Energy storage system optimization operation method, power management system and related equipment

Abstract

The embodiment of the present application provides an energy storage system optimization operation method, a power management system and related equipment. In the embodiment of the present application, the MPC optimization framework is introduced to dynamically update the energy storage charging and discharging strategy, so as to obtain a more effective energy storage charging and discharging strategy; the energy storage system is charged and discharged by controlling the energy storage power target value of the energy storage system at multiple future moments, and by controlling whether a third party entity participates in the declaration of auxiliary services, the energy utilization rate is jointly improved.

Description

Technical Field

[0001] The present application relates to the technical field of power systems, and in particular to an energy storage system optimization operation method, a power management system, and related equipment. Background Art

[0002] Third-party entities usually provide a series of value-added services related to electricity production and consumption, and play an important role in the electricity market. With the increase in the proportion of renewable energy, energy storage systems have gradually become the most important adjustable resource among third-party entities. The energy storage systems of third-party entities have multiple functions in the operation of load aggregators, such as smoothing load curves, adjusting power quality, participating in market transactions, and providing energy storage price arbitrage opportunities. These functions can help load aggregators better manage user energy needs, improve energy utilization, achieve economic benefits, and also promote the stable operation and sustainable development of the power system. At present, the energy utilization rate of third-party entities in the process of participating in the electricity market is not high. Summary of the invention

[0003] Various aspects of the present application provide an energy storage system optimization operation method, a power management system and related equipment to improve energy utilization.

[0004] An embodiment of the present application provides an energy storage system optimization operation method, comprising: predicting future operation data of a third-party entity having an energy storage system based on historical operation data of the third-party entity; constructing an optimization operation model of the energy storage system based on the future operation data, the optimization operation model at least including an objective function, the objective function using the energy storage power target value of the energy storage system at multiple future moments as a first decision variable, and using an auxiliary service declaration state quantity as a second decision variable, the auxiliary service declaration state quantity representing whether the third-party entity declares an auxiliary service; solving the objective function with the goal of optimizing energy utilization to obtain the variable value of the first decision variable and the variable value of the second decision variable; and jointly controlling the charging and discharging process and the auxiliary service declaration process of the energy storage system based on the variable value of the first decision variable and the variable value of the second decision variable to optimize the operation process of the energy storage system.

[0005] The embodiment of the present application also provides a power management system, including: at least one third-party entity, a load aggregator and a power trading center; the load aggregator is used to predict the future operation data of the third-party entity based on the historical operation data of the third-party entity with an energy storage system; construct an optimized operation model of the energy storage system based on the future operation data, the optimized operation model at least including an objective function, the objective function uses the energy storage power target value of the energy storage system at multiple future moments as the first decision variable, and uses the auxiliary service declaration state quantity as the second decision variable, the auxiliary service declaration state quantity characterizing whether the third-party entity declares auxiliary services; with the goal of optimizing energy utilization, solving the objective function to obtain the variable value of the first decision variable and the variable value of the second decision variable; issuing the variable value of the first decision variable to the third-party entity so that the third-party entity can control the charging and discharging of the energy storage system according to the variable value of the first decision variable; issuing the variable value of the second decision variable to the third-party entity so that the third-party entity can declare auxiliary services to the power trading center according to the variable value of the second decision variable.

[0006] An embodiment of the present application also provides an electronic device, including: a memory and a processor; the memory is used to store a computer program; the processor is coupled to the memory, and is used to execute the computer program to execute the steps in the method for optimizing the operation of an energy storage system.

[0007] An embodiment of the present application also provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the processor is enabled to implement the steps in the method for optimizing the operation of an energy storage system.

[0008] The embodiment of the present application also provides a computer program product, including a computer program / instruction. When the computer program / instruction is executed by a processor, the processor is enabled to implement the steps in the method for optimizing the operation of an energy storage system.

[0009] In the embodiment of the present application, an MPC optimization framework is introduced to dynamically update the energy storage charging and discharging strategy, so as to obtain a more effective energy storage charging and discharging strategy; by controlling the energy storage power target value of the energy storage system at multiple future moments to control the charging and discharging of the energy storage system, and by controlling whether a third party entity participates in declaring auxiliary services, the energy utilization rate is jointly optimized. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0011] Figure 1 is an exemplary application scenario diagram;

[0012] Figure 2A flow chart of a method for optimizing operation of an energy storage system provided in an embodiment of the present application;

[0013] Figure 3 , Figure 4 , Figure 5 , Figure 6 They are respectively a kind of waveform diagram;

[0014] Figure 7 The power waveform diagram corresponding to the energy storage operation strategy of the no-invitation scenario;

[0015] Figure 8 The power waveform diagram corresponding to the energy storage operation strategy for the peak shaving invitation scenario;

[0016] Fig. 9 The power waveform diagram corresponding to the energy storage operation strategy for the valley filling invitation scenario;

[0017] Fig.10 The power waveform diagram corresponding to the energy storage operation strategy of the multi-invitation scenario;

[0018] Fig.11 A schematic diagram of the structure of an energy storage system optimization operation device provided in an embodiment of the present application;

[0019] Fig.12 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0020] In order to make the purpose, technical solution and advantages of the present application clearer, the technical solution of the present application will be clearly and completely described below in combination with the specific embodiments of the present application and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present application.

[0021] In the embodiments of the present application, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the access relationship of associated objects, indicating that three relationships may exist. For example, A and / or B may represent: A exists alone, A and B exist at the same time, and B exists alone, where A and B may be singular or plural. In the text description of the present application, the character " / " generally indicates that the previous and next associated objects are in an "or" relationship. In addition, in the embodiments of the present application, "first", "second", "third", etc. are only used to distinguish the contents of different objects and have no other special meanings.

[0022] First, several terms involved in the embodiments of the present application are introduced:

[0023] Third-party entities: usually provide a range of value-added services related to electricity production and consumption, and play an important role in the electricity market.

[0024] Ancillary services in the power market refer to those services that support the operation of the power system and maintain the stability and reliability of the system. These services are very important for maintaining the normal operation, safety and power quality of the power system. Ancillary services are usually managed and coordinated by power system operators such as Independent System Operators (ISO) or Regional Transmission Organizations (RTO). Power ancillary services that third parties can participate in usually include peak load regulation, spinning reserve, frequency regulation, reactive power regulation (automatic voltage control) and other varieties.

[0025] Adjustable resources in the power system generally refer to energy assets or equipment that can be adjusted and controlled as needed to adapt to changes in grid load. These adjustable resources can adjust the supply and demand balance of the power system by increasing or decreasing their output power to ensure stable operation and efficient performance of the power system. For participants in the ancillary service market, adjustable resources are usually energy storage resources and flexible load resources.

[0026] Time-of-use electricity price: Time-of-use electricity price refers to a pricing strategy that differentiates and adjusts electricity prices according to the power supply and demand relationship and cost in different time periods. Usually, power companies divide a day into different time periods, such as peak hours and off-peak hours, and set different electricity price levels for different time periods. Time-of-use electricity price reflects the time-based and seasonal changes in the power market, and aims to encourage users to reduce electricity consumption during energy peak hours and increase electricity consumption during energy off-peak hours, thereby effectively balancing the load demand of the power system.

[0027] Energy storage spread arbitrage is a strategy that uses energy storage technology to trade in the energy market. Energy storage facilities can purchase electricity at a lower price during off-peak hours for charging, and then sell electricity at a higher price during peak hours to achieve profits. This strategy takes advantage of price fluctuations caused by differences in supply and demand in the energy market and makes profits by trading at different times.

[0028] Ancillary service declaration arbitrage refers to the behavior of participants in the power market to obtain additional profits by using market rules and price differences by declaring ancillary services (such as frequency regulation, spinning reserve, non-spinning reserve, black start service, etc.). This behavior usually involves a deep understanding of the operation mechanism of the power market and the precise application of market rules.

[0029] Ancillary service compensation: Ancillary service compensation refers to some specific functions in the power system, which are provided to ensure the stability and reliability of the power grid. These services include frequency regulation, voltage regulation, reactive power compensation, capacity reserve, etc., with the purpose of responding in time and maintaining the balance of the power grid when faced with load changes or sudden failures in the power system. Compensation refers to the fees paid by the power grid for the equipment or resources that provide these ancillary services.

[0030] Dynamic constraint adjustment: refers to dynamically adjusting the constraints of the optimization problem according to changes in system status or the impact of external events during the optimization problem solving process. This mechanism enables the optimization algorithm to flexibly adapt to changing environments and conditions and respond better in real-time systems.

[0031] Peak shaving: Peak shaving refers to the service provided by a third-party independent entity that accepts dispatch instructions and adjusts its own electricity consumption behavior to increase or decrease the electricity load in order to alleviate the situation where the load rate of the generator set is lower than the specified range during the low load period or the positive and standby resources of the power generation are insufficient during the peak load period. It is divided into valley filling peak shaving and peak shaving.

[0032] Model Predictive Control (MPC) is an advanced control strategy that is widely used in the field of industrial process control, especially in systems with multiple variables, strong coupling, and complex constraints. MPC combines dynamic model prediction and optimization techniques to determine control actions by continuously solving an optimization problem.

[0033] Optimization algorithm based on MPC framework: The optimization algorithm based on model predictive control (MPC) is a method that uses the MPC control framework to solve multi-variable, multi-constrained optimization problems. It combines dynamic system modeling, future prediction and optimization techniques to select the optimal control input at each moment to achieve a specific performance indicator. The MPC-based optimization algorithm continuously iterates to find a series of future control inputs that can optimize the performance indicators of the system. The optimization algorithm takes into account the dynamic characteristics and constraints of the system, so that it can better adapt to the nonlinearity, time-varying and constraint requirements of the system.

[0034] Power forecasting refers to the process of estimating and predicting power demand or power generation at a certain moment or time period in the future in the power system. This forecast is usually based on historical data, weather information, load characteristics and other relevant factors, and is carried out using technologies such as mathematical modeling, statistical analysis and machine learning. Power forecasting can help power system operators and market participants make reasonable scheduling decisions and market transactions to cope with uncertainties in the power system. It affects the supply and demand balance, power generation planning, energy storage scheduling and other aspects of the power system.

[0035] Third-party entities usually provide a series of value-added services related to electricity production and consumption, and play an important role in the electricity market. With the increase in the proportion of renewable energy, energy storage systems have gradually become the most important adjustable resource among third-party entities. The energy storage systems of third-party entities have multiple functions in the operation of load aggregators, including smoothing load curves, regulating power quality, participating in market transactions, and providing energy storage price arbitrage opportunities. These functions can help load aggregators better manage user energy needs, improve energy utilization, achieve economic benefits, and also promote the stable operation and sustainable development of the power system. At present, the energy utilization rate of third-party entities in the process of participating in the electricity market is not high.

