Energy storage system operation cost optimization control method and terminal

Abstract

The invention relates to the technical field of power system control, in particular to an energy storage system operation cost optimization control method and a terminal, and the method comprises the steps: building an efficiency function containing temperature for a PCS and a DC / DC for a micro-grid system, and building a target function and a power balance constraint condition by taking the real-time operation cost as a target; obtaining the efficiency data of the battery, and obtaining the efficiency data of the PCS and the DC / DC at different temperature points according to the efficiency functions of the PCS and the DC / DC; temperature data and power data of the PCS and the DC / DC are obtained in real time, and target efficiency data corresponding to the battery, the PCS and the DC / DC are obtained through fitting according to the temperature data; substituting the target efficiency data into the target function and solving, and performing optimization control according to a solving result; optimization control is performed on the energy storage system, and the operation cost of the energy storage system is reduced.

Description

[0001] This divisional application is based on the mother patent of an invention patent with an application date of October 24, 2024, an application number of 202411488905.8, and a title of "An Optimization Control Method and Terminal for the Operating Cost of an Energy Storage System Based on Efficiency". Technical Field

[0002] The present invention relates to the technical field of power system control, and particularly to an optimization control method and terminal for the operating cost of an energy storage system. Background Art

[0003] DC distribution networks have broad development prospects in future power systems due to their advantages such as low line losses, high power supply reliability, ease of access for distributed power sources and energy storage devices, excellent power quality, environmental protection, and low equipment investment. At the same time, with the continuous progress of technology and the continuous promotion of policies, DC distribution networks will achieve large-scale, intelligent, standardized, and flexible development.

[0004] The basic components of a DC microgrid consist of lithium-ion batteries or other types of energy storage batteries, photovoltaic, power conversion system (PCS) for energy storage, DC / DC converters, DC loads, and an energy management system. Load DC conversion, flexible DC networking of converters, and intelligent energy management and optimization control are the core and development trends of such grid systems. For a photovoltaic energy storage charging system, the main direction of optimization control is the effective utilization of energy and orderly charging. For scenarios with relatively fixed loads, such as daily power loads in industrial and commercial energy storage, efficiently allocating the AC power supply ratio and scheduling the power supply of photovoltaic and different series-connected energy storage batteries become the main objectives of energy management and regulation. With the maturity of battery thermal management technology, in a general system containing the above power units, the operating temperature of the battery can be controlled in a highly efficient area with small fluctuations through liquid cooling thermal management. However, in occasions with high requirements for equipment economy, various power converters still need to adopt air-cooled heat dissipation methods, and there is a lack of constant temperature control in the electrical cabin. Under different climate conditions, load conditions, and heat dissipation conditions, there will be significant differences in the temperature inside the electrical cabin and the temperature of the main radiator of the converter. There are also quite significant differences in the average temperature of converters at different installation positions within the same electrical cabin.

[0005] The typical DC load is for the main industrial and commercial energy storage scenarios. The battery, PV, and AC grid jointly supply power to the load. Under the topology where there are multiple battery clusters or battery strings, and multiple PCS strings can be independently controlled, the output power of each battery string through DC / DC and the DC output power of the PCS are scheduled through the energy management system, generally based on the state of the battery. Against the backdrop of the gradual improvement of independent battery thermal management and battery consistency, factors such as the difference in converter efficiency and the temperature difference in the electrical compartment gradually manifest their impact on operating economy. A method is needed to control the power output of each converter using an optimization method after considering the impact of converter efficiency differences on operating economic indicators based on the logic of electricity price peak-valley regulation. Summary of the Invention

[0006] The technical problem to be solved by the present invention is: to provide an optimization control method and terminal for the operating cost of an energy storage system, so as to optimize the operating cost of the energy storage system.

[0007] To solve the above technical problem, the technical solution adopted by the present invention is:

[0008] An efficiency-based optimization control method for the operating cost of an energy storage system, including the steps of:

[0009] S1. For the microgrid system, establish an efficiency function including temperature for the PCS and DC / DC, and construct an objective function and a power balance constraint condition with the real-time operating cost as the goal;

[0010] S2. Obtain the efficiency data of the battery, and according to the efficiency functions of the PCS and DC / DC, obtain the efficiency data of the PCS and DC / DC at different temperature points;

[0011] S3. Real-time obtain the temperature data and power data of the PCS and DC / DC, and fit according to the temperature data to obtain the target efficiency data corresponding to the battery, PCS, and DC / DC;

[0012] S4. Substitute the target efficiency data into the objective function and solve it, and perform optimization control according to the solution result.

