Unit combination and power scheduling method for dynamic reconfigurable energy storage station

Abstract

The invention discloses a unit combination and power scheduling method of a dynamic reconfigurable energy storage station, which is suitable for a non-full load scheduling working condition in a battery energy storage station, comprises two optimization objectives of unit combination and power scheduling, and aims to improve the operation benefit of the battery energy storage station and reduce the operation cost. According to the method, multiple factors such as the running state cost, the starting cost, the discharge loss cost and the running loss cost of the battery energy storage unit are combined, the power output constraint condition of the dynamic reconfigurable battery is introduced, and reasonable selection and efficient power distribution of the battery energy storage unit in the battery energy storage station are achieved. On the premise of meeting the load requirement, the loss of the battery energy storage unit is reduced to the greatest extent, the damage to the battery caused by excessive discharge is avoided, and the operation cost of the battery energy storage station is effectively reduced. The method can improve the charging and discharging efficiency of the energy storage equipment, optimize the power output of the battery, and ensure the overall economic benefit of the battery energy storage station.

Description

Technical Field

[0001] The present invention relates to the technical field of unit combination and power dispatching of a battery energy storage station, and in particular to a unit combination and power dispatching method of a dynamically reconfigurable energy storage station. Background Art

[0002] With the large-scale access of renewable energy, the power system is facing increasing scheduling pressure. Traditional power system scheduling methods often focus on the operation of conventional power generation equipment, while there are relatively few studies on the scheduling of battery energy storage systems. As a flexible scheduling method, battery energy storage systems play an increasingly important role in power systems, especially in peak-valley regulation and load balancing. At present, the scheduling problem of battery energy storage systems usually focuses on meeting load demand, adopting a simple full-station unit equalization strategy, ignoring the operating cost of the system. At the same time, the operation of battery energy storage stations is not always at full load. In particular, the charging and discharging process of battery energy storage systems is affected by many factors. Therefore, when scheduling and allocating power, it is often necessary to consider unit combination and cost issues.

[0003] The unit commitment problem is an optimization problem, which is used to dispatch the number of power supply units enabled in the power system within a certain time range. The economic dispatch problem is another optimization problem, which is used to dispatch the output power level of the target unit within a certain time range to meet the demand at the lowest cost, given the pre-determined number of target units. In both problems, it is assumed that the demand of the power system is known. How to reasonably select the unit combination and distribute the power under different load demands to achieve optimal cost control is an important technical challenge faced by battery energy storage station scheduling. Therefore, it is particularly important to design a unit combination and power dispatch method for a dynamically reconfigurable energy storage station. Summary of the invention

[0004] In order to solve the problems of unreasonable unit selection and power allocation, excessively high operating costs, etc. in the power scheduling of battery energy storage stations, the purpose of the present invention is to provide a unit combination and power scheduling method for a dynamically reconfigurable energy storage station, aiming to reduce the system operating cost through reasonable unit combination and power allocation strategy, and ensure the economy and stability of the battery energy storage station under non-full load conditions.

[0005] In order to achieve the above object, the present invention adopts the following technical solution:

[0006] A unit combination and power dispatching method for a dynamically reconfigurable energy storage electric field station comprises the following steps:

[0007] S1: Establish an objective function that considers the operating status cost of the battery energy storage unit;

[0008] S2: Establish an objective function that considers the startup cost of the battery energy storage unit;

[0009] S3: Establish an objective function that takes into account the operating discharge loss cost of the battery energy storage unit;

[0010] S4: Establish an objective function that takes into account the operating loss cost of the battery energy storage unit;

[0011] S5: Consider the constraints of the power output and state of charge of the battery energy storage unit;

[0012] S6: According to the acquired state parameters of all units in the energy storage station, select an appropriate unit combination for power distribution;

[0013] S7: Perform power allocation for the selected unit combination, and use the particle swarm algorithm to optimally solve the cost function of the allocation result to obtain the optimal unit state and power allocation result.

