Multi-energy-storage-considered collaborative optimization operation method for low-carbon micro-grid

Abstract

A multi-energy-storage-considered collaborative optimization operation method for a low-carbon microgrid comprises the steps that electricity-hydrogen energy, electricity-ammonia energy and electricity-natural gas mutual conversion devices are introduced through multi-energy-storage-system modeling, and a collaborative optimization model of a microgrid system is constructed with the minimum total operation cost of the microgrid containing a hybrid energy storage system as a target function; and the optimized operation of the micro-grid is realized. According to the method, collaborative optimization scheduling of hydrogen storage, ammonia storage, natural gas energy storage and battery energy storage is comprehensively considered, the energy efficiency of the low-carbon micro-grid is improved through multi-stage conversion of the energy flow, new energy consumption is promoted, and the operation cost of the micro-grid is reduced.

Description

Technical Field

[0001] The present invention relates to a technology in the field of power systems, specifically a method for coordinated optimization operation of multiple energy storages for low-carbon microgrids. Background Art

[0002] Carbon emissions from the power system account for about 25% of the world's total carbon emissions. As global warming continues to intensify, research on reducing carbon emissions from the power system has attracted more and more attention. Developing renewable energy is an effective way to reduce carbon emissions and operating costs. The deployment of renewable energy facilities, mainly wind turbines and photovoltaics, in the power system helps solve the problem of excessive carbon emissions. However, high penetration of renewable energy leads to unstable energy flow in the energy system due to the randomness, volatility and intermittency of its output. Summary of the invention

[0003] In view of the shortcomings of the prior art that only battery energy storage systems are considered, but hydrogen and ammonia storage equipment are not considered, the flexibility is insufficient, and the requirement for long-term energy storage is difficult to achieve, the present invention proposes a method for coordinated optimization operation of multiple energy storages for low-carbon microgrids, which comprehensively considers the coordinated optimization scheduling of hydrogen storage, ammonia storage, natural gas energy storage and battery energy storage, improves the energy efficiency of low-carbon microgrids through multi-stage conversion of energy flows, promotes the consumption of new energy, and reduces the operating costs of microgrids.

[0004] The present invention is achieved through the following technical solutions:

[0005] The present invention relates to a method for coordinated optimization operation of a low-carbon microgrid taking into account multiple energy storage systems. By introducing electricity-hydrogen energy, electricity-ammonia energy, and electricity-natural gas conversion devices through multi-energy storage system modeling, a coordinated optimization model of the microgrid system is constructed with the total cost of microgrid operation containing a hybrid energy storage system as the objective function, thereby achieving optimized operation of the microgrid. Technical Effects

[0006] The present invention adopts a microgrid day-ahead operation strategy that includes renewable energy and a hybrid energy storage system, and through the mutual coordination of various forms of energy storage such as hydrogen storage, ammonia storage, natural gas storage, and battery storage, the energy storage in the microgrid has better flexibility, can more effectively respond to power load demand, absorb and utilize renewable energy, and reduce the total scheduling cost and the amount of renewable energy abandoned. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] Figure 1 It is a schematic diagram of the application scenario of the present invention;

[0008] In the figure: WT wind turbine output, PV photovoltaic output, TL tie line power purchase, LL load demand, BS battery energy storage;

[0009] Figure 2This is a schematic diagram of the changes in renewable energy output and load power demand;

[0010] Figure 3 This is a schematic diagram of electricity price changes;

[0011] Figure 4 This is a schematic diagram of the power output of microgrid equipment;

[0012] Figure 5 Schematic diagram of energy status of each energy storage system;

[0013] Figure 6 It is a flow chart of the present invention. DETAILED DESCRIPTION

[0014] like Figure 1 As shown, this embodiment relates to a method for collaborative optimization operation of a low-carbon microgrid considering multiple energy storages, including:

[0015] Step 1: Modeling of multi-energy storage system, including:

[0016] a) Hydrogen energy storage system model, including:

[0017] Model of hydrogen energy stored in a hydrogen storage tank

[0018] The lifetime decay cost corresponding to the power fluctuation of hydrogen energy storage in T scheduling periods per day