[0036] To this end, the embodiment of the present application provides an energy storage system optimization operation method, a power management system and related equipment. In the embodiment of the present application, the MPC optimization framework is introduced to dynamically update the energy storage charging and discharging strategy, so that a more effective energy storage charging and discharging strategy can be obtained; by controlling the energy storage power target value of the energy storage system at multiple future moments to control the charging and discharging of the energy storage system, and by controlling whether a third party entity participates in the declaration of auxiliary services, the energy utilization rate can be jointly improved.

[0037] The following specific embodiments are used to describe in detail the technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The following is a detailed description of the technical solutions provided by each embodiment of the present application in conjunction with the accompanying drawings.

[0038] Figure 1 is an exemplary application scenario diagram. Figure 1 The application scenario shown includes multiple third-party entities such as third-party entity 1 and third-party entity 2, load aggregators, and power trading centers. Load aggregators such as virtual power plants can efficiently aggregate multiple third-party entities and participate in demand response and spot transactions in the power market to achieve peak shaving and valley filling in a more cost-effective manner. The third-party entity has an energy storage system that can store and release energy for the third-party entity. The power trading center issues ancillary service invitations to invite market participants (such as power plants, energy storage facilities, third-party entities, etc.) to submit quotations for ancillary services they are willing to provide. After collecting quotations from all market participants, the power trading center selects the most suitable supplier based on a certain optimization algorithm (usually cost minimization or other criteria specified by market rules) and announces the winning bid results.

[0039] Specifically, taking any third-party entity as an example, see Figure 1 As shown in ①, the load aggregator collects historical operation data of third-party entities; see Figure 1 As shown in ②, the load aggregator also monitors the ancillary service invitations and ancillary service bids issued by the power trading center. Figure 1 As shown in ③, the load aggregator formulates energy storage charging and discharging strategies and ancillary service application strategies based on the historical operation data of the third-party entity, the ancillary service invitation situation and the ancillary service bidding situation, and returns the energy storage charging and discharging strategies and ancillary service application strategies to the third-party entity. The third-party entity optimizes the operation of the energy storage system according to the energy storage charging and discharging strategy, that is, controls the charging and discharging of the energy storage system. Figure 1 As shown in Figure ④, the third-party entity decides whether to issue an auxiliary service declaration instruction to the power trading center based on the ancillary service declaration strategy. The auxiliary service declaration instruction is used to instruct the third-party entity to participate in the ancillary service declaration. As a result, the load aggregator interacts with the third-party entity and the power trading center to optimize the operation of the energy storage system and enable the third-party entity to better participate in the ancillary services of the power market.

[0040] Figure 2 A flow chart of a method for optimizing the operation of an energy storage system provided in an embodiment of the present application. Figure 2 , the method may include the following steps:

[0041] 201. Predict the future operating data of a third-party entity with an energy storage system based on its historical operating data.

[0042] In actual applications, the historical operation data of a third-party entity may be various operation data of the third-party entity in a historical period. Optionally, the historical operation data of a third-party entity may include, but are not limited to: the actual value of the mains power of the third-party entity at multiple historical moments, the actual value of the energy storage power of the energy storage system at multiple historical moments, and the actual value of the SOC (State of Charge) data of the energy storage system at multiple historical moments. Among them, the historical moment is earlier than the current time, the actual value of the mains power is the actual mains power generated, the actual value of the energy storage power is the actual energy storage power generated, the energy storage power includes charging power or discharging power, and the actual SOC value is the actual SOC generated.

[0043] In actual applications, there is no limit on the sampling interval of historical operation data. For example, sampling is performed every 15 minutes, so that data at 96 moments can be collected throughout the day. Figure 3 Taking as an example, the actual value of the mains power, the actual value of the energy storage power and the actual value of the SOC at each moment are collected at a certain sampling interval. Figure 3 In the waveform shown, from top to bottom: AC power ( Figure 3 The waveform (red curve) of grid_load_pwr) and the energy storage power ( Figure 3The green curve of the waveform (denoted as ess_pwr) in the figure), SOC ( Figure 3 The waveform (green curve) is denoted as soc in the figure. Figure 3 In the waveform shown, the horizontal axis is the time axis, and the times on the time axis are: 13:00, 14:00, 15:00... and other times, and the vertical axes are the mains power, energy storage power, and SOC size.

[0044] In actual applications, the direction of the mains power is divided into positive downstream and negative online. Positive downstream refers to the direction in which the power flows from the main grid to the user end; negative online refers to the direction in which the power flows from the user end back to the main grid. In this embodiment, the user end can be a third-party entity.

[0045] In practical applications, the energy storage power direction is divided into positive discharge and negative charge. When the energy storage power is greater than 0, the energy storage power is the discharge power, and the energy storage system discharges to the outside; when the energy storage power is less than 0, the energy storage power is the charging power, and the energy storage system is charged.

[0046] In this embodiment, the mains power and the energy storage power are added to obtain the non-adjustable power component, which can be regarded as the result of decomposing the mains power. Figure 4 For example, Figure 4 The red curve in the middle represents the mains power ( Figure 4 The waveform of grid_load_pwr) Figure 4 The green curve in the middle represents the energy storage power ( Figure 4 The waveform of ess_pwr in Figure 4 The blue curve in the middle represents the non-adjustable power component ( Figure 4 The waveform is recorded as non_adj_pwr in Figure 4 It can be seen that the non-adjustable power component at each time is equal to the sum of the mains power and the energy storage power at each time.

[0047] In practical applications, the future operation data of a third-party entity can be predicted based on the historical operation data of the third-party entity. The future operation data includes, but is not limited to: the target value of the non-adjustable power component of the third-party entity at multiple future moments, the target value of the mains power of the third-party entity at multiple future moments, and the target SOC value of the energy storage system at multiple future moments. There is no restriction on this. The target value of the non-adjustable power component refers to the expected non-adjustable power component at a future moment; the target mains power value refers to the expected mains power at a future moment, and the target SOC value refers to the expected SOC at a future moment.

[0048] Optionally, in order to make accurate predictions, the implementation method of predicting the future operation data of the third-party entity based on the historical operation data of the third-party entity with the energy storage system is as follows: according to the actual value of the city power of the third-party entity at multiple historical moments, and the actual value of the energy storage power of the energy storage system of the third-party entity at multiple historical moments, determine the actual value of the non-adjustable power component of the third-party entity at multiple historical moments; and according to the actual value of the non-adjustable power component of the third-party entity at multiple historical moments, predict the target value of the non-adjustable power component of the third-party entity at multiple future moments. Figure 5 For example, Figure 5 The blue part of the curve of the non-adjustable power component in represents the actual value of the non-adjustable power component collected from 2024-04-13 to 2024-04-18; Figure 5 The blue part of the curve of the non-adjustable power component in the graph represents the actual value of the non-adjustable power component collected from 2024-04-13 to 2024-04-18 ( Figure 5 non_adj_pwr); Figure 5 The red part of the curve of the non-adjustable power component in the graph represents the predicted target value of the non-adjustable power component on 2024-04-19 ( Figure 5 fcst_non_adj_pwr).

[0049] In some optional embodiments, more future operation data can be predicted. Based on this, the target values ​​of the mains power of the third party at multiple future moments are determined according to the target values ​​of the energy storage power of the energy storage system at multiple future moments and the target values ​​of the non-adjustable power components of the third party at multiple future moments.

[0050] 202. Construct an optimal operation model of the energy storage system based on future operation data. The optimal operation model at least includes an objective function. The objective function uses the energy storage power target value of the energy storage system at multiple future moments as the first decision variable, and uses the auxiliary service declaration state quantity as the second decision variable. The auxiliary service declaration state quantity represents whether a third-party entity declares an auxiliary service.

[0051] In practical applications, when constructing an optimized operation model of an energy storage system based on future operation data, constraints can be set based on the future operation data; an objective function can be set based on the future operation data; and an optimized operation model can be constructed based on the constraints and the objective function.

[0052] Optionally, setting constraints based on future operating data is implemented as follows: for any one of multiple future moments, determining a third auxiliary variable at the future moment according to the initial value of the energy storage power and the target value of the energy storage power of the energy storage system at the future moment, the third auxiliary variable characterizing the charging and discharging behavior of the energy storage system; determining the charging power and discharging power of the energy storage system at the future moment according to the third auxiliary variable at the future moment, and determining the charging state variable and discharging state variable of the energy storage system at the future moment according to the charging power and discharging power of the energy storage system at the future moment; setting a first constraint, the first constraint is used to limit the sum of the charging state variable and the discharging state variable of the energy storage system at the future moment; setting a second constraint, the second constraint is used to limit the upper and lower limits of the charging power of the energy storage system at the future moment; setting a third constraint, the third constraint is used to limit the upper and lower limits of the discharge power of the energy storage system at the future moment.

[0053] Further optionally, in order to better optimize the operation of the energy storage system, setting constraints based on future operation data also includes: for any future moment among multiple future moments, determining the variable of the number of changes in the charging state of the energy storage system at the future moment according to the charging state variable of the energy storage system at the future moment and the previous future moment; and, determining the variable of the number of changes in the discharge state of the energy storage system at the future moment according to the discharge state variable of the energy storage system at the future moment and the previous future moment; determining the total number of charging state changes based on the variable of the number of changes in the charging state of the energy storage system at multiple future moments, and determining the total number of discharge state changes of the energy storage system based on the variable of the number of changes in the discharge state of the energy storage system at multiple future moments; setting a fourth constraint, the fourth constraint is used to limit the upper and lower limits of the total number of changes in the charging state of the energy storage system; setting a fifth constraint, the fifth constraint is used to limit the upper and lower limits of the total number of changes in the discharge state of the energy storage system.

[0054] Further optionally, in order to better optimize the operation of the energy storage system, setting constraints based on future operation data also includes: performing parameter identification based on the actual state of charge SOC values ​​of the energy storage system at multiple historical moments to obtain the charging efficiency and discharging efficiency of the energy storage system; for any future moment among the multiple future moments, determining the SOC target value of the energy storage system at the future moment based on the charging efficiency, the discharging efficiency, the charging power of the energy storage system at the future moment, the discharging power of the energy storage system at the future moment, and the SOC target value of the energy storage system at the previous future moment; and setting a sixth constraint condition, which is used to limit the upper and lower limits of the SOC target value of the energy storage system at the future moment.

[0055] For ease of understanding, take the example of dividing the whole day into 96 moments. When building the optimization operation model, define the initial value of the energy storage power at each moment as: p_ess_init(t)=0, t∈[0,95], t∈Z, where Z represents an integer set and t represents a moment. Define the target value of the energy storage power at each moment as: p_ess_target(t), t∈[0,95], t∈Z.

