Transformer substation multi-energy complementary capacity configuration optimization method and system considering waste heat recovery

Abstract

The invention relates to a transformer substation multi-energy complementary capacity configuration optimization method considering waste heat recovery, and the method comprises the steps: firstly building output models of distributed wind power, distributed photovoltaic and energy storage equipment and waste heat thermoelectric power generation equipment in a transformer substation energy supply system; then calculating the load of the typical energy consumption scene of the transformer substation; taking the highest renewable energy utilization rate and the lowest operation cost in a transformer station energy supply system as optimization targets, and taking a power supply system electric power balance constraint, a photovoltaic wind power output constraint, a power supply system output constraint, a start-stop time constraint and an energy storage system output constraint as constraint conditions; establishing a transformer substation multi-energy complementary capacity configuration multi-objective optimization model; and finally, solving the optimization model by adopting a genetic algorithm to obtain a multi-energy complementary capacity configuration scheme of the transformer substation, and the method effectively configures renewable energy sources and waste heat resources, so that the energy utilization efficiency is improved.

Description

Technical Field

[0001] The invention relates to a method for optimizing the configuration of multi-energy complementary capacity of a transformer substation considering waste heat recovery, and belongs to the technical field of distributed energy. Background Art

[0002] With the rapid development of renewable energy, traditional power systems are facing many challenges, such as the increased volatility and uncertainty of the power grid after the access of renewable energy, and the impact on the stability and security of power supply. In order to meet these challenges, multi-energy complementary technology has gradually become a research hotspot. Multi-energy complementary systems achieve efficient utilization and stable supply of energy by integrating multiple energy forms (including wind energy, solar energy, energy storage systems, etc.). In addition to renewable energy, the utilization potential of waste heat resources in substations also needs to be explored and improved. Although the ultimate goal of transformer heat dissipation is to reduce the internal temperature of the transformer and ensure that the transformer works at a safe temperature, potential energy waste is caused in the process of heat dissipation. According to incomplete statistics, the energy lost in the form of heat by 35-220kV voltage level step-down transformers in urban power grids accounts for about 1.6% of the power generation. However, how to effectively configure these renewable energy and waste heat resources in substations to achieve optimal energy utilization efficiency and grid stability is an urgent problem to be solved. Summary of the invention

[0003] In order to solve the problems existing in the prior art, the present invention provides a substation multi-energy complementary capacity configuration optimization method for effectively configuring renewable energy and waste heat resources, thereby improving energy utilization efficiency.

[0004] In order to achieve the above object, the technical solution proposed by the present invention is: a substation multi-energy complementary capacity configuration optimization method considering waste heat recovery, characterized in that it includes the following steps:

[0005] Establish output models for distributed wind power, distributed photovoltaics, energy storage equipment and waste heat thermoelectric power generation equipment in the substation energy supply system;

[0006] Calculate the load of the substation in typical energy usage scenarios, including the electric load, heat load and cooling load in the heating season, cooling season and transition season;

[0007] Taking the highest utilization rate of renewable energy and the lowest operating cost in the substation energy supply system as the optimization goal, and taking the power balance constraint of the energy supply system, the photovoltaic and wind power output constraint, the power supply system output constraint, the start-stop time constraint, and the energy storage system output constraint as the constraint conditions, a multi-objective optimization model for the substation multi-energy complementary capacity configuration is established;

[0008] The genetic algorithm is used to solve the multi-objective optimization model of substation multi-energy complementary capacity configuration, and the substation multi-energy complementary capacity configuration scheme is obtained.

[0009] The further design of the above technical solution is as follows: Step S1 includes the following:

[0010] The distributed photovoltaic output model is:

[0011]

[0012] in, is the maximum output power of the photovoltaic unit in period t, is the output power of the photovoltaic unit under standard conditions, I stc is the solar radiation intensity under standard conditions, T stc is the standard condition temperature, I t is the solar radiation intensity in period t, T t is the temperature during time period t, α is the power temperature coefficient of the photovoltaic cell;

[0013] The distributed wind power output model is:

[0014]

[0015] in, is the maximum output power of the wind turbine in period t, v t is the wind speed in time period t, v in is the cut-in wind speed, v rated is the rated wind speed, v out is the cut-out wind speed, is the rated output power of the wind turbine;

[0016] The output model of the energy storage device is:

[0017]

[0018] Where E i,t is the storage capacity of energy storage device i in time period t; σ i is the self-consumption rate; P bess-in,i,t and P bess-out,i,t are the charging and discharging power of energy storage device i in time period t; η in,i and η out,i are the charging and discharging efficiencies of energy storage device i respectively;

[0019] The output model of the waste heat temperature difference power generation equipment is:

[0020]

[0021] Among them, P hr is the actual output power of the temperature difference power generation system, T h , T care the hot end temperature and cold end temperature of the thermoelectric power generation component, U hr is the output voltage of the temperature difference power generation system, α m is the Seebeck coefficient of the thermoelectric unit, R m is the internal resistance of the temperature difference power generation module itself, and R is the external load resistance.

