A method and system for energy storage dispatch for peak demand reduction and peak valley arbitrage

Abstract

The present application relates to a kind of energy storage scheduling method and system for peak demand reduction and peak valley arbitrage.The method comprises the following steps: setting parameters and initializing;at each time t in the demand control time period, the real-time discharge power is calculated according to the current remaining available power of the energy storage system and the remaining time to the end of demand control;at the end of each demand metering period, the grid-side demand and the load-side demand of the period are calculated, and the grid-side maximum demand and the load-side maximum demand are updated in real time;the real-time load power is continuously monitored and obtained, if the real-time load power is greater than the grid-side maximum demand, the energy storage system is controlled to discharge according to the calculated real-time discharge power to reduce demand;otherwise, control the energy storage system to execute standby;whether to enable peak valley arbitrage mode is judged based on the dynamic discharge power and the realized demand reduction amount;if enabled, the energy storage system continuously discharges according to the dynamic discharge power;if not enabled, the energy storage system remains in demand lock control mode.

Description

Technical Field

[0001] This invention relates to the field of energy storage system control technology, and more specifically to an energy storage scheduling method and system for peak demand reduction and peak-valley arbitrage. Background Technology

[0002] In many countries and regions, electricity billing systems include not only electricity price but also demand price based on peak demand, which often constitutes a significant proportion of a user's total electricity bill. Meanwhile, the price difference between peak and off-peak hours provides an economic opportunity for peak-valley arbitrage. Energy storage systems, as flexible energy regulation devices, can reduce peak demand by smoothing load fluctuations during the demand metering cycle and realize peak-valley arbitrage profits through off-peak charging and peak-peak discharging. Therefore, synergistically optimizing the demand management and peak-valley arbitrage functions of energy storage systems is an important requirement for industrial and commercial users to reduce cost pressures.

[0003] In existing technologies, energy storage dispatching technologies mainly fall into three categories: First, time-based dispatching technology, which divides the day into output, backup, and standby phases and sets demand management as the priority objective to determine dispatching instructions. This approach achieves coordinated control of demand management and peak shaving to a certain extent. Second, dispatching technology that introduces SOC (State of Charge) threshold management. This approach determines whether to execute peak-valley arbitrage or prioritize demand reduction by judging whether the current SOC exceeds the demand backup threshold. This approach achieves dynamic coordination of the two functions through SOC threshold management and has certain practical value. Third, a three-layer planning model is adopted, which achieves comprehensive optimization of energy storage dispatching through energy storage capacity planning, day-ahead optimization dispatching based on load forecasting, and real-time control correction. This approach can achieve better economic benefits.

[0004] However, all three methods have certain technical limitations: For time-segmentation-based scheduling methods, the control strategy is relatively fixed, relying on pre-set time-segmentation and target power calculation methods. In actual operation, it may not be able to fully adapt to real-time load changes and the dynamic state of energy storage capacity. For SOC threshold management scheduling technology, its core relies on the scientific setting of demand and reserve power thresholds. The setting of these thresholds depends on historical data and experience, so the practical application scope of this technology is narrow, and the problem of frequent switching of control strategies when SOC approaches the threshold is difficult to solve. For scheduling technology based on the three-level planning model, its scheme is highly dependent on the accuracy of load forecasting. When load forecasting deviates, the charging and discharging plan formulated in the previous day may not be effectively executed, resulting in unstable demand reduction effects. Moreover, the computational complexity of the three-level planning model is high, requiring high computing resources and algorithm optimization capabilities, which presents certain implementation difficulties in practical engineering applications. Summary of the Invention

[0005] The purpose of this invention is to provide an energy storage scheduling method and system for peak demand reduction and peak-valley arbitrage. This method has the advantage of ensuring the stability of demand reduction without relying on historical data and forecasts. It can coordinate demand control and peak-valley arbitrage, and fully release the stored energy while ensuring the safety margin of demand, thereby maximizing the arbitrage income during high electricity price periods and improving the overall return on investment of the energy storage system.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0007] In a first aspect, the present invention provides an energy storage scheduling method for peak demand reduction and peak-valley arbitrage, the method comprising:

[0008] S01, Set energy storage system parameters and demand metering cycle. and demand control time period And initialize the initial SOC of the energy storage system and the maximum demand on the grid side. Maximum demand on the load side Peak-valley arbitrage discharge power ;

[0009] S02 performs real-time dynamic discharge power calculation, based on the current remaining available power of the energy storage system at each time t within the demand control period. Remaining time until distance demand control ends Real-time calculation of dynamic discharge power ;

[0010] S03, at the end of each demand metering cycle, calculate the grid-side demand for that cycle. With load-side demand And update the maximum demand on the grid side in real time. and the maximum demand on the load side ;

[0011] S04, continuously monitor and acquire real-time load power. If the real-time load power Greater than the maximum demand on the grid side Then the energy storage system is controlled to discharge according to the calculated real-time power. Discharge to reduce demand; otherwise, control the energy storage system to enter standby mode.

