Light-storage-charging multi-body optimization method based on distributed robust optimization

Abstract

The invention relates to a light-storage-charging multi-body optimization method based on distributed robust optimization, and the method comprises the following steps: 1, analyzing the composition of a light-storage-charging integrated system, including an electric vehicle charging load, a conventional load, a photovoltaic system and an energy storage system, and carrying out the modeling of the electric vehicle charging load; 2, constructing a deterministic light-storage-charging multi-body optimization model based on the electric vehicle charging load model, and researching supply and demand balance of photovoltaic, energy storage, charging load and other bodies by taking the lowest operation cost of the light-storage-charging integrated system as a target; and 3, in consideration of the influence of photovoltaic output uncertainty in the photovoltaic-storage-charging integrated system, a distributed robust optimization method is adopted to convert and solve the deterministic model. According to the invention, the optimal light-storage-charging multi-body optimization method can be formulated on the basis of considering uncertainty factors, the photovoltaic consumption rate is improved, and the power utilization stability and safety of the light-storage-charging integrated system are ensured.

Description

Technical Field

[0001] The present application relates to the field of electric power, and in particular to a light-storage-charging multi-agent optimization method based on distributed blue-rod optimization. Background Art

[0002] In the existing research or technology on the optimization of photovoltaic-storage-charging multi-agent, the modeling of electric vehicle charging load is relatively simple, which is not enough to characterize the characteristics of electric vehicle power load. At the same time, in the existing photovoltaic-storage-charging multi-agent optimization, either a deterministic optimization method is used, that is, photovoltaic output is considered as predicted output, or a simple interval uncertainty set is used to describe the uncertainty of photovoltaic output, without considering the actual distribution characteristics of photovoltaic output. Moreover, the uncertainty of photovoltaic output is particularly prominent in the optimization of photovoltaic-storage-charging multi-agent. It is urgent to carry out optimization research on photovoltaic-storage-charging multi-agent on the basis of considering uncertainty. Summary of the invention

[0003] The purpose of the embodiments of the present application is to provide a multi-agent optimization method for light-storage-charging based on distributed blue rod optimization, so as to improve the photovoltaic absorption rate and ensure the stable and safe power consumption of the integrated light-storage-charging system.

[0004] To achieve the above objectives, this application provides the following technical solutions:

[0005] The embodiment of the present application provides a light-storage-charging multi-agent optimization method based on distributed blue rod optimization, comprising the following steps:

[0006] Step 1: Analyze the composition of the integrated photovoltaic-storage-charging system, including electric vehicle charging load, conventional load, photovoltaics, and energy storage, and model the electric vehicle charging load;

[0007] Step 2: Based on the electric vehicle charging load model, a deterministic photovoltaic-storage-charging multi-agent optimization model is constructed. The goal is to minimize the operating cost of the photovoltaic-storage-charging integrated system, and the supply and demand balance of photovoltaic, energy storage, charging load and other entities is studied;

[0008] Step 3: Considering the impact of photovoltaic output uncertainty in the integrated photovoltaic-storage-charging system, the distributed robust optimization method is used to transform and solve the above deterministic model.

[0009] The conventional load is predicted based on historical energy consumption data, and the photovoltaic power is predicted based on long-term meteorological data.

[0010] The modeling of the electric vehicle charging load is specifically as follows:

[0011] Based on the historical data of electric vehicles combined with the probability model, the charging load demand of electric vehicles is simulated.

[0012] The daily mileage m of electric vehicles follows a log-normal distribution, and its probability density function is:

[0013]

[0014] In the formula, μ 1 , σ 1 are the mean and standard deviation of ln m,

[0015] Initial state of charge of an electric vehicle when it leaves a parking spot It obeys uniform distribution, and its probability density function is:

[0016]

[0017] In the formula, a and b are The lower and upper limits of

[0018] The state of charge of an electric vehicle when it arrives at a charging station is:

[0019]

[0020] In the formula, E h is the battery capacity of the electric vehicle; E b The power consumption of electric vehicles per 100 kilometers.

