A hierarchical optimization design method for distributed energy supply system

Abstract

The present invention discloses a hierarchical optimization design method for a distributed energy supply system, comprising the following steps: S1, establishing a multi-objective optimization model of a distributed energy supply system according to the stability, high efficiency and economic index requirements of the system; S2, proposing a hierarchical optimization solution method for the multi-objective optimization model; S3, using a hierarchical optimization algorithm to solve the multi-objective optimization model to obtain the optimal design of the system. The present invention introduces the idea of ​​tolerant sequence hierarchical solution into the multi-objective optimization solution, sorts each objective function according to its importance, optimizes and solves each objective function in turn, and optimizes the latter objective function within the optimal solution domain of the previous objective function, ultimately realizing the integrated planning, design and operation optimization of the distributed energy supply system, achieving high-efficiency energy supply, achieving economic construction and operation, and achieving the goal of environmental friendliness.

Description

Technical Field

[0001] The present invention belongs to the field of distributed energy supply, and in particular relates to a hierarchical optimization design method for a distributed energy supply system. Background Art

[0002] With the continuous development of science and technology, stability and efficiency are two very important indicators for distributed systems whose core units are combined cooling, heating and power, and connected with renewable energy sources such as photovoltaics and wind turbines. Stability is the basic condition that a distributed energy supply system must first meet, and it is also the key to ensuring social security energy supply. Under the premise of meeting stability conditions, the ultimate optimization goal of various combined cooling, heating and power systems is to coordinate and optimize the efficiency of energy supply, the economy of distributed energy supply systems (including construction investment economy and operation economy), and the environmental friendliness of the system.

[0003] Under different climatic conditions and different building space conditions, the distributed energy supply systems planned to meet the stable and efficient indicators are different. Therefore, how to meet the needs of users in different scenarios for efficient and stable distributed energy supply systems for combined cooling, heating and power is an urgent problem to be solved.

[0004] At present, the optimization design of distributed energy supply systems mainly focuses on the single-objective or multi-objective optimization of the system's economy and environment, but rarely involves the stability and efficiency of distributed energy supply systems. Moreover, in the optimization design process of distributed energy supply systems, when solving the established multi-objective optimization model, most of them adopt the method of converting the multi-objective into a single objective through the Lagrangian function form or directly solving the multi-objective optimization model to obtain a non-inferior solution set. This method cannot obtain a better planning scheme, and it is even more impossible to achieve hierarchical control between objectives.

[0005] It can be seen that there is no method to optimize the distributed energy supply system by using the tolerant hierarchical idea. In view of the above problems, a distributed energy supply system hierarchical optimization design method is designed. Summary of the invention

[0006] In view of the shortcomings of the prior art, the purpose of the present invention is to provide a hierarchical optimization design method for a distributed energy supply system, which solves the problem that the prior art lacks optimization of the stability and efficiency of the distributed energy supply system, and solves the problem that multiple objectives are converted into a single objective in the form of a Lagrangian function or the multi-objective optimization model is directly solved to obtain a non-inferior solution set, but a better planning scheme cannot be obtained and hierarchical control between objectives cannot be achieved.

[0007] The purpose of the present invention can be achieved through the following technical solutions:

[0008] A distributed energy supply system hierarchical optimization design method comprises the following steps:

[0009] S1. Establish a multi-objective optimization model for the distributed energy supply system based on the system's stability, efficiency and economy requirements;

[0010] S2. A hierarchical optimization solution method is proposed for multi-objective optimization models;

[0011] S3. Use hierarchical optimization algorithm to solve the multi-objective optimization model to obtain the optimal design of the system.

[0012] Furthermore, the multi-objective optimization model of the distributed energy supply system constructed in S1 includes the following steps:

[0013] S1.1. Establish the first-level optimization objectives;

[0014] Establish the first-level optimization target f1 with the energy shortage rate as the system stability indicator,

[0015]

[0016] S1.2, establish the second-level optimization objectives;

[0017] Establish the second-level optimization goal f2 with comprehensive energy utilization as the system efficiency index,

[0018]

[0019] S1.3, establish the third level optimization goal;

[0020] Establish the third-level optimization goal f3 with annual total cost as the system economic index,

[0021] min C ATC =C inv +C pur +C om (3)

[0022]

[0023]

[0024]

[0025]

[0026] S1.4, establish the first level of optimization constraints;

[0027] The constraints of this layer are divided into equality constraints and inequality constraints. The equality constraints include the balance of the three energies of cooling, heating and electricity of the whole system. The inequality constraints mainly require that the capacity range of each device should be between the upper and lower limits, and the charging and discharging rate of the energy storage device should be within a certain range. The inequality constraints ensure the normal, safe and stable operation of the equipment.