[0013] An optimization control method for the operating cost of an energy storage system, including the steps of:

[0014] S1. For the microgrid system, establish an efficiency function including temperature for the PCS and DC / DC, and construct an objective function and a power balance constraint condition:

[0015] Wherein, the real-time operating cost is composed of the cost of obtaining energy from the grid and the cost of obtaining energy from the battery;

[0016] The cost of obtaining energy from the battery is determined according to the grid electricity price at off-peak hours, the battery charge-discharge efficiency, and the real-time efficiency of the DC / DC used by the battery, and is expressed as a function including temperature;

[0017] S2. Based on the efficiency data of the battery and the temperature data and power data of the PCS and DC / DC obtained in real time, solve the objective function through an optimization algorithm, and perform optimal control according to the solution result.

[0018] To solve the above technical problems, the technical solution adopted by the present invention is as follows:

[0019] An optimization control terminal for the operating cost of an energy storage system based on efficiency includes a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps in the above-mentioned optimization control method for the operating cost of an energy storage system based on efficiency are implemented.

[0020] An optimization control terminal for the operating cost of an energy storage system includes a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the following steps are implemented:

[0021] S1. For the microgrid system, establish an efficiency function including temperature for the PCS and DC / DC, and construct an objective function and a power balance constraint condition with the real-time operating cost as the goal:

[0022] Among them, the real-time operating cost is composed of the cost of obtaining energy from the grid and the cost of obtaining energy from the battery;

[0023] The cost of obtaining energy from the battery is determined according to the grid electricity price at off-peak hours, the battery charge-discharge efficiency, and the real-time efficiency of the DC / DC used by the battery, and is expressed as a function including temperature;

[0024] S2. Based on the efficiency data of the battery and the temperature data and power data of the PCS and DC / DC obtained in real time, solve the objective function through an optimization algorithm, and perform optimal control according to the solution result.

[0025] The beneficial effect of the present invention is that: the optimization control method and terminal for the operating cost of an energy storage system of the present invention consider that the operating efficiencies of the PCS and DC / DC are different at different temperatures, establish an efficiency function including temperature for the PCS and DC / DC, construct an objective function and a power balance constraint condition with the real-time operating cost as the goal, and solve the objective function based on the real-time operating conditions, so as to optimize the control of the energy storage system and reduce the operating cost of the energy storage system. Description of the Drawings

[0026] Figure 1 Flow chart of an efficiency-based optimal control method for the operating cost of an energy storage system according to an embodiment of the present invention;

[0027] Figure 2 Specific flow chart of an efficiency-based optimal control method for the operating cost of an energy storage system according to an embodiment of the present invention;

[0028] Figure 3 Structural schematic diagram of a power system exemplified by an embodiment of the present invention;

[0029] Figure 4 Structural diagram of an efficiency-based optimal control terminal for the operating cost of an energy storage system according to an embodiment of the present invention;

[0030] Label description:

[0031] 1. An efficiency-based optimal control terminal for the operating cost of an energy storage system; 2. A processor; 3. A memory. Specific implementation manners

[0032] To describe in detail the technical content, achieved objectives and effects of the present invention, the following is described in conjunction with the implementation manners and with reference to the accompanying drawings.

[0033] Please refer to Figure 1 and Figure 2 , an efficiency-based optimal control method for the operating cost of an energy storage system, comprising the steps of:

[0034] S1. For a microgrid system, establish an efficiency function including temperature for the PCS and DC / DC, and construct an objective function and a power balance constraint condition with the real-time operating cost as the objective;

[0035] S2. Obtain the efficiency data of the battery, and obtain the efficiency data of the PCS and DC / DC at different temperature points according to the efficiency functions of the PCS and DC / DC;

[0036] S3. Real-time obtain the temperature data and power data of the PCS and DC / DC, and obtain the corresponding target efficiency data of the battery, PCS and DC / DC by fitting according to the temperature data;

[0037] S4. Substitute the target efficiency data into the objective function and solve it, and perform optimal control according to the solution result.

