Optical storage isolated network active power coordination control method considering energy storage SOC state interval
By dividing the SOC state of the energy storage system into multiple intervals and setting corresponding power control coefficients, an active coordination control model for the optical storage network was established, and the problem that the existing technology did not consider the SOC state was solved, and more accurate control strategies and more efficient energy storage management were achieved.
Patent Information
- Application Number
- CN202411861509.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-17
- Publication Date
- 2025-05-16
AI Technical Summary
The existing active control method for optical storage orphan networks does not consider the energy storage SOC state, making it difficult to obtain a more accurate control strategy.
By dividing the SOC state of the energy storage system into several state intervals, setting the power control coefficients of each state interval, and establishing a power control strategy formula based on these coefficients and photovoltaic system load, building a multi-objective active coordinated control model, and finally obtaining the optimal solution by solving the model.
A more refined control strategy is implemented, which can more effectively manage the charging and discharging process of the energy storage system, ensuring that the system achieves frequency stability while maximizing the effectiveness of the energy storage system.
Smart Images
Figure CN120016608A_ABST
Abstract
Description
Technical Field
[0001] The present application belongs to the field of renewable energy power generation technology, and specifically, relates to a method for coordinated control of active power of photovoltaic and storage isolated grids taking into account energy storage SOC state intervals. Background Art
[0002] In recent years, with the rapid development of the economic level, the structure of new energy power systems, led by photovoltaics, has undergone tremendous changes. Due to the intermittent and unstable nature of photovoltaic power generation, effective energy management and control combined with energy storage systems has become a key issue. In the isolated grid operation mode, the active power control of photovoltaic and photovoltaic storage systems is particularly important, which directly affects the stability and reliability of the power grid. Research on the active power control of photovoltaic and photovoltaic storage systems in isolated grids can not only improve the self-sufficiency of the power system and reduce dependence on traditional fossil energy, but also provide strong guarantees for emergency power supply under grid failures and extreme weather conditions. Therefore, it is of great significance to study the active power control of photovoltaic and photovoltaic storage systems in isolated grids.
[0003] At present, the algorithms for solving the active power control strategy of PV-storage isolated grid mainly include dynamic programming algorithms, intelligent algorithms, etc. For dynamic programming algorithms, the computational complexity and memory requirements of dynamic programming algorithms will increase sharply when dealing with larger-scale problems. Intelligent algorithms have strong adaptability and flexibility and can handle various types of objective functions and constraints. This makes them perform well when dealing with different types of PV-storage isolated grid optimization problems. Intelligent algorithms do not rely on the precise mathematical model of the system, but are based on heuristic search and evaluation. This allows them to be applied in complex, uncertain and nonlinear systems without the need for precise system modeling. Both dynamic programming algorithms and intelligent algorithms do not consider the state of SOC when solving the active power control strategy of PV-storage isolated grid, making it difficult to obtain a more accurate control strategy. Summary of the invention
[0004] The technical problem solved by the present application is: how to provide a method for coordinated control of active power of photovoltaic and energy storage isolated grids taking into account the energy storage SOC state range, which can accurately predict the change trend in real time and perform shortage compensation.
[0005] The present application provides a method for coordinated control of active power of a photovoltaic energy storage isolated grid considering the energy storage SOC state interval, the control method comprising:
[0006] Divide the SOC state of the energy storage system into several state intervals;
[0007] Setting a power control coefficient for each state interval, and establishing a power control strategy formula according to the power control coefficient and the photovoltaic system load;
[0008] Constraints are constructed using energy storage systems, isolated grid safe operation and the power control strategy formula to establish a multi-objective active power coordinated control model.
[0009] Solve the active coordinated control model to obtain an optimal solution.
[0010] Optionally, the method of dividing the SOC state of the energy storage system into a plurality of state intervals includes:
[0011] The SOC state of the energy storage system is divided into three state intervals, namely a low SOC interval, a medium SOC interval and a high SOC interval, wherein the SOC value of the low SOC interval is less than a preset lower limit value, the SOC value of the high SOC interval is greater than a preset upper limit value, and the SOC value of the medium SOC interval is greater than the preset lower limit value and less than the preset upper limit value.
