CN114997460A - Regional micro-energy network operation optimization method considering maximum consumption of renewable energy - Google Patents

Regional micro-energy network operation optimization method considering maximum consumption of renewable energy Download PDF

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CN114997460A
CN114997460A CN202210353253.1A CN202210353253A CN114997460A CN 114997460 A CN114997460 A CN 114997460A CN 202210353253 A CN202210353253 A CN 202210353253A CN 114997460 A CN114997460 A CN 114997460A
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朱蕾蕾
王斌
李小鹏
刘劲磊
徐静
杨佳梁
周琼
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Shanghai Electrical Engineering Design Co ltd
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Abstract

The invention discloses a regional micro energy network operation optimization method considering maximum consumption of renewable energy, which comprises the following steps of S1: inputting a user side load requirement; s2: establishing a model of energy supply end equipment; s3: establishing a micro-energy network system model; s4: establishing a linearized system model; the linearized system model is divided into two sublayers, an optimization layer and a running layer; the regional micro energy network operation optimization method considering the maximum consumption of renewable energy is different from the prior art, the system model and the optimization model of the wind-light combustion-storage regional micro energy network are established based on the characteristics of different types of loads of local users on the basis of considering the volatility and the randomness of regional wind-light resources, double-layer configuration optimization logic is designed, the goals of the maximum wind-light permeability and the minimum wind-light rejection rate are taken as targets, the wind-light rejection rate can be effectively increased through mixed integer nonlinear programming solution, configuration planning, response schemes, load distribution suggestion support and the like are provided for users and factories.

Description

Regional micro-energy network operation optimization method considering maximum consumption of renewable energy
Technical Field
The invention relates to the technical field of energy optimization scheduling, in particular to a regional micro-energy network operation optimization method considering maximum consumption of renewable energy.
Background
The patent 'dispatching optimization method of multi-energy coupling comprehensive energy system' provides a dispatching optimization method of a multi-energy coupling regional micro-energy network, according to the characteristics of energy and time scales in the regional micro-energy network, the difference of electricity, cold, heat and gas multi-energy flows on the dispatching time scale is considered, a day-ahead dispatching plan and a day-inside rolling optimization dispatching are provided, the random fluctuation of renewable energy and load is eliminated, and stable and economic operation is achieved. The primary scheduling plan obtained by the day-ahead scheduling utilizes the in-day hierarchical rolling re-optimization to timely and effectively correct power fluctuation caused by wind, light and load prediction errors, so that the day-ahead scheme of the system is corrected, stable and economic operation among various energy sources is ensured, the balance of supply and demand of the system is realized, and the operation cost is reduced.
The patent 'a sectional type comprehensive energy system operation optimization method based on mixed integer nonlinear programming' provides a multi-objective day-ahead optimization scheduling method considering voltage and air pressure control, firstly, 24 hours are divided into five time intervals according to the sectional electricity price of 'valley-peak-level', an optimal optimization variable matrix of the five time intervals is calculated through mixed integer nonlinear programming, then, an objective function and a constraint condition of each time interval are respectively constructed, and finally, an optimization result of each time interval is sequentially calculated in a refining mode through the mixed integer nonlinear programming. The method can reduce the number of the model constraint conditions as much as possible, realize the optimal configuration of the operation among multiple energy sources of the regional micro energy network and realize the maximum economic benefit.
The patent 'a regional comprehensive energy system planning optimization method based on double-layer optimization' is a regional micro energy network planning optimization method based on double-layer optimization, aiming at the characteristics of wind energy and energy storage in a regional micro energy network, an optimized scheduling model suitable for the regional micro energy network is established, random problems are converted into deterministic problems, and an effective optimized scheduling implementation method is provided for the optimized scheduling of the regional micro energy network under the condition that the system safety is ensured. Compared with the traditional regional micro energy grid optimization scheduling method, the operation scheme obtained by optimizing the scheduling model is flexibly adjusted under different wind power output scenes, so that the economical efficiency of the comprehensive system is improved, the influence of the fluctuation of the wind power output on the power grid is reduced, and the stability and the reliability are better.