[0012] S05, based on the dynamic discharge power Compared with the realized demand reduction Determine whether to enable peak-valley arbitrage mode; if enabled, the energy storage system will continue to discharge according to the dynamic discharge power; if not enabled, the energy storage system will maintain the demand-locked control mode.

[0013] As a preferred embodiment of the present invention, in step S02, the dynamic discharge power The specific calculation method is as follows:

[0014]

[0015] ; ≥0.01

[0016] in, Let be the remaining available electricity of the energy storage system at time t; For charge and discharge efficiency; From the current moment The remaining control time until demand control ends; This represents the maximum allowable discharge power of the energy storage system.

[0017] As a preferred embodiment of the present invention, the remaining available power The specific calculation method is as follows:

[0018]

[0019] in, The current state of charge; For energy storage capacity; This represents the lower limit of the SOC's operating range.

[0020] As a preferred embodiment of the present invention, in step S05, the specific condition for activating the peak-valley arbitrage mode is as follows:

[0021]

[0022]

[0023] When dynamic discharge power Greater than the realized demand reduction At that time, the peak-valley arbitrage mode is activated.

[0024] As a preferred embodiment of the present invention, the method for calculating the energy storage discharge power in step S04 is specifically as follows:

[0025]

[0026]

[0027]

[0028] in, Let t be the remaining available power of the energy storage system. This refers to the maximum discharge power based on the remaining power of the energy storage system; Effective discharge power; This represents the final calculated real-time discharge power.

[0029] As a preferred embodiment of the present invention, this energy storage scheduling method for peak demand reduction and peak-valley arbitrage further includes updating the SOC state of the energy storage system:

[0030] When the energy storage system discharges, the State of Charge (SOC) is updated as follows:

[0031]

[0032] When the energy storage system is charging, the State of Charge (SOC) is updated as follows:

[0033]

[0034] Furthermore, range constraints are applied to the SOC:

[0035]

[0036] in, The current state of charge; The state of charge at the next moment; To control the time step; This refers to the real-time discharge power. Real-time charging power; For energy storage capacity; This represents the lower limit of the SOC's operating range. This represents the upper limit of the SOC's operating range.

[0037] As a preferred embodiment of the present invention, this energy storage dispatching method for peak demand reduction and peak-valley arbitrage also includes real-time grid power. Calculation and output:

[0038] .

[0039] As a preferred embodiment of the present invention, a pre-charging step is further included before step S02: during the demand control period. Previously, the energy storage system was charged to full capacity.

[0040] As a preferred embodiment of the present invention, in step S03, the maximum demand on the power grid side is updated in real time. and the maximum demand on the load side The method is as follows: at the start of the next demand metering cycle, calculate the grid-side demand of the previous cycle. and load-side demand ;

[0041]

[0042]

[0043] in, For time conversion factor, / This is a time conversion factor used to convert the increase in electrical energy within a period into an average power value based on hours. and The load energy at the beginning and end of the cycle; and This refers to the stored electrical energy at the beginning and end of the cycle.

[0044]

[0045]

[0046] Secondly, the present invention provides an energy storage dispatching system for peak demand reduction and peak-valley arbitrage, the system comprising:

[0047] The parameter setting and initialization module is used to set the energy storage system parameters and demand metering cycle. and demand control time period And initialize the initial SOC of the energy storage system and the maximum demand on the grid side. Maximum demand on the load side Peak-valley arbitrage discharge power ;

[0048] The dynamic discharge power calculation module is used to perform real-time dynamic discharge power calculations at each time t within the demand control period, based on the current remaining available power of the energy storage system. Remaining time until distance demand control ends Real-time calculation of dynamic discharge power ;

[0049] The demand update module is used to calculate the grid-side demand at the end of each demand metering cycle. With load-side demand And update the maximum demand on the grid side in real time. and the maximum demand on the load side ;

[0050] The monitoring and execution module is used to continuously monitor and obtain real-time load power. If the real-time load power Greater than the maximum demand on the grid side Then the energy storage system is controlled to discharge according to the calculated real-time power. Discharge to reduce demand; otherwise, control the energy storage system to enter standby mode.

