CN117289605A - Fuzzy logic energy storage system control method - Google Patents
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Abstract
The invention relates to the technical field of energy storage control, in particular to a fuzzy logic energy storage system control method. The method comprises the following steps: step 1: defining a state variable of the energy storage system; step 2: converting state variables composed of quantized data into fuzzy sets by using a fuzzification function; step 3: defining a fuzzy logic rule set; step 4: performing Monte Carlo simulation to obtain a Monte Carlo simulation result, and representing the predicted state of the energy storage system; step 5: combining the fuzzy logic rule set and the Monte Carlo simulation result to generate a decision output result; step 6: performing risk assessment by using a risk assessment function; step 7: searching an optimal control strategy based on a risk assessment result; step 8: performing system control based on an optimal control strategy; step 9: updating the fuzzy logic rule set and the Monte Carlo super-parameter set. The invention can improve the energy storage efficiency, optimize the system control strategy, enhance the system adaptability and reduce the operation cost.
Description
Technical Field
The invention belongs to the technical field of energy storage control, and particularly relates to a fuzzy logic energy storage system control method.
Background
With the increasing demand for energy and the interest in renewable energy sources, the importance of energy storage systems in the power industry is increasing. The energy storage system is capable of efficiently storing electrical energy and releasing it when needed to balance the power network, improve the reliability of the power system, and support large-scale integration of renewable energy sources. However, the design and control of energy storage systems face a number of challenges, including energy storage efficiency, operational stability, and adaptability to uncertainty.
An electrical energy storage system is a critical energy technology that can convert electrical energy into other forms and then back when needed. Such techniques have a variety of applications in the power industry, including energy storage, power load balancing, backup power and power quality improvement. In recent years, with the large-scale integration of renewable energy sources (such as solar and wind), the demand and importance of energy storage systems has increased further.
The energy storage system can effectively solve the problems of intermittence and unpredictability of renewable energy sources. For example, wind power generation and solar power generation are affected by weather conditions, and the power yield may fluctuate. The energy storage system may store excess electrical energy and then release it when needed to balance the power network. This helps to ensure stability and reliability of the power supply, reducing the risk of power interruption. In addition, the energy storage system can also improve the efficiency of the power system. Conventional power systems typically generate additional electrical energy during peak hours and then waste during low peak hours. By the energy storage system, redundant electric energy can be stored during low peaks and then supplied during high peaks, so that the efficiency of the power system is improved, and the resource waste is reduced.
The control strategy of the energy storage system needs to be adjusted according to different application scenes and system requirements. This includes determining when to charge, when to discharge, and how to respond to external power demand changes. Developing an effective control strategy is a complex task that often requires consideration of various factors such as electricity prices, electrical loads, availability of renewable energy sources, and the like. The energy storage system needs to be able to accommodate the uncertainty. Both the power demand and the production of renewable energy sources may be affected by weather and other external factors. Thus, the energy storage system needs to have flexibility to adjust under different conditions.
Disclosure of Invention
The invention mainly aims to provide a fuzzy logic energy storage system control method which can improve energy storage efficiency, optimize a system control strategy, enhance system adaptability and reduce operation cost.
In order to solve the problems, the technical scheme of the invention is realized as follows:
a method of fuzzy logic energy storage system control, the method comprising:
step 1: defining a state variable of the energy storage system; the state variable describing a current state of the energy storage system; the state variable comprises a plurality of quantized data;
step 2: converting state variables composed of quantized data into fuzzy sets by using a fuzzification function;
Step 3: defining a fuzzy logic rule set;
step 4: taking the state variable as an initial condition, adding random disturbance, taking the fuzzy logic rule set as one element in the super-parameter set, setting values of other elements in the super-parameter set, and performing Monte Carlo simulation to obtain a Monte Carlo simulation result, wherein the predicted energy storage system state is represented;
step 5: combining the fuzzy logic rule set and the Monte Carlo simulation result to generate a decision output result;
step 6: based on the decision output result, performing risk assessment by using a risk assessment function to obtain a risk assessment result;
step 7: searching an optimal control strategy based on a risk assessment result;
step 8: based on the optimal control strategy, performing system control, and updating state variables of the energy storage system;
step 9: based on the state variables of the new energy storage system, the fuzzy logic rule set and the Monte Carlo super-parameter set are updated.
Further, the quantized data in the state variables at least includes: energy E, load L, input power P in Output power P out System voltage V and temperature T; the defined state variables are: s=f (E, L, P in ,P out ,T,V)。
Further, in step 2, the following formula is used to convert the state variable composed of quantized data into a fuzzy set:
Wherein f fuzzy As fuzzification functions, E, L, P in ,P out T, V are input variables of the blurring function; mu (mu) E (E),μ L (L),…,μ V (V) each being a membership function, each membership function assigning a fuzzy membership value from 0 to 1 to each specific input variable value; sigma (sigma) E ,σ L ,…,σ V Standard deviations of the corresponding input variables are respectively adopted; f (S) is a fuzzy set, and the fuzzy set comprises a plurality of fuzzy sets.
