Reversible solid oxide microgrid capacity planning method based on multi-working-condition operation

Abstract

The invention provides a micro-grid capacity planning method for reversible solid oxide based on multi-working-condition operation, and the method comprises the steps: building an RSOC multi-working-condition operation model of a reversible solid oxide battery and a double-layer capacity planning model of a micro-grid; carrying out capacity planning on a new energy generator set, a reversible solid oxide battery, a hydrogen storage bank and electric heating equipment in the micro-grid; a capacity planning model at the upper layer of the double-layer planning model takes the minimum comprehensive cost of the micro-grid as a target, and a dispatching model at the lower layer of the double-layer planning model of the micro-grid takes the minimum annual operation cost and punishment cost of the island micro-grid as a target; the upper layer constraint of the micro-grid is the capacity constraint of each to-be-planned device, the lower layer constraint comprises the wind and light constraint, the RSOC constraint, the hydrogen storage reservoir constraint, the electric heating device constraint, the electric energy supply and demand balance constraint, the hydrogen energy supply and demand balance constraint and the heat energy supply and demand balance constraint, and the use efficiency of energy and the economical efficiency of system operation can be effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, in particular to a microgrid capacity planning method for reversible solid oxides based on multi-condition operation. Background Art

[0002] With the rapid development of energy storage technology, power system planning has higher requirements for improving energy use efficiency and operation flexibility. In the development of the energy system, the collaborative optimization among different energy forms has always been the core proposition of the industry development. Only the multi-energy complementarity of the mutual utilization of different energy forms can achieve the maximum energy efficiency. However, there are significant restrictions in the traditional multi-device combined heat and power system. The energy transfer between multiple devices undergoes multiple conversions, resulting in additional energy losses, reducing the energy interaction efficiency, and severely restricting the economy and operation flexibility of the multi-energy system.

[0003] The new energy storage technology can effectively improve the flexibility of the system to better cope with the volatility problem of the system under the participation of large-scale clean energy. Among current various energy storage technologies, the reversible solid oxide battery integrates a solid oxide electrolyzer and a solid oxide fuel cell, is applicable to the scenarios with a relatively high self-sufficiency rate of the microgrid or non-excessive photovoltaic power generation capacity, can achieve the bidirectional conversion between electricity and hydrogen, has a compact structure with a reduced floor area and a higher conversion efficiency, and can effectively achieve the electrical hydrogen storage when used in conjunction with a hydrogen storage tank. However, the current research on the conversion of the working state of the reversible solid oxide battery is still lacking.

[0004] To some extent, the planning result of the system for the device depends on the use efficiency of the device. Currently, there is little research on the efficiency change of the reversible solid oxide battery at home and abroad, and the relationship between the operation efficiency of the reversible solid oxide battery and factors such as the stack temperature and the net output power of the device has not been deeply explored. From the perspective of energy, fully considering the influence of the peripheral thermal auxiliary system on the thermal balance of the reversible solid oxide battery stack, considering the interactive coupling relationship between capacity configuration and operation optimization, and establishing a two-layer planning model for the microgrid to ensure the accuracy and economy of the planning result. Summary of the Invention

[0005] The present invention proposes a microgrid capacity planning method for reversible solid oxides based on multi-condition operation, which can effectively improve the energy use efficiency and the economy of system operation.

[0006] The present invention adopts the following technical solutions.

[0007] A microgrid capacity planning method for reversible solid oxides based on multi-condition operation, the planning method conducts capacity planning for new energy generating units, reversible solid oxide batteries, hydrogen storage tanks, and electric heating equipment in the microgrid by establishing an RSOC multi-condition operation model of the reversible solid oxide battery and a two-layer capacity planning model of the microgrid;

[0008] The upper-layer capacity planning model of the bi-level planning model aims to minimize the comprehensive cost of the microgrid, including the annual value of the capacity planning cost of the microgrid, the annual operating cost, and the penalty cost; the lower-layer scheduling model of the microgrid bi-level planning model aims to minimize the annual operating cost and penalty cost of the islanded microgrid;

[0009] The upper-layer constraints of the microgrid are the capacity constraints of each device to be planned, including the available capacity constraint of the wind-solar generator set, the available capacity constraint of the RSOC, the available capacity constraint of the hydrogen storage tank, and the available capacity constraint of the electric heating; the lower-layer constraints include the wind-solar constraint, the RSOC constraint, the hydrogen storage tank constraint, the electric heating equipment constraint, the power supply-demand balance constraint, the hydrogen energy supply-demand balance constraint, and the heat energy supply-demand balance constraint.

[0010] The planning method uses the whale optimization algorithm to generate an initial whale population, where each population represents a capacity configuration scheme; the CPLEX solver is called to optimize the scheduling of the microgrid system based on the multi-condition reversible solid oxide battery to obtain the optimal operation strategy;

[0011] The corresponding conditions of the RSOC multi-condition operation model include the SOFC in the power generation condition, the SOEC in the electrolysis condition, the shutdown condition, and the hot standby condition. The RSOC multi-condition operation model is expressed by the formula:

[0012]

[0013] Among them, formula 1 represents the mutually exclusive characteristics between the operating conditions of the RSOC unit; formula 2 represents that the shutdown condition and the hot standby condition of the RSOC cannot be directly converted, and the power generation condition and the electrolysis condition cannot be directly converted; formulas 3 - 4 represent the shortest working and shutdown times of the RSOC unit respectively;