[0051] Arbitrage discrimination module, used to determine the dynamic discharge power Compared with the realized demand reduction Determine whether to enable peak-valley arbitrage mode; if enabled, the energy storage system will continue to discharge according to the dynamic discharge power; if not enabled, the energy storage system will maintain the demand-locked control mode.

[0052] In summary, the present invention has the following beneficial effects:

[0053] This invention, through a dynamic discharge power calculation method and a demand locking control mechanism, enables the system to adaptively adjust its discharge behavior based on the real-time state of charge and remaining control time of the energy storage system, ensuring that the stored energy is evenly and reasonably distributed throughout the entire demand control period. It effectively avoids the risk of demand reduction failure due to load forecast deviations, significantly improves the robustness of energy storage scheduling in complex load scenarios, and thus significantly reduces users' demand electricity costs.

[0054] This invention fully taps the discharge potential of energy storage systems during periods of high electricity prices by using demand control and peak-valley arbitrage in synergistic judgment conditions, achieving synergistic optimization of multiple economic benefits. While ensuring that the demand reduction effect is not affected, the system can intelligently decide whether to activate the peak-valley arbitrage mode in the later stages of the demand control period by comparing the dynamic discharge power with the realized demand reduction. This strategy solves the problem of existing methods retaining a large amount of residual electricity after the control period ends, improving the utilization rate and return on investment of energy storage power, enabling the energy storage system to simultaneously obtain the dual benefits of reduced demand electricity costs and peak-valley arbitrage.

[0055] The control strategy of this invention is simple and efficient, easy to implement in engineering and promote application. The scheduling method has low algorithm complexity and small computational load, and can be deployed in conventional energy storage management systems without the need for high-performance computing equipment or complex optimization algorithms. Furthermore, since the system parameters are easy to set, only basic physical parameters and demand control time periods need to be set, without the need for complex parameter tuning based on historical data, which greatly reduces the difficulty of system debugging and subsequent maintenance. It has broad application prospects under the electricity price systems of different countries and regions. Attached Figure Description

[0056] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0057] Figure 1 This is a flowchart of the method of the present invention;

[0058] Figure 2 This is a system structure block diagram of the present invention;

[0059] Figure 3 This is an analysis chart of the energy storage scheduling results in Example 3;

[0060] Figure 4 This is a comparison chart of demand in Example 3. Detailed Implementation

[0061] The subject matter described herein will now be discussed with reference to exemplary embodiments. It should be understood that these embodiments are discussed merely to enable those skilled in the art to better understand and implement the subject matter described herein, and are not intended to limit the scope, applicability, or examples set forth in the claims. The function and arrangement of the elements discussed may be changed without departing from the scope of this specification. Various processes or components may be omitted, substituted, or added as needed in the various examples. For example, the described methods may be performed in a different order than described, and steps may be added, omitted, or combined. Furthermore, features described in some examples may be combined in other examples.

[0062] Example 1

[0063] like Figure 1 As shown, this embodiment provides a detailed explanation of the implementation of the above-mentioned energy storage scheduling method for peak demand reduction and peak-valley arbitrage. The method includes:

[0064] S01, Set energy storage system parameters and demand metering cycle. and demand control time period And initialize the initial SOC of the energy storage system and the maximum demand on the grid side. Maximum demand on the load side Peak-valley arbitrage discharge power ;

[0065] First, this step aims to initialize the system parameters, which requires setting basic system parameters for different specifications and types of energy storage systems. In this embodiment, the basic parameters of the energy storage system include energy storage capacity C (unit: kWh) and maximum charge / discharge power. (Unit: kW) Charge / discharge efficiency and SOC operating range In addition, it is necessary to set the demand metering cycle. (Usually 15 minutes) and demand control time period .

[0066] Next, initialize the control variables, including the initial SOC of the energy storage system and the maximum demand on the grid side. (Initial value is empty), maximum demand on the load side (Initial value is empty) and peak-valley arbitrage discharge power (Initial value is 0).