Further, in step 3, the fuzzy logic rule set is defined using the following formula:
wherein R is a fuzzy logic rule set; g (F (S)) is a mapping function, which represents the input state F (S) based on blurring, and a fuzzy logic rule set R is constructed;representing union operations, i.e. all fuzzy rules r i Is combined withInto the fuzzy logic rule set R; n is the number of fuzzy rules; r is (r) i Is the ith fuzzy rule; each fuzzy rule is an IF-THEN statement, and defines a corresponding output result under a specific input condition; f (S) →D i Representing fuzzy rule r i Based on the input state F (S) a decision output D is obtained i ；IF x i is A i THEN y i is B i X in (2) i And y i Is a fuzzy variable; a is that i And B i Is a fuzzy set corresponding to the front piece and the back piece respectively; if x i Conform to fuzzy set A i Definition of (1), then y i Belonging to fuzzy set B i 。
Further, the Monte Carlo simulation in step 4 is performed using the following formula:
wherein N is Monte Carlo simulation times; m (N, S) is a prediction result obtained by Monte Carlo simulation and represents a predicted energy storage system state obtained by N simulation iterations under the condition of a given state variable S; zeta type toy i Random disturbance added in the ith simulation; Θ is a hyper-parameter set comprising: fuzzy logic rule set, time step, total time, energy storage system charging efficiency and energy storage system discharging efficiency.
Further, in step 5, the fuzzy logic rule and the Monte Carlo simulation result are combined by using the following formula to generate a decision output result:
d is a decision output result obtained after post-blurring processing; k is a function combining the fuzzy logic rule set R and the Monte Carlo simulation result M (N, S) into D; omega is a post-blurring processing parameter set; m is M j (N, S) is one result produced for each monte carlo simulation;representing the intersection of all decision outputs, m being the number of decision outputs; />Is a union operator; />Representing a composite set of results produced by all monte carlo simulations.
Further, in step 6, the risk assessment result is obtained by calculating using the following formula:
Wherein, risk is a Risk assessment result; ρ is a risk density function; Γ is a risk preference parameter; u is a decision space variable; d is a differential operator.
Further, in step 7, the following formula is used, and based on the risk assessment result, an optimal control strategy is found;
wherein lambda is 1 Is a first weight parameter; lambda (lambda) 2 Is a second weight parameter; c (D) is a decision cost function;is a viable decision space; d (D) opt Is the optimal control strategy.
Further, in step 8, the following formula is used, based on the optimal control strategy, the system control is performed, and the state variable of the energy storage system is updated:
wherein S is new The state variable is the updated state variable of the energy storage system; psi phi type 1 Is a first control parameter; psi phi type 2 Is a first control parameter; Δt is the step of the time-in-time,is the system state gradient of the state variable relative to the decision output result.
The control method of the fuzzy logic energy storage system has the following beneficial effects: during operation of the energy storage system, it is inevitable to face uncertainties and variations. For example, the production of renewable energy sources is affected by weather conditions and power demand may change over time. The fuzzy logic energy storage system control method converts the state variable into the fuzzy set, so that the system has stronger adaptability. The fuzzy logic rule set can adjust the control strategy according to the real-time observation data, so that different operation conditions can be well adapted. This means that the energy storage system can continue to operate efficiently in an uncertainty environment, whether in extreme weather conditions or in situations where the power demand fluctuates severely. Another important advantage of the present invention is optimizing the system control strategy. The control strategy of the energy storage system needs to take into account a number of factors, such as electrical load, electrical price, availability of renewable energy sources, etc. Conventional control methods are generally relatively rigid and difficult to adapt to different operating conditions. While the fuzzy logic energy storage system control method allows the system to make intelligent decisions in an uncertainty environment. The charging and discharging strategy can be dynamically adjusted according to actual conditions, so that renewable energy sources are utilized to the greatest extent, and the energy source purchasing cost is minimized. The flexibility and the adaptivity enable the energy storage system to be more intelligent, and the requirements of the electric power market can be better met.
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Fig. 1 is a flow chart of a control method of a fuzzy logic energy storage system according to an embodiment of the present invention.
Detailed Description
The control method of the fuzzy logic energy storage system is provided, and the operation strategy of the energy storage system is optimized by converting state variables into fuzzy sets and applying fuzzy logic rule sets and Monte Carlo simulation. The invention can improve the energy storage efficiency, optimize the system control strategy, enhance the system adaptability and reduce the operation cost.
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
The following will describe in detail.
The terms "first," "second," "third," "fourth" and the like in the description and in the claims and in the above drawings, if any, are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments described herein may be implemented in other sequences than those illustrated or otherwise described herein. .