[0014] The mathematical model between the net output electric power and the power generation efficiency of the RSOC multi-condition operation model under the SOFC condition is expressed by the formula:

[0015]

[0016] In the formula: and are the power generation efficiency and heat generation efficiency of the SOFC at time t respectively; is the unit net power generation of the SOFC; is the nominal electric power of the SOFC; are the net power generation and heat generation power of the SOFC at time t respectively; is the hydrogen consumption mass of the SOFC at time t; L hv is the high calorific value of hydrogen;

[0017] When the RSOC is in the SOEC operating condition, renewable energy provides the total system power including the balance-of-plant (BOP) with peripheral thermal assistance system. Therefore, the power consumed by the BOP is very limited, only accounting for 1% of the total system power of the RSOC. At this time, the electrolysis efficiency increases with the increase of the total power, but the change range is only about 1%. Therefore, the electrolysis efficiency under the electrolysis condition is regarded as a constant. The mathematical expressions for hydrogen production and heat consumption are as follows:

[0018]

[0019] In the formula: are the power consumption and heat consumption of the SOEC at time t, respectively; is the hydrogen production mass of the SOEC at time t; is t the heat consumption coefficient of the SOEC at time is t the electrolysis efficiency of the SOEC at time

[0020] In the multi-condition operation model of the RSOC, its stack temperature is observed and controlled in real time to ensure that the RSOC is at an appropriate working temperature. Specifically:

[0021] Under the SOFC operating condition, a large amount of heat is generated by the stack reaction. The preheater in the BOP can be regarded as a heat exchanger. Therefore, the stack heat balance is determined by the total heat power heat loss brought by the environment heat power participating in heat recovery jointly. Among them, the heat recovery power is numerically equal to the heat production power

[0022] Under the SOEC operating condition, the stack reaction absorbs heat. The preheater in the BOP can be regarded as a heater to provide heat for the stack to maintain the working temperature. Therefore, the stack heat balance is determined by the heat power consumed by the stack reaction heat loss heat power provided by the BOP jointly; among them, the heat consumption power of the stack reaction is numerically equal to the heat consumption power The relationship between the stack temperature evolution and heat balance of the RSOC considering the BOP is expressed as follows:

[0023]

[0024] T c =0.0225455T RSOC +279.627 public work 14;

[0025]

[0026] In the formula: is the stack temperature of RSOC at time t; T S is the reaction time; is the heat capacity of the RSOC device; V RSOC is the volume of the RSOC device; l is the thickness of the insulation layer of the RSOC device; T c is the temperature of the cold insulation surface of the RSOC device; is the power consumption of the peripheral thermal management system at time t; is the efficiency of the peripheral thermal management system; a, b, c are the heat loss related coefficients.

[0027] In the double-layer capacity planning model, the capacity planning cost of the microgrid mainly consists of the initial investment cost C of the microgrid Inv and the equipment replacement cost C Rep and is expressed by the formula:

[0028]

[0029]

[0030] In the formula: L MG represents the planned life cycle of the electro-thermal-hydrogen coupled microgrid; r is the discount rate; N RSOC , N HES and N PTH respectively represent the planned capacities of RSOC, hydrogen storage tank and electric heating equipment; N PV , N PW respectively represent the planned capacities of photovoltaic and wind power; and respectively represent the unit investment costs corresponding to RSOC, hydrogen storage tank and electric heating equipment; respectively represent the unit investment costs of photovoltaic and wind power; and respectively represent the unit replacement costs of RSOC, hydrogen storage tank and electric heating equipment; respectively represent the unit replacement costs of photovoltaic and wind power; the calculation formula of the unit replacement cost is as follows:

[0031] In the formula: L RSOC , L HES and L PTH respectively represent the expected service lives of RSOC, hydrogen storage tank and electric heating equipment; L PV , L PW respectively represent the expected service lives of photovoltaic generator sets and wind turbine generator sets.

[0032] In the double-layer capacity planning model, the annual operating cost of the microgrid includes the equipment maintenance cost Cwe 1. RSOC start - stop cost C uv and equipment salvage value C eq Among them, the equipment salvage value refers to the remaining value after equipment depreciation at the end of the micro - grid operation period;

[0033] C op = C we + C uv - C eq Formula 20;

[0034]

[0035]

[0036] Where: ω s,o is the weight coefficient of season s and scenario o. When s takes values of 1, 2, and 3, they represent summer, winter, and transition seasons respectively, and o is the corresponding typical scenario; are the outputs of RSOC, hydrogen storage tank, and electric heating equipment at time t in season s and scenario o respectively; are the outputs of photovoltaic power generation units and wind power generation units at time t in season s and scenario o respectively; c we,RSOC , c we,HES and c we,PTH are the operation and maintenance unit prices of RSOC, hydrogen storage tank, and electric heating equipment respectively; c we,PV , c we,PW are the operation and maintenance unit prices of photovoltaic power generation units and wind power generation units respectively; T y is the number of dispatching cycles in a year; 0 - 1 variable and represent the start - up state of RSOC at time t in season s and scenario o, being 1 indicates RSOC hot start, being 1 indicates RSOC cold start; c uv1 and c uv2 are the costs of RSOC hot start and cold start respectively; η eq is the equipment salvage value rate.

