CN111667131A - Multi-objective optimization method and system for energy supply end installation design - Google Patents

Multi-objective optimization method and system for energy supply end installation design Download PDF

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CN111667131A
CN111667131A CN201910169712.9A CN201910169712A CN111667131A CN 111667131 A CN111667131 A CN 111667131A CN 201910169712 A CN201910169712 A CN 201910169712A CN 111667131 A CN111667131 A CN 111667131A
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power
time
formula
energy
heat
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Inventor
张新鹤
李克成
刘铠诚
闫华光
何桂雄
张志刚
王旭东
霍现旭
李树鹏
王锰
于航
黄子硕
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Tongji University
State Grid Corp of China SGCC
China Electric Power Research Institute Co Ltd CEPRI
State Grid Tianjin Electric Power Co Ltd
State Grid Jiangsu Electric Power Co Ltd
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Tongji University
State Grid Corp of China SGCC
China Electric Power Research Institute Co Ltd CEPRI
State Grid Tianjin Electric Power Co Ltd
State Grid Jiangsu Electric Power Co Ltd
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    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06315—Needs-based resource requirements planning or analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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Abstract

The invention provides a multi-objective optimization method and a system for energy supply end installation design, and the technical scheme provided by the invention comprises the following steps: acquiring the cooling, heating and power loads of buildings in the area, and analyzing the geographical, resource, weather and area load characteristics in the area to obtain primarily selected energy equipment; obtaining the total carbon emission in the life cycle of the primarily selected energy equipment based on the cold, heat and power loads and the basic parameters of energy conversion in the primarily selected energy equipment; and bringing the total carbon emission into a constructed multi-objective function, and solving by adopting a pareto algorithm to obtain an optimal configuration scheme of the installed capacity of each energy source. The technical scheme provided by the invention can reduce the carbon emission and solve the contradiction between the rapid development of cities and towns, the shortage of energy and the deterioration of the environment.

Description

Multi-objective optimization method and system for energy supply end installation design
Technical Field
The invention relates to the field of regional energy planning design, in particular to a multi-objective optimization method and system for energy supply end installation design.
Background
Towns are the subject of coping with climate change and developing low carbon economy. Towns consume 80% of the world's energy and their greenhouse gas emissions account for as much as 80% of the total global volume. The rapid development of cities and towns is contradictory to the shortage of energy and the deterioration of environment. In the existing planning system, the traditional planning methods are mostly adopted for power planning, gas planning, heating power planning of central heating areas in most areas and the like, namely, the safety and reliability of energy supply are emphasized from the supply side, the maximum load under the condition of extreme energy utilization is superposed, and the cold and hot loads are overestimated to ensure the supply safety factor, so that the phenomena that the more energy is saved at the terminal of the demand side, the worse the energy efficiency of the supply side is are easily caused.
Disclosure of Invention
The technical scheme provided by the invention is as follows:
a multi-objective optimization method for energy supply end installation design comprises the following steps:
acquiring the cooling, heating and power loads of buildings in the area, and analyzing the geographical, resource, weather and area load characteristics in the area to obtain primarily selected energy equipment;
obtaining the total carbon emission in the life cycle of the primarily selected energy equipment based on the cold, heat and power loads and the basic parameters of energy conversion in the primarily selected energy equipment;
and bringing the total carbon emission into a constructed multi-objective function, and solving by adopting a pareto algorithm to obtain an optimal configuration scheme of the installed capacity of each energy source.
Preferably, the acquiring of the cooling, heating and power loads of the buildings in the area and the analyzing of the geographical, resource, weather and area load characteristics in the area to obtain the primarily selected energy equipment includes:
simulating and calculating the cooling, heating and power loads of buildings in the area by using building energy consumption simulation software;
and matching and analyzing the geography, resources and weather in the region by combining with the region load characteristics to obtain the primary energy equipment.
Preferably, the obtaining of the total carbon emission in the life cycle of the primarily selected energy device based on the cooling, heating and power loads and the basic parameters of each energy conversion in the primarily selected energy device includes:
determining basic parameters of each energy conversion device according to market data and technical literature;
determining regional energy prices according to the geographical location and the policy information;
the initial investment of an energy system, the running cost of the whole life cycle, the maintenance cost of equipment and the like are treated into equal-year-value cost and total carbon emission in the life cycle;
preferably, the multi-objective function includes the following calculation formula:
min∑Ec
in the formula: ecIs the carbon emission.
Preferably, the total carbon emission is as follows:
Figure BDA0001986528530000021
in the formula: ecCarbon emissions;
Figure BDA0001986528530000022
converting carbon emission into natural gas;
Figure BDA0001986528530000023
η is the real-time power output of CHP uniteThe generating efficiency of the cogeneration unit is obtained;
Figure BDA0001986528530000024
η for generating heat of boilerbEfficiency of heat production for filtration;
Figure BDA0001986528530000025
converting carbon emission for power grid purchasing;
Figure BDA0001986528530000026
and purchasing electric quantity for the power grid.
