Calculation Method for Reliable Capacity of Energy Storage

Abstract

The present invention relates to a method for calculating the credible capacity of energy storage, comprising the following steps: S1, introducing the value at risk (VaR) and the conditional value at risk (CVaR) mechanism; S2, establishing an objective function for calculating the credible capacity of energy storage; S3, identifying the constraint conditions; S4, calculating the credible capacity β of energy storage. The present invention provides a new idea for calculating the credible capacity of energy storage, which is beneficial to effectively evaluating the contribution of energy storage to the system reliability, and further ensuring the capacity adequacy of the power system.

Description

Technical Field

[0001] The present invention relates to the field of electrical engineering, and more specifically, to a method for calculating the reliable capacity of energy storage. Background Art

[0002] As the configured capacity of energy storage in the power system is getting higher and higher, it is becoming increasingly important to reasonably evaluate the contribution degree of energy storage to the reliability of the power system. At present, the existing confidence capacity calculation methods mainly evaluate based on the historical outage status of units. This method is only applicable to thermal power units and renewable energy units. Due to the characteristics of energy storage that it can both charge and discharge, the original evaluation method is no longer applicable, which cannot guarantee the reliability of the power system and causes over-investment and waste of resources. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide a method for calculating the reliable capacity of energy storage, aiming to solve the reasonable evaluation of the contribution degree of energy storage to the system reliability.

[0004] The technical solution adopted by the present invention to solve its technical problems is to construct a method for calculating the reliable capacity of energy storage, including the following steps:

[0005] S1. Introduce the Value at Risk (VaR) and the Conditional Value at Risk (CVaR) mechanism;

[0006] S2. Establish an objective function for calculating the reliable capacity of energy storage;

[0007] S3. Confirm the constraint conditions;

[0008] S4. Calculate the reliable capacity β of energy storage.

[0009] According to the above solution, in the step S1, the Value at Risk (VaR) represents the maximum possible loss of a certain financial asset within a specific future time at the confidence level α:

[0010] VaR α (X) = inf{t: P(x ≤ t) ≥ α}

[0011] The Conditional Value at Risk (CVaR) represents the expected loss when the loss exceeds VaR at the established confidence level α:

[0012] CVaR α = E(-X | -X ≥ VaR α )

[0013] It is derived from mathematical deduction that CVaR is a convex risk measure, and there is a solution with the minimum risk for the portfolio optimization of CVaR.

[0014] According to the above solution, in step S2, when solving the reliable capacity, finding the maximum value of the reliable capacity of the energy storage is equivalent to finding the minimum value of the net load during the peak load period, that is, the minimum value of the area corresponding to the net load curve during the peak load period. The specific model is expressed as:

[0015] Objective function:

[0016] where H is the H-th peak load moment, represents the system net load at the (H + 1)-th peak moment, and π h is the difference between the net load at moment h and , is the constraint coefficient.

[0017] According to the above solution, in step S3, the constraint conditions include:

[0018] Load and net load constraint NL h = L h + Bi h - Bo h (2)

[0019] Peak load segment constraint

[0020] Non-peak load segment constraint π h ≥ 0 (4)

[0021] Energy storage power continuity Bl h = Bl h-1 + η·Bi h - Bo h (5)

[0022] Energy storage upper limit constraint Bl h ≤ Bl Max (6)

[0023] Maximum discharge constraint 0 ≤ Bo h ≤ Bp Max (7)

[0024] Maximum charge constraint 0 ≤ Bi h ≤ Bp Max (8)

[0025] Restrict the energy storage from charging during non-peak hours

[0026] where L h is the load of the system at moment h, Bi h is the charging power of the energy storage at moment h, Bo h is the discharging power of the energy storage at moment h, and Bl his the power level of the energy storage at time h, Bl h-1 is the power level of the energy storage at time h-1, η is the charging efficiency of the energy storage, Bl Max is the power upper limit of the energy storage, Bp Max is the maximum charge and discharge power of the energy storage.

[0027] According to the above scheme, in step S3, the credible capacity β of the energy storage:

[0028]

[0029] Implementing the energy storage credible capacity calculation method of the present invention has the following beneficial effects:

[0030] 1. Based on the conditional value at risk in the investment risk measurement method, the present invention evaluates the contribution of the energy storage to the system reliability. First, the risk investment function is analogized to establish an energy storage charge and discharge model. When solving the credible capacity of the energy storage, finding the maximum value of the credible capacity of the energy storage is equivalent to finding the minimum value of the net load during the peak load period, that is, the minimum value of the area corresponding to the net load curve during the peak load period;

[0031] 2. The present invention establishes an energy storage credible capacity evaluation model based on the risk investment function, considering the influence of system load, energy storage power and capacity, and renewable energy penetration rate on the energy storage credible capacity, which has important significance for the future energy storage capacity planning of the power system, is conducive to ensuring the reliability of the power system, and does not cause over-investment and resource waste. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] The present invention will be further described below in conjunction with the drawings and embodiments. In the drawings:

[0033] Figure 1 is the schematic diagram of the load probability distribution of the energy storage credible capacity calculation method of the present invention;

[0034] Figure 2 is the system load and net load duration curve of the energy storage credible capacity calculation method of the present invention;

[0035] Figure 3 is the load and net load distribution curve during the peak period of the energy storage credible capacity calculation method of the present invention;

[0036] Figure 4 is the credible capacity under different energy storage powers and working times of the energy storage credible capacity calculation method of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0037] In order to have a clearer understanding of the technical features, objectives and effects of the present invention, the specific implementation manners of the present invention will now be described in detail with reference to the drawings.