[0038] As can be seen from the above description, the beneficial effects of the present invention are as follows: A method for optimizing the operation cost of an energy storage system based on efficiency according to the present invention considers that the operation efficiencies of the PCS and the DC / DC are different at different temperatures, establishes efficiency functions including temperature for the PCS and the DC / DC, constructs an objective function and power balance constraint conditions with the real-time operation cost as the target, and solves the objective function based on the real-time operation situation, thereby optimizing the control of the energy storage system and reducing the operation cost of the energy storage system.

[0039] Further, the construction of the objective function includes the steps:

[0040] When all PCSs are rectifier outputs and all batteries are discharging, within the set control interval D, there is an objective function for the real-time operation cost:

[0041] Cost total =Cost Grid +Cost BAT ;

[0042] Among them, Cost Grid is the cost of obtaining energy from the power grid, and Cost BAT is the cost of obtaining energy from the battery.

[0043] As can be seen from the above description, the composition of the real-time operation cost includes the cost of obtaining energy from the power grid and the cost of obtaining energy from the battery.

[0044] Further, the determination of the cost Cost Grid of obtaining energy from the power grid includes:

[0045]

[0046] P PCS_j is the rectifier-direction output power of the jth PCS, and η PCS_j is the real-time efficiency of the jth PCS, expressed as a function based on temperature and output power:

[0047] η PCS_j =f1(T PCS_j ,P PCS_j );

[0048] G price is the real-time grid electricity price, and N is the number of PCSs.

[0049] As can be seen from the above description, the cost of obtaining energy from the power grid needs to consider the PCS output power and the efficiency of the PCS, and the operation efficiency of the PCS needs to consider the operation temperature of the PCS.

[0050] Further, the cost Cost BATThe determination includes:

[0051]

[0052] P DC_i is the output power of the i-th DC / DC, and η DC_i is the real-time efficiency of the i-th DC / DC, expressed as a function of temperature and output power:

[0053] η DC_i = f2(T DC_i , P DC_i );

[0054] η Charge_i and η Disharge_i are the charging efficiency and discharging efficiency of the i-th battery, which are functions of the battery temperature T BAT_i and the cumulative discharge charge Q BAT_i respectively:

[0055] η Charge_i = f3(T BAT_i , Q BAT_i );

[0056] η Charge_i = f3(T BAT_i , Q BAT_i );

[0057] G price_low is the grid electricity price at valley electricity time, and η DC_Chg_i is the average efficiency of the i-th DC / DC under the determined charging ratio under the battery charging condition. M is the number of DC / DCs, corresponding to the number of batteries.

[0058] As can be seen from the above description, the cost of obtaining energy from the battery also needs to consider the output power and efficiency of the DC / DC, and at the same time, the charging efficiency and discharging efficiency of the battery itself.

[0059] Furthermore, the construction of the power balance constraint conditions includes the steps:

[0060] To meet the power requirement of the DC load P Load , there is a constraint condition:

[0061]

[0062] where P PV is the sum of the input powers of all photovoltaics on the DC bus, P DC_i is the output power of the i-th DC / DC, P PCS_j is the rectifying direction output power of the j-th PCS. N is the number of PCSs, and M is the number of DC / DCs, corresponding to the number of batteries.

[0063] As described above, the sum of the DC output powers of all M DC / DCs is equal to the difference between the DC load power and the PV power, which is the same as the sum of the rectified output powers of all N PCSs.

[0064] Furthermore, obtaining the battery efficiency includes the steps of:

[0065] For each type of the M batteries, based on the detection data, obtain the charging efficiency value η BAT_S and the discharging efficiency value η BAT_E of this type in two states: at the initial stage Q of the lifespan and at the end stage Q of the lifespan, and when the operating temperature is at T BAT ; Interpolate the charging efficiency value and the discharging efficiency value to obtain the efficiency matrix of each battery: Charge and discharging efficiency value η Discharge ; Interpolate the charging efficiency value and the discharging efficiency value to obtain the efficiency matrix of each battery:

[0066]

[0067] As described above, for batteries applied to energy storage, they all need to undergo performance tests that meet national standards, including tests of charge and discharge efficiency, or obtain them through the type tests of suppliers. Currently, liquid cooling thermal management methods are mainly used for energy storage batteries, which can always control the battery operating temperature within a relatively small temperature change range, and the efficiency change within this range is not significant. Therefore, set the average value of the battery operating temperature at T BAT , and the efficiency parameters are obtained by testing the battery at this temperature. The testing principles at the initial state and the end moment of the battery lifespan are the same.