[0022] Furthermore: the typical energy usage scenario loads include the electric load, heat load and cooling load in the heating season, cooling season and transition season.

[0023] Further: The electric load, heat load and cooling load in the heating season are calculated as follows:

[0024]

[0025] in, is the electricity load during the heating season period t, is the heat load during the heating season period t, is the cooling load during period t of the heating season;

[0026] The calculation formula for the electric load, heating load and cooling load in the cooling season is as follows:

[0027]

[0028] in, is the electricity load during the cooling season period t, is the heat load during cooling season period t, is the cooling load during cooling season period t;

[0029] The calculation formulas for the electrical load, heating load and cooling load in the transition season are as follows:

[0030]

[0031] in, is the electricity load during the transition period t, is the heat load during the transition season period t, is the cooling load during the transition season period t;

[0032] Furthermore: the substation multi-energy complementary capacity configuration optimization model includes two layers, the upper optimization model takes the lowest total cost of the multi-energy complementary power supply system as the optimization goal; the lower optimization model takes the highest renewable energy utilization rate and the lowest daily operating cost of the multi-energy complementary power supply system as the optimization goal.

[0033] Further: the objective function F0 of the upper optimization model is:

[0034] F0=minC dev-eav

[0035]

[0036] Among them, C dev-eav is the annual cost of the equipment, i is the discount rate, and l is the life cycle of the system; is the capital recovery coefficient, C init , C om , C rep are the total initial investment cost of the equipment, annual operation and maintenance cost, and the present value of the total replacement cost, respectively. ex is the interaction cost between the multi-energy complementary power supply system and the distribution network, C DR The cost of participating in demand-side response for a multi-energy complementary power supply system;

[0037] The constraints of the upper optimization model include decision variable constraints, power balance constraints, charge and discharge power constraints, and state of charge constraints;

[0038] The decision variable constraints are:

[0039]

[0040] Among them, N wt-max 、N pv-max 、N bess-max 、N hr-max N wt 、N pv 、N bess 、N hr The upper limit value, N wt 、N pv 、N bess 、N hr They represent the number of wind turbines, the number of photovoltaic power generation units, the number of battery energy storage units, and the number of waste heat thermoelectric generators respectively;

[0041] The power balance constraint is:

[0042] P wt,h +P pv,h -P bess-in,h +P bess-out,h +P hr,h =P load,h

[0043] Among them, P wt,h , P pv,h , P bess-in,h , P bess-out,h , P hr,h , P load,h They are the wind turbine output power, photovoltaic power output power, battery energy storage charging power, battery energy storage discharging power, waste heat thermoelectric generator output power, and load power at time h respectively;

[0044] The operating characteristics of energy storage equipment are constrained as follows:

[0045]

[0046] Among them, λ i,max and λ i,min are the maximum and minimum charge states of energy storage device i respectively; W i is the capacity of energy storage device i; P bess-in,i,max and P bess-out,i,max are the maximum charging and discharging powers of energy storage device i respectively.

[0047] Further: the objective function F of the lower layer optimization model is:

[0048] F=min(F1,F2)

[0049] Among them, F1 indicates that the daily operation cost of the multi-energy complementary power supply system is the lowest, and F2 indicates that the utilization rate of renewable energy in the multi-energy complementary power supply system is the highest;

[0050]

[0051] Among them, C bess is the dispatch cost of energy storage in the hth period, C ΔPV is the cost of photovoltaic abandonment, C ΔWT is the wind curtailment cost of the wind turbine, C Δhr is the heat abandonment cost of waste heat temperature difference power generation and thermoelectric conversion, and its calculation formula is:

[0052] C bess =k bess (P bess-out / k bo +P bess-in k bi )Δt

[0053] C ΔPV =k pv ΔP pv Δt

[0054] C ΔWT =k wt ΔP wt Δt

[0055] C Δhr =k hr ΔP hr Δt

[0056] Among them, k bess is the unit dispatch cost of the energy storage system, k pv is the unit cost of photovoltaic power abandonment, ΔP pv is the reduced power of the photovoltaic system in the multi-energy complementary system, k wtis the unit wind abandonment cost of the wind turbine, k hr The unit heat rejection cost of thermoelectric conversion for waste heat temperature difference power generation and ΔP wt is the power reduction of the fan system in the multi-energy complementary system, ΔP wt is the power reduction of the fan system in the multi-energy complementary system, P bess-out is the discharge power of the energy storage device, P bess-in is the charging power of the energy storage device, k bi =ΔP hr is the power reduced by the heat loss of waste heat temperature difference power generation, Δt is the scheduling time,

[0057]

[0058] Among them, P PV is the total photovoltaic power generation, P WT is the total power generated by the wind turbine;

[0059] The constraints of the upper optimization model include power balance constraints, energy storage equipment operation characteristics constraints, and wind and solar power curtailment constraints;

[0060] The power balance constraint is:

[0061] P load,h =P wt,h -ΔP wt +P pv,h -ΔP pv +P bess-out,h -P bess-in,h

[0062] Among them, P load,h , P pv,h , P bess-out,h , P bess-in,h , P wt,h They are load power, photovoltaic power generation output power, battery energy storage charging power, battery energy storage discharging power, and wind turbine output power at time h respectively;

[0063] The operating characteristics of energy storage equipment are constrained as follows:

[0064] The constraints on the amount of wind and solar power abandoned are:

[0065]

[0066] A substation multi-energy complementary capacity configuration optimization system considering waste heat recovery, comprising:

[0067] The model generation module obtains the basic information of the multi-energy complementary power supply system, and establishes the multi-objective optimization model of the multi-energy complementary capacity configuration of the substation as mentioned above according to the basic information;

[0068] The acquisition module obtains the configuration data of the multi-objective optimization model for the multi-energy complementary capacity configuration of the substation; the configuration data includes simulation time, optimization cycle, wind / solar output parameters and load parameters;

[0069] The output module simulates and runs the multi-objective optimization model of the substation multi-energy complementary capacity configuration according to the configuration data, obtains the configuration plan, and configures the multi-energy complementary power supply system according to the configuration plan.

[0070] An electronic device comprises a memory, a processor and a computer program, wherein the computer program is stored in the memory and is configured to be executed by the processor to implement the above-mentioned substation multi-energy complementary capacity configuration optimization method considering waste heat recovery.

[0071] A computer-readable storage medium stores a computer program, which, when executed, controls a device where the computer-readable storage medium is located to execute the above-mentioned substation multi-energy complementary capacity configuration optimization method considering waste heat recovery.

[0072] Compared with the prior art, the present invention has the following advantages:

[0073] The present invention takes the highest total energy utilization rate and the lowest cost of the system operation as the optimization goal, takes the power balance constraint of the power supply system, the photovoltaic wind power processing constraint, the output constraint of the heating system, the start-stop time constraint, and the capacity constraint of the energy storage system as the constraint conditions, establishes a multi-objective optimization model for the multi-energy complementary capacity configuration of the substation, and solves the model using a genetic algorithm. The present invention optimizes the multi-energy complementary capacity configuration of the substation, realizes the efficient utilization of energy and the recovery of the abandoned heat of the substation, improves the economy of the substation energy system, and improves the efficiency of energy utilization in the substation, reduces energy waste, and especially realizes the efficient utilization of waste heat resources. Through the genetic algorithm, the stability and security of the power grid are ensured, and the fluctuation of electricity demand can be effectively responded to. The configuration ratio of various energy sources is optimized, so that the system operation cost is reduced and the economic benefit is improved. A flexible energy management solution is provided, which is adaptable to different environments and needs and has strong practicality. The use of waste heat resources reduces the dependence on other energy sources and further improves the comprehensive benefits of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] Figure 1 It is a schematic diagram of the flow chart of the method for optimizing the configuration of multi-energy complementary capacity of a substation according to an embodiment;

[0075] Figure 2 Flowchart for solving the two-level optimization model architecture using genetic algorithm. DETAILED DESCRIPTION

[0076] The present invention is described in detail below with reference to the accompanying drawings and specific embodiments.

[0077] Embodiment 1

[0078] This embodiment proposes a substation multi-energy complementary capacity configuration optimization method considering waste heat recovery technology, such as Figure 1 As shown, the following steps are included:

[0079] Step S1, establishing an output model of distributed wind power, distributed photovoltaic, energy storage equipment and waste heat temperature difference power generation equipment in the substation energy supply system;

[0080] ① The principle of photovoltaic power generation is to use photovoltaic modules to convert solar energy into electrical energy based on the photovoltaic effect. The output of photovoltaic units is related to the intensity and temperature of solar radiation. Its output model is:

[0081]

[0082] in, is the maximum output power of the photovoltaic unit in period t, is the output power of the photovoltaic unit under standard conditions, I stc is the solar radiation intensity under standard conditions, T stc is the standard condition temperature, I t is the solar radiation intensity in period t, T t is the temperature during time period t, α is the power temperature coefficient of the photovoltaic cell;

[0083] ②The wind turbine output is related to wind speed, cut-in wind speed, rated wind speed, and cut-out wind speed. Its output model is:

[0084]

[0085] in, is the maximum output power of the wind turbine in period t, v t is the wind speed in time period t, v in is the cut-in wind speed, v rated is the rated wind speed, v out is the cut-out wind speed, is the rated output power of the wind turbine;

[0086] ③The output model of energy storage equipment is:

[0087]

[0088] Where E i,t is the storage capacity of energy storage device i in time period t; σ i is the self-consumption rate; P bess-in,i,t and P bess-out,i,t are the charging and discharging power of energy storage device i in time period t; η in,i and ηout,i are the charging and discharging efficiencies of energy storage device i respectively.

[0089] ④ The waste heat thermoelectric generator converts the thermoelectric potential into electrical energy based on the Seebeck effect, and its output model is:

[0090]

[0091] Among them, P hr is the actual output power of the temperature difference power generation system, T h , T c are the hot end temperature and cold end temperature of the thermoelectric power generation component, U hr is the output voltage of the temperature difference power generation system, α m is the Seebeck coefficient of the thermoelectric unit, R m is the internal resistance of the temperature difference power generation module itself, and R is the external load resistance.