The above patents all research the operation optimization problem of the micro-energy grid system, but most of them aim at the lowest operation cost, no deep research is made on how to improve the proportion of renewable energy sources in energy structures and fully consume the renewable energy sources, and with the development of a regional micro energy source network, large-scale renewable energy source equipment is connected into a system, the fluctuation and the randomness of the energy source system bring challenges to the supply and demand matching of the energy system, and the low-carbon, clean and sustainable effects of the regional micro-energy grid can be really realized only by improving the penetration ratio of renewable energy sources in the energy system, reducing unnecessary wind and light abandonment and exploring the balance interaction between the quality and the energy flow of wind, light, fuel, storage and cold, hot and electric energy, therefore, it is very important to optimize the system operation in consideration of the maximum consumption of renewable energy, and for this purpose, we propose a method for optimizing the operation of a regional micro energy grid in consideration of the maximum consumption of renewable energy.
Disclosure of Invention
The invention aims to provide a regional micro energy network operation optimization method considering the maximum consumption of renewable energy sources so as to solve the problems in the background technology.
In order to achieve the purpose, the invention provides the following technical scheme: a regional micro energy network operation optimization method considering maximum consumption of renewable energy sources comprises the following steps:
s1: inputting a user side load requirement;
s2: establishing a model of energy supply end equipment;
s3: establishing a micro-energy network system model;
according to the model established in the step S2, taking the configuration capacity of key components of the micro energy grid system as a decision variable, and carrying out operation optimization based on mixed integer nonlinear programming;
the optimization problem for the micro-energy grid system is expressed in the following form:
Figure BDA0003581655420000021
according to the above optimization problem, determining an objective function to be optimized as:
Figure BDA0003581655420000022
Figure BDA0003581655420000023
Figure BDA0003581655420000024
wherein REP (renewable Energy networking) refers to wind and light permeability, CR (Current Rate) refers to wind and light abandoning rate, and W refers to wind and light abandoning rate wind,used 、W solar,used Refers to the part of the electricity used by the user from wind-solar power generation, W gas,used 、W grid,used Refers to the part of the user electricity consumption purchased from a gas turbine and a main power grid, Wind wasted 、Solar wasted Is used for wind power generation and photovoltaic power generation in a time period T due to the limitation of energy storage capacity and the likeThe wasted power is combined with the two indexes according to the weighting, and an optimization function considering the maximum consumption of the renewable energy sources can be obtained.
According to the optimization objective function, establishing an optimization scheduling constraint condition:
(1) the inequality constrains:
Aineq×[x]≤bineq
the inequality constraints considered by the optimization mainly include:
Figure BDA0003581655420000031
Figure BDA0003581655420000032
Figure BDA0003581655420000033
the constraints in the above equation represent: the prime mover operation capacity is not greater than the rated capacity, not less than (N-1) times the rated capacity, wherein: e0 denotes rated capacity, N denotes the number of prime movers selected; the steam yield produced by the waste heat boiler is less than the maximum steam yield of the heat-carrying smoke of the prime motor, wherein: qgas represents heat carried by smoke of a prime motor, eta HRSG represents the efficiency of a waste heat boiler, HHRSG represents the steam yield of the waste heat boiler; the amount of steam required by the bromine cooler is not more than the total amount of steam produced by the waste heat boiler and the gas boiler, wherein: hgas _ boil represents the steam yield of the gas boiler, QLibr represents the capacity of the bromine refrigerator, and COP represents the refrigeration coefficient of performance of the bromine refrigerator.
The energy storage charging and discharging power can not exceed the energy storage rated power:
P in ，P out <P cs,N
and (4) energy storage battery capacity constraint:
the charge in the energy storage battery at each moment should be between the upper and lower limits of the battery charge constraint:
S lower,lmit <S t <S N
energy storage multiplying power constraint:
P cs,N ＝S N /k
and in the formula, k is the energy multiplying power of the energy storage battery.