Preferably, the multi-objective function further includes the following calculation formula:
min∑(Cin+Co+Cm)
in the formula: cinInitial investment for the year value converted to the full life cycle; c0Energy costs for annual operation; cmFor equipment maintenance costs.
Preferably, the constructing of the multi-objective function further includes: constructing the following constraint conditions for the multi-objective function:
power consumption balance constraint, cold quantity balance constraint and heat quantity balance constraint.
Preferably, the obtaining of the optimal configuration scheme of installed capacity of each energy source by solving with the pareto algorithm includes:
setting an optimal distance and a worst distance in a normalized domain;
and selecting a global optimal solution from the pareto frontier non-dominated solution set as a carbon emission and economically optimal installed capacity configuration based on the optimal distance and the worst distance.
Preferably, the power consumption balance constraint is as follows:
Figure BDA0001986528530000031
in the formula:
Figure BDA0001986528530000032
the time-by-time electricity consumption of the electricity consumption equipment;
Figure BDA0001986528530000033
the time-by-time power consumption for lighting;
Figure BDA0001986528530000034
the time-by-time electricity consumption of the power distribution system;
Figure BDA0001986528530000035
the electricity consumption of the electric refrigerator is time by time;s，hfor buying the electricity, ifs，hIf the system needs to purchase power to the power grid as 1s，hThe redundant power of the system is on line as 0;
Figure BDA0001986528530000036
purchasing electric quantity for the power grid;
Figure BDA0001986528530000037
the power of the CHP unit is output in real time;
Figure BDA0001986528530000038
to sell power to the grid.
Preferably, the cold balance constraint is as follows:
Figure BDA0001986528530000039
in the formula:
Figure BDA00019865285300000310
the refrigeration capacity is electric refrigeration capacity;
Figure BDA00019865285300000311
the refrigeration capacity of lithium bromide; LC (liquid Crystal)s，hIs a time-by-time cooling load.
Preferably, the heat balance constraint is as follows:
Figure BDA0001986528530000041
in the formula:
Figure BDA0001986528530000042
the heat quantity of the co-production equipment;
Figure BDA0001986528530000043
heating the boiler;
Figure BDA0001986528530000044
heat is consumed for the lithium bromide;
Figure BDA0001986528530000045
the heat storage quantity of the heat storage equipment;
Figure BDA0001986528530000046
is the heat release of the heat storage device; LHs，hFor a time-wise thermal load.
Preferably, the global optimal solution is selected from the pareto frontier non-dominated solution set based on the optimal distance and the worst distance, as shown in the following formula:
Figure BDA0001986528530000047
in the formula: ED (electronic device)i+Setting the optimal distance in the normalized domain as the worst distance in the normalized domain;
wherein:
the optimal distance set in the normalized domain is as follows:
Figure BDA0001986528530000048
in the formula:
Figure BDA0001986528530000049
is the optimum value of the objective function j.
Figure BDA00019865285300000410
Is a function value representing that the pareto solution centralized point i corresponds to the objective function j under the normalized scale;
the optimal distance set in the normalized domain is as follows:
Figure BDA00019865285300000411
in the formula:
Figure BDA00019865285300000412
is the worst value of the objective function j.
A multi-objective optimization system for energy end-to-end installation design, comprising:
an acquisition module: the system comprises a power supply, a power supply control module and a power supply control module, wherein the power supply control module is used for acquiring the cold, heat and power loads of buildings in an area, and analyzing the geographical, resource, weather and area load characteristics in the area to obtain primary energy equipment;
a calculation module: the energy conversion system is used for obtaining the total carbon emission in the life cycle of the primarily selected energy equipment based on the cold, heat and power load and the basic parameters of each energy conversion in the primarily selected energy equipment;
an optimization module: and the method is used for bringing the total carbon emission into a constructed multi-objective function, and solving by adopting a pareto algorithm to obtain an optimal configuration scheme of the installed capacity of each energy source.
Preferably, the optimization module includes:
the system comprises a multi-target function submodule, a multi-target function constraint condition submodule and a preference submodule;
the multi-objective function submodule comprises the following calculation formula:
min∑Ec
in the formula: ecCarbon emissions;
wherein the total carbon emission is shown as the following formula:
Figure BDA0001986528530000051
in the formula: ecCarbon emissions;
Figure BDA0001986528530000052
converting carbon emission into natural gas;
Figure BDA0001986528530000053
η is the real-time power output of CHP uniteThe generating efficiency of the cogeneration unit is obtained;
Figure BDA0001986528530000054
η for generating heat of boilerbEfficiency of heat production for filtration;
Figure BDA0001986528530000055
converting carbon emission for power grid purchasing;
Figure BDA0001986528530000056
purchasing electric quantity for the power grid;
the multi-objective function constraint condition submodule comprises: the device comprises a power consumption balance constraint unit, a cold quantity balance constraint unit and a heat quantity balance constraint unit;
wherein, the power consumption balance constraint unit is as follows:
Figure BDA0001986528530000061
in the formula:
Figure BDA0001986528530000062
the time-by-time electricity consumption of the electricity consumption equipment;
Figure BDA0001986528530000063
the time-by-time power consumption for lighting;
Figure BDA0001986528530000064
the time-by-time electricity consumption of the power distribution system;
Figure BDA0001986528530000065
the electricity consumption of the electric refrigerator is time by time;s，hfor buying the electricity, ifs，hIf the system needs to purchase power to the power grid as 1s，hThe redundant power of the system is on line as 0;
Figure BDA0001986528530000066
purchasing electric quantity for the power grid;
Figure BDA0001986528530000067
the power of the CHP unit is output in real time;
Figure BDA0001986528530000068
selling power to the grid;
the cold quantity balance constraint unit is shown as the following formula:
Figure BDA0001986528530000069
in the formula:
Figure BDA00019865285300000610
the refrigeration capacity is electric refrigeration capacity;
Figure BDA00019865285300000611
the refrigeration capacity of lithium bromide; LC (liquid Crystal)s，hIs a time-by-time cooling load.
