CN111445107A - Multi-objective optimization configuration method for cold-heat-power combined supply type micro-grid - Google Patents

Multi-objective optimization configuration method for cold-heat-power combined supply type micro-grid Download PDF

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CN111445107A
CN111445107A CN202010135058.2A CN202010135058A CN111445107A CN 111445107 A CN111445107 A CN 111445107A CN 202010135058 A CN202010135058 A CN 202010135058A CN 111445107 A CN111445107 A CN 111445107A
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cchp system
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王小利
郑刘康
蒋保臣
吴子栋
韩钊
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Shandong University
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Abstract

The invention relates to a multi-target optimization configuration method for a combined cooling heating and power supply type microgrid, which comprises the steps of uniformly converting cold energy and heat energy into electric energy for scheduling by constructing an energy supply equipment model of a CCHP system and an energy flow diagram thereof, analyzing the energy cost of each equipment in different time periods in terms of the operation cost and the maintenance cost of the equipment, and determining a scheduling flow diagram of energy output by each equipment in the CCHP system according to the size relationship of the energy cost; constructing an objective function configured by the CCHP system, solving constraint conditions of the function and determining an evaluation index for solving an objective function value of the system; and finding the best configuration scheme by improving the PSO algorithm under the condition of meeting the evaluation index. The power supply reliability of the CCHP system after optimized configuration reaches more than 99%, the investment in the service cycle of the CCHP system is small, the pollution to the environment is minimal, and the cost of the CCHP system in the operation cycle is effectively reduced.

Description

Multi-objective optimization configuration method for cold-heat-power combined supply type micro-grid
Technical Field
The invention belongs to the technical field of micro-grids, relates to a combined cooling heating and power type micro-grid technology, and particularly relates to a combined cooling heating and power type micro-grid multi-objective optimization configuration method.
Background
When the cold-hot electricity combined supply type micro-grid operates in an island, equipment with low operation cost such as a solar photovoltaic power generation system and a wind power generation system is mainly used as a first output unit, and when the electric energy is not supplied enough, the equipment is provided by output units with relatively low operation cost such as a gas turbine. The cold load and the heat load related in the traditional micro-grid system are generated by a compression trial refrigerating machine and an electric boiler of a separate supply device, the whole energy supply process is accompanied by low energy use efficiency, for example, the utilization rate of natural gas of a gas turbine is only thirty percent, and sixty percent of energy contained in smoke is directly discharged into the atmosphere, so that the atmosphere is polluted, and huge resource waste is generated.
In a Combined Cooling Heating and Power (CCHP) system, a gas turbine is used as a power output device, and exhaust gas generated in the power generation process of the gas turbine is reused in the CCHP system to be converted into heat energy in a heating season and converted into cold energy in a cooling season, so that pollution-free natural gas is utilized in different levels of heat under the combustion condition, and the graded utilization of energy is realized. Compared with a distribution Supply (SP) system consisting of distributed power supplies, the CCHP system not only improves the utilization rate of energy, but also reduces the pollution to the environment, and is an energy supply mode advocating vigorous development at present.
Although the CCHP system has the characteristics of high energy utilization rate and low environmental pollution relative to the SP system, the configuration of the whole CCHP system from the perspective of improving energy utilization rate, reducing environmental pollution and enhancing robustness of the system is a key problem for the whole CCHP system configuration research (Hu, R.; Ma, J.; L i, Z.K.; L u, Q.; Zhang, D.H.; Qian, X.; Optimal and application reliability analysis of distributed combined-heated-powered system of the system in Zhang, D.H.; Dianwang Jiang Jishu 2017,41, 418) sets multiple gains generated by the CCHP system as a solution target of the whole model, gives a CCHP system configuration mode under different load structural requirements, and the whole CCHP system fails to perform system performance under the background of multiple annual data and system performance characteristics in the future, and the condition of the system reliability analysis of the system (the system, the method, the system, the method, the system, the method, the system, the method, the system, the method, the system, the method, the system, the method, the system.
In summary, the conventional CCHP system configuration method has the following problems: (1) the system construction cost considered during system configuration is not comprehensive, for example, the land cost and the grid-connected inverter equipment cost are not considered in the system configuration, and the construction and operation cost is not complete and cannot approach to the actual engineering application. (2) In the system optimization configuration, the constructed system is not fully combined with actual engineering application, for example, a system model is constructed only under fixed data, the reliability of the system model is not verified, and the power supply reliability is poor. (3) The advantage of the accumulated investment cost of the system during the operation period of the system construction is not reflected in the initial stage of the system construction. Therefore, how to improve the power supply reliability of the CCHP system, reduce the construction and operation cost, and reduce the environmental pollution is an urgent problem to be solved for optimizing the CCHP system.
Disclosure of Invention
The invention provides a combined cooling heating and power type microgrid multi-target optimization configuration method aiming at the problems of poor reliability, high operation cost and the like of the existing combined cooling heating and power type microgrid optimization configuration, which can improve the power supply reliability of a CCHP system, reduce the construction operation cost and reduce the environmental pollution.
In order to achieve the purpose, the invention provides a cooling, heating and power combined supply type micro-grid multi-objective optimization configuration method, which comprises the following steps:
constructing an energy supply equipment model of a CCHP system and an energy flow diagram thereof;
calculating the cost of each distributed power supply in the CCHP system for generating each kilowatt-hour of electric energy;
calculating the cost of cold energy and heat energy generated by refrigerating and heating equipment in the CCHP system, and converting the cost into the cost of electric energy per kilowatt hour;
determining a dispatching flow chart of energy of a CCHP system energy supply device according to the cost of the calculated electric energy;
the minimum investment capital of the CCHP system is taken as a target, and an objective function C is solvedtotalConstructing a CCHP system configuration model according to the minimum value on the premise of meeting constraint conditions and evaluation indexes, wherein the target function CtotalExpressed as:
Ctotal＝Cfi+Cgas+Cma+Cin+Ce+Csu(1)
in the formula, CfiRepresents the purchase cost of the equipment, CgasRepresents the fuel consumption cost, CmaRepresents the maintenance cost of the equipment, CinRepresents the installation cost of the apparatus, CeRepresents the environmental cost, CsuRepresents the CCHP system revenue cost;
by solving an objective function C of a CCHP system configuration modeltotalAnd acquiring the optimal configuration of the CCHP system.
Preferably, the energy supply equipment model comprises a solar photovoltaic battery pack, a wind generating set, a gas turbine, a storage battery energy storage device, a direct-fired lithium bromide absorption type water chilling unit, a direct-fired lithium bromide absorption type water warming unit, an energy storage device, an electric refrigerator and an electric boiler.
