Optimal control method for hybrid energy storage microgrid based on reversible solid oxide battery

Abstract

The present invention discloses a microgrid optimization control method for hybrid energy storage based on reversible solid oxide batteries, which belongs to the technical field of reversible solid oxide fuel cells. The method includes: when the distributed renewable energy in the microgrid does not match the load demand, according to the real-time operating state of the lithium battery and the reversible solid oxide battery and the real-time source-load difference (the difference between the power output of the distributed renewable energy device and the load power), input into the controller of the system, taking the minimum total attenuation of the lithium battery energy storage system and the reversible solid oxide battery system as the objective function, taking the optimal output power of the lithium battery and the reversible solid oxide battery as the control variable, and taking the source-load difference and the safe operation of each distributed energy device as the constraint condition, the optimization algorithm in the controller calculates the optimal output power of the lithium battery and the reversible solid oxide battery in the next state, thereby realizing the operation of the microgrid system in a more efficient and economical state.

Description

Technical Field

[0001] The present invention belongs to the technical field of reversible solid oxide fuel cells, and more specifically, relates to an optimization control method for a hybrid energy storage microgrid based on a reversible solid oxide battery. Background Art

[0002] Renewable energy sources such as wind and solar are often difficult to directly match with load demand due to their seasonal and intermittent characteristics, so microgrids that can manage and control these energy sources have emerged. Reversible solid oxide fuel cells (RSOCs) can work in two different modes. They can not only compensate for the demand by burning hydrogen to generate electricity, but also use the surplus electricity to produce hydrogen to store energy when there is surplus wind and solar power. Therefore, they have become a very important part of the energy management of microgrids. However, due to the high operating temperature of reversible solid oxide fuel cells and the immature development of technology, they are often not suitable as the only energy storage or power generation device in microgrids. Researchers usually add lithium battery energy storage systems (BESS) to cope with fluctuations and maintain the stable operation of microgrids.

[0003] In the existing research on microgrids based on reversible solid oxide batteries, there is often an over-reliance on lithium battery energy storage systems to absorb excess wind and solar energy, and the discrepancy between wind and solar energy and load is given priority to lithium batteries; while foreign research on reversible solid oxide batteries is more mature than that in China, and lithium battery energy storage is often directly abandoned and reversible solid oxide batteries are used directly. However, both lithium batteries and reversible solid oxide fuel cells have their optimal power operating ranges, and different output powers have a great impact on their efficiency and lifespan. If they are not controlled, the efficiency and economy of the system will be greatly affected. Summary of the invention

[0004] In view of the above defects or improvement needs of the prior art, the present invention provides an optimization control method for a hybrid energy storage microgrid based on a reversible solid oxide battery, the purpose of which is to control the output power of the lithium battery and the reversible solid oxide battery, and to improve the operating efficiency and economy of the microgrid based on the reversible solid oxide fuel cell.

[0005] To achieve the above object, according to one aspect of the present invention, a hybrid energy storage microgrid optimization control method based on a reversible solid oxide battery is provided, comprising:

[0006] S1. Obtain the source-load difference between the distributed renewable energy power generation power and the load demand power (the difference between the distributed renewable energy output power and the load demand power);

[0007] S2. Construct an optimization model; the optimization model takes the minimum total attenuation of the lithium battery energy storage system and the reversible solid oxide battery system as the objective function, takes the output power of the lithium battery and the reversible solid oxide fuel cell as the control variable, and takes satisfying the source-load difference and the safe operation of each distributed energy device as the constraint condition;

[0008] S3. Obtain the current state of charge, discharge depth and ambient temperature of the lithium battery, as well as the output power and operating time of the reversible solid oxide fuel cell, input them into the optimization model, and solve the optimal output power of the lithium battery and the reversible solid oxide fuel cell in the next state.

