CN113094919B - Power distribution system reliability assessment method considering demand response and user-side comprehensive energy - Google Patents
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Abstract
The invention discloses a power distribution system reliability evaluation method considering demand response and user side comprehensive energy, which comprises the steps of generating an initial annual load curve and forming a load curve after implementing power price type demand response; inputting system original parameters and program termination parameters; thirdly, performing time sequence Monte Carlo simulation; step four, element state sampling is carried out, and a system element state sequence of the whole year is obtained; fifthly, when the sampling time T is reached, the annual reliability index of the system is calculated; step six: verifying whether the simulation time reaches the specified simulation time limit, and if not, returning to the step two; step seven: and finishing the Monte Carlo simulation to obtain the reliability index of the system. The invention can improve the reliability of the power distribution system.
Description
Technical Field
The invention relates to the field of power distribution system reliability evaluation, in particular to a power distribution system reliability evaluation method considering demand response and user-side comprehensive energy.
Background
Different from the traditional single-energy power distribution system, the power distribution system considering the comprehensive energy has the advantages that due to the fact that the multiple energy sources have direct or indirect coupling relation, the loads also only comprise electric loads, cold loads, hot loads and the like, and various energy forms and corresponding loads have respective characteristics (such as time delay characteristics of cold and hot load energy supply), the reliability evaluation is complex. If the reliability evaluation scheme of the conventional power distribution system is still applied to the integrated energy system, the following problems need to be solved: firstly, the comprehensive energy power distribution system has the characteristic of coupling of various energy sources, and the relation between different energy sources is complex, so that a unique reliability evaluation system suitable for the comprehensive energy system needs to be established for power distribution systems with different reliability evaluation systems such as reliability indexes, models, processes and the like and power distribution systems with single energy sources. Secondly, the characteristics of different cold and hot loads and electric loads in the comprehensive energy have unique dynamic response, and if a Monte Carlo sampling simulation method is adopted, the problem that how to take the dynamic characteristics into consideration is difficult to solve is also solved.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a power distribution system reliability assessment method considering demand response and user-side comprehensive energy so as to improve the reliability of a power distribution system. .
One technical scheme for achieving the above purpose is as follows: a power distribution system reliability assessment method considering demand response and user side comprehensive energy comprises the following steps:
the method comprises the following steps: generating an initial annual load curve, and correcting the data of the original load curve according to the time-of-use electricity price and the elasticity coefficient matrix of the electricity price to form a load curve after implementing electricity price type demand response;
step two: inputting original parameters of a system and program termination parameters, wherein the original parameters comprise line length, user number, element fault rate and repair rate, the program termination parameters comprise maximum simulation age and standard variance, the state of each element of the system is initialized, and the initialization time t is 0;
step three, starting time sequence Monte Carlo simulation, selecting a group of random numbers, generating the normal operation time and the fault repair time of each element, and determining the operation time before each element fails;
the equipment in the model has a normal operation state and a fault state, and the process that the equipment is changed alternately between the normal operation state and the fault restoration state is obtained by calculating the operation time and the restoration time of each equipment. It is assumed that both the failure rate and the repair rate of the device follow an exponential distribution. The runtime to failure (ttf) and repair time (ttr) parameters of the device are generated using various probability distributions and random variables. The uniform distribution is directly generated using a uniform random number generator and this number is converted to TTF and TTR by equations (1) and (2):
wherein λ and μ are failure rate and repair rate of the element, respectively, and γ and β are randomly generated numbers between 0 and 1;
step four, element state sampling is carried out to obtain a system element state sequence of the whole year, and when element faults are sampled or the current moment load exceeds the power supply capacity is verified, firstly, the load of a fault downstream area is considered to be transferred through a connecting line or an isolated island operation mode supported by a distributed power supply so as to update the element state sequence; recording the power failure times and power failure time of each load;
step five: when the sampling time T is reached, counting and accumulating the power failure times and power failure time of each load all the year around so as to calculate the annual reliability index of the system;
step six: verifying whether the simulation time reaches the specified simulation time limit, and if not, returning to the step two;
step seven: and finishing the Monte Carlo simulation to obtain the reliability index of the system.
