Capacity demand curve calculation method and system based on reliability evaluation

Abstract

The invention relates to the field of reliability evaluation of power markets and power systems, in particular to a capacity demand curve calculation method based on reliability evaluation, which comprises the following steps: acquiring basic data of operation of a power system; the fluctuation condition of the system load and the operation condition of the generator set are obtained based on the basic data, and a Monte Carlo method is adopted to simulate the state of the generator set to obtain fault-free working time TTF and maintenance time TTR; according to the fault-free working time TTF and the maintenance time TTR, the total available capacity and the total load of the system in each time period are calculated, reliability indexes of the system under different installed capacities are obtained, and then a system prediction annual reliability curve is obtained. And carrying out differential processing on the system prediction annual reliability curve by adopting a reliability index LOLE to obtain an incremental reliability-installed capacity curve. And introducing an adjustment coefficient according to the obtained incremental reliability data to obtain a final capacity curve.

Description

Technical Field

[0001] The present invention relates to the field of power market and power system reliability assessment, and in particular to a capacity demand curve calculation method and system based on reliability assessment. Background Art

[0002] In the context of the current global energy transformation, the power system is facing unprecedented challenges and opportunities. With the large-scale access to renewable energy such as wind and solar power, the operation mode and market mechanism of the power system urgently need to be updated to adapt to these changes. The intermittent and volatile characteristics of renewable energy make the balance of power supply and demand more complicated, and put forward higher requirements for the stable operation of the power system.

[0003] Limitations of the existing capacity market mechanism:

[0004] Centralized capacity market: transactions are organized and managed by the system operator, and all power generation companies and flexibility resource suppliers submit capacity quotations and supply information to the operator. Although it is convenient for unified planning and management and can effectively ensure the overall reliability of the system, the concentration of market information may lead to market manipulation and unfair competition, and the complex decision-making process may affect market efficiency.

[0005] Decentralized capacity market: Power generation companies and flexibility resource suppliers conduct capacity transactions independently through bilateral contracts or multilateral trading platforms. The market is highly flexible and both parties have strong autonomy, but lack of unified supervision and coordination may lead to insufficient or excessive capacity in local areas, and relatively high transaction costs.

[0006] With the continuous advancement of technology, new flexible resource technologies such as energy storage systems and demand response may emerge rapidly, leading to the depreciation of existing investment in flexible resources. To deal with this risk, it is necessary to establish a technology evaluation and update mechanism and timely adjust the technical standards and entry thresholds of the capacity market. At the same time, risks such as market price fluctuations and policy changes also need to be reduced through reasonable market design.

[0007] In summary, designing an effective capacity guarantee mechanism is of great significance for ensuring the adequacy and stability of the power system. This invention aims to propose a multi-power capacity market trading mechanism under a new power system, which guides the rational allocation of resources and improves the economy and reliability of the power system through a scientific and reasonable capacity demand curve calculation method. This method will provide a new perspective and tool for the power market to adapt to the ever-changing market demand and technological progress. Summary of the invention

[0008] In order to solve the above technical problems, the present invention provides a capacity demand curve calculation method based on reliability evaluation, comprising the following steps:

[0009] Step 1: Obtain basic data on power system operation;

[0010] Step 2: Based on the basic data, the fluctuation of system load and the operation status of the generator set are obtained, and the state of the generator set is simulated by using the Monte Carlo method to obtain the trouble-free working time TTF and the maintenance time TTR;

[0011] Step 3: Based on the trouble-free working time TTF and the maintenance time TTR, calculate the total available capacity and total load of the system during the peak and valley load periods, obtain the reliability index of the system under different installed capacities, and then obtain the system predicted annual reliability curve;

[0012] Step 4: Use the reliability index LOLE to perform differential processing on the system forecast annual reliability curve to obtain the incremental reliability-installed capacity curve;

[0013] Step 5: Introduce the adjustment coefficient according to the incremental reliability data obtained in step 4 to obtain the final capacity curve;

[0014] Furthermore, in step 1, the basic data includes historical load data of the power system, available capacity of the generator sets, mean time to failure MTTF, and mean time to repair MTTR.

