Comprehensive energy system optimization method and system considering photo-thermal and energy storage

Abstract

The invention provides a method and a system for optimizing a comprehensive energy system considering photo-thermal and energy storage. The method comprises the following steps: acquiring original data required by energy consumption demand and energy output of a weak grid; establishing a comprehensive energy system containing photo-thermal and energy storage multi-energy coupling; constructing a mathematical model of key equipment in the integrated energy system; in consideration of economy and environmental protection, an objective function of minimizing total cost is taken as an upper layer optimization objective, equipment capacity is optimized, a result is fed back to a lower layer, the lower layer takes minimization of carbon emission and operation cost as objective functions, and the operation cost is fed back to the upper layer, so that a double-layer optimization framework is formed; and solving each layer of objective function of the double-layer optimization framework by adopting a genetic algorithm, wherein an obtained result is an optimized scheme. The problem of unstable power supply and demand in a weak power grid environment can be effectively solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of energy system optimization, and specifically relates to an optimization method and system for an integrated energy system taking into account solar thermal and energy storage. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] Some areas are often at the end of the power grid due to ultra-high altitude, ultra-low temperature, sparse population and other reasons. The grid is fragile, the power supply reliability is low, and the power supply is unstable. The traditional power supply method is difficult to meet the growing energy demand, especially for high-load mineral resource development projects and industrial parks. Power outages are frequent, seriously affecting production and economic benefits. In addition, the traditional energy supply model relies on fossil energy, resulting in resource depletion, price fluctuations and environmental pollution, and can no longer meet the needs of sustainable development in these areas.

[0004] As a highly efficient renewable energy technology, CSP can make full use of the abundant solar energy resources in these areas and has obvious advantages. CSP can convert sunlight into heat energy and then into electricity, which can utilize solar energy on a large scale and provide relatively stable power output, which is very suitable for the energy needs of these areas.

[0005] However, due to the influence of day and night changes and weather factors, the power generation of CSP systems is unstable and prone to large power supply fluctuations, especially on cloudy days, cloud cover or at night, resulting in a significant drop in output power. Therefore, relying solely on CSP cannot meet the high requirements for power continuity and stability in these areas, especially when power demand fluctuates greatly or the load is high, the reliability of power supply is difficult to guarantee.

[0006] To solve this problem, energy storage technology is considered to be the key to balancing supply and demand fluctuations and ensuring stable power supply. Although traditional energy storage technology can alleviate the volatility of solar thermal power generation to a certain extent, in weak power grid areas, traditional energy storage systems face problems such as limited capacity, high cost, and short service life, making it difficult to cope with large-scale, long-term high-demand loads. Especially in these areas, electricity demand usually presents a continuous peak, and the capacity limitations of traditional battery energy storage make it impossible to provide sufficient energy storage capacity and effectively deal with power outages caused by unstable solar thermal power generation.

[0007] At the same time, existing energy system optimization methods usually assume that the grid structure is relatively complete, and fail to fully consider practical problems such as unstable grids, large load fluctuations, and uncertainty in renewable energy generation in weak-power areas. Most existing optimization models rely on a relatively stable grid environment, ignoring power fluctuations at the end of the grid, volatility in energy storage demand, and uncertainty in renewable energy generation. In summary, traditional optimization methods cannot effectively deal with the complex power system problems in these areas, and it is difficult to meet the high requirements for energy stability and reliability, making it more difficult to achieve efficient and stable energy supply in weak-power areas. Therefore, it is necessary to propose a new integrated energy system and optimization framework to address the above problems. Summary of the invention

[0008] In order to solve the above problems, the present invention proposes an optimization method and system for an integrated energy system taking into account solar thermal and energy storage. The present invention can effectively solve the problem of unstable power supply and demand in a weak power grid environment.

[0009] According to some embodiments, the present invention adopts the following technical solutions:

[0010] A method for optimizing a comprehensive energy system considering solar thermal energy and energy storage comprises the following steps:

[0011] Obtain the raw data needed for weak grid energy demand and energy output;

[0012] Establish a comprehensive energy system that combines solar, thermal and energy storage;

[0013] Constructing mathematical models of key equipment in the integrated energy system;

[0014] Considering economy and environmental protection, the upper layer optimizes equipment capacity with the objective function of minimizing total cost as the optimization target, and feeds the result to the lower layer. The lower layer minimizes carbon emissions and operating costs as the objective function, and feeds the operating costs to the upper layer, forming a two-layer optimization framework.

[0015] The genetic algorithm is used to solve the objective functions of each layer of the two-layer optimization framework, and the result obtained is the optimized solution.

[0016] As an optional implementation, the energy consumption demand of the weak grid includes power supply demand, heating demand and cooling demand; the original data required for energy output includes wind speed and light intensity.