Further, in step four, the process of element state sampling is as follows: and generating a group of random numbers and distributing the random numbers to each element, generating the normal operation time and the fault repair time of each element according to the equations (1) and (2), and generating the operation-fault periodic variation sequence of each element in the simulation time period according to the time passage under the condition that the initial state of the system element is determined, thereby obtaining different state combinations of the system.
According to the method for evaluating the reliability of the power distribution system, the demand response and the user side comprehensive energy are taken into consideration, the reliability evaluation of the user side demand response-considering comprehensive energy power distribution system can be realized, and a design scheme with the highest reliability of the power distribution system can be selected according to a data result after the different types of comprehensive demand responses of the user side are taken into consideration, so that the reliability of the power distribution system is improved. .
Drawings
FIG. 1 is a schematic diagram of an exemplary structure of a method for evaluating reliability of a power distribution system in consideration of demand response and user-side integrated energy according to the present invention;
FIG. 2 is a typical daily load graph before and after a demand response event;
fig. 3 is a flowchart illustrating a method for evaluating reliability of a power distribution system in consideration of demand response and user-side integrated energy according to the present invention.
Detailed Description
In order to better understand the technical solution of the present invention, the following detailed description is made by specific examples:
the method for evaluating reliability of a power distribution system based on demand response and user-side integrated energy provided by the present invention is described in detail by taking the power distribution network shown in fig. 1 as an example.
Referring to fig. 3, the method for evaluating reliability of a power distribution system considering demand response and integrated energy at a user side according to the present invention includes the following steps in sequence:
the method comprises the following steps: and generating an initial annual load curve, and correcting the data of the original load curve according to the time-of-use electricity price and the elasticity coefficient matrix of the electricity price to form a load curve after implementing electricity price type demand response.
The peak-valley time period is divided by adopting a classical clustering method, and the dividing result is as follows:
the time-of-use electricity price adopts a method for solving an optimization model, an objective function is set to be the minimum load difference value in peak-valley time periods, the electric power company can be considered to obtain the economic benefit as much as possible, the marginal cost of power supply in the valley time periods is used as a boundary condition, the electricity prices in the peak time period, the flat time period and the valley time period are sequentially reduced, and the total electricity consumption of the user side before and after the time-of-use electricity price, namely the constraint that the total amount of the load in the day is unchanged, is used as a constraint condition to solve. The electricity prices at the peak valley-leveling time are respectively 0.85 yuan/(KW h), 0.60 yuan/(KW h) and 0.30 yuan/(KW h).
The elastic coefficient matrix is set as
The original load curve is generated according to a classic IEEE-RBTS79 load percentage model, and a typical daily load curve after the demand response is implemented is obtained according to the obtained time-of-use electricity price of the peak-valley period and the original load, as shown in FIG. 2.
Step two: inputting original parameters of a system (line length, user number, failure rate of each element, repair rate and the like) and program termination parameters (maximum simulation age, standard deviation and the like). And initializing the states of all elements of the system, wherein the initialization time t is 0.
Step three: and starting time sequence Monte Carlo simulation, selecting a group of random numbers, generating the normal operation time and the fault repair time of each element, and determining the operation time before each element fails.
The equipment in the model has a normal operation state and a fault state, and the process that the equipment is changed alternately between the normal operation state and the fault restoration state is obtained by calculating the operation time and the restoration time of each equipment. It is assumed that both the failure rate and the repair rate of the device follow an exponential distribution. The runtime to failure (ttf) and repair time (ttr) parameters of the device are generated using various probability distributions and random variables. The uniform distribution is directly generated using a uniform random number generator and this number is converted to TTF and TTR by equations (1) and (2):
where λ and μ are failure rate and repair rate of the element, respectively, and γ and β are randomly generated numbers between 0 and 1.
Step four: and carrying out element state sampling to obtain a system element state sequence all year round. When element failure is sampled or the fact that the load exceeds the power supply capacity at the current moment is verified, firstly, the load of a failure downstream area is considered to be transferred through a connecting line or an island operation mode supported by a distributed power supply, and therefore the element state sequence is updated. And recording the power failure times and power failure time of each load.