The heat balance constraint unit is represented by the following formula:
Figure BDA00019865285300000612
in the formula:
Figure BDA00019865285300000613
the heat quantity of the co-production equipment;
Figure BDA00019865285300000614
heating the boiler;
Figure BDA00019865285300000615
heat is consumed for the lithium bromide;
Figure BDA00019865285300000616
the heat storage quantity of the heat storage equipment;
Figure BDA00019865285300000617
is the heat release of the heat storage device; LHs，hFor hourly thermal loading;
the preferred sub-module is shown as follows:
Figure BDA00019865285300000618
in the formula: ED (electronic device)i+Setting the optimal distance in the normalized domain as the worst distance in the normalized domain;
wherein:
the optimal distance set in the normalized domain is as follows:
Figure BDA0001986528530000071
in the formula:
Figure BDA0001986528530000072
is the optimum value of the objective function j.
Figure BDA0001986528530000073
Is a function value representing that the pareto solution centralized point i corresponds to the objective function j under the normalized scale;
the optimal distance set in the normalized domain is as follows:
Figure BDA0001986528530000074
in the formula:
Figure BDA0001986528530000075
is the worst value of the objective function j.
Compared with the prior art, the invention has the beneficial effects that:
(1) the technical scheme provided by the invention comprises the following steps: acquiring the cooling, heating and power loads of buildings in the area, and analyzing the geographical, resource, weather and area load characteristics in the area to obtain primarily selected energy equipment; obtaining the total carbon emission in the life cycle of the primarily selected energy equipment based on the cold, heat and power loads and the basic parameters of energy conversion in the primarily selected energy equipment; and bringing the total carbon emission into a constructed multi-objective function, and solving by adopting a pareto algorithm to obtain an optimal configuration scheme of the installed capacity of each energy source. The technical scheme provided by the invention can reduce the carbon emission and solve the contradiction between the rapid development of cities and towns, the shortage of energy and the deterioration of the environment.
(2) The technical scheme provided by the invention provides theoretical and technical support for regional energy planning design, particularly fully considers the influence of heat storage equipment on the configuration of an energy system in a model, so that a planning result has more guiding value and significance, and a calculation result can also provide a reference basis for an operation scheduling strategy of a regional energy system.
Drawings
FIG. 1 is a schematic flow diagram of the present invention;
FIG. 2 is a schematic structural view of the present invention;
FIG. 3 is a schematic view of the overall structure of the present invention;
FIG. 4 is a schematic diagram of a low-side arm gas capacitor according to the present invention;
FIG. 5 is a schematic diagram of a voltage processing unit according to the present invention.
Detailed Description
For a better understanding of the present invention, reference is made to the following description taken in conjunction with the accompanying drawings and examples.
Example 1:
a multi-objective optimization method for energy supply end installation design is shown in figure 1 and comprises the following steps:
s1, acquiring the cooling, heating and power loads of buildings in the area, and analyzing the geographical, resource, weather and area load characteristics in the area to obtain primarily selected energy equipment;
s2, obtaining the total carbon emission in the life cycle of the primarily selected energy equipment based on the cold, heat and power loads and the basic parameters of energy conversion in the primarily selected energy equipment;
and S3, the total carbon emission is brought into the constructed multi-objective function, and the pareto algorithm is adopted to solve to obtain the optimal configuration scheme of the installed capacity of each energy source.
At present, the most effective means for improving the utilization level of clean energy is to use the price as a lever, use the instantaneous supply and demand balance of the power system as a basis and consider the safe operation of the power system to formulate reasonable real-time electricity price. The energy supply scheme which aims at utilizing the clean energy to the maximum is realized by taking the electricity price as the guidance and taking the stored energy as the assistance, so that the aim of equivalently converting the utilization of the clean energy to the maximum can be achieved to aim at minimizing the operation cost of the micro-grid.