[0068] Furthermore, obtaining the efficiency data of PCS and DC / DC at different temperature points includes:

[0069] Under the condition of y temperature points where the main radiator temperatures of PCS and DC / DC converters can be adjusted, test the data matrices η PCS and η DC of the output x power point efficiencies of each type of PCS and DC / DC:

[0070]

[0071] As described above, under the condition of y temperature points where the main radiator temperatures of PCS and DC / DC converters can be adjusted, test the data matrices η PCS and η DC of the output x power point efficiencies of each type of them, which are the discretized values of f1 and f2 in the above text.

[0072] Furthermore, step S3 includes:

[0073] Due to the different installation positions and heat dissipation conditions of each PCS and DC / DC, the temperatures during operation are different, resulting in efficiency differences. Set the control interval D, obtain the main radiator temperatures and output powers of N PCSs and M DC / DCs at time t, and form a data matrix:

[0074] P PCS (t) = [P PCS_1 (t)…P PCS_N (t)], T PCS (t) = [T PCS_1 (t)…T PCS_N (t)];

[0075] P DC (t) = [P DC_1 (t)…P DC_M (t)], T DC (t) = [T DC_1 (t)…T DC_M (t)];

[0076] And obtain the efficiency value matrix of each PCS and DC / DC of each model at time t through the interpolation method:

[0077] η PCS (t) = [η PCS_1 (t)…η PCS_N (t)];

[0078] η DC (t) = [η DC_1 (t)…η DC_M (t)].

[0079] As can be seen from the above description, by obtaining the temperatures and output powers of PCSs and DC / DCs at the same time, a data matrix of temperature and power is established, and the corresponding efficiency matrix is obtained.

[0080] Furthermore, step S4 includes:

[0081] Substitute the target efficiency data into the target function and solve it through an optimization algorithm;

[0082] Perform optimization control according to the solution result.

[0083] As can be seen from the above description, the solution of the target function is completed through an optimization algorithm, so as to perform optimization control.

[0084] Please refer to Figure 4, An efficiency-based operation cost optimization control terminal for an energy storage system, including a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps in the above-mentioned efficiency-based operation cost optimization control method for an energy storage system are implemented.

[0085] The efficiency-based operation cost optimization control method and terminal of the present invention are applicable to the operation cost optimization control of an energy storage system, and particularly applicable to the operation cost optimization control of a DC-compliant microgrid system.

[0086] Please refer to Figure 1 and Figure 2 , Example 1 of the present invention is as follows:

[0087] An efficiency-based operation cost optimization control method for an energy storage system, including the steps of:

[0088] S1. For a microgrid system, establish efficiency functions including temperature for the PCS and DC / DC, and construct an objective function and a power balance constraint condition with the real-time operation cost as the goal.

[0089] In this embodiment, let the efficiency of N PCSs be η PCS_1 ~η PCS_N , and the efficiency of M DC / DCs be η DC_1 ~η DC_M . Assume that the bi-directional efficiency of each DC / DC is the same (when the bi-directional efficiency of the DC / DC is different, it does not affect the implementation of the control method in this case).

[0090] In this embodiment, M batteries correspond to M DC / DCs one by one. Assume that the charging efficiency and discharging efficiency of each battery are inversely proportional to the cumulative discharge power of the battery (i.e., the efficiency decreases after the battery ages).

[0091] Assume that the battery only charges at valley electricity prices and can discharge at non-valley electricity times; the charging efficiencies of the M batteries are η Charge_1 ~η Charge_M , and the discharging efficiencies are η Disharge_1 ~η Disharge_M .

[0092] The construction of the objective function includes the steps of:

[0093] When all PCSs are rectifier outputs and all batteries are discharging, within the set control interval D, there is an objective function for the real-time operation cost:

[0094] Cost total =Cost Grid +Cost BAT ;

[0095] Among them, Cost Grid is the cost of obtaining energy from the power grid, and Cost BAT is the cost of obtaining energy from the battery;

[0096] The cost of obtaining energy from the power grid, Cost Grid is determined as follows:

[0097]

[0098] P PCS_j is the rectification-direction output power of the jth PCS, and η PCS_j is the real-time efficiency of the jth PCS, expressed as a function of temperature and output power:

[0099] η PCS_j = f1(T PCS_j , P PCS_j );

[0100] G price is the real-time power grid electricity price, and N is the number of PCSs.