[0021] The starting charging time T of the electric vehicle at the station follows a normal distribution, and its probability density function is:

[0022]

[0023] In the formula, μ c , σ c are the mean and standard deviation of TR,

[0024] According to S EV The charging time of electric vehicles can be calculated as:

[0025]

[0026] Where P che is the rated charging power of the charging pile; η EV Charging efficiency for electric vehicles,

[0027] The charging end time is:

[0028] T k =T s +T c

[0029] Therefore, the charging power requirement of each electric vehicle is:

[0030]

[0031] After multiple simulations, the average value is the charging load power of electric vehicles at a single charging station:

[0032]

[0033] Where M c is the number of simulations; N c is the total number of electric vehicles, i.e., the daily vehicle flow.

[0034] The construction of the deterministic light-storage-charging multi-agent optimization model is specifically as follows:

[0035] min(F Sub +F ESS +F EVCS +F PV )

[0036]

[0037] Where: F Sub 、F ESS 、F EVCS 、F PV They are the cost of purchasing electricity from the main grid, the cost of energy storage operation and maintenance, the cost of charging station operation and maintenance, and the penalty fee for abandoning solar power. They are the unit price of electricity purchased from the main grid at time t, the unit price of energy storage operation and maintenance, the unit price of charging station operation and maintenance, and the unit price of penalty for abandoned solar power. They are respectively the power purchased from the main grid, the energy storage charging power, the energy storage discharging power, the electric vehicle equivalent load power, and the photovoltaic abandoned power at time t.

[0038] Considering the influence of the uncertainty of photovoltaic output in the integrated photovoltaic-storage-charging system, the distributed robust optimization method is used to transform and solve the above deterministic model. Specifically,

[0039] Based on the established deterministic photovoltaic-storage-charging multi-agent optimization model, a two-stage distributed blue-rod photovoltaic-storage-charging multi-agent optimization model is proposed: the first stage determines the main grid purchase power; the second stage determines the energy storage charging and discharging power, electric vehicle charging load power, and photovoltaic abandoned light power. This is the result obtained when the main grid purchase power in the first stage is known. The first stage variable is represented by x, which is the main grid purchase power, and the second stage variable is represented by y. s Specifically, it is the energy storage charging and discharging power, the electric vehicle charging load power, and the photovoltaic abandoned power. At the same time, multiple scenarios are used to describe the uncertainty of photovoltaic output. The above deterministic model can be expressed as the following uncertainty form:

[0040]

[0041] Compared with the prior art, the beneficial effect of the present invention is that the present application can better describe the uncertainty of photovoltaic output, and then formulate the optimal light-storage-charging multi-agent optimization method based on considering the uncertainty factors, improve the photovoltaic absorption rate, and ensure the stable and safe power consumption of the integrated light-storage-charging system. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments of the present application will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0043] Figure 1 It is a flow chart of the method of this application. DETAILED DESCRIPTION

[0044] The technical solutions in the embodiments of the present application will be described below in conjunction with the drawings in the embodiments of the present application. It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in the subsequent drawings.

[0045] The terms "comprises," "comprising," or any other variation thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus that includes the element.

[0046] The terms "first", "second", etc. are only used to distinguish one entity or operation from another entity or operation, and should not be understood as indicating or implying relative importance, nor should they be understood as requiring or implying any such actual relationship or order between these entities or operations.

[0047] like Figure 1 The embodiment of the present application provides a light-storage-charging multi-agent optimization method based on distributed blue rod optimization. Step 1: First, analyze the composition of the "photovoltaic + energy storage + charging station" integrated system (referred to as light-storage-charging), including electric vehicle charging load, conventional load, photovoltaic, and energy storage. The electric vehicle charging load accounts for a relatively high proportion of the total load (charging load + conventional power load). First, model the electric vehicle charging load;

[0048] Step 2: Based on the electric vehicle charging load model, a deterministic photovoltaic-storage-charging multi-agent optimization model is constructed. The goal is to minimize the operating cost of the photovoltaic-storage-charging integrated system, and the supply and demand balance of photovoltaic, energy storage, charging load and other entities is studied;

[0049] Step 3: Considering the impact of photovoltaic output uncertainty in the photovoltaic-storage-charging integrated system, the distributed robust optimization method is used to transform and solve the above deterministic model. Specifically, a combined paradigm is used to describe the uncertainty of photovoltaic output to reduce the impact of prediction errors and output volatility on the stable operation of the photovoltaic-storage-charging integrated system.

[0050] The integrated photovoltaic-storage-charging system described in step 1 is specifically:

[0051] The integrated solar-storage-charging system consists of electric vehicle charging load, conventional load, photovoltaics, and energy storage. The conventional load can be predicted based on historical energy consumption data, and the photovoltaic power can be predicted based on long-term meteorological data.