[0056] In order to distinguish the charging and discharging behavior of the energy storage system, the third auxiliary variable p_ess_diff(t) is introduced. p_ess_diff(t)=p_ess_init(t)-p_ess_target(t), t∈[0,95], t∈Z;

[0057] When p_ess_diff(t)>0, the charging power of the energy storage system p_ess_char(t)=p_ess_diff(t); When p_ess_diff(t)≤0, the charging power of the energy storage system p_ess_char(t)=0;

[0058] When p_ess_diff(t)<0, the discharge power of the energy storage system p_ess_disc(t)=-p_ess_diff(t); when p_ess_diff(t)>=0, the discharge power of the energy storage system p_ess_disc(t)=0.

[0059] The target value of the energy storage power p_ess_target(t) can be expressed using the charging power and the discharging power as: p_ess_target(t)=p_ess_init(t)+p_ess_disc(t)-p_ess_char(t), t∈[0,95], t∈Z.

[0060] Define the charging state variable state_ess_char(t) that reflects the charging state of the energy storage system. state_ess_char(t)∈{0,1},t∈[0,95]. state_ess_char(t) takes the value of 0, indicating no charging; state_ess_char(t) takes the value of 1, indicating charging. Define the discharge state variable state_ess_disc(t) that reflects the discharge state of the energy storage system. state_ess_disc(t)∈{0,1},t∈[0,95]. state_ess_disc(t) takes the value of 0, indicating no discharging; state_ess_disc(t) takes the value of 1, indicating discharging.

[0061] state_ess_char(t)+state_ess_disc(t)≤1; this constraint is also the first constraint.

[0062] When p_ess_char(t)>0, state_ess_char(t)=1; when p_ess_char(t)≤0, state_ess_char(t)=0;

[0063] When p_ess_disc(t)>0, state_ess_disc(t)=1; when p_ess_disc(t)≤0, state_ess_disc(t)=0.

[0064] Define the variable state_ess_char_change(t) for the number of charging state changes, state_ess_char_change(t)∈{0,1}, t∈[0,95], t∈Z; state_ess_char_change(t) takes the value of 1, indicating that the charging state at time t changes once, and state_ess_char_change(t) takes the value of 0, indicating that the charging state at time t does not change.

[0065] Define the discharge state change count variable state_ess_disc_change(t), state_ess_disc_change(t)∈{0,1}, t∈[0,95], t∈Z; state_ess_disc_change(t) takes the value of 1, indicating that the discharge state at time t changes once; state_ess_disc_change(t) takes the value of 0, indicating that the discharge state at time t does not change.

[0066] state_ess_char_change(t)=|state_ess_char(t)-state_ess_char(t-1)|, that is, the absolute value of the difference between the charging state variable state_ess_char(t) at time t and the charging state variable state_ess_char(t-1) at time t-1 is equal to the charging state change count variable state_ess_char_change(t) at time t;

[0067] state_ess_disc_change(t)=|state_ess_disc(t)-state_ess_disc(t-1)|, that is, the absolute value of the difference between the discharge state variable state_ess_disc(t) at time t and the discharge state variable state_ess_disc(t-1) at time t is equal to the discharge state change count variable state_ess_disc_change(t) at time t.

[0068] In practical applications, parameter identification can also be performed based on the actual SOC values ​​of the energy storage system at multiple historical moments to obtain the charging efficiency and discharging efficiency of the energy storage system.

[0069] The charging efficiency of the energy storage system is defined as char_efft, char_efft=|Δchar_soc / (Δeng / ess_eng)|. Δchar_soc represents the change in char_soc within the Δt time interval, char_soc represents the SOC during the charging phase; ess_eng represents the total energy storage, that is, the maximum amount of electrical energy that the energy storage system can store; Δeng represents the change in the energy storage energy within the Δt time interval.

[0070] The discharge efficiency of the energy storage system is defined as disc_efft=|(Δeng / ess_eng) / Δdisc_soc|, where Δdisc_soc represents the change in disc_soc within the Δt time interval, and disc_soc represents the SOC during the discharge phase.

[0071] by Figure 6 For example, Figure 6 The waveforms shown are from top to bottom: SOC waveform, charging efficiency of energy storage system ( Figure 6 The waveform of the energy storage system (denoted as ess_chr_efft) and the discharge efficiency of the energy storage system ( Figure 6 The waveform is denoted as ess_disc_efft in the figure.

[0072] In this embodiment, the SOC target value at each moment is defined as soc(t):

[0073] soc(t)=soc(t-1)+{[p_ess_char(t)×char_efft-p_ess_discr(t) / disc_efft]×Δt} / ess_eng;

[0074] Among them, soc(t)∈[0, 1],∈[0, 95], t∈Z, Z is a positive integer, efft is the charging efficiency, disc_efft is the discharge efficiency, Δt is the time interval, and ess_eng is the total energy storage.

[0075] Set the second constraint: When state_ess_char(t)=1,

[0076] min_char_p≤p_ess_target(t)≤max_char_p, where min_char_p is the minimum charging power; max_char_p is the maximum charging power;

[0077] Set the third constraint: When state_ess_disc(t)=1,

[0078] min_disc_p≤p_ess_target(t)≤max_disc_p, where min_disc_p is the minimum discharge power and max_disc_p is the maximum discharge power;

[0079] Set the fourth constraint: min_char_times≤ ≤max_char_times, where min_char_times is the minimum number of charging times and max_char_times is the maximum number of charging times; It means that the state_ess_char_change(t) at each future moment is summed to obtain the total number of charging state changes.

[0080] Set the fifth constraint: min_disc_times≤ ≤max_disc_times, where min_disc_times is the minimum number of discharges and max_disc_times is the maximum number of discharges; It means that the state_ess_disc_change(t) at each future moment is summed to obtain the total number of discharge state changes.

[0081] The sixth constraint condition is set as follows: min_soc≤soc(t)≤max_soc, where min_soc is the set soc lower limit value, max_soc is the soc upper limit value; soc(t) represents the SOC target value at time t.

[0082] In this embodiment, an objective function is established with the goal of optimizing energy utilization. On the one hand, energy utilization is optimized by controlling the target value of energy storage power of the energy storage system at multiple future moments to control the charging and discharging of the energy storage system. On the other hand, energy utilization is also optimized by controlling whether a third-party entity participates in the declaration of auxiliary services. Furthermore, in the process of optimizing energy utilization, energy storage price difference arbitrage and auxiliary service declaration arbitrage are used to fully ensure that the third-party entity obtains a good return.

[0083] In practical applications, the implementation method of setting the objective function according to future operation data is as follows: determine the energy storage operation cost function according to the initial energy storage power value and the target energy storage power value of the energy storage system at multiple future moments; determine the power purchase cost function according to the target city power value of the third party at multiple future moments; determine the electricity cost function with a first decision variable according to the energy storage operation cost function and / or the power purchase cost function; if auxiliary service invitation data is detected, establish an auxiliary service declaration reward function according to the auxiliary service invitation data; the auxiliary service declaration reward function has a second decision variable, and obtain the objective function according to the auxiliary service declaration reward function and the electricity cost function.

[0084] It is worth noting that the energy storage operation cost function or the electricity purchase cost function can reflect the energy storage price difference arbitrage; the ancillary service declaration reward function can reflect the ancillary service declaration arbitrage. In actual applications, third-party entities participating in the declaration of ancillary services in the electricity market can be rewarded with corresponding resource returns. Resource returns can be, for example, currency, valuable entities or preferential electricity prices, etc., but are not limited to these. The ancillary service declaration reward function can reflect the resource returns of third-party entities participating in the declaration of ancillary services. In actual applications, the ancillary service declaration reward function can be flexibly set as needed according to the different forms of resource returns, and there are no restrictions on this.

[0085] For example, according to the target value of the energy storage power at time t p_ess_target(t); p_ess_target(t)=p_ess_init(t)+p_ess_disc(t)-p_ess_char(t), the discharge power p_ess_disc(t) and the charging power p_ess_char(t) at time t can be derived.

[0086] Define the energy storage unit power charging cost as unit_p_char_cost(t), t∈[0,95], t∈Z, then the total charging cost function cost_char= , that is, the total charging cost function is constructed based on the sum of the product of the charging power at each moment and the charging cost per unit power of the energy storage.

[0087] Define the energy storage unit power discharge cost as unit_p_disc_cost(t), t∈[0,95], t∈Z, then the total discharge cost function cost_disc= , that is, the total discharge cost function is constructed based on the sum of the product of the discharge power at each moment and the unit power discharge cost of the energy storage.

[0088] In practical applications, the total charging cost function and the total discharging cost function can be added or weighted to obtain the energy storage operation cost function.

[0089] Assume that the target value of the mains power at time t is , the mains power target value at time t is recorded as p_load_target(t), p_load_target(t)=p_non_adj(t)-p_ess_target(t), where p_non_adj(t) is the target value of the non-adjustable power component at time t; p_ess_target(t) is the energy storage power target value at time t.

[0090] Assume that the electricity purchase cost function is recorded as cost_p, and the electricity price per unit time is elect_price(t). , that is, the sum of the products of the city power target value, the unit time electricity price and the time interval at each moment is equal to the electricity purchase cost function.

[0091] In practical applications, the energy storage operation cost function and the electricity purchase cost function can be added, weighted summed or subtracted to obtain the electricity cost function.

[0092] For example, set the electricity cost function as follows:

[0093] Min elect_cost=cost_p+cost_disc+cost_char (1)

[0094] Among them, Min means minimization, that is, when solving the electricity cost function, the goal is to minimize the value of the electricity cost function elect_cost. It can be understood that the electricity cost function constructed based on the energy storage operation cost function and / or the electricity purchase cost function is a function that aims at energy storage price difference arbitrage.

[0095] In practical applications, the ancillary service invitation data includes, but is not limited to: the type of ancillary service, the invitation period of ancillary service, the ancillary service declared price, and the ancillary service declared quantity. Among them, the ancillary service declared quantity represents the specific quantity of ancillary services that the power market participants are willing to provide, usually expressed in megawatts (MW) or megawatt hours (MWh). The ancillary service declared price refers to the price that the market participants are willing to accept for the ancillary services provided. The price is usually expressed in monetary units per megawatt hour or per megawatt ( / MW).

[0096] In this embodiment, the ancillary service invitation situation in the power market is detected, and the constraint conditions and the ancillary service declaration reward function are dynamically updated according to the ancillary service invitation data.