[0028] S1.5, establish the constraints for the second-level optimization;

[0029] This layer of optimization is completed on the basis of the first layer of optimization, and its constraint condition is the stable domain of the first layer of optimization, or a series of combination schemes;

[0030] S1.6. Establish constraints for the third-level optimization;

[0031] This layer of optimization is completed on the basis of the second layer of optimization, and its constraints are the efficient domain of the second layer of optimization, or a series of combination schemes.

[0032] Furthermore, in formula (1), |ΔW E,T | is the undersupply of the total power load of the user in the time interval T; |ΔW H,T | is the undersupply of the user's heat load in the time interval T; |ΔW C,T | is the undersupply of cooling load of the user in the time interval T; W E,T is the total electrical load in the T period; W H,T is the total heat load in the T period; W C,T is the total cooling load in the T period; P ΔE is the shortage rate of total electric load power; P ΔH P is the shortage rate of heat load power; ΔC is the shortage rate of cooling load power;

[0033] In formula (2), η is the annual average comprehensive energy utilization rate; W is the annual net output power, in kilowatt-hours (kWh); Q1 is the annual total waste heat heating, in megajoules (MJ); Q2 is the annual total waste heat cooling, in megajoules (MJ); B is the annual total gas consumption, in cubic meters (m3). 3 );Q L The low calorific value of the gas, in megajoules per cubic meter (MJ / m 3 );Q s The net annual energy stored in the energy storage device, in megajoules (MJ);

[0034] In formula (3), C ATC is the total annual cost of the energy supply system (yuan); C inv is the total investment in energy supply system equipment (yuan); C pur The total cost of natural gas and electricity consumed by the energy supply system in a year; Com Total cost of maintenance and management of the combined cooling, heating and power system (yuan);

[0035] In formula (4), CRF represents the investment recovery rate, r represents the interest rate, and n is the service life of the equipment;

[0036] In formula (5), is the unit capacity price of brand j for type i equipment (yuan / kW); is the i-th type of equipment, j is the brand of a certain equipment, when When , it indicates that the device of this brand is selected; is the rated capacity of brand j of type i equipment; is the investment payback rate of brand j for type i equipment;

[0037] In formula (6), C NG is the natural gas price (yuan / kWh); is the annual gas consumption of the gas turbine (kWh); The annual gas consumption of the gas boiler (kWh); The total amount of electricity purchased by the system from the power grid annually (kWh); is the total amount of electricity sold by the system to the grid in a year (kWh); C im is the price of electricity purchased by the system from the power grid (yuan / kWh); C ex The price of electricity sold by the system to the grid (yuan / kWh);

[0038] In formula (7), is the annual maintenance and management cost per unit capacity of equipment of type i and brand j (yuan / kWh).

[0039] Furthermore, S1.4 includes the following constraint balance conditions:

[0040] ⑴Balance constraints of electric energy

[0041]

[0042] (2) Thermal energy balance constraints

[0043]

[0044] ⑶ Balance of cold energy

[0045]

[0046] ⑷Selection of each equipment

[0047]

[0048]

[0049] ⑸ Constraints on energy exchange between CCHP and power grid

[0050]

[0051]

[0052]

[0053]

[0054] ⑹ Equipment capacity constraints

[0055]

[0056]

[0057]

[0058]

[0059]

[0060]

[0061] ⑺Battery Constraints

[0062]

[0063]

[0064]

[0065]

[0066]

[0067] ⑻Constraints of heat storage

[0068]

[0069]

[0070]

[0071]

[0072]

[0073]

[0074] ⑼ Constraints of Photovoltaic Panels

[0075]

[0076]

[0077] ⑽ Constraints of gas boilers

[0078]

[0079]

[0080] ⑾Constraints of heat exchanger

[0081]

[0082] ⑿ Constraints of gas turbines

[0083]

[0084]

[0085] Furthermore, in formula (8), E demand,s,h is the user's electricity load demand, E PV,s,h is the photovoltaic power generation, E GT,s,h is the power generation of the gas turbine, E im,s,h E is the amount of electricity purchased from the power grid, ex,s,h E is the amount of electricity sold to the power grid, bat-out,s,h is the discharge capacity of the battery, E bat-in,s,h is the charge of the battery, E ec,s,h is the power consumption of the electric refrigerator;

[0086] In formula (9), is the user's heat load demand, η e is the efficiency of the heat exchanger, is the heat output of the gas boiler, is the heating capacity of the gas turbine, is the heat released by the heat storage tank, is the heat storage capacity of the heat storage tank, is the heat consumption of the absorption chiller;

