CN113328443A

CN113328443A - Energy consumption optimization control method for side of transformer area

Info

Publication number: CN113328443A
Application number: CN202110672510.3A
Authority: CN
Inventors: 崔高颖; 邵雪松; 陈霄; 黄奇峰; 蔡奇新; 周玉; 易永仙; 祝宇楠; 支亚薇
Original assignee: State Grid Jiangsu Electric Power Co ltd Marketing Service Center; State Grid Corp of China SGCC; State Grid Jiangsu Electric Power Co Ltd
Current assignee: State Grid Jiangsu Electric Power Co ltd Marketing Service Center; State Grid Corp of China SGCC; State Grid Jiangsu Electric Power Co Ltd
Priority date: 2021-06-17
Filing date: 2021-06-17
Publication date: 2021-08-31

Abstract

A platform side energy consumption optimization control method is characterized in that a multi-energy system model is established based on an energy hub; the method comprises the steps that the optimal 24-hour running cost of the multi-energy system is taken as a target function, and an energy utilization optimization model of the multi-energy system is constructed by combining the constraint of the running limit of the multi-energy system and the constraint of a running mechanism; solving an energy utilization optimization model of the multi-energy system based on a particle swarm algorithm to obtain output optimization data of each energy subsystem in the multi-energy system; the comprehensive optimization control method for the energy system with centralized and distributed cooperation and fusion of renewable energy and fossil energy is realized, the comprehensive utilization efficiency of multiple energy sources is improved, and the aims of energy conservation, environmental protection, economic operation and the like are fulfilled.

Description

Energy consumption optimization control method for side of transformer area

Technical Field

The invention relates to the technical field of energy coordination control, in particular to a station area side-oriented energy consumption optimization control method.

Background

Along with the development of economic society, the intimacy of energy and human life is continuously increased, and the scientificity and rationality of energy production, distribution, transportation and use not only concern the energy industry itself, but also have important influence on various aspects such as economic transformation development, environmental protection, social safety and the like. Although the construction of urban energy systems in China currently obtains great achievements, the problems of difficult renewable energy consumption, serious environmental pollution, low overall energy efficiency and the like are still serious. The traditional urban energy supply in China is mainly divided into several items such as electric power, gas and heat supply, and the items are respectively completed by corresponding energy departments. The energy supply mode which is divided into different classes and lacks of overall planning not only causes the waste of resources and facilities but also cannot exert the complementary advantages of the properties of various energy sources because the interconversion property among different energy sources is not considered, thereby influencing the improvement of the utilization efficiency of the whole energy sources.

In the prior art, energy-saving transformation is usually carried out on a single energy-using object, and although a certain energy-saving benefit is obtained, the energy-saving potential generated by the overall optimization of the multi-energy complementary energy network system is far greater than the energy-saving effect of the energy-saving transformation on the single energy-using object. Moreover, in the existing energy-saving control method of the multi-energy complementary energy network, the comprehensive consideration of energy utilization of a plurality of energy utilization and capacity objects is lacked, so that the system cannot be comprehensively optimized; in addition, the complex energy system theory and optimization method system for regional energy planning is weak, so that the corresponding multi-energy combined supply and coordinated interconnection optimization technologies are relatively few, and particularly, the system-oriented test and simulation capability is still to be improved.

Therefore, it is necessary to construct a comprehensive optimization control method for an energy system with centralized and distributed cooperation and fusion of renewable energy and fossil energy.

Disclosure of Invention

In order to solve the defects in the prior art, the invention aims to provide a district-oriented energy consumption optimization control method, realize a comprehensive optimization control method of an energy system integrating centralized and distributed cooperation and renewable energy and fossil energy, improve the comprehensive utilization efficiency of multiple energy sources, and realize the aims of energy conservation, environmental protection, economic operation and the like.

The invention adopts the following technical scheme.