(2) Constraint of equality
Photovoltaic power generation power equality constraint
P pv ＝A pv *F pv *(1-0.005(T-25))*η pv
Power balance constraint
Figure BDA0003581655420000041
Q Libr +Q chill ＝Q user
H HRSG +H gas_boil ＝H user +H Libr
The above equation represents the balance and matching of the system output and the user's cold, heat and electricity respectively.
Wherein SN is the rated capacity in the energy storage battery, P cs,N For the power rating of the energy storage arrangement,
Figure BDA0003581655420000042
in order to purchase electric power for a utility,
Figure BDA0003581655420000043
in order to have the capacity of the transformer,
Figure BDA0003581655420000044
for increasing transformer capacity, c is discharge rate, P pv Is the power of the photovoltaic power and is,
Figure BDA0003581655420000045
is the discharge power;
Figure BDA0003581655420000046
in order to charge the power, the charging power,
Figure BDA0003581655420000047
for power on the net, P load For load power, F pv For photovoltaic irradiation, T is the ambient temperature eta pv The photovoltaic efficiency, delta is a value of 0 or 1, epsilon is a minimum value, M is a maximum value, Qgas represents heat carried by smoke of a prime motor, eta HRSG represents the efficiency of a waste heat boiler, HHRSG represents the steam yield of the waste heat boiler, Hgas _ boil represents the steam yield of a gas boiler, QLibr represents the capacity of a bromine cooler, COP represents the refrigeration coefficient of performance of the bromine cooler, Qchilll represents the refrigeration capacity of an electric refrigeration unit, and COPchill represents the refrigeration coefficient of performance of the electric refrigeration unit.
S4: establishing a linearized system model;
the linearized system model is divided into two sublayers, an optimization layer and an operation layer;
(1) optimization layer
And (4) carrying out iterative solution on decision variables and related constraints of the micro energy network system model in the step S3 through an MINLP solver, generating the number of samples, calling an OPTI tool box based on a Matlab platform, solving the mixed integer nonlinear programming problem through a BONMIN algorithm, and realizing the operation optimization algorithm.
(2) Running layer
And for each sample combination generated by the optimization layer, the operation layer sequentially calculates, can obtain the renewable permeability and the abandoned wind and abandoned light rate of the system in the operation time through calculation, and performs the next iteration according to the returned data through automatically changing operation parameters until an optimal solution set is obtained.
Preferably, the user terminal described in step S1 includes a residential building, a new energy automobile, and the like.
Preferably, the energy supply end equipment in the step S2 includes a small wind power generator, a solar power generator, an energy storage battery, a gas turbine, a bromine refrigerator and the like.
Preferably, the samples in step S3 include wind power installation amount, photovoltaic installed power, number of gas turbines, waste heat boiler capacity, lithium bromide refrigerant capacity, electric refrigeration capacity, energy storage element capacity, and the like.
Compared with the prior art, the invention has the beneficial effects that:
the method is different from the prior art, a system model and an optimization model of the wind-light-combustion-storage regional micro-energy network are established based on the characteristics of different types of loads of local users on the basis of considering the fluctuation and randomness of regional wind-light resources, double-layer configuration optimization logic is designed, the goals of maximum wind-light permeability and minimum wind-light abandonment rate are taken as the goals, the wind-light consumption can be effectively increased through mixed integer nonlinear programming solution, and configuration planning, response schemes, load distribution suggestion support and the like are provided for the users and the plant area.