[0052] The electric vehicle charging load in step 1 is specifically:

[0053] The charging load demand of electric vehicles is simulated based on the historical data of electric vehicles and the probability model.

[0054] The daily mileage m of electric vehicles follows a log-normal distribution, and its probability density function is:

[0055]

[0056] In the formula, μ 1 , σ 1 are the mean and standard deviation of ln m respectively.

[0057] Initial state of charge of an electric vehicle when it leaves a parking spot It obeys uniform distribution, and its probability density function is:

[0058]

[0059] In the formula, a and b are lower and upper limits.

[0060] The state of charge of an electric vehicle when it arrives at a charging station is:

[0061]

[0062] In the formula, E h is the battery capacity of the electric vehicle; E b It is the power consumption of electric vehicles per 100 kilometers.

[0063] The starting charging time T of the electric vehicle at the station follows a normal distribution, and its probability density function is:

[0064]

[0065] In the formula, μ c , σ c are the mean and standard deviation of TR respectively.

[0066] According to S EV The charging time of electric vehicles can be calculated as:

[0067]

[0068] Where P che is the rated charging power of the charging pile; η EV Charging efficiency for electric vehicles.

[0069] The charging end time is:

[0070] T k =T s +T c

[0071] Therefore, the charging power requirement of each electric vehicle is:

[0072]

[0073] After multiple simulations, the average value is the charging load power of electric vehicles at a single charging station:

[0074]

[0075] Where M c is the number of simulations; N c is the total number of electric vehicles, i.e., the daily vehicle flow.

[0076] Step 2 establishes a deterministic light-storage-charging multi-agent optimization model, specifically: min(F Sub +F ESS +F EVCS +F PV )

[0077]

[0078] Where: F Sub 、F ESS 、F EVCS 、F PV They are the cost of purchasing electricity from the main grid, the cost of energy storage operation and maintenance, the cost of charging station operation and maintenance, and the penalty fee for abandoning solar power. They are the unit price of electricity purchased from the main grid at time t, the unit price of energy storage operation and maintenance, the unit price of charging station operation and maintenance, and the unit price of penalty for abandoned solar power. They are respectively the power purchased from the main grid, the energy storage charging power, the energy storage discharging power, the electric vehicle equivalent load power, and the photovoltaic abandoned power at time t.

[0079] The distribution robust optimization method described in step 3 is specifically expressed as follows:

[0080] Based on the above-established deterministic light-storage-charging multi-agent optimization model, a two-stage distributed blue-rod light-storage-charging multi-agent optimization model is proposed: the first stage determines the main network purchase power; the second stage determines the energy storage charging and discharging power, electric vehicle charging load power, and photovoltaic abandoned light power, which is the result obtained when the main network purchase power in the first stage is known. The first stage variable is represented by x, which is the main network purchase power, and the second stage variable is represented by y s Specifically, it is the energy storage charging and discharging power, the electric vehicle charging load power, and the photovoltaic abandoned power. At the same time, multiple scenarios are used to describe the uncertainty of photovoltaic output. The above deterministic model can be expressed as the following uncertainty form:

[0081]

[0082] Considering the uncertainty of photovoltaic output, a theoretical distribution p containing uncertainty variables is constructed based on historical data. s 0 , and this is achieved by establishing a confidence set that is consistent with the actual distribution, expressed as:

[0083]

[0084] Where: θ 1 ,θ ∞ are the probability allowable deviation limits under the corresponding constraints of 1-norm and ∞-norm respectively; Ω p For p s feasible domain; p is the element p s vector; p 0 is the theoretical probability distribution of the uncertainty variable. The confidence set is constructed based on the given confidence level and amount of historical data, which is expressed as:

[0085]

[0086] Let the right sides of the above inequality be equal to α 1 and α ∞ , then it is expressed as:

[0087]

[0088] It can be seen that as the amount of historical data increases, the value of θ becomes smaller, which means that the confidence set with the same confidence level shrinks as the amount of historical data increases. When the amount of historical data increases to ∞, θ 1 and θ ∞ When it decreases to 0, it means that the initial distribution is the true distribution.

