Micro-grid operation system considering state of energy storage unit

Abstract

The application provides a micro-grid operation system considering the state of an energy storage unit, which comprises a micro-grid grid-connected instruction set multi-objective rolling optimization model, a battery energy storage system power distribution model considering the state of the energy storage unit, a micro-grid operation result multi-dimensional evaluation decision model and a battery energy storage system; the micro-grid grid-connected instruction set multi-objective rolling optimization model sends a Pareto solution set to the battery energy storage system power distribution model considering the state of the energy storage unit and the micro-grid operation result multi-dimensional evaluation decision model; the battery energy storage system power distribution model considering the state of the energy storage unit sends an optimal distribution scheme and the Pareto solution set to the micro-grid operation result multi-dimensional evaluation decision model; and the micro-grid grid-connected instruction set multi-objective rolling optimization model performs optimization for the next period based on the optimal operation scheme of the current period. The application can reduce the number of state switching of the battery energy storage system and reduce the SOC inconsistency level.

Description

Technical Field

[0001] The present invention belongs to the technical field of microgrid control, and in particular relates to a microgrid operation system that takes the state of an energy storage unit into consideration. Background Art

[0002] The national standard GBT34930-2017, "Microgrid Integration into Distribution Network Operation Control Specifications," stipulates that when microgrids are connected to distribution networks with voltage levels of 10kV or above, they must adhere to the following requirements: "Tie-line active power control must be implemented according to the requirements of the grid dispatching organization" and "The maximum and rate of change of exchange power must be within the range specified by the grid dispatching organization." Current research focuses on utilizing battery energy storage systems (BESS) to meet both microgrid power leveling and error reduction requirements. However, using BESS solely to optimize microgrid grid-connected power in real time has limited effectiveness and hinders the full realization of its adjustable potential.

[0003] Extending the service life of battery energy storage systems is one of the important means to improve the economic efficiency of microgrid operations. In microgrid scenarios, battery energy storage systems are generally composed of multiple battery energy storage units (BESUs). However, for energy storage units composed of single cells connected in series, if the internal characteristics of the energy storage units are ignored during the microgrid regulation process and they are simplified into large single cells, the consistency of the state of health (SOH) and state of charge (SOC) of the single cells within the energy storage units will deteriorate due to improper operation. In addition, the life of the energy storage unit is not only closely related to its SOH consistency and SOC consistency, but also affected by the number of charge and discharge state transitions. Reducing the number of charge and discharge state transitions can effectively extend the service life of the battery energy storage system. Therefore, it is necessary to pay more attention to the power allocation strategy of the battery energy storage system when studying the microgrid operation strategy.

[0004] The existing technology has technical problems such as not taking into account the accuracy and volatility requirements that need to be met for microgrid grid connection, as well as the number of charge and discharge conversions, SOC consistency, and charge and discharge efficiency that need to be considered in the power distribution of energy storage units. Summary of the Invention

[0005] To overcome the technical problems of the above-mentioned prior art that fail to take into account the accuracy and volatility requirements that need to be met for microgrid grid connection, as well as the number of charge and discharge conversions, SOC consistency, and charge and discharge efficiency that need to be considered in the power allocation of energy storage units, the present invention proposes a microgrid operation system that considers the status of energy storage units, including a multi-objective rolling optimization model for a microgrid grid connection instruction set, a power allocation model for a battery energy storage system that considers the status of energy storage units, a multi-dimensional evaluation and decision-making model for microgrid operation results, and a battery energy storage system;

[0006] Based on the operating requirements of the microgrid and the battery energy storage system, an evaluation system and evaluation indicators are constructed;

[0007] The multi-objective rolling optimization model of the microgrid grid-connected instruction set is used to use a multi-objective exponential distribution optimization algorithm to obtain the Pareto solution of the battery energy storage system's charge and discharge power in the current period, and send the Pareto solution to the battery energy storage system power allocation model considering the energy storage unit status and the multi-dimensional evaluation and decision model of the microgrid operation results;

[0008] The battery energy storage system power allocation model considering the energy storage unit status is used to obtain, based on the Pareto solution set, an optimal allocation scheme for the charge and discharge power of each battery energy storage system in the Pareto solution set among the energy storage units selected within the battery energy storage system, and send the optimal allocation scheme and the Pareto solution set to the multi-dimensional evaluation decision model for the microgrid operation results;

[0009] The multidimensional evaluation decision model for the microgrid operation results is used to determine the decision indicators based on the Pareto solution set, the optimal allocation plan, the evaluation system, and the evaluation indicators; use the Pareto solution set of the hierarchical analysis method to make a decision based on the decision indicators to obtain the optimal operation plan for this period, and send the optimal operation plan for this period to the multi-objective rolling optimization model of the microgrid grid connection instruction set;

[0010] The multi-objective rolling optimization model of the microgrid grid-connected instruction set is also used to optimize the next period based on the optimal operation plan of the current period.