[0037] The two - layer capacity planning model introduces the penalty costs of wind and light abandonment and load shedding to improve the energy utilization rate of the micro - grid system. The formula is;

[0038]

[0039] Where: ε cl , ε pe are the penalty coefficients of wind and light abandonment and power load shedding respectively; ε hy , ε te are the penalty coefficients of hydrogen load shedding and heat load shedding respectively; The curtailment of photovoltaic power and wind power at s season, o scenario, and t moment, respectively; The load shedding of electricity and heat at s season, o scenario, and t moment, respectively; Is the hydrogen load shedding at s season, o scenario, and t moment.

[0040] For the upper-layer capacity constraint of the double-layer capacity planning model, the planned capacity of the equipment to be planned in the microgrid does not exceed its boundary range, which is specifically expressed by the following formula:

[0041]

[0042] In the formula: Respectively represent the upper and lower bounds of the planned capacity of the photovoltaic power generation unit; Respectively represent the upper and lower bounds of the planned capacity of the wind power generation unit; Respectively represent the upper and lower bounds of the planned capacity of RSOC; Respectively represent the upper and lower bounds of the planned capacity of the hydrogen storage tank; Respectively represent the upper and lower bounds of the planned capacity of the electric heating equipment.

[0043] For the lower-layer scheduling constraint of the double-layer capacity planning model, the output of each device in the microgrid does not exceed the corresponding boundary conditions, and the output of the device should not exceed its available power generation upper limit while satisfying the corresponding capacity constraint. The constraints on the output of wind and light, the output constraint of RSOC, and the ramp constraint of RSOC are specifically expressed by the following formula:

[0044]

[0045] In the formula: Respectively represent the actual output of the photovoltaic power generation unit and the wind power generation unit; Respectively represent the available power generation data sampled in real time for photovoltaic and wind power in the area where the microgrid is located. And Respectively represent the upper and lower boundaries of the output rate of RSOC in the SOFC and SOEC states, which are related to the capacity of RSOC. As shown in the above formula, the correlation coefficients are represented by And Indicates; In terms of the upper and lower limits of the ramp of RSOC, And Respectively are the upper and lower limits of the ramp power of RSOC in the SOFC and SOEC states.

[0046] In the lower-layer constraints of the microgrid, the upper and lower limits of the hydrogen storage and release mass of the hydrogen storage equipment, the upper and lower limits of the ramp of the hydrogen storage and release of the hydrogen storage tank, and the output constraint of the electric heating equipment are expressed by the formula:

[0047]

[0048] In the formula: respectively represent the upper and lower bounds of the hydrogen storage mass in the hydrogen storage tank, which are related to the capacity of the hydrogen storage tank, as shown in formula (C7), and the correlation coefficients are determined by denoted; and respectively represent the upper and lower bounds of the hydrogen release mass in the hydrogen storage tank, which are also related to the capacity of the hydrogen storage tank, and the correlation coefficients are determined by and denoted; The 0-1 variable K t is the hydrogen storage and release flag of the hydrogen storage tank at time t. A value of 1 indicates that the hydrogen storage tank is storing hydrogen, and a value of 0 indicates hydrogen release; In the ramp up and down limits constraints of hydrogen storage and release in the hydrogen storage tank, are respectively the ramp up and down limits of hydrogen storage in the hydrogen storage tank; and are respectively the ramp up and down limits of hydrogen release in the hydrogen storage tank; is the output of the electric heating device at time t.

[0049] In the lower layer constraints of the microgrid, the power balance constraint of electric energy, the hydrogen energy balance constraint, and the heat energy balance constraint are expressed by the following formulas:

[0050]

[0051] In the formula: respectively represent the wind power output, photovoltaic power output, and RSOC output in the SOFC state at time t; respectively represent the real-time electric load, power consumption of the electric heating device, RSOC electrolysis power in the SOEC state, and power consumption of the peripheral heat management system at time t; respectively represent the recoverable heat available and the heat production power of the electric heating equipment at time t; represents the real-time heat load demand; respectively represent the hydrogen release mass of the hydrogen storage tank and the hydrogen production of RSOC in the SOEC state at time t; respectively represent the hydrogen storage mass of the hydrogen storage tank, the hydrogen consumption of RSOC in the SOFC state, and the real-time hydrogen load demand at time t.

[0052] The RSOC (Reversible Solid Oxide Cells) power generation technology adopted in the present invention is a technology that uses a reversible solid oxide fuel cell (Reversible Solid Oxide Fuel Cells, rSOC) for power generation. The rSOC technology can realize the reversible switching between the two modes of electrolytic gas production and fuel cell power generation, thus playing an important role in the energy Internet. Compared with the prior art, the present invention has the following beneficial effects:

[0053] On the one hand, the four - mode operation of RSOC can effectively reduce the curtailment of wind and light in the micro - grid system and improve the energy consumption level. On the other hand, the hot - standby mode under the four - mode operation enables the rapid conversion between the SOEC mode and the SOFC mode, making it not restricted by the shortest shutdown time, reducing the start - stop cost, and enhancing the operation flexibility of the micro - grid system.

