Improved industrial and commercial energy storage demand management method

Abstract

The invention relates to the technical field of electric energy management, and discloses an improved industrial and commercial energy storage demand management method, which comprises the following steps: firstly, acquiring electric energy data; establishing a demand management objective function, wherein the establishment of the demand management objective function specifically comprises establishment of an objective function and constraint conditions based on peak valley arbitrage and demand management; solving a target function; the charging and discharging time nodes are controlled through the energy storage system, and the algorithm of the steps is embedded into the energy storage system. The constraint conditions comprise an electric quantity balance constraint, an energy and power constraint, a charging and discharging power constraint, a battery electric quantity constraint, a load demand constraint and an energy storage system operation constraint, and the electric quantity balance constraint is at any moment t, and charging and discharging of the battery cannot be carried out at the same time. The method has more flexible peak-valley electricity price difference response capability, can dynamically adjust the charging and discharging strategy according to the actual electricity price fluctuation condition, maximizes the utilization of the electricity price advantages in different time periods, and improves the income level of an enterprise.

Description

Technical Field

[0001] The present invention relates to the technical field of electric energy management, and in particular to an improved industrial and commercial energy storage demand management method. Background Art

[0002] Electricity costs account for a large proportion of a company's production costs. "Lowering electricity costs" is often the most direct and effective way to reduce production costs, and the basic electricity cost is an important component of the electricity cost. The calculation formula is: Basic electricity cost = billing capacity or maximum demand × basic electricity price. How to effectively manage demand, timely adjust the reported demand according to production electricity consumption, and choose a reasonable billing method to maximize savings on basic electricity costs is the issue that corporate users are most concerned about. Currently, demand management combined with energy storage is the most common management method on the market. If energy storage is regarded as a computer, EMS is like an operating system. However, the current EMS demand management on the market has the following defects:

[0003] First, regarding demand reporting, traditional methods lack accurate demand forecasting capabilities and are unable to effectively analyze data characteristics in a timely manner, making it difficult to accurately predict peak and valley power demand, thus affecting the accuracy of demand estimates. There is also uncertainty regarding energy storage charging and discharging, as well as demand management. Static demand values: Traditional methods typically set static demand values ​​based on historical load data or fixed demand, ignoring the time-varying and flexible nature of load fluctuations and failing to respond promptly to energy market fluctuations and changes in user demand.

[0004] Second, in terms of operations and management, demand reporting traditionally relies on manual estimation of maximum demand. Manual meter reading and the creation of statistical analysis spreadsheets each month require approximately six days of work. Historical data is analyzed using Excel spreadsheets, and then retrospectively analyzed based on this data to determine whether changes to billing methods are necessary. This results in significant labor costs. Since data cannot be transmitted externally by default, a local operations and maintenance team is required at the power plant, resulting in high labor and management costs. Lack of intelligent management: Traditional methods often rely on manual judgment and empirical rules, lacking automated, intelligent demand management strategies. This results in low operational efficiency and fails to maximize the potential of the energy storage system.

[0005] Third, in terms of application scenarios, traditional energy storage EMS applications are mostly on the power supply side and the grid side. Due to the small capacity, large number, wide dispersion, and high operation and maintenance costs of industrial and commercial energy storage sites, they cannot support local manned operation and inevitably require remote operation and maintenance monitoring, which traditional energy storage EMS technology cannot support.

[0006] Fourth, regarding professional requirements, industrial and commercial energy storage is primarily used for peak shaving and valley filling, complemented by demand control strategies and backflow protection to achieve objectives such as dynamic capacity expansion and off-grid backup. Due to the instability of renewable energy generation and the volatility of loads, static energy storage strategies struggle to meet current market demands. More dynamic, intelligent strategies are needed. These strategies combine time-of-use electricity prices, photovoltaic forecasts, load fluctuations, and protection targets. Based on relevant algorithms, risk predictions are used to dynamically formulate charging and discharging strategies in real time. This is the only way to effectively achieve overall economic efficiency. By rationally utilizing batteries and minimizing excessive degradation, the economic viability of the energy storage system itself is ensured. Traditional methods fail to fully consider factors such as electricity price curves and load forecasts, making it impossible to formulate optimal charging and discharging strategies, resulting in low economic efficiency. To address these issues, an improved approach to demand management for industrial and commercial energy storage is needed. Summary of the Invention

[0007] The purpose of this invention is to provide an improved method for managing industrial and commercial energy storage demand. This method offers a more flexible response to peak and valley electricity price fluctuations, dynamically adjusting charging and discharging strategies based on actual price fluctuations, maximizing the advantages of electricity prices in different time periods and improving enterprise profitability.