[0092] Step S2: Calculate the load of the typical energy usage scenario of the substation, including the electric load, heat load and cooling load in the heating season, cooling season and transition season;

[0093] ①1①Calculate the electric load, heat load and cooling load in the heating season. The calculation formula is as follows:

[0094]

[0095] in, is the electricity load during the heating season period t, is the heat load during the heating season period t, is the cooling load during period t of the heating season;

[0096] ② Calculate the electricity load, heat load and cooling load in the cooling season. The calculation formula is as follows:

[0097]

[0098] in, is the electricity load during the cooling season period t, is the heat load during cooling season period t, is the cooling load during cooling season period t;

[0099] ③ Calculate the electrical load, heat load and cooling load in the transition season. The calculation formula is as follows:

[0100]

[0101] in, is the electricity load during the transition period t, is the heat load during the transition season period t, is the cooling load during the transition season period t.

[0102] S3. Taking the highest utilization rate of renewable energy (distributed wind power and distributed photovoltaic power) in the substation energy supply system and the lowest operating cost as the optimization goal, and taking the power supply system power balance constraint, photovoltaic wind power output constraint, power supply system output constraint, start and stop time constraint, and energy storage system output constraint as constraint conditions, a multi-objective optimization model for substation multi-energy complementary capacity configuration is established.

[0103] A two-layer optimization configuration model for a multi-energy complementary power supply system includes an upper optimization model and a lower optimization model. The upper optimization model optimizes the power supply economy of the multi-energy complementary power supply system, that is, minimizes the total cost as the optimization goal, and optimizes the configuration capacity of each power source in the multi-energy complementary power supply system. The lower optimization model determines the economic dispatch plan of the multi-energy complementary power supply system with the highest utilization rate of renewable energy and the lowest daily operating cost as the optimization goals.

[0104] Among them, the upper optimization model is composed of objective function 1 and constraint condition 1;

[0105] Objective function 1, the upper optimization model takes the minimum total cost of the multi-energy complementary power supply system as the optimization goal, and the expression is as follows:

[0106] minC dev-eav (1)

[0107] In formula (1), C dev-eav is the annual cost of the equipment, and:

[0108]

[0109] In formula (2), i is the discount rate, l is the system life cycle (in years);

[0110] is the capital recovery coefficient, C init , C om , C rep They are the total initial investment cost of the equipment, annual operation and maintenance cost, and the present value of the total replacement cost, and:

[0111]

[0112] In formula (3), C wt,in ,C pv,in ,C bess,in ,C invr,in are the initial investment costs of wind turbines, photovoltaic power generation, energy storage equipment, and inverters, respectively. wt,om ,C pv,om ,C bess,om ,C invr,omare the present value of operation and maintenance costs of wind turbines, photovoltaic power generation units, energy storage equipment, and inverters, respectively. bess,rep is the present value of the replacement cost of the energy storage equipment, N wt is the number of wind turbines, N pv is the number of photovoltaic power generation units, N bess is the number of battery storage cells and N invr is the number of inverters;

[0113] In formula (2), C ex The interaction cost between the multi-energy complementary power supply system and the distribution network. When the multi-energy complementary power supply system is connected to the grid, different operation modes can be selected, mainly including the surplus power grid-connected operation mode and the full power grid-connected operation mode.

[0114] The comprehensive operating income generated by the multi-energy complementary power supply system using the surplus power grid operation mode includes: the income from the multi-energy complementary system selling electricity to the superior power grid, the cost of purchasing electricity from the superior power grid, the subsidy for photovoltaic power generation, and the electricity cost saved by the user load. At this time, the interaction cost C between the multi-energy complementary power supply system and the distribution network is ex It can be expressed as:

[0115]

[0116] In formula (4), c w is the normal electricity price, c s is the normal electricity price, c pv-bu Subsidy for photovoltaic power generation, sell The power purchased and sold by the multi-energy complementary system to the distribution network, P PV is the total photovoltaic power generation and P load,t is the user load power;

[0117] The comprehensive operating income generated by the full-on grid operation mode of the multi-energy complementary power supply system includes: the income from the sale of electricity from photovoltaic and wind power to the upper grid, the income from low-charge and high-discharge of energy storage batteries, and the electricity cost paid by the user load. At this time, the interaction cost C between the multi-energy complementary power supply system and the distribution network is ex It can be expressed as:

[0118]

[0119] In formula (5), C MES-s is the annual operating net income of the multi-energy complementary system, c PV 、c WT is the benchmark electricity price of photovoltaic and wind power, P WT is the total power generated by the wind turbine, P BS_D , P BS_C are the total discharge and charging power of the energy storage system respectively;

[0120] In formula (2), C DR The cost of the multi-energy complementary power supply system participating in the demand-side response can be calculated based on a step-by-step multi-electricity price demand-side response model. The step-by-step multi-electricity price demand-side response model takes 24 hours as a scheduling cycle, sets compensation fees for different time periods according to the time-of-use electricity price policy, and constrains the number of interruptions, single interruption capacity, and continuous interruption time. The specific model is as follows:

[0121]

[0122] In formula (6), H1~H4 represent the electricity price level, ξ m_H It represents the unit capacity cost of m-type loads at different electricity price levels, L l_d,t , L in,t , L out,t , L G_t , L MES_i,d,t , respectively represent the load without considering demand response, the transferred-in load, the transferred-out load, the grid-connected load power output, and the grid-consumed load power output.