[0015] Furthermore, in step 2, the initial state of the generator set is set to be available. For each generator set, the trouble-free working time TTF and the maintenance time TTR are generated through Monte Carlo simulation, and the formula is as follows:

[0016]

[0017] Among them, U1 and U2 are random numbers uniformly distributed in the interval [0,1], and λ and μ are the failure rate and repair rate of the unit respectively.

[0018] Furthermore, in step 3, specifically, the load loss expectancy (LOLE) is selected as the evaluation indicator. The calculation of LOLE will take into account various possible operating states of the system and their duration, thereby providing a comprehensive system reliability evaluation. The calculation formula is as follows:

[0019] LOLE=p m ·t m +p n ·t n

[0020] Among them, p m is the probability that the system state is normal, t m is the duration of the system's normal state; p n is the probability that the system state is a fault state, t n is the duration of the system fault state.

[0021] Furthermore, in step 4, the difference formula used is as follows:

[0022] ΔLOLE=LOLE i+1 -LOLE i

[0023] ΔLOLE is the change in LOLE, LOLE i+1 To increase the LOLE value after installed capacity, LOLE i It is the LOLE value before adding installed capacity.

[0024] Furthermore, in step 5, an adjustment coefficient k is introduced, and the specific formula is as follows:

[0025] P demand = k × ΔLOLE

[0026] Among them, P demand is the price on the capacity demand curve. The adjustment factor k is a conversion factor used to map incremental reliability (such as ΔLOLE) to capacity price.

[0027] Calculate the conversion factor based on a cost-benefit analysis:

[0028] First, the additional cost ΔC required to improve the reliability of the power system to a certain extent (for example, the reliability index is increased from R1 to R2) is calculated.

[0029] This may include the construction cost of additional standby units, grid upgrade and transformation costs, increased maintenance costs, etc. These costs need to be considered comprehensively and can be determined through detailed engineering economic analysis.

[0030] Secondly, calculate the benefit increment ΔB brought by the improved reliability. The benefit can be considered from the perspective of reducing power outage losses.

[0031] For example, for industrial users, power outages may cause production interruptions and huge economic losses; for commercial users, power outages may affect business operations and lead to reduced revenue. ΔB is calculated by evaluating the power outage losses of different user types and combining the reduction in the number of power outages or power outage time after reliability improvement.

[0032] Finally, the conversion factor can be calculated by the ratio of the incremental benefit to the incremental cost, i.e.

[0033] According to another aspect of the present invention, there is also provided a capacity demand curve calculation system based on reliability evaluation, comprising:

[0034] Data collection and processing unit, to obtain basic data on power system operation;

[0035] The state simulation unit obtains the fluctuation of system load and the operation status of the generator set based on the basic data, and uses the Monte Carlo method to simulate the state of the generator set to obtain the trouble-free working time TTF and maintenance time TTR;

[0036] The reliability assessment unit calculates the total available capacity and total load of the system during peak and valley load periods based on the trouble-free working time TTF and the maintenance time TTR, obtains the reliability index of the system under different installed capacities, and then obtains the predicted annual reliability curve of the system;

[0037] The incremental reliability analysis unit uses the reliability index LOLE to perform differential processing on the system prediction annual reliability curve to obtain the incremental reliability-installed capacity curve;

[0038] The capacity curve calculation unit introduces an adjustment coefficient according to the obtained incremental reliability data to obtain a final capacity demand curve.

[0039] This technical solution is of great significance for ensuring the adequacy and stability of the power system by designing an effective capacity guarantee mechanism. The present invention aims to propose a multi-power capacity market trading mechanism under a new power system, guide the rational allocation of resources through a scientific and reasonable capacity demand curve calculation method, and improve the economy and reliability of the power system. This method will provide a new perspective and tool for the power market to adapt to the ever-changing market demand and technological progress. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 It is a schematic diagram of the process of the present invention. DETAILED DESCRIPTION

[0041] The principles and features of the present invention are described below in conjunction with the accompanying drawings. The examples given are only used to explain the present invention and are not used to limit the scope of the present invention.

