An intelligent energy storage integration management method for an industrial site

Abstract

The application discloses an intelligent energy storage integration management method for an industrial field, first, data of an energy storage system (ESS) is collected by a monitoring device; based on historical load data given by the energy storage system (ESS), an energy management system (EMS) is constructed to minimize long running costs; the constructed energy management system (EMS) is tested for dispatch, predicted data of power generation and load use are input, corresponding weighting coefficients are set according to different hours of a day to reflect energy demand in peak hours and off-peak hours; parameters of the energy management system (EMS) are adjusted by user input demand, the energy management system (EMS) updates an optimization strategy in real time according to the user input demand and performs visual display, and the user checks specific optimization suggestions and expected saved costs. The above method can help users better manage energy consumption, and maximally reduces demand charges of a power system without affecting normal operation of the power system.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of energy management, and particularly relates to an intelligent energy storage integrated management method for an industrial site. BACKGROUND

[0002] With the development of renewable energy, the penetration of renewable energy in distribution networks is increasing. However, the intermittency and volatility of photovoltaic power generation will affect the stable operation of the distribution network, and the most significant problem is voltage control. Traditional voltage control methods (such as voltage and reactive power control (VVC)) mainly rely on on-load tap changer (OLTC), capacitor bank (CB) and voltage regulator (VR) and other devices. However, these devices have slow response time and high cost, which makes these traditional voltage control devices unable to effectively respond to the rapid changes in photovoltaic output. The current problems can be solved by deploying an energy storage system (ESS) at the back end of the meter. The ESS can provide voltage control by storing excess energy and releasing energy when needed, which can smooth the fluctuations of photovoltaic power generation and ensure the stability of the voltage.

[0003] However, existing research mainly focuses on the operational needs of utilities, ignoring the economic benefits of commercial and industrial customers. For commercial and industrial customers, demand charges are a significant cost. If an optimized energy storage system is operated, the demand charge cost of the customer can be reduced, bringing economic benefits to the customer. In addition, the degradation cost of the battery is an important factor affecting the economic efficiency of the system. The life of the battery is closely related to its depth of discharge (DOD). In order to prolong the life of the battery, the charging and discharging strategy of the battery needs to be optimized, 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

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

[0005] The purpose of the present application is achieved by the following technical solutions:

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

[0007] Step 1, first use a monitoring device to collect data of an energy storage system ESS, the collected data is stored in an Excel table as prediction data for subsequent power generation and load use;

[0008] Step 2, based on the given historical load data of the energy storage system ESS, the energy management system EMS is constructed to minimize the long running cost and optimize the energy storage configuration of the energy storage system ESS to obtain the best investment-return scheme;

[0009] Step 3, the constructed energy management system EMS is tested, the predicted data of power generation and load use are input, and the corresponding weighting coefficients are set according to different hours of a day to reflect the energy demand in peak period and non-peak period;

[0010] Step 4, the user inputs the parameters of the demand adjustment energy management system EMS, the energy management system EMS updates the optimization strategy in real time according to the user input demand and visualizes the display, and the user views the specific optimization suggestion and the expected saved cost.

[0011] From the above technical solutions provided by the present application, the above method can help users better manage energy consumption, and maximize the reduction of demand charges of the power system without affecting the normal operation of the power system. BRIEF DESCRIPTION OF DRAWINGS

[0012] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and those skilled in the art can obtain other drawings according to these drawings without creative labor.

[0013] Figure 1 The intelligent energy storage integrated management method flowchart of the industrial field provided by the embodiments of the present application. DETAILED DESCRIPTION

[0014] The technical solutions in the embodiments of the present application will be described clearly and completely in the following with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, not all embodiments of the present application, which do not constitute a limitation to the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative labor are within the protection scope of the present application.

[0015] As Figure 1 The intelligent energy storage integrated management method flowchart of the industrial field provided by the embodiments of the present application is shown, the method comprises:

[0016] Step 1, first, the data of the energy storage system ESS is collected by using monitoring equipment (such as intelligent electric meter, flow meter, etc.), the collected data is stored in the form of Excel table as the subsequent predicted data of power generation and load use;

[0017] In this step, a 2976*12 matrix can be 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 the data for the specified month is extracted, so that the power load data for 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 the data into smaller, more manageable parts (e.g., by month), statistical analysis or machine learning model training tasks can be performed more easily.