[0097] In some optional embodiments, the implementation method of establishing an auxiliary service declaration reward function according to the auxiliary service invitation data is as follows: determining at least one first future moment in the invitation period among multiple future moments according to the auxiliary service invitation data, and determining the actual response load of the third party subject at the first future moment according to the baseline load and the mains power target value of the third party subject at the first future moment; establishing an auxiliary service declaration reward function according to the actual response load of the third party subject at at least one first future moment, the auxiliary service declaration state quantity and the first auxiliary state variable; wherein the first auxiliary state variable is used to characterize the numerical range of the ratio of the actual response load to the auxiliary service declaration quantity. It can be understood that the auxiliary service declaration reward function characterizes the auxiliary service declaration arbitrage. Optionally, when establishing the auxiliary service declaration reward function, the auxiliary service declaration price can also be taken as a consideration. In actual applications, the first auxiliary state variable can be one or more, and the coefficient of the actual response load corresponding to each first auxiliary state variable is different. The auxiliary service declaration reward function can be established according to the actual response load or auxiliary service declaration quantity of the third party subject at at least one first future moment, the auxiliary service declaration state quantity, each first auxiliary state variable and its corresponding coefficient. Exemplarily, the actual response load or auxiliary service declaration amount at the first future moment, the auxiliary service declaration price, the first auxiliary state variable and its corresponding coefficient are multiplied to obtain a first product result under the first auxiliary state variable at the first future moment; various operations such as summing, averaging or weighted summing are performed on the first product results under each first auxiliary state variable at the first future moment to obtain an intermediate result at the first future moment; the intermediate result at the first future moment and the auxiliary service declaration state amount are multiplied to obtain a target result at the first future moment; various operations such as accumulation, averaging or weighted summing are performed on the target results of each first future moment to obtain an auxiliary service declaration reward function.

[0098] In practical applications, the actual values ​​of the mains power of the third-party subject in the historical period can be statistically analyzed to obtain the baseline load of the third-party subject. For example, the actual values ​​of the mains power of the third-party subject in the historical period can be averaged or weighted summed to obtain the baseline load of the third-party subject.

[0099] For example, the auxiliary service declaration amount at time t is defined as ause_dec_p(t), where t can only be obtained from the invitation period of the auxiliary service, t∈Z;

[0100] The actual response load of the peak shaving auxiliary service at time t is defined as ause_res_p(t), ause_res_p(t)=base_line_p(t)-p_load_target(t); wherein base_line_p(t) represents the baseline load at time t.

[0101] Define the actual response load ause_res_p(t) of the valley filling auxiliary service at time t, ause_res_p(t)=p_load_target(t)-base_line_p(t).

[0102] Define the ancillary service declared price as ause_dec_price(t);

[0103] The auxiliary service reporting state is defined as ause_dec_state(t)∈{0,1}, where when the value of ause_dec_state(t) is 0, it indicates that the auxiliary service is reported; when the value of ause_dec_state(t) is 1, it indicates that the auxiliary service is not reported.

[0104] In practical applications, the number of the first auxiliary state variables can be flexibly set based on the flexible auxiliary service compensation rules. For example, the auxiliary service compensation rules are: the actual response load ause_res_p(t) of the third-party subject at time t is greater than 120% of the auxiliary service declaration ause_dec_p(t) at time t, and the part exceeding 120% is not compensated, and there is only partial income at time t; the actual response load ause_res_p(t) of the third-party subject at time t is less than 70% of the auxiliary service declaration ause_dec_p(t) at time t, and there is no income at time t; the actual response load ause_res_p(t) of the third-party subject at time t is greater than 70% of the auxiliary service declaration ause_dec_p(t) at time t and less than 120% of the auxiliary service declaration ause_dec_p(t) at time t, then the auxiliary service declaration ause_dec_p(t) at time t is compensated, and there is income at time t. Based on the above rules, the first auxiliary state variables are set to be y1, y2, and y3. The values ​​of y1, y2, and y3 are either 0 or 1.

[0105] Among them, if ause_res_p(t) / ause_dec_p(t)<0.7, y1(t)=1, and the coefficient corresponding to y1(t) is 0;

[0106] If 0.7≤ause_res_p(t) / ause_dec_p(t)<1.2, y2(t)=1, and the coefficient corresponding to y2(t) is 1;

[0107] If 1.2≤ause_res_p(t) / ause_dec_p(t), y3(t)=1, and the coefficient corresponding to y3(t) is 1.2;

[0108] y1(t)+y2(t)+y3(t)=1.

[0109] Based on the above, the auxiliary service reporting reward function ause_dec_revenue can be formulated as follows:

[0110] (2)

[0111] Among them, for formula (2), the value of time t is taken as each time in the invitation period, and for time t, calculation is performed; each time in the invitation period is taken as of The sum is accumulated to obtain the auxiliary service reporting reward function ause_dec_revenue.

[0112] Optionally, when setting constraints related to auxiliary services, you can set one or more of the following:

[0113] Setting the seventh constraint condition, which is used to limit the value of the auxiliary service declaration status quantity and the value of the auxiliary service declaration quantity;

[0114] An eighth constraint condition is set, where the eighth constraint condition is used to limit the magnitude relationship between the actual response load of the third party subject and the baseline load at the first future moment;

[0115] Setting a ninth constraint condition, where the ninth constraint condition is used to limit the magnitude of the response rate of the actual response load of the third party subject at the first future moment;

[0116] The tenth constraint condition is set, which is used to limit the upper limit of the ancillary service reporting amount.

[0117] For example, for peak shaving auxiliary services, one or more of the following constraints may be set:

[0118] The seventh constraint condition is set as follows: Since partial declaration is not allowed for auxiliary services, only the following situations are allowed during the invitation period: ause_dec_state(t) at each moment in the invitation period is all 1, that is, the auxiliary service participates in the declaration, and the auxiliary service declaration amount ause_dec_p(t)>0 is constrained at this time; ause_dec_state(t) at each moment in the invitation period is all 0, that is, the auxiliary service does not participate in the declaration, and the auxiliary service declaration amount ause_dec_p(t)=0 is constrained at this time.

[0119] The eighth constraint condition is set to: max[ause_res_p(t)]<max[base_line_p(t)], which means that the maximum value of the auxiliary service declaration state quantity at time t is less than the maximum value of the baseline load at time t.

[0120] The eighth constraint condition may also be set as: mean[ause_res_p(t)]<mean[base_line_p(t)], which means: the average value of the auxiliary service declaration state quantity at time t is less than the average value of the baseline load at time t.

[0121] The ninth constraint condition is set to: ause_res_p(t) / ause_dec_p(t)>0.5, ause_res_p(t) / ause_dec_p(t) represents the actual load response rate, that is, the ratio of ause_res_p(t) to ause_dec_p(t) represents the actual load response rate.

[0122] The tenth constraint condition is set to: ause_dec_p(t)<ause_dec_p_max, which means that the ancillary service reported amount ause_dec_p(t) is less than the upper limit value ause_dec_p_max of the ancillary service reported amount.

[0123] For example, for the valley filling auxiliary service, one or more of the following constraints can be set:

[0124] The seventh constraint condition is set as follows: Since partial declaration is not allowed for auxiliary services, only the following situations are allowed during the invitation period: ause_dec_state(t) at each moment in the invitation period is all 1, that is, the auxiliary service participates in the declaration, and the auxiliary service declaration amount ause_dec_p(t)>0 is constrained at this time; ause_dec_state(t) at each moment in the invitation period is all 0, that is, the auxiliary service does not participate in the declaration, and the auxiliary service declaration amount ause_dec_p(t)=0 is constrained at this time.

[0125] The eighth constraint condition is set to: min[ause_res_p(t)]>min[base_line_p(t)], which means: the minimum value of the auxiliary service declaration state quantity at time t is greater than the minimum value of the baseline load at time t.

[0126] The eighth constraint condition may also be set as: mean[ause_res_p(t)]>mean[base_line_p(t)], which means: the average value of the auxiliary service declaration state quantity at time t is greater than the average value of the baseline load at time t.

[0127] The ninth constraint condition is set to: ause_res_p(t) / ause_dec_p(t)>0.5, ause_res_p(t) / ause_dec_p(t) represents the actual load response rate, that is, the ratio of ause_res_p(t) to ause_dec_p(t) represents the actual load response rate.

[0128] The tenth constraint condition is set to: ause_dec_p(t)<ause_dec_p_max, which means that the ancillary service reported amount ause_dec_p(t) is less than the upper limit value ause_dec_p_max of the ancillary service reported amount.

[0129] In practical applications, third-party entities that participate in the application of ancillary services in the power market and win the bid for ancillary services can be rewarded with corresponding resource returns. In practical applications, the reward function for winning the bid for ancillary services can be flexibly set as needed according to the different forms of resource returns, and there is no restriction on this; the reward function for winning the bid for ancillary services can reflect the resource returns of third-party entities that win the bid for ancillary services.

[0130] In some optional embodiments, in order to better optimize the operation of the energy storage system, if it is determined that the auxiliary service has been won in the invitation period according to the detected auxiliary service winning data, then an auxiliary service winning reward function is established according to the actual response load of the third party at at least one first future moment, the auxiliary service winning amount and the second auxiliary state variable; wherein the second auxiliary state variable is used to characterize the numerical range of the ratio of the actual response load to the auxiliary service winning amount; accordingly, according to the auxiliary service declaration reward function and the electricity cost function, the implementation method of obtaining the objective function is: according to the auxiliary service declaration reward function, the auxiliary service winning reward function and the electricity cost function, the objective function is obtained. It can be understood that the auxiliary service winning reward function can also characterize the auxiliary service declaration arbitrage to a certain extent. Optionally, when establishing the auxiliary service declaration reward function, the auxiliary service winning price can also be considered as a factor. Of course, the objective function can also be obtained based on the auxiliary service winning reward function and the electricity cost function.

[0131] In practical applications, the second auxiliary state variable may be one or more, and the coefficient of the actual response load corresponding to each second auxiliary state variable is different. An auxiliary service winning reward function can be established based on the actual response load or the auxiliary service winning bid quantity, the auxiliary service declared state quantity, each second auxiliary state variable and its corresponding coefficient of the third party at at least one first future moment. Exemplarily, for any second auxiliary state variable, the actual response load or the auxiliary service winning bid quantity, the auxiliary service winning bid price, the second auxiliary state variable and its corresponding coefficient are multiplied to obtain the second product result under the second auxiliary state variable at the first future moment; the second product results under each second auxiliary state variable at each first future moment are summed, averaged or weighted summed, etc., to obtain the auxiliary service winning reward function.

[0132] In practical applications, the ancillary service winning bid data include, but are not limited to: the winning bid quantity of ancillary services and the winning bid price of ancillary services. The winning bid quantity of ancillary services refers to the specific quantity of ancillary services that a market participant is selected to provide, usually expressed in megawatts (MW) or megawatt hours (MWh). For example, a power plant may be selected to provide 50MW of frequency regulation services. The winning bid price of ancillary services refers to the price actually obtained by market participants for the ancillary services provided. Assume that the winning bid quantity of ancillary services at time t is defined as ause_bid_p(t), and the winning bid price of ancillary services at time t is defined as ause_bid_price(t).