[0017] As an optional implementation, the integrated energy system includes a photovoltaic power generation group, a wind power generation group, a gas power generation group, a heat absorption tower, a high-temperature storage tank, a steam generator, a steam generator, a low-temperature storage tank, a generator, a compressed air energy storage system, an absorption refrigerator and an electric refrigerator. The heat absorption tower heats the fluid by concentrating solar energy, and stores the heat in the high-temperature storage tank together with the waste heat from gas power generation; the high-temperature storage tank transfers the heat energy to the steam generator to generate high-pressure steam to drive the steam generator to generate electricity, and the electricity is input into the power grid together with the photovoltaic power generation group, the wind power generation group and the gas power generation group for power supply; when the power grid has surplus power supply, the compressed air energy storage system converts the electrical energy into air internal energy and stores it in the high-pressure gas storage tank, while recovering the compressed heat and storing it in the high-temperature storage tank for external heating; when the power grid is insufficient, the compressed air energy storage system releases the air internal energy through the expander and converts it into mechanical energy to drive the generator to generate electricity, and the cooled fluid transfers the cold to the low-temperature storage tank together with the absorption refrigerator and the electric refrigerator for external cooling.

[0018] As an optional implementation, the key equipment includes a photovoltaic generator set, a wind generator set, a gas generator set, a compressed air energy storage system, an absorption refrigerator and an electric refrigerator.

[0019] As an optional implementation, the process of constructing the mathematical model of the key equipment in the integrated energy system includes: the mathematical model of photovoltaic power generation, which is expressed as follows:

[0020] P PV (t) = P STC I[1+k(T PV -T r )] / I STC

[0021] Among them, P PV is the photovoltaic power generation power; I is the light intensity; P STC is the maximum test power under standard test conditions; k is the power temperature coefficient, and its value is -0.45% / K; T PV is the temperature of photovoltaic power generation components;

[0022] The mathematical model of wind power generation is expressed as follows:

[0023]

[0024] Among them, P WT is the wind power generation power; P r is the rated power; v is the actual wind speed; v r is the rated wind speed; v in is the cut-in wind speed; v out To cut out the wind speed;

[0025] The mathematical model of solar thermal power generation is expressed as follows:

[0026]

[0027] P Photothermal (t) = η ele η solar Q solar

[0028] Among them, P Photothermal is the CSP power; Q solar is the solar energy input; q solar is the solar input per unit time; R is the instantaneous normal direct irradiance at a certain moment; A hel is the mirror area of ​​the photothermal system; η solar is the thermal efficiency of the photothermal system; P Photothermal Output electrical energy for the solar thermal system; η ele is the electrical efficiency of the solar thermal system;

[0029] The mathematical model of gas-fired power generation is expressed as follows:

[0030]

[0031] Among them, G PGU is the amount of natural gas consumed; η pe (t), η te (t) are the electrical efficiency and thermal efficiency of the gas generator respectively; Q jw is the waste heat of the cylinder jacket water; Q exh is the waste heat of exhaust gas; Q loss is heat loss; Q re is the recoverable heat; jw is the efficiency of the jacket water heat exchanger; η exh is the exhaust gas heat exchanger efficiency.

[0032] As an optional implementation, the process of constructing a mathematical model of key equipment in the integrated energy system includes:

[0033] The mathematical model of compressed air energy storage is expressed as follows:

[0034]

[0035]

[0036] Among them, P CAES,in is the compressor output power; Q h,com Output heat to the compressor; P CAES,out is the expander output power; Q h,tur Output heat to the expander; Q c,tur N is the cooling capacity output by the expander;com is the compression level; q com,a (t) is the mass flow rate of air; R g is the gas constant; T com,in,i is the inlet temperature of the i-th stage compressor; T 0 is the ambient temperature; η com,m represents the compressor electrical energy-mechanical energy conversion efficiency; γ is the specific heat capacity ratio index of air; η com,ist is the compressor adiabatic efficiency; com,i is the pressure ratio of the i-th stage compressor, that is, the ratio of the outlet and inlet pressures of the i-th stage compressor; c w and q com,w (t) represent the specific heat capacity of water and the mass flow rate of water in the compressor heat exchanger respectively; T com,w,out,i and T com,w,in,i Respectively represent the water temperature at the outlet and inlet of the i-th stage heat exchanger of the compressor; η tur,ist is the adiabatic efficiency of the expander; N tur is the total expansion order; q tur,a (t) and T tur,in,i They represent the air flow rate and the inlet air temperature of the i-th stage expander respectively; τ tur,i is the expansion ratio of the i-th stage expander; q tur,w (t) represents the mass flow rate of water in the expander heat exchanger; T tur,w,in,i represents the water temperature at the inlet of the i-th stage heat exchanger of the expander; T c Indicates the chilled water temperature.