[0019] Step 2, based on the given historical load data of the energy storage system ESS, construct the energy management system EMS to minimize the long-run cost and optimize the energy storage configuration of the energy storage system ESS, and obtain the best investment-return scheme;

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

[0021]

[0022] s.t. 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, b represent the cost parameters of battery energy storage system, i.e. yuan / kWh, yuan / kW; r E ∑(L t +P inv,t )Δt represents the kilowatt-hour electricity fee, r E is the kilowatt-hour electricity price, L t is the actual power load measured by the electric meter at time t; r d max(L t +P inv，t ) represents the demand charge, r d is the unit price of demand charge, and the billing period is usually one month; the electric meter will record the maximum load value max(L t +P inv，t ) in a time interval of Δt (for example, fifteen minutes) throughout the entire period; α is the return on investment coefficient; J represents the sum of all expenses;

[0029] The constraint condition s.t. is as follows:

[0030] P inv，min ≤P inv，t ≤P inv，max

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

[0032] For the amount of electricity stored by the battery system:

[0033] E bat，t =E bat，t-1 +P bat，t Δt

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

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

[0036] E min ≤E bat，t ≤E max

[0037] Where E max and E min represent the upper and lower bounds of the amount of electricity stored by the battery system of the energy storage system ESS, respectively;

[0038] Pinv，dis，t and P inv，ch，t For optimization variables, refer to discharging power and charging power at inverter terminal; in order to map out discharging power and charging power from energy storage system (ESS) to inverter terminal, introduce charging and discharging efficiency, convert as follows:

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

[0040] Wherein η dis and η ch are discharging efficiency and charging efficiency respectively;

[0041] At inverter terminal, provide reference power P inv，t as sum of discharging power and charging power, expressed as:

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

[0043] The last constraint indicates that the last power of battery should be equal to the initial power of battery, that is:

[0044] E bat，T = E bat，0

[0045] E bat，0 represents initial power of battery; E bat，T represents power of battery at time T;

[0046] In specific implementation, because electricity fee is usually charged monthly, the value of J can be analyzed monthly.

[0047] For each reference power P inv，t , obtain optimal operating condition, the demand charge of this time period is calculated by:

[0048] r d max(L t + P inv，t )

[0049] After collecting demand charge and energy charge data of all months, annual cost profit report can be calculated, that is, minimizing the sum of all expenses J.

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

[0051] Step 3, dispatch test on the constructed energy management system EMS, input the forecast data of power generation and load use, set the corresponding weighting coefficient according to different hours of the day to reflect the energy demand in peak hours and off-peak hours;

[0052] In this step, in peak hours, the problems involved are demand charge, voltage variation and equipment degradation; in off-peak hours, the goal is to minimize voltage variation and degradation problems, wherein:

[0053] 1) Demand charge

[0054] Demand charge refers to the peak demand charge service of NYSEG, the power company uses demand meters to measure power flow, and uses demand meters to record the highest power flow in the peak period during the billing period, and in the off-peak period, the State Grid Corporation will not charge demand charge, and the billing customers will pay demand charge according to the highest average 15-minute flow during the billing period. Demand charge is expressed as:

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

[0056] Where C demand is the total demand charge during the billing period; p demand is the demand charge in $ / KW; P load,t is the actual power load measured in the demand meter at time t;

[0057] The reduction of demand charge is achieved by reducing the daily peak demand of each billing period;

[0058] 2) Voltage variation

[0059] Due to photovoltaic and load oscillation at the load point, the voltage on the distribution network will fluctuate, in order to quantify the fluctuation, the following voltage variation coefficient is used:

[0060]

[0061] Where V variation represents voltage variation; V t is the voltage vector containing all voltage amplitudes 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 the voltage amplitude of the node, in volts; V t，i is the voltage magnitude of the load point i at time t; K Pi is the sensitivity coefficient with respect to the vector of the actual power injection; K Qi represents the vector of the sensitivity coefficient multiplied by the reactive power; ΔP i，t and ΔQ i，t are the active and reactive power injections at node i at time t;

[0066] In a specific implementation, the purpose of the voltage control in the embodiment of the present application is to deal with the intermittency of the user-side photovoltaic, so the tap switch regulator is disabled because it does not belong to the user-side control period.

