Distributed renewable energy system based on methanol energy storage and configuration optimization method

Abstract

The invention discloses a distributed renewable energy system based on methanol energy storage and a configuration optimization method. The system comprises a photovoltaic unit, a battery, a P2M module, a methanol storage tank, an M2P module, an ORC module and a power grid power supply. The photovoltaic unit converts solar energy into electric energy and directly supplies power to users. According to the configuration optimization method, the minimum leveling cost of kilowatt-hour electricity, the minimum carbon emission factor of kilowatt-hour electricity and the minimum electricity abandoning rate are taken as optimization targets, and the installed capacity of a photovoltaic storage tank, a battery storage tank and a methanol storage tank is taken as decision variables; performing configuration optimization solution on the system to obtain an optimal configuration scheme of each device; according to the system, methanol rectification and hydrogen purification processes are not needed, the system flow is simplified, and the system investment is reduced; according to the method, the system flow can be simplified, the consumption of renewable energy sources is improved, and the operation cost is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of distributed energy systems, and in particular to a distributed renewable energy system based on methanol energy storage and a configuration optimization method. Background Art

[0002] Due to its high dependence on weather conditions, the ratio of peak output to average output of photovoltaic systems is relatively high, ranging from about 4:1 to 10:1, resulting in a high power abandonment rate. The rapid increase in the proportion of renewable energy has brought major challenges in maintaining the stability and reliability of renewable energy systems. In this regard, long-term energy storage technology, characterized by long-term continuous energy supply and large capacity, has shown great potential in alleviating diurnal and even seasonal fluctuations in renewable energy.

[0003] The solar distributed energy system based on methanol storage mainly includes photovoltaic, PEM (Proton Exchange Membrane) electrolyzer, methanol synthesis, methanol reforming, high-temperature PEM fuel cell and tail gas boiler. The system can avoid methanol distillation and hydrogen purification process, thereby reducing energy loss and investment cost. However, due to the diverse reaction temperature ranges of each reaction module, it is still a challenge to improve the round-trip efficiency through effective system coupling. Summary of the invention

[0004] Purpose of the invention: The purpose of the present invention is to provide a distributed renewable energy system based on methanol energy storage and a configuration optimization method, which can simplify the system process and reduce system investment, and achieve minimum electricity cost, minimum carbon emissions and minimum power abandonment rate.

[0005] Technical solution: A distributed renewable energy system based on methanol energy storage, including photovoltaic units, batteries, P2M modules, methanol storage tanks, M2P modules, ORC modules and grid power supply; The photovoltaic unit converts solar energy into electrical energy, directly supplying electricity to users: When the power of the photovoltaic unit is greater than the user's power load, the excess photovoltaic power is first stored in the battery; if the battery is full, the excess photovoltaic power is converted into methanol through the P2M module and stored; if the methanol storage tank is full and there is still excess photovoltaic power, the excess power will be regarded as abandoned power; When the photovoltaic power is less than the user's power load, the battery will first supplement the power supply; if the battery power is insufficient, the methanol solution in the methanol storage tank will provide power through the M2P module and the ORC module at the same time; if the methanol is exhausted and the power demand is still not met, the power grid will provide power.

[0006] Furthermore, the P2M module includes a booster water pump, a first preheater, an electrolyzer, a compressor, a mixer, a second preheater, a reactor, a cooler, a first gas-liquid separator, a pressure reducing valve and a second gas-liquid separator. After the water is pressurized by the booster water pump, it is input into the second preheater for preheating, and the preheated water is electrolyzed into oxygen and hydrogen in the electrolyzer; the hydrogen is pressurized to 50 bar by the compressor and mixed with the circulating gas in the mixer; the mixed gas is preheated to 230°C in the second preheater; the mixed gas undergoes a methanol synthesis reaction in the reactor; the synthesis gas generated in the reactor first heats the mixed gas in the second preheater, then is cooled in the cooler, and then the unreacted gas is separated by the first gas-liquid separator, and the unreacted gas is returned to the mixer; the liquid methanol solution separated by the first gas-liquid separator is firstly reduced in pressure by the pressure reducing valve, and then enters the second gas-liquid separator.