[0054] When the present invention is applied to the electro - thermal - hydrogen - coupled island micro - grid based on RSOC, it has high operation flexibility and energy supply reliability. Among them, as an energy hub, RSOC can effectively stabilize the supply balance of electricity, heat, and hydrogen in the electro - thermal - hydrogen - coupled micro - grid. The waste - heat recovery of RSOC not only relieves the heating pressure of the electric heating equipment but also improves the energy use efficiency. With the reduction of the investment cost of RSOC, it is beneficial for the island micro - grid system to gradually increase the planned capacities of RSOC and the hydrogen storage tank. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] The present invention will be further described in detail below in conjunction with the drawings and specific embodiments:

[0056] Appendix Figure 1 is a schematic diagram of the process of the present invention;

[0057] Appendix Figure 2 is a schematic diagram of the RSOC mode conversion of the present invention considering BOP;

[0058] Appendix Figure 3 is a schematic diagram of the daily operation characteristics of the present invention in summer;

[0059] Appendix Figure 4 is a schematic diagram of the daily operation characteristics of the present invention in winter;

[0060] Appendix Figure 5 is a schematic diagram of the daily operation characteristics of the present invention in the transition season;

[0061] Appendix Figure 6 is a schematic diagram of the influence of the RSOC investment cost on the planning result of the present invention;

[0062] Appendix Figure 7 is a schematic diagram of the iterative process of the whale optimization algorithm of the present invention;

[0063] Appendix Figure 8 is a topological schematic diagram of the electro - thermal - hydrogen - coupled island micro - grid based on RSOC in the embodiment. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0064] As shown in the figure, for the micro - grid capacity planning method based on the multi - mode operation of reversible solid oxide, the planning method conducts capacity planning for new energy generating units, reversible solid oxide batteries, hydrogen storage tanks, and electric heating equipment in the micro - grid by establishing an RSOC multi - mode operation model of reversible solid oxide batteries and a two - layer capacity planning model of the micro - grid;

[0065] The capacity planning model in the upper layer of the bi-level planning model aims to minimize the comprehensive cost of the microgrid, including the annual value of the capacity planning cost of the microgrid, etc., the annual operating cost, and the penalty cost; the scheduling model in the lower layer of the microgrid bi-level planning model aims to minimize the annual operating cost and the penalty cost of the islanded microgrid;

[0066] The upper-layer constraints of the microgrid are the capacity constraints of each device to be planned, including the available capacity constraint of the wind-solar generating unit, the available capacity constraint of the RSOC, the available capacity constraint of the hydrogen storage tank, and the available capacity constraint of the electric heating; the lower-layer constraints include the wind-solar constraint, the RSOC constraint, the hydrogen storage tank constraint, the electric heating equipment constraint, the electric energy supply-demand balance constraint, the hydrogen energy supply-demand balance constraint, and the heat energy supply-demand balance constraint.

[0067] The planning method uses the whale optimization algorithm to generate an initial whale population, where each population represents a capacity configuration scheme; calls the CPLEX solver to optimize the scheduling of the microgrid system based on the multi-condition reversible solid oxide battery to obtain the optimal operation strategy;

[0068] The corresponding conditions of the RSOC multi-condition operation model include the SOFC in the power generation condition, the SOEC in the electrolysis condition, the shutdown condition, and the hot standby condition. The RSOC multi-condition operation model is expressed by the formula:

[0069]

[0070] Among them, formula 1 represents the mutually exclusive characteristics between the operating conditions of the RSOC unit; formula 2 represents that the shutdown condition and the hot standby condition of the RSOC cannot be directly converted, and the power generation condition and the electrolysis condition cannot be directly converted; formulas 3 - formula 4 respectively represent the shortest working and shutdown times of the RSOC unit;

[0071] The mathematical model between the net output electric power and the power generation efficiency of the RSOC multi-condition operation model under the SOFC condition is expressed by the formula:

[0072]

[0073] In the formula: and are respectively the power generation efficiency and the heat generation efficiency of the SOFC at time t; is the unit net power generation of the SOFC; is the nominal electric power of the SOFC; are respectively the net power generation and the heat generation power of the SOFC at time t; is the hydrogen consumption mass of the SOFC at time t; L hv is the high calorific value of hydrogen;

[0074] When the RSOC is in the SOEC operating condition, renewable energy provides the total system power including the balance of plant (BOP) of the peripheral thermal auxiliary system. Therefore, the power consumed by the BOP is very limited, only accounting for 1% of the total system power of the RSOC. At this time, the electrolysis efficiency increases with the increase of the total power, but the change range is only about 1%. Therefore, the electrolysis efficiency under the electrolysis condition is regarded as a constant, and its mathematical expressions for hydrogen production and heat consumption are as follows:

[0075]

[0076] In the formula: are the power consumption and heat consumption of the SOEC at time t, respectively; is the hydrogen production mass of the SOEC at time t; is t the heat consumption coefficient of the SOEC at time is t the electrolysis efficiency of the SOEC at time

[0077] In the multi-condition operation model of the RSOC, its stack temperature is observed and controlled in real time to ensure that the RSOC is at an appropriate working temperature. Specifically:

[0078] Under the SOFC operating condition, a large amount of heat is generated by the stack reaction. The preheater in the BOP can be regarded as a heat exchanger. Therefore, the stack heat balance is determined by the total heat power heat loss brought by the environment heat power participating in heat recovery jointly. Among them, the heat recovery power is numerically equal to the heat generation power

[0079] Under the SOEC operating condition, the stack reaction absorbs heat. The preheater in the BOP can be regarded as a heater to provide heat for the stack to maintain the working temperature. Therefore, the stack heat balance is determined by the heat power consumed by the stack reaction heat loss heat power provided by the BOP jointly; among them, the heat consumption power of the stack reaction is numerically equal to the heat consumption power The relationship between the stack temperature evolution and heat balance of the RSOC considering the BOP is expressed as follows:

[0080]

[0081] In the formula: is the stack temperature of the RSOC at time t; T S is the reaction time; is the heat capacity of the RSOC device; V RSOCis the volume of the RSOC device; l is the thickness of the thermal insulation layer of the RSOC device; T c is the temperature of the insulated cold surface of the RSOC device; is the power consumption of the peripheral thermal management system at time t; is the efficiency of the peripheral thermal management system; a, b, and c are the coefficients related to heat loss.