The method for optimizing and controlling energy consumption of the platform area side comprises the following steps:

step 1, based on an energy hub, establishing a multi-energy system model by taking energy input data of each energy subsystem as input data and output data of each energy subsystem as output data;

step 2, based on a multi-energy system model, constructing an energy utilization optimization model of the multi-energy system by taking the optimal 24-hour running cost of the multi-energy system as a target function and combining the constraint of the running limit of the multi-energy system and the constraint of a running mechanism;

wherein the objective function satisfies the following relation:

wherein F is the 24-hour running cost of the multi-energy system, Q_G(t) the natural gas quantity, Q, purchased by the multi-energy system in the tth_D(t) the amount of diesel oil purchased by the multi-energy system in the tth hour, C_G(t) the purchase price of natural gas at the t hour, C_D(t) the purchase price of the diesel oil in the tth hour;

the constraints of the operation limit of the multi-energy system comprise energy supply equipment power constraints and energy storage equipment capacity constraints; constraints on the operating mechanism of the multi-energy system include: electric power balance constraint, thermal power balance constraint, cold power balance constraint and power network power flow constraint;

and 3, solving the energy utilization optimization model of the multi-energy system by taking the energy input data and the operation constraint of various energy sources as input data based on the particle swarm optimization to obtain output optimization data of each energy subsystem in the multi-energy system.

Preferably, the first and second electrodes are formed of a metal,

in the step 1, each energy subsystem comprises a cogeneration unit, a diesel generator, a gas boiler unit, an ice storage system, a hot water storage tank system and a V2G battery replacement station.

In step 1, establishing a multi-energy system model comprises:

step 1.1, building an energy network, wherein the energy network comprises a power network for transmitting electric energy and a fluid network for transmitting heat energy; the power network and the fluid network are coupled by virtue of the pump and the total heat load balance, wherein the total heat load balance meets the following relational expression:

P_T＝P_Th+P_Te

in the formula, P_ThHeat, P, supplied to the user for the fluid network_TeHeat provided to the user for the power network;

step 1.2, based on a network theory, selecting a branch where a pump is located as a tree branch and the rest branches as connecting branches in a power network, and selecting a branch where the pump is located as a tree branch and the rest branches as connecting branches in a fluid network; numbering each branch, each node and each basic loop, and selecting the direction of each branch and each basic loop; each branch is a section of line without branches in the network, each node is an end point of each branch, and each basic loop is a closed loop which comprises and only comprises one continuous branch; the direction of each branch is selected to be the same as the current, and the direction of each basic loop is the clockwise direction;

step 1.3, selecting common nodes of all loops as reference nodes, and establishing a multi-energy system model according to the generalized kirchhoff law by using the following relational expression:

in the formula (I), the compound is shown in the specification,

a is an energy network incidence matrix,

h is an extensive flow matrix, the extensive flow matrix is a charge flow matrix in the process of transmitting electric energy by the power network, the extensive flow matrix is an entropy flow matrix in the process of transmitting heat energy by the fluid network,

B_fin the form of a matrix of elementary loops,

and delta X is a matrix of the difference value of the intensities of the two ends of the line, the matrix of the difference value of the intensities of the two ends of the line is a matrix of the potential difference value in the process of transferring electric energy by the power network, and the matrix of the difference value of the intensities of the two ends of the line is a matrix of the temperature difference value of the two ends of the line in the process of transferring heat energy by the fluid network.

Preferably, the first and second electrodes are formed of a metal,

in step 2, the power constraint of the energy supply equipment comprises the following steps: the method comprises the following steps of (1) gas internal combustion engine power constraint, air conditioner power constraint, flue gas absorption type refrigerating unit power constraint, compression type electric refrigerating unit power constraint, gas boiler power constraint and waste heat boiler power constraint;

energy storage device capacity constraints include: the capacity of the storage battery is restricted, the capacity of the heat storage tank is restricted, the capacity of the ice storage is restricted, the capacity of the heat pump is restricted, and the capacity of the water chilling unit is restricted.

In step 2, the electric power balance constraint satisfies the following relational expression:

in the formula (I), the compound is shown in the specification,

P_buyin order to purchase the electric quantity,

P_sellin order to sell the electricity quantity,

P_PV，kis the power generation amount of the kth photovoltaic cell, wherein N_PVRepresents the total number of photovoltaic cells,

P_GE，iis the power generation amount of the ith gas internal combustion engine, wherein N_GEIndicates the total number of gas-fired internal combustion engines,