[0007] Furthermore, the second gas-liquid separator further separates the unreacted gas, a part of the gas is discharged through the vent valve, and the remaining gas is pressurized by the compressor and returned to the mixer; the liquid methanol solution separated by the second gas-liquid separator enters the methanol storage tank for storage and is used in the methanol reforming hydrogen production process in the M2P module.

[0008] Furthermore, the reaction temperature of the methanol synthesis reaction is 250-300° C., and the reaction pressure is 1-50 bar.

[0009] Furthermore, the M2P module includes a third preheater, a methanol reforming reactor, a HT-PEMFC, a fourth preheater and a burner. The methanol solution in the methanol storage tank is heated to 180°C in the third preheater, and then enters the methanol reforming reactor for a methanol reforming reaction to generate a reformed gas; the reformed gas enters the anode of the HT-PEMFC as a fuel, and the air is heated to 180°C through the fourth preheater and enters the HT-PEMFC, and the reformed gas and air undergo an electrochemical reaction to generate electrical energy and thermal energy; the generated electrical energy is used to meet the user's electrical load, and the thermal energy is used to drive the ORC module to generate electricity; the unreacted gas in the HT-PEMFC enters the burner and burns to generate high-temperature flue gas, and the high-temperature flue gas provides heat for the third preheater and the methanol reforming reactor.

[0010] Furthermore, the ORC module includes a working fluid pump, an evaporator, a turbine and a condenser. The ethanol working fluid enters the evaporator after being pressurized by the working fluid pump and is vaporized into a gaseous state; the high-pressure gaseous working fluid expands in the turbine and converts its internal energy into mechanical energy; the low-pressure exhaust gas of the turbine enters the condenser, is cooled into a liquid state, and enters the working fluid pump again.

[0011] Any of the above-mentioned configuration optimization methods for distributed renewable energy systems comprises the following steps: S1, input hourly meteorological parameters and hourly electric load data to obtain hourly photovoltaic power generation; S2, based on distributed renewable energy systems, takes the minimum power abandonment rate, minimum levelized cost per kilowatt-hour and minimum carbon emission factor per kilowatt-hour as optimization objectives, and uses non-genetic optimization algorithms to carry out system configuration optimization to obtain the optimal solution set; S3, substitute each solution in the optimal solution set into the decision plan one by one, and take the solution with the minimum value of the decision plan as the optimal solution.

[0012] Furthermore, the hourly power generation of photovoltaic The expression is as follows: , where is the installed capacity of the PV unit; The definition of curtailment rate CR is as follows: , where is the annual power abandonment of the system; is the annual photovoltaic power generation; The definition of levelized cost of electricity (LCOE) is as follows: , , where Indicates the total annual expenses; is the electrical load; is the investment cost of the kth component; is the maintenance factor; is the return on investment; is the life of the system; is the specific price of grid electricity; Indicates the amount of electricity provided by the power grid; The definition of carbon emission factor (CEF) per kilowatt-hour is as follows: , where is the carbon emission factor of the power grid; The optimization objective function is expressed as: , ,in, , Represent the installed capacity of batteries and methanol storage tanks respectively.

[0013] Furthermore, the decision-making scheme uses the square weighted sum method, which is expressed as: , where: is the optimization target, i=1,2,3 represent the levelized cost per kilowatt-hour, carbon emission factor per kilowatt-hour and power abandonment rate respectively; is the weight factor of each optimization objective; is the ideal target value.