Preferably, the operation cost, the maintenance cost and the power generation subsidy cost of each distributed power supply are calculated according to the historical meteorological conditions and the commodity price level of the region where the CCHP system is located, and then the cost of each distributed power supply for generating electricity energy per kilowatt-hour is obtained.
Preferably, the equipment purchase cost CfiComprises the following steps:
Figure BDA0002397037210000041
wherein n is the total number of distributed power sources,
Figure BDA0002397037210000042
the unit price of the device of the i-th type,
Figure BDA0002397037210000043
the power output quantity of the ith distributed power supply is obtained;
Figure BDA0002397037210000044
as a function of the number of i distributed power sources;
Figure BDA0002397037210000045
the unit price of the ith grid-connected inverter equipment is;
Figure BDA0002397037210000046
solving the number function of the ith kind of grid-connected inverter equipment;
the fuel consumption cost CgasComprises the following steps:
Figure BDA0002397037210000047
in the formula (I), the compound is shown in the specification,t is the time point of equipment operation in the CCHP system;
Figure BDA0002397037210000048
power output for gas turbine at time t ηMTL HV is the heat value of natural gas, and the value is 9.7 kW.h/m3；ΔtMTFor gas turbine operating hours;
Figure BDA0002397037210000049
consuming the cold energy generated by natural gas for the lithium bromide water chilling unit at the time t;
Figure BDA00023970372100000410
η for lithium bromide hot water unit consuming heat energy generated by natural gas at time tBT_coolEfficiency of generating cold energy for lithium bromide chiller consuming natural gas at time t ηBT_hotThe efficiency of generating heat energy for the lithium bromide hot water unit consuming natural gas at the moment t; Δ tBT_gas_coolWorking time for the lithium bromide water chilling unit to consume natural gas to generate cold energy; Δ tBT_gas_hotWorking time for a lithium bromide hot water unit to consume natural gas to generate heat energy;
Figure BDA0002397037210000051
the unit price of the natural gas at the t moment;
the equipment maintenance cost CmaComprises the following steps:
Figure BDA0002397037210000052
in the formula (I), the compound is shown in the specification,
Figure BDA0002397037210000053
the operation and maintenance cost of the equipment when generating electric energy per kilowatt hour for the ith distributed power supply;
Figure BDA0002397037210000054
the output power of the distributed power supply at the t moment is obtained; Δ t is the operating time of the distributed power supply;
Figure BDA0002397037210000055
the periodic maintenance cost of the ith equipment;
the equipment installation cost CinComprises the following steps:
Figure BDA0002397037210000056
in the formula (I), the compound is shown in the specification,
Figure BDA0002397037210000057
the installation cost of each distributed power supply is ith;
Figure BDA0002397037210000058
the occupied area required by the ith distributed power supply; clandA circulation price for land per unit area;
the environmental cost CeComprises the following steps:
Figure BDA0002397037210000059
wherein M is the amount and type of pollutant generated in the CCHP system αjβ for the unit treatment cost for treating the jth pollution gas according to the international standardijA proportionality coefficient for producing a jth polluting gas per unit output power for the ith plant; pi(t) the output of the ith equipment at t, Q the harmful gas type which can be purified by the occupied land, L the total cost type of the air purification by the occupied land, l the cost type of the air purification by the occupied land, αlqCost for purifying q gases for land occupied by CCHP system αqPurifying the proportion coefficient of q kinds of gas for the land occupied by the CCHP system;
revenue cost C of the CCHP systemsuComprises the following steps:
Figure BDA0002397037210000061
in the formula, K is used for generating power by new energyThe number of types of subsidies;
Figure BDA0002397037210000062
the output power of the distributed power supply at the ith moment is obtained; ckThe price of the kth new energy power generation subsidy;
Figure BDA0002397037210000063
purchasing electric quantity from the power grid at the time t;
Figure BDA0002397037210000064
the price of purchasing electricity from the power grid at the moment t;
Figure BDA0002397037210000065
selling the electric quantity of electricity to the power grid at the moment t;
Figure BDA0002397037210000066
the price of electricity sold to the power grid at time t.
Preferably, the objective function CtotalThe satisfied constraint conditions comprise energy balance constraint conditions, energy supply unit output constraint conditions and energy storage unit energy storage constraint conditions, wherein:
the energy balance constraint is expressed as:
Figure BDA0002397037210000067
Figure BDA0002397037210000068
Figure BDA0002397037210000069
in the formula (I), the compound is shown in the specification,
Figure BDA00023970372100000610
the power is the output power in unit time of the ith distributed power supply;
Figure BDA00023970372100000611
the demand quantity of the electric load at the t time point in the system;
Figure BDA00023970372100000612
the cold energy output by the ith cold energy output equipment in unit time;
Figure BDA00023970372100000613
the demand of the cooling load at the t time point in the system;
Figure BDA00023970372100000614
the heat energy output in unit time of the ith heat energy output equipment;
Figure BDA00023970372100000615
demand for system thermal load at time t;
the energy supply unit output constraint condition is expressed as:
Figure BDA00023970372100000616
Figure BDA00023970372100000617
Figure BDA0002397037210000071
in the formula (I), the compound is shown in the specification,
Figure BDA0002397037210000072
the lower limit of the output of the ith power output device,
Figure BDA0002397037210000073
the upper limit of the output of the ith power output device,
Figure BDA0002397037210000074
the lower limit of the output force of the ith cold energy output unit,
Figure BDA0002397037210000075
the upper limit of the output force of the ith cold energy output unit,
Figure BDA0002397037210000076
the lower limit of the output force of the ith heat energy output unit,
Figure BDA0002397037210000077
the output limit of the ith heat energy output unit is set;
the energy storage constraint condition of the energy storage unit is represented as:
(1-DOD)QR≤QBAT(t)≤QBAT_max(t) (14)
0≤Qcool(t)≤Qcool_max(15)
0≤Qhot(t)≤Qhot_max(16)
in the formula, QBAT_max(t) is the rated capacity of the storage battery system at the moment t; qBAT(t) the capacity of the storage battery system at the time t; qRIs the rated capacity of the battery system; DOD (%) is the maximum allowable depth of discharge of the battery pack; qcool_maxThe rated cold storage capacity of the cold storage tank is provided; qcool(t) the cold storage quantity of the cold storage tank at the t moment; qhot_maxRated heat storage capacity of the heat storage tank; qhotAnd (t) is the heat storage quantity of the heat storage tank at the t moment.