Example 1: referring to fig. 1, a fuzzy logic energy storage system control method, the method comprising:
step 1: defining a state variable of the energy storage system; the state variable describing a current state of the energy storage system; the state variable comprises a plurality of quantized data;
the state variable is a key indicator describing the current condition of the energy storage system. These variables not only reflect the instantaneous state of the system, but may also affect the future performance and stability of the system. Thus, proper selection of the state variables is critical to subsequent control activities. Typically, these variables are based on the operating principles of the system, design parameters, and external conditions that may affect system performance and safety. All selected state variables must be capable of being quantified, i.e. they can be measured or calculated and expressed in numerical form. This is because the fuzzy logic control system requires numerical input to make the blurring process and subsequent control decisions. The quantized data may be obtained from sensors (e.g., temperature sensors, current sensors, etc.), system monitoring devices, or calculated by specific algorithms and models.
The state variables of the energy storage system may change over time, external environmental conditions, system loads, and the like. Thus, in defining state variables, it is desirable to take into account their dynamics and ensure that the control system is able to obtain updated values of the state variables in real time or periodically. In some cases, the directly acquired state variable data may require further processing for fuzzy logic control. Such as data filtering (to remove noise), normalization (to adjust the data to within a standard range), or feature engineering (e.g., extracting more meaningful information for control from the raw data).
Step 2: converting state variables composed of quantized data into fuzzy sets by using a fuzzification function;
fuzzification is a core concept in fuzzy logic that involves converting conventional, accurate input data (usually real or integer numbers) into members of a fuzzy set that can be understood and operated on by a fuzzy logic system. The main purpose of the obfuscation process is to enable the computer system to understand and handle the human obfuscation concepts, such as "high temperature", "medium voltage" or "low battery". Membership functions play a key role in the blurring process. This is a function of mapping each specific input value to a value between 0 and 1, which represents the degree to which the input value belongs to a certain fuzzy set.
For example, for a state variable of "temperature", there may be a membership function that maps a specific temperature value into a fuzzy set of "high temperature". If a "high temperature" is defined as exceeding 30 ℃, temperatures below 30 ℃ may have lower membership values, while temperatures exceeding 30 ℃ have higher membership values. Membership functions may take different forms, including triangular, trapezoidal, bell-shaped, gaussian, etc. Which form is selected depends on the requirements of the particular application and the nature of the state variables. The membership functions are designed to take into account how to most accurately reflect the relationship between the input values and the fuzzy concepts. For example, if the change in state variable is very sensitive to the operation of the system, it may be desirable for one membership function to be able to divide the different membership intervals more finely. In a fuzzy logic system, it is necessary to define a set of fuzzy sets, each set corresponding to a fuzzy concept such as "high", "medium" and "low".
Each fuzzy set is defined by its membership function that describes how specific input data is mapped to the fuzzy set. In the example of an energy storage system, possible fuzzy sets include "high voltage", "medium voltage" and "low voltage". Once the membership functions and fuzzy sets are defined, the fuzzification process may begin. For each state variable of the system, its membership in each fuzzy set is calculated using its corresponding membership function. Eventually, each state variable will be represented as a set of fuzzy sets of membership values that will be used for subsequent fuzzy logic reasoning and decision making.
Step 3: defining a fuzzy logic rule set;
the fuzzy logic rules generally follow the format of "IF-THEN" (IF-THEN). In the IF section, a rule defines a set of input conditions; in the "THEN" (THEN) section, rules define control actions to be taken when the conditions of the "if" section are satisfied. For example, a simple fuzzy logic rule might be: "if (battery voltage is low) and (load is high)," then (charge rate is increased) ". A rule set is a set of multiple fuzzy logic rules. Each rule corresponds to a particular situation that the energy storage system may encounter and defines an appropriate control response for that situation.
Constructing rule sets requires a system designer or domain expert to have an in-depth knowledge of the behavior and response of the energy storage system. Typically, these rules are based on experience, system analysis, simulation studies, and/or historical performance data. The fuzzy logic rules require quantifying the relationship between the input conditions and the control output. This means that the rule needs to specify not only which input conditions trigger the rule, but how these conditions affect the control decision. For example, in "if the battery voltage is very low, the charge rate is greatly increased", the relationship between the "very low" voltage and the "greatly increased" charge rate is regularly quantified. The conditions in the rules typically involve evaluation of fuzzy set members, which requires the use of membership functions. Further, when the rule contains a plurality of conditions (for example, "if the battery voltage is low AND the load is high"), it is necessary to evaluate the authenticity of the entire condition using a fuzzy logic operation (such as fuzzy AND, OR, etc.). Fuzzy arithmetic allows the system to process information in a non-binary (non-black-and-white) manner, thereby enabling uncertainty and ambiguity to be handled.
Step 4: taking the state variable as an initial condition, adding random disturbance, taking the fuzzy logic rule set as one element in the super-parameter set, setting values of other elements in the super-parameter set, and performing Monte Carlo simulation to obtain a Monte Carlo simulation result, wherein the predicted energy storage system state is represented;
Monte Carlo simulation is a computational algorithm that relies on repeated random sampling to obtain numerical results, typically used to evaluate and understand the effects of uncertainty and complexity in statistical models. This approach is particularly useful for energy storage systems because these systems are often subject to uncertainty from multiple sources. The state variable is a parameter describing the current state of the energy storage system. In this step, the initial state of the system is known and is based on the state variables defined in step 1. Random perturbations are randomness introduced in the simulation to simulate real world uncertainties and contingencies. For example, the future output of the battery may fluctuate randomly due to temperature, service life, or other unpredictable factors.