[0014] Compared with the prior art, the present invention has the following significant effects: 1. The distributed renewable energy system constructed by the present invention does not require methanol distillation in the methanol reforming hydrogen production process by adopting the P2M module; the reformed gas in the M2P module is directly used as fuel to enter the anode of the HT-PEMFC without the need to purify the hydrogen, which simplifies the system process and reduces the system investment; on the other hand, the system adopts the organic Rankine cycle (ORC) subsystem to recover the heat of the methanol synthesizer and the fuel cell, provide additional electricity, and realize the efficient utilization of waste heat; 2. The configuration optimization method of the present invention can obtain the optimal system configuration according to the benefit requirements of the distributed photovoltaic energy system in terms of energy, economy and carbon emissions, realize cross-seasonal energy storage, and have a lower cost per kilowatt-hour, carbon emissions and power abandonment rate. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 is a system schematic diagram of the present invention; Figure 2 It is a schematic diagram of the specific operation process of the P2M module; Figure 3 This is a schematic diagram of the operation process of the M2P module; Figure 4 This is a schematic diagram of the operation process of the ORC module; Figure 5 is a flow chart of the configuration optimization method of the present invention; Figure 6 It is a schematic diagram of the optimal solution set obtained by the configuration optimization method; Figure 7 This is a schematic diagram of methanol reserves hourly throughout the year. DETAILED DESCRIPTION

[0016] The present invention is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0017] like Figure 1 The figure shows a schematic diagram of a distributed renewable energy system based on methanol energy storage, which includes a photovoltaic unit, a battery, a P2M (Power-to-Methanol) module, a methanol storage tank, an M2P (Methanol-to-Power) module, an ORC module, and a grid power supply.

[0018] The energy dispatching method of the distributed renewable energy system is as follows: the photovoltaic unit converts solar energy into electrical energy, which can directly supply electricity to users. When the power of the photovoltaic unit is greater than the user's electrical load, the excess photovoltaic power is first stored in the battery; if the battery is full, the excess photovoltaic power is converted into methanol through the P2M module and stored; if the methanol storage tank is full and there is still excess photovoltaic power, the excess power will be regarded as abandoned power. When the photovoltaic power is less than the user's electrical load, the battery will first supplement the power supply; if the battery is insufficient, the methanol solution in the methanol storage tank will provide power simultaneously through the M2P module and the ORC module; if the methanol is exhausted and the power demand is still not met, the power grid will serve as the last guarantee.

[0019] Figure 2 The specific operation process diagram of the P2M module is shown in FIG. 1 . Water is pressurized to 30 bar by a booster pump 1 and then preheated to 60°C by a first preheater 2. In the electrolyzer 3, water is electrolyzed into oxygen and hydrogen. Hydrogen is pressurized to 50 bar by a compressor 4 and mixed with the circulating gas in a mixer 5. The mixed gas is preheated to 230°C in a second preheater 6. The mixed gas undergoes a methanol synthesis reaction in a reactor 7 at a reaction temperature of 250 to 300°C and a reaction pressure of 1 to 50 bar. The synthesis gas produced by the reactor 7 first heats the mixed gas in the second preheater 6 and then is cooled to 50°C in a cooler 8. The synthesis gas is separated from the unreacted gas by a first gas-liquid separator 9 and returned to the mixer 5. The liquid methanol solution separated by the first gas-liquid separator 9 is first reduced in pressure by a pressure reducing valve 10 and then enters a second gas-liquid separator 11. The second gas-liquid separator 11 further separates the unreacted gas. Part of the gas is discharged through the vent valve 12, and the remaining gas is pressurized by the compressor 13 and returned to the mixer 5. The liquid methanol solution separated by the second gas-liquid separator 11 enters the methanol storage tank 14 for storage. The molar composition of methanol and water in the methanol solution is about 1:1, which can be directly used in the methanol reforming hydrogen production process in the M2P module without the need for methanol distillation, simplifying the system process and reducing system investment.

