Intelligent energy storage integrated management method for industrial site

Abstract

The invention discloses an intelligent energy storage integrated management method for an industrial site. Firstly, data of an energy storage system ESS is collected by using monitoring equipment; based on historical load data given by an energy storage system (ESS), constructing an energy management system (EMS) to minimize long operation cost; carrying out scheduling test on the constructed energy management system EMS, inputting prediction data of power generation and load use, and setting corresponding weighting coefficients according to different hours in one day to reflect energy demands in peak periods and off-peak periods; a user inputs a demand to adjust parameters of an energy management system (EMS), the energy management system (EMS) updates an optimization strategy in real time according to the demand input by the user and carries out visual display, and the user checks specific optimization suggestions and cost expected to be saved. According to the method, the user can be helped to better manage energy consumption, and under the condition that normal operation of the electric power system is not affected, the demand electric charge of the electric power system is reduced to the maximum extent.

Description

Technical Field

[0001] The present invention relates to the technical field of energy management, and in particular to an intelligent energy storage integrated management method for an industrial site. Background Art

[0002] With the development of renewable energy, the penetration rate of renewable energy in the distribution network continues to increase. However, the intermittent and volatile nature of photovoltaic power generation will affect the stable operation of the distribution network. The most significant problem is voltage control. Traditional voltage control methods (such as volt-var control (VVC)) mainly rely on devices such as on-load tap changers (OLTCs), capacitor banks (CBs), and voltage regulators (VRs). However, these devices have slow response times and high costs, which makes these traditional voltage control devices unable to effectively cope with the rapid changes in photovoltaic output. These problems currently encountered can be solved by deploying energy storage systems (ESS) at the "back end of the meter". ESS can provide voltage control by storing excess energy and releasing energy when needed, which can smooth the fluctuations of photovoltaic power generation to ensure voltage stability.

[0003] However, existing research focuses on the operational needs of utilities, but ignores the economic benefits of commercial and industrial customers. For commercial and industrial customers, demand charges are a huge cost. If the energy storage system is optimized, the customer's demand charge cost can be reduced, bringing economic benefits to the customer. In addition, the degradation cost of the battery is also an important factor affecting the economic efficiency of the system. The life of the battery is closely related to its depth of charge and discharge (DOD). To extend the battery life, it is necessary to optimize the battery charge and discharge strategy, so that the battery life can be maximized while meeting the voltage control requirements. However, the existing technology does not have a corresponding energy management solution. Summary of the invention

[0004] The purpose of the present invention is to provide an intelligent energy storage integrated management method for industrial sites, which can help users better manage energy consumption and minimize the demand electricity charges of the power system without affecting the normal operation of the power system.

[0005] The objective of the present invention is achieved through the following technical solutions:

[0006] An intelligent energy storage integrated management method for an industrial site, the method comprising:

[0007] Step 1: First, use the monitoring equipment to collect the data of the energy storage system ESS. The collected data is stored in the form of an Excel table as the prediction data for subsequent power generation and load use;

[0008] Step 2: Based on the historical load data given by the energy storage system ESS, an energy management system EMS is constructed to minimize the long-term operating costs and optimize the energy storage configuration of the energy storage system ESS to obtain the best investment-return solution;

[0009] Step 3: Conduct a scheduling test on the constructed energy management system EMS, input the forecast data of power generation and load usage, and set corresponding weighting coefficients according to different hours of the day to reflect the energy demand during peak and non-peak hours;

[0010] Step 4: The user inputs the requirements to adjust the parameters of the energy management system EMS. The energy management system EMS updates the optimization strategy in real time according to the requirements input by the user and displays it visually. The user views the specific optimization suggestions and the estimated cost savings.

[0011] It can be seen from the technical solution provided by the present invention that the above method can help users better manage energy consumption and minimize the demand electricity charges of the power system without affecting the normal operation of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0013] Figure 1 A schematic diagram of a flow chart of an intelligent energy storage integrated management method for an industrial site provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0014] The following is a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments, which does not constitute a limitation of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0015] like Figure 1 The figure is a schematic flow chart of an intelligent energy storage integrated management method for an industrial site provided by an embodiment of the present invention, the method comprising:

[0016] Step 1: First, use monitoring equipment (such as smart meters, flow meters, etc.) to collect data from the energy storage system ESS. The collected data is stored in the form of an Excel spreadsheet as prediction data for subsequent power generation and load use;

[0017] In this step, a 2976*12 matrix can be specifically defined to store data for 12 months of the year, the data stored in the Excel spreadsheet can be allocated to the matrix by month, and then the data of the specified month can be extracted, so that the power load data of each month can be processed or analyzed separately.