[0019] Start-up costs of hydrogen electrolysis and hydrogen fuel cells

[0020] Closure costs for hydrogen electrolysis and hydrogen fuel cells

[0021] Maintenance costs of hydrogen electrolysis equipment and hydrogen fuel cells Where: t is the sequence number of the scheduling period, represents the amount of hydrogen energy stored in the scheduling period t, η ED and η FC are the energy conversion efficiency of the electrolytic hydrogen production device and the hydrogen fuel cell, P t ED , P t HFC are the electric powers of the hydrogen electrolysis device and the hydrogen fuel cell respectively, Δt is the time span of the scheduling period; ν λ_FL is the life attenuation cost corresponding to the unit power fluctuation of the electrolytic hydrogen production device and the hydrogen fuel cell, P t λ The electric power of the hydrogen electrolysis device and hydrogen fuel cell, is the start and stop status of the electrolytic hydrogen production device and the hydrogen fuel cell, ED and FC are the electrolytic hydrogen production device and the hydrogen fuel cell respectively; ν λ_U and ν λ_D are the unit start-up and shutdown costs of the electrolytic hydrogen production device and the hydrogen fuel cell, and are the start and stop states of the hydrogen electrolysis device and the hydrogen fuel cell, ν λ_M It is the unit time maintenance cost of the hydrogen electrolysis device and hydrogen fuel cell.

[0022] b) Ammonia energy storage system model, including:

[0023] Ammonia energy stored in ammonia tank in: is the energy contained in the ammonia storage tank during the scheduling period t, η AD is the energy conversion efficiency of the ammonia synthesis unit, η AFC is the energy conversion efficiency of the ammonia fuel cell, P t AD is the electrical power of the ammonia synthesis unit, P t AFC is the electrical power of the ammonia synthesis unit.

[0024] c) Natural gas energy storage system, including:

[0025] Energy contained in natural gas tanks in: is the energy contained in the natural gas storage tank, η MD is the energy conversion efficiency of the methanogenation device, η NGG is the energy conversion efficiency of the gas turbine, P t MD is the electrical power of the methanogenation device, P t NGG is the electrical power of the gas turbine.

[0026] d) Battery energy storage system, including:

[0027] Relationship between energy stored in battery energy storage and charging and discharging power in: is the energy stored in the battery, η BC and η BD are the energy conversion efficiency during the charging and discharging process of electrochemical energy storage, P t BC and P t BD are the electrical power during charging and discharging, respectively.

[0028] Attenuation cost corresponding to the charge and discharge cycle of electrochemical energy storage Where: BCLis the unit cycle cost.

[0029] Step 2: Coordinated optimization of microgrids containing hybrid energy storage systems: Based on the mathematical model of the multi-energy storage system established in step 1 and combined with the microgrid operation mechanism, a coordinated optimization model of the microgrid system is constructed.

[0030] The coordinated optimization model of the microgrid system described above takes minimizing the daily operating cost as the objective function, specifically: minimizing the total cost minC of the microgrid operation containing the hybrid energy storage system Overall =C TL +C λ_FL +C λ_U +C λ_D +C λ_M +C U +C D , including: the cost of purchasing electricity from the main grid Cutting the cost of renewable energy output Carbon trading costs is the main grid time-of-use electricity price, P t TL is the tie line power; ν U_PV , ν U_WT Respectively reduce the cost of photovoltaic and wind power, P t PV _ max , P t WT _ max , P t PV , P t WT are the maximum and actual output of photovoltaic and wind power, respectively, D is the carbon trading price, E L is the carbon emission quota per unit of electricity generated, is the carbon emission per unit of electricity generated by the methanogenation device, is the carbon emission per unit of electricity generated by the gas turbine.