[0012] Optionally, the power control strategy formula is expressed as:
[0013]
[0014] Where P control is the controlled power, P PV is the photovoltaic power generation power, P load is the current load power, K low Indicates the power control coefficient in the low SOC range, K medium Indicates the power control coefficient in the medium SOC range, K high represents the power control coefficient in the high SOC range, Indicates the preset lower limit value, Indicates the preset upper limit value.
[0015] Optionally, the constraints constructed with the energy storage system are:
[0016]
[0017] Where: R dis_max is the maximum discharge rate, R ch_max is the maximum charging rate, P dis is the energy storage charging power, P ch is the energy storage discharge power.
[0018] Optionally, the constraints for building in isolated network safe operation are:
[0019] Δf≤Δf max , Δv≤Δv max
[0020] Where Δf max is the maximum frequency allowed, Δv max They are the maximum allowable voltage fluctuations; Δf is the frequency, and Δv is the voltage fluctuation.
[0021] Optionally, the constraint condition constructed by the power control strategy formula is:
[0022] The constraints of the low SOC range are: K low ·(P PV -P load )≤P dis_max , P dis_max is the maximum discharge power of the energy storage system;
[0023] The constraints of the SOC interval are: ch_max ≥K medium ·(P PV -P load )≥P dis_max , P ch_max is the maximum charging power of the energy storage system;
[0024] The constraints of the high SOC interval are: K high ·(P PV -P load )≤P ch_max .
[0025] Optionally, the multi-objective active coordinated control model is expressed as:
[0026] F(x)=min(F1+F2),
[0027] Where F1 is the frequency fluctuation objective function, F2 is the SOC over-limit rate objective function, f(t) is the actual frequency at time t, and f rate is the rated frequency, F1 is the sum of the frequency fluctuations during the entire time period, is the time period that exceeds the upper limit value, This is the time period when the lower limit is crossed.
[0028] Optionally, the method for solving the active coordinated control model is to introduce a particle swarm algorithm on the basis of a standard firefly algorithm to form a firefly-particle swarm hybrid algorithm to solve the active coordinated control model.
[0029] The present application provides a method for coordinated control of active power of a photovoltaic energy storage isolated grid considering the energy storage SOC state interval, which has the following technical effects:
[0030] By considering the SOC state and dividing it, a more detailed control strategy can be implemented, thereby managing the charging and discharging process of the energy storage system more effectively and accurately. Considering the energy storage system and power control coefficient can ensure that the system maximizes the utility of the energy storage system while achieving frequency stability. BRIEF DESCRIPTION OF THE DRAWINGS
[0031] Figure 1A flow chart of the main steps of a method for coordinated control of active power of a photovoltaic energy storage and isolated grid considering energy storage SOC state intervals according to one or more embodiments;
[0032] Figure 2 The present invention is a flowchart of a solution process of a firefly-particle swarm hybrid algorithm according to one or more embodiments. DETAILED DESCRIPTION
[0033] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.
[0034] Before describing the various embodiments of the present application in detail, the technical concept of the present application is first briefly described: At present, the state of SOC is not considered when solving the active control strategy of the photovoltaic storage isolated network, and it is difficult to obtain a more accurate control strategy. For this reason, the present application provides a method for coordinated control of active power of photovoltaic storage isolated network considering the energy storage SOC state interval. By dividing the SOC state of the energy storage system into several state intervals, combining the power control coefficient of each state interval, the energy storage system and the constraints of safe operation of the isolated network, a multi-objective active coordinated control model is established, and finally the optimal solution is obtained by solving the model to achieve active power coordinated control. By considering the SOC state and dividing it, a more detailed control strategy can be implemented, so as to more effectively and accurately manage the charging and discharging process of the energy storage system. Considering the energy storage system and the power control coefficient, it can ensure that the system maximizes the utility of the energy storage system while achieving frequency stability. The specific principles of the method for coordinated control of active power of photovoltaic storage isolated network considering the energy storage SOC state interval of the present application are described below in combination with more embodiments.