[0133] In practical applications, the number of the second auxiliary state variables can be flexibly set based on the flexibly set auxiliary service compensation rules. For example, the second auxiliary state variables are x1, x2, and x3, and the values ​​of x1, x2, and x3 are either 0 or 1.

[0134] Among them, if ause_res_p(t) / ause_bid_p(t)<0.7, x1(t)=1, and the coefficient corresponding to x1(t) is 0;

[0135] If 0.7≤ause_res_p(t) / ause_bid_p(t)<1.2, x2(t)=1, and the coefficient corresponding to x2(t) is 1;

[0136] If ause_res_p(t) / ause_bid_p(t)≥1.2, x3(t)=1, and the coefficient corresponding to x3(t) is 1.2;

[0137] x1(t)+x2(t)+x3(t)=1.

[0138] Based on the above, the auxiliary service bidding reward function ause_bid_revenue can be formulated as follows:

[0139] (3)

[0140] In formula (3), the value of time t is taken as the time in the invitation period, and for time t, the calculation is , the The sum is accumulated to obtain the auxiliary service bid reward function ause_bid_revenue.

[0141] 203. With the goal of optimizing energy utilization, solve the objective function and obtain the variable value of the first decision variable and the variable value of the second decision variable.

[0142] 204. Based on the variable value of the first decision variable and the variable value of the second decision variable, jointly control the charging and discharging process and the auxiliary service application process of the energy storage system to optimize the operation process of the energy storage system.

[0143] In practical applications, various operations may be performed on one or more of the electricity cost function, the auxiliary service application reward function, and the auxiliary service bid winning reward function to obtain the objective function.

[0144] Assuming that the objective function is recorded as Y, if there is no invitation period (that is, no auxiliary service invitation data is detected), the objective function is:

[0145] Min Y = elect_cost;

[0146] If there is an invitation period, the objective function is:

[0147] Min Y=(elect_cost-ause_dec_revenue);

[0148] If there is an invitation period and the bid for auxiliary services is successful, the objective function is:

[0149] Min Y=(elect_cost-ause_dec_revenue-ause_bid_revenue), or, Min Y=(elect_cost-ause_bid_revenue).

[0150] Solve the objective function under the constraints, obtain the target energy storage power values ​​of the energy storage system at multiple future moments according to the variable value of the first decision variable corresponding to the minimum value of the objective function, and obtain the value of the auxiliary service declaration state quantity according to the second decision variable corresponding to the minimum value of the objective function. Generate an energy storage charging and discharging strategy based on the target energy storage power values ​​of the energy storage system at multiple future moments, and control the charging and discharging of the energy storage system according to the energy storage charging and discharging strategy. Determine the auxiliary service declaration strategy based on the value of the auxiliary service declaration state quantity, and determine whether the auxiliary service declaration is required based on the auxiliary service declaration strategy.

[0151] In the embodiment of the present application, a self-consistent and complete optimization modeling is performed for energy storage resources, energy storage price difference arbitrage, and auxiliary service declaration arbitrage, and the MPC optimization framework is introduced to dynamically update the energy storage charging and discharging strategy, so that a more effective energy storage charging and discharging strategy can be obtained, thereby improving energy utilization. Furthermore, in the process of optimizing energy utilization, while ensuring that the energy storage price difference arbitrage income does not decrease, market means are used as much as possible to obtain the auxiliary service declaration arbitrage income to fully ensure that the third party entity obtains a better resource return.

[0152] The technical solution provided in the embodiment of the present application introduces an MPC optimization framework to dynamically update the energy storage charging and discharging strategy, so as to obtain a more effective energy storage charging and discharging strategy; by controlling the energy storage power target value of the energy storage system at multiple future moments to control the charging and discharging of the energy storage system, and by controlling whether a third party entity participates in declaring auxiliary services, the energy utilization rate can be jointly improved.

[0153] In addition, the technical solution provided in the embodiments of the present application also has one or more of the following advantages:

[0154] 1. Dynamic constraint adjustment: The optimization objectives and constraints of the energy storage system can be dynamically adjusted according to the real-time market environment and system status.

[0155] 2. Model predictive control technology: Using MPC technology, the patent can perform real-time system status prediction and make flexible optimization control decisions based on the prediction results.

[0156] 3. Multivariable optimization: It solves the multivariable optimization problem, takes into account multiple variables such as historical data, market prices, system operating status, etc., and solves the optimal scheduling strategy through optimization algorithms.

[0157] 4. Energy storage price difference arbitrage: It provides technical means that can be flexibly adjusted according to market price changes to maximize energy storage price difference arbitrage.

[0158] 5. Real-time response capability: Based on MPC technology, real-time system status prediction and optimization adjustment can be achieved, making the system have strong real-time response capability.

[0159] 6. Ancillary service optimization: By collaboratively optimizing user-side energy storage resources, it can provide a variety of ancillary services such as peak shaving and valley filling to meet the power system's demand for ancillary services.

[0160] 7. Multi-invitation collaborative optimization: Using MPC technology, user-side energy storage resources can respond to multiple auxiliary service invitations at the same time, meet the needs of different auxiliary services, and effectively coordinate between these services.

[0161] 8. Optimized scheduling: By comprehensively considering multiple ancillary service invitations and market prices, the patent may provide a corresponding optimized scheduling method, so that the energy storage system can better meet the needs of multiple ancillary services and effectively allocate them between different services.

[0162] In general, the optimization algorithm based on the model predictive control (MPC) framework can dynamically consider the impact of the latest changes in energy storage charging and discharging strategies on the total power consumption, and can dynamically adjust local constraints according to the ancillary service invitation situation to balance the arbitrage income of energy storage price differences and the maximization of the arbitrage income of ancillary service applications, and support energy storage optimization decision-making technology for simultaneous application of multiple ancillary service invitations. It makes up for the shortcomings of traditional control algorithms in the operation of user-side energy storage resources, making the energy storage system more intelligent, flexible and efficient, which is of great significance to improving the operation level and market competitiveness of load aggregators. It can help load aggregators to reasonably use user-side energy storage resources to cope with various challenges in the power market.

[0163] For ease of understanding and description, several exemplary scenario embodiments are introduced below:

[0164] It is worth noting that the total electricity cost calculated below is calculated based on the electricity purchase cost, energy storage cost or arbitrage income reported by ancillary services.

[0165] (1) No invitation scenario, only using energy storage price difference arbitrage: the total electricity cost is 3902, of which the electricity purchase cost is 3675 and the energy storage cost is 227. Figure 7 The power waveform diagram corresponding to the energy storage operation strategy in the no-invitation scenario. Figure 7 The waveforms shown include: Mains power target value ( Figure 7 target_p) waveform, initial value of energy storage power ( Figure 7 base_pre) waveform, energy storage power target value ( Figure 7 bat_soure) waveform, target value of non-adjustable power component ( Figure 7 other_source) waveform, adjust power ( Figure 7 The sum of the non-adjustable power component target value and the energy storage power target value is equal to the regulated power.

[0166] (2) Peak shaving invitation scenario, using energy storage price difference arbitrage while considering ancillary service peak shaving

[0167] The first peak shaving invitation scenario:

[0168] The baseline load at each time is: {"11:00:00": 200, "11:15:00": 200, "11:30:00": 200, "11:45:00": 200}; / / The baseline load at 11:00:00, 11:15:00, 11:30:00, 11:45:00, etc. is 200.

[0169] For the first peak shaving invitation scenario, the energy storage system's optimization operation model determines that the baseline load of the ancillary service invitation is too low. The energy storage system adjusts the mains power target value to below the baseline load and there is little room for arbitrage profit. Therefore, the output is a conclusion that the application is not recommended. The output results of the energy storage system's optimization operation model are as follows:

[0170] state_vars:{'11:00:00': -0.0, '11:15:00': -0.0, '11:30:00': -0.0, '11:45:00': -0.0} / / The auxiliary service declaration state quantity state_vars at 11:00:00, 11:15:00, 11:30:00, 11:45:00, etc. is 0, indicating that the auxiliary service declaration state quantity indicates that no auxiliary service declaration is performed;

[0171] declare: {'11:00:00': 0.0'11:15:00': 0.0, '11:30:00': 0.0, '11:45:00': 0.0} / / The value of declare for the auxiliary service declaration at 11:00:00, 11:15:00, 11:30:00, 11:45:00, etc. is 0.

[0172] load_resp: {'11:00:00': -388.313, '11:15:00': -388.693, '11:30:00': -365.063, '11:45:00': -345.256} / / The actual response load load_resp value at 11:00:00, 11:15:00, 11:30:00, 11:45:00, etc.; base_line: {'11:00:00': 200, '11:15:00': 200, '11:30:00': 200, '11:45:00': 200} / / The value of the baseline load base_line at each time, such as 11:00:00, 11:15:00, 11:30:00, and 11:45:00.

[0173] target_pwr:{'11:00:00': 588.313, '11:15:00': 588.693, '11:30:00': 565.063, '11:45:00': 545.256} / / The values ​​of the AC power target at 11:00:00, 11:15:00, 11:30:00, 11:45:00, etc.

[0174] In addition, the power waveform corresponding to the energy storage operation strategy of the first peak shaving invitation scenario is the same as the power waveform of the non-invitation scenario. The total electricity cost of the first peak shaving invitation scenario is 3902, of which the electricity purchase cost is 3675 and the energy storage cost is 227.

[0175] The second peak shaving invitation scenario: the baseline load at each time is: {"11:00:00": 450, "11:15:00": 450, "11:30:00": 450, "11:45:00": 450} / / The baseline load at 11:00:00, 11:15:00, 11:30:00, 11:45:00, etc. is 450.

[0176] For the second peak shaving invitation scenario, the energy storage system's optimized operation model determines that the baseline load of the ancillary service invitation is reasonable, and that energy storage resources can be used for arbitrage. It also appropriately shifts the energy storage charging and discharging intervals, and tries to earn as much arbitrage income from ancillary service applications as possible while maintaining energy storage price difference arbitrage. The output results of the energy storage system's optimized operation model are as follows:

[0177] state_vars:{'11:00:00': 1.0,'11:15:00': 1.0,'11:30:00': 1.0,'11:45:00': 1.0} / / The auxiliary service declaration state quantity state_vars at 11:00:00, 11:15:00, 11:30:00, 11:45:00, etc. has a value of 1, indicating that the auxiliary service declaration state quantity represents the auxiliary service declaration;

[0178] declare: {'11:00:00': 162.399, '11:15:00': 162.083, '11:30:00': 200.0, '11:45:00': 198.28} / / The value of declare of the auxiliary service declaration quantity at 11:00:00, 11:15:00, 11:30:00, 11:45:00, etc.

[0179] load_resp: {'11:00:00': 194.879, '11:15:00': 194.499, '11:30:00': 218.129, '11:45:00': 237.936} / / The actual response load load_resp value at 11:00:00, 11:15:00, 11:30:00, 11:45:00, etc.