S1, acquiring the cooling, heating and power loads of buildings in the area, and analyzing the geographical, resource, weather and area load characteristics in the area to obtain primarily selected energy equipment;
the technical scheme adopted by the invention is as shown in figure 2: simulating and calculating the cooling, heating and power loads of buildings in the area by using building energy consumption simulation software; obtaining a matching primary energy technology by geographic, resource, meteorological analysis and regional load characteristic analysis in a region; determining basic parameters of each energy conversion device according to market data and technical literature;
s2, obtaining the total carbon emission in the life cycle of the primarily selected energy equipment based on the cold, heat and power loads and the basic parameters of energy conversion in the primarily selected energy equipment;
determining regional energy prices based on the geographic location and policy information is shown in fig. 4; the initial investment of an energy system, the running cost of the whole life cycle, the maintenance cost of equipment and the like are treated into equal annual value cost and total carbon emission in the life cycle,
s3, the total carbon emission is brought into a constructed multi-objective function, and a pareto algorithm is adopted to solve to obtain an optimal configuration scheme of installed capacity of each energy source;
and solving the pareto front curve to decide a global optimal solution, and obtaining the type and installed capacity configuration of the supply technology by analyzing the pareto front.
Specifically, a partial load efficiency model of each device is selected according to the existing research, then early-stage load simulation and device initial selection are carried out, and an economic low-carbon objective function is further established:
min∑(Cin+Co+Cm) (1)
min∑Ec(2)
Cinrepresenting the equal annual value initial investment converted to the full life cycle; coRepresenting annual operating energy costs; cmRepresents a maintenance charge; ecIs the carbon emission.
1) Initial investment cost of equipment:
C′in＝C′in-chp+C′in-boiler+C′in-HS+C′in-OT(3)
Cin＝C′in×CRF (4)
Figure BDA0001986528530000101
C′inrepresenting the total initial investment cost; c'in-chpRepresenting the initial investment of the combined supply system; c'in-boilerRepresents the initial investment of the gas boiler; c'in-HSRepresenting the initial investment of the heat storage equipment; c'in-OTRepresents the initial investment of a cooling tower, a water pump, a pipe network and the like; r is annual interest rate; and n is the service life of the system.
2) Annual operating costs:
Co＝Cf+Ce(6)
that is, the annual running cost includes a gas cost CfAnd electric charge (large grid) Ce。
a gas cost:
Figure BDA0001986528530000102
in the formula, CfIs the natural gas price; s and h are season and time respectively;
Figure BDA0001986528530000103
η is the real-time power output of CHP uniteThe generating efficiency of the CHP unit is obtained;
Figure BDA0001986528530000104
η for generating heat of boilerbThe heat production efficiency of the boiler is improved.
b, electric charge:
the electric charge consists of basic electric charge and electric power charge, and the basic electric charge has two metering modes, namely the basic electric charge is calculated according to the capacity of the transformer and the maximum active power of the electricity.
The basic electricity charge calculated from the transformer capacity is:
Figure BDA0001986528530000105
wherein the content of the first and second substances,
Figure BDA0001986528530000106
is the price per unit capacity of the transformer; catransformerIs the transformer capacity.
The basic electricity fee calculated according to the maximum active power of the electricity consumption is as follows:
Figure BDA0001986528530000111
wherein the content of the first and second substances,
Figure BDA0001986528530000112
is the price per unit active power;
Figure BDA0001986528530000113
the electricity is purchased from the large power grid time by time every month.
The electricity charge is as follows:
Figure BDA0001986528530000114
in the formula (I), the compound is shown in the specification,
Figure BDA0001986528530000115
the time-by-time electricity price;
Figure BDA0001986528530000116
the electricity is purchased from a large power grid time by time.
Therefore, the annual electricity consumption cost is:
Figure BDA0001986528530000117
0r
Figure BDA0001986528530000118
3) equipment maintenance cost:
the operation and maintenance costs of the equipment are composed of fixed maintenance costs and variable maintenance costs.
Figure BDA0001986528530000119
In the formula, αtFixing a maintenance cost coefficient for each device; catInstalled capacity for each equipment βtChanging a maintenance cost coefficient for each device;
Figure BDA00019865285300001110
the total hourly output for each device.
4) Carbon emissions
The carbon emission path has three main paths, namely, the carbon emission generated indirectly by purchasing power of a power grid, the carbon emission generated directly by consuming gas by a gas turbine, and the carbon emission generated directly by consuming gas by a gas boiler.
Figure BDA00019865285300001111
In the formula (I), the compound is shown in the specification,
Figure BDA00019865285300001112
carbon emission coefficient per unit energy production.
Further comprising the following steps: the constraints in the configuration problem include total amount constraints (energy conservation) and equipment constraints, and the total amount constraints include power consumption balance constraints and cold (heat) amount balance constraints; the equipment constraint comprises equipment output constraint and equipment capacity total constraint, and the established constraint conditions are as follows:
1) and (3) power consumption balance constraint:
Figure BDA0001986528530000121
in the formula (I), the compound is shown in the specification,
Figure BDA0001986528530000122
for time-by-time consumption of power-consuming apparatusAn amount;
Figure BDA0001986528530000123
the time-by-time power consumption for lighting;
Figure BDA0001986528530000124
the time-by-time electricity consumption of the power distribution system;
Figure BDA0001986528530000125
the electricity consumption of the electric refrigerator is time by time;s，hthe sign position is marked for buying electricity.