[0037] As an optional implementation, the upper layer optimization takes minimizing the total cost as the objective function, which is expressed as follows:

[0038]

[0039] In the formula, C r is the total cost of system operation; C i,var is the daily operating cost of each device in the system; N d is the number of days; N i,r is the capacity of the i-th device; C i,v is the unit purchase cost of the i-th equipment; r is the conversion coefficient, T i is the life span of the i-th equipment;

[0040] The optimization variable mentioned above is the mirror area S of the photothermal system. Photothermal 、Distributed photovoltaic capacity N PV 、Wind turbine capacity N WT , Compressed air energy storage capacity N CAES , High temperature storage tank capacity N TES 、Cryogenic storage tank capacity N CES and are the optimization variables for the electric cooling ratio R;

[0041] The constraints described above are expressed as follows:

[0042] S Photothermal ≤S Photothermal,max

[0043] N PV ≤N PV,max

[0044] 0 <N WT <N WT,max

[0045] N CAES,min ≤N CAES ≤N CAES,max

[0046] N TES,min <N TES <N TES,max

[0047] N CES,min <N CES <N CES,max

[0048] R min <R<R max

[0049] Among them, N PV Represents the total area occupied by distributed photovoltaics; N PV,max is the total area of ​​distributed photovoltaic installation; N WT is the capacity of the wind turbine; N WT,max is the upper limit of wind turbine capacity; N CAES,min is the lower limit of air compression energy storage capacity; N CAES is the compressed air energy storage capacity; N CAES,max N is the upper limit of air compression energy storage capacity; TES is the capacity of high temperature storage tank; N TES,max N is the upper limit of high temperature storage tank; TES,min is the lower limit of high temperature storage tank; N CES is the capacity of the cryogenic storage tank; N CES,max The upper limit of the low temperature storage tank; N CES,min is the lower limit of low temperature storage tank; R min is the lower limit of the electric cooling ratio; R is the electric cooling ratio; R max It is the upper limit of electric cooling ratio.

[0050] As an alternative embodiment, the lower layer is optimized to minimize CO 2 The emission and operating cost objective functions are expressed as follows:

[0051]

[0052]

[0053] In the formula, CO 2 E IESS For CO 2 Total emissions; is the carbon dioxide emission coefficient of natural gas; is the total amount of natural gas; μ e is the carbon dioxide emission coefficient of power grid electricity; P grid is the total amount of electricity purchased; C IESS is the system operation cost; C grid The cost of purchasing electricity from the power grid; C com The operation and maintenance costs of system equipment; Cost of natural gas consumed for the system;

[0054] The optimization variable described in the lower layer is the output H of the compressed air energy storage system CAES ;

[0055] The constraint conditions described in the lower layer are expressed as H CAES <N CAES .

[0056] As an optional implementation method, the process of using genetic algorithm to solve the objective functions of each layer of the two-layer optimization framework includes: initializing the population, each individual represents a device capacity configuration and operation scheduling strategy; evaluating the fitness of each individual, respectively according to the total cost minimization objective of the upper layer and the CO 2 The fitness value is calculated with the goal of minimizing emissions and operating costs;

[0057] Perform selection operations to select individuals with higher fitness, and generate a new generation of population through crossover and mutation operations; in the iterative process, repeat evaluation, selection, crossover and mutation until the stopping condition is met, such as reaching the maximum number of generations or fitness convergence;

[0058] Output the optimal individual, obtain the optimal equipment capacity configuration and operation scheduling strategy, and achieve the optimization of the solar thermal integrated energy system.

[0059] An optimization system for a comprehensive energy system considering solar thermal and energy storage, comprising:

[0060] A data acquisition module is used to obtain the raw data required for the energy consumption demand and energy output of the weak grid;

[0061] System establishment module, used to establish a comprehensive energy system with multiple energy coupling including solar thermal and energy storage;

[0062] A system mathematical modeling module, used to construct a mathematical model of key equipment in the integrated energy system;

[0063] The two-layer optimization model building module is used to consider economic and environmental protection. The upper layer optimizes the equipment capacity with the objective function of minimizing the total cost as the upper optimization target, and feeds the results back to the lower layer. The lower layer minimizes carbon emissions and operating costs as the objective function, and feeds the operating costs back to the upper layer, forming a two-layer optimization framework.

[0064] The optimization solution module is used to solve the objective functions of each layer of the double-layer optimization framework using a genetic algorithm, and the result obtained is the optimized solution.

[0065] Compared with the prior art, the present invention has the following beneficial effects:

[0066] (1) The present invention proposes a comprehensive energy system model based on the energy demand of weak grid and the multi-factor coupling characteristics of various energy output characteristics, which can effectively solve the problem of unstable power supply and demand in a weak grid environment. The model optimizes the coordinated scheduling of different energy sources by considering the volatility of solar thermal power generation, the charging and discharging characteristics of energy storage equipment, and the fluctuation of grid load. When solar thermal power generation fluctuates, the energy storage system adjusts the power supply through reasonable charging and discharging to ensure the stability of the grid. This multi-energy coupling model can improve the reliability and flexibility of the system and effectively deal with the power supply problems in weak grid areas. Through precise energy scheduling and optimal configuration, the model significantly improves the utilization rate of renewable energy, reduces the risks brought by fluctuations in power supply and demand, and ensures the stability of the grid and the efficient use of energy.