[0101] The cost of obtaining energy from the battery, Cost BAT is determined as follows:

[0102]

[0103] P DC_i is the output power of the ith DC / DC, and η DC_i is the real-time efficiency of the ith DC / DC, expressed as a function of temperature and output power:

[0104] η DC_i = f2(T DC_i , P DC_i );

[0105] η Charge_i and η Disharge_i are the charging efficiency and discharging efficiency of the ith battery, which are functions of the battery temperature T BAT_i and the cumulative discharged electricity Q BAT_i respectively:

[0106] η Charge_i = f3(T BAT_i , Q BAT_i );

[0107] η Charge_i = f3(T BAT_i , Q BAT_i );

[0108] G price_low is the power grid electricity price at the valley electricity time, and η DC_Chg_i$\eta _{i}$ is the average efficiency of the $i$-th DC / DC at a determined charging rate under battery charging conditions, and $M$ is the number of DC / DCs, corresponding to the number of batteries.

[0109] The construction of the power balance constraint condition includes the steps:

[0110] To meet the power requirement of the DC load $P_{d}$, there is a constraint condition: Load

[0111]

[0112] Where $P_{pv}$ PV is the sum of the input powers of all PVs on the DC bus, $P_{i}$ DC_i is the output power of the $i$-th DC / DC, $P_{j}^{r}$ PCS_j is the rectification direction output power of the $j$-th PCS, $N$ is the number of PCSs, and $M$ is the number of DC / DCs, corresponding to the number of batteries.

[0113] The meaning of this formula is that the sum of the DC output powers of all $M$ DC / DCs and the sum of the rectification output powers of all $N$ PCSs are equal to the difference between the DC load power and the PV power.

[0114] S2. Obtain the efficiency data of the batteries, and according to the efficiency functions of the PCSs and DC / DCs, obtain the efficiency data of the PCSs and DC / DCs at different temperature points.

[0115] In this embodiment, for a set of microgrid systems, all $N$ PCSs, all $M$ DC / DCs, and $M$ batteries may have different models.

[0116] The acquisition of the battery efficiency includes the steps:

[0117] For each model among the $M$ batteries, according to the detection data, obtain the charging efficiency value $\eta _{c}$ BAT_S and the discharging efficiency value $\eta _{d}$ BAT_E of this model in two states, namely at the initial stage $Q_{0}$ BAT of the life and at the end stage $Q_{L}$ Charge of the life, and the operating temperature is at $T$ Discharge ; interpolate the charging efficiency value and the discharging efficiency value to obtain the efficiency matrix of each battery:

[0118]

[0119] Description: (1) For batteries applied to energy storage, they all need to undergo performance tests that meet national standards, including tests of charge-discharge efficiency, or obtain them through the type tests of suppliers. Currently, liquid cooling thermal management methods are mainly used for energy storage batteries, and the operating temperature of the batteries can always be controlled within a relatively small temperature change range, and the efficiency change within this range is not significant. In this embodiment, the average value of the battery operating temperature is controlled at T BAT , and the efficiency parameter is obtained, that is, the battery is tested at this temperature.

[0120] (2) The efficiency test in (1) above is the test value at the initial state of the life. The same test is carried out at the end of the life to obtain the test value.

[0121] Obtaining the efficiency data of PCS and DC / DC at different temperature points includes:

[0122] Under the condition of y temperature points where the main radiator temperatures of PCS and DC / DC converters can be adjusted, test the data matrix η of the output x power point efficiencies of each model of PCS and DC / DC PCS and η DC :

[0123]

[0124] Among them, the quantities of x and y are determined according to the test conditions.

[0125] If there are 2 models of PCS and 2 models of DC / DC in the system, 2 corresponding efficiency data matrices are obtained.

[0126] In this embodiment, the measured efficiency matrix data is stored in the memory of the microgrid energy management system.

[0127] S3. Real-time obtain the temperature data and power data of PCS and DC / DC, and obtain the corresponding target efficiency data of the battery, PCS and DC / DC according to the temperature data fitting.