The process of element state sampling is as follows: and generating a group of random numbers and distributing the random numbers to each element, wherein the normal operation time and the fault repair time of each element can be generated according to the equations (1) and (2), and under the condition that the initial state of the system element is determined, the periodic variation sequence of the operation-fault of each element in the simulation time period can be generated according to the time passage, so that different state combinations of the system are obtained. In actual programming, the uptime of each element can be compared after each generation of a set of random numbers, the element with the smallest uptime is found, namely the element which is the first to fail, the system state sequence is changed, and the state sequence and the last state duration are recorded. And then repeating the process after generating a new group of random numbers so as to simulate the whole simulation time length T.
Step five: and when the sampling time T is reached, counting and accumulating the power failure times and power failure time of each load all the year around so as to calculate the annual reliability index of the system.
Step six: and verifying whether the simulation times (simulation years) reach a specified value, and if not, returning to the step two.
Step seven: and finishing the Monte Carlo simulation to obtain the reliability index of the system.
Based on the steps, the reliability evaluation of the comprehensive energy distribution system considering the user side demand response can be realized.
For the power distribution system shown in fig. 1, the following four schemes are designed for comparison:
the first scheme is as follows: not considering user side demand response;
scheme II: implementing electricity price type electricity load demand response;
the third scheme is as follows: implementing demand response of the electricity price type heat load on the basis of the scheme II;
and the scheme is as follows: and considering the demand response of the electricity price type and the incentive type, and evaluating the reliability index.
The reliability of the power distribution system is improved in sequence from the first scheme to the fourth scheme, and the reliability of the power distribution system is improved after the comprehensive demand responses of different types at the user side are considered.
It should be understood by those skilled in the art that the above embodiments are only for illustrating the present invention and are not to be used as a limitation of the present invention, and that changes and modifications to the above described embodiments are within the scope of the claims of the present invention as long as they are within the spirit and scope of the present invention.
Claims (2)
1. A method for evaluating reliability of a power distribution system considering demand response and user-side integrated energy is characterized by comprising the following steps:
the method comprises the following steps: generating an initial annual load curve, and correcting the data of the original load curve according to the time-of-use electricity price and the elasticity coefficient matrix of the electricity price to form a load curve after implementing electricity price type demand response;
step two: inputting original parameters of a system and program termination parameters, wherein the original parameters comprise line length, user number, element fault rate and repair rate, the program termination parameters comprise maximum simulation age and standard variance, the state of each element of the system is initialized, and the initialization time t is 0;
step three, starting time sequence Monte Carlo simulation, selecting a group of random numbers, generating the normal operation time and the fault repair time of each element, and determining the operation time before each element fails;
the method comprises the steps that equipment in a model has a normal operation state and a fault state, the process that the equipment is changed alternately between the normal operation state and the fault repair state is obtained by calculating the operation time and the repair time of each piece of equipment, the fault rate and the repair rate of the equipment are assumed to be in accordance with exponential distribution, various probability distributions and random variables are utilized to generate operation time TTF (time to failure) and repair time TTR (time to repair) parameters of the equipment, a uniform random number generator is utilized to directly generate uniform distribution, and the parameters are converted into TTF and TTR by an equation (1) and an equation (2):
wherein λ and μ are failure rate and repair rate of the element, respectively, and γ and β are randomly generated numbers between 0 and 1;
step four, element state sampling is carried out to obtain a system element state sequence of the whole year, and when element faults are sampled or the current moment load exceeds the power supply capacity is verified, firstly, the load of a fault downstream area is considered to be transferred through a connecting line or an isolated island operation mode supported by a distributed power supply so as to update the element state sequence; recording the power failure times and power failure time of each load;
step five: when the sampling time T is reached, counting and accumulating the power failure times and power failure time of each load all the year around so as to calculate the annual reliability index of the system;
step six: verifying whether the simulation time reaches the specified simulation time limit, and if not, returning to the step two;
step seven: and finishing the Monte Carlo simulation to obtain the reliability index of the system.
2. The method as claimed in claim 1, wherein in the fourth step, the process of element state sampling comprises: and generating a group of random numbers and distributing the random numbers to each element, generating the normal operation time and the fault repair time of each element according to the formula (1) and the formula (2), and generating the operation-fault periodic variation sequence of each element in the simulation time period according to the time passage under the condition that the initial state of the system element is determined, thereby obtaining different state combinations of the system.
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