[0020] Figure 3Schematic diagram of the operation process of the M2P module. The methanol solution in the methanol storage tank 14 can be directly used as a raw material for the methanol reforming hydrogen production process without additional water supplementation. First, it is heated to 180°C in the third preheater 21, and then enters the methanol reforming reactor 22 for methanol reforming reaction to produce reformed gas. Since the HT-PEMFC (high temperature proton exchange membrane fuel cell) 23 has a certain tolerance to CO, the reformed gas directly enters the anode of the HT-PEMFC 23 as fuel without the need to purify the hydrogen. The air is heated to 180°C through the fourth preheater 24, and the reformed gas entering the HT-PEMFC 23 reacts electrochemically with the air to generate electricity and heat. The generated electricity is used to meet the user's electrical load, and the heat is used to drive the ORC module to generate electricity. The unreacted gas in the HT-PEMFC 23 enters the burner 25 for combustion, generating high-temperature flue gas. The high-temperature flue gas provides heat for the third preheater 21 and the methanol reforming reactor 22.

[0021] Figure 4 Schematic diagram of the operation process of the ORC module. Ethanol with a critical temperature of 240.85°C is selected as the working fluid. The ethanol working fluid is pressurized by the working fluid pump 31 and enters the evaporator 32 to be vaporized into a gaseous state. The high-pressure gaseous working fluid expands in the turbine generator 33 to convert the internal energy into electrical energy. The low-pressure exhaust gas of the turbine generator 33 enters the condenser 34, is cooled to a liquid state, and enters the working fluid pump 31 again.

[0022] Figure 5 This is a flow chart of a distributed renewable energy system configuration optimization method based on methanol energy storage. The implementation steps are as follows: Step 1: Input hourly meteorological parameters (including temperature and solar radiation intensity) and hourly electrical load data to obtain the hourly photovoltaic power generation. ,as follows: , where is the installed capacity of the PV unit.

[0023] Step 2, based on Figure 1 The distributed renewable energy system designed in the paper is optimized by system configuration to obtain the optimal solution set.

[0024] The objectives of the distributed renewable energy system configuration optimization method include minimum power curtailment rate, minimum levelized cost of electricity, and minimum carbon emission factor per kilowatt-hour.

[0025] The definition of curtailment rate CR is as follows: (1) Where: is the annual power abandonment of the system; is the annual photovoltaic power generation.

[0026] The definition of levelized cost of electricity (LCOE) is as follows: (2) (3) Where: Indicates the total annual expenses; is the electrical load; is the investment cost of the kth component; is the maintenance factor, which is 3%; is the return on investment, which is 6%; is the life of the system, which is 20 years in this embodiment; is the specific price of grid electricity, which is $0.136 / kWh; Indicates the amount of electricity provided by the power grid.

[0027] The definition of carbon emission factor (CEF) per kilowatt-hour is as follows: (4) Where: is the carbon emission factor of the grid, which is 0.968 kg / kWh.

[0028] Based on the three performance indicators of minimum power abandonment rate, minimum levelized cost per kilowatt-hour and minimum carbon emission factor per kilowatt-hour, the optimization objective function can be expressed as: (5) The installed capacity of photovoltaic units, batteries and methanol storage tanks is used as the decision variable of the optimization method, which can be expressed as: (6) in, , Represent the installed capacity of batteries and methanol storage tanks respectively.

[0029] Among them, the optimization algorithm uses the non-dominated sorting genetic algorithm, and the optimal front-end individual coefficient, population size and maximum number of evolutionary iterations are set to 0.3, 0.4 and 200 respectively.

[0030] The decision-making scheme uses the square weighted sum method, which can be expressed as: (5) Where: is the optimization target, i=1,2,3 represent the levelized cost per kilowatt-hour, carbon emission factor per kilowatt-hour and power abandonment rate respectively; is the weight factor of each optimization objective, and the weights of the three objective functions are [1 / 3 1 / 3 1 / 3] respectively; is the ideal target value. The ideal target values ​​of the three objective functions are [0.1 0.01 0.001] respectively.