P_BT，outis the output power of the storage battery,

P_BT，inis the input power of the storage battery,

P_acfor the power consumption of the air-conditioning system at the time t, P_ecFor the power consumption of the compression-type electric refrigerator at the time t, P_{hp_h}Is the power consumption, P, of the heat pump for heating at time t_{hp_c}Is the power consumption, P, of the heat pump for cooling at the time t_loadThe electric load of the system at the t moment;

the thermal power balance constraint comprises a space thermal load thermal power balance constraint and a hot water load thermal power balance constraint, wherein the space thermal load thermal power balance constraint satisfies the following relational expression:

the hot water load thermal power balance constraint satisfies the following relational expression:

in the formula (I), the compound is shown in the specification,

H_gb，j1for the space heat load generated by the jth gas boiler,

H_gb，j2for the hot water load produced by the jth gas boiler,

H_h，out1is the space heat load generated by the heat storage tank,

H_h，out2is the load of hot water generated by the heat storage tank,

H_hp1for the space heat load generated by the heat pump,

H_hp2for the hot water load generated by the heat pump,

H_b，out1for the space heat load generated by the waste heat boiler,

H_b，out2is the hot water load generated by the waste heat boiler,

H_load1for the space heat load of the multi-energy system at the t moment,

H_load2for the hot water load of the multi-energy system at time t,

H_h，inis the load of hot water consumed by the heat storage tank,

N_gbthe total number of the gas-fired boilers;

the cold power balance constraint comprises a space cold load cold power balance constraint and a cold water load cold power balance constraint, wherein the space cold load cold power balance constraint satisfies the following relational expression:

Q_ac+Q_sar1+Q_gar1+Q_ec1+Q_c1+Q_hp+Q_ic，out1＝Q_load1

the cold water load cold power balance constraint satisfies the following relational expression:

Q_sar2+Q_gar2+Q_ec2+Q_ic，out2+Q_c2＝Q_ic，in+Q_load2

in the formula (I), the compound is shown in the specification,

Q_acfor the space cooling load generated by the air conditioning system,

Q_sar1for space cooling loads generated by vapor absorption chiller units,

Q_sar2for the cold water load generated by a vapor absorption chiller,

Q_gar1is the space cold load generated by the smoke absorption refrigerating unit,

Q_gar2is the cold water load generated by the smoke absorption refrigerating unit,

Q_ec1the space cold load generated by the compression type electric refrigerator,

Q_ec2is the cold water load produced by the compression type electric refrigerator,

Q_c1is the space cold load generated by the water chilling unit,

Q_c2is the cold water load generated by the water chilling unit,

Q_hpis the space cold load generated by the heat pump,

Q_ic，out1the space cold load generated for ice cold accumulation,

Q_ic，out2the cold water load generated for ice storage,

Q_ic，inthe cold water load consumed for ice storage,

Q_load1for the space cooling load of the multi-energy system at the time t,

Q_load2the cold water load of the multi-energy system at the t moment;

the power network power flow constraint comprises non-out-of-limit constraint on the interactive power flow of a user and a distribution network and upper and lower limit constraint on the node voltage of the whole network, wherein the non-out-of-limit constraint satisfies the following relational expression:

in the formula (I), the compound is shown in the specification,

for the line load active power of network nodes l to n at time t,

for the line-load active power minimum limit from network node l to node n,

for the maximum limit of the line load active power of the network node l to the node n,

for line-flow reactive power of network nodes l to n at time t,

for the line-flow reactive power minimum limit from network node l to node n,

for the line flow reactive power maximum limit from network node l to node n,

l，n∈n_node，n_nodethe total number of all nodes in the network;

the upper and lower limit constraints satisfy the following relational expression:

in the formula (I), the compound is shown in the specification,

is the voltage at the node l and,

the upper and lower limits of the voltage at node l.

Preferably, the first and second electrodes are formed of a metal,

the step 3 comprises the following steps:

step 3.1, taking energy input data and operation constraints of various energy sources as input data, and taking hourly output data of each energy subsystem as an optimization variable;

step 3.2, defining each optimized variable as a random particle swarm, and initializing;

3.3, taking the random particle swarm as input data of iterative computation, taking a target function as a fitness function, and iteratively updating an individual extreme value and a global extreme value of the random particle swarm according to preset particle swarm parameters to determine the position and the speed of the particles; obtaining a particle swarm optimization solution after multiple iterations; the preset particle swarm parameters comprise the size of the particle swarm and the iteration times;

step 3.4, verifying whether the comprehensive utilization efficiency of the cogeneration unit reaches the lowest or not under the obtained particle swarm optimization solution: if the comprehensive utilization efficiency of the cogeneration unit reaches the minimum standard, the obtained particle swarm optimization solution is output optimization data of each energy subsystem in the multi-energy system every hour; and if the comprehensive utilization efficiency of the cogeneration unit does not reach the minimum standard, returning to the step 3.1, and adding a minimum heat-power ratio constraint condition to adjust the heat-power ratio of the cogeneration unit.