[0089] Based on the above method, the uncertainty of photovoltaic output is processed to obtain a data-driven distributed blue-rod photovoltaic-storage-charging multi-agent optimization model, which is expressed as:

[0090]

[0091] The above description is only an embodiment of the present application and is not intended to limit the protection scope of the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A light-storage-charging multi-agent optimization method based on distributed blue rod optimization, characterized in that: The following steps are involved: Step 1: Analyze the composition of the integrated photovoltaic-storage-charging system, including electric vehicle charging load, conventional load, photovoltaics, and energy storage, and model the electric vehicle charging load; Step 2: Based on the electric vehicle charging load model, a deterministic photovoltaic-storage-charging multi-agent optimization model is constructed. The goal is to minimize the operating cost of the photovoltaic-storage-charging integrated system, and the supply and demand balance of photovoltaic, energy storage, charging load and other entities is studied; Step 3: Considering the impact of photovoltaic output uncertainty in the integrated photovoltaic-storage-charging system, the distributed robust optimization method is used to transform and solve the above deterministic model.

2. The light-storage-charging multi-agent optimization method based on distributed blue rod optimization according to claim 1 is characterized in that: The conventional load is predicted based on historical energy consumption data, and the photovoltaic power is predicted based on long-term meteorological data.

3. The light-storage-charging multi-agent optimization method based on distributed blue rod optimization according to claim 1 is characterized in that: The modeling of the electric vehicle charging load is specifically as follows: Based on the historical data of electric vehicles combined with the probability model, the charging load demand of electric vehicles is simulated. The daily mileage m of electric vehicles follows a log-normal distribution, and its probability density function is: In the formula, μ1 and σ1 are the mean and standard deviation of ln m, respectively. Initial state of charge of an electric vehicle when it leaves a parking spot It obeys uniform distribution, and its probability density function is: In the formula, a and b are The lower and upper limits of The state of charge of an electric vehicle when it arrives at a charging station is: In the formula, E h is the battery capacity of the electric vehicle; E b The power consumption of electric vehicles per 100 kilometers. The starting charging time T of the electric vehicle at the station follows a normal distribution, and its probability density function is: In the formula, μ c , σ c are the mean and standard deviation of TR, According to S EV The charging time of electric vehicles can be calculated as: Where P che is the rated charging power of the charging pile; η EV Charging efficiency for electric vehicles, The charging end time is: T k =T s +T c Therefore, the charging power requirement of each electric vehicle is: After multiple simulations, the average value is the charging load power of electric vehicles at a single charging station: Where M c is the number of simulations; N c is the total number of electric vehicles, i.e., the daily vehicle flow.

4. The light-storage-charging multi-agent optimization method based on distributed blue-rod optimization according to claim 1 is characterized in that: The construction of the deterministic light-storage-charging multi-agent optimization model is specifically as follows: min(F Sub +F ESS +F EVCS +F PV ) Where: F Sub 、F ESS 、F EVCS 、F PV They are the cost of purchasing electricity from the main grid, the cost of energy storage operation and maintenance, the cost of charging station operation and maintenance, and the penalty fee for abandoning solar power. They are the unit price of electricity purchased from the main grid at time t, the unit price of energy storage operation and maintenance, the unit price of charging station operation and maintenance, and the unit price of penalty for abandoned solar power. They are respectively the power purchased from the main grid, the energy storage charging power, the energy storage discharging power, the electric vehicle equivalent load power, and the photovoltaic abandoned power at time t.

5. The light-storage-charging multi-agent optimization method based on distributed blue-rod optimization according to claim 1 is characterized in that: Considering the influence of the uncertainty of photovoltaic output in the integrated photovoltaic-storage-charging system, the distributed robust optimization method is used to transform and solve the above deterministic model. Specifically, Based on the established deterministic photovoltaic-storage-charging multi-agent optimization model, a two-stage distributed blue-rod photovoltaic-storage-charging multi-agent optimization model is proposed: the first stage determines the main grid purchase power; the second stage determines the energy storage charging and discharging power, electric vehicle charging load power, and photovoltaic abandoned light power. This is the result obtained when the main grid purchase power in the first stage is known. The first stage variable is represented by x, which is the main grid purchase power, and the second stage variable is represented by y. s Specifically, it is the energy storage charging and discharging power, electric vehicle charging load power, and photovoltaic abandoned light power. At the same time, multiple scenarios are used to describe the uncertainty of photovoltaic output. The above deterministic model can be expressed as the following uncertainty form: In the formula: A is the first-stage variable coefficient; B is the second-stage variable coefficient; X is the first-stage variable set; Y is the second-stage variable set; N s is the number of scenes.

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