When the system needs to purchase the power grid from the power grid:s，h1 is ═ 1; when the redundant power of the system is on line, the following steps are carried out:s，h＝0。
2) cold quantity balance constraint:
Figure BDA0001986528530000126
in the formula (I), the compound is shown in the specification,
Figure BDA0001986528530000127
the refrigeration capacity is electric refrigeration capacity;
Figure BDA0001986528530000128
the refrigeration capacity of lithium bromide; LC (liquid Crystal)s，hIs a time-by-time cooling load.
3) And (3) heat balance constraint:
Figure BDA0001986528530000129
in the formula (I), the compound is shown in the specification,
Figure BDA00019865285300001210
the heat quantity of the co-production equipment;
Figure BDA00019865285300001211
heating the boiler;
Figure BDA00019865285300001212
heat is consumed for the lithium bromide;
Figure BDA00019865285300001213
the heat storage quantity of the heat storage equipment;
Figure BDA00019865285300001214
is the heat release of the heat storage device; LHs，hFor a time-wise thermal load.
4) And (3) equipment constraint:
the maximum and minimum output range exists in any equipment, and the starting and stopping states exist.
For a CHP power plant, there are:
Figure BDA0001986528530000131
Figure BDA0001986528530000132
in the formula, gammae-chpIs 0, 1;
Figure BDA0001986528530000133
the minimum output of the unit;
Figure BDA0001986528530000134
representing the maximum capacity of the unit.
For electric refrigeration units, there are:
Figure BDA0001986528530000135
Figure BDA0001986528530000136
in the formula, COPecIs the efficiency of the electric refrigerator; gamma rayecIs 0, 1.
For boilers, there are:
Figure BDA0001986528530000137
Figure BDA0001986528530000138
in the formula (I), the compound is shown in the specification,
Figure BDA0001986528530000139
the time-by-time gas consumption of the boiler; gamma rayghIs 0, 1.
On the basis of the regional energy system planning model constructed by the method, the pareto frontier schematic diagram of the model can be solved by taking the formula (1) and the formula (2) as the economic objective function and the environmental protection objective function of the model respectively and adopting a meta-heuristic optimization algorithm as shown in the attached figure 5. As can be seen from the pareto frontier in fig. 5, the solved non-dominant solutions are a solution set, and therefore, a global optimal solution needs to be decided in the pareto frontier, i.e., optimal in the implementation of optimization.
The notion of best and worst distances is first given in the normalized domain, defined as follows:
Figure BDA0001986528530000141
Figure BDA0001986528530000142
in the formula, EDi+Represents the optimal distance of the pareto solution concentration point i; ED (electronic device)i-Represents the worst distance for point i;
Figure BDA0001986528530000143
representing the function value of the objective function j for the pareto solution set point i at the normalized scale.
Figure BDA0001986528530000144
Representing the optimal value of the objective function j.
Figure BDA0001986528530000145
Representing the worst value of the objective function j.
Further, an optimal decision definition can be given as follows, and point i is the global optimal solution sought:
Figure BDA0001986528530000146
example 2:
a multi-objective optimization system for energy end-to-end installation design, comprising:
an acquisition module: the system comprises a power supply, a power supply control module and a power supply control module, wherein the power supply control module is used for acquiring the cold, heat and power loads of buildings in an area, and analyzing the geographical, resource, weather and area load characteristics in the area to obtain primary energy equipment;
a calculation module: the energy conversion system is used for obtaining the total carbon emission in the life cycle of the primarily selected energy equipment based on the cold, heat and power load and the basic parameters of each energy conversion in the primarily selected energy equipment;
an optimization module: and the method is used for bringing the total carbon emission into a constructed multi-objective function, and solving by adopting a pareto algorithm to obtain an optimal configuration scheme of the installed capacity of each energy source.
The optimization module comprises:
the system comprises a multi-target function submodule, a multi-target function constraint condition submodule and a preference submodule;
the multi-objective function submodule comprises the following calculation formula:
min∑Ec
in the formula: ecCarbon emissions;
wherein the total carbon emission is shown as the following formula:
Figure BDA0001986528530000151
in the formula: ecCarbon emissions;
Figure BDA0001986528530000152
converting carbon emission into natural gas;
Figure BDA0001986528530000153
η is the real-time power output of CHP uniteThe generating efficiency of the cogeneration unit is obtained;
Figure BDA0001986528530000154
η for generating heat of boilerbEfficiency of heat production for filtration;
Figure BDA0001986528530000155
converting carbon emission for power grid purchasing;
Figure BDA0001986528530000156
purchasing electric quantity for the power grid;
the multi-objective function constraint condition submodule comprises: the device comprises a power consumption balance constraint unit, a cold quantity balance constraint unit and a heat quantity balance constraint unit;
wherein, the power consumption balance constraint unit is as follows:
Figure BDA0001986528530000157
in the formula:
Figure BDA0001986528530000158
the time-by-time electricity consumption of the electricity consumption equipment;
Figure BDA0001986528530000159
the time-by-time power consumption for lighting;
Figure BDA00019865285300001510
the time-by-time electricity consumption of the power distribution system;
Figure BDA00019865285300001511
the electricity consumption of the electric refrigerator is time by time;s，hfor buying the electricity, ifs，hIf the system needs to purchase power to the power grid as 1s，hThe redundant power of the system is on line as 0;
Figure BDA00019865285300001512
purchasing electric quantity for the power grid;
Figure BDA00019865285300001513
the power of the CHP unit is output in real time;
Figure BDA00019865285300001514
selling power to the grid;
the cold quantity balance constraint unit is shown as the following formula:
Figure BDA00019865285300001515
in the formula:
Figure BDA00019865285300001516
the refrigeration capacity is electric refrigeration capacity;
Figure BDA00019865285300001517
the refrigeration capacity of lithium bromide; LC (liquid Crystal)s，hIs a time-by-time cooling load.