In Monte Carlo simulation, the fuzzy logic rule sets are considered hyper-parameters, meaning that they are high-level settings that control the behavior of the simulation. The hyper-parameters may also include the time frame of the simulation, the type of uncertainty to be considered, the specific shape of the membership functions, etc. At the start of the simulation, the initial conditions of the system state variables are known. The simulation is then repeated multiple times, each time with random changes in state variables (based on defined random perturbations), and the effect of these changes on the system state is recorded.
Each simulation iteration generates a set of possible future system states. After repeated a sufficient number of iterations, a probability distribution of possible states of the system can be obtained. After the simulation is completed, a large amount of data about the possible states of the energy storage system is obtained. These data will typically be used to create probability distributions describing the likelihood of various different outcomes.
These probability distributions may then be used to guide decision making. The data provided by the Monte Carlo simulation needs to be combined with a fuzzy logic rule set to formulate a control strategy. This is because the fuzzy logic rule sets provide a framework for interpreting the simulation data and making decisions based thereon. In combination with the Monte Carlo simulation results and the fuzzy logic rule set, the system generates a decision output, which is typically a specific guide as to how the operation of the energy storage system should be adjusted. This output takes into account the uncertainty and takes advantage of the ability of the fuzzy logic to handle the inaccuracy, thereby providing a robust decision method that accommodates uncertain environmental changes. Note that monte carlo simulation is not a one-time process. Over time, and with new input data received by the system, the simulation will need to be re-performed to reflect the latest system state and information. Similarly, the fuzzy logic rule sets may also need to be updated and adjusted based on new operational data and experience.
Step 5: combining the fuzzy logic rule set and the Monte Carlo simulation result to generate a decision output result;
step 5 first requires interpretation of the output of the monte carlo simulation. This is typically manifested as a series of possible future states and probabilities associated therewith. These states and probabilities constitute predictive models for future performance of the system, including possible energy costs, storage levels, demand changes, etc. It is important to understand that these results are not deterministic, but rather express the possibility and risk of various results. A fuzzy logic rule set is a predefined set of rules that describe actions that should be taken under certain conditions. These rules are based on state variables of the energy storage system and use the form of fuzzy sets to handle uncertainty and ambiguity. These rules guide how decisions are made based on simulation results according to the principles of fuzzy logic (e.g., if-then rules). For example, "if the remaining charge of the battery is low and the data from the monte carlo simulation shows a high probability of an increase in grid demand within the next few hours, then the energy storage is increased.
Step 6: based on the decision output result, performing risk assessment by using a risk assessment function to obtain a risk assessment result;
Step 7: searching an optimal control strategy based on a risk assessment result;
step 8: based on the optimal control strategy, performing system control, and updating state variables of the energy storage system;
step 9: based on the state variables of the new energy storage system, the fuzzy logic rule set and the Monte Carlo super-parameter set are updated.
In step 9, first, new energy storage system state variable data needs to be collected, which will reflect the performance and behavior of the system under the latest control strategy. The performance of the system is evaluated using the new state variable data. This may include comparing actual performance to desired performance, taking into account metrics in terms of safety and efficiency of the system, etc. The actual performance is compared with the expected performance, and a performance gap is identified. Determine which aspects need improvement, and how. If some rules in the fuzzy logic rule set are found to be no longer applicable or require improvement, the rules may be updated based on the results of the performance analysis. This may involve adding new rules, modifying existing rules, or deleting rules that are no longer valid. The Monte Carlo super-parameter set may include parameter values of random perturbations used in the simulation as well as other simulation parameters. Based on the results of the performance evaluation, the parameter values are adjusted to better reflect the behavior of the system. This may include increasing the number of simulations, altering the amplitude or frequency of the disturbance, etc. After updating the fuzzy logic rule set and the Monte Carlo super parameter set, monte Carlo simulation needs to be performed again. This simulation will be based on updated rules and parameters in order to evaluate the performance of the new control strategy. The new control strategies are verified and validated, ensuring that they perform well in practical energy storage systems. This may require actual testing or monitoring during actual operation. This process is typically a loop iterative process. Step 9 may be repeated periodically with the operation of the system and the continuous accumulation of data, continuously optimizing the control strategy of the energy storage system to accommodate the continuously changing conditions and demands.
Specifically, the system combines the results of the Monte Carlo simulation with a fuzzy logic rule set. This is not just a simple data superposition process, but a complex analysis and synthesis process. The system must consider a number of possible future states and use a set of fuzzy logic rules to determine the best action in each case. This means that the system may need to trade-off between different rules and potential results to determine the optimal, least risky course of action. After analysis and synthesis, the system generates a decision output, which is an explicit guideline of action that instructs the system how to operate to minimize risk and optimize performance. This output may be very specific including how to adjust the energy storage level, when to buy or sell energy, how to adjust to account for anticipated demand changes, etc.