[0008] The present invention is implemented as follows: the present invention provides an improved industrial and commercial energy storage demand management method, which is specifically performed in the following steps:

[0009] S1: Acquire electric energy data; wherein the electric energy data includes electric energy market data, energy storage device data, and electric energy consumption data; the electric energy market data includes day-ahead electricity prices, real-time electricity prices, and peak-valley electricity price differences; the energy storage device data includes capacity, charge and discharge efficiency, and charge and discharge rate parameters; the electric energy consumption data includes electric load curves and load demand curves;

[0010] S2: Establishing a demand management objective function; establishing a demand management objective function specifically includes establishing an objective function and constraints based on peak-valley arbitrage and demand management, where the objective function F = f(electricity price cost, electricity reward and penalty cost, benefit factor...); specifically, as shown in formula (1);

[0011] F=f(p ch -p dch )*dt*n_1_price′+demand_price*gamma Formula (1)

[0012] Among them, p ch Represents the decision vector of charging power, p dch represents the decision variable of discharge power, dt represents the price interval, demand_price represents the demand price, and gamma represents the optimized demand power.

[0013] The constraints include power balance constraints, energy and power constraints, charge and discharge power constraints, battery power constraints, load demand constraints, energy storage system operation constraints and legal and regulatory constraints. The power balance constraint states that at any time t, the battery cannot be charged and discharged at the same time, i.e. u d +u ch ≤1;

[0014] The battery capacity at the next moment of the energy and power constraints is equal to the battery capacity at this moment plus the charging capacity or the capacity subtracted, as shown in formula (2)

[0015] T st (2:end)==T st (1:end-1)+η ch *p ch *dt-(p dch / η d )*dt Formula (2).

[0016] The charge and discharge power constraints include that the power during the charging and discharging process is less than or equal to the maximum charge / discharge power, the difference between the battery's charging power and discharge power is less than or equal to the difference between the transformer power and the production power, the production power is greater than or equal to the difference between the battery's discharge power and charging power, and the difference between the battery's charging power and discharge power is less than or equal to the difference between the demand power and the production power, as shown in formula (3).

[0017]

[0018] S3: Solve the objective function; the battery power constraint is that the battery power should be large

[0019] is equal to 0 and less than or equal to the battery capacity, as shown in formula (4);

[0020] 0≤T st ≤T full Formula (4).

[0021] The load demand constraint is to set corresponding load demand limits according to user needs and electricity consumption characteristics;

[0022] The energy storage system operation constraints are set based on factors such as the operating status and lifespan of the energy storage equipment. Dynamic programming and integer programming optimization algorithms are used to solve the objective function, adjusting the demand management lower limit in real time. Based on the current system status and forecast data, the objective function is repeatedly calculated and the demand management lower limit is dynamically modified, thereby optimizing the objective function and achieving automatic demand adjustment. This is specifically performed as follows:

[0023] S 3.1 :Set the initial value X0 as the lower limit of demand and calculate F(X0);

[0024] S 3.2 : Use dynamic programming to calculate the increment ΔX, let X1 = X0 + ΔX;

[0025] S 3.3 : Repeat S 3.1 -S 3.2 ; Calculate the lower limit of X's influence demand that maximizes F(X).

[0026] S4: Controlling the charging and discharging time nodes through the energy storage system; the energy storage system includes an energy management system, a power control system and a battery management system;

[0027] Based on the output results of the improved demand management method in steps S1-S3, the energy management system in the energy storage system is used to control the charging and discharging time nodes. The energy management system controls the power control system to perform charging or discharging operations based on the collected electricity prices and the charge and discharge status parameters of the energy storage equipment and the charge and discharge plan instructions. The specific operations of the energy storage system performing demand control are as follows:

[0028] Monitor the real-time power of the transformer. When the power on the low-voltage side of the transformer exceeds the demand, the energy storage system starts discharging / increasing the discharge power, reducing the transformer output and ensuring that the transformer power exceeds the limit.