[0067] (2) The present invention proposes a two-layer optimization structure that comprehensively considers the output volatility of solar thermal power generation, the charging and discharging characteristics of energy storage, and the stability of power supply of the power grid, and can achieve the optimal balance between economy and environmental protection under weak power grid conditions. The upper optimization goal is to minimize the total system cost and reasonably configure the capacity of solar thermal power generation and energy storage equipment, thereby reducing the system construction and operation costs. The lower optimization goal is to minimize carbon dioxide emissions and system operating costs, and reduce dependence on traditional energy and carbon emissions by optimizing the charging and discharging scheduling of the energy storage system. This two-layer optimization framework can dynamically adjust the system operation strategy under the conditions of changes in power grid load and uncertainty in new energy power generation to ensure the stability and efficient operation of the power grid.

[0068] (3) The present invention proposes a double-layer optimization solution method for a weak power grid environment, which uses a genetic algorithm to perform double-layer optimization on the energy system in a weak power grid environment, which can effectively solve the challenges brought by system complexity and uncertainty. The genetic algorithm gradually optimizes the equipment capacity configuration and energy storage scheduling strategy by simulating natural selection and genetic operations, ensuring that the optimal solution is found under the conditions of variable power load and renewable energy output fluctuations. This method can not only effectively reduce the total system cost and carbon dioxide emissions, but also improve the adaptability and flexibility of the system, ensuring a stable power supply in weak grid areas.

[0069] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0071] Figure 1 is a block diagram of a comprehensive energy system of an embodiment;

[0072] Figure 2 The present invention is a flowchart of an optimization method for an integrated energy system taking into account solar thermal energy and energy storage. DETAILED DESCRIPTION

[0073] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0074] It should be noted that the following detailed descriptions are all illustrative and intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0075] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0076] In the absence of conflict, the embodiments in this application and the features in the embodiments may be combined with each other.

[0077] Embodiment 1

[0078] First, the following terms are explained:

[0079] A weak grid refers to a regional grid structure in the power system where power transmission and dispatching are difficult due to insufficient grid structure, capacity or stability. It is usually located on the edge of the grid or in areas far away from the main grid.

[0080] A method for optimizing a comprehensive energy system based on a weak grid and considering solar thermal and energy storage. Figure 2 As shown, the following steps are included:

[0081] Collect energy demand and renewable energy output data in weak grid areas, and analyze grid load fluctuations and energy supply characteristics;

[0082] Design an integrated energy system that combines CSP and energy storage, combining CSP power generation, energy storage systems and other renewable energy sources to achieve stability and flexibility in energy supply;

[0083] Mathematically model the integrated energy system and accurately describe the individual system components;

[0084] Based on economic and environmental considerations, a two-layer optimization framework is constructed. The upper layer goal is to minimize the total system cost and adjust the equipment capacity, and the lower layer goal is to minimize carbon dioxide emissions and system operating costs, and optimize the charging and discharging scheduling of the energy storage system.

[0085] Genetic algorithm is used to solve the objective function and output the optimal system configuration and operation strategy.

[0086] The energy consumption demand of the weak grid in step 1 includes power supply demand, heating demand and cooling demand; the original data required for energy output includes wind speed and light intensity.

[0087] The integrated energy system containing solar thermal energy, energy storage and multi-energy coupling described in step 2 is as follows Figure 1 , which consists of a photovoltaic generator set, a wind generator set, a gas generator set, a heat absorption tower, a high-temperature storage tank, a steam generator, a steam generator, a low-temperature storage tank, a generator, a compressed air energy storage, an absorption refrigerator and an electric refrigerator. The heat absorption tower heats the fluid by concentrating solar energy, and stores the heat in the high-temperature storage tank together with the waste heat from gas power generation. The high-temperature storage tank transfers the heat energy to the steam generator, generating high-pressure steam to drive the steam generator to generate electricity, and the electricity is input into the grid together with photovoltaic, wind power and gas power generation. When the grid has sufficient power supply, the compressed air energy storage system converts the electrical energy into air internal energy and stores it in the high-pressure gas storage tank, while recovering the compressed heat and storing it in the high-temperature storage tank for external heating. When the grid power supply is insufficient, the compressed air energy storage releases the air internal energy through the expander and converts it into mechanical energy to drive the generator to generate electricity, and the cooled fluid transfers the cold to the low-temperature storage tank together with the absorption refrigerator and the electric refrigerator for external cooling.

[0088] Step 3 describes the mathematical modeling of the integrated energy system containing multiple energy couplings including photovoltaic, thermal and energy storage. The equipment includes photovoltaic power generation, wind power generation, photovoltaic power generation, compressed air energy storage, absorption refrigerator, and electric refrigerator.