[0009] Furthermore, the attenuation model of the lithium battery energy storage system is:

[0010] A BESS =A cyc +A cal

[0011] A cyc Indicates the degree of cycle aging, C is the capacity of the battery (Ah), CL n AH is the number of cycles of the life cycle of lithium batteries under standard test conditions, n represents the standard test conditions, eq_i Indicates the equivalent cumulative AH number of lithium battery charge and discharge in each sampling period, A cal is the degree of calendar aging, t is the operating time of the lithium battery, t n is the detection cycle of the lithium battery energy storage system by the controller when the microgrid is running, Q loss is the capacity lost by the lithium battery, SOC is the state of charge of the lithium battery, and T is the ambient temperature of the lithium battery.

[0012] Furthermore, the reversible solid oxide battery system attenuation model is:

[0013] A RSOC =∑A SOFC +∑A SOEC

[0014] A SOFC is the decay rate of the SOFC stack changing with time under rated conditions, P SOFC is the output power of SOFC, R SOFC_N is the operating power of SOFC under rated conditions, a and α are constants, A SOEC =γ·A SOFC , γ is an empirical constant.

[0015] According to another aspect of the present invention, a hybrid energy storage microgrid optimization control system based on a reversible solid oxide battery is provided, comprising: a source-load difference acquisition module, a first sensing unit, a second sensing unit and a controller;

[0016] The source-load difference acquisition module is used to obtain the source-load difference between the distributed renewable energy power generation power and the load demand power;

[0017] The first sensing unit is used to obtain the current state of charge, discharge depth and ambient temperature of the lithium battery;

[0018] The second sensing unit is used to obtain the output power and operating time of the reversible solid oxide battery;

[0019] The controller is used to receive the output data of the first sensor unit and the second sensor unit, and run the optimization model to solve the optimal output power of the lithium battery and the reversible solid oxide fuel cell in the next state; the optimization model uses the minimum total attenuation of the lithium battery energy storage system and the reversible solid oxide battery system as the objective function, the output power of the lithium battery and the reversible solid oxide fuel cell as the control variable, and satisfies the source-load difference and the safe operation of each distributed energy device as the constraint condition.

[0020] In general, compared with the prior art, the above technical solution conceived by the present invention can achieve the following beneficial effects.

[0021] When the distributed renewable energy in the microgrid does not match the load demand, the method of the present invention no longer processes all of it first by the lithium battery energy storage system and then compensates by the reversible solid oxide battery. Instead, the attenuation rate is obtained based on the attenuation aging model of BESS and RSOC and the current operating state. Under the conditions of satisfying the real-time source-load difference and the safe operation of the distributed energy equipment, the power allocation scheme that minimizes the attenuation of the microgrid is calculated within each time step, thereby effectively improving the operating life of the microgrid.

[0022] The present invention combines the advantages of rapid dynamic response of lithium batteries in microgrids and high energy efficiency of reversible solid oxide fuel cells, so that the RSOC system and BESS system jointly participate in the compensation of the source-load gap in the microgrid, which can reduce the pressure of the two when they are running alone, and avoid the situation where energy shortage or energy surplus cannot be solved when they are running alone. At the same time, the combination of the two can give play to their respective advantages and avoid deficiencies, so that renewable energy can be better absorbed and utilized, and the reliability, safety and efficiency of the overall operation of the microgrid are also improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 A topological diagram of the microgrid shown in the present invention;

[0024] Figure 2A flow chart of the energy management method provided by the present invention;

[0025] Figure 3 Flowchart of the hybrid optimization algorithm used for optimal control (GA-PSO)

[0026] Figure 4 The operation and attenuation of the system under the greedy control method of BESS. (a) represents the output power and state of charge of BESS and the output power of RSOC, and (b) represents the attenuation rate of BESS and RSOC.