[0038] Such as Figures 1-4As shown in the figure, the method for calculating the credible capacity of energy storage in the present invention includes the following steps:

[0039] S1. Introduce Value at Risk (VaR) and Conditional Value at Risk (CVaR) mechanisms

[0040] Value at Risk (VaR) represents the maximum possible loss of a financial asset within a specific future time period at a credible level α:

[0041] VaR α (X) = inf{t: P(x ≤ t) ≥ α}

[0042] Conditional Value at Risk (CVaR) represents the expected loss when the loss exceeds VaR at a given credible level α:

[0043] CVaR α = E(-X | -X ≥ VaR α )

[0044] It can be known from mathematical derivation that CVaR is a convex risk measure. Therefore, there must be a solution with the minimum risk for portfolio optimization based on CVaR.

[0045] S2. Establish an objective function for calculating the credible capacity of energy storage

[0046] When solving the credible capacity, finding the maximum value of the credible capacity of energy storage is equivalent to finding the minimum value of the net load during the peak load period. That is, the minimum value of the area corresponding to the net load curve during the peak load period. Therefore, the specific model can be expressed as:

[0047] Objective function:

[0048] Among them, H is the Hth peak load moment, represents the system net load at the (H + 1)th peak moment, π h is the difference between the net load at time h and , is the constraint coefficient.

[0049] S3. Constraint conditions:

[0050] Load and net load constraint NL h = L h + Bi h - Bo h (2)

[0051] Peak load section constraint

[0052] Non-peak load section constraint π h ≥ 0 (4)

[0053] Energy storage power continuity Bl h= Bl h-1 + η·Bi h - Bo h (5)

[0054] Energy storage upper limit constraint Bl h ≤ Bl Max (6)

[0055] Maximum discharge constraint 0 ≤ Bo h ≤ Bp Max (7)

[0056] Maximum charge constraint 0 ≤ Bi h ≤ Bp Max (8)

[0057] Restrict energy storage charging during off-peak hours

[0058] where L h is the load at time h of the system, Bi h is the charging power of the energy storage at time h, Bo h is the discharging power of the energy storage at time h, Bl h is the power level of the energy storage at time h, Bl h-1 is the power level of the energy storage at time h - 1, η is the charging efficiency of the energy storage, Bl Max is the power upper limit of the energy storage, Bp Max is the maximum charge and discharge power of the energy storage.

[0059] S4. Calculate the credible capacity β of the energy storage:

[0060]

[0061] Example 1

[0062] Select the load data for 8736 hours in the IEEE reliability model RTS-79. With reference to the battery data of a certain energy storage technology development company, select 4 energy storages with a capacity of 400 MWh, a power of 25 MW, a working duration of 4 h, and a cycle efficiency of 0.85.

[0063] The calculated load duration curve and the net load duration curve considering the participation of the energy storage are shown in Figure 2 and Figure 3 respectively. At this time, the credible capacity of the energy storage is 61.88%. When the power is constant, the distribution of the credible capacity values of the energy storage under different working times is calculated as shown in Figure 4 and is shown in Figure 4It can be seen that the reliable capacity value of energy storage is directly proportional to the working time and inversely proportional to the energy storage power. When the working time is 1 h, the reliable capacity values at different powers are very close, at 6-28%, so the working duration has a great influence on the reliable capacity value of energy storage. When the working time is 8-10 h and the energy storage power is 10 MW, the reliable capacity gradually approaches 100%. Thus, it can be seen that small-power energy storage scheduling is more flexible and has a higher utilization rate at this load level.

[0064] The embodiments of the present invention have been described above in conjunction with the accompanying drawings. However, the present invention is not limited to the above specific embodiments. The above specific embodiments are merely illustrative and not restrictive. Under the inspiration of the present invention, those of ordinary skill in the art can also make many forms without departing from the purpose of the present invention and the scope protected by the claims. All of these fall within the protection scope of the present invention.

Claims

1. A method for calculating the credible capacity of energy storage, characterized in that, It includes the following steps: S1. Introduce the Value-at-Risk (VaR) and the Conditional Value-at-Risk (CVaR) mechanism; The Value-at-Risk (VaR) represents the maximum possible loss of a financial asset over a specific future time period at a confidence level α: The Conditional Value-at-Risk (CVaR) represents the expected loss when the loss exceeds VaR at a given confidence level α: Derived from mathematical derivation, CVaR is a convex risk measure, and there is a solution with the minimum risk for the portfolio optimization of CVaR; S2. Establish an objective function for calculating the credible capacity of energy storage; When solving for the credible capacity, finding the maximum value of the credible capacity of energy storage is equivalent to finding the minimum value of the net load during the peak load period, that is, the minimum value of the area corresponding to the net load curve during the peak load period. The specific model is expressed as: where H is the H-th peak load time, represents the system net load at the (H + 1)-th peak time, is the difference between the net load at time h and the difference, is the constraint coefficient; S3. Confirm the constraint conditions; The constraint conditions include: Among them, is the load at time h of the system, is the charging power of the energy storage at time h, is the discharging power of the energy storage at time h, is the power level of the energy storage at time h, is the power level of the energy storage at time h-1, is the charging efficiency of the energy storage, is the power upper limit of the energy storage, is the maximum charge-discharge power of the energy storage; S4. Calculate the reliable energy storage capacity ; Reliable energy storage capacity : 。

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