Preferably, the evaluation indexes include a reliability evaluation index, an economic evaluation index and an environmental protection evaluation index, wherein:
the objective function of the reliability evaluation index is expressed as:
ηsys＝ηeqηgirdηpower(17)
Figure BDA0002397037210000078
in the formula, ηsysη for CCHP system stabilityeqSelecting high reliability devices and adding spare devices during system configuration for device stability in CCHP system ηeqη percentgirdAdding C when configuring CCHP system for load node stabilityfiThe selective purchasing of high output electric energy quality and stable operation are added with emergency equipment, so that ηgirdη percentpowerThe energy supply and demand reliability in the CCHP system is realized; n is a CCHP system planning construction period;
Figure BDA0002397037210000079
the electric energy output by the CCHP system at the time t according to the load;
Figure BDA0002397037210000081
is the total load capacity of the CCHP system in the ith year;
the economic evaluation index is expressed as the cumulative investment cost of the CCHP system:
Figure BDA0002397037210000082
in the formula, Y is the service life time of the CCHP system;
Figure BDA0002397037210000083
for the revenue cost of the i-th CCHP system,
Figure BDA0002397037210000084
for the i-th CCHP system fuel consumption cost,
Figure BDA0002397037210000085
for the i-th CCHP system equipment maintenance cost,
Figure BDA0002397037210000086
for the environmental cost of the i-th CCHP system,
Figure BDA0002397037210000087
the cost of electrical energy to purchase the grid for the ith year CCHP system;
the environmental protection evaluation index refers to the emission reduction rate η of the polluted gas after the CCHP system replaces the traditional functional system and after the SP systemeIs shown byComprises the following steps:
Figure BDA0002397037210000088
in the formula, PsysIs the total load capacity of the CCHP system.
Preferably, the objective function C is optimized by adopting an improved particle swarm optimization algorithmtotalAnd solving, wherein in the improved particle swarm optimization algorithm, a population particle speed and position updating formula is as follows:
Figure BDA0002397037210000089
Figure BDA00023970372100000810
in the formula (I), the compound is shown in the specification,
Figure BDA00023970372100000811
the population particle speed at the k +1 th iteration; omega is the inertia weight coefficient, and the weight coefficient,
Figure BDA00023970372100000812
the population particle speed at the kth iteration is obtained; c. C1Is an individual extreme acceleration constant; r is1Is [0,1 ]]Random numbers transformed within a range;
Figure BDA00023970372100000813
is the individual extreme value at the k iteration;
Figure BDA00023970372100000814
the position of the search area at the k iteration is obtained; c. C2Is a group extreme acceleration constant; r is2Is [0,1 ]]Random numbers transformed within a range;
Figure BDA00023970372100000815
the number is the group extremum at the k iteration;
Figure BDA00023970372100000816
the position of the search area in the k +1 th iteration is obtained;
the inertia weight coefficient ω satisfies a condition that decreases as the number of iterations increases, and the inertia weight coefficient ω satisfying the above condition is determined by equation (23), where equation (23) is expressed as:
ω(k)＝ωstar+(ωstar-ωend)(2k/Tmax-(k/Tmax)2) (23)
in the formula, ω (k) is a weight coefficient in the kth iteration; k is the current iteration number; omegastarIs the initial inertial weight; omegaendIs the inertial weight iterated to the maximum number of times is; t ismaxIs the maximum point in time at which the equipment in the CCHP system is operating.
Compared with the prior art, the invention has the advantages and positive effects that:
the method comprises the steps of constructing energy supply equipment of the CCHP system and an energy flow diagram thereof, uniformly converting cold energy and heat energy into electric energy for scheduling, analyzing the energy cost of each equipment in different kilowatt-hours generated by each equipment in different time periods from the aspects of the operation cost and the maintenance cost of the equipment, and determining the scheduling flow diagram of the energy output by each equipment in the CCHP system according to the size relationship of the energy cost; constructing an objective function configured by the CCHP system, solving constraint conditions of the function and determining an evaluation index for solving an objective function value of the system; and finding the best configuration scheme by improving the PSO algorithm under the condition of meeting the evaluation index. According to the CCHP system configuration scheme obtained by the configuration method, the power supply reliability of the CCHP system reaches more than 99%, the investment in the CCHP system in the use period is small, the pollution to the environment is minimal, and the cost of the CCHP system in the operation period is effectively reduced.
Drawings
FIG. 1 is an energy flow diagram of a CCHP system according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of the cost of generating electricity per kilowatt-hour for a distributed power supply according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of the cost of electricity generated by a cooling and heating apparatus per kilowatt-hour according to an embodiment of the present invention;
FIG. 4 is a flow chart of energy scheduling of energy supply equipment according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of typical daily electricity, cold, and heat load demands for different load configurations in accordance with an embodiment of the present invention;
FIG. 6 is a typical daily power generation curve of a fan and a photovoltaic system for different load structure time periods according to an embodiment of the present invention;
FIG. 7 is a diagram of a CCHP system configuration model constructed in accordance with an embodiment of the present invention;
FIG. 8 is a statistical schematic of daily missing load data according to an embodiment of the present invention;
FIG. 9 is a diagram illustrating the amount of each load loss and the reliability of the system according to an embodiment of the present invention;
FIG. 10 is a schematic diagram illustrating the cumulative usage cost of the CCHP system, the SP system, and the conventional energy supply system during the usage time according to the embodiment of the present invention;
fig. 11 is a schematic diagram of environmental costs of different energy consuming devices of each system under different energy supplying modes according to an embodiment of the present invention.
Detailed Description
The invention is described in detail below by way of exemplary embodiments. It should be understood, however, that elements, structures and features of one embodiment may be beneficially incorporated in other embodiments without further recitation.
An embodiment of the invention provides a cooling, heating and power combined supply type micro-grid multi-objective optimization configuration method, which comprises the following steps:
and S1, constructing an energy supply equipment model of the CCHP system and an energy flow diagram of the energy supply equipment model. The energy supply equipment model comprises a solar photovoltaic battery pack, a wind generating set, a gas turbine, a storage battery energy storage device, a direct-fired lithium bromide absorption type water chilling unit, a direct-fired lithium bromide absorption type water warming unit, an energy storage device, an electric refrigerator and an electric boiler; the energy flow diagram of the CCHP system is shown in fig. 1.
S2, calculating the cost per kilowatt-hour of electricity generated by each distributed power source in the CCHP system.
In this embodiment, the CCHP system operates in a grid-connected mode or an island mode, the gas turbine power generation system uses natural gas as fuel, and the generated electric power is used for supplying electric power in the system. And calculating the operation cost, the maintenance cost and the power generation subsidy cost of each distributed power supply according to the historical meteorological conditions and the commodity price level of the region where the CCHP system is located, and further obtaining the cost of each distributed power supply for generating electricity energy per kilowatt-hour (see figure 2).