[0011] Preferably, the multi-objective rolling optimization model of the microgrid grid-connected instruction set includes: a multi-objective optimization model of the microgrid grid-connected instruction set and a rolling optimization model;

[0012] The microgrid grid-connected instruction set multi-objective optimization model is used to construct objective functions and constraints;

[0013] The rolling optimization model is used to accept and solve the multi-objective optimization model of the microgrid grid-connected instruction set within the current period of microgrid operation and a limited period thereafter, based on the optimal operation plan of the previous period obtained by the multi-dimensional evaluation decision model of the microgrid operation results.

[0014] Preferably, the objective function includes: a function for the minimum number of unqualified periods of grid-connected power scheduling within the scheduling range, a function for the minimum number of unqualified periods of grid-connected power fluctuation, and a function for the minimum cumulative charge and discharge throughput of the battery energy storage system;

[0015] The constraints include: grid-connected power scheduling and fluctuation qualified state constraints, battery energy storage system power constraints, and battery energy storage system energy constraints.

[0016] Preferably, the battery energy storage system power allocation model considering the energy storage unit status includes: a unit grouping submodule, a unit screening submodule and a power allocation submodule;

[0017] The unit grouping submodule is configured to receive and, based on the Pareto solution of the battery energy storage system's charge and discharge power, divide the energy storage units into two groups for operation in the current period based on the number of charge and discharge switching times of the battery energy storage system, and send the grouped energy storage units to the unit screening submodule. Prior to each charge and discharge of the battery energy storage system, the grouping results of the previous period are inherited or modified through a dynamic grouping mechanism.

[0018] The unit screening submodule is configured to determine the potential size and number of energy storage units that participate in the response of the grouped energy storage units according to the unit response potential calculation method, and complete the screening of energy storage units based on the number of energy storage units participating in the response and the potential size; and send the screened energy storage units to the power distribution submodule;

[0019] The power distribution submodule is used to establish a battery energy storage system power distribution model for the screened energy storage units, with the goal of minimizing the degree of inconsistency in the battery state of charge within the energy storage units, and use an energy valley optimization algorithm to calculate the optimal distribution plan for charging and discharging power among the selected energy storage units; and send the optimal distribution plan to the multidimensional evaluation and decision model for the microgrid operation results.

[0020] Preferably, the multi-dimensional evaluation decision model for microgrid operation results is specifically used for:

[0021] Based on the Pareto solution set, the optimal allocation plan, the evaluation system, and the evaluation indicators, weighting the microgrid operation result indicators using the analytic hierarchy process, and determining the weights of the indicators according to the microgrid operation result indicators and the evaluation system; the evaluation indicators include an indicator for the number of unqualified periods of grid-connected power scheduling, an indicator for the number of unqualified periods of grid-connected power fluctuation, an indicator for the cumulative charge and discharge throughput of the battery energy storage system, an indicator for the number of charge and discharge conversions, and an indicator for the degree of inconsistency of the state of charge;

[0022] The linear proportional method in the linear dimensionless method is used to perform dimensionless processing on the indicators as follows:

[0023] Dimensionless results of indicators The calculation is as follows:

[0024]

[0025] In the above formula: is the dimensionless data of the charging and discharging efficiency index of solution i in the Pareto solution set; x i is the actual value of the charging and discharging efficiency index of solution i in the Pareto solution set; are the x corresponding to each solution i Minimum value.

[0026] Dimensionless results of the indicators for the number of periods with unqualified grid power scheduling, the number of periods with unqualified grid power fluctuation, the cumulative charge and discharge throughput of the battery energy storage system, the number of charge and discharge conversions, and the degree of inconsistency of the state of charge. The calculation is as follows:

[0027]

[0028] In the above formula: is the dimensionless data of indicator j in solution i in the Pareto solution set; x ij is the actual value of indicator j of solution i in the Pareto solution set; is the x corresponding to each solution ij Maximum value;

[0029] After dimensionless processing of the evaluation index, the decision index DM of solution i in the Pareto solution set is obtained: i for:

[0030]

[0031] In the above formula: w v,j is the weight of indicator j in the evaluation system; j is the jth indicator;

[0032] Based on the decision indicator DM i A decision is made on the Pareto solution set of the charging and discharging power of the battery energy storage system to obtain an optimal operation plan, and the optimal operation plan is sent to the multi-objective rolling optimization model of the microgrid grid-connected instruction set for optimization in the next period.