[0123] Constraint 1, including: decision variable constraint, power balance constraint 1, charge and discharge power constraint 1, and state of charge constraint 1;

[0124] The decision variable constraint is used to limit the decision variable within a valid and reasonable range when performing optimization calculations. Its mathematical expression is as follows:

[0125]

[0126] In formula (7), N wt-max 、N pv-max 、N bess-max 、N hr-max N wt 、N pv 、N bess 、N hr The upper limit value can be set according to the actual investment budget and installation area, N wt 、N pv 、N bess 、N hr They represent the number of wind turbines, the number of photovoltaic power generation units, the number of battery energy storage units, and the number of waste heat thermoelectric generators respectively;

[0127] Power balance constraint 1 is the constraint condition that the operating power of the multi-energy complementary power supply system should meet, and its mathematical expression is:

[0128] P wt,h +P pv,h -Pbess-in,h +P bess-out,h +P hr,h =P load,h (8)

[0129] In formula (8), P wt,h , P pv,h , P bess-in,h , P bess-out,h , P hr,h , P load,h They are the wind turbine output power, photovoltaic power output power, battery energy storage charging power, battery energy storage discharging power, waste heat thermoelectric generator output power, and load power at time h respectively;

[0130] Energy storage device operation characteristic constraint 1 is used to represent the maximum limit of the charging and discharging power of the energy storage device. Its mathematical expression is:

[0131]

[0132] In formula (9), λ i,max and λ i,min are the maximum and minimum charge states of energy storage device i respectively; W i is the capacity of energy storage device i; P bess-in,i,max and P bess-out,i,max are the maximum charging and discharging powers of energy storage device i respectively.

[0133] In the two-layer optimization configuration model of the multi-energy complementary power supply system, the lower optimization model is composed of objective function 2 and constraint condition 2;

[0134] Objective function 2 takes the maximum utilization rate of renewable energy and the minimum daily operating cost of the multi-energy complementary power supply system as the optimization goal, and its mathematical expression is:

[0135] F=min(F1,F2) (10)

[0136] In formula (10), F is the optimization target of objective function 2, which contains two optimization sub-targets F1 and F2. F1 represents the minimum daily operation cost of the multi-energy complementary power supply system, and F2 represents the minimum unutilized rate of renewable energy in the multi-energy complementary power supply system.

[0137] In objective function 2, the mathematical expression of the corresponding optimization sub-objective F1 is:

[0138]

[0139] In formula (11), C bess is the dispatch cost of energy storage in the hth period, C ΔPV is the cost of photovoltaic abandonment, C ΔWT is the wind curtailment cost of the wind turbine, CΔhr is the heat abandonment cost of waste heat temperature difference power generation and thermoelectric conversion, and its calculation formula is:

[0140] C bess =k bess (P bess-out / k bo +P bess-in k bi )Δt (12)

[0141] C ΔPV =k pv ΔP pv Δt (13)

[0142] C ΔWT =k wt ΔP wt Δt (14)

[0143] C Δhr =k hr ΔP hr Δt (15)

[0144] Among them, k bess is the unit dispatch cost of the energy storage system, k pv is the unit cost of photovoltaic power abandonment, ΔP pv is the reduced power of the photovoltaic system in the multi-energy complementary system, k wt is the unit wind abandonment cost of the wind turbine, k hr The unit heat rejection cost of thermoelectric conversion for waste heat temperature difference power generation and ΔP wt is the power reduction of the fan system in the multi-energy complementary system, P bess-out is the discharge power of the energy storage device, P bess-in is the charging power of the energy storage device, k bi =ΔP hr is the power reduced by the heat loss of waste heat temperature difference power generation, and Δt is the scheduling time;

[0145] The renewable energy unutilization rate is defined as the percentage of the ratio of wind and solar power abandonment to renewable energy power generation. In objective function 2, the mathematical expression of the corresponding optimization sub-goal F2 is:

[0146]

[0147] In formula (16), P PV is the total photovoltaic power generation, P WT is the total power generated by the wind turbine, ΔP pv The reduced power and ΔP of the photovoltaic system in the multi-energy complementary system wt To reduce the power of the fan system in the multi-energy complementary system;

[0148] Constraint 2 includes: power balance constraint 2, energy storage equipment operation characteristic constraint 2 and wind and solar power abandonment constraint;

[0149] Power balance constraint 2 is the constraint condition that the system operating power should meet during scheduling operation. Its mathematical expression is:

[0150] P load,h =P wt,h -ΔP wt +P pv,h -ΔP pv +P bess-out,h -P bess-in,h (17)

[0151] Among them, P load,h , P pv,h , P bess-out,h , P bess-in,h , P wt,h They are load power, photovoltaic power generation output power, battery energy storage charging power, battery energy storage discharging power, and wind turbine output power at time h respectively.