[0087] In formula (10), is the user's cooling load demand, is the cooling capacity of the absorption chiller, Refrigeration capacity of the electric refrigerator;

[0088] In formulas (11) and (12), Indicates whether the device is selected, i is the type of device, j is the jth brand of the i-th type of device;

[0089] In formula (13), The maximum value of electricity sold by the system to the grid;

[0090] In formula (14), The maximum amount of electricity that the system can purchase from the grid;

[0091] Formula (15) indicates that the system requires that energy cannot be fed in and out simultaneously;

[0092] Formula (16) indicates that during the early morning load valley period, no electric energy can be fed into the grid;

[0093] In formulas (17), (18), (19), (20), (21) and (22), CAP limit is the capacity limit of the device, is the minimum load rate of the selected gas turbine, is the minimum load rate of the selected absorption chiller, is the minimum load rate of the selected electric refrigerator, is the minimum load rate of the selected gas boiler, is the minimum load rate of the selected heat recovery device, is the maximum load rate of the selected gas turbine, is the maximum load rate of the selected absorption chiller, is the maximum load rate of the selected electric refrigerator, is the maximum load rate of the selected gas boiler, is the maximum load rate of the selected heat recovery device;

[0094] In formulas (23), (24), (25), (26) and (27), is the energy storage efficiency of the battery, is the charging power, is the discharge power, is the maximum charging rate, is the maximum discharge rate, and formula (27) constrains that charging and discharging cannot be performed simultaneously;

[0095] In formulas (28), (29), (30), (31), (31), (32) and (33), is the heat storage efficiency of the heat storage tank, is the heating power, is the heat release power, is the maximum charging and discharging rate. Formula (33) constrains that charging and discharging cannot be performed simultaneously.

[0096] In formulas (34) and (35), SRI represents the solar radiation index, T represents the temperature, and E PVRepresents the electrical energy generated by photovoltaic panels, A PV is the area of ​​the photovoltaic panel, η PV is the efficiency of the solar panel.

[0097] Furthermore, the multi-objective hierarchical optimization method in S2 comprises the following steps:

[0098] S2.1. Sort each objective function by importance. Suppose the importance of each objective function is: f1(x), f2(x), f3(x);

[0099] S2.2, taking f1(x) as the optimization target, optimize the following equation and obtain the optimal solution;

[0100]

[0101] S2.3, in {f1 *} to optimize f2(x) within a certain range of the set;

[0102]

[0103] This level of optimization is completed on the basis of the first level optimization, and its constraint condition is the stable domain of the first level optimization, that is, firstly relax the range of the optimal value of the first level objective function, and take a certain wide capacity ε1 (ε1>0), that is, when optimizing the second level objective function, the constraint of the first level objective function is expanded to a range near its optimal value;

[0104] S2.4, in {f2 *} to optimize f3(x) within a certain range of the set;

[0105]

[0106] This layer of optimization is completed on the basis of the second layer optimization, and its constraint condition is the efficient domain range of the second layer optimization; first, the range of the optimal value of the second layer objective function is relaxed, and a certain wide capacity ε2 (ε2>0) is taken. That is, when optimizing the third layer objective function, the constraint of the second layer objective function is expanded to a range near its optimal value.

[0107] A distributed energy supply system includes: renewable energy power generation, batteries, electric refrigerators, natural gas, gas turbines, gas boilers, absorption refrigerators, heat storage tanks, electrical loads, cooling loads and heating loads.

[0108] Furthermore, renewable energy power generation, batteries and gas turbines will transmit the generated electrical energy to electrical loads and electric refrigerators, and batteries can realize the transfer of electrical energy; natural gas supplies energy to gas turbines and gas boilers, and gas turbines and gas boilers will supply the generated heat energy to absorption refrigerators, heat storage tanks and heat loads, and heat storage tanks can realize the transfer of heat energy; electric refrigerators and absorption refrigerators will convert the received energy into cold energy and transmit it to cold loads.