Example 2: on the basis of the above embodiment, the quantized data in the state variables at least includes: energy E, load L, input power P in Output power P out System voltage V and temperature T; the defined state variables are: s=f (E, L, P in ,P out ,T,V)。
Example 3: based on the above embodiment, the following formula is used in step 2 to convert the state variable composed of quantized data into a fuzzy set:
Wherein f fuzzy As fuzzification functions, E, L, P in ,P out T, V are input variables of the blurring function; mu (mu) E (E),μ L (L),…,μ V (V) Each membership function is used for distributing a fuzzy membership value from 0 to 1 to each specific input variable value; sigma (sigma) E ,σ L ,…,σ V Standard deviations of the corresponding input variables are respectively adopted; f (S) is a fuzzy set, and the fuzzy set comprises a plurality of fuzzy sets.
Specifically, f fuzzy Is a blurring function whose function is to convert the actual measured value into blurred values that describe the degree to which the input data belongs to each of the blurred sets (e.g. "low", "medium", "high"). E, L, P in ,P out T, V are input variables of the blurring function, i.e. actual parameter values obtained from the system, which represent energy, load, input power, output power, temperature and system voltage, respectively. Mu (mu) E (E),μ L (L),…,μ V (V) are membership functions, each mapping an actual input variable value to a value between 0 and 1, indicating the degree to which the input corresponds to a particular fuzzy set. For example, if μ E (E) =0.8, which may mean that the current energy level "high" of the system has a membership of 80%. Sigma (sigma) E ,σ L ,…,σ V Is the standard deviation of the input variables, and is used for normalizing the output of the membership function so that the membership function is not affected by the dimension and the distribution range of the original data. The standard deviation is an index for measuring the dispersion degree of a group of data, and is used for adjusting the membership value, so as to ensure the robustness of the blurring process. F (S) is the final fuzzy set consisting of fuzzy values each processed and normalized (divided by standard deviation) by membership function. This fuzzy set provides input for subsequent fuzzy logic reasoning.
Example 4: on the basis of the above embodiment, the fuzzy logic rule set is defined in step 3 using the following formula:
wherein R is a fuzzy logic rule set; g (F (S)) is a mapping function, which represents the input state F (S) based on fuzzification, and a fuzzy logic rule is constructedA set R;representing union operations, i.e. all fuzzy rules r i Is incorporated into the fuzzy logic rule set R; n is the number of fuzzy rules; r is (r) i Is the ith fuzzy rule; each fuzzy rule is an IF-THEN statement, and defines a corresponding output result under a specific input condition; f (S) →D i Representing fuzzy rule r i Based on the input state F (S) a decision output D is obtained i ；IF x i is A i THEN y i is B i X in (2) i And y i Is a fuzzy variable; a is that i And B i Is a fuzzy set corresponding to the front piece and the back piece respectively; if x i Conform to fuzzy set A i Definition of (1), then y i Belonging to fuzzy set B i 。
In particular, the purpose of this fuzzy logic rule set is to match the fuzzy result of the input state variable with a set of fuzzy rules to determine the appropriate decision output. Each fuzzy rule is based on a front piece condition and a back piece condition, and an output result is deduced according to the ambiguity of the input state. The fuzzy logic control method can be used in an energy storage system to automatically adjust a control strategy according to the system state so as to achieve various targets, such as improving performance, prolonging the service life of a battery and the like. F (S): this is a blurred representation of the system state S. When you have a system state S (which may include various types of inputs such as temperature, pressure, etc.), F (S) is the process of converting these specific state values into fuzzy sets or fuzzy values. For example, a particular temperature value (e.g., 22.5 degrees) may be converted to a "warm" fuzzy set member. A is that i : these are fuzzy sets involved in the fuzzy rule front-part. They represent the input variable x i Such as "low", "medium" or "high", etc. These are not derived directly from F (S), but rather are based on fuzzy sets set by the system designer to explain how the input values should be understood and classified. For example, there may be an ambiguity set "A1" representing "low power" defining what power level is being usedConsidered "low". B (B) i : these are fuzzy sets involved in the fuzzy rule middleware. They represent the output variable y i Is a control behavior derived based on fuzzy logic reasoning of rule widgets and systems. For example, if the power is low (satisfying a certain a i ) A corresponding B i May be a "high charge rate".
Example 5: on the basis of the above embodiment, the monte carlo simulation in step 4 is performed using the following formula:
wherein N is Monte Carlo simulation times; m (N, S) is a prediction result obtained by Monte Carlo simulation and represents a predicted energy storage system state obtained by N simulation iterations under the condition of a given state variable S; zeta type toy i Random disturbance added in the ith simulation; Θ is a hyper-parameter set comprising: fuzzy logic rule set, time step, total time, energy storage system charging efficiency and energy storage system discharging efficiency.