[0152] The energy storage device operation characteristic constraint 2 has the same mathematical expression as formula (9).

[0153] The wind and solar power curtailment constraint is the value range limit that the wind and solar power curtailment should meet, and its mathematical expression is:

[0154]

[0155] S4, and use genetic algorithm to solve the model and obtain the optimal solution for the multi-energy complementary capacity configuration of the substation.

[0156] Collect the data and information required for the optimization configuration calculation of the multi-energy complementary power supply system, and on this basis, obtain the optimization configuration plan of the multi-energy complementary power supply system based on the optimization solution of the two-layer optimization configuration model of the multi-energy complementary power supply system; input project information, which is the basis for subsequent photovoltaic, wind power, energy storage, and waste heat power generation module modeling and cost model modeling; input load information through the load information input module, and the input load information is the basis for subsequent distributed energy system energy balance modeling; input cost information through the cost information input module, and the input cost information is the basis for subsequent economic evaluation modeling; input equipment information through the equipment information input module, and the input equipment information is the basis for subsequent equipment modeling of each energy module.

[0157] Specifically, it is to obtain the configuration data of the double-layer optimization model of the multi-energy complementary power supply system;

[0158] Perform initialization settings. After the initialization is completed, simulate the double-layer optimization model of the multi-energy complementary power supply system according to the configuration data, obtain the configuration plan, and configure the multi-energy complementary power supply system according to the configuration plan.

[0159] Among them, the configuration plan is an economic dispatch plan for a multi-energy complementary power supply system.

[0160] Among them, the configuration data includes: simulation duration, optimization cycle, wind / solar output parameters and load parameters.

[0161] Among them, the multi-objective optimization model of the multi-energy power supply system includes:

[0162] An optimization model, wherein the optimization model is constructed according to an upper-level objective function and constraint conditions, takes the total cost of the multi-energy complementary power supply system during the investment period as a target, and optimizes the configuration capacity of each power source in the multi-energy complementary power supply system;

[0163] The lower-level optimization model is constructed according to the lower-level objective function and constraint conditions, and determines the configuration scheme of the multi-energy complementary power supply system with the renewable energy utilization rate and daily operation cost of the multi-energy complementary power supply system as the target.

[0164] The configuration scheme is obtained, including:

[0165] Generate an initial population of a preset size, determine the initial value of the wind turbine capacity, the initial value of the inverter capacity, the energy storage output range and the amount of waste heat power generation that can be recycled for the multi-energy complementary power supply system;

[0166] According to the initial value of wind turbine capacity, initial value of inverter capacity, energy storage output range and recoverable waste heat power generation of the multi-energy complementary power supply system, determine the optimal scheduling plan and energy storage output range in each period;

[0167] In this step, for the established multi-objective optimization mathematical model, Matlab software is used to write a calculation program, in which a multi-objective optimization function based on a genetic algorithm is called, and equipment capacity configurations are randomly generated and continuously updated and iterated to obtain the optimal equipment capacity configuration with the highest system renewable energy consumption rate and the lowest system cost. Figure 2 As shown in the figure, the genetic algorithm solution to the two-layer optimization model includes the following steps:

[0168] Step 1: Initialize the model;

[0169] Step 2: Obtain basic information of the multi-energy complementary power supply system, including equipment parameters, load data and energy prices;

[0170] Step 3: Randomly generate initial population data;

[0171] Step 4: Input the population into the lower optimization model, solve the lower optimization model, and return the solution result to the upper optimization model;

[0172] Step 5: Calculate the objective function value based on the penalty function method in the upper optimization model to find the current optimal individual and optimal population;

[0173] Step 6: Determine whether the number of iterations has been reached, if yes, output the optimal result, otherwise proceed to step 7;

[0174] Step 7: Perform selection, crossover and mutation operations of the genetic algorithm to update and generate a new population, and continue with step 4.

[0175] Step S5, determine the energy storage capacity according to the optimal scheduling plan and the output range of each energy storage period, and determine the optimal individual and the optimal population according to the operating conditions of the multi-energy complementary power supply system; update the optimal individual and the optimal population in a preset manner, and after terminating the update, output the optimal result, which is the configuration plan.

[0176] The optimal individual and the optimal population are updated in a preset manner, and after the update is terminated, the optimal result is output, and the optimal result is the configuration scheme.