[0067] S02 performs real-time dynamic discharge power calculation, based on the current remaining available power of the energy storage system at each time t within the time period. Remaining time until distance demand control ends Real-time calculation of dynamic discharge power ;

[0068] The dynamic discharge power is calculated in real time at each time t within the demand control period. :

[0069] First, calculate the remaining available power of the energy storage system:

[0070]

[0071] Then calculate the remaining time from the current moment until the demand control ends:

[0072]

[0073] in, The current state of charge; For energy storage capacity; This represents the lower limit of the SOC's operating range.

[0074] To avoid division by zero, the minimum remaining time is set to 0.01 hours. ≥0.01.

[0075] The formula for calculating dynamic discharge power is:

[0076]

[0077] in, Let be the remaining available electricity of the energy storage system at time t; For charge and discharge efficiency; From the current moment The remaining control time until demand control ends; This represents the maximum allowable discharge power of the energy storage system. By discharging using dynamic discharge power, it can be ensured that the remaining energy stored can be evenly distributed over the remaining control time, without exceeding the maximum discharge power limit of the energy storage system.

[0078] Dynamic discharge power The dynamic discharge power changes continuously over time and with power consumption. In the early stages of the demand control period, when the remaining time is relatively long, the dynamic discharge power... The remaining time is relatively small to avoid premature depletion of stored energy; in the later stages of the demand control period, the remaining time is shortened, and the dynamic discharge power is increased. The corresponding increase ensures that the remaining power can be fully utilized. This dynamic adjustment mechanism fundamentally solves the problem of uneven distribution of energy storage power in existing methods. It ensures that there is always sufficient power reserve to cope with load peaks throughout the entire demand control period, while avoiding excessive remaining energy storage power at the end of the demand control period. More importantly, this method is calculated entirely based on the real-time SOC of the energy storage system and the current time, without relying on historical data and load forecasting. This greatly improves the stability and reliability of the control strategy, enabling the energy storage system to stably achieve demand reduction effects under various load change scenarios.

[0079] S03, at the end of each demand metering cycle, calculate the grid-side demand for that cycle. With load-side demand And update the maximum demand on the grid side in real time. and the maximum demand on the load side ;

[0080] At the start of each demand metering cycle (e.g., 0, 15, 30, and 45 minutes into the hour), record the load energy at that moment. and energy storage At the start of the next demand metering cycle, calculate the grid-side demand and load-side demand of the previous cycle:

[0081] The formula for calculating grid demand is:

[0082]

[0083] The formula for calculating load-side demand is:

[0084]

[0085] The coefficient 4 is because the demand measurement cycle is 15 minutes (0.25 hours), and the average power over 15 minutes needs to be converted into hourly power.

[0086] Update the maximum demand on the grid side and the maximum demand on the load side:

[0087]

[0088]

[0089] S04, continuously monitor and acquire real-time load power. If the real-time load power Greater than the maximum demand on the grid side Then the energy storage system is controlled to discharge according to the calculated real-time power. Discharge to reduce demand; otherwise, control the energy storage system to enter standby mode.

[0090] During the demand control period, based on real-time load power and the recorded maximum demand on the grid side This determines the discharge behavior of the energy storage system. The judgment condition is:

[0091]

[0092] When discharge is required, calculate the actual discharge power.

[0093] First, calculate the maximum dischargeable power based on the remaining energy storage capacity:

[0094]

[0095] in To control the time step (usually 1 minute, or 1 / 60 of an hour).

[0096] Then calculate the effective discharge power, which is the portion that does not exceed the difference between the load power and the maximum demand:

[0097]

[0098] The final real-time discharge power is:

[0099]

[0100] This mechanism tracks and records the historical maximum demand on the power grid side in real time. This is used as a dynamic control benchmark, and only when the real-time load power... The energy storage system only discharges when the demand exceeds the control threshold. This demonstrates that this step establishes an adaptive control threshold, which is not a pre-set fixed value but dynamically increased based on actual operating conditions. Initially, during the demand control period, the control target is lower, making it easier for the energy storage system to trigger discharge. As the control process progresses, whenever a new demand peak occurs, the control target automatically increases to that peak level, forming a gradually rising control threshold. The advantage of this mechanism is that when the load power is lower than the existing maximum demand, the energy storage system does not discharge and remains in standby mode, thus saving valuable stored energy. Traditional methods often use fixed control targets or prediction-based targets, which can easily lead to over-discharge or under-discharge. The automatic adjustment mechanism of this invention can flexibly adjust according to the actual load situation, ensuring both demand reduction and maximizing the saving of stored energy, creating conditions for subsequent peak-valley arbitrage. This intelligent control strategy significantly improves the energy utilization efficiency and economic benefits of the energy storage system.