Drawings
FIG. 1 is a block flow diagram of the present invention;
FIG. 2 is a block diagram of the operation of the present invention;
FIG. 3 is a block diagram of a zone type micro power grid according to the present invention;
FIG. 4 is a table showing the operation of the components under three loads in accordance with the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1-4, the present invention provides a technical solution: a regional micro energy network operation optimization method considering maximum consumption of renewable energy sources comprises the following steps:
s1: inputting a user side load requirement;
s2: establishing a model of energy supply end equipment;
s3: establishing a micro-energy network system model;
according to the model established in the step S2, taking the configuration capacity of key components of the micro energy grid system as a decision variable, and carrying out operation optimization based on mixed integer nonlinear programming;
the optimization problem for the micro-energy grid system is expressed in the following form:
Figure BDA0003581655420000061
according to the above optimization problem, determining an objective function to be optimized as:
Figure BDA0003581655420000062
Figure BDA0003581655420000063
Figure BDA0003581655420000064
wherein REP (renewable Energy networking) refers to wind and light permeability, CR (Current Rate) refers to wind and light abandoning rate, and W refers to wind and light abandoning rate wind,used 、W solar,used Refers to the part of the electricity used by the user from wind-solar power generation, W gas,used 、W grid,used Refers to the part of the electricity consumption of the user from the gas turbine and the main power grid, Wind wasted 、Solar wasted For the power wasted by wind power generation and photovoltaic power generation due to the reasons of energy storage capacity limitation and the like in the time period T, the optimization function considering the maximum consumption of the renewable energy can be obtained by combining the two indexes according to weighting.
According to the optimization objective function, establishing an optimization scheduling constraint condition:
(1) the inequality constrains:
Aineq×[x]≤bineq
the inequality constraints considered by the optimization mainly include:
Figure BDA0003581655420000065
Figure BDA0003581655420000071
Figure BDA0003581655420000072
the constraints in the above equation represent: the prime mover operation capacity is not greater than the rated capacity, not less than (N-1) times the rated capacity, wherein: e0 denotes rated capacity, N denotes the number of prime movers selected; the steam yield produced by the waste heat boiler is less than the maximum steam yield of the heat-carrying smoke of the prime motor, wherein: qgas represents heat carried by smoke of a prime motor, eta HRSG represents the efficiency of a waste heat boiler, HHRSG represents the steam yield of the waste heat boiler; the steam quantity required by the bromine cooler is not more than the total steam quantity produced by the waste heat boiler and the gas boiler, wherein: hgas _ boil represents the steam yield of the gas boiler, QLibr represents the capacity of the bromine cooler, and COP represents the coefficient of performance of the refrigeration of the bromine cooler.
The energy storage charging and discharging power can not exceed the energy storage rated power:
P in ，P out <P cs,N
and (4) energy storage battery capacity constraint:
the charge in the energy storage battery at each moment should be between the upper and lower limits of the battery charge constraint:
S lower,lmit <S t <S N
energy storage multiplying power constraint:
P cs,N ＝S N /k
in the formula, k is the energy multiplying power of the energy storage battery.
(2) Constraint of equality
Photovoltaic power generation power equality constraint
P pv ＝A pv *F pv *(1-0.005(T-25))*η pv
Power balance constraint
Figure BDA0003581655420000081
Q Libr +Q chill ＝Q user
H HRSG +H gas_boil ＝H user +H Libr
The above equation represents the balance and matching of the system output with the user's cold, heat, and electricity, respectively.
Wherein SN is the rated capacity in the energy storage battery, P cs,N For the power rating of the energy storage arrangement,
Figure BDA0003581655420000082
in order to purchase electric power for a utility,
Figure BDA0003581655420000083
in order to have the capacity of the transformer,
Figure BDA0003581655420000084
for increasing transformer capacity, c is discharge rate, P pv In order to be the photovoltaic power,
Figure BDA0003581655420000085
is the discharge power;
Figure BDA0003581655420000086
in order to be able to charge the power,
Figure BDA0003581655420000087
for power on the net, P load For load power, F pv For photovoltaic irradiation, T is ambient temperature eta pv The photovoltaic efficiency, delta is a value of 0 or 1, epsilon is a minimum value, M is a maximum value, Qgas represents heat carried by smoke of a prime motor, eta HRSG represents the efficiency of a waste heat boiler, HHRSG represents the steam yield of the waste heat boiler, Hgas _ boil represents the steam yield of a gas boiler, QLibr represents the capacity of a bromine cooler, COP represents the refrigeration coefficient of performance of the bromine cooler, Qchilll represents the refrigeration capacity of an electric refrigeration unit, and COPchill represents the refrigeration coefficient of performance of the electric refrigeration unit.