[0177] Embodiment 2

[0178] The substation multi-energy complementary capacity configuration optimization system considering waste heat recovery in this embodiment includes: a model generation module, which obtains basic information of the multi-energy complementary power supply system, and establishes a multi-objective optimization model for the substation multi-energy complementary capacity configuration as described in the first embodiment according to the basic information;

[0179] The acquisition module obtains the configuration data of the multi-objective optimization model for the multi-energy complementary capacity configuration of the substation; the configuration data includes simulation time, optimization cycle, wind / solar output parameters and load parameters;

[0180] The output module simulates and runs the multi-objective optimization model of the multi-energy complementary capacity configuration of the substation according to the configuration data, obtains the configuration scheme by adopting the method of the first embodiment, and configures the multi-energy complementary power supply system according to the configuration scheme.

[0181] This embodiment also provides an electronic device, including a memory, a processor and a computer program, wherein the computer program is stored in the memory and is configured to be executed by the processor to implement the substation multi-energy complementary capacity configuration optimization method considering waste heat recovery as described in any one of claims 1 to 7.

[0182] This embodiment also provides a computer-readable storage medium, in which a computer program is stored. When the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the substation multi-energy complementary capacity configuration optimization method considering waste heat recovery as described in any one of claims 1 to 7.

[0183] The technical solutions of the present invention are not limited to the above-mentioned embodiments, and any technical solutions obtained by equivalent replacement methods fall within the scope of protection required by the present invention.

Claims

1. A method for optimizing the configuration of multi-energy complementary capacity of a substation considering waste heat recovery, characterized in that: The steps include: Establish output models for distributed wind power, distributed photovoltaics, energy storage equipment and waste heat thermoelectric power generation equipment in the substation energy supply system; Calculate the load of the substation in typical energy usage scenarios, including the electric load, heat load and cooling load in the heating season, cooling season and transition season; Taking the highest utilization rate of renewable energy and the lowest operating cost in the substation energy supply system as the optimization goal, and taking the power balance constraint of the energy supply system, the photovoltaic and wind power output constraint, the power supply system output constraint, the start-stop time constraint, and the energy storage system output constraint as the constraint conditions, a multi-objective optimization model for the substation multi-energy complementary capacity configuration is established; The genetic algorithm is used to solve the multi-objective optimization model of substation multi-energy complementary capacity configuration, and the substation multi-energy complementary capacity configuration scheme is obtained.

2. The substation multi-energy complementary capacity configuration optimization method considering waste heat recovery according to claim 1 is characterized by: Step S1 includes the following: The distributed photovoltaic output model is: in, is the maximum output power of the photovoltaic unit in period t, is the output power of the photovoltaic unit under standard conditions, I stc is the solar radiation intensity under standard conditions, T stc is the standard condition temperature, I t is the solar radiation intensity in period t, T t is the temperature during time period t, α is the power temperature coefficient of the photovoltaic cell; The distributed wind power output model is: in, is the maximum output power of the wind turbine in period t, v t is the wind speed in time period t, v in is the cut-in wind speed, v rated is the rated wind speed, v out is the cut-out wind speed, is the rated output power of the wind turbine; The output model of the energy storage device is: Where E i，t is the storage capacity of energy storage device i in time period t; σ i is the self-consumption rate; P bess-in，i，t and P bess-out，i，t are the charging and discharging power of energy storage device i in time period t; η in，i and η out，i are the charging and discharging efficiencies of energy storage device i respectively; The output model of the waste heat temperature difference power generation equipment is: Among them, P hr is the actual output power of the temperature difference power generation system, T h , T c are the hot end temperature and cold end temperature of the thermoelectric power generation component, U hr is the output voltage of the temperature difference power generation system, α m is the Seebeck coefficient of the thermoelectric unit, R m is the internal resistance of the temperature difference power generation module itself, and R is the external load resistance.

3. The substation multi-energy complementary capacity configuration optimization method considering waste heat recovery according to claim 2 is characterized by: The typical energy usage scenario loads include the electricity load, heat load and cooling load in the heating season, cooling season and transition season.

4. The substation multi-energy complementary capacity configuration optimization method considering waste heat recovery according to claim 3 is characterized by: The electric load, heating load and cooling load in the heating season are calculated as follows: in, is the electricity load during the heating season period t, is the heat load during heating season period t, is the cooling load during period t of the heating season; The calculation formula for the electric load, heating load and cooling load in the cooling season is as follows: in, is the electricity load during the cooling season period t, is the heat load during cooling season period t, is the cooling load during cooling season period t; The calculation formulas for the electrical load, heating load and cooling load in the transition season are as follows: in, is the electricity load during the transition period t, is the heat load during the transition season period t, is the cooling load during the transition season period t.

5. The substation multi-energy complementary capacity configuration optimization method considering waste heat recovery according to claim 4 is characterized by: The substation multi-energy complementary capacity configuration optimization model includes two layers, the upper layer optimization model takes the minimum total cost of the multi-energy complementary power supply system as the optimization goal; The lower optimization model takes the maximum utilization rate of renewable energy and the minimum daily operating cost of the multi-energy complementary power supply system as the optimization objectives.