The heat balance constraint unit is represented by the following formula:
Figure BDA0001986528530000161
in the formula:
Figure BDA0001986528530000162
the heat quantity of the co-production equipment;
Figure BDA0001986528530000163
heating the boiler;
Figure BDA0001986528530000164
heat is consumed for the lithium bromide;
Figure BDA0001986528530000165
the heat storage quantity of the heat storage equipment;
Figure BDA0001986528530000166
is the heat release of the heat storage device; LHs，hFor hourly thermal loading;
the preferred sub-module is shown as follows:
Figure BDA0001986528530000167
in the formula: ED (electronic device)i+Setting the optimal distance in the normalized domain as the worst distance in the normalized domain;
wherein:
the optimal distance set in the normalized domain is as follows:
Figure BDA0001986528530000168
in the formula:
Figure BDA0001986528530000169
is the optimum value of the objective function j.
Figure BDA00019865285300001610
Is a function value representing that the pareto solution centralized point i corresponds to the objective function j under the normalized scale;
the optimal distance set in the normalized domain is as follows:
Figure BDA00019865285300001611
in the formula:
Figure BDA00019865285300001612
is the worst value of the objective function j.
Example 3:
an example scenario is shown in fig. 3, which takes into account the effect of heat storage on the configuration of the regional energy system.
Taking an example as a regional complex, and acquiring annual cooling and heating load data by a regional cooling and heating load simulation method; as shown in the attached table 1, the initial investment cost of part of equipment is shown, in addition, the regional energy system also comprises auxiliary equipment such as a water pump, a cooling tower and the like, and the investment cost of the auxiliary equipment is 35% of the investment cost of the attached table 1. The price of natural gas is fixed price, the price of electricity on the internet is fixed value, and the price of electricity purchased is stepped price of electricity, so that the invention is fully utilized to embody economic leverage and realize the best fusion point of economic cost and carbon emission. And the attached table 2 is maintenance cost of the equipment, the attached table 3 is specific performance parameters of the equipment, and in order to reduce the solving difficulty of the model, the performance parameters such as efficiency of the equipment in the system are assumed to be constants.
Solving a planning equation by using a mathematic tool Matlab to obtain: the equipment selected by the comprehensive energy system is as follows: a generator set with capacity of 578 kW; a gas boiler with the capacity of 102 kW; the electric refrigerating unit has the capacity of 743 kW; an absorption chiller unit having a capacity of 438; the capacity of the heat storage equipment is 913 kW. The capacity of the heat pump equipment is 856 kW.
Initial investment unit price of attached table 1 part equipment
Figure BDA0001986528530000171
TABLE 2 maintenance costs of the respective equipments
Figure BDA0001986528530000172
Figure BDA0001986528530000181
Table 3 attached hereto specific parameters of each plant
Figure BDA0001986528530000182
Figure BDA0001986528530000191
It is to be understood that the embodiments described are only a few embodiments of the present invention, and not all 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.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The present invention is not limited to the above embodiments, and any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention are included in the scope of the claims of the present invention which are filed as the application.

Claims (14)

1. A multi-objective optimization method for energy supply end installation design is characterized by comprising the following steps:
acquiring the cooling, heating and power loads of buildings in the area, and analyzing the geographical, resource, weather and area load characteristics in the area to obtain primarily selected energy equipment;
obtaining the total carbon emission in the life cycle of the primarily selected energy equipment based on the cold, heat and power loads and the basic parameters of energy conversion in the primarily selected energy equipment;
and bringing the total carbon emission into a constructed multi-objective function, and solving by adopting a pareto algorithm to obtain an optimal configuration scheme of the installed capacity of each energy source.
2. The multi-objective optimization method according to claim 1, wherein the acquiring of the cooling, heating and power loads of the buildings in the area and the analyzing of the geographical, resource, weather and area load characteristics in the area to obtain the primary energy devices comprises:
simulating and calculating the cooling, heating and power loads of buildings in the area by using building energy consumption simulation software;
and matching and analyzing the geography, resources and weather in the region by combining with the region load characteristics to obtain the primary energy equipment.