[0014] The specific method of step S1 is as follows:

[0015] S1.1: Assume that the state cost of the battery energy storage unit is:

[0016] C state,i =(C soc,i *(1-SOC i )+C soh,i *(1-SOH i )) (1)

[0017] In the formula, C SOC,i is the cost coefficient related to the state of charge, 1-SOC i The effect of deviation from the optimal power state, C SOH,i is the cost coefficient related to health status, 1-SOH i for the effects of deviations from optimal health;

[0018] S1.2: Consider the start and stop status x of the battery energy storage unit i , x i =0 means shutdown state, x i =1 indicates the startup state, so the objective function of the state cost of all battery energy storage units in the energy storage station is:

[0019] C state,i =x i *(C soc,i *(1-SOC i )+C soh,i *(1-SOH i )) (2).

[0020] The specific method of step S2 is as follows:

[0021] S2.1: Assume the startup cost of the battery energy storage unit is:

[0022]

[0023] In the formula, C start is the fixed startup cost, a is the cost coefficient linearly related to the downtime, Indicates the attenuation of the impact of a period of downtime on the unit startup cost, T off It is the downtime of the battery energy storage unit.

[0024] S2.2: Consider the start and stop status x of the battery energy storage unit i , x i =0 means shutdown state, x i =1 indicates the startup state, so the objective function of the startup cost of all battery energy storage units in the energy storage station is:

[0025]

[0026] The specific method of step S3 is as follows:

[0027] S3.1: Assume that the discharge loss cost of the battery energy storage unit is:

[0028]

[0029] Where P d,i represents the output power of the i-th battery energy storage unit, η d,i represents the discharge efficiency of the i-th battery energy storage unit, γ represents the nonlinear coefficient of discharge efficiency loss, and β represents the influence index, reflecting the nonlinear relationship.

[0030] S3.2: Consider the start and stop status x of the battery energy storage unit i , x i =0 means shutdown state, x i =1 indicates the start-up state, so the objective function of the discharge loss cost of all battery energy storage units in the energy storage station is:

[0031]

[0032] The specific method of step S4 is as follows:

[0033] S4.1: Assume that the power loss coefficient of the i-th battery energy storage unit in the battery energy storage station is The discharge efficiency of the battery of the i-th energy storage unit is η1; then the objective function of the battery loss cost of the battery energy storage unit can be established as:

[0034]

[0035] Where P i is the output power of the i-th battery energy storage unit, T is the working time, and n is the number of battery energy storage units.

[0036] In step S5, the main constraints considered are:

[0037] S5.1: Power output constraints

[0038] The design considers the power range constraints of the dynamic reconfigurable battery group on the energy storage inverter. The state of the dynamic reconfigurable battery determines the voltage on the DC side of the energy storage inverter. Different reconstruction states correspond to different voltage values. The DC side voltage of the inverter is taken as V dc , the maximum output current of the inverter is fixed, then the maximum output power of the inverter can be obtained as:

[0039] Pmax=Vdc*Imax / 1000 (8)

[0040] In the formula, I max is the maximum output current of the inverter.

[0041] S5.2: Battery energy storage unit state of charge constraints

[0042] During the operation of the battery energy storage unit, the state of charge of the energy storage battery should be set to a certain threshold to ensure the long-term life of the energy storage battery. The calculation formula for the state of charge parameter of the energy storage battery after power output is as follows:

[0043] SOC new =SOC i -((P i *T) / E max ) (9)

[0044] In the formula, SOC i is the state of charge of the battery of the i-th battery energy storage unit, E max is the maximum capacity of the energy storage battery, T is the working time of the battery energy storage unit, SOC new It is the state of charge of the energy storage battery after power output.

[0045] Take the minimum threshold of the energy storage battery state of charge as SOC min , then the state of charge constraint of the battery energy storage unit is:

[0046] SOC new ≥SOC min (10).

[0047] The specific methods of steps S6 and S7 are as follows:

[0048] By collecting the status parameters of all battery energy storage units in the energy storage site, the status of each energy storage unit is evaluated, and several groups of battery energy storage unit operating states are randomly generated. Power is allocated according to the operating states, and the cost objective function is calculated for each group of operating states and power allocation results. The particle swarm algorithm is used to obtain the optimal unit state and power allocation results.