[0033] Compared with the closest prior art, the present invention has the following beneficial effects:

[0034] It can maximize the grid-connected performance of the microgrid while reducing the charge and discharge throughput of the battery energy storage system. On this basis, it can achieve the optimal distribution of charge and discharge power among the energy storage units in the battery energy storage system, reduce the number of charge and discharge state switching times of the battery energy storage system, and reduce the SOC inconsistency level. It improves the economic benefits of the battery energy storage system throughout its life cycle and has certain engineering reference value. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 This is the microgrid operation framework diagram considering the energy storage unit status of the present invention

[0036] Figure 2 This is the rolling scheduling plan implementation flow chart of the present invention

[0037] Figure 3 This is a diagram of the dynamic grouping mechanism of energy storage units taking into account SOC and SOC consistency of the present invention. DETAILED DESCRIPTION

[0038] The specific implementation methods of the present invention will be further described in detail below with reference to the accompanying drawings.

[0039] The present invention provides a microgrid operation system that considers the state of energy storage units, including a multi-objective rolling optimization model for a microgrid grid-connected instruction set, a battery energy storage system power allocation model that considers the state of energy storage units, a multi-dimensional evaluation and decision-making model for microgrid operation results, and a battery energy storage system.

[0040] Based on the operating requirements of the microgrid and the battery energy storage system, an evaluation system and evaluation indicators are constructed;

[0041] The multi-objective rolling optimization model of the microgrid grid-connected instruction set is used to use a multi-objective exponential distribution optimization algorithm to obtain the Pareto solution of the battery energy storage system's charge and discharge power in the current period, and send the Pareto solution to the battery energy storage system power allocation model considering the energy storage unit status and the multi-dimensional evaluation and decision model of the microgrid operation results;

[0042] The battery energy storage system power allocation model considering the energy storage unit status is used to obtain, based on the Pareto solution set, an optimal allocation scheme for the charge and discharge power of each battery energy storage system in the Pareto solution set among the energy storage units selected within the battery energy storage system, and send the optimal allocation scheme and the Pareto solution set to the multi-dimensional evaluation decision model for the microgrid operation results;

[0043] The multidimensional evaluation decision model for the microgrid operation results is used to determine the decision indicators based on the Pareto solution set, the optimal allocation plan, the evaluation system, and the evaluation indicators; use the hierarchical analysis method to determine the decision indicators for the Pareto solution set, make decisions based on the decision indicators, obtain the optimal operation plan for this period, and send the optimal operation plan for this period to the multi-objective rolling optimization model of the microgrid grid connection instruction set;

[0044] The multi-objective rolling optimization model of the microgrid grid-connected instruction set is also used to optimize the next period based on the optimal operation plan of the current period.

[0045] The multi-objective rolling optimization model for the microgrid grid-connected instruction set comprehensively considers the accuracy and smoothness requirements that the microgrid grid-connected power must meet, establishes an optimization model with the goals of minimizing the number of unqualified periods for grid-connected power scheduling, minimizing the number of unqualified periods for grid-connected power fluctuation, and minimizing the cumulative charging and discharging throughput of the BESS. The multi-objective exponential distribution optimization algorithm (MOEDO) is used to obtain the BESS charging and discharging power, and fully utilizes the high-precision ultra-short-term predicted power. The optimization model is embedded in the rolling plan to improve the BESS's ability to support subsequent regulation needs.

[0046] The BESS power allocation model that considers the energy storage unit status refers to a BESS power allocation strategy that is constructed by combining a unit grouping module, a unit screening module, and a power allocation module based on the acquisition of the BESS charge and discharge power. This achieves optimal distribution of charge and discharge power among different energy storage units within the BESS, thereby extending the service life of the BESS.

[0047] Reference Figure 1 The BESS power allocation model considering the energy storage unit status of the present invention consists of a unit grouping submodule, a unit screening submodule and a power allocation submodule.

[0048] The unit grouping submodule divides the energy storage units into two groups for operation based on the number of charge and discharge switching times of the battery energy storage system (referred to as the "two-grouping method"). Before each power allocation, the grouping results of the previous time period are inherited or revised through the dynamic grouping mechanism.

[0049] The unit screening submodule is to determine the number and potential size of energy storage units participating in the response based on the unit response potential calculation method, so as to complete the screening of energy storage units.

[0050] The power allocation submodule is to establish a BESS power allocation model according to the principle of optimal consistency of battery state of charge within the energy storage unit after completing the selection of energy storage units through the unit grouping submodule and the unit screening submodule, and use the Energy Valley Optimization (EVO) algorithm to calculate the optimal allocation scheme of charging and discharging power among the selected energy storage units.

[0051] The three modules are described in detail below.

[0052] (1) Unit grouping submodule

[0053] In order to balance the charging and discharging capacity of BESS, the SOH and ΔS of the energy storage unit are comprehensively considered when grouping in the initial period. OH , state of charge and ΔS OCThe difference in index values ​​is based on the ranking results of the reference unit response potential calculation method, and the response potential of the priority charge / discharge groups is ensured to be as similar as possible. During the remaining time periods, the dynamic grouping mechanism designed by the present invention is used to update the grouping status of the energy storage units in real time.