Specifically, the initialization parameters: the total number of monte carlo simulations N is set. The hyper-parameter set Θ is determined, which may include a fuzzy logic rule set, a simulated time step, a simulated total time, a charging and discharging efficiency of the energy storage system, and the like. Acquiring current state variables S including current energy E, load L and input power P in Output power P out System voltage V, temperature T, etc.
Simulation was performed: for each simulation i (from 1 to N): generating random disturbance ζ i . This may be a random number from a particular probability distribution (e.g., normal distribution, uniform distribution, etc.) used to simulate randomness and uncertainty in the real world. Based on the current state S, random disturbance ζ using function h i And the super-parameter set theta calculates a new state of the energy storage system. The function h may be a physical model, a mathematical model, or a data-based predictive model for predicting the response of the system under the current conditions. Recording a single timeResults of the simulation.
Analysis results: once all N simulations were completed, the average of all simulation results was calculated. This average valueRepresenting the average energy storage system state predicted in all simulations.
Making a decision: based on the simulation results M (N, S), in combination with risk assessment and other decision criteria, an optimal control strategy or operation plan for the energy storage system is determined. For example, if the simulation results show that a certain state has a high probability of causing energy loss or system failure, a decision maker may take measures to avoid such a state.
In this expression, ζ i,in 、ξ i,out And xi i,L Is a random disturbance that affects the input power, output power, and load, respectively. This function calculates the expected variation of the system energy E in the time step Δt, taking into account the random disturbance and the charge-discharge efficiency. Time step delta t and energy charging efficiency eta of energy storage system charge， Energy release efficiency eta of energy storage system discharge Energy E, load L, input power P in Output power P out The system voltage V and the temperature T are parameters thereof.
Example 6: based on the above embodiment, in step 5, the fuzzy logic rule and the monte carlo simulation result are combined to generate a decision output result by using the following formula:
d is a decision output result obtained after post-blurring processing; k is a function combining the fuzzy logic rule set R and the Monte Carlo simulation result M (N, S) into D; omega is a post-blurring processing parameter set; m is M j (N, S) is eachOne result of the sub-monte carlo simulation;representing the intersection of all decision outputs, m being the number of decision outputs; />Is a union operator; />Representing a composite set of results produced by all monte carlo simulations.
Specifically, D: this is the final decision output result. It represents the final decision of the energy storage system control method, and the result is obtained by combining the fuzzy logic rule set R and the Monte Carlo simulation result M (N, S) and performing post-blurring treatment. D reflects the control strategy or action that the system should take. k (R, M (N, S), Ω): this is a function that accepts three input parameters: a fuzzy logic rule set R, a Monte Carlo simulation result M (N, S), and a post-fuzzification processing parameter set omega. The task of this function is to combine these inputs to produce the final decision output D. The specific functional form may be defined according to the application and requirements. Omega: this is a set of parameters for the post-blurring process, including parameters and rules for controlling the subsequent processing of the blurred output results. Post-blurring is the process of converting the result of the blurring output into a clear decision output, and these parameters can adjust the method and rules of post-blurring. M is M j (N, S): this represents one result of each run of the Monte Carlo simulation, where j represents the sequence number of the simulation. In N simulations, N different results will be obtained, each reflecting a different evolution path of the system for a given state variable S.This is the intersection of all decision outputs, where m represents the number of decision outputs. The intersection operation will take into account common features between the multiple decision outputs. This can be used to ensure a final blockThe policy output is consistent in several respects. />This is a union operator that is used to merge together the different decision output results. The union operation considers a number of possible decision paths to comprehensively consider different decision choices. And combining the rules in the fuzzy logic rule set R with the Monte Carlo simulation result M (N, S), and generating a final decision output D through post-blurring processing. This process takes into account information from different sources, including rules based on fuzzy logic and data obtained through multiple Monte Carlo simulations. The post-fuzzification parameter Ω may control the method of post-fuzzification to ensure that the generated decision output meets the needs and goals of the system. The final decision output D may be used for control and optimization of the energy storage system to meet specific control objectives and constraints.
Example 7: based on the above embodiment, the risk assessment result is calculated in step 6 using the following formula:
wherein, risk is a Risk assessment result; ρ is a risk density function; Γ is a risk preference parameter; u is a decision space variable; d is a differential operator.
The risk assessment is calculated by multiplying the risk density function ρ (u; Γ) with the difference u-D of the decision space variable u and the final decision D and integrating the whole decision space. This process aims to quantify the risk level at different decisions to help the decision maker select the decision with lower risk. The selection of the risk preference parameter Γ may affect the outcome of the risk assessment. Different Γ values may reflect the degree of preference of different decision makers for risks. A larger Γ value may indicate that the decision maker is more averted risk, while a smaller Γ value may indicate that the decision maker is more willing to bear a certain risk.