[0109] Beneficial effects of the present invention:

[0110] 1. The hierarchical optimization design method for distributed energy supply systems proposed in the present invention introduces the idea of ​​tolerant sequence hierarchical design into the multi-objective optimization solution. By sorting each objective function according to its importance, each objective function is optimized in turn. The latter objective function is optimized within the optimal solution domain of the previous objective function, and finally the integrated planning, design and operation optimization of the distributed energy supply system are realized, which can achieve the goal of high-efficiency energy supply, economical construction and operation, and environmental friendliness. BRIEF DESCRIPTION OF THE DRAWINGS

[0111] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0112] Figure 1 is a schematic diagram of a distributed energy supply system according to an embodiment of the present invention;

[0113] Figure 2 is a flow chart of a hierarchical optimization method according to an embodiment of the present invention;

[0114] Figure 3 This is a typical day cooling, heating and power load diagram in spring according to an embodiment of the present invention;

[0115] Figure 4 This is a typical day cooling, heating and power load diagram in summer according to an embodiment of the present invention;

[0116] Figure 5 This is a typical day cooling, heating and power load diagram in autumn according to an embodiment of the present invention;

[0117] Figure 6 This is a typical day cooling, heating and power load diagram in winter according to an embodiment of the present invention;

[0118] Figure 7 is a curve diagram of solar radiation density variation on a typical day in spring according to an embodiment of the present invention;

[0119] Figure 8is a graph showing changes in solar radiation density on a typical day in summer according to an embodiment of the present invention;

[0120] Fig. 9 is a curve diagram of solar radiation density variation on a typical day in autumn according to an embodiment of the present invention;

[0121] Fig.10 1 is a curve diagram of solar radiation density variation on a typical day in winter according to an embodiment of the present invention. DETAILED DESCRIPTION

[0122] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0123] like Figure 1 As shown, a distributed energy supply system includes: renewable energy power generation, storage battery, electric refrigerator, natural gas, gas turbine, gas boiler, absorption refrigerator, heat storage tank, electric load, cold load and heat load. Renewable energy power generation, storage battery and gas turbine transmit the generated electric energy to the electric load and electric refrigerator, and storage battery can realize the transfer of electric energy; natural gas supplies energy to gas turbine and gas boiler, gas turbine and gas boiler supply the generated heat energy to absorption refrigerator, heat storage tank and heat load, and heat storage tank can realize the transfer of heat energy; electric refrigerator and absorption refrigerator convert the received energy into cold energy and transmit it to cold load.

[0124] Under the condition that the energy demand of the actual environment is met, the distributed energy supply system can operate stably and efficiently. A three-layer objective function is established. According to the importance of each layer of objectives, the stability, economy and efficiency indicators of the system are considered and analyzed layer by layer. In this embodiment, the first layer realizes that the system can operate stably under the condition that the energy demand of the actual environment is met; the second layer is to further optimize the efficiency planning results in the stable domain based on the stability of the optimization results of the first layer; the third layer is to optimize the economy of the system based on the optimization of the second layer. The optimization process is as follows: Figure 2 shown.

[0125] A distributed energy supply system hierarchical optimization design method comprises the following steps:

[0126] S1. Establish a multi-objective optimization model for the distributed energy supply system based on the system's stability, efficiency and economy requirements;

[0127] S2. A hierarchical optimization solution method is proposed for multi-objective optimization models;

[0128] S3. Use hierarchical optimization algorithm to solve the multi-objective optimization model to obtain the optimal design of the system.

[0129] S1 constructs a multi-objective optimization model for distributed energy supply systems, including the following steps:

[0130] S1.1. Establish the first-level optimization objectives;

[0131] The first-level optimization target f1 is established with the energy shortage rate as the system stability indicator.

[0132] The following formula (1) is proposed:

[0133]

[0134] In formula (1), |ΔW E,T | is the undersupply of the total power load of the user in the time interval T; |ΔW H,T The undersupply of the user's heat load in the time interval T; |ΔW C,T | is the undersupply of cooling load of the user in the time interval T; W E,T is the total electrical load in the T period; W H,T is the total heat load in the T period; W C,T is the total cooling load in the T period; P ΔE is the shortage rate of total electric load power; P ΔH P is the shortage rate of heat load power; ΔC It is the shortage rate of cooling load power.

[0135] S1.2, establish the second-level optimization objectives;

[0136] Establish the second-level optimization goal f2 with comprehensive energy utilization as the system efficiency indicator.

[0137] The following formula (2) is proposed:

[0138]

[0139] In formula (2), η is the annual average comprehensive energy utilization rate; W is the annual net output power, in kilowatt-hours (kWh); Q1 is the annual total waste heat heating, in megajoules (MJ); Q2 is the annual total waste heat cooling, in megajoules (MJ); B is the annual total gas consumption, in cubic meters (m3). 3 );Q L The low calorific value of the gas, in megajoules per cubic meter (MJ / m 3 );Q s It is the net annual stored energy of the energy storage device, measured in megajoules (MJ).

[0140] S1.3, establish the third level optimization goal;

[0141] Establish the third-level optimization goal f3 with annual total cost as the system economic index

[0142] The following equations (3), (4), (5), (6) and (7) are proposed:

[0143] min C ATC =C inv +C pur +C om (3)

[0144] In formula (3), C ATC is the total annual cost of the energy supply system (yuan); C inv is the total investment in energy supply system equipment (yuan); C pur The total cost of natural gas and electricity consumed by the energy supply system in a year; C om The total cost of maintenance and management of the combined heating, cooling and power system (yuan).