[0054] After determining the group status, the BESS charge and discharge instructions need to be distributed to the two unit groups. When the charge and discharge groups are unable to handle the charge and discharge tasks alone, the priority group will be fully dispatched, and the non-priority group will assist in filling the power shortage based on the unit screening submodule and power allocation submodule of the present invention.

[0055] Suppose there are two battery groups A and B respectively, the priority charging group and the priority discharging group, ΔS OC Indicates the degree of inconsistency of the state of charge of the energy storage unit. Taking the charging process of period t as an example, the dynamic grouping mechanism of energy storage units considering the state of charge and the consistency of the state of charge proposed in this paper is explained. The specific process is as follows: Figure 3 As shown:

[0056] 1: If only group A responds to the charging command during the t-1 period, the power allocation result of the responding unit is constrained by the state of charge warning state and ΔS OC The early warning state constraint check is shown in equations (1) and (2) respectively. If one of the two constraints is not satisfied, the energy storage unit is added to group B during time period t. At the same time, the charging capacity of the energy storage units in group B is sorted using the energy storage unit response potential sorting method. The energy storage units with strong charging capacity are selected to join group A, so that the number of energy storage units in groups A and B is equal again. The grouping process when group B responds to the discharge command alone is similar to the above process.

[0057] SOC i,alert,min ≤SOC i (t-1)≤SOC i,alert,max (1)

[0058] |ΔS OC,i (t-1)|≤ΔS OC,i,alert (2)

[0059] Where: SOC i (t-1) and ΔS OC,i (t-1) are the state of charge value and ΔS of the i-th energy storage unit in the t-1th period respectively OC value; SOC i,alert,min and SOC i,alert,max Set the lower limit and upper limit of the charge state warning state of the i-th energy storage unit respectively; ΔS OC,i,alert is the ΔS of the i-th energy storage unit OC The warning state setting value, when the constraint is not met, the energy storage unit is in the warning state.

[0060] 2: During the t-1 period, groups A and B jointly respond to the charging instruction. If group A cannot complete the charging demand, the energy storage unit response potential ranking method is used to select y units with strong charging capabilities in group B to assist in completing the charging shortage, and the y energy storage units that assist in charging in group B are used as the candidate unit set for group A. At the same time, the power allocation results of the response units in group A are subject to charge state warning state constraints and ΔS OC The warning status constraint check selects x energy storage units in the warning status as the group B candidate unit set.

[0061] When y>x, the number of energy storage units exchanged between the two groups is y, and the candidate unit set of group A remains unchanged. The discharge capacity of the energy storage units in group A is sorted using the response potential sorting method, and yx energy storage units that originally did not change the grouping status are added to the candidate unit set of group B in order;

[0062] When x>y, the number of energy storage units exchanged between the two groups is x, and the set of alternative units in group B remains unchanged. The charging capacity of the energy storage units in group B is sorted using the response potential sorting method, and xy energy storage units that originally did not change the grouping status are added to the set of alternative units in group A in sequence.

[0063] (2) Unit screening submodule

[0064] The unit response potential calculation method F of the present invention is shown in formula (3). F is the weighted sum of f4 and f5, k4 and k5 are the weight values ​​of f4 and f5 respectively, and the smaller F is, the greater the potential of the energy storage unit to participate in the response.

[0065] F=k4f4+k5f5 (3)

[0066]

[0067]

[0068] In order to prevent over-discharge and over-charge, the present invention sets the function f4 based on the state of charge level of the energy storage unit as shown in formula (4). min SOC is the lower limit of the state of charge allowed value. max The upper limit of the state of charge allowed value, SOC m is the midpoint of the allowable state of charge value, and its calculation formula is shown in (5). min ,SOC m ], in order to avoid the energy storage unit from being affected by over-discharge and affecting its service life, it is prioritized during charging and its response order is reduced during discharge. When the state of charge of the energy storage unit is [SOC m ,SOC max], in order to avoid overcharging behavior that damages the unit life, f4 is increased during charging to reduce its charging sequence, and f4 is reduced during discharging to increase its discharging sequence.

[0069] Considering the extreme SOH in the energy storage unit and the inconsistency between SOH and state of charge, function f5 is constructed, where ΔS OH,i is the SOH range in the i-th energy storage unit (the difference between the highest SOH and the lowest SOH of the single battery in the energy storage unit); S OH,i is the SOH of the i-th energy storage unit, which is determined by the single cell with the lowest SOH in the energy storage unit; ΔS OC,i (t) is the difference in state of charge between the single cell with the lowest SOH and the single cell with the highest SOH in the i-th energy storage unit in the t-th period; r is the set value of the state of charge inconsistency degree.