[0128] In this embodiment, by obtaining the temperature and power data of the operating PCS and DC / DC, fitting the efficiency of the operating point according to the stored data, substituting it into the real-time operating cost objective function, and obtaining the operating power of PCS and DC / DC that minimizes the objective function value through the optimization algorithm and real-time control, so as to minimize the real-time operating cost of the system. Each PCS and DC / DC has different installation positions and heat dissipation conditions. Especially for a system with an air-cooled power converter, the operating temperatures are different, resulting in efficiency differences.

[0129] Step S3 includes:

[0130] Due to the different installation positions and heat dissipation conditions of each PCS and DC / DC, the temperatures during operation are different, resulting in efficiency differences. Set the control interval D, obtain the main radiator temperatures and output powers of N PCSs and M DC / DCs at time t, and form a data matrix:

[0131] P PCS (t)=[P PCS_1 (t)…P PCS_N (t)], T PCS (t)=[T PCS_1 (t)…T PCS_N (t)];

[0132] P DC (t)=[P DC_1 (t)…P DC_M (t)], T DC (t)=[T DC_1 (t)…T DC_M (t)];

[0133] And obtain the efficiency value matrix of each PCS and DC / DC of each model at time t through the interpolation method:

[0134] η PCS (t)=[η PCS_1 (t)…η PCS_N (t)];

[0135] η DC (t)=[η DC_1 (t)…η DC_M (t)].

[0136] S4. Substitute the target efficiency data into the target function and solve it, and perform optimization control according to the solution result;

[0137] Step S4 includes:

[0138] Substitute the target efficiency data into the target function and solve it through an optimization algorithm;

[0139] Perform optimization control according to the solution result.

[0140] In this embodiment, with the goal of minimizing the real-time operating cost target function at time t, solve for the given values of P PCSj (t + 1), P DCi (t + 1).

[0141] Target function:

[0142]

[0143] Constraint conditions:

[0144]

[0145] P DC_i (t)<P DC_i_limit ,P PCS_j (t)<P PCS_j_limit ;

[0146] Wherein, P DC_i_limit , P PCS_j_limit are respectively the power limit values of the converters.

[0147] Initial conditions:

[0148]

[0149] Please refer to Figure 3 , Example 2 of the present invention is:

[0150] In this embodiment, an example is given of the method for optimizing and controlling the operating cost of an energy storage system based on efficiency described in Embodiment 1 above.

[0151] There is Figure 3 a power system as shown, the number N of PCSs is 2, of the same model, with a single - unit power limit of 150 kW; the number M of DC / DCs is 2, of the same model, with a single - unit power limit of 120 kW; the total output power of the photovoltaic power generation unit at time t is 50 kW, and the DC load power is 500 kW; 2 batteries are of the same model; the valley - time electricity price G price_low = 0.4 / kWh, and the real - time grid electricity price G price = 0.8 / kWh within the control interval.

[0152] Under the condition of the battery operating temperature T BAT in thermal management control, assume that the two batteries have the same initial - life and end - of - life condition efficiencies: η Charge = [0.970, 0.970; 0.905, 0.905]; η Discharge = [0.975, 0.975; 0.915, 0.915]; obtain the PCS and DC / DC efficiency test matrices η PCS and η DC for 4 temperatures; the cumulative discharge electricity of the two batteries is set to 0 and 2×e5 kwh respectively, and the cumulative discharge electricity at the end of life is set to 1×e6 kwh; use the interpolation method to obtain the charge / discharge efficiencies of the two batteries within the control interval as 0.9700, 0.9570 and 0.9750, 0.9630 respectively.

[0153] Note: The efficiency values are for illustrative examples and do not represent actual test results.

[0154] Let the operating power of the two PCSs at time t be:

[0155] P PCS (t) = [127, 122] kW;

[0156] The temperature is:

[0157] T PCS (t) = [38, 10];

[0158] The operating power of the DC / DC is:

[0159] P DC (t) = [105, 108] kW;

[0160] The temperature is:

[0161] T DC (t) = [0, 10].

[0162] Satisfy the constraint conditions:

[0163]

[0164] P DC_i (t) < P DC_i_limit , P PCS_j (t) < P PCS_j_limit .

[0165] Note: The temperature values are for example calculations and do not represent actual operating results.