[0101] S05, based on the dynamic discharge power Compared with the realized demand reduction Determine whether to enable peak-valley arbitrage mode; if enabled, the energy storage system will continue to discharge according to the dynamic discharge power; if not enabled, the energy storage system will maintain the demand-locked control mode.

[0102] In the final stage of the demand control period (such as the last two hours), peak-valley arbitrage judgment is performed at the beginning of each demand measurement cycle.

[0103] First, calculate the demand reduction already achieved by the energy storage system:

[0104]

[0105] This value represents the demand reduction capability that the energy storage system has achieved through discharge;

[0106] Then compare the dynamic discharge power with the demand reduction:

[0107]

[0108] When peak-valley arbitrage is enabled, the peak-valley arbitrage discharge power is set as follows:

[0109]

[0110] During this demand metering period, even if the load power does not exceed the maximum demand, the energy storage system will still operate according to... Discharge the stored energy to make full use of periods with high electricity prices.

[0111] When peak-valley arbitrage is not enabled The energy storage system maintains a demand-locked control mode to reserve power to cope with possible load surges.

[0112] In addition, this embodiment also includes a step for updating the SOC state of the energy storage system:

[0113] When the energy storage system discharges, the SOC update formula is:

[0114]

[0115] When the energy storage system is charging (during non-demand control periods), the SOC update formula is:

[0116]

[0117] Furthermore, range constraints are applied to the SOC:

[0118]

[0119] in, The current state of charge; The state of charge at the next moment; To control the time step; This refers to the real-time discharge power. Real-time charging power; For energy storage capacity; This represents the lower limit of the SOC's operating range. This represents the upper limit of the SOC's operating range.

[0120] This condition is determined by comparing dynamic discharge power. Compared with the realized demand reduction The system intelligently decides whether to activate peak-valley arbitrage. It utilizes demand reduction... Demand reduction is used as an indicator for assessing safety margin. This represents the demand reduction capability that the energy storage system has already achieved, when the dynamic discharge power... When it exceeds this capability, it indicates that even according to the dynamic discharge power... Even with continuous discharge, the energy storage system still has sufficient energy reserves to cope with potential load surges, at which point peak-valley arbitrage mode can be safely activated. This judgment condition cleverly combines the safety of demand control with the economics of peak-valley arbitrage, fully tapping the discharge potential of the energy storage system during periods of high electricity prices while ensuring that the demand reduction effect is not affected. Existing technologies often treat demand control and peak-valley arbitrage as two independent functions, making it difficult to achieve effective coordination. However, this invention, by introducing this judgment condition, achieves seamless connection and dynamic balance between the two functions, enabling the energy storage system to fully release stored energy in the final stage of the demand control period, avoiding economic losses caused by excess energy, and significantly improving the overall economic efficiency and return on investment of the energy storage system.

[0121] In addition, this embodiment also includes real-time grid power. Calculation and output steps:

[0122]

[0123] This power refers to the power purchased from the grid. Through the scheduling of the energy storage system, the peak power of the grid is effectively reduced. At the same time, during periods of high electricity prices, the amount of electricity purchased is reduced by discharging, thereby realizing peak-valley arbitrage profits.

[0124] It should be noted that in this embodiment, during the demand control period... Previously, the energy storage system was charged to full capacity.

[0125] Example 2

[0126] like Figure 2 As shown, this embodiment provides an energy storage dispatching system for peak demand reduction and peak-valley arbitrage. The system includes:

[0127] The parameter setting and initialization module is used to set the energy storage system parameters and demand metering cycle. and demand control time period And initialize the initial SOC of the energy storage system and the maximum demand on the grid side. Maximum demand on the load side Peak-valley arbitrage discharge power ;

[0128] The dynamic discharge power calculation module is used to perform real-time dynamic discharge power calculations at each time t within the demand control period, based on the current remaining available power of the energy storage system. Remaining time until distance demand control ends Real-time calculation of dynamic discharge power ;

[0129] The demand update module is used to calculate the grid-side demand at the end of each demand metering cycle. With load-side demand And update the maximum demand on the grid side in real time. and the maximum demand on the load side ;

[0130] The monitoring and execution module is used to continuously monitor and obtain real-time load power. If the real-time load power Greater than the maximum demand on the grid side Then the energy storage system is controlled to discharge according to the calculated real-time power. Discharge to reduce demand; otherwise, control the energy storage system to enter standby mode.