[0082] In the double-layer capacity planning model, the capacity planning cost of the microgrid mainly consists of the initial investment cost C Inv of the microgrid and the equipment replacement cost C Rep and is expressed by the formula:

[0083]

[0084] In the formula: L MG represents the planned life cycle of the electro-thermal-hydrogen coupled microgrid; r is the discount rate; N RSOC , N HES and N PTH respectively represent the planned capacities of the RSOC, hydrogen storage tank, and electric heating equipment; N PV , N PW respectively represent the planned capacities of photovoltaic and wind power; and respectively represent the unit investment costs corresponding to the RSOC, hydrogen storage tank, and electric heating equipment; respectively represent the unit investment costs of photovoltaic and wind power; and respectively represent the unit replacement costs of the RSOC, hydrogen storage tank, and electric heating equipment; respectively represent the unit replacement costs of photovoltaic and wind power; the calculation formula for the unit replacement cost is as follows:

[0085] In the formula: L RSOC , L HES and L PTH respectively represent the expected service lives of the RSOC, hydrogen storage tank, and electric heating equipment; L PV , L PW respectively represent the expected service lives of the photovoltaic generator set and the wind turbine generator set.

[0086] In the double-layer capacity planning model, the annual operating cost of the microgrid includes the equipment maintenance cost C we , the start-stop cost C uv of the RSOC, and the equipment salvage value C eq . The equipment salvage value refers to the remaining value after the equipment is depreciated at the end of the microgrid operation;

[0087] C op = C we + Cuv -C eq Formula 20;

[0088]

[0089]

[0090] Where: ω s,o is the weight coefficient of season s and scenario o, where s takes the values of 1, 2, and 3 representing summer, winter, and transition seasons respectively, and o is the corresponding typical scenario; are the outputs of RSOC, hydrogen storage tank, and electric heating equipment at time t in season s and scenario o respectively; are the outputs of photovoltaic power generation unit and wind power generation unit at time t in season s and scenario o respectively; c we,RSOC , c we,HES and c we,PTH are the operation and maintenance unit prices of RSOC, hydrogen storage tank, and electric heating equipment respectively; c we,PV , c we,PW are the operation and maintenance unit prices of photovoltaic power generation unit and wind power generation unit respectively; T y is the number of dispatching cycles in a year; 0-1 variable and represent the start-up state of RSOC at time t in season s and scenario o, being 1 indicates the hot start of RSOC, being 1 indicates the cold start of RSOC; c uv1 and c uv2 are the costs of hot start and cold start of RSOC respectively; η eq is the equipment residual value rate.

[0091] The two-layer capacity planning model introduces the penalty costs for wind curtailment, light curtailment, and load shedding to improve the energy utilization rate of the microgrid system. The formula is;

[0092]

[0093] Where: ε cl , ε pe are the penalty coefficients for wind curtailment, light curtailment, and load shedding respectively; ε hy , ε te are the penalty coefficients for hydrogen load shedding and heat load shedding respectively; are the amounts of light curtailment and wind curtailment at time t in season s and scenario o respectively; are the amounts of electric load shedding and heat load shedding at time t in season s and scenario o respectively; is the amount of hydrogen load shedding at time t in season s and scenario o.

[0094] For the above-mentioned two-layer capacity planning model, in terms of the upper-layer capacity constraint, the planned capacity of the equipment to be planned in the microgrid does not exceed its boundary range, which is specifically expressed by the following formula:

[0095]

[0096] In the formula: respectively represent the upper and lower bounds of the planned capacity of the photovoltaic power generation unit; respectively represent the upper and lower bounds of the planned capacity of the wind power generation unit; respectively represent the upper and lower bounds of the planned capacity of the RSOC; respectively represent the upper and lower bounds of the planned capacity of the hydrogen storage tank; respectively represent the upper and lower bounds of the planned capacity of the electric heating equipment.

[0097] For the above-mentioned two-layer capacity planning model, in terms of the lower-layer scheduling constraint, the output of each device in the microgrid does not exceed the corresponding boundary conditions, and the output of the device should not exceed its available power generation upper limit while satisfying the corresponding capacity constraint. The constraints on the output of wind and light, the output constraint of the RSOC, and the RSOC ramp constraint are specifically expressed by the following formula:

[0098]

[0099] In the formula: respectively represent the actual outputs of the photovoltaic power generation unit and the wind power generation unit; respectively represent the available power generation data of the real-time sampling of photovoltaic and wind power in the area where the microgrid is located. and respectively represent the upper and lower boundaries of the output rate of the RSOC in the SOFC and SOEC states, which are related to the capacity of the RSOC. As shown in the above formula, the correlation coefficients are represented by and ; In terms of the upper and lower limits of the RSOC ramp constraint, and respectively represent the upper and lower limits of the ramp power of the RSOC in the SOFC and SOEC states.