[0027] Figure 5 The operation and attenuation of the system under the optimal control method proposed in the present invention, (a) represents the output power and charge state of BESS and the output power of RSOC, (b) represents the attenuation rate of BESS and RSOC. DETAILED DESCRIPTION

[0028] In order to make the purpose, technical scheme and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0029] The present invention proposes a hybrid energy storage microgrid optimization control method based on a reversible solid oxide battery, and its topology diagram is as follows: Figure 1 As shown, the control flow chart is as follows Figure 2 As shown. The technical solution of the present invention is as follows. First, an aging model of a lithium battery energy storage system (BESS) is constructed. Lithium batteries mainly play the role of peak shaving and valley filling in microgrids. In island mode, they can improve the utilization rate of wind and solar energy. In grid-connected mode, they can participate in the electricity trading market and improve the economic benefits of microgrids. The attenuation of lithium batteries can be divided into calendar aging and cycle aging according to the degradation mechanism. Calendar aging describes the degradation of batteries during storage, that is, when no current is applied to the battery, while cycle aging describes the degradation of batteries that occurs during battery charging and discharging. Therefore, calendar aging and cycle aging attenuation models are constructed respectively.

[0030] Calendar Aging:

[0031]

[0032] Where Q lossis the capacity lost by the lithium battery (percentage), SOC is the state of charge of the lithium battery, T is the ambient temperature of the lithium battery (K), and t is the operating time of the lithium battery (days); in addition, A = 165400, α = 1, β = 4148K, γ = 0.5. In order to make this calendar aging model more suitable for complex working conditions such as drastic changes in SOC, it is transformed into the following form:

[0033]

[0034]

[0035] Where A cal The calendar aging degree is usually defined as the need for replacement when the battery capacity decays by 20%, i.e. the aging degree is 100%. n It is the detection cycle of the controller for the lithium battery energy storage system when the microgrid is running.

[0036] Cyclic Aging:

[0037]

[0038]

[0039] CL DoD =12180DoD 3 +34990DoD 2 +97540DoD+52900

[0040] Where CL dis , CL ch , CL DoD They are respectively lithium battery with discharge rate I dis 、Charging rate I ch And the discharge depth DoD is a function of the independent variable, and the cycle life of the lithium battery under the corresponding conditions is obtained.

[0041] Then, the calendar aging and cycle aging of lithium batteries are integrated using the weighted AH method. The increase in the value of the weight factor indicates that the stress will accelerate the degradation of lithium batteries. This method assumes that the cumulative AH number of the battery cycle before the end of its life is constant, so the battery life is estimated by calculating the equivalent AH during operation. In addition, in order to cope with the situation where the charge and discharge rate of lithium batteries changes dramatically during the actual operation of the microgrid, a charge or discharge cycle should be decomposed into multiple small segments for calculation.

[0042]

[0043] Where AH ch With AH dis is the equivalent cumulative AH number of the lithium battery charge and discharge in each sampling period, CLdis_n , CL DoD_n With CL ch_n It is the cycle life of lithium battery under standard test conditions (charge and discharge rate and discharge depth are both 1).

[0044]

[0045] In the above formula, A cyc Indicates the degree of cycle aging, C is the capacity of the battery (Ah), CL n AH is the number of cycles of the life cycle of lithium batteries under standard test conditions, n represents the standard test conditions, eq_i It represents the equivalent cumulative AH number of the lithium battery charge and discharge in each sampling period. Therefore, in the above formula, the numerator is the sum of the equivalent AH number accumulated in each sampling period, and the denominator is the cumulative available AH number of the lithium battery in theory throughout its life cycle.

[0046] Therefore, the comprehensive attenuation rate of lithium batteries is:

[0047] A BESS =A cyc +A cal

[0048] A BESS It is the comprehensive attenuation rate of the lithium battery. When its value reaches 100%, the service life of the lithium battery is considered to have ended.

[0049] Subsequently, a decay and aging model of the reversible solid oxide battery (RSOC) system was built. Since the factors causing decay and the decay rates in the two modes are very different, aging and decay models of solid oxide fuel cells (SOFC) and solid oxide electrolyzers (SOEC) were built respectively, and the functional relationship between output power and system efficiency was given.