[0067] 3) Degradation cost

[0068] Generally speaking, the battery life can be regarded as the calendar life and the cycle life, the former refers to the time from the date of production to the end of the battery life, which is irrelevant to operation; the latter is related to the charging and discharging decision and the working condition. The model of the degradation cost is represented as:

[0069]

[0070] where battery replacement cost represents the battery replacement cost; energy throughput incycle represents the total energy charging and discharging in a cycle;

[0071] Further represented as:

[0072]

[0073] where C bat represents the battery capital cost; L bat (DOD) represents the battery life coefficient with respect 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 the State Grid Corporation only charges customers for peak demand usage fees during peak hours, this means that the objective function will be different at different times of the day, specifically:

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

[0076] J = a C demand + b V variation + g C d

[0077] Wherein, a, b and g are the weighted coefficients of the multi-objective function;

[0078] In off-peak hours, the requirement of reducing demand charges is cancelled, and the weighted coefficient a in the objective function is 0.

[0079] Step 4, the user inputs the parameters of the demand adjustment energy management system EMS, and the energy management system EMS updates the optimization strategy in real time according to the user input demand and performs visual display, and the user views the specific optimization suggestion and the expected saved cost.

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

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

[0082] It is worth noting that the contents not described in detail in the embodiments of the present application belong to the prior art known to those skilled in the art.

[0083] Taking a specific example, according to the electric energy use of a certain industrial and commercial user in a certain city in 2023, the energy management scheme described in the embodiments of the present application is tested, and the monthly experimental results of the whole year are shown in the following table:

[0084]

[0085] From the experimental results, the method described in the embodiments of the present application has the following advantages:

[0086] 1. Compared with the mainstream ESS planning and running products, the method described in the embodiments of the present application can process 15min optimization interval, and better meet the calculation demand of demand charge;

[0087] 2. The method can autonomously and intelligently evaluate the demand response request according to the demand response request, greatly improving the work efficiency of the operator;

[0088] 3、The method can successfully reduce the load peak according to the demand, thereby achieving the purpose of reducing the cost, reducing the operation cost for the user, and improving the economic benefit;

[0089] 4、The method constructs strong data analysis capability, helps the user to comprehensively understand the change trend and use efficiency of the energy consumption, and thereby formulates the scientific and reasonable energy management strategy.

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

[0091] The above description is merely preferred specific embodiments of the present application, but the protection scope of the present application is not limited thereto. Any changes or replacements within the technical scope disclosed by the present application can be easily conceived by those skilled in the art, and should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims. The information disclosed in the background section of the present application is merely intended to deepen the understanding of the general background of the present application, and should not be regarded as acknowledging or implying in any form that the information constitutes the prior art known by those skilled in the art.