[0145]

[0146] In formula (4), CRF represents the investment recovery rate, r represents the interest rate, and n is the useful life of the equipment.

[0147]

[0148] In formula (5), is the unit capacity price of brand j for type i equipment (yuan / kW); is the i-th type of equipment, j is the brand of a certain equipment, when When , it indicates that the device of this brand is selected; is the rated capacity of brand j of type i equipment; is the investment recovery rate of brand j for type i equipment.

[0149]

[0150] In formula (6), C NG is the natural gas price (yuan / kWh); is the annual gas consumption of the gas turbine (kWh); The annual gas consumption of the gas boiler (kWh); The total amount of electricity purchased by the system from the power grid annually (kWh); is the total amount of electricity sold by the system to the grid in a year (kWh); C im is the price of electricity purchased by the system from the power grid (yuan / kWh); C ex The price of electricity sold by the system to the grid (yuan / kWh).

[0151]

[0152] In formula (7), is the annual maintenance and management cost per unit capacity of equipment of type i and brand j (yuan / kWh).

[0153] S1.4, establish the first level of optimization constraints;

[0154] The constraints optimized at this level are divided into equality constraints and inequality constraints. The equality constraints include the balance of the three types of energy: cold, heat and electricity of the entire system. The inequality constraints mainly require that the capacity range of each device should be between the upper and lower limits, and the charging and discharging rates of the energy storage device should be within a certain range. The inequality constraints ensure the normal, safe and stable operation of the equipment.

[0155] ⑴Balance constraints of electric energy

[0156]

[0157] In formula (8), E demand,s,h is the user's electricity load demand, E PV,s,h is the photovoltaic power generation, E GT,s,h is the power generation of the gas turbine, E im,s,h E is the amount of electricity purchased from the power grid, ex,s,h E is the amount of electricity sold to the power grid, bat-out,s,h is the discharge capacity of the battery, E bat-in,s,h is the charge of the battery, E ec,s,h is the power consumption of the electric refrigerator.

[0158] (2) Thermal energy balance constraints

[0159]

[0160] In formula (9), is the user's heat load demand, η e is the efficiency of the heat exchanger, is the heat output of the gas boiler, is the heating capacity of the gas turbine, is the heat released by the heat storage tank, is the heat storage capacity of the heat storage tank, is the heat consumption of the absorption chiller.

[0161] ⑶ Balance of cold energy

[0162]

[0163] In formula (10), is the user's cooling load demand, is the cooling capacity of the absorption chiller, The cooling capacity of an electric refrigerator.

[0164] ⑷Selection of each equipment

[0165]

[0166]

[0167] In formulas (11) and (12), Indicates whether the device is selected, i is the type of device, and j is the jth brand of the i-th type of device.

[0168] ⑸ Constraints on energy exchange between CCHP and power grid

[0169]

[0170] In formula (13), It is the maximum value of electricity sold by the system to the grid.

[0171]

[0172] In formula (14), The maximum amount of electricity the system can purchase from the grid.

[0173]

[0174] Formula (15) indicates that the system requires that energy cannot be fed in and out at the same time.

[0175]

[0176] Formula (16) indicates that during the early morning load valley period, no electric energy can be fed into the grid.

[0177] ⑹ Equipment capacity constraints

[0178]

[0179]

[0180]

[0181]

[0182]

[0183]

[0184] In formulas (17), (18), (19), (20), (21) and (22), CAP limit is the capacity limit of the device, is the minimum load rate of the selected gas turbine, is the minimum load rate of the selected absorption chiller, is the minimum load rate of the selected electric refrigerator, is the minimum load rate of the selected gas boiler, is the minimum load rate of the selected heat recovery device, is the maximum load rate of the selected gas turbine, is the maximum load rate of the selected absorption chiller, is the maximum load rate of the selected electric refrigerator, is the maximum load rate of the selected gas boiler, is the maximum load rate of the selected heat recovery device.

[0185] ⑺Battery Constraints

[0186]

[0187]

[0188]

[0189]

[0190]

[0191] In formulas (23), (24), (25), (26) and (27), is the energy storage efficiency of the battery, is the charging power, is the discharge power, is the maximum charging rate, is the maximum discharge rate. Formula (27) constrains that charging and discharging cannot be performed simultaneously.