[0180] base_line: {'11:00:00': 450, '11:15:00': 450, '11:30:00': 450, '11:45:00': 450} / / The values ​​of the baseline load base_line at 11:00:00, 11:15:00, 11:30:00, 11:45:00, etc.

[0181] target_pwr:{'11:00:00':255.121,'11:15:00':255.501,'11:30:00':231.871,'11:45:00':212.064} / / The values ​​of the AC power target at 11:00:00, 11:15:00, 11:30:00, 11:45:00, etc.

[0182] in addition, Figure 8 The power waveform diagram corresponding to the energy storage operation strategy for the peak shaving invitation scenario. Figure 8 The figure shows the power waveform corresponding to the energy storage operation strategy of the second peak shaving invitation scenario. Figure 8 The waveforms shown include: Mains power target value ( Figure 8 target_p) waveform, initial value of energy storage power ( Figure 8 base_pre) waveform, energy storage power target value ( Figure 8 bat_soure) waveform, target value of non-adjustable power component ( Figure 8 other_source) waveform, adjust power ( Figure 8 The total electricity cost of the second peak shaving invitation scenario is 3888, of which the electricity purchase cost is 3769, the energy storage cost is 224, and the ancillary service arbitrage income is 105.

[0183] (3) Valley filling invitation scenario, using energy storage price difference arbitrage while considering ancillary services to fill the valley

[0184] The first type of valley filling invitation: The baseline load at each time is: {"08:00:00": 350, "08:15:00": 350, "08:30:00": 350, "08:45:00": 350} / / The baseline load at 08:00:00, 08:15:00, 08:30:00, 08:45, etc. is 350.

[0185] For the first valley-filling invitation scenario, the energy storage system's optimization operation model determines that the baseline load of the ancillary service invitation is too high. The energy storage system adjusts the mains power target value to above the baseline load and there is little room for valley-filling arbitrage to gain profits. Therefore, the output is a conclusion that the application is not recommended. The output results of the energy storage system's optimization operation model are as follows:

[0186] state_vars: {'08:00:00': -0.0, '08:15:00': -0.0, '08:30:00': -0.0, '08:45:00'-0.0} / / The value of the auxiliary service declaration state quantity state_vars at 08:00:00, 08:15:00, 08:30:00, 08:45, etc. is 0, indicating that the auxiliary service declaration state quantity indicates that no auxiliary service declaration is performed;

[0187] declare: {'08:00:00': 0.0, '08:15:00': 0.0, '08:30:00': 0.0, '08:45:00': 0.0} / / The value of declare of the auxiliary service declaration quantity at each time such as 08:00:00, 08:15:00, 08:30:00 and 08:45.

[0188] load_resp: {'08:00:00': -246.606, '08:15:00': -240.416, '08:30:00': -300.0, '08:45:00': -298.608} / / The actual response load load_resp value at 08:00:00, 08:15:00, 08:30:00, 08:45, etc.

[0189] base_line: {'08:00:00': 350, '08:15:00': 350, '08:30:00': 350, '08:45:00': 350} / / The value of the baseline load base_line at 08:00:00, 08:15:00, 08:30:00, 08:45, etc.

[0190] target_pwr: {'08:00:00': 103.394, '08:15:00': 109.584, '08:30:00': 50.0, '08:45:00': 51.392} / The values ​​of the AC power target at 08:00:00, 08:15:00, 08:30:00, 08:45, etc.

[0191] In addition, the power waveform corresponding to the energy storage operation strategy of the first valley filling invitation scenario is the same as the power waveform of the non-invitation scenario. The total electricity cost of the first valley filling invitation scenario is 3902, of which the electricity purchase cost is 3674 and the energy storage cost is 227.

[0192] The second valley filling invitation: The baseline load at each moment is:

[0193] {'08:00:00': 100, '08:15:00': 100, '08:30:00': 100, '08:45:00': 100} / / The baseline load at 08:00:00, 08:15:00, 08:30:00, 08:45, etc. is 100.

[0194] For the second valley filling invitation scenario, the energy storage system's optimized operation model determines that the baseline load of the ancillary service invitation is reasonable, and that energy storage resources can be used for arbitrage, and appropriately shifts the energy storage charging and discharging intervals, in order to earn as much arbitrage income as possible from ancillary service declarations while maintaining energy storage price difference arbitrage. The output results of the energy storage system's optimized operation model are as follows:

[0195] state_vars:{'08:00:00': 1.0,'08:15:00': 1.0,'08:30:00': 1.0,'08:45:00': 1.0} / / The auxiliary service declaration state quantity state_vars at 08:00:00, 08:15:00, 08:30:00, 08:45, etc. has a value of 1, indicating that the auxiliary service declaration state quantity represents the auxiliary service declaration;

[0196] declare: {'08:00:00': 20.0, '08:15:00': 20.0, '08:30:00': 37.619, '08:45:00': 38.779} / / The value of declare of the auxiliary service declaration quantity at 08:00:00, 08:15:00, 08:30:00, 08:45, etc.

[0197] load_resp:{'08:00:00': 14.0, '08:15:00': 14.0, '08:30:00': 45.143, '08:45:00': 46.534} / / The actual response load load_resp value at 08:00:00, 08:15:00, 08:30:00, 08:45, etc.

[0198] base_line: {'08:00:00': 100, '08:15:00': 100, '08:30:00': 100, '08:45:00': 100} / / The value of the baseline load base_line at 08:00:00, 08:15:00, 08:30:00, 08:45, etc.

[0199] target_pwr:{'08:00:00':114.0,'08:15:00':114.0,'08:30:00':145.142,'08:45:00':146.534} / / The values ​​of the AC power target at 08:00:00, 08:15:00, 08:30:00, 08:45, etc.

[0200] in addition, Fig. 9 The power waveform diagram corresponding to the energy storage operation strategy for the valley filling invitation scenario. Fig. 9 The figure shows the power waveform corresponding to the energy storage operation strategy of the second valley filling invitation scenario. Fig. 9 The waveforms shown include: Mains power target value ( Fig. 9 target_p) waveform, initial value of energy storage power ( Fig. 9 base_pre) waveform, energy storage power target value ( Fig. 9 bat_soure) waveform, target value of non-adjustable power component ( Fig. 9 other_source) waveform, adjust power ( Figure 8 The total electricity cost of the second valley filling invitation scenario is 3889, of which the electricity purchase cost is 3683, the energy storage cost is 222, and the ancillary service declaration arbitrage income is 14.

[0201] (4) In multiple invitation scenarios, use energy storage price difference arbitrage while considering multiple auxiliary service invitation arbitrage

[0202] Multiple invitation scenarios include valley filling invitation and peak shaving invitation:

[0203] The baseline load of the valley filling invitation is: {'08:00:00': 100, '08:15:00': 100, '08:30:00': 100, '08:45:00': 100} / / The baseline loads at 08:00:00, 08:15:00, 08:30:00, 08:45, etc. are 100.

[0204] The baseline load of the peak shaving invitation is: {"11:00:00": 450, "11:15:00": 450, "11:30:00": 450, "11:45:00": 450} / / The baseline load at 11:00:00, 11:15:00, 11:30:00, 11:45:00, etc. is 450.

[0205] The optimized operation model of the energy storage system determines that the multi-invitation scenario can more fully increase revenue by adjusting energy storage resources, so the energy storage charging and discharging power is appropriately adjusted to earn as much auxiliary service subsidies as possible while maintaining price difference arbitrage. The output results of the optimized operation model of the energy storage system are as follows:

[0206] The output of the valley filling invitation is:

[0207] state_vars: {'08:00:00': 1.0, '08:15:00': 1.0, '08:30:00': 1.0, '08:45:00': 1.0}

[0208] declare: {'08:00:00': 20.0, '08:15:00': 20.0, '08:30:00': 37.619, '08:45:00': 38.779}

[0209] load_resp: {'08:00:00':14.0, '08:15:00':14.0, '08:30:00':45.143, '08:45:00':46.534}

[0210] base_line: {'08:00:00': 100, '08:15:00': 100, '08:30:00': 100, '08:45:00': 100}

[0211] target_pwr: {'08:00:00':114.0, '08:15:00'114.0, '08:30:00':145.142, '08:45:00':146.534}

[0212] The output result of the peak shaving invitation is:

[0213] state_vars: {'11:00:00': 1.0, '11:15:00': 1.0, '11:30:00': 1.0, '11:45:00': 1.0}

[0214] declare: {'11:00:00': 162.4, '11:15:00': 162.082, '11:30:00': 181.774, '11:45:00': 198.28}

[0215] load_resp: {'11:00:00': 194.879, '11:15:00': 194.499, '11:30:00': 218.129, '11:45:00': 237.936}

[0216] base_line: {'11:00:00': 450, '11:15:00': 450, '11:30:00': 450, '11:45:00': 450}

[0217] target_pwr: {'11:00:00': 255.121, '11:15:00': 255.501, '11:30:00': 231.871, '11:45:00': 212.064}

[0218] in addition, Fig.10 The power waveform diagram corresponding to the energy storage operation strategy of the multi-invitation scenario. Fig.10 The waveforms shown include: Mains power target value ( Fig.10 target_p) waveform, initial value of energy storage power ( Fig.10 base_pre) waveform, energy storage power target value ( Fig.10 bat_soure) waveform, target value of non-adjustable power component ( Fig.10 other_source) waveform, adjust power ( Fig.10 The total electricity cost of the multi-invitation scenario is 3888, of which the electricity purchase cost is 3773, the energy storage cost is 225, and the ancillary service declaration arbitrage income is 120.

[0219] The embodiment of the present application also provides a power management system, including: at least one third-party entity, a load aggregator and a power trading center; the load aggregator is used to predict the future operation data of the third-party entity based on the historical operation data of the third-party entity with the energy storage system; the optimization operation model of the energy storage system is constructed based on the future operation data, and the optimization operation model at least includes an objective function, the objective function uses the energy storage power target value of the energy storage system at multiple future moments as the first decision variable, and uses the auxiliary service declaration state quantity as the second decision variable, and the auxiliary service declaration state quantity represents whether the third-party entity declares auxiliary services; with the goal of optimizing energy utilization, solving the objective function, obtaining the variable value of the first decision variable and the variable value of the second decision variable; sending the variable value of the first decision variable to the third-party entity so that the third-party entity can control the charging and discharging of the energy storage system according to the variable value of the first decision variable; sending the variable value of the second decision variable to the third-party entity so that the third-party entity can declare auxiliary services to the power trading center according to the variable value of the second decision variable. Optionally, the load aggregator is a virtual power plant.

[0220] The implementation methods and technical effects of each step in the method for optimizing the operation of an energy storage system by load aggregation can be found in the aforementioned embodiments and will not be described in detail here.