[0166] Obtain the efficiency value matrix of each two PCSs and DC / DC at time t through the interpolation method.

[0167] η PCS (t) = [0.9497, 0.9508];

[0168] η DC (t) = [0.9528, 0.9493];

[0169] Note: The efficiency values are the calculation results based on the example; the interpolation method is not limited, and in this embodiment, the "spline" method can be used to fit the efficiency function and obtain the interpolation.

[0170] Take the control interval D as 60 s, use the above data at time t, and use the neural network optimization algorithm to calculate the power that minimizes the real-time operating cost objective function in the t + 1 period as:

[0171] P PCS (t + 1) = [61.37, 149.48] kW;

[0172] P DC(t + 1) = [120.00, 119.15] kW;

[0173] Cost total (t + 1) = 4.8867 yuan / 60 s.

[0174] As a comparison, at time t + 1, it still operates under the initial conditions in 3 without optimization, and Cost is calculated. total (t + 1) = 5.2069 yuan / 60 s.

[0175] The third embodiment of the present invention is as follows:

[0176] An optimization control terminal 1 for the operating cost of an energy storage system based on efficiency, including a processor 2, a memory 3, and a computer program stored in the memory 3 and operable on the processor 2. When the processor 2 executes the computer program, it implements the steps in the optimization control method for the operating cost of an energy storage system based on efficiency described in the above Embodiment 1 or 2.

[0177] In summary, the optimization control method and terminal for the operating cost of an energy storage system based on efficiency provided by the present invention consider that the operating efficiencies of the PCS and DC / DC are different at different temperatures, establish efficiency functions including temperature for the PCS and DC / DC, and construct an objective function and power balance constraint conditions with the real-time operating cost as the goal, and solve the objective function based on the real-time operating conditions, so as to optimize the control of the energy storage system and reduce the operating cost of the energy storage system.

[0178] The control method for a string-type energy storage system based on efficiency optimization of the present invention, under the condition of considering temperature inconsistency, enables the converter with higher efficiency to increase output, so that the operating cost is optimized and controlled. During the period when the electricity price of the peak-valley electricity price implementation is high and the load fluctuation is small, the electricity cost is minimized by this method.

[0179] At the same time, the influence of the efficiencies of batteries with different lifetimes on the operating cost is considered. Through the algorithm, the discharge power of batteries in different lifetime stages can be automatically balanced, the utilization rate of battery assets can be optimized, and the battery lifetimes measured by Regge electricity tend to be consistent.

[0180] The above are only the embodiments of the present invention, and do not limit the patent scope of the present invention accordingly. Any equivalent transformation made by using the content of the specification and drawings of the present invention, or directly or indirectly applied in the related technical fields, is equally included in the patent protection scope of the present invention.

Claims

1. A method for optimizing and controlling the operation cost of an energy storage system, characterized in that: Includes steps: S1. For the microgrid system, establish an efficiency function including temperature for PCS and DC / DC, and construct the objective function and power balance constraints with real-time operation cost as the target: The real-time operation cost is composed of the cost of obtaining energy from the power grid and the cost of obtaining energy from the battery; The cost of obtaining energy from the battery is determined by the grid electricity price during off-peak hours, the battery charging and discharging efficiency, and the real-time efficiency of the DC / DC used by the battery, expressed as a function including temperature; S2. Based on the efficiency data of the battery and the temperature data and power data of the PCS and DC / DC acquired in real time, the objective function is solved by an optimization algorithm, and optimization control is performed according to the solution result.

2. The energy storage system operation cost optimization control method according to claim 1, characterized in that: The objective function is constructed as follows: When all PCSs are rectified output and all batteries are discharged, within the set control interval D, the objective function of the real-time operating cost is: Cost total =Cost Grid +Cost BAT ; Among them, Cost Grid is the cost of obtaining energy from the grid. BAT is the cost of getting energy from the battery.

3. The method for optimizing the operation cost of an energy storage system according to claim 2, characterized in that: Cost of getting energy from the grid Grid The determination includes: P PCS_j is the output power in the rectified direction of the jth PCS, η PCS_j is the real-time efficiency of the jth PCS, expressed as a function based on temperature and output power: η PCS_j =f1(T PCS_j ,P PCS_j ); G price is the real-time grid electricity price, and N is the number of PCSs.