Specifically, the calculation formula of the power generation cost of the photovoltaic cell per kilowatt-hour is as follows:
Figure BDA0002397037210000111
in the formula, CPVThe cost of photovoltaic cell power generation per kilowatt-hour; pPVThe annual power generation amount of the single photovoltaic cell is obtained; cbuy_PVPurchase cost for a single photovoltaic cell; cland_PVThe land use cost is reduced for a single photovoltaic cell every year; cma_PVThe annual maintenance cost for a single photovoltaic cell; cin_PVReduced installation cost for a single photovoltaic cell; cbe_PVThe photovoltaic power generation patch is subsidized for each kilowatt-hour.
The calculation formula of the power generation cost of the wind driven generator per kilowatt hour is as follows:
Figure BDA0002397037210000112
in the formula, CWTThe cost of generating electricity for each kilowatt hour of the wind driven generator; pWTThe annual power generation amount of a single wind driven generator is obtained; cbuy_WTPurchasing cost for a single wind turbine; cland_WTThe land use cost is reduced for a single wind driven generator every year; cma_WTThe maintenance cost is reduced for a single wind driven generator every year; cin_WTThe installation cost of a single wind driven generator is reduced; cbe _ WT is a patch for wind generator generation per kilowatt-hour.
The calculation formula of the power generation cost of the gas turbine per kilowatt hour is as follows:
Figure BDA0002397037210000113
in the formula, CMTCost of gas turbine power generation per kilowatt-hour; pMTConsuming the generated energy of 1 cubic meter of natural gas for the gas turbine; cgasThe price of natural gas per cubic meter; cma_MTMaintenance costs per kilowatt-hour of electrical energy generated for the gas turbine.
The price of the electric energy is determined according to the price of the electricity in the region where the CCHP system is located.
And S3, calculating the cost of the cold energy and the heat energy generated by the refrigerating and heating equipment in the CCHP system (see figure 3), and converting the cost into the cost of electric energy per kilowatt hour.
And S4, determining a dispatching flow chart of the energy supply equipment of the CCHP system according to the cost of the calculated electric energy (see FIG. 4). Since the CCHP system distinguishes the functional periods of the peak hour and the valley hour, the device with the minimum processing cost is preferentially selected at a specific time according to the device output cost calculated in fig. 2 and 3.
S5, solving an objective function C by taking the minimum invested funds of the CCHP system as an objectivetotalConstructing a CCHP system configuration model according to the minimum value on the premise of meeting constraint conditions and evaluation indexes, wherein the target function CtotalExpressed as:
Ctotal＝Cfi+Cgas+Cma+Cin+Ce+Csu(1)
in the formula, CfiRepresents the purchase cost of the equipment, CgasRepresents the fuel consumption cost, CmaRepresents the maintenance cost of the equipment, CinRepresents the installation cost of the apparatus, CeRepresents the environmental cost, CsuRepresenting the CCHP system revenue cost.
In particular, the equipment purchase cost CfiComprises the following steps:
Figure BDA0002397037210000121
wherein n is the total number of distributed power sources,
Figure BDA0002397037210000122
the unit price of the device of the i-th type,
Figure BDA0002397037210000123
the power output quantity of the ith distributed power supply is obtained;
Figure BDA0002397037210000124
as a function of the number of i distributed power sources;
Figure BDA0002397037210000125
the unit price of the ith grid-connected inverter equipment is;
Figure BDA0002397037210000126
and solving the number function of the ith grid-connected inverter equipment.
The fuel consumption cost CgasComprises the following steps:
Figure BDA0002397037210000127
in the formula, T is the time point of equipment operation in the CCHP system;
Figure BDA0002397037210000128
power output for gas turbine at time t ηMTL HV is the heat value of natural gas, and the value is 9.7 kW.h/m3；ΔtMTFor gas turbine operating hours;
Figure BDA0002397037210000129
consuming the cold energy generated by natural gas for the lithium bromide water chilling unit at the time t;
Figure BDA0002397037210000131
η for lithium bromide hot water unit consuming heat energy generated by natural gas at time tBT_coolEfficiency of generating cold energy for lithium bromide chiller consuming natural gas ηBT_hotThe efficiency of generating heat energy for the lithium bromide water heater unit consuming natural gas; Δ tBT_gas_coolFor consumption of lithium bromide water chilling unitWorking time of natural gas for generating cold energy; Δ tBT_gas_hotWorking time for a lithium bromide hot water unit to consume natural gas to generate heat energy;
Figure BDA0002397037210000132
is the unit price of natural gas at time t.
The equipment maintenance cost CmaComprises the following steps:
Figure BDA0002397037210000133
in the formula (I), the compound is shown in the specification,
Figure BDA0002397037210000134
the operation and maintenance cost of the equipment when generating electric energy per kilowatt hour for the ith distributed power supply;
Figure BDA0002397037210000135
the output power of the distributed power supply at the t moment is obtained; Δ t is the operating time of the distributed power supply;
Figure BDA0002397037210000136
is the periodic maintenance cost of the ith equipment.
The equipment installation cost CinComprises the following steps:
Figure BDA0002397037210000137
in the formula (I), the compound is shown in the specification,
Figure BDA0002397037210000138
the installation cost of each distributed power supply is ith;
Figure BDA0002397037210000139
the occupied area required by the ith distributed power supply is occupied; clandA circulation price for land per unit area;
the environmental cost CeComprises the following steps:
Figure BDA00023970372100001310
wherein M is the type of pollutant generated in the CCHP system αjβ for the unit treatment cost for treating the jth pollution gas according to the international standardijA proportionality coefficient for producing a jth polluting gas per unit output power for the ith plant; pi(t) the output of the ith equipment at t, Q the harmful gas type which can be purified by the occupied land, L the total cost type of the air purification by the occupied land, l the cost type of the air purification by the occupied land, αlqCost for purifying q gases for land occupied by CCHP system αqAnd purifying the proportionality coefficient of q gases for the land occupied by the CCHP system.
Revenue cost C of the CCHP systemsuComprises the following steps:
Figure BDA0002397037210000141
in the formula, K is the number of the new energy power generation subsidies;
Figure BDA0002397037210000142
the output power of the distributed power supply at the ith moment is obtained; ckThe price of the kth new energy power generation subsidy;
Figure BDA0002397037210000143
purchasing electric quantity from the power grid at the time t;
Figure BDA0002397037210000144
the price of purchasing electricity from the power grid at the moment t;
Figure BDA0002397037210000145
selling the electric quantity of electricity to the power grid at the moment t;
Figure BDA0002397037210000146
the price of electricity sold to the power grid at time t.
The constraint condition in the CCHP system is to enable the whole CCHP system to have high operation reliability and economic environmental protection, and the reliability standard of the power distribution network is divided into three levels according to the IEEE Std 1366-plus 2003 standard and the China D L T836-2012 index standard of power distribution network power supply reliability.
In particular, the objective function CtotalThe satisfied constraint conditions comprise an energy balance constraint condition, an energy supply unit output constraint condition and an energy storage unit energy storage constraint condition.