[0049] Compared with the existing method, the present invention has the following beneficial effects:

[0050] The present invention combines the start and stop status of the battery energy storage unit in the battery energy storage station, the state parameters of the energy storage battery and other factors, and designs a unit combination and power scheduling method of a dynamically reconfigurable energy storage station to improve the operating efficiency of the battery energy storage station. By introducing the particle swarm optimization algorithm to solve the battery energy storage unit combination, the reasonable selection of battery energy storage units and the efficient allocation of power in the battery energy storage station are achieved, and the power constraint conditions of the dynamically reconfigurable battery are introduced to ensure that the load demand is met while reducing the system operating cost. This method optimizes the power output of the energy storage device, reduces the discharge loss of the battery, and avoids damage to the equipment caused by excessive discharge. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 This is the power dispatch structure diagram of the battery energy storage station;

[0052] Figure 2 is the power output value of each unit after the model is solved;

[0053] Figure 3 This is a cost comparison chart of power output of different unit combinations;

[0054] Figure 4 It is the charge state parameter diagram of each energy storage unit after power output;

[0055] Figure 5 This is the flow chart of particle swarm algorithm. DETAILED DESCRIPTION

[0056] The present invention is described in detail below with reference to the accompanying drawings and specific embodiments.

[0057] The present invention proposes a unit combination and power dispatching method for a dynamically reconfigurable energy storage station. The method solves the unit combination by introducing a particle swarm optimization algorithm, thereby achieving a reasonable selection of units in a battery energy storage station and efficient power allocation. While ensuring that the load demand is met, the operating cost of the battery energy storage station is reduced.

[0058] The study of the unit combination of battery energy storage units is of great significance, mainly in terms of improving the stability of the power grid, improving the economy and operating efficiency of battery energy storage stations, and enhancing the emergency response capabilities of the power system. Through reasonable unit combination, the configuration of battery energy storage stations can be optimized to ensure effective regulation when the power grid load fluctuates, reduce operating costs and extend the equipment life cycle. In addition, unit combination optimization helps to balance the volatility of renewable energy, which is of great practical significance for achieving efficient, safe and economical operation of the power system.

[0059] Energy storage stations usually adopt a 3S architecture consisting of a battery management system, a power conversion system, and an energy management system. The three work together to convert the battery energy into power according to the grid demand, and manage the battery charging and discharging process through corresponding protection control logic to ensure the safe operation of the station. The typical structure diagram of power dispatching of energy storage stations is as follows: Figure 1 shown.

[0060] according to Figure 1 The structural diagram of energy storage station scheduling can assume that the total output power command given by the battery energy storage station is P, and there are n battery energy storage units in the station. In the process of objective function design and solution, the battery state parameters and total power command in the energy storage station are known. The steps of a unit combination and power scheduling method for a dynamically reconfigurable energy storage station are as follows:

[0061] 1) Consider the objective function of designing the operating cost of the battery energy storage unit:

[0062] Assume the state cost of the battery energy storage unit is:

[0063] C state,i =(C soc,i *(1-SOC i )+C soh,i *(1-SOH i )) (1)

[0064] In the formula, C SOC,i is the cost coefficient related to the state of charge, 1-SOC i The effect of deviation from the optimal power state, C SOH,i is the cost coefficient related to health status, 1-SOH i The impact of deviation from a healthy power state.

[0065] Consider the start and stop status of the battery energy storage unit x i , x i =0 means shutdown state, x i =1 indicates the startup state, so the objective function of the state cost of all battery energy storage units in the energy storage station is:

[0066] Cstate,i =x i *(C soc,i *(1-SOC i )+C soh,i *(1-SOH i )) (2)

[0067] The operating state parameters of the battery energy storage unit directly affect the battery's service life and performance. As the battery is used, the state parameters gradually change. Excessive discharge or charging may cause the battery to decay faster, thereby increasing the cost of subsequent maintenance and replacement. By optimizing these parameters, the battery's decay can be delayed, the long-term operation and maintenance costs can be reduced, and the economic efficiency of the energy storage site can be improved.