[0131] Arbitrage discrimination module, used to determine the dynamic discharge power Compared with the realized demand reduction Determine whether to enable peak-valley arbitrage mode; if enabled, the energy storage system will continue to discharge according to the dynamic discharge power; if not enabled, the energy storage system will maintain the demand-locked control mode.

[0132] Example 3

[0133] This embodiment uses real data from a group of industrial and commercial users' energy storage scheduling in a certain region to further explain the implementation of the above method.

[0134] The electricity pricing structure for an energy storage system configured for industrial and commercial users in a certain region is as follows: demand price 97.6 local currency / kW, high-price period (14:00-22:00) energy price 0.3132 local currency / kWh, and low-price period energy price 0.2723 local currency / kWh. The energy storage system is configured with a capacity of 225 kWh, a maximum charge / discharge power of 100 kW, a charge / discharge efficiency of 92%, and a state of charge (SOC) operating range of 10%-100%. The demand metering cycle is 15 minutes, and the demand control period is set from 14:00 to 22:00, coinciding with the high-price period.

[0135] The specific implementation process of the energy storage scheduling method of the present invention is as follows:

[0136] First, during the low-electricity-price period each day (22:00-14:00 the next day), the energy storage system is charged to full capacity (SOC=100%) to ensure sufficient power reserves for demand control and peak-valley arbitrage. During the demand control period (14:00-22:00), the energy storage system is scheduled according to the method of this invention.

[0137] At 14:00, the remaining control time is calculated to be 8 hours, the remaining usable energy storage capacity is 202.5 kWh (225 × (1-0.1)), and the dynamic discharge power is calculated to be... kW. During the first demand metering cycle (14:00-14:15), when the load power exceeds the dynamic discharge power, the energy storage system discharges according to the dynamic discharge power. At 14:15, the grid-side demand for this cycle is recorded as 290.01 kW, and the updated maximum demand is 290.01 kW.

[0138] Depend on Figure 3 and Figure 4 As shown, during subsequent demand metering cycles, the energy storage system continuously tracks and updates the maximum demand. When the real-time load power exceeds the maximum demand, the energy storage system discharges according to the dynamic discharge power to control the grid-side power below the maximum demand; when the real-time load power is lower than the maximum demand, the energy storage system stands still and does not discharge, saving electricity. As time progresses, the maximum demand gradually increases from 290.01 kW to 370.90 kW, forming a dynamically rising control benchmark; the dynamic discharge power is dynamically adjusted as the remaining time decreases and the remaining electricity is consumed, ensuring that the energy storage power can be evenly distributed throughout the entire demand control period.

[0139] At 20:00, entering the final two hours of the demand control period, the system began executing peak-valley arbitrage judgment. At this time, the maximum demand on the load side was 401.20 kW, the maximum demand on the grid side was 370.90 kW, and the realized demand reduction was 30.30 kW; the calculated dynamic discharge power was 73.51 kW, which was greater than the demand reduction of 30.30 kW, meeting the conditions for peak-valley arbitrage to be activated; the system activated the peak-valley arbitrage mode, and in the subsequent demand metering cycle, even if the load power did not exceed the maximum demand, the energy storage system continued to discharge according to the dynamic discharge power, making full use of the high electricity price period to release the stored energy; at 22:00, the demand control period ended, the SOC of the energy storage system dropped to 10%, and the energy storage capacity utilization rate reached 90%.

[0140] from Figure 3 and Figure 4 As can be seen, in this embodiment, the peak demand on the grid side is effectively reduced, and peak-valley arbitrage is achieved in the final stage of the demand control period. The energy storage SOC is reduced from 100% to 10%, and the energy storage capacity utilization rate reaches 90%, which significantly improves the economic benefits of the energy storage system.

[0141] Example 4

[0142] This embodiment further explains the implementation of the above method using real data from the energy storage scheduling of a group of industrial and commercial users in another region.