The energy supply of the whole CCHP system needs to meet the load requirement of the CCHP system at any time, and the stable operation of the whole CCHP system can be ensured. Thus, the energy balance constraint is expressed as:
Figure BDA0002397037210000151
Figure BDA0002397037210000152
Figure BDA0002397037210000153
in the formula (I), the compound is shown in the specification,
Figure BDA0002397037210000154
the power is the output power in unit time of the ith distributed power supply;
Figure BDA0002397037210000155
the demand quantity of the electric load at the t time point in the system;
Figure BDA0002397037210000156
the cold energy output by the ith cold energy output equipment in unit time;
Figure BDA0002397037210000157
the demand of the cooling load at the t time point in the system;
Figure BDA0002397037210000158
the heat energy output in unit time of the ith heat energy output equipment;
Figure BDA0002397037210000159
is the demand of the system heat load at the t-th time point.
The stability of the CCHP system is mainly ensured by cold energy, heat energy and electric energy output by the energy output units, and the energy output of each energy output unit in the system also has a range. Determining the output constraints of each energy output unit is crucial to solving the number of each output unit in the model. In order to realize the output constraint of the energy supply unit, the output constraint condition of the energy supply unit is expressed as:
Figure BDA00023970372100001510
Figure BDA00023970372100001511
Figure BDA00023970372100001512
in the formula (I), the compound is shown in the specification,
Figure BDA00023970372100001513
the lower limit of the output of the ith power output device,
Figure BDA00023970372100001514
for the i-th power output deviceThe limit is that the temperature of the molten steel is limited,
Figure BDA00023970372100001515
the lower limit of the output force of the ith cold energy output unit,
Figure BDA00023970372100001516
the upper limit of the output force of the ith cold energy output unit,
Figure BDA00023970372100001517
the lower limit of the output force of the ith heat energy output unit,
Figure BDA00023970372100001518
the upper limit of the output of the ith heat energy output unit.
In a CCHP system, an energy storage unit should meet energy storage constraint conditions at any time, wherein the energy storage constraint conditions of the energy storage unit are expressed as follows:
(1-DOD)QR≤QBAT(t)≤QBAT_max(t) (14)
0≤Qcool(t)≤Qcool_max(15)
0≤Qhot(t)≤Qhot_max(16)
in the formula, QBAT_max(t) is the rated capacity of the storage battery system at the moment t; qBAT(t) the capacity of the storage battery system at the time t; qRIs the rated capacity of the battery system; DOD (%) is the maximum allowable depth of discharge of the battery pack; qcool_maxThe rated cold storage capacity of the cold storage tank is provided; qcool(t) the cold storage quantity of the cold storage tank at the t moment; qhot_maxRated heat storage capacity of the heat storage tank; qhotAnd (t) is the heat storage quantity of the heat storage tank at the t moment.
Specifically, the evaluation indexes include a reliability evaluation index, an economic evaluation index and an environmental protection evaluation index, wherein:
the objective function of the reliability evaluation index is expressed as:
ηsys＝ηeqηgirdηpower(17)
Figure BDA0002397037210000161
in the formula, ηsysη for CCHP system stabilityeqSelecting high reliability devices and adding spare devices during system configuration for device stability in CCHP system ηeqη percentgirdAdding C when configuring CCHP system for load node stabilityfiThe selective purchasing of high output electric energy quality and stable operation are added with emergency equipment, so that ηgirdη percentpowerThe energy supply and demand reliability in the CCHP system is realized; n is a CCHP system planning construction period;
Figure BDA0002397037210000162
the electric energy output by the CCHP system at the time t according to the load;
Figure BDA0002397037210000163
is the total load capacity of the CCHP system of the ith year.
Compared with the SP system, the CCHP system has the advantages that: most of heat energy and cold energy are obtained by recycling the flue gas of the gas turbine, so that the operating cost of the whole system is reduced, and the Chinese government has certain quota subsidy and electric energy recycling policy for solar energy and wind energy power generation, so that the whole CCHP system is a system capable of gradually recycling the cost. The economic evaluation index is expressed as the cumulative investment cost of the CCHP system:
Figure BDA0002397037210000171
in the formula, Y is the service life time of the CCHP system;
Figure BDA0002397037210000172
for the revenue cost of the i-th CCHP system,
Figure BDA0002397037210000173
for the i-th CCHP system fuel consumption cost,
Figure BDA0002397037210000174
for the i-th CCHP system equipment maintenance cost,
Figure BDA0002397037210000175
for the environmental cost of the i-th CCHP system,
Figure BDA0002397037210000176
the cost of electrical energy to the grid for the ith year CCHP system.
The main output units in the micro-grid of the CCHP system are a photovoltaic battery pack, a wind generating set and a gas turbine generating unit, and the grid power interaction unit is used as an auxiliary unit for energy output. 60% of electric energy in China is generated by a thermal power generator taking coal as fuel. Coal combustion with production of large amounts of NOx、SOx、COxThe environmental evaluation index refers to the emission reduction rate η of the polluted gas after the CCHP system replaces the traditional functional system and after the SP systemeExpressed as:
Figure BDA0002397037210000177
in the formula, PsysIs the total load capacity of the CCHP system.
S6, solving an objective function C of a CCHP system configuration modeltotalAnd acquiring the optimal configuration of the CCHP system.
The PSO algorithm is commonly used for solving the objective function because of the advantages of random global search, fast convergence, high efficiency and the like. But at the same time its disadvantages are also evident, for example: in the optimizing process, the particles miss the global optimal solution in the diving process. When applying the PSO algorithm to deal with a multi-dimensional complex problem, the algorithm may fall into a locally optimal solution, which is called premature convergence.
Specifically, in order to avoid the above problem occurring in solving the objective function, in the embodiment of the present invention, an improved particle swarm optimization algorithm is adopted to solve the objective function CtotalAnd solving, wherein in the improved particle swarm optimization algorithm, a population particle speed and position updating formula is as follows:
Figure BDA0002397037210000181
Figure BDA0002397037210000182
in the formula (I), the compound is shown in the specification,
Figure BDA0002397037210000183
the population particle speed at the k +1 th iteration; omega is the inertia weight coefficient, and the weight coefficient,
Figure BDA0002397037210000184
the population particle speed at the kth iteration is obtained; c. C1Is an individual extreme acceleration constant; r is1Is [0,1 ]]Random numbers transformed within a range;
Figure BDA0002397037210000185
is the individual extreme value at the k iteration;
Figure BDA0002397037210000186
the position of the search area at the k iteration is obtained; c. C2Is a group extreme acceleration constant; r is2Is [0,1 ]]Random numbers transformed within a range;
Figure BDA0002397037210000187
the number is the group extreme value in the K iteration;
Figure BDA0002397037210000188
is the search area position at the k +1 th iteration.