[0100] In the lower-layer constraints of the microgrid, the upper and lower limits of the hydrogen storage and release mass of the hydrogen storage device, the upper and lower limits of the hydrogen storage and release ramp of the hydrogen storage tank, and the output constraint of the electric heating equipment are expressed by the formula:

[0101]

[0102] In the formula: respectively represent the upper and lower bounds of the hydrogen storage mass of the hydrogen storage tank, which are related to the capacity of the hydrogen storage tank. As shown in formula (C7), the correlation coefficient is represented by ; and respectively represent the upper and lower bounds of the hydrogen release mass of the hydrogen storage reservoir, which are also related to the capacity of the hydrogen storage reservoir. The correlation coefficient is determined by and ; the binary variable K t is the hydrogen storage and release flag of the hydrogen storage reservoir at time t. A value of 1 indicates that the hydrogen storage reservoir stores hydrogen, and a value of 0 indicates hydrogen release. In the ramp up and down limits constraints for hydrogen storage and release in the hydrogen storage reservoir, are the ramp up and down limits for hydrogen storage in the hydrogen storage reservoir respectively; and are the ramp up and down limits for hydrogen release in the hydrogen storage reservoir respectively; is the output of the electric heating device at time t.

[0103] In the lower - layer constraints of the microgrid, the power balance constraint, hydrogen energy balance constraint, and heat energy balance constraint are expressed by the following formulas:

[0104]

[0105] In the formula: respectively represent the wind power output, photovoltaic power output, and RSOC output under the SOFC state at time t; respectively represent the real - time electrical load, power consumption of the electric heating device, RSOC electrolysis power under the SOEC state, and power consumption of the peripheral thermal management system at time t; respectively represent the available recovered heat and heat production power of the electric heating equipment at time t; represents the real - time heat load demand; respectively represent the hydrogen release mass of the hydrogen storage reservoir and the hydrogen production of RSOC under the SOEC state at time t; respectively represent the hydrogen storage mass of the hydrogen storage reservoir, the hydrogen consumption of RSOC under the SOFC state, and the real - time hydrogen load demand at time t.

[0106] Example 1:

[0107] This example presents a microgrid capacity planning based on a multi - condition reversible solid oxide battery, and the operation steps are as Figure 1 shown.

[0108] Taking a certain island as an example, a microgrid is established to verify the effectiveness of the proposed model in this invention. The basic parameters of the equipment required for the construction of the island microgrid are shown in Table 1:

[0109] Table 1 Parameters of related equipment

[0110]

[0111] The selection of the RSOC operation mode has a great impact on the operation and dispatching results of the island microgrid. In this regard, based on whether to consider the auxiliary role of BOP and whether to consider the thermal standby condition, the operation of RSOC is compared and analyzed, and the comparison results are shown in Table 2. Among them, the operation mode without considering the thermal standby condition is called the RSOC three-condition operation mode.

[0112] Table 2 Annual comprehensive cost analysis of the island microgrid under different RSOC operation modes

[0113]

[0114] Example 2:

[0115] In this example, to improve the problem of difficult energy transmission in remote islands, an RSOC is used as an energy node to construct an integrated electricity-thermal-hydrogen island microgrid system. The microgrid structure is as Figure 8 shown.

[0116] The equipment to be planned in this example includes wind turbines, photovoltaic generators, RSOC, hydrogen storage tanks, and electric heating devices. Through reasonable planning and operation of the above equipment, the demands of electric load, hydrogen load, and thermal load in the microgrid can be met. Among them, the BOP device is an auxiliary equipment to meet the working temperature of the RSOC stack, so the BOP capacity is not configured, and its capacity value is 1.3 times the configured RSOC capacity. Since the capacity of the electrolyzer is usually expressed in rated power, the RSOC capacity is defined as its rated power.

[0117] When the wind and light outputs are sufficient, the RSOC operates in the SOEC state, and the excess electric energy is converted into hydrogen energy and stored in the hydrogen storage tank. At this time, the hydrogen load can be met by two methods: hydrogen production by the RSOC in real time and hydrogen supply from the hydrogen storage tank; when the wind and light outputs are insufficient, the RSOC operates in the SOFC state, converting the hydrogen energy in the hydrogen storage tank into electric energy. At the same time, its high-temperature tail gas can be supplied to the thermal load through the waste heat recovery device, reducing the pressure on the electric heating device to meet the thermal load demand and realizing the efficient utilization of energy. As follows:

[0118] Optimal capacity configuration results of the integrated electricity-thermal-hydrogen island microgrid

[0119]

[0120] Annual comprehensive cost analysis of the microgrid under different scenarios

[0121]

[0122] The above are the preferred embodiments of the present invention. All changes made according to the technical solutions of the present invention and whose functional effects do not exceed the scope of the technical solutions of the present invention belong to the protection scope of the present invention.

Claims

1. A method for microgrid capacity planning based on a reversible solid oxide operating under multiple working conditions, characterized in that: The planning method conducts capacity planning for new energy generating units, reversible solid oxide cells, hydrogen storage tanks, and electric heating equipment in the microgrid by establishing an RSOC multi-condition operation model for reversible solid oxide cells and a two-layer capacity planning model for the microgrid. The capacity planning model in the upper layer of the two-layer planning model aims to minimize the comprehensive cost of the microgrid, including the equivalent annual value of the capacity planning cost of the microgrid, the annual operation cost, and the penalty cost; the scheduling model in the lower layer of the microgrid two-layer planning model aims to minimize the annual operation cost and penalty cost of the islanded microgrid. The upper-layer constraints of the microgrid are the capacity constraints of each device to be planned, including the available capacity constraints of wind-solar generating units, RSOC available capacity constraints, hydrogen storage tank available capacity constraints, and electric heating available capacity constraints; the lower-layer constraints include wind-solar constraints, RSOC constraints, hydrogen storage tank constraints, electric heating equipment constraints, power supply-demand balance constraints, hydrogen energy supply-demand balance constraints, and heat energy supply-demand balance constraints.