[0192] ⑻Constraints of heat storage

[0193]

[0194]

[0195]

[0196]

[0197]

[0198]

[0199] In formulas (28), (29), (30), (31), (31), (32) and (33), is the heat storage efficiency of the heat storage tank, is the heating power, is the heat release power, is the maximum charge and discharge heat rate. Formula (33) constrains that charge and discharge heat cannot be performed simultaneously.

[0200] ⑼ Constraints of Photovoltaic Panels

[0201]

[0202]

[0203] In formulas (34) and (35), SRI represents the solar radiation index, T represents the temperature, and E PV Represents the electrical energy generated by photovoltaic panels, A PV is the area of ​​the photovoltaic panel, η PV is the panel efficiency, and SRI0 = 1000W / m 2 , T0 = 25, AM0 = 1.5, P1 = 0.2820, P2 = 0.3967, P3 = -0.4473, P4 = -0.093, P5 = 0.1601. The efficiency of photovoltaic panels is related to the light intensity per unit area, temperature, and air volume.

[0204] ⑽ Constraints of gas boilers

[0205]

[0206]

[0207] ⑾Constraints of heat exchanger

[0208]

[0209] ⑿ Constraints of gas turbines

[0210]

[0211]

[0212] S1.5, establish the constraints for the second-level optimization;

[0213] This layer of optimization is completed on the basis of the first layer of optimization, and its constraints are the stable domain of the first layer of optimization, or a series of combination schemes.

[0214] S1.6. Establish constraints for the third-level optimization;

[0215] This layer of optimization is completed on the basis of the second layer of optimization, and its constraints are the efficient domain of the second layer of optimization, or a series of combination schemes.

[0216] The multi-objective hierarchical optimization method in S2 includes the following steps:

[0217] S2.1. Sort each objective function by importance. Suppose the importance of each objective function is: f1(x), f2(x), f3(x);

[0218] S2.2, taking f1(x) as the optimization target, optimize the following equation and obtain the optimal solution;

[0219]

[0220] S2.3, in {f1 *} to optimize f2(x) within a certain range of the set;

[0221]

[0222] First, relax the range of the optimal value of the first-level objective function and take a certain wide capacity ε1 (ε1>0). That is, when optimizing the second-level objective function, expand the constraints of the first-level objective function to a range near its optimal value.

[0223] S2.4, in {f2 *} to optimize f3(x) within a certain range of the set;

[0224]

[0225] First, relax the range of the optimal value of the second-level objective function and take a certain wide capacity ε2 (ε2>0). That is, when optimizing the third-level objective function, expand the constraints of the second-level objective function to a range near its optimal value.

[0226] In this example, the annual load consists of four typical days in spring, summer, autumn and winter. The load and solar radiation density vary as follows: Figure 3 — Fig.10 In the optimization design, four types of gas turbines, three types of absorption chillers, three types of electric chillers, three types of gas boilers and two types of energy storage devices are provided for selection. The parameters of each type of equipment are shown in Table 1-Table 4. Through hierarchical optimization solution, the planning and operation scheme of the system is shown in Table 5.

[0227] Table 1 Performance parameters of four types of gas turbines

[0228] Device Type Rated capacity(MW) Electrical efficiency Thermal efficiency Investment cost (yuan / kW) A 0.8 0.398 0.475 4500 B 1.3 0.425 0.447 4250 C 3 0.43 0.426 3690 D 9.5 0.485 0.413 4760

[0229] Table 2 Performance parameters of three types of absorption chillers

[0230] Device Type Refrigeration coefficient Investment cost (yuan / KW) Maintenance cost coefficient (yuan / KWh) A 1.44 2000 0.047 B 1.41 1800 0.044 C 1.33 1100 0.042

[0231] Table 3 Performance parameters of three types of electric refrigerators

[0232] Device Type Refrigeration coefficient Investment cost (yuan / KW) Maintenance cost coefficient (yuan / KWh) A 3-1 1000 0.041 B 4.2 1650 0.046 C 5.4 1900 0.051

[0233] Table 4 Performance parameters of three types of gas boilers

[0234] Device Type Thermal efficiency Investment cost (yuan / KW) Maintenance cost coefficient (yuan / KWh) A 0.88 260 0.045 B 0.90 320 0.052 C 0.91 340 0.064

[0235] Table 5 Results of optimization scheme for distributed energy supply system

[0236] equipment Equipment selection Equipment capacity (KW) Number of devices Gas turbine or internal combustion engine Device C 3000 4 Absorption Chiller Device A 7732.6 1 Electric Refrigerator Device C 10000 1 Gas boiler Device C 15542.8 1 Battery - 2.5MW 1 Heat storage tank - 2.0MW 1

[0237] By solving the model, the result is: when the stability margin does not exceed 5%, the average annual comprehensive energy utilization efficiency of the distributed energy supply system reaches 88.62%, which is much higher than the 70% energy utilization efficiency stipulated in the "Technical Regulations for Gas-fired Combined Heat and Power Engineering", and can achieve stable and efficient planning and design of the system.