The inertia weight coefficient ω satisfies a condition that decreases as the number of iterations increases, and the inertia weight coefficient ω satisfying the above condition is determined by equation (23), where equation (23) is expressed as:
ω(k)＝ωstar+(ωstar-ωend)(2k/Tmax-(k/Tmax)2) (23)
in the formula, ω (k) is a weight coefficient in the kth iteration; k is the current iteration number; omegastarIs the initial inertial weight; omegaendIs the inertial weight iterated to the maximum number of times is; t ismaxIs the maximum point in time at which the equipment in the CCHP system is operating.
According to the method, when the CCHP system is optimally configured, an economic target, a reliability target and an environmental target in the CCHP system are all converted into a same system investment target, an economic target function is expressed as fixed cost investment, system operation and investment of the system, the reliability target of the system is converted into the system investment to increase the quality stability of energy output equipment and the reliability of energy output, and under the load requirement, the energy supply is guaranteed to be more than 99%; the environmental objective of the system is expressed as the pollutant treatment cost generated by the system, and after the system obtains electric energy through the power grid, because 60% of the electric energy of the power grid is generated by the traditional thermal power generation, the environmental cost converted in the system when the electric energy of the power grid is used is expressed as NO generated by the thermal power plant for producing electric energy per kilowatt hourx、SOx、COxThe cost of pollutant disposal. The CCHP system after optimized configuration has the advantages of small investment in the use period and minimal pollution to the environment, and the cost of the CCHP system in the operation period is effectively reduced.
To illustrate the effectiveness of the above method, the following further illustrates the above method in connection with specific examples.
The CCHP system energy supply/demand is analyzed.
(1) Load analysis
Taking 2016 year load demand for an industrial park as an example, the total area of the industrial park is 2.05 x 105m2The average annual hourly average electric load is about 350KWh, the hourly load demand of the heating season (11.16-3.31) is about 115KWh in terms of converted electric energy amount, and the hourly load of the cooling season (5.15-9.15)The required amount is about 218KWh in terms of converted electric energy, the energy supply of the whole system is divided according to the energy supply time period and the type of energy supply, the electric loads of the whole year are divided into electric loads in a heating season, a cooling season and an overseason (a season in which neither heating nor cooling is performed), the heat load in the heating season and the cooling load in the cooling season, and various loads are shown in fig. 5. The different load areas are beneficial to the optimization of the configuration, and the unreasonable planning of the equipment capacity is avoided.
(2) Solar power generation and wind power generation output analysis
The system construction site is selected in a certain area of the smoke platform, the photoelectric conversion efficiency of the area is calculated to be 16.15% and 26.5% through a power output mathematical model of the solar photovoltaic cell and the wind driven generator according to meteorological data such as wind speed and temperature taking hours as scales in one year in the local area, and the energy output of the solar photovoltaic cell set and the wind driven generator set of the whole system is distinguished according to the time intervals of different load structures, as shown in fig. 6.
A CCHP system configuration model meeting the constraint conditions is obtained according to the objective function and the constraint conditions, referring to fig. 7, points 4000000 in the model all meet the constraint conditions of the system and all can allow the system to run reliably, but C corresponding to each pointtotalThis is different from one another, namely, the various costs involved in the formula (1) are different.
Solving an objective function of the CCHP system configuration model according to an improved particle swarm optimization algorithm, and calculating the cost of CCHP system construction to be 3.385 x 10 according to evaluation indexes under the condition of meeting constraint conditions5The element, the capacity of each energy delivery device, is shown in table 1.
TABLE 1
Energy output device name Installed capacity/KW
Solar generator set 1140.4
Wind power generator set 980
Gas turbine generator set 325
Direct-fired lithium bromide cold water (hot water) unit 260
Electric refrigerator set 283
Electric boiler unit 268.7
Accumulator battery 1200
Heat storage tank 2000
Cold storage tank 4500
The reliability of the CCHP system is evaluated according to the formula (17) and the formula (18), the difference between the system load cold, heat and power load demand data and the energy provided by the system is counted, and the reliability of the system is evaluated. Wind and light data and energy demand data of the locations of the systems in 2017, 2018 and 2019 are input, the running data of the statistical system is used for excavating the missing amount of the annual statistical load, and the daily missing amount of the load, the missing amount of various loads per year and the reliability of the system are calculated, and the method is specifically shown in fig. 8 and 9.
And according to the economic evaluation index of the CCHP system, estimating the wind and light data and the load data of the system in twenty years of construction and use by taking an average value according to the wind and light data of the system location and the load data of the garden in the last four years. The annual performance of the CCHP system is calculated by the formula (19)
Figure BDA0002397037210000201
The annual performance of the SP system is calculated by the formula (24)
Figure BDA0002397037210000202
Conventional industrial parks only use electrical energy to satisfy the load demands used in the industrial park, so that their energy operating costs are calculated as the cost of the electrical energy used, of conventional energy supply systems
Figure BDA0002397037210000211
Calculated from equation (25). Equations (24) and (25) are expressed as:
Figure BDA0002397037210000212
Figure BDA0002397037210000213
in the formula (I), the compound is shown in the specification,
Figure BDA0002397037210000214
the accumulated investment cost of the SP system; cSP_fiPurchase cost for SP system equipment; cSP_inCost for SP system equipment installation;
Figure BDA0002397037210000215
for the SP system revenue cost of the ith year;
Figure BDA0002397037210000216
SP system fuel consumption cost for the ith year;
Figure BDA0002397037210000217
cost for SP system equipment maintenance in the ith year;
Figure BDA0002397037210000218
SP system environmental cost for the ith year;
Figure BDA0002397037210000219
the cost of electrical energy to purchase the grid for the year i SP system;
Figure BDA00023970372100002110
the accumulated investment cost of the traditional energy supply system is saved;
Figure BDA00023970372100002111
the environmental cost of the traditional energy supply system in the ith year;
Figure BDA00023970372100002112
the cost of electric energy from the grid is purchased for the traditional energy supply system of the ith year.
As shown in fig. 10, it can be seen from the image that the CCHP construction cost is the highest, the SP system is the second lowest, and the conventional energy supply system is the lowest, but from the perspective of long-term development of the system, because there is a profit in the CCHP system and the SP system, the accumulated investment in the service life is gradually reduced as the service life increases, the accumulated investment cost of the CCHP system after the optimized configuration of the embodiment is lower than that of the conventional energy supply system from the 13 th year of the service life of the system, and the total investment of the system accounts for only 50% of that of the conventional energy supply system within 20 years of planned service. Compared with an SP system, the CCHP system after the optimized configuration has the advantages that the investment on cost of related equipment such as energy secondary utilization is increased, most of heat energy and cold energy of the CCHP system are obtained by recycling tail gas of a gas turbine, the operation cost of the system is low, the total investment cost of the CCHP system after the optimized configuration is lower than that of the SP system when the investment of the system is used in the eleventh year, and the CCHP system has the most obvious advantages in the aspect of economy.