2. The microgrid capacity planning method of a reversible solid oxide based on multi-condition operation according to claim 1, wherein: The planning method uses the whale optimization algorithm to generate an initial whale population, where each population represents a capacity configuration scheme. Call the CPLEX solver to optimize the scheduling of the microgrid system based on the multi-condition reversible solid oxide cell to obtain the optimal operation strategy. The corresponding conditions of the RSOC multi-condition operation model include the power generation condition SOFC, the electrolysis condition SOEC, the shutdown condition, and the hot standby condition. The RSOC multi-condition operation model is expressed by the formula: Among them, formula 1 represents the mutually exclusive characteristics between the operating conditions of the RSOC unit; formula 2 represents that the shutdown condition and the hot standby condition of the RSOC cannot be directly converted, and the power generation condition and the electrolysis condition cannot be directly converted; formulas 3 - 4 respectively represent the shortest working and shutdown times of the RSOC unit. The mathematical model between the net output electric power and the power generation efficiency of the RSOC multi-condition operation model under the SOFC condition is expressed by the formula: Where: and are the power generation efficiency and heat generation efficiency of the SOFC at time t, respectively; is the net power generation per unit of the SOFC; is the nominal electric power of the SOFC; are the net power generation and heat generation powers of the SOFC at time t, respectively; is the mass of hydrogen consumed by the SOFC at time t; L hv is the higher heating value of hydrogen; When the RSOC is in the SOEC condition, renewable energy provides the total system power including the peripheral heat-assisted system BOP. Regarding the electrolysis efficiency under the electrolysis condition as a constant, the mathematical expressions for hydrogen production and heat consumption are as follows: Wherein: are the power consumption and heat consumption powers of the SOEC at time t, respectively; is the hydrogen production mass of the SOEC at time t; is t the heat consumption coefficient of the SOEC at time is t the electrolysis efficiency of the SOEC at time 3. The microgrid capacity planning method for reversible solid oxides based on multi-condition operation according to claim 1, characterized in that: In the RSOC multi-condition operation model, the stack temperature is observed and controlled in real time to ensure that the RSOC is at an appropriate operating temperature, specifically: Under SOFC operating conditions, a large amount of heat is generated by the stack reaction. The preheater in the BOP can be regarded as a heat exchanger. Therefore, the stack heat balance is determined by the total heat power generated by the stack reaction heat loss to the environment heat power participating in heat recovery jointly determined, where the heat recovery power is numerically equal to the heat generation power at this time Under the SOEC condition, the stack reaction absorbs heat, and the preheater in the BOP can be regarded as a heater to provide heat for the stack to maintain the operating temperature. Therefore, the stack heat balance is determined by the heat power consumed by the stack reaction Heat loss Heat power provided by BOP jointly determined; among them, the heat consumption power of the stack reaction is numerically equal to the heat consumption power at this time The relationship between the stack temperature evolution and the heat balance considering the RSOC of the BOP is expressed as follows: T c =0.0225455T RSOC +279.627 Public Works 14; Wherein: is the stack temperature of the RSOC at time t; T S is the reaction time; is the heat capacity of the RSOC device; V RSOC is the volume of the RSOC device; l is the thickness of the thermal insulation layer of the RSOC device; T c is the temperature of the insulated cold surface of the RSOC device; is the power consumption of the peripheral thermal management system at time t; is the efficiency of the peripheral thermal management system; a, b, c are heat loss related coefficients.

4. The microgrid capacity planning method for reversible solid oxide based on multi-condition operation according to claim 1, wherein: In the double-layer capacity planning model, the capacity planning cost of the microgrid mainly consists of the initial investment cost C of the microgrid Inv and the equipment replacement cost C Rep which can be expressed by the formula as follows: where: L MG represents the planned life cycle of the electro-thermal hydrogen-coupled microgrid; r is the discount rate; N RSOC , N HES and N PTH represent the planned capacities of the RSOC, hydrogen storage tank, and electric heating equipment, respectively; N PV , N PW represent the planned capacities of the photovoltaic and wind power, respectively; and represent the unit investment costs corresponding to the RSOC, hydrogen storage tank, and electric heating equipment, respectively; represent the unit investment costs of the photovoltaic and wind power, respectively; and represent the unit replacement costs of the RSOC, hydrogen storage tank, and electric heating equipment, respectively; represent the unit replacement costs of the photovoltaic and wind power, respectively; The calculation formula for the unit replacement cost is as follows: Where: L RSOC , L HES and L PTH are the expected service lives of the RSOC, the hydrogen storage reservoir, and the electric heating equipment respectively; L PV , L PW are the expected service lives of the photovoltaic power generation unit and the wind power generation unit respectively.