[0035] Specifically, Figure 1 As shown, the active power coordination control method of the photovoltaic energy storage isolated grid considering the energy storage SOC state interval of the first embodiment includes:
[0036] Step S10: Divide the SOC state of the energy storage system into several state intervals;
[0037] Step S20: setting a power control coefficient for each state interval, and establishing a power control strategy formula according to the power control coefficient and the photovoltaic system load;
[0038] Step S30: Constraints are constructed using the energy storage system, isolated grid safe operation and the power control strategy formula to establish a multi-objective active power coordinated control model;
[0039] Step S40: solving the active coordinated control model to obtain an optimal solution.
[0040] Specifically, in step S10, a preset upper limit value of the SCO state is introduced. Preset lower limit The energy storage system SOC state interval is divided into three state intervals, specifically:
[0041] Low SOC interval: When the SOC of the energy storage system is at a low level, it means that the energy storage system is insufficient and needs to be replenished in time to avoid damage to the energy storage system caused by deep discharge. The SOC value in the low SOC interval is less than the preset lower limit, that is,
[0042] Medium SOC interval: At this time, the SOC of the energy storage system is in an intermediate range. The energy storage in this state range works in the optimal efficiency range, which is conducive to extending battery life and improving system operating efficiency. The SOC value in the medium SOC interval is greater than the preset lower limit and less than the preset upper limit, that is,
[0043] High SOC interval: When the SOC of the energy storage system is at a high level, the energy storage is close to saturation. At this time, further charging should be avoided to prevent overcharging from damaging the energy storage system. The SOC value in the high SOC interval is greater than the preset upper limit, that is,
[0044] In one or more embodiments, the power control strategy formula of step S20 is expressed as:
[0045] P control =K(SOC)·(P PV -P load ),
[0046] Where P control is the controlled power, P PV is the photovoltaic power generation power, P load is the current load power, K low Indicates the power control coefficient in the low SOC range, K medium Indicates the power control coefficient in the medium SOC range, K high represents the power control coefficient in the high SOC range, Indicates the preset lower limit value, Indicates the preset upper limit value.
[0047] In one or more embodiments, in step S30, in order to limit the change of battery charge and discharge rate and avoid excessive fatigue and loss of the battery, the constraint conditions constructed by the energy storage system are:
[0048]
[0049] In the formula, R dis_max is the maximum discharge rate, R ch_max is the maximum charging rate, Pdis is the energy storage charging power, P ch is the energy storage discharge power.
[0050] For example, to ensure stable operation of the system in isolated grid mode and avoid drastic fluctuations in frequency and voltage, the constraints for safe operation of isolated grid are:
[0051] Δf≤Δf max , Δv≤Δv max
[0052] Where Δf max is the maximum frequency allowed, Δv max They are the maximum allowable voltage fluctuations; Δf is the frequency, and Δv is the voltage fluctuation.
[0053] Exemplarily, the constraints constructed by the power control strategy formula are as follows:
[0054] In the low SOC range, the power control coefficient K low Small, mainly limiting the discharge power of energy storage, photovoltaic power generation is used for load first, and the rest is used for charging. Therefore, the constraint condition of the low SOC interval is: K low ·(P PV -P load )≤P dis_max , P dis_max is the maximum discharge power of the energy storage system.
[0055] In the middle SOC range, the power control coefficient K medium Medium, energy storage can be charged and discharged flexibly, and energy storage can be effectively charged and discharged while ensuring load demand. Therefore, the constraints of the medium SOC interval are: P ch_max ≥K medium ·(P PV -P load )≥P dis_max , P ch_max is the maximum charging power of the energy storage system.
[0056] In the high SOC range, the power control coefficient K high The main limitation is that the battery charging power is limited, photovoltaic power generation is used for load first, energy storage charging is reduced or stopped, and the excess photovoltaic power is consumed or limited by other means. Therefore, the constraint condition of the high SOC interval is: K high ·(P PV -P load )≤P ch_max .