In step 3.4, the comprehensive utilization efficiency of the cogeneration unit meets the following relational expression:

in the formula, Q_eFor electric energy output by cogeneration units, Q_hHeat energy, Q, output by cogeneration units_inThe sum of the heat values of diesel oil and natural gas is consumed for the cogeneration unit.

In step 3.4, the thermoelectric ratio is adjusted by the constraint condition of the minimum thermoelectric ratio represented by the following relational expression:

R′_CHPmin＝R_CHPmin+ΔR_CHP

in the formula (II), R'_CHPminFor adjusted minimum thermoelectric ratio, R_CHPminFor minimum thermoelectric ratio before adjustment, Δ R_CHPIs the minimum thermoelectric ratio constraint value.

Compared with the prior art, the method has the advantages that the method realizes multi-energy combined supply and coordinated interconnection optimization technology by constructing a complex energy system theory and an optimization method system facing regional energy planning, plays a positive role in innovating an energy service mode, guaranteeing the balance of supply and demand of a power grid, improving the economic operation level of the power grid, improving the overall efficiency of a regional energy system, reducing environmental pollution, developing an intelligent power grid, reducing the total investment and operation management cost of a social energy system and optimizing an energy structure, and provides an effective technical means for greatly reducing the energy consumption in a short period.

Drawings

FIG. 1 is a flow chart of the energy consumption optimization control method for the side of the platform of the invention;

FIG. 2 is a schematic diagram of a simple energy network in the optimization control method for energy consumption at the platform side according to the present invention;

FIG. 3 is a schematic diagram illustrating numbering of branches, nodes and basic loops of an energy network in the method for controlling energy consumption optimization of a platform side;

FIG. 4 is a flow chart of a PSO optimization algorithm in the optimization control method for energy consumption of the platform side;

FIG. 5 is a graph of spring cold, hot, and electrical load curves for a campus oriented to one embodiment of the energy consumption optimization control method for the side of the campus;

FIG. 6 is a graph of the cool, hot and electrical load in summer of a campus of one embodiment of the optimization control method for energy consumption of the power-consumption management system of the invention;

FIG. 7 is a diagram of the cold, hot and electric load curves in autumn of a park according to one embodiment of the energy consumption optimization control method for the side of the park;

FIG. 8 is a graph of winter cold, hot, and electrical loads for a campus according to one embodiment of the optimal control method for energy consumption on the power-consumption side of the campus of the present invention;

FIG. 9 illustrates the natural gas prices for seasonal parks in accordance with one embodiment of the invention directed to a power utility optimization control method;

FIG. 10 is a graph of thermoelectric ratio versus energy conversion efficiency for one embodiment of the energy use optimization control method for the side of the platform of the present invention;

FIG. 11 is a schematic diagram of the output of the ice storage air conditioner in spring and the equivalent electrical load before and after the individual consideration of the cold load according to an embodiment of the optimal control method for energy consumption of the platform area side;

FIG. 12 is a schematic diagram of equivalent electrical loads before and after output of a summer ice storage air conditioner and independent consideration of cold load in an embodiment of an energy consumption optimization control method for a side of a platform area according to the present invention;

FIG. 13 is a schematic diagram of equivalent electrical loads before and after output of an autumn ice storage air conditioner and independent consideration of cold load, according to an embodiment of an energy consumption optimization control method for a platform area side;

FIG. 14 is a schematic diagram of equivalent electrical loads before and after the output of the ice storage air conditioner in winter and the independent consideration of the cold load according to an embodiment of the optimal control method for energy consumption of the side of the platform;

FIG. 15 is a graph of summer diesel engine output before and after consideration of cold load alone for an embodiment of the power consumption optimization control method for the side of the platform of the present invention;

FIG. 16 is a graph comparing the output of the hot water storage tank in spring and the gas price in the embodiment of the optimal control method for energy consumption facing the platform area side;