[0018] The above data organization method is commonly used in the power industry to analyze consumption patterns, predict loads, or optimize resource allocation. By breaking down data into smaller, more manageable parts (for example, by month), tasks such as statistical analysis or machine learning model training can be performed more easily.

[0019] Step 2: Based on the historical load data given by the energy storage system ESS, an energy management system EMS is constructed to minimize the long-term operating costs and optimize the energy storage configuration of the energy storage system ESS to obtain the best investment-return solution;

[0020] In this step, based on the historical load data, demand electricity charges, electricity charges and customer operation mode given by the energy storage system ESS, the charging / discharging curve at the set time interval is calculated as the output reference to find the cost profit and specific E max and P max ; E max P represents the upper limit of the amount of electricity stored in the battery system of the energy storage system ESS. max represents the maximum power of the power electronic equipment; the specific optimization problem is expressed as:

[0021]

[0022] s P inv，min ≤P inv，t ≤P inv，max ,

[0023] E min ≤E bat，t ≤E max ,

[0024] E bat，t =E bat，t-1 +P bat，t Δt,

[0025] P bat，t =-P inv，dis，t / η dis +P inv，ch，t η ch ,

[0026] P inv，t =-P inv，dis，t +P inv，ch，t .

[0027] E bat，T =Ebat，0 ,

[0028] Where T represents 24 hours; a and b represent the cost parameters of the battery energy storage system, i.e., RMB / kWh and RMB / kW; r E ∑(L t +P inv,t )Δt represents the electricity cost, r E is the electricity price, L t is the actual power load measured by the meter at time t; r d max(L t +P inv，t ) represents the demand electricity cost, r d is the unit price of the demand electricity charge, and the billing period is usually one month; the meter will record the maximum load value max (L t +P inv，t );α is the return on investment coefficient;J is the sum of all expenses;

[0029] The constraints st are as follows:

[0030] P inv，min ≤P inv，t ≤P inv，max

[0031] Where P inv，min and P inv，max They represent the minimum power and maximum power of the power electronic equipment respectively;

[0032] Calculation of the amount of power stored in the battery system:

[0033] E bat，t =E bat，t-1 +P bat，t Δt

[0034] Among them, E batt P is the amount of electricity stored in the battery system of the energy storage system ESS at time t; bat，t is the output power of the energy storage system ESS at time t;

[0035] The amount of electricity stored in the battery system E bat，t Need to meet:

[0036] E min ≤E bat，t ≤E max

[0037] Where E max and E min They represent the upper and lower limits of the amount of electricity stored in the battery system of the energy storage system ESS respectively;

[0038] Pinv，dis，t and P inv，ch，t To optimize the variables, it refers to the discharge power and charging power of the inverter terminal; in order to map the discharge power and charging power from the energy storage system ESS to the inverter terminal, the charging and discharging efficiency is introduced, and the conversion is as follows:

[0039] P bat，t =-P inv，dis，t / η dis +P inv，ch，t η ch

[0040] where η dis and η ch They are discharge efficiency and charge efficiency respectively;

[0041] A reference power P is provided at the inverter terminals as the sum of the discharge power and the charging power inv，t , expressed as:

[0042] P inv，t =-P inv，dis，t +P inv，ch，t

[0043] The last constraint states that the final charge of the battery should be equal to the initial charge of the battery, that is:

[0044] E bat，T =E bat，0

[0045] E bat，0 Indicates the initial charge of the battery; E bat，T Indicates the battery's charge at the last moment T;

[0046] In specific implementation, since electricity is usually charged on a monthly basis, the J value can be analyzed on a monthly basis.

[0047] For each reference power P inv，t To obtain the optimal operating conditions, the demand electricity charge for this period of time is calculated by the following formula:

[0048] r d max(L t +P inv，t )

[0049] After collecting the demand charge and kilowatt-hour charge data for all months, the annual cost-benefit report can be calculated, which is to minimize the sum of all expenses J.