[0050] After long-term testing of the performance of the SOFC stack system by researchers, data support was provided for the exploration of its attenuation law. The study found that there are many factors that will cause the attenuation of the SOFC stack performance, and the degree of influence caused by each factor is different. In order to reduce the calculation burden and simplify the optimization procedure, the present invention uses the fully coupled SOFC attenuation model verified by industry researchers to describe the performance attenuation of the SOFC stack under rated conditions over time, and fits the attenuation curve obtained in the experiment into a function to facilitate subsequent control.

[0051] After analysis and comparison, fitting the attenuation function into a power function is the best. The function expression and parameters after fitting are as follows:

[0052] A SOFC =a·t α

[0053] Where ASOCF is the decay rate of the SOFC stack under rated conditions over time, t is the operation time of the stack (that is, the overall operation time of the battery energy storage system and the microgrid), in hours (h), and the fitting result is a=1.541×10 -3 , α = 0.4167. The fitting results show that the determination coefficient R 2 =0.9919, the fitting accuracy is good.

[0054] Studies have shown that the output power of SOFC will affect the decay rate of the stack. Usually, within a certain range, the lower the output power, the higher the system efficiency. Under full power output, the system efficiency is the lowest and the decay rate will be greatly improved. However, the lower the power, the better. This lower limit is generally taken as 1 / 7 of the output power of the SOFC system, that is, a power lower than this will also cause a sharp drop in system efficiency and accelerated decay. Assuming that the effect of power on the decay rate is linear, that is, when operating within the range, the decay rate of the stack increases with the increase of output power, so consider introducing a weighting factor λ that varies with output power to the decay of the SOFC stack, and λ increases monotonically within the power range. Therefore, the stack performance decay function that varies with output power is introduced as follows, P SOFC is the output power of SOFC, P SOFC_N The operating power of SOFC under rated conditions:

[0055]

[0056] Compared with SOFC, SOEC was studied later and decays faster. There is no clear conclusion on the mechanism of SOEC decay. Recent studies generally believe that the changes in material composition and structure under high temperature and long-term operation are the core issues leading to SOEC performance decay. The decay of the oxygen electrode is the main reason for the decline in SOEC performance and thus affecting its service life. In the present invention, the decay curve of SOEC is similar to that of SOFC but with a faster decay rate, and γ is an empirical constant.

[0057] A SOEC =γ·A SOFC

[0058] Since the performance decay of BOP components is much slower than that of the fuel cell stack, and the RSOC system considered in the present invention is a pure hydrogen system, which does not include the reformer with the fastest performance decay among the BOP components, the present invention does not consider the impact of the decay of BOP components on the system performance, and takes the decay of the fuel cell stack as the overall decay of the system life.

[0059] Therefore, the overall attenuation rate of the RSOC system is the sum of the cumulative attenuation rates in the two modes:

[0060] A RSOC =ΣA SOFC+∑A SOEC

[0061] After building the attenuation and aging model of lithium battery energy storage and RSOC system, control can be applied on this basis. However, before that, it is necessary to determine the capacity of each distributed energy device in the microgrid by optimizing the configuration according to the specific application scenario, combined with the constraints and objective function under the actual situation.

[0062] After determining the rated capacity of each distributed energy device in the microgrid, when the microgrid is put into operation, the real-time source load data and the status of the energy storage device at each moment are first collected and input into the server for control, so as to first obtain the power data that needs to be compensated or absorbed by the energy storage device. Then, according to the attenuation and aging model of the above-mentioned BESS and RSOC system configured in the server, the objective function of the microgrid operation is set, that is, the comprehensive attenuation rate of BESS and RSOC is minimized. At the same time, various constraints for the safe operation of the microgrid are set, including the maximum power constraint of the wind and solar units, the upper limit of the charge and discharge power and the state of charge constraint of the battery energy storage system, the slope constraint of the output power tracking of RSOC, and the constraint that the power that needs to be absorbed or compensated at each moment is the sum of the output powers of BESS and RSOC. With the set objective function as the goal, the hybrid optimization algorithm GA-PSO is used (the algorithm solution flow chart is shown in the figure). Figure 3 ), calculate the optimal power configuration scheme of the microgrid at the next moment, and send the corresponding control signal to enable the BESS and RSOC systems to track the optimal power they have configured.