[0070]

[0071] Taking the charging situation as an example, the objective function is specifically explained, that is, P B (t)>0.

[0072] 1: When ΔS OC,i (t)P B (t)<0 and |ΔS OC,i (t)|>r, P B (t)>0,ΔS OC,i (t)<0, it can be known that the energy storage unit is in a valley imbalance state. Since the state of charge of the short-board battery in the energy storage unit is low during the valley imbalance, the energy storage unit is more suitable for charging at this time. Charging can alleviate the inconsistency of the state of charge of the energy storage unit. At this time, |ΔS OC,i (t)|>r, the degree of inconsistency of the state of charge in the energy storage unit has exceeded the set value, so the charging order of the energy storage unit is the highest level;

[0073] 2: When ΔS OC,i (t)P B (t)<0 and |ΔS OC,i When (t)|≤r, although the state of the energy storage unit is valley imbalance, the degree of charge inconsistency within the energy storage unit is still below the set value. Mild charge inconsistency does not have a significant impact on the life of the energy storage unit, so the charging order is set to the second level;

[0074] 3: When ΔS OC,i (t)P B When (t)≥0, P B (t)>0,ΔS OC,i(t)≥0, the energy storage unit is in a peak imbalance state, and the state of charge of the short-board battery in the energy storage unit is high. In this case, the energy storage unit is more suitable for discharging to reduce the degree of peak imbalance, and is not suitable for charging. The charging order of the energy storage unit is set to the lowest.

[0075] On the basis of obtaining the response potential of the energy storage unit, the present invention determines the number of energy storage units to be scheduled based on the minimum unit action quantity calculation method to complete the screening of the energy storage units. The result x obtained by the calculation method must satisfy formula (7).

[0076]

[0077] (4) Power distribution submodule

[0078] After selecting the optimal energy storage units participating in the response through the unit grouping and unit screening submodules, the optimal power distribution among different energy storage units is achieved. This paper establishes a BESS power distribution mathematical model with the goal of optimizing state of charge consistency and uses EVO to solve it.

[0079] 1: Objective function

[0080] In order to extend the service life of BESS, the objective function is established based on the optimal consistency of the state of charge of the energy storage units in BESS as the allocation principle, as shown in formula (8).

[0081]

[0082] Where: Represents the charging and discharging power P of the i-th energy storage unit ES,i (t) ΔS generated OC , the calculation formula is shown in formula (9).

[0083]

[0084] Where: ΔS OC,i (t-1) is the ΔS of the i-th energy storage unit OC The value at time t-1; E N,i is the rated capacity of the i-th energy storage unit; is the charge and discharge efficiency of the i-th energy storage unit in period t, and the calculation method is shown in formula (10).

[0085]

[0086] Where: P ES,i * (t) is P ES,i (t)P ESN,i The per-unit value obtained as a benchmark is calculated as shown in formula (11).

[0087]

[0088] Furthermore, the multi-objective rolling optimization model of the microgrid grid-connected instruction set includes: a multi-objective optimization model of the microgrid grid-connected instruction set and a rolling optimization model;

[0089] The microgrid grid-connected instruction set multi-objective optimization model is used to construct objective functions and constraints;

[0090] The rolling optimization model is used to accept and solve the multi-objective optimization model of the microgrid grid-connected instruction set within the current period of microgrid operation and a limited period thereafter, based on the optimal operation plan of the previous period obtained by the multi-dimensional evaluation decision model of the microgrid operation results.

[0091] Furthermore, the objective function includes: a function for the minimum number of unqualified periods of grid-connected power scheduling within the scheduling range, a function for the minimum number of unqualified periods of grid-connected power fluctuation, and a function for the minimum cumulative charge and discharge throughput of the battery energy storage system;

[0092] The function of the minimum number of unqualified periods for grid-connected power scheduling within the scheduling range is as follows:

[0093]

[0094] Where: f1 is the minimum number of unqualified periods for grid-connected power dispatch within the dispatch range, n is the number of periods within the dispatch range; U acc (t) is the qualified state variable of microgrid grid-connected power dispatch in period t, U acc When (t) is 0, the grid-connected power dispatch is qualified, U acc When (t) is 1, the grid-connected power scheduling is unqualified;

[0095] The function of the minimum number of unqualified periods of grid-connected power fluctuation is as follows:

[0096]

[0097] Where: f2 is the minimum number of unqualified periods of grid-connected power fluctuation, U wave (t) is the qualified state variable of the microgrid grid-connected power fluctuation during period t, U wave When (t) is 0, the grid power fluctuation is qualified, U wave When (t) is 1, the grid-connected power fluctuation is unqualified;