[0089] The mathematical model of photovoltaic power generation is expressed as follows:

[0090] P PV (t) = P STC I[1+k(T PV -Tr )] / I STC (1)

[0091] Where: P PV is the photovoltaic power generation power; I is the light intensity; P STC The maximum test power under standard test conditions (light intensity I STC 1000W / m 2 , T r is 25℃); k is the power temperature coefficient, its value is -0.45% / K; T PV is the temperature of the photovoltaic power generation component.

[0092] The mathematical model of wind power generation is expressed as follows:

[0093]

[0094] Where: P WT is the wind power generation power; P r is the rated power; v is the actual wind speed; v r is the rated wind speed; v in is the cut-in wind speed; v out To cut out wind speed.

[0095] The mathematical model of solar thermal power generation is expressed as follows:

[0096]

[0097] P Photothermal (t) = η ele η solar Q solar (4)

[0098] Where P Photothermal is the CSP power; Q solar is the solar energy input; q solar is the solar input per unit time; R is the instantaneous normal direct irradiance at a certain moment; A hel is the mirror area of ​​the photothermal system; η solar is the thermal efficiency of the photothermal system; P Photothermal Output electrical energy for the solar thermal system; η ele is the electrical efficiency of the solar thermal system.

[0099] The mathematical model of gas-fired power generation is expressed as follows:

[0100]

[0101] In the formula, G PGU is the amount of natural gas consumed; η pe (t), η te(t) are the electrical efficiency and thermal efficiency of the gas generator respectively; Q jw is the waste heat of the cylinder jacket water; Q exh is the waste heat of exhaust gas; Q loss is heat loss; Q re is the recoverable heat; jw is the efficiency of the jacket water heat exchanger; η exh is the exhaust gas heat exchanger efficiency.

[0102] Preferably, the mathematical model of compressed air energy storage is expressed as follows:

[0103]

[0104] Where P CAES,in is the compressor output power; Q h,com Output heat to the compressor; P CAES,out is the expander output power; Q h,tur Output heat to the expander; Q c,tur N is the cooling capacity output by the expander; com is the compression level; q com,a (t) is the mass flow rate of air (kg / s); R g is the gas constant, with a value of 287.1 J / (kg·K); T com,in,i is the inlet temperature of the i-th stage compressor (K); T 0 is the ambient temperature; η com,m represents the compressor electrical energy-mechanical energy conversion efficiency; γ is the specific heat capacity ratio index of air; η com,ist is the compressor adiabatic efficiency; com,i is the pressure ratio of the i-th stage compressor, that is, the ratio of the outlet and inlet pressures of the i-th stage compressor. w and q com,w (t) represents the specific heat capacity of water (4.2 kJ / (kg·K)) and the mass flow rate of water in the compressor heat exchanger (kg / s); T com,w,out,i and T com,w,in,i Respectively represent the water temperature at the outlet and inlet of the i-th stage heat exchanger of the compressor. η tur,ist is the adiabatic efficiency of the expander; N tur is the total expansion order; q tur,a (t) and T tur,in,i Respectively represent the air flow rate (kg / s) and the inlet air temperature of the i-th stage expander; τ tur,i is the expansion ratio of the i-th stage expander; q tur,w (t) represents the mass flow rate of water in the expander heat exchanger (kg / s); T tur,w,in,i represents the water temperature at the inlet of the i-th stage heat exchanger of the expander; T c Indicates the chilled water temperature.

[0105] In the two-layer optimization described in step 4, the upper layer optimizes the equipment capacity with the goal of minimizing the total cost and feeds the results back to the lower layer; the lower layer minimizes CO 2 Taking emissions and operating costs as the goal, optimize equipment operation scheduling, and feed back operating costs to the upper level to achieve interaction between the upper and lower levels.

[0106] The objective function of minimizing the total cost described above is expressed as follows:

[0107]

[0108] In the formula, C r is the total cost of system operation; C i,var is the daily operating cost of each device in the system; N d is the number of days; N i,r is the capacity of the i-th device; C i,v is the unit purchase cost of the i-th equipment; r is the conversion coefficient, which is taken as 8% in this paper; T i is the life span of the ith equipment.

[0109] The optimization variable mentioned above is the mirror area S of the photothermal system. Photothermal 、Distributed photovoltaic capacity N PV 、Wind turbine capacity N WT , Compressed air energy storage capacity N CAES , High temperature storage tank capacity N TES 、Cryogenic storage tank capacity N CES and are the optimization variables of the electric cooling ratio R.