[0063] Due to the abundance of distributed power sources in microgrids and the complexity of various electrical and non-electrical equipment, it is difficult to build a physical experimental platform. Therefore, the implementation case of the present invention uses the Matlab / Simulink simulation platform for simulation experiments. Each model built on the platform is compared and verified with the physical experimental data, and the error is small and within an acceptable range.

[0064] Example 1

[0065] Firstly, models of distributed renewable power sources are built on the Matlab / Simulink simulation platform, including photovoltaic power generation units and wind power generation units, and the models are built based on the relationship between their various performance indicators and output power.

[0066] Then build a lithium battery energy storage system (BESS) model, including the output power and state of charge model, as well as the above-mentioned aging attenuation model.

[0067] Then, a reversible solid oxide battery (RSOC) model was built, including the models of its BOP components and the battery stack. The battery stack model also included sub-models such as the battery stack temperature sub-model and the electrochemical sub-model.

[0068] Finally, the control method proposed in the present invention is combined with the optimization algorithm, and the aging models of the BESS and RSOC systems are integrated into a function as the objective function of the optimization control algorithm. Then, the function is implemented in the s-function module on the Matlab / Simulink simulation platform through the m language. Figure 3 The solution process of the hybrid optimization algorithm shown in the figure provides effective global search capability and search efficiency by combining the particle swarm algorithm with the genetic algorithm, which serves as the energy management and control module of the microgrid.

[0069] By finding a suitable application scenario, the capacity of photovoltaic and wind power generation units can be determined according to the available ground and roof area of ​​the selected scenario, so the total amount of distributed renewable energy in the microgrid can be determined first. Then, based on the total capacity of renewable energy, the total capacity of the lithium battery energy storage system and the reversible solid oxide battery system can be determined. After determining the capacity of each distributed energy device in the microgrid, the capacity parameters of each device are input into the built simulation model for simulation.

[0070] First, the operation of the microgrid is simulated according to the existing BESS greedy control method, that is, the surplus or shortage of wind and solar energy is first compensated by the lithium battery energy storage system. When the BESS cannot absorb the energy, the RSOC system is started. After the simulation, the operation of the microgrid under the control of this method is observed. The simulation results are as follows: Figure 4 As shown in (a)-(b) in the figure. Then, the optimization control method proposed in the present invention is introduced to detect the real-time source-load difference and control the optimal output power distribution to the BESS and RSOC systems. The simulation is run for the same time as the previous method to observe the power distribution of BESS and RSOC, the attenuation rate and other performance parameters of the microgrid during operation. The simulation results are shown in Figure 5 As shown in (a)-(b). Comparing the difference in the impact of the two control methods on the attenuation rate of a single component and the system as a whole, it can be clearly seen that under the optimal control method proposed in the present invention, the power curves of BESS and RSOC are smoother and more stable, and under the same simulation running time, the attenuation rates of both are also smaller than those of the previous control method. Therefore, it can be concluded that the optimal control method for a hybrid energy storage microgrid based on reversible solid oxide batteries proposed in the present invention can effectively improve the stability of microgrid operation and reduce the attenuation rate of the main energy storage and energy supply units of the system, thereby achieving the purpose of making the microgrid system operate in a more efficient and economical state.

[0071] Example 2

[0072] Depending on the different objective functions set, the energy management under the control of the optimal control method proposed in the present invention will distribute and control the output power according to different trajectories. In Example 1, the minimum attenuation is used as the objective function, but in actual engineering applications, economic costs and benefits are often more valued. By investigating and studying the initial investment cost and operation and maintenance cost of the BESS and RSOC systems, the economic cost consumption corresponding to their attenuation rates can be further obtained. Integrating this relationship into the objective function of the optimal control algorithm in Example 1 can be further converted into finding the most economical power allocation operation trajectory for the microgrid. Similarly, if the system pursues the highest efficiency operation, the function between the system efficiency and the output power can be used as the fitness function, and the output power that makes the system most efficient in each step can be found through optimal control.