FIG. 17 is a diagram of the comparison between the output of the heat storage water tank in summer and the gas price in the embodiment of the optimal control method for energy consumption facing the platform area side;

FIG. 18 is a comparison graph of the output of the autumn heat storage water tank and the gas price facing to the platform area side energy consumption optimization control method;

FIG. 19 is a graph comparing the output of the hot water storage tank in winter and the gas price in the embodiment of the optimal control method for the energy consumption of the side of the platform;

FIG. 20 is a graph comparing the spring output and the gas price for an embodiment of the energy consumption optimization control method facing the platform area side of the present invention;

FIG. 21 is a diagram of the comparison of summer output and gas price for an embodiment of the optimal control method for energy consumption at the side of a platform;

FIG. 22 is a diagram of comparing autumn output and gas prices facing to an embodiment of the method for controlling energy consumption optimization of the platform area side;

fig. 23 is a graph comparing the output in winter and the gas price of an embodiment of the optimal energy consumption control method facing the platform area side of the invention.

Detailed Description

The present application is further described below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present application is not limited thereby.

As shown in fig. 1, a method for controlling energy consumption optimization facing a platform area side includes:

step 1, based on an energy hub, taking energy input data of each energy subsystem as input data, and taking output data of each energy subsystem as output data, and establishing a multi-energy system model.

In particular, the amount of the solvent to be used,

in step 1, the energy subsystems include but are not limited to: the system comprises a heat and power cogeneration unit (CHP), a diesel generator, a gas boiler unit, an ice storage system, a hot water storage tank system and a V2G battery replacement station.

In the preferred embodiment, a CHP with a afterburning waste heat boiler is arranged in the multi-energy system facing the platform area side to change the thermoelectric ratio of the energy supply side, and the CHP can collect heat energy generated by the micro-combustion engine in the power generation process by using the waste heat boiler so as to improve the comprehensive energy efficiency; the load loss rate of the system can be obviously reduced by installing the diesel engine power generation system so as to improve the reliability of the system; the gas boiler can be used as auxiliary heat production equipment to work in cooperation with CHP to supply heat load together; the ice storage system can be used to provide cooling load for the park; the high electric energy storage installation cost is considered, so that the capacity configuration of a battery energy storage system is reduced, the excess capacity of a large-scale storage system is realized by taking heat storage as a main energy storage mode and through electric-thermal coupling, the load curve is finally stabilized, and the reliability of the system is improved; the V2G trades power station can utilize electric automobile's energy storage ability and gardens comprehensive energy system to interact to play the effect of buffer system load, with the stability of improvement system.

It is noted that those skilled in the art can select different types of energy subsystems according to engineering practice, and the preferred embodiment of the present invention is a non-limiting preferred choice.

Furthermore, in step 1, the output data of the CHP is the thermoelectric ratio, i.e. the ratio of the thermal power actually output by the CHP to the actual output electric power, and satisfies the following relation:

in the formula (I), the compound is shown in the specification,

R_CHPis the heat-electricity ratio of the cogeneration unit,

P_CHPhis the output thermal power of the cogeneration unit,

P_CHPefor the output electric power of the cogeneration unit,

P_CHPinis the input power of the cogeneration unit,

η_CHPhfor the heat conversion efficiency of the cogeneration unit,

η_CHPethe electricity conversion efficiency of the cogeneration unit;

in the preferred embodiment, in order to change the output thermoelectric ratio of the CHP, the diesel afterburning amount needs to be increased to improve the heat conversion efficiency of the CHP, which may increase the operation cost of the system, so the operating constraint condition of the cogeneration unit is defined, and the thermoelectric ratio is adjusted by the fuel afterburning amount, and the following relation is satisfied:

R_CHP(t)＝K_a1(K_b1P₀(t)+K_b2)+K_a2

in the formula (I), the compound is shown in the specification,

P₀(t) is the amount of diesel afterburning,

K_a1for the first enhancement factor of the thermoelectric ratio,

K_a2for the first enhancement factor of the thermoelectric ratio,

K_b1the first influencing factor is a supplementary firing of the fuel,

K_b2a second influencing factor for fuel oil afterburning;

in the preferred embodiment, K_a1、K_a2、K_b1、K_b2Set at 4.4483, 1.2857, 0.0051209, 0.00038, respectively. Therefore, the mathematical relationship between the post-combustion fuel amount and the heat conversion efficiency can be expressed as:

η_CHPh＝η_CHPeR_CHP＝η_CHPe[K_a1(K_b1P₀(t)+K_b2)+K_a2]

from the above formula, the electrical conversion efficiency of the CHP is almost constant, and the heat conversion efficiency of the CHP can be adjusted only by adding the post-combustion fuel oil, so as to adjust the heat-electricity ratio of the CHP.