S4: establishing a linearized system model;
the linearized system model is divided into two sublayers, an optimization layer and a running layer;
(1) optimization layer
And (2) carrying out iterative solution on decision variables and related constraints of the micro energy network system model in the step S3 through an MINLP solver, generating the number of samples, calling an OPTI tool box based on a Matlab platform, solving a mixed integer nonlinear programming problem through a BONMIN algorithm, and realizing an operation optimization algorithm, wherein the BONMIN is an algorithm and software of a local MINLP solver convex mixed integer nonlinear program, is a LP/NLP-based branch-and-bound algorithm, and is used for processing the convex MINLP optimization problem. The branch-and-bound algorithm is a direct popularization of the branch-and-bound algorithm for solving the MILP in the MINLP problem, and the algorithm comprises three key steps of branching, node selection and pruning.
(2) Running layer
And for each sample combination generated by the optimization layer, the operation layer sequentially calculates, through calculation, the operation layer can obtain the reproducible permeability and the wind and light abandoning rate of the system in the operation time, and the operation layer performs the next iteration according to the returned data and automatically changes the operation parameters until the optimal solution set is obtained.
Further, the user terminal described in step S1 includes a residential building, a new energy automobile, and the like.
Further, the energy supply end equipment in the step S2 includes a small wind power generator, a solar power generator, an energy storage battery, a gas turbine, a bromine refrigerator, and the like.
Further, the samples in step S3 include the wind power installation amount, the photovoltaic installed power, the number of gas turbines, the capacity of the waste heat boiler, the capacity of the lithium bromide refrigerant, the electric refrigeration capacity, the capacity of the energy storage element, and the like.
Selecting three groups of typical load test comprehensive energy system optimization results: three sets of typical loads are shown in the following table,
table one: load condition
Electric load kW Heat load kW Cooling load kW
Transitional season 5900 2100 1500
(Summer) 7200 700 6150
Winter season 6085 5950 390
Wherein:
Case-A is 3 months spring load, including 5900kW of electric load, 2100kW of heat load and 1500kW of cold load.
Case-B is 7-month summer load, including electric load 7200kW, heat load 700kW and cold load 6150 kW.
The Case-C is the load of 11 months in winter, and comprises electric load 6085kW, heat load 5950kW and cold load 390 kW.
The comprehensive energy system configuration in the embodiment comprises: 1200 kW gas turbines and 5 1000kW gas turbines; 2 5000kW waste heat boilers; 2 4000kW absorption type bromine coolers; 1 piece of 500kW electric refrigeration equipment, 1 piece of 1600kW photovoltaic power generation system consisting of 5000 unit plates; 10 500kW wind power generators; 1 1000kW power storage device; 1 1000kW cold storage plant. Meanwhile, the micro energy network can purchase electric energy from the power grid, and the electricity purchasing quantity is limited to be not more than 10% of the load in order to achieve the purpose of local consumption of wind and light resources. In the optimization process, the decision variable is the power output of each component of the system, and the optimization variable is a renewable energy consumption index considering the wind and light permeability and the rejection rate.
Table two: micro energy network configuration table
Figure BDA0003581655420000101
The output of each component after operation optimization is as follows:
1) operation in a transition season:
electrical loading: 4 1000kW combustion engines are selected to run at full load, the output power is 4000kW, and the output power of 1 1000kW combustion engine is 478 kW; the photovoltaic power generation capacity is 912kW, and the wind power generation capacity is 1200 kW; and 303kW of electricity is purchased from a power grid. Heat load: 1 5000kW waste heat boiler is selected to operate, and the output power is 3434 kW. Cold load: 1 4000kW lithium bromide refrigerator is selected to operate, and the output power is 1500 kW.