[0029] S5: Embed the algorithm from steps S1-S4 into the energy storage system. Specifically, an open-architecture EMS, such as a light EMS, provides expansion interfaces. Design the demand optimization module to be pluggable and insert it into the expansion interface of the energy management system host. Simply inserting the algorithm module enables the system to receive signals and accept charge and discharge instructions, thus providing the EMS with automated demand management capabilities.

[0030] Furthermore, the present invention provides a computer-readable storage medium, wherein the storage medium stores a computer program, and when the computer program is executed by a main controller, the method described above is implemented.

[0031] Compared with the prior art, the present invention has the following beneficial effects:

[0032] 1. Through a complete data collection system, a reliable and stable data storage mechanism is established to collect, manage, and coordinate control of BMS, PCS, and other systems, enabling intelligent management such as real-time monitoring and fault alarms. Unlike traditional EMSs that rely primarily on manual configuration, this invention takes external objectives, such as profit maximization, power consumption limits, and energy use priorities, and inputs internal system conditions to determine the optimal system output. This frees power plant managers from reliance on manual configuration, overcoming issues such as inefficiency and sub-optimal power plant performance.

[0033] 2) Significantly reduce operation and maintenance costs

[0034] By replacing peak transformer capacity with energy storage devices, users can lower transformer costs and reduce the cycle and expense of transformer expansion investment. This invention enables intelligent monitoring, systematized experience, and refined management, significantly reducing operation and maintenance costs, and eliminating unnecessary on-site personnel.

[0035] 3) Supporting industrial and commercial energy storage EMS application scenarios

[0036] The energy storage EMS combination kit combines a small box with an embedded algorithm into an EMS management system, receiving signals and issuing charging and discharging strategies in real time. It is easy to operate and has a wide range of applications. It covers different scenarios, including standalone energy storage, photovoltaic power stations, and energy storage vehicles.

[0037] 4) Charging and discharging strategy based on optimal profit target algorithm

[0038] Peak-valley arbitrage: By deploying an energy storage system and adopting a two-charge, two-discharge strategy, charging during valley / flat periods and discharging during peak hours, profits can be generated by arbitrage between peak and valley electricity prices. On the one hand, rational and planned charging and discharging can reduce peak loads, fill valley loads, and control demand spike thresholds, thus preventing frequent start-up and shutdown of impact loads that could damage power supply and consumer equipment. On the other hand, due to the difference in time-of-use electricity prices, surplus electricity sales, demand charges, and maintenance expenses can be reduced, all of which improve economic benefits.

[0039] Demand management: After configuring the energy storage system, it can intelligently adjust power. When the load power exceeds the set value, the energy storage system automatically reduces the charging power or switches to a discharge state, reducing demand electricity charges. Demand-side response is also one of the main ways for industrial and commercial energy storage projects to generate profits. Through grid demand-side response services, certain financial subsidies can be obtained.

[0040] The present invention has a more flexible ability to respond to differences in peak and valley electricity prices, and can dynamically adjust the charging and discharging strategy according to actual electricity price fluctuations, maximize the use of electricity price advantages in different time periods, and improve the company's profit level. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without paying any creative work.

[0042] Figure 1 It is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0043] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the invention for which protection is sought, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0044] In this embodiment, please refer to Figure 1 , follow these steps:

[0045] S1: Obtaining electric energy data; wherein the electric energy data includes electric energy market data, energy storage equipment data and electric energy consumption data, wherein the electric energy market data includes day-ahead electricity prices, real-time electricity prices, and peak-valley electricity price differences; the energy storage equipment data includes capacity, charge and discharge efficiency, and charge and discharge rate parameters; the electric energy consumption data includes electric load curves and load demand curves; as shown in Table 1:

[0046] Table 1 Electric energy data table

[0047] Time 0:00 0:15 0:30 … 16:45 17:00 17:15 … 23:15 23:30 23:45 electricity prices 0.357 0.354 0.354 … 1.206 1.206 0.830 … 0.330 0.330 0.330 load 117.0 102.5 104.5 … 332 316 314 … 138.5 156.5 130.0

[0048] S2: Establishing a demand management objective function; establishing a demand management objective function specifically includes establishing an objective function and constraints based on peak-valley arbitrage and demand management, where the objective function F = f(electricity price cost, electricity reward and penalty cost, benefit factor...); specifically, as shown in formula (1);

[0049] F=f(p ch -p dch )*dt*n_1_price′+demand_price*gamma Formula (1)

[0050] Among them, p ch Represents the decision vector of charging power, p dch represents the decision variable of discharge power, dt represents the price interval, demand_price represents the demand price, and gamma represents the optimized demand power.