Further, in step 1, the output data of the diesel generator is output power, and the following relational expression is satisfied:

P_DE＝η_DEP_DEmin

in the formula (I), the compound is shown in the specification,

P_DEis the output power of the diesel generator,

η_DEin order to achieve the energy conversion efficiency of the diesel generator,

P_DEminthe input power of the diesel generator;

the operation constraint condition of the diesel generator meets the following relational expression:

P_DEmin＜＜P_DE＜＜P_DEmax

in the formula (I), the compound is shown in the specification,

P_DEminfor starting diesel generatorsThe minimum input power is the minimum input power,

P_DEmaxto maintain maximum input power for diesel generator operation.

Further, in step 1, the output data of the gas-fired boiler unit is output power, and the following relational expression is satisfied:

P_GB＝η_GBP_GBmin

in the formula (I), the compound is shown in the specification,

P_GBis the output power of the gas boiler unit,

η_GBin order to improve the energy conversion efficiency of the gas boiler unit,

η_GB＝(100-a₁-a₂-a₃) %, wherein a₁For heat loss of exhaust gas, a₂Loss due to incomplete combustion of chemical species, a₃Heat dissipation loss; in the preferred embodiment, a₁、a₂、a₃Set to 9.87, 0.95 and 2.18, respectively.

The operation constraint conditions of the gas boiler unit comprise operation capacity constraint and operation time constraint, wherein the operation capacity constraint satisfies the following relational expression:

Q_GBHmin≤Q_GBH(t)≤Q_GBHmax

in the formula (I), the compound is shown in the specification,

Q_GBHminis the minimum operation capacity of the gas boiler unit,

Q_GBHmaxthe maximum operation capacity of the gas boiler unit;

the runtime constraint satisfies the following relationship:

T_GBON(t)≥T_GBONmin

T_GBOFF(t)≥T_GBOFFmin

in the formula (I), the compound is shown in the specification,

T_GBON(t) is the accumulated opening time of the gas boiler unit at the t-th moment,

T_GBOFF(t) is the accumulated closing time of the gas boiler unit at the t-th moment,

T_GBONminfor the minimum operation time of the gas boiler plant,

T_GBOFFminis the minimum off-time of the gas boiler plant.

Further, in step 1, the output data of the ice storage system is refrigeration power, and the following relational expression is satisfied:

P_C＝P_CinEER

in the formula (I), the compound is shown in the specification,

P_Cis the refrigerating power of the ice cold storage system,

P_Cinis the input power of the ice storage system,

the EER is the energy efficiency ratio of the ice storage system, and in the preferred embodiment, the energy efficiency ratio is taken to be 3.1.

The ice storage and melting work of the ice storage system is mainly completed in the ice storage tank. The operation constraint condition of the ice cold storage system meets the following relational expression:

in the formula (I), the compound is shown in the specification,

Q_Cice(t) is the ice storage capacity of the ice storage tank in the ice storage system,

Q_Ciceminis the minimum ice storage capacity of an ice storage tank in the ice cold storage system,

Q_Cicemaxis the maximum ice storage amount of an ice storage tank in the ice storage system,

Q_w(t) is the ice melting amount of an ice storage tank in the ice storage system,

Q_wminis the minimum ice melting amount of an ice storage tank in the ice cold storage system,

Q_wmaxis the maximum ice melting amount of an ice storage tank in the ice storage system,

M_CICE(t) setting an ice making operation mode of the dual-working-condition main machine,

M_Cwand (t) setting the ice melting operation mode of the dual-working-condition host machine.