[0238] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0239] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments, and the above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, and these changes and improvements all fall within the scope of the present invention to be protected.

Claims

1. A hierarchical optimization design method for a distributed energy supply system, characterized in that: The steps include: S1. Establish a multi-objective optimization model for the distributed energy supply system based on the system's stability, efficiency and economy index requirements; S2. A hierarchical optimization solution method is proposed for multi-objective optimization models; S3, using hierarchical optimization algorithm to solve the multi-objective optimization model to obtain the optimal design of the system; The S1 constructs a multi-objective optimization model for a distributed energy supply system, including the following steps: S1.

1. Establish the first-level optimization objectives; Establish the first-level optimization target f1 with the energy shortage rate as the system stability indicator, S1.2, establish the second level optimization goal; Establish the second-level optimization goal f2 with comprehensive energy utilization as the system efficiency index, S1.3, establish the third level optimization goal; Establish the third-level optimization goal f3 with annual total cost as the system economic index, my C ATC =C inv +C pur +C om (3) S1.4, establish the first level of optimization constraints; The constraints of this layer are divided into equality constraints and inequality constraints. The equality constraints include the balance of the three energies of cooling, heating and electricity of the whole system. The inequality constraints mainly require that the capacity range of each device should be between the upper and lower limits, and the charging and discharging rate of the energy storage device should be within a certain range. The inequality constraints ensure the normal, safe and stable operation of the equipment. S1.5, establish the constraints for the second-level optimization; This layer of optimization is completed on the basis of the first layer of optimization, and its constraint condition is the stable domain of the first layer of optimization, or a series of combination schemes; S1.

6. Establish constraints for the third-level optimization; This layer of optimization is completed on the basis of the second layer of optimization, and its constraints are the efficient domain of the second layer of optimization, or a series of combination schemes; In formula (1), |ΔW E,T | is the undersupply of the total power load of the user in the time interval T; |ΔW H,T The undersupply of heat load of the user in the time interval T; |ΔW C,T | is the undersupply of cooling load of the user in the time interval T; W E,T is the total electrical load in the T period; W H,T is the total heat load in the T period; W C,T is the total cooling load in the T period; P ΔE is the shortage rate of total electric load power; P ΔH P is the shortage rate of heat load power; ΔC is the shortage rate of cooling load power; In formula (2), η is the annual average comprehensive energy utilization rate; W is the annual net output of electricity, in kilowatt-hours (kWh); Q1 is the annual total waste heat heating, in megajoules (MJ); Q2 is the annual total waste heat cooling, in megajoules (MJ); B is the annual total gas consumption, in cubic meters (m 3 );Q L The low calorific value of the gas, in megajoules per cubic meter (MJ / m 3 );Q s The net annual energy stored in the energy storage device, in megajoules (MJ); In formula (3), C ATC is the total annual cost of the energy supply system, C inv is the total investment in energy supply system equipment, C pur is the total cost of natural gas and electricity consumed by the energy supply system per year, C om is the total cost of maintenance and management of the combined cooling, heating and power system, in yuan; In formula (4), CRF represents the investment recovery rate, r represents the interest rate, and n is the service life of the equipment; In formula (5), is the unit capacity price of brand j of type i equipment, in yuan / kW; is the i-th type of equipment, j is the brand of a certain equipment, when When , it indicates that the device of this brand is selected; is the rated capacity of brand j of type i equipment; is the investment payback rate of brand j for equipment of type i; In formula (6), C NG is the natural gas price, C im is the price at which the system purchases electricity from the grid, C ex The price of electricity sold by the system to the grid, in yuan / kWh; is the annual gas consumption of the gas turbine, is the annual gas consumption of the gas boiler, The total amount of electricity purchased by the system from the power grid each year, The total amount of electricity sold by the system to the grid per year, in kilowatt-hours; In formula (7), is the annual maintenance and management cost per unit capacity of equipment of type i and brand j, in yuan / kWh; S1.4 includes the following constraint balance conditions: ⑴Balance constraints of electric energy (2) Thermal energy balance constraints ⑶ Balance of cold energy ⑷Selection of each equipment ⑸ Constraints on energy exchange between CCHP and power grid ⑹ Equipment capacity constraints ⑺Battery Constraints ⑻Constraints of heat storage ⑼ Constraints of Photovoltaic Panels ⑽ Constraints of gas boilers ⑾Constraints of heat exchanger ⑿ Constraints of gas turbines In formula (8), E demand,s,h is the user's electricity load demand, E PV,s,h is the photovoltaic power generation, E GT,s,h is the power generation of the gas turbine, E im,s,h E is the amount of electricity purchased from the power grid, ex,s,h E is the amount of electricity sold to the power grid, bat-out,s,h is the discharge capacity of the battery, E bat-in,s,h is the charge of the battery, E ec,s,h is the power consumption of the electric refrigerator; In formula (9), is the user's heat load demand, η e is the efficiency of the heat exchanger, is the heat output of the gas boiler, is the heating capacity of the gas turbine, is the heat released by the heat storage tank, is the heat storage capacity of the heat storage tank, is the heat consumption of the absorption chiller; In formula (10), is the user's cooling load demand, is the cooling capacity of the absorption chiller, Refrigeration capacity of the electric refrigerator; In formulas (11) and (12), Indicates whether the device is selected, i is the type of device, j is the jth brand of the i-th type of device; In formula (13), The maximum value of electricity sold by the system to the grid; In formula (14), The maximum amount of electricity that the system can purchase from the grid; Formula (15) indicates that the system requires that energy cannot be fed in and out simultaneously; Formula (16) indicates that during the early morning load valley period, no electric energy can be fed into the grid; In formulas (17), (18), (19), (20), (21) and (22), CAP limit is the capacity limit of the device, is the minimum load rate of the selected gas turbine, is the minimum load rate of the selected absorption chiller, is the minimum load rate of the selected electric refrigerator, is the minimum load rate of the selected gas boiler, is the minimum load rate of the selected heat recovery device, is the maximum load rate of the selected gas turbine, is the maximum load rate of the selected absorption chiller, is the maximum load rate of the selected electric refrigerator, is the maximum load rate of the selected gas boiler, is the maximum load rate of the selected heat recovery device; In formulas (23), (24), (25), (26) and (27), is the energy storage efficiency of the battery, is the charging power, is the discharge power, is the maximum charging rate, is the maximum discharge rate, and formula (27) constrains that charging and discharging cannot be performed simultaneously; In formulas (28), (29), (30), (31), (31), (32) and (33), is the heat storage efficiency of the heat storage tank, is the heating power, is the heat release power, is the maximum charging and discharging rate. Formula (33) constrains that charging and discharging cannot be performed simultaneously. In formulas (34) and (35), SRI represents the solar radiation index, T represents the temperature, and E PV Represents the electrical energy generated by photovoltaic panels, A PV is the area of ​​the photovoltaic panel, η PV is the efficiency of the solar panel.