Claims

1. An intelligent energy storage integration management method for an industrial site, characterized by, The method comprises: Step 1, first, the monitoring device is used to collect data of the energy storage system ESS, and the collected data is stored in the form of an Excel table as prediction data for subsequent power generation and load use; Step 2, based on the given historical load data of the energy storage system ESS, an energy management system EMS is constructed to minimize long running costs and optimize the energy storage configuration of the energy storage system ESS to obtain an optimal investment-return scheme; Step 3, the constructed energy management system EMS is tested for scheduling, the prediction data for power generation and load use are input, and corresponding weighting coefficients are set according to different hours of a day to reflect energy demand in peak hours and off-peak hours; In step 3, in the peak hours, the problems of demand charge, voltage variation and equipment degradation are involved; in the off-peak hours, the target is to minimize voltage variation and equipment degradation problems, wherein: 1) demand charge The total demand charge is represented by recording the highest power flow in the peak hours during the billing period using a demand meter: C demand = p demand ·max(P load,t ) where C demand is the total demand charge during the billing period; p demand is the demand charge in $ / KW; P load，t is the actual power load measured in the demand meter at time t; The reduction of demand charge is achieved by reducing the daily peak demand of each billing period; 2) voltage variation Due to the photovoltaic and load oscillation of the load point, the voltage on the distribution network will fluctuate, in order to quantify the fluctuation, the following voltage variation coefficient is used: where V variation represents the voltage variation; V t is the voltage vector containing all voltage amplitudes of the feeder at time t; V ref represents the designed voltage vector; The mapping between voltage amplitude and injected power is represented 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 t，i represents the fluctuation of the node voltage amplitude, in volts; V t，i is the voltage magnitude at the load point i at time t; K Pi is the sensitivity coefficient with respect to the vector of actual power injection; K Qi represents the vector of the sensitivity coefficient multiplied by the reactive power; ΔP i，t and ΔQ i，t are the active and reactive power injections at load point i at time t; 3) degradation cost The model of degradation cost is represented as: Wherein, battery repalcement cost represents the battery replacement cost; energy throughput in cycle represents the total energy of charging and discharging in the use cycle; Further represented as: where C bat represents the battery capital cost; L bat (DOD) represents the battery life factor relative to the depth of discharge DOD; P bat，t is the output power of the energy storage system ESS at time t; At is the time interval; In the peak hours, the system will reduce the peak demand, minimize the voltage variation and optimize the degradation cost, and the objective function is represented as: J = a - C demand + β - V variation + γ - C d Wherein, J represents the sum of all overheads; and alpha, beta and gamma are the weighting coefficients of the multi-objective function; In the off-peak hours, the requirement of reducing demand charge is cancelled, and the weighting coefficient alpha in the objective function is 0; Step 4, the user inputs the parameters of the demand adjustment energy management system EMS, and the energy management system EMS updates the optimization strategy in real time according to the user input demand and performs visual display, and the user views the specific optimization suggestions and the expected saved cost. 2.The intelligent energy storage integration management method of an industrial site according to claim 1, wherein, In step 1, a 2976*12 matrix is defined for storing data of 12 months in a year, the data stored in the Excel table is allocated to the matrix by month, and then the data of a specified month is extracted, so that the power load data of each month can be processed or analyzed separately. 3.The intelligent energy storage integration management method of an industrial site according to claim 1, wherein, The process of step 2 is specifically as follows: Based on the given historical load data, demand charge, energy charge and customer operation mode of the energy storage system ESS, the charging / discharging curve of the set time interval is calculated as the output reference, and the cost profit and specific E max and P max ; E max represents the upper limit of the power stored in the battery system of the energy storage system ESS, P max represents the maximum power of the power electronic device; the specific optimization problem is: s.t. P inv，min ≤ P inv，t ≤ P inv，max , E min ≤E bat，t ≤E max , E bat，t = E bat，t-1 + P bat，t Δt, P bat，t = -P inv，dis，t / η dis + P inv，ch，t η ch , P inv，t = -P inv，dis，t + P inv，ch，t , E bat，T = E bat，0 , Wherein, T represents 24 hours; a, b represent cost parameters of battery energy storage system; r E ∑(L t +P inv，t )Δt represents the kilowatt-hour electricity charge, r E is the kilowatt-hour electricity price, L t is the actual power load measured by the electric meter at t time; r d max(L t +P inv，t ) represents the demand charge expense, r d is the unit price of demand charge; the electric meter will record the maximum load value nmax(L t +P inv，t ) in a time interval of Δt in the entire time period; α is the investment return coefficient; J represents the sum of all expenses; The constraint condition s.t. is as follows: P inv，min ≤P inv，t ≤P inv，max where P inv，min and P inv，max represent the minimum and maximum power of the power electronics device, respectively; For the calculation of the stored power of the battery system: E bat，t = E bat，t-1 + P bat，t Δt wherein E bat，t is the stored energy of the battery system of the energy storage system ESS at time t; P bat,t is the output power of the energy storage system ESS at time t; The amount of stored energy E of the battery system bat，t The following needs to be met: E min ≤E bat，t ≤E max where E max and E min represent the upper and lower bounds of the stored energy of the battery system of the energy storage system (ESS), respectively; P inv，dis，t and P inv，ch，t As optimization variables, the discharging and charging power at the inverter terminal are referred to; in order to map the discharging and charging power from the energy storage system ESS to the inverter terminal, the charge-discharge efficiency is introduced, which is converted as follows: P bat，t = -P inv，dis，t / η dis +R inv，ch，t η ch where η dis and η ch are the discharge and charge efficiencies, respectively; At the inverter terminal a reference power P is provided which is the sum of the discharging power and the charging power inv，t is represented as: P inv，t = -P inv，dis，t +P inv，ch，t The last constraint condition indicates that the last power of the battery should be equal to the initial power of the battery, that is: E bat，T = E bat.0 E bat，0 represents the initial electric quantity of the battery; E bat，T represents the electric quantity of the battery at the last time T For each reference power P inv，t The demand charge for this period of time is calculated by the following equation: r d max(L t +P inv，t ) After collecting the demand charge and watt-hour charge data of all months, the annual cost and profit report can be calculated, that is, the sum of all overheads J is minimized. 4.The intelligent energy storage integration management method of an industrial site according to claim 1, wherein, In step 4, the visualization performed by the energy management system EMS comprises three graphs: a load curve of the interaction of the power load and the energy storage system, a state percentage curve of the system, and a daily net charge and discharge power curve.

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