[0042] refer to Figure 1 The purpose of the present invention is to provide a capacity demand curve calculation method based on reliability assessment, which can accurately reflect the capacity demand of the power system under different capacity prices and provide decision support for capacity trading in the power market.

[0043] The present invention solves the problem by the following technical solutions:

[0044] Step 1: Data collection and preprocessing. Collect key information such as historical load data of the power system, available capacity of generator sets, mean time to failure (MTTF), mean time to repair (MTTR), etc. After data collection is completed, perform data cleaning, outlier processing and data formatting to facilitate subsequent analysis and simulation.

[0045] Step 2: Generator status simulation. Through the data collection in step 1, the basic data of power system operation is obtained. These data provide the current status of the system, including the fluctuation of system compliance and the operating status of the generator set. In order to deeply understand the operating characteristics of the generator set and predict its possible performance in the future, it is now necessary to simulate the status of the generator set.

[0046] The initial state of the generator set is set to be available. For each generator set, the trouble-free working time TTF and maintenance time TTR are generated through Monte Carlo simulation. The formula is as follows:

[0047]

[0048] Among them, P(S i ) is the system state S i The probability of T(S i ) is state S i duration, i represents the availability or failure state of the system.

[0049] Step 3: System reliability assessment. In step 2, the trouble-free working time (TTF) and maintenance time (TTR) of each generator set were obtained through Monte Carlo simulation. These simulation results provide a detailed view of the possible operating status of the generator set in the future. Next, use this data to calculate the total available capacity and total load of the system in each period to evaluate the system reliability level. The loss of load expectation (LOLE) is selected as the evaluation indicator. The calculation of LOLE will take into account the various possible operating states of the system and their duration, thereby providing a comprehensive system reliability assessment. The calculation formula is as follows:

[0050] LOLE=p m ·t m +p n ·t n

[0051] Among them, p m is the probability that the system state is normal, t m is the duration of the system's normal state; p n is the probability that the system state is a fault state, t n is the duration of the system fault state.

[0052] Step 4: Incremental reliability analysis. In step 3, the reliability level of the power system under different conditions was evaluated. In order to further understand the relationship between system reliability and installed capacity, an incremental reliability analysis is needed.

[0053] The reliability index of the system at different installed capacities, LOLE, obtained in step 3, is used to perform differential processing on the system reliability curve to obtain the relationship between incremental reliability and installed capacity. This analysis will help understand the specific impact of increasing installed capacity on system reliability and provide a basis for making effective capacity investment decisions. The differential formula is as follows:

[0054] ΔLOLE=LOLE i+1 -LOLE i

[0055] ΔLOLE is the change in LOLE, LOLE i+1 To increase the LOLE value after installed capacity, LOLE i It is the LOLE value before adding installed capacity.

[0056] Step 5: Demand curve mapping. Using the incremental reliability data obtained in step 4, the adjustment coefficient k is introduced to map the incremental reliability level to the corresponding capacity price. This mapping process converts technical parameters into economic parameters to form a capacity demand curve. This curve will provide decision support for capacity trading in the power market and help market participants understand the changes in system demand for capacity at different price levels. The formula is as follows:

[0057] P demand = k × ΔLOLE

[0058] Among them, P demand is the price on the capacity demand curve. The adjustment factor k is a conversion factor used to map incremental reliability (such as ΔLOLE) to capacity price. It reflects the market's sensitivity to changes in reliability and willingness to pay. Through this coefficient, changes in technical performance (such as improved reliability) can be converted into economic value.

[0059] In summary, the demand curve obtained through the above steps shows that as the installed capacity increases, the system reliability improves (LOLE decreases), but the capacity price also increases. Based on this capacity demand curve, the system operator can decide the capacity bid to be submitted in the upcoming capacity market auction to ensure that the system reliability requirements are met while maintaining economy.