[0031] The constraints of the coordinated optimization model of the microgrid system include:

[0032] Constraints on energy stored in energy storage devices

[0033] Charging power constraints of energy storage devices

[0034] Discharge power constraints of energy storage equipment

[0035] Charging and discharging operation time constraints

[0036] Minimum on-time constraints for electrochemical energy storage and fuel cells

[0037] Minimum off-time constraints for electrochemical energy storage and fuel cells

[0038] in: Indicates the upper and lower limits of the energy contained in the energy storage device. Indicates the charging and discharging status, Indicates the maximum and minimum values ​​of charge and discharge;

[0039] The output constraints of renewable energy equipment, namely wind turbines and photovoltaics, include: t WT_min ≤P t WT ≤P t WT_max , P t PV_min ≤P t PV ≤P t PV_max , where: P t WT_min , P t WT_max Respectively represent the upper and lower limits of the wind power generation device power, P t PV_min , P t PV_max They respectively represent the upper and lower limits of the power of the photovoltaic power generation device.

[0040] The power flow constraints in the system include: power balance relationship constraints in the microgrid P t TL +P t FC +P t AFC +P t NCG +P t BD +P t WT +P t PV -P t ED -P t AD -P t MD -P t BC -P t LL =0, Where: Pt LL represents the load demand of the low-carbon microgrid, P t TL Indicates the interconnection line power purchased from the main power grid when energy is insufficient.

[0041] Step 3: Construct a dispatching mode including a hybrid energy storage system:

[0042] The microgrid includes a hydrogen energy storage system, an ammonia energy storage system, a natural gas energy storage system, and a battery energy storage system. The remaining electric energy of the new energy unit's electric energy supply load is stored in multiple ways through water electrolysis to produce hydrogen and further ammonia synthesis, methanation, and battery energy storage. When the output of new energy is less than the load demand, the stored energy is supplied to the power load in multiple forms such as hydrogen, ammonia fuel cells, natural gas generators, and battery energy storage. The remaining shortfall is supplemented by the upper power grid through transmission lines. In the process of energy scheduling, the daily operating cost is minimized as the optimization goal, and the solver is called to solve the problem to obtain the output value of the microgrid equipment containing the hybrid energy storage system at each moment.

[0043] Compared with the microgrid dispatching mode that only includes battery energy storage systems or hydrogen energy storage systems, the microgrid dispatching mode that includes hybrid energy storage systems comprehensively considers hydrogen energy storage, ammonia energy storage, natural gas energy storage and battery energy storage. It has a larger storage scale, higher flexibility, and full mobilization of a variety of flexible resources. It can effectively meet the charging and discharging needs brought about by changes in source and load, increase the consumption of new energy, and reduce operating costs.

[0044] After specific practical experiments, Figure 1 In the low-carbon microgrid with renewable energy power generation equipment shown in the figure, an optimization scheduling model is established with the goal of minimizing the total operating cost of the microgrid including reducing the output cost of renewable energy and the carbon trading cost. The YALMIP and GUROBI solvers in MATLAB are called to solve the problem, and the daily operation scheduling strategy of the low-carbon microgrid is obtained.

[0045] like Figure 1 As shown in the figure, the low-carbon microgrid system includes: renewable energy power generation equipment wind turbines and photovoltaics, upper grid transmission lines, power loads, battery energy storage, and a variety of energy storage systems that convert electrical energy into other forms of energy. The hydrogen energy storage system includes a water electrolysis device, a hydrogen storage tank, and a hydrogen fuel cell. The ammonia energy storage system includes: a synthetic ammonia device that produces ammonia from hydrogen, an ammonia storage tank, and an ammonia fuel cell. The natural gas energy storage includes: a methanogenization device, a natural gas tank, and a natural gas generator. Figure 2 As shown in the figure, it is the output of the new energy unit and the power demand of the power load, such as Figure 3 The figure shows the electricity price curve at each time.

[0046] 1) Hybrid energy storage system dispatch results: The dispatch results of the low-carbon microgrid including the hybrid energy storage system are as follows: Figure 4 and Figure 5 shown. Figure 4 As shown, it is the power output of each device in the microgrid. Figure 5 As shown in the figure, the state of charge of hydrogen storage, battery energy storage, ammonia storage, and natural gas energy storage systems. As can be seen from the figure, the demand for power load is mainly supplied by wind turbines and photovoltaics. When the electricity price is low, more electricity is purchased from the main grid, while when the electricity price is high, more energy storage is used. Hydrogen energy storage has high power and is relatively stable because hydrogen has a high energy density and can be flexibly converted between electricity, ammonia, and natural gas. The state of charge of battery energy storage changes frequently because compared with other energy storage methods, battery energy storage has a fast response time and low start-stop cost, which can balance frequent power fluctuations.