[0110] The constraints described above are expressed as follows:

[0111] S Photothermal ≤S Photothermal,max (12)

[0112] N PV ≤N PV,max (13)

[0113] 0 <N WT <N WT,max (14)

[0114] N CAES,min ≤N CAES ≤N CAES,max (15)

[0115] N TES,min <N TES <N TES,max (16)

[0116] N CES,min <N CES <N CES,max(17)

[0117] R min <R<R max (18)

[0118] Where N PV Represents the total area occupied by distributed photovoltaics; N PV,max is the total area of ​​distributed photovoltaic installation; N WT is the capacity of the wind turbine; N WT,max is the upper limit of wind turbine capacity; N CAES,min is the lower limit of air compression energy storage capacity; N CAES is the compressed air energy storage capacity; N CAES,max N is the upper limit of air compression energy storage capacity; TES is the capacity of high temperature storage tank; N TES,max N is the upper limit of high temperature storage tank; TES,min is the lower limit of high temperature storage tank; N CES is the capacity of the cryogenic storage tank; N CES,max The upper limit of the low temperature storage tank; N CES,min is the lower limit of low temperature storage tank; R min is the lower limit of the electric cooling ratio; R is the electric cooling ratio; R max It is the upper limit of electric cooling ratio.

[0119] Minimizing CO as described below 2 The emission and operating cost objective functions are expressed as follows:

[0120]

[0121] In the formula, CO 2 E IESS For CO 2 Total emissions; is the carbon dioxide emission coefficient of natural gas; is the total amount of natural gas; μ e is the carbon dioxide emission coefficient of power grid electricity; P grid is the total amount of electricity purchased; C IESS is the system operation cost; C grid The cost of purchasing electricity from the power grid; C com The operation and maintenance costs of system equipment; Cost of natural gas consumed by the system.

[0122] The optimization variable described in the lower layer is the output H of the compressed air energy storage system CAES .

[0123] The constraints described in the following layer are expressed as follows:

[0124] H CAES <N CAES(twenty one)

[0125] The genetic algorithm described in step 5 is used to solve the objective function in step 4. First, the population is initialized, and each individual represents a device capacity configuration and operation scheduling strategy; then, the fitness of each individual is evaluated, and the total cost minimization objective of the upper layer and the CO 2 The fitness value is calculated with the goal of minimizing emissions and operating costs; then, a selection operation is performed to select individuals with higher fitness, and a new generation of population is generated through crossover and mutation operations; in the iterative process, evaluation, selection, crossover and mutation are repeated until the stopping condition is met, such as reaching the maximum number of generations or fitness convergence; finally, the optimal individual is output to obtain the optimal equipment capacity configuration and operation scheduling strategy, thereby realizing the optimization of the solar thermal integrated energy system.

[0126] The method of the above embodiment can be applied to an integrated energy system for external heating demand in winter, by connecting the solar thermal power generation and energy storage system with the local gas boiler to ensure stable heating and power supply in the low temperature season. When the solar thermal power generation system and energy storage equipment cannot fully meet the demand, the gas boiler will serve as an auxiliary energy to supplement the heat source and provide stable power output for the power grid.

[0127] The method of the above embodiment can be applied to industrial waste heat recovery in steel plants. After the exhaust gas is cooled by the cooling system, it is converted into steam by the waste heat boiler to drive the generator to supply electricity. Solar thermal power generation stores heat energy through heat absorption towers and high-temperature storage tanks to supplement electricity to the power grid. The energy storage system recovers heat during the compression process and provides electricity to ensure stable operation of the power grid when the power demand is high. Through double-layer optimization, the best balance between system cost, operating efficiency and environmental protection is achieved.

[0128] Embodiment 2

[0129] An optimization system for a comprehensive energy system considering solar thermal and energy storage, comprising:

[0130] A data acquisition module is used to obtain the raw data required for the energy consumption demand and energy output of the weak grid;

[0131] System establishment module, used to establish a comprehensive energy system with multiple energy coupling including solar thermal and energy storage;

[0132] A system mathematical modeling module, used to construct a mathematical model of key equipment in the integrated energy system;

[0133] The two-layer optimization model building module is used to consider economic and environmental protection. The upper layer optimizes the equipment capacity with the objective function of minimizing the total cost as the upper optimization target, and feeds the results back to the lower layer. The lower layer minimizes carbon emissions and operating costs as the objective function, and feeds the operating costs back to the upper layer, forming a two-layer optimization framework.

[0134] The optimization solution module is used to solve the objective functions of each layer of the double-layer optimization framework using a genetic algorithm, and the result obtained is the optimized solution.

[0135] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0136] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0137] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0138] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0139] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made by those skilled in the art within the spirit and principle of the present invention without creative labor shall be included in the protection scope of the present invention.

Claims

1. A method for optimizing a comprehensive energy system considering solar thermal energy and energy storage, characterized in that: The following steps are involved: Obtain the raw data needed for weak grid energy demand and energy output; Establish a comprehensive energy system that combines solar, thermal and energy storage; Constructing mathematical models of key equipment in the integrated energy system; Considering economy and environmental protection, the upper layer optimizes equipment capacity with the objective function of minimizing total cost as the optimization target, and feeds the result to the lower layer. The lower layer minimizes carbon emissions and operating costs as the objective function, and feeds the operating costs to the upper layer, forming a two-layer optimization framework. The genetic algorithm is used to solve the objective functions of each layer of the two-layer optimization framework, and the result obtained is the optimized solution.