ρ (u; Γ): this is a risk density function that describes the probability distribution of the occurrence of different decision values u. It is a function of the decision space variable u, the parameter Γ can adjust its shape reflecting the decision maker's preference for risk. u: this is a decision space variable representing a decision. In energy storage system control, there may be a plurality of decision variables that describe the selection of different control strategies or operating parameters. D: this is the final decision, i.e. the control strategy or operating parameters selected by the energy storage system. Γ: this is a risk preference parameter that can adjust the shape of the risk density function to reflect the different preferences of the decision maker for risk. A larger Γ value indicates that the decision maker is more risk sensitive and a smaller Γ value indicates that the decision maker is more willing to bear a certain risk. D (u-D): this part represents the difference between the decision space variable u and the final decision D and is differentiated by the differentiation operator D. C: this is the sign of the integral, meaning that the whole decision space is integrated from negative infinity to positive infinity to calculate the risk under different decisions.
The main role of this formula is to evaluate the difference in risk of different decisions. It allows the decision maker to quantify the risk level under different decisions and to select the most appropriate decision strategy. By calculating the integral, the formula takes into account the influence of the risk density function ρ (u; Γ) on all possible decision values u in the decision space. This may help the decision maker identify which decisions may lead to higher risk and which decisions may be relatively safe. The risk preference parameter Γ may be used to adjust the outcome of the risk assessment to reflect the different preferences of the decision maker for risk. Different Γ values may lead to different risk assessment results, thereby helping a decision maker to make decisions according to their personal preferences. The final risk assessment results may be used for decision support. The decision maker may choose the optimal decision to balance between risk and performance based on the risk assessment while taking into account his preference for risk.
Wherein,
k is a shape parameter.
Example 8: based on the above embodiment, the following formula is used in step 7 to find an optimal control strategy based on the risk assessment result;
wherein lambda is 1 Is a first weight parameter; lambda (lambda) 2 Is a second weight parameter; c (D) is a decision cost function; Is a viable decision space; d (D) opt Is the optimal control strategy.
Specifically, D opt : this is the optimal control strategy, which is the best decision found by the optimization problem. The decision has a minimum overall cost, wherein the overall cost takes into account both the risk assessment results and the decision cost.This means +.>The decision D in (c) is optimized to minimize the overall cost in brackets later. Lambda (lambda) 1 And lambda (lambda) 2 : this is a weight parameter used to balance the importance between risk assessment results and decision costs. They may be set according to the nature of the problem and the decision maker may adjust them to reflect their different preferences for risk and cost. Risk: this is the risk assessment result calculated in example 7. It represents an assessment of the decision in terms of risk. c (D): this is a decision cost function that represents the cost or penalty of selecting a particular decision D. The decision cost may be a function of various factors, such as resource consumption, time cost, economic cost, etc., depending on the context of the application, may be a linear function, representing a coefficient of linear cost associated with the decision. This form of cost function assumes that cost is a linear relationship with decision.
The principle of this formula is by weighing risk and costTo select an optimal control strategy. The goal of the optimization is to find a decision D opt So that the overall cost is minimized. The comprehensive cost is composed of two parts: the first part is lambda 1 Risk, which represents the Risk assessment result of the decision, by multiplying by the weight parameter lambda 1 To control its importance in the optimization. Larger lambda 1 Values will make decisions more risk-intensive, and smaller values will be more cost-intensive. The second part is lambda 2 c (D) which represents the cost of the decision by multiplying by the weighting parameter lambda 2 To control its importance in the optimization. Larger lambda 2 Values will make decisions more cost-intensive and smaller values will be more risk-intensive.
Example 9: based on the above embodiment, the following formula is used in step 8, and based on the optimal control strategy, the system control is performed, and the state variable of the energy storage system is updated:
wherein S is new The state variable is the updated state variable of the energy storage system; psi phi type 1 Is a first control parameter; psi phi type 2 Is a first control parameter; Δt is the step of the time-in-time,is the system state gradient of the state variable relative to the decision output result.
Specifically, S new : this is the updated state variable of the energy storage system. During control, the state variables are updated according to the selected control strategy to reflect the new state of the system. Δt: this is a step of time representing the time interval between each control operation. It is a fixed amount of time for discretizing the time and simulating the state changes of the system. Psi phi type 1 Sum phi 2 : this is the control parameter that is used to adjust the way the state variable is updated. They may be determined based on the selection of the control strategy and the system requirements.This is the partial derivative of the state variable S with respect to time t, representing the rate of change of the state variable over time. It describes how state variables evolve over time, typically determined by the system dynamics equations. />This is the system state gradient of the state variable S with respect to the decision output D. It shows the sensitivity of the state variables with respect to the control strategy, i.e. how the state variables respond when different control strategies are selected.
This formula describes the state variable update process in the control of the energy storage system. At each time step Δt, the state variable S will evolve over time, depending on the system dynamics and the choice of control strategy. The update process takes into account two factors:this section describes the evolution of state variables over time. />Representing the time rate of change of the state variable, ψ 1 Is a control parameter that can be used to adjust the time evolution of the state variable. Different psi 1 The values may cause the state variables to evolve at different rates. />This portion represents the system state gradient of the state variable relative to the decision output result. Representing the degree of response of the state variable to different control strategies, t 2 Is another control parameter for adjusting the sensitivity of the state variable to different decisions. Different psi 2 The values may result in the state variables responding differently to different control strategies.