[0031] Follow the above steps to solve and get the optimal solution set, such as Figure 6 As shown. Figure 6 It can be seen that the trends of LCOE and CEF show that although a larger PV power supply can reduce carbon emissions, it will also lead to an increase in LCOE. As LCOE increases, the rate of decline of CEF slows down, while the curtailment rate CR increases significantly. This shows that an overly large system configuration will reduce the effect of CEF and lead to increased system investment and serious PV curtailment.

[0032] Step 3: Based on the decision plan selected in step 2, substitute each solution in the optimal solution set into formula (5) one by one. The solution that makes formula (5) obtain the minimum value is the optimal solution.

[0033] According to the decision-making method of the present invention, the optimal configurations for the two cases are listed in Table 1. In contrast, Case 1 achieves lower LCOE (0.2078 $ / kWh), CEF (0.0429 kg / kWh), and CR (0.82%) with a smaller PV capacity and a larger methanol storage tank. In Case 2, the larger PV capacity leads to increased investment and more power abandonment. Under the same power load, the LCOE of Case 2 is 14.7% higher than that of Case 1.

[0034] Table 1 Optimal configuration of distributed renewable energy systems

[0035] Figure 7 is the hourly methanol reserves throughout the year. In Case 1, the solar radiation intensity gradually increased in the first half of the year, and the excess photovoltaic energy was stored through the P2M process, resulting in a continuous increase in methanol reserves and reaching a peak at the 5466th hour. In the second half of the year, the solar radiation intensity gradually decreased. The stored methanol was used to supplement the electricity supply through the M2P process, resulting in a continuous decrease in methanol reserves until it was exhausted at the 8284th hour. However, in Case 2, due to the lack of obvious patterns in solar radiation intensity, methanol reserves showed continuous fluctuations: in the first half of the year, methanol inventories dropped to zero several times; in the second half of the year, methanol inventories gradually increased. In both cases, the trend of methanol storage over time indicates that methanol is suitable for long-term energy storage.

Claims

1. A distributed renewable energy system based on methanol energy storage, characterized in that: It includes photovoltaic units, batteries, P2M modules, methanol storage tanks, M2P modules, ORC modules and grid power supply; The photovoltaic unit converts solar energy into electrical energy, directly supplying electricity to users: When the power of the photovoltaic unit is greater than the user's power load, the excess photovoltaic power is first stored in the battery; if the battery is full, the excess photovoltaic power is converted into methanol through the P2M module and stored; if the methanol storage tank is full and there is still excess photovoltaic power, the excess power will be regarded as abandoned power; When the photovoltaic power is less than the user's electrical load, the battery first supplements the power supply; If the battery power is insufficient, the methanol solution in the methanol storage tank provides electricity through the M2P module and the ORC module simultaneously; if the methanol is exhausted and the power demand is still not met, the power grid provides electricity.

2. The distributed renewable energy system based on methanol energy storage according to claim 1 is characterized in that: The P2M module comprises a booster water pump (1), a first preheater (2), an electrolyzer (3), a compressor (4), a mixer (5), a second preheater (6), a reactor (7), a cooler (8), a first gas-liquid separator (9), a pressure reducing valve (10) and a second gas-liquid separator (10). After being pressurized by the booster water pump (1), water is input into the second preheater (2) for preheating. The preheated water is electrolyzed into oxygen and hydrogen in the electrolyzer (3); the hydrogen is pressurized to 50 bar, and mixed with the circulating gas in the mixer (5); the mixed gas is preheated to 230°C in the second preheater (6); the mixed gas undergoes a methanol synthesis reaction in the reactor (7); the synthesis gas produced in the reactor (7) is first heated in the second preheater (6) to heat the mixed gas, then cooled in the cooler (8), and then separated from the unreacted gas by the first gas-liquid separator (9), and the unreacted gas is returned to the mixer (5); the liquid methanol solution separated by the first gas-liquid separator (9) is first reduced in pressure by the pressure reducing valve (10), and then enters the second gas-liquid separator (11).