[0047] 2) Comparison of different dispatching modes: As shown in Table 1, the implementation effect of the present invention is compared with the effect of the existing dispatching mode. Mode 1 does not include any energy storage, Mode 2 includes battery energy storage scheduling, and Mode 3 includes hydrogen energy storage scheduling. The proposed dispatching mode that takes into account hydrogen energy storage, ammonia energy storage, natural gas energy storage and battery energy storage is Mode 4. Compared with the existing microgrid dispatching modes 1 to 3, the dispatching mode proposed by the present invention reduces the amount of renewable energy abandoned by 71.08%, 69.69%, and 28.30%, respectively, and reduces the operating costs by 23.49%, 21.39%, and 4.43%, respectively.

[0048] Table 1 Comparison of different scheduling modes

[0049] Compared with the existing technology, this method can achieve the lowest daily operating cost and renewable energy abandonment by adding a hybrid energy storage system to the low-carbon microgrid and establishing a corresponding optimization scheduling model strategy compared with the low-carbon microgrid scheduling mode without an energy storage system or only considering a battery or hydrogen energy storage system.

[0050] The above-mentioned specific implementation can be partially adjusted in different ways by those skilled in the art without departing from the principle and purpose of the present invention. The protection scope of the present invention shall be based on the claims and shall not be limited by the above-mentioned specific implementation. Each implementation scheme within its scope shall be subject to the constraints of the present invention.

Claims

1. A method for the coordinated optimization operation of multiple energy storages for low-carbon microgrids, characterized in that: Through the modeling of multiple energy storage systems, electricity-hydrogen energy, electricity-ammonia energy, and electricity-natural gas conversion devices are introduced. The coordinated optimization model of the microgrid system is constructed with the objective function of minimizing the total cost of microgrid operation containing hybrid energy storage systems. Four different scheduling modes are constructed, including: excluding energy storage equipment, including battery energy storage, including hydrogen energy storage, and including hybrid energy storage systems to achieve optimized operation.

2. The method for coordinated optimization operation of low-carbon microgrids considering multiple energy storage according to claim 1 is characterized in that: The multi-energy storage system modeling specifically includes: a) Hydrogen energy storage system model, including: Model of hydrogen energy stored in a hydrogen storage tank The lifetime decay cost corresponding to the power fluctuation of hydrogen energy storage in T scheduling periods per day Start-up costs of hydrogen electrolysis and hydrogen fuel cells Closure costs for hydrogen electrolysis and hydrogen fuel cells Maintenance costs of hydrogen electrolysis equipment and hydrogen fuel cells Where: t is the sequence number of the scheduling period, represents the amount of hydrogen energy stored in the scheduling period t, η ED and η FC are the energy conversion efficiency of the electrolytic hydrogen production device and the hydrogen fuel cell, P t ED , P t HFC are the electric powers of the hydrogen electrolysis device and the hydrogen fuel cell respectively, Δt is the time span of the scheduling period; ν λ_FL is the life attenuation cost corresponding to the unit power fluctuation of the electrolytic hydrogen production device and the hydrogen fuel cell, P t λ The electric power of the hydrogen electrolysis device and hydrogen fuel cell, is the start and stop status of the electrolytic hydrogen production device and the hydrogen fuel cell, ED and FC are the electrolytic hydrogen production device and the hydrogen fuel cell respectively; ν λ_U and ν λ_D are the unit start-up and shutdown costs of the electrolytic hydrogen production device and the hydrogen fuel cell, and are the start and stop states of the hydrogen electrolysis device and the hydrogen fuel cell, ν λ_M The unit time maintenance cost of the electrolytic hydrogen production device and hydrogen fuel cell; b) Ammonia energy storage system model, including: Ammonia energy stored in ammonia tank in: is the energy contained in the ammonia storage tank during the scheduling period t, η AD is the energy conversion efficiency of the ammonia synthesis unit, η AFC is the energy conversion efficiency of the ammonia fuel cell, P t AD is the electrical power of the ammonia synthesis unit, P t AFC is the electrical power of the ammonia synthesis unit; c) Natural gas energy storage system, including: Energy contained in natural gas tanks in: is the energy contained in the natural gas storage tank, η MD is the energy conversion efficiency of the methanogenation device, η NGG is the energy conversion efficiency of the gas turbine, P t MD is the electrical power of the methanogenation device, P t NGG is the electrical power of the gas turbine; d) Battery energy storage system, including: Relationship between energy stored in battery energy storage and charging and discharging power in: is the energy stored in the battery, η BC and η BD are the energy conversion efficiency during the charging and discharging process of electrochemical energy storage, P t BC and P t BD are the electrical power during charging and discharging, respectively; Attenuation cost corresponding to the charge and discharge cycle of electrochemical energy storage Where: BCL is the unit cycle cost.