[0098] The minimum cumulative charge and discharge throughput function of the battery energy storage system is as follows:

[0099]

[0100] Where: f3 is the minimum number of unqualified periods of grid-connected power fluctuation, PBESS (t) is the charge and discharge capacity of the battery energy storage system during period t; T is the control step size of the battery energy storage system;

[0101] The constraints include: grid-connected power scheduling and fluctuation qualified state constraints, battery energy storage system power constraints, and battery energy storage system energy constraints;

[0102] The grid-connected power scheduling and fluctuation qualified state constraints are expressed as follows:

[0103]

[0104] Where: acc (t) is the accuracy of the microgrid grid-connected power in period t, indicating the accuracy of the microgrid grid-connected power and the dispatch plan. The calculation formula is shown in formula (16); is the allowable error of the scheduling plan curve; wave (t) is T wave The power fluctuation rate within the time scale is calculated as shown in formula (17); T wave Active power fluctuation rate limit within the time scale.

[0105]

[0106]

[0107] Where: P plan (t) is the microgrid dispatching plan power during period t; C new is the installed capacity of the new energy units in the microgrid; P M (t) is the grid-connected power of the microgrid in period t;

[0108] The power constraint of the battery energy storage system is expressed as follows:

[0109]

[0110] Where: P ESN,i is the rated charge and discharge power of the i-th energy storage unit; P BESS (t) is the charge and discharge power of the battery energy storage system in period t;

[0111] The energy constraint of the battery energy storage system is expressed as follows:

[0112]

[0113] Where: S BESS (t) is the equivalent state of charge of the battery energy storage system; E A,i is the actual capacity of the i-th energy storage unit, and the calculation formula is shown in formula (20); η PBESSS is the preset charge and discharge efficiency before the power allocation result of the battery energy storage system is determined; BESS，min 、S BESS，max are the lower and upper limits of the equivalent state of charge of the battery energy storage system respectively;

[0114] E A,i =E N,i ×S OH,i (20)

[0115] Where: E N,i is the rated capacity of the i-th energy storage unit; S OH,i is the health status of the i-th energy storage unit. Since the energy storage unit is constrained by the short-board battery, the health status of the energy storage unit is represented by the health status of the short-board battery.

[0116] Furthermore, the power allocation model of the battery energy storage system considering the state of the energy storage unit includes: a unit grouping submodule, a unit screening submodule and a power allocation submodule;

[0117] The unit grouping submodule is configured to receive and, based on the Pareto solution of the battery energy storage system's charge and discharge power, divide the energy storage units into two groups for operation in the current period based on the number of charge and discharge switching times of the battery energy storage system, and send the grouped energy storage units to the unit screening submodule. Prior to each charge and discharge of the battery energy storage system, the grouping results of the previous period are inherited or modified through a dynamic grouping mechanism.

[0118] The unit screening submodule is configured to determine the potential size and number of energy storage units that participate in the response of the grouped energy storage units according to the unit response potential calculation method, and complete the screening of energy storage units based on the number of energy storage units participating in the response and the potential size; and send the screened energy storage units to the power distribution submodule;

[0119] The power distribution submodule is used to establish a battery energy storage system power distribution model for the screened energy storage units, with the goal of minimizing the degree of inconsistency in the battery state of charge within the energy storage units, and use an energy valley optimization algorithm to calculate the optimal distribution plan for charging and discharging power among the selected energy storage units; and send the optimal distribution plan to the multidimensional evaluation and decision model for the microgrid operation results.

[0120] Furthermore, the multi-dimensional evaluation decision model for microgrid operation results is specifically used to:

[0121] Based on the Pareto solution set, the optimal allocation plan, the evaluation system, and the evaluation indicators, weighting the microgrid operation result indicators using the hierarchical analysis method, and determining the weights of the indicators according to the microgrid operation result indicators and the evaluation system;

[0122] The linear proportional method in the linear dimensionless method is used to perform dimensionless processing on the indicators as follows:

[0123] Taking into account the various indicators in the multidimensional evaluation system, due to the different dimensions, there are conflicts between them, making it difficult to achieve the optimal sub-goals at the same time. The linear proportional method in the linear dimensionless method is used. According to the formulation of relevant indicators, through the indicator data transformation, the indicators with larger values ​​are better and the indicators with smaller values ​​are better are dimensionless.

[0124] Dimensionless results of indicators as follows:

[0125]

[0126] In the above formula: is the dimensionless data of the charging and discharging efficiency index of solution i in the Pareto solution set; x i is the actual value of the charging and discharging efficiency index of solution i in the Pareto solution set; are the x corresponding to each solution i Minimum value.