6. The substation multi-energy complementary capacity configuration optimization method considering waste heat recovery according to claim 5 is characterized by: The objective function F0 of the upper optimization model is: F0=minC dev-eav Among them, C dev-eav is the annual cost of the equipment, i is the discount rate, and l is the life cycle of the system; is the capital recovery coefficient, C init , C om , C rep are the total initial investment cost of the equipment, annual operation and maintenance cost, and the present value of the total replacement cost, respectively. ex is the interaction cost between the multi-energy complementary power supply system and the distribution network, C DR The cost of participating in demand-side response for a multi-energy complementary power supply system; The constraints of the upper optimization model include decision variable constraints, power balance constraints, charge and discharge power constraints, and state of charge constraints; The decision variable constraints are: Among them, N wt-max 、N pv-max 、N bess-max 、N hr-max N wt 、N pv 、N bess 、N hr The upper limit value, N wt 、N pv 、N bess 、N hr They represent the number of wind turbines, the number of photovoltaic power generation units, the number of battery energy storage units, and the number of waste heat thermoelectric generators respectively; The power balance constraint is: P wt，h +P pv，h -P bess-in，h +P bess-out，h +P hr，h =P load，h Among them, P wt，h , P pv，h , P bess-in，h , P bess-out，h , P hr，h , P load，h They are the wind turbine output power, photovoltaic power output power, battery energy storage charging power, battery energy storage discharging power, waste heat thermoelectric generator output power, and load power at time h respectively; The operating characteristics of energy storage equipment are constrained as follows: Among them, λ i，max and λ i，min are the maximum and minimum charge states of energy storage device i respectively; W i is the capacity of energy storage device i; P bess-in，i，max and P bess-out，i，max are the maximum charging and discharging powers of energy storage device i respectively.

7. The substation multi-energy complementary capacity configuration optimization method considering waste heat recovery according to claim 6 is characterized by: The objective function F of the lower optimization model is: F=min(F1,F2) Among them, F1 indicates that the daily operation cost of the multi-energy complementary power supply system is the lowest, and F2 indicates that the utilization rate of renewable energy in the multi-energy complementary power supply system is the highest; Among them, C bess is the dispatch cost of energy storage in the hth period, C ΔPV is the cost of photovoltaic abandonment, C ΔWT is the wind curtailment cost of the wind turbine, C Δhr is the heat abandonment cost of waste heat temperature difference power generation and thermoelectric conversion, and its calculation formula is: C bess =k bess (P bess-out / k bo +P bess-in k bi )Δt C ΔPV =k pv ΔP pv Δt C ΔWT =k wt ΔP wt Δt C Δhr =k hr ΔP hr Δt Among them, k bess is the unit dispatch cost of the energy storage system, k pv is the unit cost of photovoltaic power abandonment, ΔP pv is the reduced power of the photovoltaic system in the multi-energy complementary system, k wt is the unit wind abandonment cost of the wind turbine, k hr The unit heat rejection cost of thermoelectric conversion for waste heat temperature difference power generation and ΔP wt is the power reduction of the fan system in the multi-energy complementary system, ΔP wt is the power reduction of the fan system in the multi-energy complementary system, P bess-out is the discharge power of the energy storage device, P bess-in is the charging power of the energy storage device, k bi =ΔP hr is the power reduced by the heat loss of waste heat temperature difference power generation, Δt is the scheduling time, Among them, P PV is the total photovoltaic power generation, P WT is the total power generated by the wind turbine; The constraints of the upper optimization model include power balance constraints, energy storage equipment operation characteristics constraints, and wind and solar power curtailment constraints; The power balance constraint is: P load，h =P wt，h -ΔP wt +P pv，h -ΔP pv +P bess-out，h -P bess-in，h Among them, P load，h , P pv，h , P bess-out，h , P bess-in，h , P wt，h They are load power, photovoltaic power generation output power, battery energy storage charging power, battery energy storage discharging power, and wind turbine output power at time h respectively; The operating characteristics of energy storage equipment are constrained as follows: The constraints on the amount of wind and solar power abandoned are:

8. A substation multi-energy complementary capacity configuration optimization system considering waste heat recovery, characterized in that: include: A model generation module, which obtains basic information of the multi-energy complementary power supply system, and establishes a multi-objective optimization model for the multi-energy complementary capacity configuration of a substation according to any one of claims 1 to 7 based on the basic information; The acquisition module obtains the configuration data of the multi-objective optimization model for the multi-energy complementary capacity configuration of the substation; the configuration data includes simulation time, optimization cycle, wind / solar output parameters and load parameters; The output module simulates and runs the multi-objective optimization model of the substation multi-energy complementary capacity configuration according to the configuration data, obtains the configuration plan, and configures the multi-energy complementary power supply system according to the configuration plan.

9. An electronic device, characterized in that: It includes a memory, a processor and a computer program, wherein the computer program is stored in the memory and is configured to be executed by the processor to implement the substation multi-energy complementary capacity configuration optimization method considering waste heat recovery as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, which, when executed, controls the device where the computer-readable storage medium is located to execute the substation multi-energy complementary capacity configuration optimization method considering waste heat recovery as described in any one of claims 1 to 7.

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