[0050] It is worth noting that each annual cost-benefit report corresponds to the selected energy storage system size (E max , P max ); if another pair of parameters needs to be analyzed, the optimization needs to be recomputed.

[0051] Step 3: Conduct a scheduling test on the constructed energy management system EMS, input the forecast data of power generation and load usage, and set corresponding weighting coefficients according to different hours of the day to reflect the energy demand during peak and non-peak hours;

[0052] In this step, during peak hours, the issues involved are demand charges, voltage variations, and equipment degradation; during off-peak hours, the goal is to minimize voltage variations and degradation, where:

[0053] 1) Demand Charge

[0054] Demand charges refer to NYSEG's peak demand charge service. The power company uses a demand meter to measure power flow. The demand meter records the highest power flow during the peak period of the billing period. During non-peak hours, the State Grid Corporation will not charge demand charges. Bill customers pay demand charges based on the highest average 15-minute flow during the billing period. Demand charges are expressed as:

[0055] C demand =P demand ·max(P load，t )

[0056] Among them C demand is the total demand charge for the billing period; p demand P is the demand electricity charge in $ / KW; load,t is the actual electrical load measured in the demand meter at time t;

[0057] Demand charge reductions are achieved by reducing daily peak demand during each billing period;

[0058] 2) Voltage changes

[0059] Due to photovoltaic and load oscillations at the load point, the voltage on the distribution network will fluctuate. In order to quantify the fluctuations, the following voltage variation coefficient is used:

[0060]

[0061] Where V variation Indicates voltage change; V t is the voltage vector containing all voltage magnitudes of the feeder at time t; V ref represents the designed voltage vector;

[0062] The mapping between voltage amplitude and injected power is expressed as:

[0063] V t，i =V t-1，i +ΔV t，i

[0064] ΔV t，i =K Pi ΔP i，t +K Qi ΔQ i，t

[0065] Where ΔV represents the fluctuation of node voltage amplitude, in volts; V t，i is the voltage at load point i at time t; K Pi is the vector of sensitivity coefficient relative to actual power injection; K Qi A vector representing the sensitivity factor multiplied by the reactive power; ΔP i，t and ΔQ i，t is the active power and reactive power injection at node i at time t;

[0066] In a specific implementation, the purpose of voltage control in the embodiment of the present invention is to handle the intermittency of photovoltaic power generation on the user side, so the tap switch regulator is disabled because it does not belong to the user side control period.

[0067] 3) Degradation costs

[0068] Generally speaking, battery life can be considered as calendar life and cycle life. The former refers to the time from the date of production to the end of the battery life, which is independent of operation; the latter is related to charging and discharging decisions and working conditions. The degradation cost model is expressed as:

[0069]

[0070] Among them, battery replacement cost refers to the battery replacement cost; energy throughput incycle refers to the total charge and discharge energy in the usage cycle;

[0071] Further expressed as:

[0072]

[0073] Among them C bat represents the battery capital cost; L bat (DOD) represents the battery life coefficient relative to DOD (Depth of Discharge); P bat，t is the output power of the energy storage system ESS at time t; Δt is the time interval, which can be set to 15 minutes.

[0074] Since State Grid only charges customers for peak demand usage during peak hours, this means that the objective function will be different at different times of the day, specifically:

[0075] During peak hours, the system will reduce peak demand, minimize voltage variation, and optimize degradation cost. The objective function is expressed as:

[0076] J=α·C demand +β·V variation +γ·C d

[0077] Among them, α, β and γ are the weighted coefficients of the multi-objective function;

[0078] During off-peak hours, the requirement to reduce demand charges is eliminated, and the weighting coefficient α in the objective function is 0.

[0079] Step 4: The user inputs the requirements to adjust the parameters of the energy management system EMS. The energy management system EMS updates the optimization strategy in real time according to the requirements input by the user and displays it visually. The user views the specific optimization suggestions and the estimated cost savings.

[0080] In this step, the visualization display of the energy management system EMS includes three charts: the load curve of the interaction between the power load and the energy storage system, the system state (SOC, State of Charge) percentage curve, and the daily net charge and discharge power curve.

[0081] In the specific implementation, the optimization solver of the energy management system EMS can be realized by using the CVX toolbox library in Matlab.