3. The multi-objective optimization method according to claim 1, wherein the obtaining of the total carbon emission in the life cycle of the primarily selected energy equipment based on the cooling, heating and power loads and the basic parameters of each energy conversion in the primarily selected energy equipment comprises:
determining basic parameters of each energy conversion device according to market data and technical literature;
determining regional energy prices according to the geographical location and the policy information;
the initial investment of an energy system, the running cost of the whole life cycle, the maintenance cost of equipment and the like are treated into equal-year-value cost and total carbon emission in the life cycle.
4. The multi-objective optimization method of claim 3, wherein the multi-objective function comprises the following equations:
min∑Ec
in the formula: ecIs the carbon emission.
5. The multi-objective optimization method of claim 4, wherein the total carbon emissions are as follows:
Figure FDA0001986528520000021
in the formula: ecCarbon emissions;
Figure FDA0001986528520000022
converting carbon emission into natural gas;
Figure FDA0001986528520000023
η is the real-time power output of CHP uniteThe generating efficiency of the cogeneration unit is obtained;
Figure FDA0001986528520000024
η for generating heat of boilerbEfficiency of heat production for filtration;
Figure FDA0001986528520000025
converting carbon emission for power grid purchasing;
Figure FDA0001986528520000026
and purchasing electric quantity for the power grid.
6. The multi-objective optimization method of claim 4, wherein the multi-objective function further comprises the following equations:
min∑(Cin+Co+Cm)
in the formula: cinInitial investment for the year value converted to the full life cycle; c0Energy costs for annual operation; cmFor equipment maintenance costs.
7. The multi-objective optimization method of claim 6, wherein the construction of the multi-objective function further comprises: constructing the following constraint conditions for the multi-objective function:
power consumption balance constraint, cold quantity balance constraint and heat quantity balance constraint.
8. The multi-objective optimization method according to claim 1, wherein the solving by the pareto algorithm to obtain the optimal configuration scheme of the installed capacities of the energy sources comprises:
setting an optimal distance and a worst distance in a normalized domain;
and selecting a global optimal solution from the pareto frontier non-dominated solution set as a carbon emission and economically optimal installed capacity configuration based on the optimal distance and the worst distance.
9. The multi-objective optimization method of claim 7, wherein the power usage balance constraint is given by:
Figure FDA0001986528520000031
in the formula:
Figure FDA0001986528520000032
the time-by-time electricity consumption of the electricity consumption equipment;
Figure FDA0001986528520000033
the time-by-time power consumption for lighting;
Figure FDA0001986528520000034
the time-by-time electricity consumption of the power distribution system;
Figure FDA0001986528520000035
the electricity consumption of the electric refrigerator is time by time;s，hfor buying the electricity, ifs，hIf the system needs to purchase power to the power grid as 1s，hThe redundant power of the system is on line as 0;
Figure FDA0001986528520000036
purchasing electric quantity for the power grid;
Figure FDA0001986528520000037
the power of the CHP unit is output in real time;
Figure FDA0001986528520000038
to sell power to the grid.
10. The multi-objective optimization method of claim 7, wherein the cold balance constraint is given by:
Figure FDA0001986528520000039
in the formula:
Figure FDA00019865285200000310
the refrigeration capacity is electric refrigeration capacity;
Figure FDA00019865285200000311
the refrigeration capacity of lithium bromide; LC (liquid Crystal)s，hIs a time-by-time cooling load.
11. The multi-objective optimization method of claim 7, wherein the heat balance constraint is given by:
Figure FDA00019865285200000312
in the formula:
Figure FDA00019865285200000313
the heat quantity of the co-production equipment;
Figure FDA00019865285200000314
heating the boiler;
Figure FDA00019865285200000315
heat is consumed for the lithium bromide;
Figure FDA00019865285200000316
the heat storage quantity of the heat storage equipment;
Figure FDA00019865285200000317
is the heat release of the heat storage device; LHs，hFor a time-wise thermal load.
12. The multi-objective optimization method of claim 8, wherein the selecting a global optimal solution from the pareto frontier non-dominated solution set based on the optimal distance and the worst distance is as follows:
Figure FDA0001986528520000041
in the formula: ED (electronic device)i+Setting the optimal distance in the normalized domain as the worst distance in the normalized domain;
wherein:
the optimal distance set in the normalized domain is as follows:
Figure FDA0001986528520000042
in the formula:
Figure FDA0001986528520000043
is the optimum value of the objective function j.
Figure FDA0001986528520000044
Is a function value representing that the pareto solution centralized point i corresponds to the objective function j under the normalized scale;
the optimal distance set in the normalized domain is as follows:
Figure FDA0001986528520000045
in the formula:
Figure FDA0001986528520000046
is the worst value of the objective function j.
13. A multi-objective optimization system for energy supply end installation design, comprising:
an acquisition module: the system comprises a power supply, a power supply control module and a power supply control module, wherein the power supply control module is used for acquiring the cold, heat and power loads of buildings in an area, and analyzing the geographical, resource, weather and area load characteristics in the area to obtain primary energy equipment;
a calculation module: the energy conversion system is used for obtaining the total carbon emission in the life cycle of the primarily selected energy equipment based on the cold, heat and power load and the basic parameters of each energy conversion in the primarily selected energy equipment;
an optimization module: and the method is used for bringing the total carbon emission into a constructed multi-objective function, and solving by adopting a pareto algorithm to obtain an optimal configuration scheme of the installed capacity of each energy source.