2. The method for optimizing a comprehensive energy system taking into account solar thermal energy and energy storage as claimed in claim 1, characterized in that: The energy consumption demand of the weak grid includes power supply demand, heating demand and cooling demand; the original data required for energy output includes wind speed and light intensity.

3. The method for optimizing a comprehensive energy system taking into account solar thermal energy and energy storage as claimed in claim 1, characterized in that: The comprehensive energy system includes a photovoltaic power generation unit, a wind power generation unit, a gas power generation unit, a heat absorption tower, a high-temperature storage tank, a steam generator, a steam generator, a low-temperature storage tank, a generator, a compressed air energy storage system, an absorption refrigerator and an electric refrigerator. The heat absorption tower heats the fluid by concentrating solar energy, and stores the heat in the high-temperature storage tank together with the waste heat from gas power generation; the high-temperature storage tank transfers the heat energy to the steam generator to generate high-pressure steam to drive the steam generator to generate electricity, and the electricity is input into the power grid together with the photovoltaic power generation unit, the wind power generation unit and the gas power generation unit for power supply; when the power grid has surplus power supply, the compressed air energy storage system converts the electrical energy into air internal energy and stores it in the high-pressure gas storage tank, while recovering the compressed heat and storing it in the high-temperature storage tank for external heat supply; when the power grid is insufficient, the compressed air energy storage system releases the air internal energy through the expander and converts it into mechanical energy to drive the generator to generate electricity, and the cooled fluid transfers the cold to the low-temperature storage tank together with the absorption refrigerator and the electric refrigerator for external cold supply.

4. The method for optimizing a comprehensive energy system taking into account solar thermal energy and energy storage as claimed in claim 1, characterized in that: The key equipment includes photovoltaic generator sets, wind generator sets, gas generator sets, compressed air energy storage systems, absorption refrigerators and electric refrigerators.

5. The method for optimizing a comprehensive energy system taking into account solar thermal energy and energy storage as claimed in claim 4, characterized in that: The process of constructing the mathematical model of the key equipment in the integrated energy system includes: the mathematical model of photovoltaic power generation, which is expressed as follows: P PV (t)=P STC I[1+k(T PV -T r )] / I STC Among them, P PV is the photovoltaic power generation power; I is the light intensity; P STC is the maximum test power under standard test conditions; k is the power temperature coefficient, and its value is -0.45% / K; T PV is the temperature of photovoltaic power generation components; The mathematical model of wind power generation is expressed as follows: Among them, P WT is the wind power generation power; P r is the rated power; v is the actual wind speed; v r is the rated wind speed; v in is the cut-in wind speed; v out To cut out the wind speed; The mathematical model of solar thermal power generation is expressed as follows: P Photothermal (t)=η ele or solar Q solar Among them, P Photothermal is the CSP power; Q solar is the solar energy input; q solar is the solar input per unit time; R is the instantaneous normal direct irradiance at a certain moment; A hel is the mirror area of ​​the photothermal system; η solar is the thermal efficiency of the photothermal system; P Photothermal Output electrical energy for the solar thermal system; η ele is the electrical efficiency of the solar thermal system; The mathematical model of gas-fired power generation is expressed as follows: Among them, G PGU is the amount of natural gas consumed; η pe (t), η te (t) are the electrical efficiency and thermal efficiency of the gas generator respectively; Q jw is the waste heat of the cylinder jacket water; Q exh is the waste heat of exhaust gas; Q loss is heat loss; Q re is the recoverable heat; jw is the efficiency of the jacket water heat exchanger; η exh is the exhaust gas heat exchanger efficiency.

6. The method for optimizing a comprehensive energy system taking into account solar thermal and energy storage as claimed in claim 1, characterized in that: The process of constructing the mathematical model of the key equipment in the integrated energy system includes: The mathematical model of compressed air energy storage is expressed as follows: Among them, P CAES,in is the compressor output power; Q h,com Output heat to the compressor; P CAES,out is the expander output power; Q h,tur Output heat to the expander; Q c,tur N is the cooling capacity output by the expander; com is the compression level; q com,a (t) is the mass flow rate of air; R g is the gas constant; T com,in,i is the inlet temperature of the i-th compressor; T0 is the ambient temperature; η com,m represents the compressor electrical energy-mechanical energy conversion efficiency; γ is the specific heat capacity ratio index of air; η com,ist is the compressor adiabatic efficiency; com,i is the pressure ratio of the i-th stage compressor, that is, the ratio of the outlet and inlet pressures of the i-th stage compressor; c w and q com,w (t) represent the specific heat capacity of water and the mass flow rate of water in the compressor heat exchanger respectively; T com,w,out,i and T com,w,in,i Respectively represent the water temperature at the outlet and inlet of the i-th stage heat exchanger of the compressor; η tur,ist is the adiabatic efficiency of the expander; N tur is the total expansion order; q tur,a (t) and T tur,in,i They represent the air flow rate and the inlet air temperature of the i-th stage expander respectively; τ tur,i is the expansion ratio of the i-th stage expander; q tur,w (t) represents the mass flow rate of water in the expander heat exchanger; T tur,w,in,i represents the water temperature at the inlet of the i-th stage heat exchanger of the expander; T c Indicates the chilled water temperature.