[0082] It is worth noting that the contents not described in detail in the embodiments of the present invention belong to the prior art known to professional and technical personnel in the field.

[0083] Taking a specific example, according to the electricity usage of a certain industrial and commercial user in a certain city in 2023, the energy management solution described in the embodiment of the present invention is used for testing, and the monthly experimental results for the whole year are shown in the following table:

[0084]

[0085] It can be seen from the experimental results that the method described in the embodiment of the present invention has the following advantages:

[0086] 1. Compared with mainstream ESS planning and operation products, the method described in the embodiment of the present invention can handle the optimization interval of 15 minutes and better meet the calculation requirements of demand electricity charges;

[0087] 2. This method can autonomously and intelligently evaluate demand response requests based on demand response requests, greatly improving the operator's work efficiency;

[0088] 3. This method can successfully reduce the load peak according to demand, thereby achieving the purpose of reducing costs, reducing operating costs for users and improving economic benefits;

[0089] 4. This method builds powerful data analysis capabilities to help users fully understand the changing trends and usage efficiency of energy consumption, so as to formulate scientific and reasonable energy management strategies.

[0090] In addition, a person skilled in the art can understand that all or part of the steps in the above-mentioned embodiment method can be implemented by instructing related hardware through a program, and the corresponding program can be stored in a computer-readable storage medium. The above-mentioned storage medium can be a read-only memory, a disk or an optical disk, etc.

[0091] The above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by any technician familiar with the technical field within the technical scope disclosed in the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims. The information disclosed in the background technology section of this article is only intended to deepen the understanding of the overall background technology of the present invention, and should not be regarded as an admission or in any form that the information constitutes prior art known to those skilled in the art.

Claims

1. An intelligent energy storage integrated management method for industrial sites, characterized in that: The method comprises: Step 1: First, use the monitoring equipment to collect the data of the energy storage system ESS. The collected data is stored in the form of an Excel table as the prediction data for subsequent power generation and load use; Step 2: Based on the historical load data given by the energy storage system ESS, an energy management system EMS is constructed to minimize the long-term operating costs and optimize the energy storage configuration of the energy storage system ESS to obtain the best investment-return solution; Step 3: Conduct a scheduling test on the constructed energy management system EMS, input the forecast data of power generation and load usage, and set corresponding weighting coefficients according to different hours of the day to reflect the energy demand during peak and non-peak hours; Step 4: The user inputs the requirements to adjust the parameters of the energy management system EMS. The energy management system EMS updates the optimization strategy in real time according to the requirements input by the user and displays it visually. The user views the specific optimization suggestions and the estimated cost savings.

2. The intelligent energy storage integrated management method for industrial sites according to claim 1, characterized in that: In step 1, a 2976*12 matrix is ​​defined to store data for 12 months of the year. The data stored in the Excel table is allocated to the matrix by month, and then the data of the specified month is extracted, so that the power load data of each month can be processed or analyzed separately.