[0221] Fig.11 This is a schematic diagram of the structure of an energy storage system optimization operation device provided in an embodiment of the present application. Fig.11 , the device may include:

[0222] A prediction module 10, for predicting future operation data of a third-party entity based on historical operation data of the third-party entity having the energy storage system;

[0223] The optimization module 20 is used to construct an optimized operation model of the energy storage system based on future operation data. The optimized operation model at least includes an objective function. The objective function uses the energy storage power target value of the energy storage system at multiple future moments as the first decision variable, and uses the auxiliary service declaration state quantity as the second decision variable. The auxiliary service declaration state quantity represents whether a third-party entity declares an auxiliary service; with the goal of optimizing energy utilization, the objective function is solved to obtain the variable value of the first decision variable and the variable value of the second decision variable; according to the variable value of the first decision variable and the variable value of the second decision variable, the charging and discharging process of the energy storage system and the auxiliary service declaration process are jointly controlled to optimize the operation process of the energy storage system.

[0224] Optionally, the historical operation data includes: actual values ​​of the mains power of the third party entity at multiple historical moments, actual values ​​of the energy storage power of the energy storage system at multiple historical moments;

[0225] Accordingly, the prediction module 10 is specifically used to: determine the actual values ​​of the non-adjustable power components of the third-party subject at multiple historical moments according to the actual values ​​of the mains power of the third-party subject at multiple historical moments and the actual values ​​of the energy storage power of the energy storage system of the third-party subject at multiple historical moments; and predict the target values ​​of the non-adjustable power components of the third-party subject at multiple future moments according to the actual values ​​of the non-adjustable power components of the third-party subject at multiple historical moments; and / or,

[0226] According to the target values ​​of energy storage power of the energy storage system at multiple future moments and the target values ​​of the non-adjustable power components of the third party at multiple future moments, the target values ​​of the mains power of the third party at multiple future moments are determined.

[0227] Optionally, when the optimization module 20 constructs an optimization operation model of the energy storage system according to future operation data, it is specifically used to:

[0228] Set constraints based on future operating data;

[0229] Set the objective function based on future operation data;

[0230] Construct an optimization operation model based on constraints and objective functions.

[0231] Optionally, when the optimization module 20 sets the objective function according to the future operation data, it is specifically used to:

[0232] Determine an energy storage operation cost function according to an initial energy storage power value and an energy storage power target value of the energy storage system at multiple future moments;

[0233] Determine the electricity purchase cost function according to the target value of the mains power of the third party at multiple future moments;

[0234] Determine an electricity cost function having a first decision variable according to the energy storage operation cost function and / or the electricity purchase cost function;

[0235] If auxiliary service invitation data is detected, establishing an auxiliary service declaration reward function having a second decision variable according to the auxiliary service invitation data;

[0236] The objective function is obtained based on the ancillary service declaration reward function and the electricity cost function.

[0237] Optionally, when the optimization module 20 establishes the auxiliary service application reward function according to the auxiliary service invitation data, it is specifically used to:

[0238] Determine at least one first future moment in the invitation period among the multiple future moments according to the ancillary service invitation data, and determine the actual response load of the third party subject at the first future moment according to the baseline load of the third party subject at the first future moment and the mains power target value;

[0239] An auxiliary service declaration reward function is established based on the actual response load of the third-party entity at at least one first future moment, the auxiliary service declaration state quantity and the first auxiliary state variable; wherein the first auxiliary state variable is used to characterize the numerical range of the ratio of the actual response load to the auxiliary service declaration quantity.

[0240] Optionally, the optimization module 20 is further configured to establish an auxiliary service winning reward function according to the actual response load of the third party entity at at least one first future moment, the auxiliary service winning amount, and a second auxiliary state variable if it is determined that the auxiliary service has won the bid in the invitation period according to the detected auxiliary service winning bid data; wherein the second auxiliary state variable is used to characterize the numerical range of the ratio of the actual response load to the auxiliary service winning amount;

[0241] Accordingly, when the optimization module 20 obtains the target function according to the auxiliary service application reward function and the electricity cost function, it is specifically used to obtain the target function according to the auxiliary service application reward function, the auxiliary service winning reward function and the electricity cost function.

[0242] Optionally, when the optimization module 20 sets the constraint conditions according to the future operation data, it is specifically used to: for any future moment among multiple future moments, determine a third auxiliary variable at the future moment according to the energy storage power initial value and the energy storage power target value of the energy storage system at the future moment, the third auxiliary variable representing the charging and discharging behavior of the energy storage system;

[0243] Determine the charging power and discharging power of the energy storage system at the future time according to the third auxiliary variable at the future time, and determine the charging state variable and discharging state variable of the energy storage system at the future time according to the charging power and discharging power of the energy storage system at the future time;

[0244] Setting a first constraint condition, where the first constraint condition is used to limit the sum of a charging state variable and a discharging state variable of the energy storage system at a future time;

[0245] Setting a second constraint condition, where the second constraint condition is used to limit the upper and lower limits of the charging power of the energy storage system at a future time;

[0246] A third constraint condition is set, and the third constraint condition is used to limit the upper and lower limits of the discharge power of the energy storage system at a future moment.

[0247] Optionally, the optimization module 20 is further used for:

[0248] For any future moment among multiple future moments, determine the variable of the number of changes in the charging state of the energy storage system at the future moment according to the charging state variable of the energy storage system at the future moment and the previous future moment; and determine the variable of the number of changes in the discharge state of the energy storage system at the future moment according to the discharge state variable of the energy storage system at the future moment and the previous future moment;

[0249] Determine the total number of charging state changes according to the variable of the number of charging state changes of the energy storage system at multiple future moments, and determine the total number of discharging state changes of the energy storage system according to the variable of the number of discharging state changes of the energy storage system at multiple future moments;

[0250] Setting a fourth constraint condition, where the fourth constraint condition is used to limit the upper and lower limits of the total number of charge state changes of the energy storage system;

[0251] A fifth constraint condition is set, and the fifth constraint condition is used to limit the upper and lower limits of the total number of discharge state changes of the energy storage system.

[0252] Optionally, the optimization module 20 is further used for:

[0253] Parameter identification is performed based on the actual state of charge (SOC) values ​​of the energy storage system at multiple historical moments to obtain the charging efficiency and discharging efficiency of the energy storage system;

[0254] For any future moment among the multiple future moments, determine the SOC target value of the energy storage system at the future moment according to the charging efficiency, the discharging efficiency, the charging power of the energy storage system at the future moment, the discharging power of the energy storage system at the future moment, and the SOC target value of the energy storage system at the previous future moment;

[0255] A sixth constraint condition is set, and the sixth constraint condition is used to limit the upper and lower limits of the SOC target value of the energy storage system at a future moment.

[0256] Optionally, the optimization module 20 is further used for:

[0257] Setting the seventh constraint condition, which is used to limit the value of the auxiliary service declaration status quantity and the value of the auxiliary service declaration quantity;

[0258] An eighth constraint condition is set, where the eighth constraint condition is used to limit the magnitude relationship between the actual response load of the third party subject and the baseline load at the first future moment;

[0259] Setting a ninth constraint condition, where the ninth constraint condition is used to limit the magnitude of the response rate of the actual response load of the third party subject at the first future moment;

[0260] The tenth constraint condition is set, which is used to limit the upper limit of the ancillary service reporting amount.

[0261] Fig.11 The device shown can perform Figure 2 The method shown in the embodiment shown, its implementation principle and technical effect are not described in detail. Fig.11 The specific manner in which each module and unit of the device shown performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0262] It should be noted that the execution subject of each step of the method provided in the above embodiment can be the same device, or the method can be executed by different devices. For example, the execution subject of steps 201 to 204 can be device A; for another example, the execution subject of steps 201 and 202 can be device A, and the execution subject of steps 203 and 204 can be device B; and so on.

[0263] In addition, in some of the processes described in the above embodiments and the accompanying drawings, multiple operations that appear in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this article or executed in parallel, and the sequence numbers of the operations, such as 201, 202, etc., are only used to distinguish between different operations, and the sequence numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions of "first", "second", etc. in this article are used to distinguish different messages, devices, modules, etc., do not represent the order of precedence, and do not limit the "first" and "second" to be different types.

[0264] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with the relevant laws, regulations and standards of relevant countries and regions, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0265] Fig.12 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. Fig.12 As shown, the electronic device includes: a memory 11 and a processor 12;

[0266] The memory 11 is used to store computer programs and can be configured to store various other data to support operations on the computing platform. Examples of such data include instructions for any application or method operating on the computing platform, contact data, phone book data, messages, pictures, videos, etc.

[0267] The memory 11 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read only memory (EEPROM), erasable programmable read only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0268] The processor 12 is coupled to the memory 11 and is used to execute the computer program in the memory 11 to execute the steps in the energy storage system optimization operation method.

[0269] Optional, such as Fig.12 As shown, the electronic device also includes: a communication component 13, a display 14, a power component 15, an audio component 16 and other components. Fig.12 Only some components are shown schematically, which does not mean that the electronic device only includes Fig.12 In addition, Fig.12 The components in the dashed box are optional components, not mandatory components, and the specific components depend on the product form of the electronic device. The electronic device of this embodiment can be implemented as a terminal device such as a desktop computer, a laptop computer, a smart phone, or an IOT (Internet of Things) device, or a server device such as a conventional server, a cloud server, or a server array. If the electronic device of this embodiment is implemented as a terminal device such as a desktop computer, a laptop computer, a smart phone, etc., it can include Fig.12 If the electronic device of this embodiment is implemented as a server device such as a conventional server, a cloud server or a server array, it may not include Fig.12 Components within the dashed box.

[0270] For the detailed implementation process of the processor executing each action, please refer to the relevant description in the aforementioned method embodiment or device embodiment, which will not be repeated here.

[0271] Accordingly, an embodiment of the present application further provides a computer-readable storage medium storing a computer program, which, when executed, can implement each step that can be executed by an electronic device in the above method embodiment.

[0272] Accordingly, an embodiment of the present application also provides a computer program product, including a computer program / instruction. When the computer program / instruction is executed by a processor, the processor is enabled to implement each step in the above method embodiment that can be executed by an electronic device.

[0273] The communication component is configured to facilitate wired or wireless communication between the device where the communication component is located and other devices. The device where the communication component is located can access a wireless network based on a communication standard, such as WiFi (Wireless Fidelity), 2G (2 Generation), 3G (3 Generation), 4G (4 Generation) / LTE (long Term Evolution), 5G (5 Generation), and other mobile communication networks, or a combination thereof. In an exemplary embodiment, the communication component receives a broadcast signal or broadcast-related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component also includes a near field communication (NFC) module to facilitate short-range communication. For example, the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra wide band (UWB) technology, Bluetooth (BT) technology, and other technologies.

[0274] The display includes a screen, and the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from a user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundary of a touch or slide action, but also detect the duration and pressure associated with the touch or slide operation.