The wind-light permeability of the system after operation optimization is 34.54%, and the wind and light abandoning rate is 0.
2) Operation in summer:
electrical loading: 4 1000kW combustion engines are selected to run at full load, the output power is 4000kW, and the output power of 1 1000kW combustion engine is 861 kW; the photovoltaic power generation capacity is 1358kW, and the wind power generation capacity is 1200 kW; and electricity is purchased 543kW from the power grid. Heat load: 2 5000kW waste heat boilers are selected to operate, 1 output power is 3000kW, and the other output power is 2560 kW. Cold load: 2 4000kW lithium bromide refrigerators were selected to operate, 1 4000kW full load output power and 2150kW output power were selected.
The wind and light permeability of the system after operation optimization is 33.04%, and the wind and light abandoning rate is 0%.
3) Operation in winter:
electrical loading: selecting 5 1000kW combustion engines to run at full load, wherein the output power is 5000kW, and the output power of 1200 kW combustion engine is 119 kW; the photovoltaic power generation capacity is 800kW, and the wind power generation capacity is 500k W; and 535kW of electricity is purchased from the power grid.
Heat load: 2 5000kW waste heat boilers are selected to operate, 1 full load output power is 5000kW, and the other output power is 1228 kW. Cold load: the output power of 1 4000kW lithium bromide refrigerator is 390 kW.
The wind-light permeability of the system after operation optimization is 18.71%, and the wind and light abandoning rate is 0%.
It should be noted that, in this document, relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.

Claims (4)

1. The method for optimizing the operation of the regional micro energy network considering the maximum consumption of the renewable energy is characterized by comprising the following steps of:
s1: inputting a user side load requirement;
s2: establishing a model of energy supply end equipment;
s3: establishing a micro-energy network system model;
according to the model established in the step S2, taking the configuration capacity of the key components of the micro energy grid system as a decision variable, and carrying out operation optimization based on mixed integer nonlinear programming;
the optimization problem for the micro-energy grid system is expressed in the following form:
Figure FDA0003581655410000011
according to the above optimization problem, determining an objective function to be optimized as:
Figure FDA0003581655410000012
Figure FDA0003581655410000013
Figure FDA0003581655410000014
wherein REP (renewable Energy networking) refers to wind and light permeability, CR (Current Rate) refers to wind and light abandoning rate, and W refers to wind and light abandoning rate wind,used 、W solar,used Refers to the part of the electricity used by the user from wind-solar power generation, W gas,used 、W grid,used Refers to the part of the electricity consumption of the user from the gas turbine and the main power grid, Wind wasted 、Solar wasted For the power wasted by wind power generation and photovoltaic power generation due to the reasons of energy storage capacity limitation and the like in the time period T, the optimization function considering the maximum consumption of the renewable energy can be obtained by combining the two indexes according to weighting.
According to the optimization objective function, establishing an optimization scheduling constraint condition:
(1) the inequality constrains:
Aineq×[x]≤bineq
the inequality constraints considered by the optimization mainly include:
Figure FDA0003581655410000021
Figure FDA0003581655410000022
Figure FDA0003581655410000023
the constraints in the above equation represent: the prime mover operation capacity is not greater than the rated capacity, not less than (N-1) times the rated capacity, wherein: e0 denotes rated capacity, N denotes the number of prime movers selected; the steam yield produced by the waste heat boiler is less than the maximum steam yield of the heat-carrying smoke of the prime motor, wherein: qgas represents heat carried by smoke of a prime motor, eta HRSG represents the efficiency of the waste heat boiler, HHRSG represents the steam yield of the waste heat boiler; the amount of steam required by the bromine cooler is not more than the total amount of steam produced by the waste heat boiler and the gas boiler, wherein: hgas _ boil represents the steam yield of the gas boiler, QLibr represents the capacity of the bromine cooler, and COP represents the coefficient of performance of the refrigeration of the bromine cooler.