14. The multi-objective optimization system of claim 13, wherein the optimization module comprises:
the system comprises a multi-target function submodule, a multi-target function constraint condition submodule and a preference submodule;
the multi-objective function submodule comprises the following calculation formula:
min∑Ec
in the formula: ecCarbon emissions;
wherein the total carbon emission is shown as the following formula:
Figure FDA0001986528520000051
in the formula: ecCarbon emissions;
Figure FDA0001986528520000052
converting carbon emission into natural gas;
Figure FDA0001986528520000053
η is the real-time power output of CHP uniteThe generating efficiency of the cogeneration unit is obtained;
Figure FDA0001986528520000054
η for generating heat of boilerbEfficiency of heat production for filtration;
Figure FDA0001986528520000055
converting carbon emission for power grid purchasing;
Figure FDA0001986528520000056
purchasing electric quantity for the power grid;
the multi-objective function constraint condition submodule comprises: the device comprises a power consumption balance constraint unit, a cold quantity balance constraint unit and a heat quantity balance constraint unit;
wherein, the power consumption balance constraint unit is as follows:
Figure FDA0001986528520000057
in the formula:
Figure FDA0001986528520000058
the time-by-time electricity consumption of the electricity consumption equipment;
Figure FDA0001986528520000059
the time-by-time power consumption for lighting;
Figure FDA00019865285200000510
the time-by-time electricity consumption of the power distribution system;
Figure FDA00019865285200000511
the electricity consumption of the electric refrigerator is time by time;s，hfor buying the electricity, ifs，hIf the system needs to purchase power to the power grid as 1s，hThe redundant power of the system is on line as 0;
Figure FDA00019865285200000512
purchasing electric quantity for the power grid;
Figure FDA00019865285200000513
the power of the CHP unit is output in real time;
Figure FDA00019865285200000514
selling power to the grid;
the cold quantity balance constraint unit is shown as the following formula:
Figure FDA0001986528520000061
in the formula:
Figure FDA0001986528520000062
the refrigeration capacity is electric refrigeration capacity;
Figure FDA0001986528520000063
the refrigeration capacity of lithium bromide; LC (liquid Crystal)s，hIs a time-by-time cooling load.
The heat balance constraint unit is represented by the following formula:
Figure FDA0001986528520000064
in the formula:
Figure FDA0001986528520000065
the heat quantity of the co-production equipment;
Figure FDA0001986528520000066
heating the boiler;
Figure FDA0001986528520000067
heat is consumed for the lithium bromide;
Figure FDA0001986528520000068
the heat storage quantity of the heat storage equipment;
Figure FDA0001986528520000069
is the heat release of the heat storage device; LHs，hFor hourly thermal loading;
the preferred sub-module is shown as follows:
Figure FDA00019865285200000610
in the formula: ED (electronic device)i+Setting the optimal distance in the normalized domain as the worst distance in the normalized domain;
wherein:
the optimal distance set in the normalized domain is as follows:
Figure FDA00019865285200000611
in the formula:
Figure FDA00019865285200000612
is the optimum value of the objective function j.
Figure FDA00019865285200000613
Is a function value representing that the pareto solution centralized point i corresponds to the objective function j under the normalized scale;
the optimal distance set in the normalized domain is as follows:
Figure FDA00019865285200000614
in the formula:
Figure FDA00019865285200000615
is the worst value of the objective function j.
CN201910169712.9A 2019-03-06 2019-03-06 Multi-objective optimization method and system for energy supply end installation design Pending CN111667131A (en)

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CN112381300A (en) * 2020-11-17 2021-02-19 国网北京市电力公司 Energy utilization system, energy analysis method and device based on energy utilization system
CN113610269A (en) * 2021-06-28 2021-11-05 天津大学 Multi-objective optimization-based rural residential building low-carbon energy system optimization method
CN113901672A (en) * 2021-11-17 2022-01-07 香港理工大学深圳研究院 Optimal design method of wind-solar complementary power energy storage system for net zero energy consumption building application
CN113988473A (en) * 2021-11-23 2022-01-28 国网北京市电力公司 Method, system, device and storage medium for configuring energy in region

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112381300A (en) * 2020-11-17 2021-02-19 国网北京市电力公司 Energy utilization system, energy analysis method and device based on energy utilization system
CN113610269A (en) * 2021-06-28 2021-11-05 天津大学 Multi-objective optimization-based rural residential building low-carbon energy system optimization method
CN113901672A (en) * 2021-11-17 2022-01-07 香港理工大学深圳研究院 Optimal design method of wind-solar complementary power energy storage system for net zero energy consumption building application
CN113901672B (en) * 2021-11-17 2022-11-08 香港理工大学深圳研究院 Optimal design method of wind-solar complementary power energy storage system for net zero energy consumption building application
CN113988473A (en) * 2021-11-23 2022-01-28 国网北京市电力公司 Method, system, device and storage medium for configuring energy in region

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