[0275] The power supply assembly provides power to various components of the device where the power supply assembly is located. The power supply assembly may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to the device where the power supply assembly is located.

[0276] The above-mentioned audio component can be configured to output and / or input audio signals. For example, the audio component includes a microphone (MIC), and when the device where the audio component is located is in an operating mode, such as a call mode, a recording mode, and a speech recognition mode, the microphone is configured to receive an external audio signal. The received audio signal can be further stored in a memory or sent via a communication component. In some embodiments, the audio component also includes a speaker for outputting an audio signal.

[0277] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may take the form of a computer program product implemented on one or more computer-readable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0278] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0279] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0280] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0281] In a typical configuration, a computing device includes one or more processors (Central Processing Unit, CPU), input / output interface, network interface and memory.

[0282] Memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.

[0283] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, Phase Change RAM (PRAM), Static Random-Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically-Erasable Programmable Read-Only Memory (EEPROM), flash memory or other memory technology, CD-ROM, Digital Versatile Disc (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices or any other non-transmission medium that can be used to store information that can be accessed by a computing device. According to the definition in this article, computer-readable media does not include temporary computer-readable media (transitory media), such as modulated data signals and carrier waves.

[0284] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0285] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.

Claims

1. A method for optimizing the operation of an energy storage system, characterized in that: include: Predicting future operating data of a third-party entity having an energy storage system based on historical operating data of the third-party entity; Constructing an optimized operation model of the energy storage system according to the future operation data, the optimized operation model at least including an objective function and constraints, the objective function using the energy storage power target value of the energy storage system at multiple future moments as a first decision variable, and using the auxiliary service declaration state quantity as a second decision variable, the auxiliary service declaration state quantity indicating whether the third party entity declares an auxiliary service; With the goal of optimizing energy utilization, solving the objective function, and obtaining the variable value of the first decision variable and the variable value of the second decision variable; According to the variable value of the first decision variable and the variable value of the second decision variable, jointly control the charging and discharging process and the auxiliary service declaration process of the energy storage system to optimize the operation process of the energy storage system; Among them, the implementation method of setting the objective function according to the future operation data is: determining the energy storage operation cost function according to the energy storage power initial value and the energy storage power target value of the energy storage system at multiple future moments; determining the power purchase cost function according to the mains power target value of the third-party entity at multiple future moments; determining the electricity cost function with the first decision variable according to the energy storage operation cost function and / or the power purchase cost function; if auxiliary service invitation data is detected, establishing the auxiliary service declaration reward function with the second decision variable according to the auxiliary service invitation data; obtaining the objective function according to the auxiliary service declaration reward function and the electricity cost function; Among them, the implementation method of setting constraint conditions according to the future operation data includes: for any future moment among the multiple future moments, determining the third auxiliary variable at the future moment according to the initial value of the energy storage power and the target value of the energy storage power of the energy storage system at the future moment, the third auxiliary variable characterizing the charging and discharging behavior of the energy storage system; determining the charging power and discharging power of the energy storage system at the future moment according to the third auxiliary variable at the future moment, and determining the charging state variable and discharging state variable of the energy storage system at the future moment according to the charging power and discharging power of the energy storage system at the future moment; setting a first constraint condition, the first constraint condition is used to limit the sum of the charging state variable and the discharging state variable of the energy storage system at the future moment; setting a second constraint condition, the second constraint condition is used to limit the upper and lower limits of the charging power of the energy storage system at the future moment; setting a third constraint condition, the third constraint condition is used to limit the upper and lower limits of the discharge power of the energy storage system at the future moment.

2. The method according to claim 1, characterized in that: The historical operation data includes: the actual value of the mains power of the third party entity at multiple historical moments, and the actual value of the energy storage power of the energy storage system at multiple historical moments; Accordingly, based on the historical operation data of the third-party entity having the energy storage system, predicting the future operation data of the third-party entity includes: Determine the actual values ​​of the non-adjustable power components of the third-party subject at multiple historical moments based on the actual values ​​of the mains power of the third-party subject at multiple historical moments and the actual values ​​of the energy storage power of the energy storage system of the third-party subject at multiple historical moments; and predict the target values ​​of the non-adjustable power components of the third-party subject at multiple future moments based on the actual values ​​of the non-adjustable power components of the third-party subject at multiple historical moments; and / or, The target values ​​of the mains power of the third party at multiple future moments are determined according to the target values ​​of the energy storage power of the energy storage system at multiple future moments and the target values ​​of the non-adjustable power components of the third party at multiple future moments.

3. The method according to claim 1, characterized in that An auxiliary service application reward function is established according to the auxiliary service invitation data, including: Determine at least one first future moment in the invitation period among multiple future moments according to the auxiliary service invitation data, and determine the actual response load of the third party entity at the first future moment according to the baseline load of the third party entity at the first future moment and the mains power target value; An auxiliary service declaration reward function is established based on the actual response load of the third-party entity at at least one first future moment, the auxiliary service declaration state quantity and the first auxiliary state variable; wherein the first auxiliary state variable is used to characterize the numerical range of the ratio of the actual response load to the auxiliary service declaration quantity.

4. The method according to claim 3, characterized in that: Also includes: If it is determined that the auxiliary service has been successfully bid for during the invitation period according to the detected auxiliary service winning bid data, then an auxiliary service winning bid reward function is established according to the actual response load of the third party entity at at least one first future time, the auxiliary service winning bid amount, and a second auxiliary state variable; wherein the second auxiliary state variable is used to characterize the numerical range of the ratio of the actual response load to the auxiliary service winning bid amount; Accordingly, the objective function is obtained according to the auxiliary service reporting reward function and the electricity cost function, including: The objective function is obtained according to the auxiliary service application reward function, the auxiliary service bid winning reward function and the electricity cost function.

5. The method according to claim 1, characterized in that Also includes: For any future moment among the multiple future moments, determining a variable of the number of changes in the charging state of the energy storage system at the future moment according to the charging state variable of the energy storage system at the future moment and the previous future moment; And, determining a variable of the number of discharge state changes of the energy storage system at the future moment according to the discharge state variable of the energy storage system at the future moment and the previous future moment; Determine the total number of charging state changes according to the variable of the number of charging state changes of the energy storage system at multiple future moments, and determine the total number of discharging state changes of the energy storage system according to the variable of the number of discharging state changes of the energy storage system at multiple future moments; Setting a fourth constraint condition, wherein the fourth constraint condition is used to limit the upper and lower limits of the total number of times the charging state of the energy storage system changes; A fifth constraint condition is set, and the fifth constraint condition is used to limit the upper and lower limits of the total number of discharge state changes of the energy storage system.

6. The method according to claim 1, characterized in that Also includes: Perform parameter identification according to actual values ​​of the state of charge (SOC) of the energy storage system at multiple historical moments to obtain charging efficiency and discharging efficiency of the energy storage system; For any one of the multiple future moments, determine the SOC target value of the energy storage system at the future moment according to the charging efficiency, the discharging efficiency, the charging power of the energy storage system at the future moment, the discharging power of the energy storage system at the future moment, and the SOC target value of the energy storage system at the last future moment; A sixth constraint condition is set, and the sixth constraint condition is used to limit the upper and lower limits of the SOC target value of the energy storage system at the future moment.

7. The method according to claim 1, characterized in that Also includes: Setting a seventh constraint condition, wherein the seventh constraint condition is used to limit the value of the auxiliary service declaration state quantity and the value of the auxiliary service declaration quantity; Setting an eighth constraint condition, wherein the eighth constraint condition is used to limit the magnitude relationship between the actual response load of the third-party subject and the baseline load at a first future moment; Setting a ninth constraint condition, wherein the ninth constraint condition is used to limit the response rate of the actual response load of the third party entity at the first future moment; A tenth constraint condition is set, and the tenth constraint condition is used to limit the upper limit value of the auxiliary service reporting amount.

8. A power management system, characterized in that: include: At least one third party entity, load aggregator and power trading center; The load aggregator is used to predict the future operation data of the third-party entity with the energy storage system based on the historical operation data of the third-party entity; construct an optimized operation model of the energy storage system based on the future operation data, and the optimized operation model at least includes an objective function and constraints, and the objective function uses the energy storage power target value of the energy storage system at multiple future moments as the first decision variable, and uses the auxiliary service declaration state quantity as the second decision variable, and the auxiliary service declaration state quantity represents whether the third-party entity declares an auxiliary service; With the goal of optimizing energy utilization, the objective function is solved to obtain the variable value of the first decision variable and the variable value of the second decision variable; the variable value of the first decision variable is sent to the third party entity so that the third party entity can control the charging and discharging of the energy storage system according to the variable value of the first decision variable; the variable value of the second decision variable is sent to the third party entity so that the third party entity can declare an auxiliary service to the power trading center according to the variable value of the second decision variable; Among them, the implementation method of setting the objective function according to the future operation data is: determining the energy storage operation cost function according to the energy storage power initial value and the energy storage power target value of the energy storage system at multiple future moments; determining the power purchase cost function according to the mains power target value of the third-party entity at multiple future moments; determining the electricity cost function with the first decision variable according to the energy storage operation cost function and / or the power purchase cost function; if auxiliary service invitation data is detected, establishing the auxiliary service declaration reward function with the second decision variable according to the auxiliary service invitation data; obtaining the objective function according to the auxiliary service declaration reward function and the electricity cost function; Among them, the implementation method of setting constraint conditions according to the future operation data includes: for any future moment among the multiple future moments, determining the third auxiliary variable at the future moment according to the initial value of the energy storage power and the target value of the energy storage power of the energy storage system at the future moment, the third auxiliary variable characterizing the charging and discharging behavior of the energy storage system; determining the charging power and discharging power of the energy storage system at the future moment according to the third auxiliary variable at the future moment, and determining the charging state variable and discharging state variable of the energy storage system at the future moment according to the charging power and discharging power of the energy storage system at the future moment; setting a first constraint condition, the first constraint condition is used to limit the sum of the charging state variable and the discharging state variable of the energy storage system at the future moment; setting a second constraint condition, the second constraint condition is used to limit the upper and lower limits of the charging power of the energy storage system at the future moment; setting a third constraint condition, the third constraint condition is used to limit the upper and lower limits of the discharge power of the energy storage system at the future moment.

9. The system according to claim 8, characterized in that The load aggregator is a virtual power plant.

10. An electronic device, characterized in that: include: Memory and processor; The memory is used to store computer programs; The processor is coupled to the memory and configured to execute the computer program to perform the steps in the method according to any one of claims 1 to 7.

11. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the processor is enabled to implement the steps in the method according to any one of claims 1 to 7.

12. A computer program product, characterized in that The method comprises a computer program / instruction, which, when executed by a processor, enables the processor to implement the steps of the method according to any one of claims 1 to 7.

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