7. The method for optimizing a comprehensive energy system taking into account solar thermal energy and energy storage as claimed in claim 1, characterized in that: The upper-level optimization takes minimizing the total cost as the objective function, which is expressed as follows: In the formula, C r is the total cost of system operation; C i,var is the daily operating cost of each device in the system; N d is the number of days; N i,r is the capacity of the i-th device; C i,v is the unit purchase cost of the i-th equipment; r is the conversion coefficient, T i is the life span of the i-th equipment; The optimization variable mentioned above is the mirror area S of the photothermal system. Photothermal 、Distributed photovoltaic capacity N PV 、Wind turbine capacity N WT , Compressed air energy storage capacity N CAES , High temperature storage tank capacity N TES 、Cryogenic storage tank capacity N CES and are the optimization variables for the electric cooling ratio R; The constraints described above are expressed as follows: S Photothermal ≤S Photothermal,max N PV ≤N PV,max 0<N WT <N WT,max N CAES,min ≤N CAES ≤N CAES,max N TES,min <N TES <N TES,max N CES,min <N CES <N CES,max R min <R<R max Among them, N PV Represents the total area occupied by distributed photovoltaics; N PV,max is the total area of ​​distributed photovoltaic installation; N WT is the capacity of the wind turbine; N WT,max is the upper limit of wind turbine capacity; N CAES,min is the lower limit of air compression energy storage capacity; N CAES is the compressed air energy storage capacity; N CAES,max N is the upper limit of air compression energy storage capacity; TES is the capacity of high temperature storage tank; N TES,max N is the upper limit of high temperature storage tank; TES,min is the lower limit of high temperature storage tank; N CES is the capacity of the cryogenic storage tank; N CES,max The upper limit of the low temperature storage tank; N CES,min is the lower limit of low temperature storage tank; R min is the lower limit of the electric cooling ratio; R is the electric cooling ratio; R max It is the upper limit of electric cooling ratio.

8. The method for optimizing a comprehensive energy system taking into account solar thermal energy and energy storage as claimed in claim 1, characterized in that: The lower layer optimizes to minimize CO2 emissions and operating cost objective functions, expressed as follows: In the formula, CO2E IESS is the total CO2 emissions; is the carbon dioxide emission coefficient of natural gas; is the total amount of natural gas; μ e is the carbon dioxide emission coefficient of power grid electricity; P grid is the total amount of electricity purchased; C IESS is the system operation cost; C grid The cost of purchasing electricity from the power grid; C com The operation and maintenance costs of system equipment; Cost of natural gas consumed for the system; The optimization variable described in the lower layer is the output H of the compressed air energy storage system CAES ; The constraint conditions described in the lower layer are expressed as H CAES <N CAES .

9. The method for optimizing a comprehensive energy system taking into account solar thermal energy and energy storage as claimed in claim 1, characterized in that: The process of solving the objective functions of each layer of the two-layer optimization framework using genetic algorithm includes: initializing the population, each individual represents a device capacity configuration and operation scheduling strategy; evaluating the fitness of each individual, and calculating the fitness value according to the total cost minimization objective of the upper layer and the CO2 emission and operation cost minimization objective of the lower layer; Perform selection operations to select individuals with higher fitness, and generate a new generation of population through crossover and mutation operations; in the iterative process, repeat evaluation, selection, crossover and mutation until the stopping condition is met, such as reaching the maximum number of generations or fitness convergence; Output the optimal individual, obtain the optimal equipment capacity configuration and operation scheduling strategy, and achieve the optimization of the solar thermal integrated energy system.

10. An optimization system for a comprehensive energy system considering solar thermal and energy storage, characterized in that: include: A data acquisition module is used to obtain the raw data required for the energy consumption demand and energy output of the weak grid; System establishment module, used to establish a comprehensive energy system with multiple energy coupling including solar thermal and energy storage; A system mathematical modeling module, used to construct a mathematical model of key equipment in the integrated energy system; The two-layer optimization model building module is used to consider economic and environmental protection. The upper layer optimizes the equipment capacity with the objective function of minimizing the total cost as the upper optimization target, and feeds the results back to the lower layer. The lower layer minimizes carbon emissions and operating costs as the objective function, and feeds the operating costs back to the upper layer, forming a two-layer optimization framework. The optimization solution module is used to solve the objective functions of each layer of the double-layer optimization framework using a genetic algorithm, and the result obtained is the optimized solution.

Images