4. The energy storage system operation cost optimization control method according to claim 2, characterized in that: Cost of getting energy from batteries BAT The determination includes: P DC_i is the output power of the i-th DC / DC, η DC_i is the real-time efficiency of the i-th DC / DC, expressed as a function based on temperature and output power: η DC_i =f2(T DC_i ,P DC_i ); η Charge_i and η Disharge_i are the charging efficiency and discharging efficiency of the ith battery, and are the battery temperature T BAT_i And the cumulative discharge capacity Q BAT_i Function: η Charge_i =f3(T BAT_i ,Q BAT_i ); η Charge_i =f3(T BAT_i ,Q BAT_i ); G price_low is the grid electricity price during off-peak hours, η DC_Chg_i is the average efficiency of the i-th DC / DC at a certain charging rate under battery charging conditions, and M is the number of DC / DCs, corresponding to the number of batteries.

5. The method for optimizing and controlling the operation cost of an energy storage system according to claim 1, characterized in that: The construction of the power balance constraint condition comprises the steps of: To meet the DC load P Load The power requirement is subject to the following constraints: Among them, P PV is the sum of all photovoltaic input power on the DC bus, P DC_i is the output power of the ith DC / DC, P PCS_j is the output power in the rectification direction of the jth PCS, N is the number of PCSs, and M is the number of DC / DCs, which corresponds to the number of batteries.

6. An energy storage system operation cost optimization control terminal, comprising a processor, a memory, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the following steps are implemented: S1. For the microgrid system, establish an efficiency function including temperature for PCS and DC / DC, and construct the objective function and power balance constraints with real-time operation cost as the target: Wherein, the real-time operation cost is composed of the cost of obtaining energy from the power grid and the cost of obtaining energy from the battery; The cost of obtaining energy from the battery is determined by the grid electricity price during off-peak hours, the battery charging and discharging efficiency, and the real-time efficiency of the DC / DC used by the battery, expressed as a function including temperature; S2. Based on the efficiency data of the battery and the temperature data and power data of the PCS and DC / DC acquired in real time, the objective function is solved by an optimization algorithm, and optimization control is performed according to the solution result.

7. The energy storage system operation cost optimization control terminal according to claim 6, characterized in that: The objective function is constructed as follows: When all PCSs are rectified output and all batteries are discharged, within the set control interval D, the objective function of the real-time operating cost is: Cost total =Cost Grid +Cost BAT ; Among them, Cost Grid is the cost of obtaining energy from the grid. BAT is the cost of getting energy from the battery.

8. The energy storage system operation cost optimization control terminal according to claim 7, characterized in that: Cost of getting energy from the grid Grid The determination includes: P PCS_j is the output power in the rectified direction of the jth PCS, η PCS_j is the real-time efficiency of the jth PCS, expressed as a function based on temperature and output power: η PCS_j =f1(T PCS_j ,P PCS_j ); G price is the real-time grid electricity price, and N is the number of PCSs.

9. The energy storage system operation cost optimization control terminal according to claim 7, characterized in that: Cost of getting energy from batteries BAT The determination includes: P DC_i is the output power of the i-th DC / DC, η DC_i is the real-time efficiency of the i-th DC / DC, expressed as a function based on temperature and output power: η DC_i =f2(T DC_i ,P DC_i ); η Charge_i and η Disharge_i are the charging efficiency and discharging efficiency of the ith battery, and are the battery temperature T BAT_i And the cumulative discharge capacity Q BAT_i Function: η Charge_i =f3(T BAT_i ,Q BAT_i ); η Charge_i =f3(T BAT_i ,Q BAT_i ); G price_low is the grid electricity price during off-peak hours, η DC_Chg_i is the average efficiency of the i-th DC / DC at a certain charging rate under battery charging conditions, and M is the number of DC / DCs, corresponding to the number of batteries.

10. The energy storage system operation cost optimization control terminal according to claim 6, characterized in that: The construction of the power balance constraint condition comprises the steps of: To meet the DC load P Load The power requirement is subject to the following constraints: Among them, P PV is the sum of all photovoltaic input power on the DC bus, P DC_i is the output power of the ith DC / DC, P PCS_j is the output power in the rectification direction of the jth PCS, N is the number of PCSs, and M is the number of DC / DCs, which corresponds to the number of batteries.

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