[0051] The constraints include power balance constraints, energy and power constraints, charge and discharge power constraints, battery power constraints, load demand constraints, energy storage system operation constraints and legal and regulatory constraints. The power balance constraint states that at any time t, the battery cannot be charged and discharged at the same time, i.e. u d +u ch ≤1;

[0052] The battery capacity at the next moment of the energy and power constraints is equal to the battery capacity at this moment plus the charging capacity or the capacity subtracted, as shown in formula (2)

[0053] T st (2:end)==T st (1:end-1)+η ch *p ch *dt-(p dch / η d )*dt Formula (2).

[0054] The charge and discharge power constraints include that the power during the charging and discharging process is less than or equal to the maximum charge / discharge power, the difference between the battery's charging power and discharge power is less than or equal to the difference between the transformer power and the production power, the production power is greater than or equal to the difference between the battery's discharge power and charging power, and the difference between the battery's charging power and discharge power is less than or equal to the difference between the demand power and the production power, as shown in formula (3).

[0055]

[0056] S3: Solve the objective function; the battery power constraint is that the battery power must be greater than or equal to 0 and less than or equal to the battery capacity, as shown in formula (4);

[0057] 0≤T st ≤T full Formula (4).

[0058] The load demand constraint is to set corresponding load demand limits according to user needs and electricity consumption characteristics;

[0059] The energy storage system operation constraints are set based on factors such as the operating status and lifespan of the energy storage equipment. Dynamic programming and integer programming optimization algorithms are used to solve the objective function, adjusting the demand management lower limit in real time. Based on the current system status and forecast data, the objective function is repeatedly calculated and the demand management lower limit is dynamically modified, thereby optimizing the objective function and achieving automatic demand adjustment. This is specifically performed as follows:

[0060] S 3.1 :Set the initial value X0 as the lower limit of demand and calculate F(X0);

[0061] S3.2 : Use dynamic programming to calculate the increment ΔX, let X1 = X0 + ΔX;

[0062] S 3.3 : Repeat S 3.1 -S 3.2 ; Calculate the lower limit of the X-influence demand that maximizes F(X). The operating results obtained from the sample data are shown in Table 2;

[0063] Table 2 Run sample data table

[0064]

[0065] S4: Control the charging and discharging time nodes through the energy storage system; the energy storage system includes an energy management system, a power control system and a battery management system.

[0066] Based on the output results of the improved demand management method in steps S1-S3, the energy management system in the energy storage system is used to control the charging and discharging time nodes. The energy management system controls the power control system to perform charging or discharging operations based on the collected electricity prices and the charge and discharge status parameters of the energy storage equipment and the charge and discharge plan instructions. The specific operations of the energy storage system performing demand control are as follows:

[0067] Monitor the real-time power of the transformer. When the power on the low-voltage side of the transformer exceeds the demand, the energy storage system starts discharging / increasing the discharge power, reducing the transformer output and ensuring that the transformer power exceeds the limit.

[0068] S5: Embed the algorithm from steps S1-S4 into the energy storage system. Specifically, an open-architecture EMS, such as a light EMS, provides expansion interfaces. Design the demand optimization module to be pluggable and insert it into the expansion interface of the energy management system host. Simply inserting the algorithm module enables the system to receive signals and accept charge and discharge instructions, thus providing the EMS with automated demand management capabilities.

[0069] In this embodiment, the present invention provides a computer-readable storage medium, wherein the storage medium stores a computer program. When the computer program is executed by a main controller, any of the methods described above is implemented.

[0070] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of protection of the present invention.

Claims

1. An improved industrial and commercial energy storage demand management method, characterized in that: Please follow the steps below: S1: Obtaining power data; S2: Establishing demand management objective function; S3: solving the objective function; S4: Control the charging and discharging time nodes through the energy storage system; S5: Embed the algorithm of steps S1-S4 into the energy storage system.