[0068] 2) Consider the objective function of designing the startup cost of the battery energy storage unit:

[0069] Assume that the startup cost of the battery energy storage unit is:

[0070]

[0071] In the formula, C start is the fixed startup cost, a is the cost coefficient linearly related to the downtime, indicating the attenuation of the impact of a period of downtime on the startup cost of the unit, T off It is the downtime of the battery energy storage unit.

[0072] Consider the start and stop status of the battery energy storage unit x i , x i =0 means shutdown state, x i =1 indicates the startup state, so the objective function of the startup cost of all battery energy storage units in the energy storage station is:

[0073]

[0074] The startup of a battery energy storage unit usually involves a certain amount of energy loss and equipment wear, especially in the case of frequent start-stop, the startup cost may increase significantly. Therefore, optimizing the startup strategy and reasonably arranging the timing of unit activation can effectively reduce the startup cost and improve the overall economic benefits of the energy storage site.

[0075] 3) Consider the objective function of designing the discharge loss cost of battery energy storage units:

[0076] Assume that the discharge loss cost of the battery energy storage unit is:

[0077]

[0078] Where P d,i represents the output power of the i-th battery energy storage unit, η d,irepresents the discharge efficiency of the i-th battery energy storage unit, γ represents the nonlinear coefficient of discharge efficiency loss, and β represents the influence index, reflecting the nonlinear relationship.

[0079] Consider the start and stop status of the battery energy storage unit x i , x i =0 means shutdown state, x i =1 indicates the start-up state, so the objective function of the discharge loss cost of all battery energy storage units in the energy storage station is:

[0080]

[0081] During the operation of the energy storage station, the charging and discharging efficiency of the battery is affected to a certain extent, especially during the high-frequency discharge process, which may cause large energy losses. Discharge losses not only reduce the overall efficiency of the energy storage station, but also increase energy consumption and operating costs. By optimizing power scheduling and reducing unnecessary discharge processes, discharge losses can be effectively reduced and the overall economic benefits of the energy storage station can be improved.

[0082] 4) Consider the objective function of designing the operating loss cost of the battery energy storage unit:

[0083] Assume that the power loss coefficient of the i-th energy storage battery unit in the battery energy storage station is The discharge efficiency of the battery of the i-th energy storage unit is η1. Then the objective function of the battery loss cost of the battery energy storage unit can be established as:

[0084]

[0085] Where P i is the output power of the i-th battery energy storage unit, T is the working time, and n is the number of battery energy storage units.

[0086] Battery loss factor It indicates the energy loss caused by internal resistance, battery chemical reaction, temperature and other factors during the operation of the energy storage unit under the condition of unit power output. This coefficient is affected by many factors, such as battery technology type, charge and discharge cycle, service life and ambient temperature. It can accurately reflect the loss characteristics of battery energy storage stations under different loads, which helps to optimize the energy efficiency and economy of battery energy storage stations.

[0087] 5) Consider the dynamic reconfigurable battery output constraints:

[0088] Design the power range constraint of the dynamic reconfigurable battery group on the energy storage inverter. The state of the dynamic reconfigurable battery determines the voltage on the DC side of the energy storage inverter. Different reconfiguration states correspond to different voltage values. Take the DC side voltage of the inverter as V dc, the maximum output current of the inverter is fixed, then the maximum output power of the inverter can be obtained as:

[0089] Pmax=Vdc*Imax / 1000 (8)

[0090] In the formula, I max is the maximum output current of the inverter.

[0091] During the operation of the battery energy storage unit, the state of charge of the energy storage battery should be set to a certain threshold to ensure the long-term life of the energy storage battery. The calculation formula for the state of charge parameter of the energy storage battery after power output is as follows:

[0092] SOC new =SOC i -((P i *T) / E max ) (9)

[0093] In the formula, SOC i is the state of charge of the battery of the i-th battery energy storage unit, E max is the maximum capacity of the energy storage battery, T is the working time of the battery energy storage unit, SOC new It is the state of charge of the energy storage battery after power output.

[0094] Take the minimum threshold of the energy storage battery state of charge as SOC min , then the state of charge constraint of the battery energy storage unit is:

[0095] SOC new ≥SOC min (10).