3. The method for coordinated optimization operation of low-carbon microgrids considering multiple energy storage according to claim 1 is characterized in that: The coordinated optimization model of the microgrid system is constructed based on the mathematical model of the multi-energy storage system and the microgrid operation mechanism. The model takes minimizing the daily operating cost as the objective function, specifically: minimizing the total cost minC of the microgrid operation containing the hybrid energy storage system Overall =C TL +C λ_FL +C λ_U +C λ_D +C λ_M +C U +C D , including: the cost of purchasing electricity from the main grid Cutting the cost of renewable energy output Carbon trading costs μ t TL is the main grid time-of-use electricity price, P t TL is the tie line power; ν U_PV , ν U_WT Reducing the cost of photovoltaic and wind power, P t PV _ max , P t WT _ max , P t PV , P t WT are the maximum and actual output of photovoltaic and wind power, respectively, D is the carbon trading price, E L is the carbon emission quota per unit of electricity generated, is the carbon emission per unit of electricity generated by the methanogenation device, is the carbon emission per unit of electricity generated by the gas turbine.

4. The method for coordinated optimization operation of low-carbon microgrids considering multiple energy storage according to claim 3 is characterized in that: The constraints of the coordinated optimization model of the microgrid system include: Constraints on energy stored in energy storage devices Charging power constraints of energy storage devices Discharge power constraints of energy storage equipment Charging and discharging operation time constraints Minimum on-time constraints for electrochemical energy storage and fuel cells Minimum off-time constraints for electrochemical energy storage and fuel cells in: Indicates the upper and lower limits of the energy contained in the energy storage device. Indicates the charging and discharging status, Indicates the maximum and minimum values ​​of charge and discharge; The output constraints of renewable energy equipment, namely wind turbines and photovoltaics, include: t WT_min ≤P t WT ≤P t WT_max , P t PV_min ≤P t PV ≤P t PV_max , where: P t WT_min , P t WT_max Respectively represent the upper and lower limits of the wind power generation device power, P t PV_min , P t PV _max They represent the upper and lower limits of the power of the photovoltaic power generation device respectively; The power flow constraints in the system include: power balance constraint P in the microgrid t TL +P t FC +P t AFC +P t NCG +P t BD +P t WT +P t PV -P t ED -P t AD -P t MD -P t BC -P t LL =0, Where: P t LL represents the load demand of the low-carbon microgrid, P t TL Indicates the interconnection line power purchased from the main power grid when energy is insufficient.

5. The method for coordinated optimization operation of low-carbon microgrids considering multiple energy storage according to claim 3 is characterized in that: The dispatching mode means that the microgrid includes a hydrogen energy storage system, an ammonia energy storage system, a natural gas energy storage system and a battery energy storage system. The remaining electric energy of the electric energy supply load of the new energy unit is stored in multiple ways through water electrolysis to produce hydrogen and further synthesize ammonia, methanation and battery energy storage. When the output of new energy is less than the load demand, the stored energy is supplied to the power load in multiple forms such as hydrogen, ammonia fuel cells, natural gas generators and battery energy storage. The remaining insufficient part is supplemented by the upper power grid through the transmission line. In the process of energy dispatching, the daily operating cost is minimized as the optimization goal, and the solver is called to solve to obtain the output value of the microgrid equipment containing the hybrid energy storage system at each moment.

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