The above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (9)
1. The control method of the fuzzy logic energy storage system is characterized by comprising the following steps:
step 1: defining a state variable of the energy storage system, the state variable describing a current state of the energy storage system, the state variable including a plurality of quantized data therein;
step 2: converting state variables composed of quantized data into fuzzy sets by using a fuzzification function;
step 3: defining a fuzzy logic rule set;
Step 4: taking the state variable as an initial condition, adding random disturbance, taking the fuzzy logic rule set as one element in the super-parameter set, setting values of other elements in the super-parameter set, and performing Monte Carlo simulation to obtain a Monte Carlo simulation result, wherein the predicted energy storage system state is represented;
step 5: combining the fuzzy logic rule set and the Monte Carlo simulation result to generate a decision output result;
step 6: based on the decision output result, performing risk assessment by using a risk assessment function to obtain a risk assessment result;
step 7: searching an optimal control strategy based on a risk assessment result;
step 8: based on the optimal control strategy, performing system control, and updating state variables of the energy storage system;
step 9: based on the state variables of the new energy storage system, the fuzzy logic rule set and the Monte Carlo super-parameter set are updated.
2. As claimed inThe method of claim 1, wherein the quantized data in the state variables comprises at least: energy E, load L, input power P in Output power P out System voltage V and temperature T; the defined state variables are: s=f (E, L, P in ,P out ,T,V)。
3. The method of claim 2, wherein the step 2 converts the state variable composed of quantized data into a fuzzy set using the following formula:
Wherein f fuzzy As fuzzification functions, E, L, P in ,P out T, V are input variables of the blurring function; mu (mu) E (E),μ L (L),…,μ V (V) each being a membership function, each membership function assigning a fuzzy membership value from 0 to 1 to each specific input variable value; sigma (sigma) E ,σ L ,…,σ V Standard deviations of the corresponding input variables are respectively adopted; f (S) is a fuzzy set, and the fuzzy set comprises a plurality of fuzzy sets.
4. The method of claim 3, wherein the fuzzy logic rule set is defined in step 3 using the formula:
wherein R is a fuzzy logic rule set; g (F (S)) is a mapping function, which represents the input state F (S) based on blurring, and a fuzzy logic rule set R is constructed;representing union operations, i.e. whatSome fuzzy rules r i Is incorporated into the fuzzy logic rule set R; n is the number of fuzzy rules; r is (r) i Is the ith fuzzy rule; each fuzzy rule is an IF-THEN statement, and defines a corresponding output result under a specific input condition; f (S) →D i Representing fuzzy rule r i Based on the input state F (S) a decision output D is obtained i ；IF x i is A i THEN y i is B i X in (2) i And y i Is a fuzzy variable; a is that i And B i Is a fuzzy set corresponding to the front piece and the back piece respectively; if x i Conform to fuzzy set A i Definition of (1), then y i Belonging to fuzzy set B i 。
5. The method of claim 4, wherein the monte carlo simulation in step 4 is performed using the following formula:
wherein N is Monte Carlo simulation times; m (N, S) is a prediction result obtained by Monte Carlo simulation and represents a predicted energy storage system state obtained by N simulation iterations under the condition of a given state variable S; zeta type toy i Random disturbance added in the ith simulation; Θ is a hyper-parameter set comprising: fuzzy logic rule set, time step, total time, energy storage system charging efficiency and energy storage system discharging efficiency.
6. The method of claim 5, wherein in step 5, the fuzzy logic rule and the monte carlo simulation result are combined to generate the decision output result using the following formula:
d is a decision output result obtained after post-blurring processing; k is a function combining the fuzzy logic rule set R and the Monte Carlo simulation result M (N, S) into D; omega is a post-blurring processing parameter set; m is M j (N, S) is one result produced for each monte carlo simulation;representing the intersection of all decision outputs, m being the number of decision outputs; / >Is a union operator; />Representing a composite set of results produced by all monte carlo simulations.
7. The method of claim 6, wherein the risk assessment result is calculated in step 6 using the following formula:
wherein, risk is a Risk assessment result; ρ is a risk density function; Γ is a risk preference parameter; u is a decision space variable; d is a differential operator.
8. The method according to claim 7, wherein the following formula is used in step 7 to find an optimal control strategy based on the risk assessment result;
wherein lambda is 1 Is a first weight parameter; lambda (lambda) 2 Is the second weight parameterThe method comprises the steps of carrying out a first treatment on the surface of the c (D) is a decision cost function;is a viable decision space; d (D) opt Is the optimal control strategy.
9. The method of claim 8, wherein in step 8, the following formula is used to perform system control based on an optimal control strategy to update state variables of the energy storage system:
wherein S is new The state variable is the updated state variable of the energy storage system; psi phi type 1 Is a first control parameter; psi phi type 2 Is a first control parameter; Δt is the step of the time-in-time,is the system state gradient of the state variable relative to the decision output result.
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