[0096] 6) Through the collected status parameters of all battery energy storage units in the energy storage station, the health status and other performance of each energy storage unit are comprehensively evaluated. The operating status of several groups of unit combinations is randomly generated, and then the power of the operating units in each group is allocated according to actual needs. The goal of power allocation is to ensure that the economic benefits of the battery energy storage station are maximized while meeting the needs of the power grid, while considering the health status of the battery energy storage unit to avoid excessive losses. The designed cost objective function includes operating status cost, startup cost, battery discharge loss cost, and operating loss cost, etc., and the particle swarm algorithm is used to solve the optimal unit status combination and power allocation plan through multiple iterations to maximize the economic benefits and operating efficiency of the battery energy storage station.

[0097] Assuming that the power output of the energy storage station is P, there are n energy storage units in the energy storage station. By selecting some units for power output and using the particle swarm algorithm to optimally solve the designed objective function, the units that need to be started in the energy storage station and the power values ​​required to be output by the corresponding units are obtained, such as Figure 2 As shown. At the same time, the method of the present invention is compared with the power equalization method and two randomly generated unit combinations in terms of cost, and a cost comparison chart of the four methods is obtained, as Figure 3 shown. It can be seen from the figure that compared with the power equalization and random combination methods, the result obtained by solving according to the unit combination and power scheduling method of the present invention has the lowest cost and the highest economy. At the same time, according to the solution result, the state of charge parameter after calculating the output power of the unit is obtained, such as Figure 4 shown. It is found that the state of charge of all units is higher than the minimum state of charge threshold, effectively ensuring the good health state of the battery. In this embodiment, the particle swarm algorithm is used to optimally solve the objective function, and the specific steps are as follows:

[0098] ① Initialize the particle swarm, including the population size N, the position x of each particle i and the velocity v i ;

[0099] ② Calculate the fitness value fit(i) of each particle, and its formula is as follows:

[0100]

[0101] ③ For each particle, compare its fitness value fit(i) with the individual extreme value. If fit(i) < pbest(i), then replace pbest(i) with fit(i);

[0102] ④ For each particle, compare its fitness value fit(i) with the global extreme value gbest. If fit(i) < gbest, then replace gbest with fit(i);

[0103] ⑤ Iteratively update the velocity v of the particle i and the position x i ;

[0104] ⑥ Perform boundary condition processing, that is:

[0105]

[0106] ⑦ Judge whether the algorithm termination condition is satisfied. If satisfied, terminate the operation and output the optimization result; otherwise, return to step ② and repeat this process until the end.

[0107] Its flowchart is as Figure 5 shown.

[0108] When selecting battery energy storage units, a large number of particle combinations are randomly generated, the power distribution and cost calculation are performed on the corresponding start-stop states, and the objective function results of different combinations are compared to obtain the optimal unit combination and the corresponding power distribution result.

Claims

1. A unit combination and power dispatching method for a dynamically reconfigurable energy storage station, characterized in that: The following steps are involved: S1: Establish an objective function that considers the operating status cost of the battery energy storage unit; S2: Establish an objective function that considers the startup cost of the battery energy storage unit; S3: Establish an objective function that takes into account the operating discharge loss cost of the battery energy storage unit; S4: Establish an objective function that takes into account the operating loss cost of the battery energy storage unit; S5: Consider the constraints of the power output and state of charge of the battery energy storage unit; S6: According to the acquired state parameters of all units in the energy storage station, select an appropriate unit combination for power distribution; S7: Perform power allocation for the selected unit combination, and use the particle swarm algorithm to optimally solve the cost function of the allocation result to obtain the optimal unit state and power allocation result.