[0143] The electricity pricing structure for an energy storage system configured for industrial and commercial users in this region is as follows: demand price 432.6 local currency / kW, high-price period (18:00-22:00) energy price 2.3075 local currency / kWh, and low-price period energy price 1.9716 local currency / kWh. The energy storage system is configured with a capacity of 261 kWh, a maximum charge / discharge power of 100 kW, a charge / discharge efficiency of 92%, and a state of charge (SOC) operating range of 10%-100%. The demand metering cycle is 15 minutes, and the demand control period is set from 18:00 to 22:00, coinciding with the high-price period.

[0144] The specific implementation process of the energy storage scheduling method of the present invention is as follows:

[0145] During the low-electricity-price period each day (22:00-18:00 the next day), the energy storage system is charged to full capacity (SOC=100%) to reserve energy for demand control and peak-valley arbitrage. During the demand control period (18:00-22:00), the energy storage system is scheduled according to the method of this invention. The demand control period in this embodiment is 4 hours, which is shorter than the 8 hours in Embodiment 3, therefore the dynamic discharge power is relatively larger.

[0146] At 18:00, the remaining control time is calculated to be 4 hours, the remaining usable energy storage capacity is 234.9 kWh (261 × (1-0.1)), and the dynamic discharge power is calculated to be... kW. In the first demand metering cycle (18:00-18:15), when the load power exceeds the dynamic discharge power, the energy storage system discharges according to the dynamic discharge power; at 18:15, the grid-side demand for this cycle is recorded and the maximum demand is updated.

[0147] During subsequent demand metering cycles, the energy storage system continuously tracks and updates the maximum demand. Due to the short demand control period, the energy storage system discharges at a relatively high frequency, resulting in a relatively large dynamic discharge power. At 20:00, entering the last two hours of the demand control period, the system begins to perform peak-valley arbitrage judgment. Assuming the maximum demand on the load side is 350kW and the maximum demand on the grid side is 325kW, the realized demand reduction is 25kW. The calculated dynamic discharge power is approximately 90kW, which is greater than the demand reduction of 25kW, satisfying the peak-valley arbitrage activation condition. The system activates the peak-valley arbitrage mode, continuously discharging during subsequent demand metering cycles to fully utilize the high electricity price period to release stored energy. At 22:00, the demand control period ends, the energy storage system's SOC drops to around 10%, and the energy storage capacity utilization rate reaches 90%.

[0148] As can be seen from the above, the method of the present invention has good adaptability and versatility. In Example 3, where the demand control period is relatively long, the dynamic discharge power is relatively small, and the energy storage system can evenly distribute the power throughout the 8 hours, ensuring both demand reduction and peak-valley arbitrage in the last two hours. In Example 4, where the demand control period is relatively short, the dynamic discharge power is relatively large, and the energy storage system can quickly respond to load changes within 4 hours, similarly achieving stable demand reduction and peak-valley arbitrage benefits. The energy storage capacity utilization rate in both cases reached approximately 90%, significantly higher than the 60%-70% of traditional methods, fully verifying the superiority of the present invention's method in fully utilizing energy storage capacity.

[0149] Several embodiments of this disclosure have been described above. These descriptions are exemplary and not exhaustive, and are not limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen to best explain the principles, practical applications, or technological improvements to the embodiments in the market, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. An energy storage dispatching method for peak demand reduction and peak-valley arbitrage, characterized in that, The methods include: S01, Set energy storage system parameters and demand metering cycle. and demand control time period And initialize the initial SOC of the energy storage system and the maximum demand on the grid side. Maximum demand on the load side Peak-valley arbitrage discharge power ; S02 performs real-time dynamic discharge power calculation, based on the current remaining available power of the energy storage system at each time t within the demand control period. Remaining time until distance demand control ends Real-time calculation of dynamic discharge power ; S03, at the end of each demand metering cycle, calculate the grid-side demand for that cycle. With load-side demand And update the maximum demand on the grid side in real time. and the maximum demand on the load side ; S04, continuously monitor and acquire real-time load power. If the real-time load power Greater than the maximum demand on the grid side Then the energy storage system is controlled to discharge according to the calculated real-time power. Discharge to reduce demand; otherwise, control the energy storage system to enter standby mode. S05, based on the dynamic discharge power Compared with the realized demand reduction Determine whether to enable peak-valley arbitrage mode; if enabled, the energy storage system will discharge according to dynamic power. Continuous discharge; if not enabled, the energy storage system remains in demand-locked control mode.