2. A distributed energy supply system hierarchical optimization design method according to claim 1, characterized in that: The multi-objective hierarchical optimization method in S2 comprises the following steps: S2.

1. Sort each objective function by importance. Suppose the importance of each objective function is: f1(x), f2(x), f3(x); S2.2, taking f1(x) as the optimization target, optimize the following equation and obtain the optimal solution; S2.3, in {f1 * } to optimize f2(x) within a certain range of the set; This level of optimization is completed on the basis of the first level optimization, and its constraint condition is the stable domain of the first level optimization, that is, firstly relax the range of the optimal value of the first level objective function, and take a certain wide capacity ε1 (ε1>0), that is, when optimizing the second level objective function, the constraint of the first level objective function is expanded to a range near its optimal value; S2.4, in Optimize f3(x) within a certain range of the set; This layer of optimization is completed on the basis of the second layer optimization, and its constraint condition is the efficient domain range of the second layer optimization; first, the range of the optimal value of the second layer objective function is relaxed, and a certain wide capacity ε2 (ε2>0) is taken. That is, when optimizing the third layer objective function, the constraint of the second layer objective function is expanded to a range near its optimal value.

3. A distributed energy supply system using a distributed energy supply system hierarchical optimization design method according to any one of claims 1 to 2, comprising: Renewable energy generation, batteries, electric chillers, natural gas, gas turbines, gas boilers, absorption chillers, thermal storage tanks, electrical loads, cooling loads and heating loads.

4. A distributed energy supply system according to claim 3, characterized in that: Renewable energy generation, batteries and gas turbines will transmit the generated electrical energy to electrical loads and electric refrigerators. Batteries can realize the transfer of electrical energy; natural gas supplies energy to gas turbines and gas boilers. Gas turbines and gas boilers will supply the generated heat energy to absorption refrigerators, heat storage tanks and heat loads. Heat storage tanks can realize the transfer of heat energy; electric refrigerators and absorption refrigerators will convert the received energy into cold energy and transmit it to cold loads.

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