[0057] In one or more embodiments, the multi-objective active coordinated control model in step S30 is expressed as:
[0058] F(x)=min(F1+F2),
[0059] Where F1 is the frequency fluctuation objective function, F2 is the SOC over-limit rate objective function, f(t) is the actual frequency at time t, and f rate is the rated frequency, F1 is the sum of the frequency fluctuations during the entire time period, is the time period that exceeds the upper limit value, This is the time period when the lower limit is crossed.
[0060] In one or more embodiments, in step S40, the method for solving the active power coordinated control model is to introduce a particle swarm algorithm based on the standard firefly algorithm to form a firefly-particle swarm hybrid algorithm to solve the active power coordinated control model. Figure 2 As shown in Figure 2, the solution process of the firefly-particle swarm hybrid algorithm includes the following steps:
[0061] Step S41: Initialize parameters. Population size n is used to represent the number of fireflies and particles; the maximum number of iterations of the algorithm L; light intensity attraction coefficient β0; absorption coefficient γ; step coefficient α; particle swarm inertia weight ω; cognitive coefficient c1 and social coefficient c2; randomly generate the initial population position x i and initial trial speed v1; initialize the individual optimal position p i and the global optimal position g;
[0062] Step S42: Calculate the light intensity. Calculate the position x of each individual i The light intensity I i For the objective function minimization problem in this embodiment, the light intensity is defined as
[0063]
[0064] Step S43: Randomly select FA algorithm or PSO algorithm to update. For each individual i, generate a random number r in the interval [0,1]. If r < 0.5, select the FA update rule; otherwise, select the PSO update rule.
[0065] Step S44: Update position and speed
[0066] Select FA algorithm:
[0067] For each pair of fireflies i and j (assuming I j >I i , that is, firefly j is brighter), perform the following steps:
[0068] Step S44.1: Calculate the distance between fireflies i and j:
[0069] r ij =x i -x j
[0070] Step S44.2: Calculate the attraction between fireflies i and j:
[0071]
[0072] Step S44.3: Update the position of the firefly:
[0073] x i =x i +β ij (x j -x i )+α∈ i
[0074] Where:∈ i is a random number that follows a standard normal distribution;
[0075] Select POS algorithm:
[0076] Update the particle's velocity and position:
[0077] v i =wv i +c1r1(p i -x i )+c2r2(gx i )
[0078] x i =x i +v i
[0079] Where: r1 and r2 are random numbers in the interval [0,1].
[0080] Step S45: update the individual best position and the global best position.
[0081] For each particle i, if the objective function value of the current particle is better than its individual best value, the individual best position is updated:
[0082] p i =x i F(x i )<F(p i )
[0083] If the objective function value of the current particle is better than the global optimal value, the global optimal position is updated:
[0084] g = x i F(x i )<F(g)
[0085] Step S46: Check and adjust boundary conditions.
[0086] Ensure that the updated positions of fireflies and particles are within the search space. If not, adjust them to the boundary.
[0087] Step S46, repeat the above steps until the stop condition is reached.
[0088] Step S47: output the optimal solution.
[0089] The active power coordinated control method of the photovoltaic energy storage isolated grid considering the energy storage SOC state interval provided in this embodiment divides the SOC state into several state spaces (for example, low SOC, medium SOC and high SOC) to achieve a more detailed control strategy, thereby more effectively managing the charging and discharging process of the energy storage system. Considering the energy storage system and the power control coefficient can ensure that the system maximizes the utility of the energy storage system while achieving frequency stability. By minimizing the SOC state index, the balanced use of the energy storage system can be achieved, the system life can be extended and the maintenance cost can be reduced. The firefly algorithm has good global search capabilities, while the particle swarm algorithm has good local search performance, and the hybrid algorithm can more effectively avoid falling into local optimality. Through this optimization method, the optimal solution of the active power coordinated control model can be found more quickly and accurately, improving the overall performance of the system.