2. The energy storage dispatching method for peak demand reduction and peak-valley arbitrage according to claim 1, characterized in that, In step S02, the dynamic discharge power The specific calculation method is as follows: ， ； ≥0.01， in, Let be the remaining available electricity of the energy storage system at time t; For charge and discharge efficiency; From the current moment The remaining control time until demand control ends; This represents the maximum allowable discharge power of the energy storage system.

3. The energy storage dispatching method for peak demand reduction and peak-valley arbitrage according to claim 2, characterized in that, The remaining available power The specific calculation method is as follows: ， in, The current state of charge; For energy storage capacity; This represents the lower limit of the SOC's operating range.

4. The energy storage dispatching method for peak demand reduction and peak-valley arbitrage according to claim 3, characterized in that, In step S05, the specific conditions for activating the peak-valley arbitrage mode are as follows: ， ， When dynamic discharge power Greater than the realized demand reduction At that time, the peak-valley arbitrage mode is activated.

5. The energy storage dispatching method for peak demand reduction and peak-valley arbitrage according to claim 4, characterized in that, In step S04, the specific method for calculating the energy storage discharge power is as follows: ， ， ， in This refers to the maximum discharge power based on the remaining power of the energy storage system; Effective discharge power; This represents the final calculated real-time discharge power.

6. The energy storage scheduling method for peak demand reduction and peak-valley arbitrage according to claim 5, characterized in that, This energy storage dispatching method for peak demand reduction and peak-valley arbitrage also includes updating the SOC status of the energy storage system: When the energy storage system discharges, the State of Charge (SOC) is updated as follows: ， When the energy storage system is charging, the State of Charge (SOC) is updated as follows: ， Furthermore, range constraints are applied to the SOC: ， in, The current state of charge; The state of charge at the next moment; To control the time step; This refers to the real-time discharge power. Real-time charging power; For energy storage capacity; This represents the lower limit of the SOC's operating range. This represents the upper limit of the SOC's operating range.

7. The energy storage dispatching method for peak demand reduction and peak-valley arbitrage according to claim 1, characterized in that, This energy storage dispatching method for peak demand reduction and peak-valley arbitrage also includes real-time grid power. Calculation and output: 。 8. The energy storage dispatching method for peak demand reduction and peak-valley arbitrage according to claim 1, characterized in that, Before step S02, a pre-charging step is also included: during the demand control period. Previously, the energy storage system was charged to full capacity.

9. The energy storage dispatching method for peak demand reduction and peak-valley arbitrage according to claim 1, characterized in that, In step S03, the maximum demand on the grid side is updated in real time. and the maximum demand on the load side The method is as follows: at the start of the next demand metering cycle, calculate the grid-side demand of the previous cycle. and load-side demand ; ， ， ， ， in, This is a time conversion factor; / This is a time conversion factor used to convert the increase in electrical energy within a period into an average power value based on hours. and The load energy at the beginning and end of the cycle; and This refers to the stored electrical energy at the beginning and end of the cycle.

10. An energy storage dispatching system for peak demand reduction and peak-valley arbitrage, characterized in that the system... include: The parameter setting and initialization module is used to set the energy storage system parameters and demand metering cycle. and demand control time period And initialize the initial SOC of the energy storage system and the maximum demand on the grid side. Maximum demand on the load side Peak-valley arbitrage discharge power ; The dynamic discharge power calculation module is used to perform real-time dynamic discharge power calculations at each time t within the demand control period, based on the current remaining available power of the energy storage system. Remaining time until distance demand control ends Real-time calculation of dynamic discharge power ; The demand update module is used to calculate the grid-side demand at the end of each demand metering cycle. With load-side demand And update the maximum demand on the grid side in real time. and the maximum demand on the load side ; The monitoring and execution module is used to continuously monitor and obtain real-time load power. If the real-time load power Greater than the maximum demand on the grid side Then the energy storage system is controlled to discharge according to the calculated real-time power. Discharge to reduce demand; otherwise, control the energy storage system to enter standby mode. Arbitrage discrimination module, used to determine the dynamic discharge power Compared with the realized demand reduction Determine whether to enable peak-valley arbitrage mode; if enabled, the energy storage system will discharge according to dynamic power. Continuous discharge; if not enabled, the energy storage system remains in demand-locked control mode.

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