrated energy parks in a market environment, characterized in that, include: Construct the constraints of multi-energy conversion equipment within the integrated energy park and the quantity-price elasticity curves of end-user electricity in the integrated energy park; Based on the constraints of the constructed multi-energy conversion equipment and the quantity-price elasticity curve of the end-user electricity, the external electricity purchase price of the integrated energy park is changed to obtain the integrated elasticity quantity-price curve of the integrated energy park. Based on the external electricity purchase price and the faults that occur in the distribution network, a two-state model of the probability distribution function of the external electricity purchase price and the component faults is constructed, and typical distribution network operation scenarios are generated. The generation of typical distribution network operation scenarios includes: considering the fluctuation of external electricity purchase price in the distribution network under market conditions, constructing an external electricity purchase price probability distribution function; considering the random faults that may occur in the distribution network, modeling the internal components of the distribution network as a two-state model; forming a distribution network component operation state set and an external electricity purchase price set based on Monte Carlo sampling; and using K-means clustering to reduce the distribution network component operation state set and external electricity purchase price set to generate typical distribution network operation scenarios. Based on external electricity purchase prices and the electricity consumption of the integrated energy park, an optimal load reduction model for the distribution network is used for operational simulation, outputting evaluation indicators. According to the reduced external electricity purchase prices under different distribution network operation scenarios, combined with the constructed comprehensive elasticity quantity-price curve, the electricity purchase volume of the integrated energy park is obtained. By solving the optimal load reduction model for the distribution network, the electricity load reduction amount under any scenario is obtained. The optimal load reduction model for the distribution network aims to minimize the costs of purchasing external electricity and reducing load within a given time period.

2. The method for assessing the reliability of a distribution network in a market environment, including the participation of integrated energy parks, as described in claim 1, is characterized in that: The constraints on the multi-energy conversion equipment within the integrated energy park include: The integrated energy park consists of internal multi-energy conversion equipment and terminal users of electricity, natural gas, and heat. The multi-energy conversion equipment includes gas turbine units, gas boilers, electric-to-gas equipment, electric heat pumps, and combined heat and power units.

3. The method for assessing the reliability of a distribution network in a market environment involving integrated energy parks, as described in claim 1 or 2, is characterized in that: The quantity-price elasticity curves for end-user electricity in the integrated energy park include... The electricity users at the end of the integrated energy park change their electricity consumption according to fluctuations in external electricity prices. The electricity consumption of the end users is negatively correlated with the external electricity price. The constraint condition of the quantity-price elasticity curve of the end users is expressed as follows: ， ， in, For end users Electricity consumption at all times The quantity-price elasticity curve for end users Duan Zai Electricity consumption at all times The quantity-price elasticity curve for end users The upper limit of electricity consumption for a given segment.

4. The method for assessing the reliability of a distribution network in a market environment involving integrated energy parks, as described in claim 3, is characterized in that: The comprehensive elasticity quantity-price curve of the integrated energy park includes, With the goal of minimizing the energy consumption cost of the integrated energy park itself, an optimal energy purchase model for the integrated energy park is established. By changing the external electricity purchase price of the integrated energy park, the external electricity purchase volume of the integrated energy park under different external electricity purchase prices is obtained, forming the comprehensive elasticity quantity-price curve of the integrated energy park.

5. The method for assessing the reliability of distribution networks in a market environment involving integrated energy parks, as described in claims 1, 2, or 4, is characterized in that: The comprehensive elasticity quantity-price curve of the integrated energy park includes, The energy costs of the integrated energy park itself mainly consist of two parts: external electricity purchase costs and external gas purchase costs, expressed as follows: ， in, For the integrated energy park in The external electricity purchase price at any given time, For the integrated energy park in External power purchase at any time For the integrated energy park in The external gas purchase price at any given time, For the integrated energy park in The amount of gas purchased from outside at any given time.

6. The method for assessing the reliability of a distribution network in a market environment involving integrated energy parks, as described in claim 5, is characterized in that: The probability distribution function of the external electricity purchase price includes... The probability distribution function of external electricity purchase prices can be viewed as a normal distribution, expressed as: ， in, For the integrated energy park in The external electricity purchase price at any given time, This represents the average external electricity purchase price. This represents the variance of the external electricity purchase price.

7. The method for assessing the reliability of a distribution network in a market environment involving integrated energy parks, as described in claim 6, is characterized in that: The optimal load reduction model for the distribution network is specifically expressed as follows: ， in, To minimize the costs of electricity purchase and sale and load reduction over the specified time period, For time period, For the integrated energy park in The external electricity purchase price at any given time, For the distribution network in Always purchase electricity from the upper-level power grid. For the distribution network in The electricity price sold to the upstream power grid at all times is a known quantity. For the distribution network in The amount of electricity sold to the upper-level power grid at all times. To reduce the unit price of electricity load, For the distribution network in The amount of power load reduction at any given time.

8. The method for assessing the reliability of distribution networks in a market environment involving integrated energy parks, as described in claims 1, 2, 4, or 7, is characterized in that: The optimal load reduction model for the distribution network includes: Solve the optimal load reduction model for the distribution network to obtain the load reduction amount under any scenario, and output the power reliability evaluation index. The reliability evaluation index is expressed by the expected power load loss as follows: ， in, To anticipate power outage, For each scene The probability, For time period, For the scene Sub-distribution network in The amount of power load reduction at any given time.

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