3. The distributed renewable energy system based on methanol energy storage according to claim 2 is characterized in that: The second gas-liquid separator (11) further separates the unreacted gas, a portion of which is discharged through the vent valve (12), and the remaining gas is pressurized by the compressor (13) and returned to the mixer (5); the liquid methanol solution separated by the second gas-liquid separator (11) enters the methanol storage tank (14) for storage and is used in the methanol reforming hydrogen production process in the M2P module.

4. The distributed renewable energy system based on methanol energy storage according to claim 2 is characterized in that: The reaction temperature of the methanol synthesis reaction is 250-300°C, and the reaction pressure is 1-50 bar.

5. The distributed renewable energy system based on methanol energy storage according to claim 1 is characterized in that: The M2P module comprises a third preheater (21), a methanol reforming reactor (22), a HT-PEMFC (23), a fourth preheater (24) and a burner (25). The methanol solution in the methanol storage tank (14) is heated to 180° C. in the third preheater (21) and then enters the methanol reforming reactor (22) for a methanol reforming reaction to generate a reformed gas. The reformed gas enters the anode of the HT-PEMFC (23) as a fuel. The air is heated to 180° C. through the fourth preheater (24) and enters the HT-PEMFC (23). The reformed gas and the air undergo an electrochemical reaction to generate electrical energy and thermal energy. The generated electrical energy is used to meet the user's electrical load, and the thermal energy is used to drive the ORC module to generate electricity. The unreacted gas in the HT-PEMFC (23) enters the burner (25) for combustion to generate high-temperature flue gas. The high-temperature flue gas provides heat for the third preheater (21) and the methanol reforming reactor (22).

6. The distributed renewable energy system based on methanol energy storage according to claim 1 is characterized in that: The ORC module comprises a working fluid pump (31), an evaporator (32), a turbine (33) and a condenser (34); the ethanol working fluid is pressurized by the working fluid pump (31) and enters the evaporator (32) to be vaporized into a gaseous state; the high-pressure gaseous working fluid expands in the turbine (33) to convert internal energy into mechanical energy; the low-pressure exhaust gas from the turbine (33) enters the condenser (34), is cooled into a liquid state, and enters the working fluid pump (31) again.

7. The configuration optimization method of a distributed renewable energy system according to any one of claims 1 to 6, characterized in that: The steps include: S1, input hourly meteorological parameters and hourly electric load data to obtain hourly photovoltaic power generation; S2, based on distributed renewable energy systems, takes the minimum power abandonment rate, minimum levelized cost per kilowatt-hour and minimum carbon emission factor per kilowatt-hour as optimization objectives, and uses non-genetic optimization algorithms to carry out system configuration optimization to obtain the optimal solution set; S3, substitute each solution in the optimal solution set into the decision plan one by one, and take the solution with the minimum value of the decision plan as the optimal solution.

8. The configuration optimization method of the distributed renewable energy system according to claim 7, characterized in that: Photovoltaic hourly power generation The expression is as follows: , where is the installed capacity of the PV unit; The definition of curtailment rate CR is as follows: , where is the annual power abandonment of the system; is the annual photovoltaic power generation; The definition of levelized cost of electricity (LCOE) is as follows: , , where Indicates the total annual expenses; is the electrical load; is the investment cost of the kth component; is the maintenance factor; is the return on investment; is the life of the system; is the specific price of grid electricity; Indicates the amount of electricity provided by the power grid; The definition of carbon emission factor (CEF) per kilowatt-hour is as follows: , where is the carbon emission factor of the power grid; The optimization objective function is expressed as: , ,in, , Represent the installed capacity of batteries and methanol storage tanks respectively.

9. The configuration optimization method of the distributed renewable energy system according to claim 7, characterized in that: The decision-making scheme uses the square weighted sum method, which is expressed as: , where: is the optimization target, i=1,2,3 represent the levelized cost per kilowatt-hour, carbon emission factor per kilowatt-hour and power abandonment rate respectively; is the weight factor of each optimization objective; is the ideal target value.

Images