2. According to the method of unit combination and power dispatching of a dynamically reconfigurable energy storage station according to claim 1, it is characterized in that: The specific method of step S1 is as follows: S1.1: Assume that the state cost of the battery energy storage unit is: C state,i =(C soc,i *(1-SOC i )+C soh,i *(1-SOH i )) (1) In the formula, C SOC,i is the cost coefficient related to the state of charge, 1-SOC i The effect of deviation from the optimal power state, C SOH,i is the cost coefficient related to health status, 1-SOH i for the effects of deviations from optimal health; S1.2: Consider the start and stop status x of the battery energy storage unit i , x i =0 means shutdown state, x i =1 indicates the startup state, so the objective function of the state cost of all battery energy storage units in the energy storage station is: C state,i =x i *(C soc,i *(1-SOC i )+C soh,i *(1-SOH i )) (2)。 3. According to the method of unit combination and power dispatching of a dynamically reconfigurable energy storage station in claim 1, it is characterized in that: The specific method of step S2 is as follows: S2.1: Assume the startup cost of the battery energy storage unit is: In the formula, C start is the fixed startup cost, a is the cost coefficient linearly related to the downtime, Indicates the attenuation of the impact of a period of downtime on the unit startup cost, T off is the downtime of the battery energy storage unit. S2.2: Consider the start and stop status of the battery energy storage unit x i , x i =0 means shutdown state, x i =1 indicates the startup state, so the objective function of the startup cost of all battery energy storage units in the energy storage station is:

4. According to the method of unit combination and power dispatching of a dynamically reconfigurable energy storage station of claim 1, it is characterized in that: The specific method of step S3 is as follows: S3.1: Assume that the discharge loss cost of the battery energy storage unit is: Where P d,i is the output power of the i-th battery energy storage unit, η d,i represents the discharge efficiency of the i-th battery energy storage unit, γ represents the nonlinear coefficient of discharge efficiency loss, and β represents the influence index, reflecting the nonlinear relationship; S3.2: Consider the start and stop status x of the battery energy storage unit i , x i =0 means shutdown state, x i =1 indicates the startup state, so the objective function of the discharge loss cost of all battery energy storage units in the energy storage station is:

5. The unit combination and power dispatching method of a dynamically reconfigurable energy storage station according to claim 1 is characterized in that: The specific method of step S4 is as follows: S4.1: Assume that the power loss coefficient of the i-th battery energy storage unit in the battery energy storage station is The discharge efficiency of the battery of the i-th energy storage unit is η1; then the objective function of the battery loss cost of the battery energy storage unit is established as: Where P i is the output power of the i-th battery energy storage unit, T is the working time, and n is the number of battery energy storage units.

6. A unit combination and power dispatching method for a dynamically reconfigurable energy storage station according to claim 1, characterized in that: The specific method of step S5 is as follows: S5.1: The design considers the power range constraints of the dynamic reconfigurable battery group on the energy storage inverter. The state of the dynamic reconfigurable battery determines the voltage on the DC side of the energy storage inverter. Different reconfiguration states correspond to different voltage values. The DC side voltage of the inverter is taken as V dc , the maximum output current of the inverter is fixed, then the maximum output power of the inverter is: Pmax=Vdc*Imax / 1000 (8) In the formula, I max is the maximum output current of the inverter; S5.2: During the operation of the battery energy storage unit, the state of charge of the energy storage battery is set to a certain threshold to ensure the long-term life of the energy storage battery; The calculation formula of the state of charge parameters of the energy storage battery after power output is as follows: SOC new =SOC i -((P i *TEA max ) (9) In the formula, SOC i is the state of charge of the battery of the i-th battery energy storage unit, E max is the maximum capacity of the energy storage battery, T is the working time of the battery energy storage unit, SOC new It is the state of charge of the energy storage battery after power output; Take the minimum threshold of the energy storage battery state of charge as SOC min , then the state of charge constraint of the battery energy storage unit is: SOC new ≥SOC min (10)。 7. A unit combination and power dispatching method for a dynamically reconfigurable energy storage station according to claim 1, characterized in that: The specific methods of steps S6 and S7 are as follows: By collecting the status parameters of all battery energy storage units in the energy storage station, the status of each energy storage unit is evaluated, and several groups of battery energy storage unit operating states are randomly generated. Corresponding power allocation is performed for each group of operating states, and the cost objective function is calculated for each group of operating states and power allocation results. The particle swarm algorithm is used to obtain the optimal unit state and power allocation results.

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