[0127] Dimensionless results of the indicators for the number of periods with unqualified grid power scheduling, the number of periods with unqualified grid power fluctuation, the cumulative charge and discharge throughput of the battery energy storage system, the number of charge and discharge conversions, and the degree of inconsistency of the state of charge. as follows:

[0128]

[0129] In the above formula: is the dimensionless data of indicator j in solution i in the Pareto solution set; x ij is the actual value of indicator j of solution i in the Pareto solution set; is the x corresponding to each solution ij Maximum value;

[0130] After dimensionless processing of the evaluation index, the decision index DM of solution i in the Pareto solution set is obtained: i for:

[0131]

[0132] In the above formula: w v,j is the weight of indicator j in the evaluation system; j is the jth indicator;

[0133] Based on the decision indicator DM iA decision is made on the Pareto solution set of the charging and discharging power of the battery energy storage system to obtain an optimal operation plan, and the optimal operation plan is sent to the multi-objective rolling optimization model of the microgrid grid-connected instruction set for optimization in the next period.

[0134] If the capacity and power constraints of energy storage units and the principles that must be followed in BESS power allocation are not considered, the BESS can be used to compensate for deviations from the planned curve and smooth out fluctuations in real time. However, BESS costs are currently high. Using real-time optimization to meet microgrid integration requirements based on existing BESS capacity configurations can exacerbate the degradation of the BESS's lifespan due to high-power charging and discharging over multiple periods. Furthermore, continuous charging and discharging can prematurely reduce the BESS's internal adjustable capacity, reducing its ability to cope with the uncertainties of renewable energy and loads. Therefore, utilizing continuously updated ultra-short-term forecast data for a global perspective, establishing a rolling optimization model, and solving an open-loop optimization problem with a finite time window for each period, comprehensively considering the microgrid's grid-connected performance and BESS allocation results under multiple periods, to obtain the optimal control instructions for the BESS in the finite future, is the most effective way to avoid BESS performance degradation.

[0135] Based on the above ideas, the present invention embeds the multi-objective optimization model of the microgrid grid-connected instruction set into the rolling optimization model. The implementation process of the entire rolling optimization model is as follows: Figure 2 As shown in the figure, the present invention divides the scheduling cycle into T time periods based on the time period division of the system data, and the scheduling range (number of time periods) is set to X. Taking the mth time period as an example, the EMS collects the charge state, ΔS of each energy storage unit at the end of the m-1 time period. OC The proposed two-stage optimization process uses state information, actual power data for renewable energy generators and loads during period m, and ultra-short-term forecast data for periods m+1 to m+X-1 as input. After completion, the optimal charge and discharge instructions for the BESS within the dispatch range are obtained. However, the EMS only issues charge and discharge power instructions for each energy storage unit during period m; instructions for periods m+1 to m+X-1 are not issued. The above steps are repeated from period 1 to period T until the optimal BESS response plan for each of these T periods is obtained.

[0136] The multidimensional evaluation decision-making model for microgrid operation results is based on the establishment of a multidimensional evaluation system for microgrid operation results. It fully considers the relationship between microgrid grid connection evaluation indicators and BESS power allocation evaluation indicators, as well as the comprehensive impact of each indicator on BESS scheduling results. It uses the Analytic Hierarchy Process (AHP) to make decisions on the optimization results of BESS charging and discharging power, so as to achieve global optimization of the microgrid system and energy storage units.

[0137] Those skilled in the art will appreciate that the embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0138] The present invention 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 invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks 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 produce 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.

[0139] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work 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 The function specified in one or more boxes.

[0140] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0141] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit its scope of protection. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that after reading the present invention, those skilled in the art can still make various changes, modifications or equivalent substitutions to the specific implementation methods of the application, but these changes, modifications or equivalent substitutions are all within the scope of protection of the pending claims.