3. The intelligent energy storage integrated management method for industrial sites according to claim 1 is characterized in that: The process of step 2 is specifically as follows: Based on the historical load data, demand electricity charges, electricity charges and customer operation mode given by the energy storage system ESS, the charging / discharging curve of the set time interval is calculated as the output reference to calculate the cost profit and specific E max and P max ; E max P represents the upper limit of the amount of electricity stored in the battery system of the energy storage system ESS. max represents the maximum power of the power electronic equipment; the specific optimization problem is: s.t.P inv，min ≤P inv，t ≤P inv，max , AND min ≤E bat，t ≤E max , AND bat，t =And bat，t-1 +P bat，t Δt, P bat，t =-P inv，dis，t / or dis +P inv，ch，t or ch , P inv，t =-P inv，dis，t +P inv，ch，t . AND bat，T =And bat，0 , Where T represents 24 hours; a and b represent the cost parameters of the battery energy storage system; r E ∑(L t +P inv，t )Δt represents the electricity cost, r E is the electricity price, L t is the actual power load measured by the meter at time t; r d max(L t +P inv，t ) represents the demand electricity cost, r d is the unit price of the demand electricity charge; the meter will record the maximum load value max(L t +P inv，t );α is the return on investment coefficient;J is the sum of all expenses; The constraints st are as follows: P inv，min ≤P inv，t ≤P inv，max Where P inv，min and P inv，max They represent the minimum power and maximum power of the power electronic equipment respectively; Calculation of the amount of power stored in the battery system: AND bat，t =And bat，t-1 +P bat，t Δt Among them, E bat，t P is the amount of electricity stored in the battery system of the energy storage system ESS at time t; bat，t is the output power of the energy storage system ESS at time t; The amount of electricity stored in the battery system E batt Need to meet: AND min ≤E bat，t ≤E max Where E max and E min They represent the upper and lower limits of the amount of electricity stored in the battery system of the energy storage system ESS respectively; P inv，dis，t and P inv，ch，t To optimize the variables, it refers to the discharge power and charging power of the inverter terminal; in order to map the discharge power and charging power from the energy storage system ESS to the inverter terminal, the charging and discharging efficiency is introduced, and the conversion is as follows: P bat，t =-P inv，dis，t / or dis +P inv，ch,t or ch where η dis and η ch They are discharge efficiency and charge efficiency respectively; A reference power P is provided at the inverter terminals as the sum of the discharge power and the charging power inv，t , expressed as: P inv，t =-P inv，dis，t +P inv，ch，t The last constraint states that the final charge of the battery should be equal to the initial charge of the battery, that is: AND bat，T =And bat，0 E bat，0 Indicates the initial charge of the battery; E bat，T Indicates the battery's charge at the last moment T; For each reference power P inv，t To obtain the optimal operating conditions, the demand electricity charge for this period of time is calculated by the following formula: r d max(L t +P inv，t ) After collecting the demand charge and kilowatt-hour charge data for all months, the annual cost-benefit report can be calculated, which is to minimize the sum of all expenses J.

4. The intelligent energy storage integrated management method for industrial sites according to claim 1, characterized in that: In step 3, during peak hours, the issues involved are demand charges, voltage variations, and equipment degradation; during off-peak hours, the goal is to minimize voltage variations and equipment degradation, where: 1) Demand Charge The demand meter is used to record the highest power flow during the peak period of the billing period. The demand charge is expressed as: C demand =p demand ·max(P load，t ) Among them C demand is the total demand charge for the billing period; p demand P is the demand electricity charge in units of $ / KW; load，t is the actual electrical load measured in the demand meter at time t; Demand charge reductions are achieved by reducing daily peak demand during each billing period; 2) Voltage changes Due to photovoltaic and load oscillations at the load point, the voltage on the distribution network will fluctuate. In order to quantify the fluctuations, the following voltage variation coefficient is used: Where V variation Indicates voltage change; V t is the voltage vector containing all voltage magnitudes of the feeder at time t; V ref represents the designed voltage vector; The mapping between voltage amplitude and injected power is expressed as: V t，i =V t-1，i +ΔV t，i ΔV t，i =K pi ΔP i，t +K Qi ΔQ i，t Where ΔV represents the fluctuation of node voltage amplitude, in volts; V t，i is the voltage at load point i at time t; K Pi is the vector of sensitivity coefficient relative to actual power injection; K Qi A vector representing the sensitivity factor multiplied by the reactive power; ΔP i，t and ΔQ i，t is the active power and reactive power injection at node i at time t; 3) Degradation costs The model of degradation cost is expressed as: Among them, battery replacement cost refers to the battery replacement cost; energy throughput in cycle refers to the total charge and discharge energy in the usage cycle; Further expressed as: Among them C bat represents the battery capital cost; L bat (DOD) represents the battery life coefficient relative to the depth of discharge DOD; P bat，t is the output power of the energy storage system ESS at time t; Δt is the time interval; During peak hours, the system will reduce peak demand, minimize voltage variation, and optimize degradation cost. The objective function is expressed as: J=α·C demand +β·V variation +γ·C d Among them, α, β and γ are the weighted coefficients of the multi-objective function; During off-peak hours, the requirement to reduce demand charges is eliminated, and the weighting coefficient α in the objective function is 0.

5. The intelligent energy storage integrated management method for industrial sites according to claim 1, characterized in that: In step 4, the visualization display provided by the energy management system EMS includes three charts: the load curve of the interaction between the power load and the energy storage system, the system status percentage curve, and the daily net charge and discharge power curve.

Images