5. The microgrid capacity planning method of a reversible solid oxide based on multi-condition operation according to claim 1, characterized in that: In the double-layer capacity planning model, the annual operating cost of the microgrid includes the equipment maintenance cost C we , the start-stop cost C uv of the RSOC, and the equipment salvage value C eq . Among them, the equipment salvage value refers to the remaining value after the equipment is depreciated at the end of the microgrid operation; C op = C we + C uv - C eq Equation 20; Where: ω s,o is the weight coefficient of the s-season o-scenario, where s takes the values of 1, 2, and 3, representing summer, winter, and transition seasons respectively, and o is the corresponding typical scenario; are the outputs of RSOC, hydrogen storage tank, and electric heating equipment at the t-th moment of the s-season o-scenario respectively; are the outputs of photovoltaic power generation units and wind power generation units at the t-th moment of the s-season o-scenario respectively; c we,RSOC 、c we,HES and c we,PTH are the operation and maintenance unit prices of RSOC, hydrogen storage tank, and electric heating equipment respectively; c we,PV 、c we,PW are the operation and maintenance unit prices of photovoltaic power generation units and wind power generation units respectively; T y is the number of scheduling cycles within a year; 0-1 variable and represent the start-up status of RSOC at the t-th moment of the s-season o-scenario, being 1 indicates the hot start of RSOC, being 1 indicates the cold start of RSOC; c uv1 and c uv2 are the costs of hot start and cold start of RSOC respectively; η eq is the residual value rate of the equipment.

6. The microgrid capacity planning method of a reversible solid oxide based on multi-condition operation according to claim 1, characterized in that: The two-layer capacity planning model introduces the penalty costs for wind and light curtailment and the penalty cost for load shedding to improve the energy utilization rate of the microgrid system. The formula is; where: ε cl , ε pe are the curtailment-of-wind penalty factor and curtailment-of-solar penalty factor respectively; ε hy , ε te are the hydrogen-load shedding penalty factor and heat-load shedding penalty factor respectively; are the amounts of curtailed solar power and curtailed wind power at time t in season s and scenario o respectively; are the amounts of shed electrical load and shed heat load at time t in season s and scenario o respectively; is the amount of shed hydrogen load at time t in season s and scenario o.

7. The microgrid capacity planning method for reversible solid oxide based on multi-condition operation according to claim 1, wherein: For the two-layer capacity planning model, in terms of the upper-layer capacity constraints, the planned capacity of the devices to be planned in the microgrid does not exceed its boundary range, specifically expressed by the formula as follows: In the formula: respectively represent the upper and lower bounds of the schedulable capacity of the photovoltaic power generation unit; respectively represent the upper and lower bounds of the schedulable capacity of the wind power generation unit; respectively represent the upper and lower bounds of the schedulable capacity of the RSOC; respectively represent the upper and lower bounds of the schedulable capacity of the hydrogen storage tank; respectively represent the upper and lower bounds of the schedulable capacity of the electric heating equipment.

8. The method for microgrid capacity planning of a reversible solid oxide based on multi-condition operation according to claim 1, characterized in that: For the two-layer capacity planning model, in terms of the lower-layer scheduling constraints, the output of each device in the microgrid does not exceed the corresponding boundary conditions, and the device output should not exceed its available power generation upper limit while satisfying the corresponding capacity constraints. The wind-solar output constraints, RSOC output constraints, and RSOC ramp constraints are specifically expressed by the formula as follows: Wherein: respectively represent the actual outputs of the photovoltaic power generation unit and the wind power generation unit; respectively represent the available power generation data obtained by real-time sampling of photovoltaic and wind power in the area where the microgrid is located. and respectively represent the upper and lower boundaries of the output rate of RSOC in the SOFC and SOEC states, which are related to the capacity of RSOC. As shown in the above formula, the correlation coefficients are determined by and denote; Regarding the upper and lower limits of the ramp of RSOC, and are respectively the upper and lower limits of the ramp power of RSOC in the SOFC and SOEC states.

9. The microgrid capacity planning method for reversible solid oxide based on multi-condition operation according to claim 1, characterized in that: In the lower-layer constraints of the microgrid, the upper and lower limits of the hydrogen storage / release mass of the hydrogen storage device, the upper and lower limits of the hydrogen storage / release ramp of the hydrogen storage tank, and the output constraints of the electric heating equipment are expressed by the formula: In the formula: respectively represent the upper and lower bounds of the hydrogen storage mass in the hydrogen storage tank, which are related to the capacity of the hydrogen storage tank, as shown in formula (C7), and the correlation coefficients are determined by indicated; and respectively represent the upper and lower bounds of the hydrogen release mass in the hydrogen storage tank, which are also related to the capacity of the hydrogen storage tank, and the correlation coefficients are determined by and indicated; The 0-1 variable K t is the hydrogen storage and release flag at time t of the hydrogen storage tank. A value of 1 indicates that the hydrogen storage tank is storing hydrogen, and a value of 0 indicates hydrogen release; In the ramp up and down limits for hydrogen storage and release in the hydrogen storage tank, are respectively the ramp up and down limits for hydrogen storage in the hydrogen storage tank; and are respectively the ramp up and down limits for hydrogen release in the hydrogen storage tank; is the output of the electric heating device at time t.

10. The microgrid capacity planning method for reversible solid oxide based on multi-condition operation according to claim 1, characterized in that: Among the lower-layer constraints of the microgrid, the power supply-demand balance constraint, the hydrogen energy supply-demand balance constraint, and the heat energy supply-demand balance constraint are expressed by the following formulas: Wherein: respectively represent the wind power output, photovoltaic power output and RSOC output under the SOFC state at time t; respectively represent the real-time electrical load, power consumption of the electric heating device, RSOC electrolysis power under the SOEC state, and power consumption of the peripheral thermal management system at time t; respectively represent the recoverable heat available and heat production power of the electric heating equipment at time t; represents the real-time heat load demand; respectively represent the hydrogen release mass of the hydrogen storage tank and the hydrogen production of RSOC under the SOEC state at time t; respectively represent the hydrogen storage mass of the hydrogen storage tank, the hydrogen consumption of RSOC under the SOFC state, and the real-time hydrogen load demand at time t.

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