According to the formula (6), the annual environmental costs generated by the system when the industrial park meets the annual load supply of the whole industrial park under the SP system, the traditional power supply and the CCHP system optimally configured according to the embodiment are respectively 102606.98 yuan, 148979.6 yuan and 584923 yuan. In fig. 11, detailed data of environmental costs generated by each system of the industrial park each year under three power supply modes are shown, and the environmental costs generated by the CCHP system are mainly divided into environmental costs generated by equipment consuming power of a power grid and environmental costs generated by equipment consuming natural gas according to the energy scheduling flow chart of fig. 4. The SP system and the conventional power supply system calculate the environmental cost of the power grid power consuming equipment and the environmental cost of the natural gas consuming equipment according to the demand of the cooling, heating and power loads. The formula (20) shows that the environmental protection index of the CCHP system after the optimized configuration of the embodiment is about 31.26% relative to the SP system, and 82.45% relative to the environmental protection index of the conventional power supply system, so the CCHP system after the optimized configuration of the embodiment has a great advantage in the aspect of environmental protection.
The above-described embodiments are intended to illustrate rather than to limit the invention, and any modifications and variations of the present invention are possible within the spirit and scope of the claims.

Claims (7)

1. A multi-objective optimization configuration method for a combined cooling heating and power type micro-grid is characterized by comprising the following steps:
constructing an energy supply equipment model of a CCHP system and an energy flow diagram thereof;
calculating the cost of each distributed power supply in the CCHP system for generating each kilowatt-hour of electric energy;
calculating the cost of cold energy and heat energy generated by refrigerating and heating equipment in the CCHP system, and converting the cost into the cost of electric energy per kilowatt hour;
determining a dispatching flow chart of energy of a CCHP system energy supply device according to the cost of the calculated electric energy;
the minimum investment capital of the CCHP system is taken as a target, and an objective function C is solvedtotalOn the premise of satisfying constraint conditions and evaluation indexesConstructing a CCHP system configuration model in a small value, wherein the target function CtotalExpressed as:
Ctotal＝Cfi+Cgas+Cma+Cin+Ce+Csu(1)
in the formula, CfiRepresents the purchase cost of the equipment, CgasRepresents the fuel consumption cost, CmaRepresents the maintenance cost of the equipment, CinRepresents the installation cost of the apparatus, CeRepresents the environmental cost, CsuRepresents the CCHP system revenue cost;
by solving an objective function C of a CCHP system configuration modeltotalAnd acquiring the optimal configuration of the CCHP system.
2. The combined cooling, heating and power type microgrid multi-objective optimization configuration method of claim 1, wherein the energy supply equipment model comprises a solar photovoltaic battery pack, a wind generating set, a gas turbine, a storage battery energy storage device, a direct-fired lithium bromide absorption type water chilling unit, a direct-fired lithium bromide absorption type water warming unit, an energy storage device, an electric refrigerator and an electric boiler.
3. The combined cooling, heating and power type microgrid multi-objective optimization configuration method as claimed in claim 2, characterized in that the operation cost, the maintenance cost and the power generation subsidy cost of each distributed power supply are calculated according to the historical meteorological conditions and the commodity price level of the region where the CCHP system is located, and then the cost of each distributed power supply for generating electricity per kilowatt-hour is obtained.
4. The combined cooling heating and power type micro-grid multi-objective optimization configuration method according to claim 1 or 3, wherein the equipment purchase cost is CfiComprises the following steps:
Figure FDA0002397037200000021
wherein n is the total number of distributed power sources,
Figure FDA0002397037200000022
the unit price of the device of the i-th type,
Figure FDA0002397037200000023
the power output quantity of the ith distributed power supply is obtained;
Figure FDA0002397037200000024
as a function of the number of i distributed power sources;
Figure FDA0002397037200000025
the unit price of the ith grid-connected inverter equipment is;
Figure FDA0002397037200000026
solving the number function of the ith kind of grid-connected inverter equipment;
the fuel consumption cost CgasComprises the following steps:
Figure FDA0002397037200000027
in the formula, T is the time point of equipment operation in the CCHP system;
Figure FDA0002397037200000028
power output for gas turbine at time t ηMTL HV is the heat value of natural gas, and the value is 9.7 kW.h/m3；ΔtMTFor gas turbine operating hours;
Figure FDA0002397037200000029
consuming the cold energy generated by natural gas for the lithium bromide water chilling unit at the time t;
Figure FDA00023970372000000210
η for lithium bromide hot water unit consuming heat energy generated by natural gas at time tBT_coolEfficiency of generating cold energy for lithium bromide chiller consuming natural gas ηBT_hotThe efficiency of generating heat energy for the lithium bromide water heater unit consuming natural gas; Δ tBT_gas_coolWorking time for the lithium bromide water chilling unit to consume natural gas to generate cold energy; Δ tBT_gas_hotWorking time for a lithium bromide hot water unit to consume natural gas to generate heat energy;
Figure FDA00023970372000000211
the unit price of the natural gas at the t moment;
the equipment maintenance cost CmaComprises the following steps:
Figure FDA00023970372000000212
in the formula (I), the compound is shown in the specification,
Figure FDA00023970372000000213
the operation and maintenance cost of the equipment when generating electric energy per kilowatt hour for the ith distributed power supply;
Figure FDA0002397037200000031
the output power of the distributed power supply at the t moment is obtained; Δ t is the operating time of the distributed power supply;
Figure FDA0002397037200000032
the periodic maintenance cost of the ith equipment;
the equipment installation cost CinComprises the following steps:
Figure FDA0002397037200000033
in the formula (I), the compound is shown in the specification,
Figure FDA0002397037200000034
the installation cost of each distributed power supply is ith;
Figure FDA0002397037200000035
the occupied area required by the ith distributed power supply;Clanda circulation price for land per unit area;
the environmental cost CeComprises the following steps:
Figure FDA0002397037200000036
wherein M is the amount and type of pollutant generated in the CCHP system αjβ for the unit treatment cost for treating the jth pollution gas according to the international standardijA proportionality coefficient for producing a jth polluting gas per unit output power for the ith plant; pi(t) the output of the ith equipment at t, Q the harmful gas type which can be purified by the occupied land, L the total cost type of the air purification by the occupied land, l the cost type of the air purification by the occupied land, αlqCost for purifying q gases for land occupied by CCHP system αqPurifying the proportion coefficient of q kinds of gas for the land occupied by the CCHP system;
revenue cost C of the CCHP systemsuComprises the following steps:
Figure FDA0002397037200000037
in the formula, K is the number of the new energy power generation subsidies;
Figure FDA0002397037200000038
the output power of the distributed power supply at the ith moment is obtained; ckThe price of the kth new energy power generation subsidy;
Figure FDA0002397037200000039
purchasing electric quantity from the power grid at the time t;
Figure FDA00023970372000000310
the price of purchasing electricity from the power grid at the moment t;
Figure FDA00023970372000000311
is time tThe amount of electricity sold to the grid;
Figure FDA00023970372000000312
the price of electricity sold to the power grid at time t.