The energy storage charging and discharging power can not exceed the energy storage rated power:
P in ，P out <P cs,N
and (4) energy storage battery capacity constraint:
the charge in the energy storage battery at each moment should be between the upper and lower limits of the battery charge constraint:
S lower,lmit <S t <S N
energy storage multiplying power constraint:
P cs,N ＝S N /k
and in the formula, k is the energy multiplying power of the energy storage battery.
(2) Constraint of equality
Photovoltaic power generation power equality constraint
P pv ＝A pv *F pv *(1-0.005(T-25))*η pv
Power balance constraint
Figure FDA0003581655410000031
Q Libr +Q chill ＝Q user
H HRSG +H gas_boil ＝H user +H Libr
The above equation represents the balance and matching of the system output and the user's cold, heat and electricity respectively.
Wherein SN is the rated capacity in the energy storage battery, P cs,N For the power rating of the energy storage arrangement,
Figure FDA0003581655410000032
in order to purchase the electric power,
Figure FDA0003581655410000033
in order to have the capacity of the transformer,
Figure FDA0003581655410000034
for increasing transformer capacity, c is discharge rate, P pv In order to be the photovoltaic power,
Figure FDA0003581655410000035
is the discharge power;
Figure FDA0003581655410000036
in order to charge the power, the charging power,
Figure FDA0003581655410000037
for power on the Internet, P load For load power, F pv For photovoltaic irradiation, T is ambient temperature eta pv The photovoltaic efficiency, delta is a value of 0 or 1, epsilon is a minimum value, M is a maximum value, Qgas represents heat carried by smoke of a prime motor, eta HRSG represents the efficiency of a waste heat boiler, HHRSG represents the steam yield of the waste heat boiler, Hgas _ boil represents the steam yield of a gas boiler, QLibr represents the capacity of a bromine cooler, COP represents the refrigeration coefficient of performance of the bromine cooler, Qchilll represents the refrigeration capacity of an electric refrigeration unit, and COPchill represents the refrigeration coefficient of performance of the electric refrigeration unit.
S4: establishing a linearized system model;
the linearized system model is divided into two sublayers, an optimization layer and a running layer;
(1) optimization layer
And (4) carrying out iterative solution on decision variables and related constraints of the micro energy network system model in the step S3 through an MINLP solver, generating the number of samples, calling an OPTI tool box based on a Matlab platform, solving the mixed integer nonlinear programming problem through a BONMIN algorithm, and realizing the operation optimization algorithm.
(2) Running layer
And for each sample combination generated by the optimization layer, the operation layer sequentially calculates, through calculation, the operation layer can obtain the reproducible permeability and the wind and light abandoning rate of the system in the operation time, and the operation layer performs the next iteration according to the returned data and automatically changes the operation parameters until the optimal solution set is obtained.
2. The method for optimizing operations of a regional micro power grid considering maximum consumption of renewable energy according to claim 1, wherein the user terminal in step S1 comprises residential buildings, new energy vehicles, and the like.
3. The method for optimizing the operation of the regional micro power grid considering the maximum consumption of the renewable energy according to claim 1, wherein the energy supply end equipment in the step S2 comprises a small wind power generator, a solar power generator, an energy storage battery, a gas turbine, a bromine cooler and the like.
4. The method for optimizing the operation of the regional micro power grid considering the maximum consumption of the renewable energy according to claim 1, wherein the samples in the step S3 include wind power installation amount, photovoltaic installation power, the number of gas turbines, waste heat boiler capacity, lithium bromide refrigerant capacity, electric refrigeration capacity, energy storage element capacity, and the like.
CN202210353253.1A 2022-04-06 2022-04-06 Regional micro-energy network operation optimization method considering maximum consumption of renewable energy Pending CN114997460A (en)

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