2. An improved industrial and commercial energy storage demand management method according to claim 1, characterized in that: In step S2, establishing the demand management objective function specifically includes establishing an objective function and constraint conditions based on peak-valley arbitrage and demand management, wherein the objective function F=f(electricity price cost, electricity reward and punishment cost, benefit factor...); specifically as shown in formula (1); F = f(p ch - p dch ) * dt * n_1_price′ + demand_price * gamma Equation (1) Among them, p ch represents the decision vector of charging power, p dch represents the decision variable of discharge power, dt represents the price interval, demand_price represents the demand price, and gamma represents the optimized demand power.

3. An improved industrial and commercial energy storage demand management method according to claim 2, characterized in that: The constraints include power balance constraints, energy and power constraints, charge and discharge power constraints, battery power constraints, load demand constraints, and energy storage system operation constraints. The power balance constraints mentioned above mean that at any time t, the battery cannot be charged and discharged at the same time, that is, u d +u ch ≤1; The battery power at the next moment of the energy and power constraints is equal to the battery power at this moment plus the charging power or the power subtracted, as shown in formula (2): T st (2:end) == T st (1:end - 1) + η ch *p ch *dt - (p dch / η d )*dt Equation (2).

4. An improved industrial and commercial energy storage demand management method according to claim 3, characterized in that: The charging and discharging power constraints include that the power during the charging and discharging process is less than or equal to the maximum charging / discharging power, the difference between the charging power and the discharging power of the battery is less than or equal to the difference between the transformer power and the production power, the production power is greater than or equal to the difference between the battery discharging power and the charging power, and the difference between the charging power and the discharging power of the battery is less than or equal to the difference between the demand power and the production power, as shown in formula (3); 5. An improved industrial and commercial energy storage demand management method according to claim 4, characterized in that: In step S5, the energy storage system includes an energy management system, a power control system and a battery management system; The electric energy data mentioned herein include electricity market data, energy storage equipment data and electricity consumption data. The electricity market data include day-ahead electricity prices, real-time electricity prices and peak-valley electricity price differences; the energy storage equipment data include capacity, charging and discharging efficiency and charging and discharging rate parameters; the electricity consumption data include electricity load curves and load demand curve data.

6. The improved industrial and commercial energy storage demand management method according to claim 1, characterized in that: In step S3, the battery power constraint is that the battery power must be greater than or equal to 0 and less than or equal to the battery capacity, as shown in formula (4); 0≤T st ≤T full Formula (4).

7. The improved industrial and commercial energy storage demand management method according to claim 1, characterized in that: In step S3, the objective function is solved by using the optimization algorithm of dynamic programming and integer programming, and the lower limit of demand management is adjusted in real time. According to the current system status and forecast data, the objective function is repeatedly calculated, and the lower limit of demand management is dynamically modified, so as to optimize the objective function and realize automatic adjustment of demand. Specifically, the following steps are performed: S 3.1 :Set the initial value X0 as the lower limit of demand and calculate F(X0); S 3.2 : Use dynamic programming to calculate the increment ΔX, let X1=X0+ΔX; S 3.3 : Repeat S 3.1 -S 3.2 ; Calculate the lower limit of X influence demand that maximizes F(X).

8. An improved industrial and commercial energy storage demand management method according to claim 1, characterized in that: In step S4, based on the output results of the improved demand management method of steps S1-S3, the energy management system in the energy storage system is used to control the time nodes of charging and discharging. The energy management system controls the power control system to perform charging or discharging operations according to the collected electricity price and the charging and discharging state parameters of the energy storage device and the charging and discharging plan instructions. The specific operations of the energy storage system to perform demand control are as follows: Monitor the real-time power of the transformer. When the power on the low-voltage side of the transformer exceeds the demand, the energy storage system starts to discharge / increase the discharge power, reducing the transformer output and ensuring that the transformer power exceeds the limit.

9. The improved industrial and commercial energy storage demand management method according to claim 1, characterized in that: In step S5, the algorithms of steps S1-S4 are embedded into the energy storage system, specifically using an open architecture EMS system, providing an expansion interface, and designing the demand optimization module as a pluggable structure, which is inserted into the expansion interface of the energy management system host.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by the main controller, the method according to any one of claims 1 to 9 is implemented.

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