Claims

1. A microgrid operation system considering the state of energy storage units, characterized in that: Including the multi-objective rolling optimization model of the microgrid grid-connected instruction set, the power allocation model of the battery energy storage system considering the state of the energy storage unit, and the multi-dimensional evaluation decision model of the microgrid operation results and the battery energy storage system; Based on the operating requirements of the microgrid and the battery energy storage system, an evaluation system and evaluation indicators are constructed; The multi-objective rolling optimization model of the microgrid grid-connected instruction set is used to use a multi-objective exponential distribution optimization algorithm to obtain the Pareto solution of the battery energy storage system's charge and discharge power in the current period, and send the Pareto solution to the battery energy storage system power allocation model considering the energy storage unit status and the multi-dimensional evaluation and decision model of the microgrid operation results; The battery energy storage system power allocation model considering the energy storage unit status is used to obtain, based on the Pareto solution set, an optimal allocation scheme for the charge and discharge power of each battery energy storage system in the Pareto solution set among the energy storage units selected within the battery energy storage system, and send the optimal allocation scheme and the Pareto solution set to the multi-dimensional evaluation decision model for the microgrid operation results; The multi-dimensional evaluation decision model for the microgrid operation results is used based on the Pareto solution set, the optimal allocation plan, the evaluation system, and the evaluation index; Using the Pareto solution set of the hierarchical analysis method to determine the decision indicators, making decisions based on the decision indicators to obtain the optimal operation plan for the current period, and sending the optimal operation plan for the current period to the multi-objective rolling optimization model of the microgrid grid connection instruction set; The multi-objective rolling optimization model of the microgrid grid-connected instruction set is further used to optimize the next period based on the optimal operation plan of the current period; The multi-objective rolling optimization model of the microgrid grid-connected instruction set includes: a multi-objective optimization model of the microgrid grid-connected instruction set and a rolling optimization model; The microgrid grid-connected instruction set multi-objective optimization model is used to construct objective functions and constraints; The rolling optimization model is used to accept and solve the multi-objective optimization model of the microgrid grid connection instruction set within a limited period of time and thereafter in the current period of microgrid operation based on the optimal operation plan of the previous period obtained by the multi-dimensional evaluation decision model of the microgrid operation results; The objective functions include: a function for the minimum number of unqualified periods for grid-connected power scheduling within the scheduling range, a function for the minimum number of unqualified periods for grid-connected power fluctuations, and a function for the minimum cumulative charge and discharge throughput of the battery energy storage system; The constraints include: grid-connected power scheduling and fluctuation qualified state constraints, battery energy storage system power constraints, and battery energy storage system energy constraints; The battery energy storage system power allocation model considering the energy storage unit status includes: a unit grouping submodule, a unit screening submodule and a power allocation submodule; The unit grouping submodule is configured to receive and, based on the Pareto solution of the battery energy storage system's charge and discharge power, divide the energy storage units into two groups for operation in the current period based on the number of charge and discharge switching times of the battery energy storage system, and send the grouped energy storage units to the unit screening submodule. Prior to each charge and discharge of the battery energy storage system, the grouping results of the previous period are inherited or modified through a dynamic grouping mechanism. The unit screening submodule is configured to determine the potential size and number of energy storage units that participate in the response of the grouped energy storage units according to the unit response potential calculation method, and complete the screening of energy storage units based on the number of energy storage units participating in the response and the potential size; and send the screened energy storage units to the power distribution submodule; The power distribution submodule is used to establish a battery energy storage system power distribution model for the screened energy storage units, with the goal of minimizing the degree of inconsistency in the battery state of charge within the energy storage units, and use an energy valley optimization algorithm to calculate the optimal distribution plan for charging and discharging power among the selected energy storage units; and send the optimal distribution plan to the multidimensional evaluation and decision model for the microgrid operation results.

2. The system according to claim 1, wherein: The multi-dimensional evaluation decision model for microgrid operation results is specifically used for: Based on the Pareto solution set, the optimal allocation plan, the evaluation system, and the evaluation indicators, weighting the microgrid operation result indicators using the analytic hierarchy process, and determining the weights of the indicators according to the microgrid operation result indicators and the evaluation system; the evaluation indicators include an indicator for the number of unqualified periods of grid-connected power scheduling, an indicator for the number of unqualified periods of grid-connected power fluctuation, an indicator for the cumulative charge and discharge throughput of the battery energy storage system, an indicator for the number of charge and discharge conversions, and an indicator for the degree of inconsistency of the state of charge; The linear proportional method in the linear dimensionless method is used to perform dimensionless processing on the indicators as follows. The dimensionless results of the indicators are The calculation is as follows: In the above formula: is the dimensionless data of the charging and discharging efficiency index of solution i in the Pareto solution set; x i is the actual value of the charging and discharging efficiency index of solution i in the Pareto solution set; are the x corresponding to each solution i Minimum value; Dimensionless results of the indicators for the number of periods with unqualified grid power scheduling, the number of periods with unqualified grid power fluctuation, the cumulative charge and discharge throughput of the battery energy storage system, the number of charge and discharge conversions, and the degree of inconsistency of the state of charge. The calculation is as follows: In the above formula: is the dimensionless data of indicator j in solution i in the Pareto solution set; x ij is the actual value of indicator j of solution i in the Pareto solution set; is the x corresponding to each solution ij Maximum value; After dimensionless processing of the evaluation index, the decision index DM of solution i in the Pareto solution set is obtained: i for: In the above formula: w v,j is the weight of indicator j in the evaluation system; j is the jth indicator; Based on the decision indicator DM i A decision is made on the Pareto solution set of the charging and discharging power of the battery energy storage system to obtain an optimal operation plan, and the optimal operation plan is sent to the multi-objective rolling optimization model of the microgrid grid-connected instruction set for optimization in the next period.

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