[0090] The specific implementation methods of the present application are described in detail above. Although some embodiments have been shown and described, those skilled in the art should understand that these embodiments can be modified and improved without departing from the principles and spirit of the present application whose scope is defined by the claims and their equivalents. These modifications and improvements should also be within the scope of protection of the present application.
Claims
1. A method for coordinated control of active power of photovoltaic energy storage and isolated grid considering energy storage SOC state interval, characterized in that: The control method comprises: Divide the SOC state of the energy storage system into several state intervals; Setting a power control coefficient for each state interval, and establishing a power control strategy formula according to the power control coefficient and the photovoltaic system load; Constraints are constructed using energy storage systems, isolated grid safe operation and the power control strategy formula to establish a multi-objective active power coordinated control model. Solve the active coordinated control model to obtain an optimal solution.
2. The method for coordinated control of active power of photovoltaic energy storage and isolated grid considering energy storage SOC state interval according to claim 1 is characterized in that: The method for dividing the SOC state of the energy storage system into a plurality of state intervals includes: The SOC state of the energy storage system is divided into three state intervals, namely a low SOC interval, a medium SOC interval and a high SOC interval, wherein the SOC value of the low SOC interval is less than a preset lower limit value, the SOC value of the high SOC interval is greater than a preset upper limit value, and the SOC value of the medium SOC interval is greater than the preset lower limit value and less than the preset upper limit value.
3. The method for coordinated control of active power of photovoltaic and energy storage isolated grid considering energy storage SOC state interval according to claim 2 is characterized in that: The power control strategy formula is expressed as: P control =K(SOC)·(P PV -P load ), Where P control is the controlled power, P PV is the photovoltaic power generation power, P load is the current load power, K low Indicates the power control coefficient in the low SOC range, K medium Indicates the power control coefficient in the medium SOC range, K high represents the power control coefficient in the high SOC range, Indicates the preset lower limit value, Indicates the preset upper limit value.
4. The method for coordinated control of active power of photovoltaic and energy storage isolated grid considering energy storage SOC state interval according to claim 3 is characterized in that: The constraints for building an energy storage system are: Where: R dis_max is the maximum discharge rate, R ch_max is the maximum charging rate, P dis is the energy storage charging power, P ch is the energy storage discharge power.
5. The method for coordinated control of active power of photovoltaic and energy storage isolated grid considering energy storage SOC state interval according to claim 1 is characterized in that: The constraints for safe operation of isolated grids are: Δf≤Δf max 、Δv≤Δv max Where Δf max is the maximum frequency allowed, Δv max They are the maximum allowable voltage fluctuations; Δf is the frequency, and Δv is the voltage fluctuation.
6. The method for coordinated control of active power of photovoltaic and storage isolated grid considering energy storage SOC state interval according to claim 3 is characterized in that: The constraints constructed with the power control strategy formula are: The constraints of the low SOC range are: K low ·(P PV -P load )≤P dis_max , Pdis_max is the maximum discharge power of the energy storage system; The constraints of the SOC interval are: ch_max ≥K medium ·(P PV -P load )≥P dis_max , Pch_max is the maximum charging power of the energy storage system; The constraints of the high SOC interval are: K high ·(P PV -P load )≤P ch_max .
7. The method for coordinated control of active power of photovoltaic energy storage and isolated grid considering energy storage SOC state interval according to claim 1 is characterized in that: The multi-objective active coordinated control model is expressed as: F(x)=min(F1+F2), Where F1 is the frequency fluctuation objective function, F2 is the SOC over-limit rate objective function, f(t) is the actual frequency at time t, and f rate is the rated frequency, F1 is the sum of the frequency fluctuations during the entire time period, is the time period that exceeds the upper limit value, This is the time period when the lower limit is crossed.
8. The method for coordinated control of active power of photovoltaic and energy storage isolated grid considering energy storage SOC state interval according to claim 1 is characterized in that: The method for solving the active coordinated control model is to introduce a particle swarm algorithm on the basis of a standard firefly algorithm to form a firefly-particle swarm hybrid algorithm to solve the active coordinated control model.