[0060] According to another aspect of the present invention, there is also provided a capacity demand curve calculation system based on reliability evaluation, comprising:

[0061] Data collection and processing unit, to obtain basic data on power system operation;

[0062] The state simulation unit obtains the fluctuation of system load and the operation status of the generator set based on the basic data, and uses the Monte Carlo method to simulate the state of the generator set to obtain the trouble-free working time TTF and maintenance time TTR;

[0063] The reliability assessment unit calculates the total available capacity and total load of the system during peak and valley load periods based on the trouble-free working time TTF and the maintenance time TTR, obtains the reliability index of the system under different installed capacities, and then obtains the predicted annual reliability curve of the system;

[0064] The incremental reliability analysis unit uses the reliability index LOLE to perform differential processing on the system reliability curve to obtain the incremental reliability-installed capacity curve;

[0065] The capacity curve calculation unit introduces an adjustment coefficient according to the obtained incremental reliability data to obtain a final capacity demand curve.

[0066] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A capacity demand curve calculation method based on reliability assessment, characterized in that: The following steps are included: Step 1: Obtain basic data on power system operation; Step 2: Based on the basic data, the fluctuation of system load and the operation status of the generator set are obtained, and the state of the generator set is simulated by using the Monte Carlo method to obtain the trouble-free working time TTF and the maintenance time TTR; Step 3: Based on the trouble-free working time TTF and the maintenance time TTR, calculate the total available capacity and total load of the system during the peak and valley load periods, obtain the reliability index of the system under different installed capacities, and then obtain the system predicted annual reliability curve; Step 4: Use the reliability index LOLE to perform differential processing on the system forecast annual reliability curve to obtain the incremental reliability-installed capacity curve; Step 5: Introduce the adjustment coefficient based on the incremental reliability data obtained in step 4 to obtain the final capacity demand curve.

2. A capacity demand curve calculation method based on reliability evaluation as claimed in claim 1, characterized in that: In step 1, the basic data includes historical load data of the power system, available capacity of the generator set, mean time to failure MTTF, and mean time to repair MTTR.

3. A capacity demand curve calculation method based on reliability assessment as claimed in claim 1, characterized in that: In step 2, the initial state of the generator set is set to be available; for each generator set, the trouble-free working time TTF and maintenance time TTR are generated through Monte Carlo simulation, and the formula is as follows: Among them, U1 and U2 are random numbers uniformly distributed in the interval [0,1], and λ and μ are the failure rate and repair rate of the unit respectively.

4. A method for calculating a capacity demand curve based on reliability assessment as claimed in claim 1, characterized in that: In step 3, specifically, the load loss expectation LOLE is selected as the evaluation index, and the calculation formula is as follows: LOLE=p m ·t m +p n ·t n Among them, p m is the probability that the system state is normal, t m is the duration of the system's normal state; p n is the probability that the system state is a fault state, t n is the duration of the system fault state.

5. The method for calculating a capacity demand curve based on reliability evaluation according to claim 1, characterized in that: In step 4, the difference formula used is as follows: ΔCLOTHES=CLOTHES i+1 - CLOTHES i ΔLOLE is the change in LOLE, LOLE i+1 To increase the LOLE value after installed capacity, LOLE i It is the LOLE value before adding installed capacity.

6. A method for calculating a capacity demand curve based on reliability assessment as claimed in claim 1, characterized in that: In step 5, the adjustment coefficient k is introduced, and the specific formula is as follows: P demand =k×ΔLOLE Among them, P demand is the price on the capacity demand curve, and the adjustment factor k is a conversion factor used to map incremental reliability to capacity price.

7. A capacity demand curve calculation system based on reliability evaluation, characterized in that: include, Data collection and processing unit, to obtain basic data on power system operation; The state simulation unit obtains the fluctuation of system load and the operation status of the generator set based on the basic data, and uses the Monte Carlo method to simulate the state of the generator set to obtain the trouble-free working time TTF and maintenance time TTR; The reliability assessment unit calculates the total available capacity and total load of the system during peak and valley load periods based on the trouble-free working time TTF and the maintenance time TTR, obtains the reliability index of the system under different installed capacities, and then obtains the predicted annual reliability curve of the system; The incremental reliability analysis unit uses the reliability index LOLE to perform differential processing on the system reliability curve to obtain the incremental reliability-installed capacity curve; The capacity curve calculation unit introduces an adjustment coefficient according to the obtained incremental reliability data to obtain a final capacity demand curve.

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