5. The combined cooling, heating and power type microgrid multi-objective optimization configuration method of claim 4, characterized in that an objective function CtotalThe satisfied constraint conditions comprise energy balance constraint conditions, energy supply unit output constraint conditions and energy storage unit energy storage constraint conditions, wherein:
the energy balance constraint is expressed as:
Figure FDA0002397037200000041
Figure FDA0002397037200000042
Figure FDA0002397037200000043
in the formula (I), the compound is shown in the specification,
Figure FDA0002397037200000044
the power is the output power in unit time of the ith distributed power supply;
Figure FDA0002397037200000045
the demand quantity of the electric load at the t time point in the CCHP system is shown;
Figure FDA0002397037200000046
the cold energy output by the ith cold energy output equipment in unit time;
Figure FDA0002397037200000047
the demand of the cooling load at the t time point in the CCHP system;
Figure FDA0002397037200000048
the heat energy output in unit time of the ith heat energy output equipment;
Figure FDA0002397037200000049
demand for system thermal load at time t;
the energy supply unit output constraint condition is expressed as:
Figure FDA00023970372000000410
Figure FDA00023970372000000411
Figure FDA00023970372000000412
in the formula (I), the compound is shown in the specification,
Figure FDA00023970372000000413
the lower limit of the output of the ith electric energy output equipment;
Figure FDA00023970372000000414
the upper limit of the output of the ith electric energy output equipment;
Figure FDA00023970372000000415
the lower limit of the output of the ith cold energy output unit;
Figure FDA00023970372000000416
the output limit of the ith cold energy output unit is the upper limit of the output of the ith cold energy output unit;
Figure FDA00023970372000000417
the lower limit of the output of the ith heat energy output unit;
Figure FDA00023970372000000418
the output limit of the ith heat energy output unit is set;
the energy storage constraint condition of the energy storage unit is represented as:
(1-DOD)QR≤QBAT(t)≤QBAT_max(t) (14)
0≤Qcool(t)≤Qcool_max(15)
0≤Qhot(t)≤Qhot_max(16)
in the formula, QBAT_max(t) is the rated capacity of the storage battery system at the moment t; qBAT(t) the capacity of the storage battery system at the time t; qRIs the rated capacity of the battery system; DOD (%) is the maximum allowable depth of discharge of the battery pack; qcool_maxThe rated cold storage capacity of the cold storage tank is provided; qcool(t) the cold storage quantity of the cold storage tank at the t moment; qhot_maxRated heat storage capacity of the heat storage tank; qhotAnd (t) is the heat storage quantity of the heat storage tank at the t moment.
6. The multi-objective optimization configuration method for the combined cooling heating and power supply type microgrid according to claim 4, characterized in that the evaluation indexes comprise a reliability evaluation index, an economic evaluation index and an environmental protection evaluation index, wherein:
the objective function of the reliability evaluation index is expressed as:
ηsys＝ηeqηgirdηpower(17)
Figure FDA0002397037200000051
in the formula, ηsysη for CCHP system stabilityeqSelecting high reliability devices and adding spare devices during system configuration for device stability in CCHP system ηeqη percentgirdAdding C when configuring CCHP system for load node stabilityfiThe selective purchasing of high output electric energy quality and stable operation are added with emergency equipment, so that ηgirdIs one percentBaihui ηpowerThe energy supply and demand reliability in the CCHP system is realized; n is a CCHP system planning construction period;
Figure FDA0002397037200000052
the electric energy output by the CCHP system at the time t according to the load;
Figure FDA0002397037200000053
is the total load capacity of the CCHP system in the ith year;
the economic evaluation index is expressed as the cumulative investment cost of the CCHP system:
Figure FDA0002397037200000054
in the formula, Y is the service life time of the CCHP system;
Figure FDA0002397037200000055
for the revenue cost of the i-th CCHP system,
Figure FDA0002397037200000061
for the i-th CCHP system fuel consumption cost,
Figure FDA0002397037200000062
for the i-th CCHP system equipment maintenance cost,
Figure FDA0002397037200000063
for the environmental cost of the i-th CCHP system,
Figure FDA0002397037200000064
the cost of electrical energy to purchase the grid for the ith year CCHP system;
the environmental protection evaluation index refers to the emission reduction rate η of the pollution gas after the CCHP system replaces the traditional electric energy supply system and the SP systemeExpressed as:
Figure FDA0002397037200000065
in the formula, PsysIs the total load capacity of the CCHP system.
7. The combined cooling, heating and power type microgrid multi-objective optimization configuration method of claim 4, characterized in that an improved particle swarm optimization algorithm is adopted to perform objective function CtotalAnd solving, wherein in the improved particle swarm optimization algorithm, a population particle speed and position updating formula is as follows:
Figure FDA0002397037200000066
Figure FDA0002397037200000067
in the formula (I), the compound is shown in the specification,
Figure FDA0002397037200000068
the population particle speed at the k +1 th iteration; omega is the inertia weight coefficient, and the weight coefficient,
Figure FDA0002397037200000069
the population particle speed at the kth iteration is obtained; c. C1Is an individual extreme acceleration constant; r is1Is [0,1 ]]Random numbers transformed within a range;
Figure FDA00023970372000000610
is the individual extreme value at the k iteration;
Figure FDA00023970372000000611
the position of the search area at the k iteration is obtained; c. C2Is a group extreme acceleration constant; r is2Is [0,1 ]]Random numbers transformed within a range;
Figure FDA00023970372000000612
is the population pole at the k-th iterationA value;
Figure FDA00023970372000000613
the position of the search area in the k +1 th iteration is obtained;
the inertia weight coefficient ω satisfies a condition that decreases as the number of iterations increases, and the inertia weight coefficient ω satisfying the above condition is determined by equation (23), where equation (23) is expressed as:
ω(k)＝ωstar+(ωstar-ωend)(2k/Tmax-(k/Tmax)2) (23)
in the formula, ω (k) is a weight coefficient in the kth iteration; k is the current iteration number; omegastarIs the initial inertial weight; omegaendIs the inertial weight iterated to the maximum number of times is; t ismaxIs the maximum point in time at which the equipment in the CCHP system is operating.
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