[0073] It will be easily understood by those skilled in the art that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. An optimal control method for hybrid energy storage microgrid based on reversible solid oxide battery, It is characterized in that include: S1. Obtain the source-load difference between the power generated by distributed renewable energy and the power required by the load; S2. Construct an optimization model; the optimization model takes the minimum total attenuation of the lithium battery energy storage system and the reversible solid oxide battery system as the objective function, takes the output power of the lithium battery and the reversible solid oxide fuel cell as the control variable, and takes satisfying the source-load difference and the safe operation of each distributed energy device as the constraint condition; S3. Obtain the current state of charge, discharge depth and ambient temperature of the lithium battery, as well as the output power and operating time of the reversible solid oxide battery, and input them into the optimization model to solve the optimal output power of the lithium battery and the reversible solid oxide fuel cell in the next state; The attenuation model of the lithium battery energy storage system is: A BESS =A cyc +A cal A cyc Indicates the degree of cycle aging, C is the capacity of the battery (Ah), CL n AH is the number of life cycle cycles of lithium batteries under standard test conditions, n represents the standard test conditions, eq_i Indicates the equivalent cumulative AH number of lithium battery charge and discharge in each sampling period, A cal is the degree of calendar aging, t is the operating time of the lithium battery, t n is the detection cycle of the lithium battery energy storage system by the controller when the microgrid is running, Q loss is the capacity lost by the lithium battery, SOC is the state of charge of the lithium battery, and T is the ambient temperature of the lithium battery; The reversible solid oxide battery system attenuation model is: A RSOC =∑A sOFC +∑A SOEC A SOFC is the decay rate of the SOFC stack changing with time under rated conditions, P SOFC is the output power of SOFC, P SOFC_N is the operating power of SOFC under rated conditions, a and α are constants, A SOEC =γ·A SOFC , γ is an empirical constant.

2. A hybrid energy storage microgrid optimization control system based on reversible solid oxide batteries, It is characterized in that include: A source-load difference acquisition module, a first sensing unit, a second sensing unit and a controller; The source-load difference acquisition module is used to obtain the source-load difference between the distributed renewable energy power generation power and the load demand power; The first sensing unit is used to obtain the current state of charge, discharge depth and ambient temperature of the lithium battery; The second sensing unit is used to obtain the output power and operating time of the reversible solid oxide battery; The controller is used to receive the output data of the first sensor unit and the second sensor unit, and run the optimization model to solve the optimal output power of the lithium battery and the reversible solid oxide fuel cell in the next state; the optimization model uses the minimum total attenuation of the lithium battery energy storage system and the reversible solid oxide battery system as the objective function, the output power of the lithium battery and the reversible solid oxide fuel cell as the control variable, and satisfies the source-load difference and the safe operation of each distributed energy device as the constraint condition; The attenuation model of the lithium battery energy storage system is: A BESS =A cyc +A cal A cyc Indicates the degree of cycle aging, C is the capacity of the battery (Ah), CL n AH is the number of life cycle cycles of lithium batteries under standard test conditions, n represents the standard test conditions, eq_i Indicates the equivalent cumulative AH number of lithium battery charge and discharge in each sampling period, A cal is the degree of calendar aging, t is the operating time of the lithium battery, t n is the detection cycle of the lithium battery energy storage system by the controller when the microgrid is running, Q loss is the capacity lost by the lithium battery, SOC is the state of charge of the lithium battery, and T is the ambient temperature of the lithium battery; The reversible solid oxide battery system attenuation model is: A RSOC =∑A SOFC +∑A SOEC A SOFC is the decay rate of the SOFC stack changing with time under rated conditions, P SOFC is the output power of SOFC, P SOFC_N is the operating power of SOFC under rated conditions, a and α are constants, A SOEC =γ·A SOFC , γ is an empirical constant.

3. An electronic device, It is characterized in that include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the steps of the method according to claim 1 are implemented when the computer program is executed by the processor.

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