Further, in step 1, the output data of the hot water storage tank system is heat energy, and the following relational expression is satisfied:

Q_WT(t)＝(1-ε_WT)Q_WT(t-1)+Q_WTh(t)-Q_WTr(t)

in the formula (I), the compound is shown in the specification,

Q_WT(t) is the heat stored by the hot water storage tank system at the time of the t,

ε_WTfor the self-heat release rate of the hot water storage tank system,

Q_WTh(t) is the operating capacity of the hot water storage tank system to store heat,

Q_WTr(t) the heat release of the hot water storage tank system;

the operation constraint condition of the hot water storage tank system meets the following relational expression:

in the formula (I), the compound is shown in the specification,

Q_WThminfor the minimum operating capacity limit of the hot water storage tank system when storing heat,

Q_WThmaxis the maximum operating capacity limit of the hot water storage tank system when storing heat,

Q_WTrminis the minimum operation capacity limit value of the hot water storage tank system when releasing heat,

Q_WTrmaxis the maximum operation capacity limit value of the hot water storage tank system when releasing heat,

Q_WTminis the minimum limit value of the heat storage quantity of the hot water storage tank system,

Q_WTmaxthe maximum limit value of the heat storage quantity of the hot water storage tank system.

Further, in step 1, the output data of the V2G power conversion station is the charging and discharging time, and respectively satisfy the following relations:

in the formula (I), the compound is shown in the specification,

t_scfor the charging start time of the V2G power conversion station,

t_c1system negative for V2G power conversion stationAt the time of the valley period of the lotus seeds,

t_c2at the end of the system load valley period of the V2G power change station,

t_chgfor the continuous charging time of the V2G power conversion station,

k^*is a random number between 0 and 1, is a parameter for stabilizing the system load fluctuation to the maximum extent and reducing the peak-valley difference of a power grid of a charging automobile which embodies the internal parameters of the power station and the demand response,

T_gthe valley period duration of the V2G power change station;

in the formula (I), the compound is shown in the specification,

t_SDfor the starting discharge moment of the V2G power conversion station,

t_d1at the time of the system load peak section of the V2G power change station,

t_d2for the end time of the system load peak section of the V2G power change station,

t_chdfor the continuous discharge time of the V2G power conversion station, T_fThe peak-segment time length of the V2G power change station is obtained;

in addition, considering the load characteristic and the energy storage characteristic of the V2G power conversion station, the operation of the power conversion station is mainly influenced by the power conversion amount L_EVAnd the limitation of the state of charge, therefore, the operation constraint condition of the V2G power conversion station meets the following relational expression:

in the formula (I), the compound is shown in the specification,

Q_EV(t) is the energy storage capacity of the V2G power conversion station at the t-th moment,

Q_EVfor the rated energy storage capacity of the V2G power conversion station,

SOC_minin order to be at a minimum state of charge,

SOC_maxat maximum state of charge;

in the present preferred embodiment of the present invention,in order to enable the V2G power station to participate in system peak regulation to the maximum extent, the SOC is set_minIs 10%, SOC_maxIs 100%.

The energy hub is an abstraction of a multi-energy system, the multi-energy system is considered to be a dual-port network with multi-form energy input and multi-energy output, and an intermediate energy conversion link is represented by a coupling matrix; the energy hub is utilized to model the multi-energy system, the collaborative optimization of energy sources in various forms can be realized, the established model is connected with different energy carriers to associate different inputs and outputs of the system, so that the supply path of the energy sources is increased, different kinds of energy sources can be coupled, and finally the economy, flexibility, safety and energy efficiency of the energy system are improved.

In particular, the amount of the solvent to be used,

in step 1, establishing a multi-energy system model comprises:

step 1.1, building an energy network, wherein the energy network comprises a power network for transmitting electric energy and a fluid network for transmitting heat energy;

the energy network comprises a plurality of forms of energy transfer, in the preferred embodiment, in a simple energy network involving only electrical and thermal energy, as shown in FIG. 2, the solid lines represent the power network; the dotted lines represent the fluid network.

The power and fluid networks rely on pumps TM and total heat load P_TBalanced to achieve coupling, in which the total heat load P_TThe balance satisfies the following relation:

P_T＝P_Th+P_Te

in the formula, P_ThHeat, P, supplied to the user for the fluid network_TeHeat provided to the user by the power network.

in the preferred embodiment, the simple energy network shown in fig. 2 is represented by fig. 3, based on network theory.

in the formula (I), the compound is shown in the specification,

a is an energy network incidence matrix,

B_fin the form of a matrix of elementary loops,

In the preferred embodiment, as shown in FIG. 3, n is selected₁、n₂For the reference node, the energy network association matrix is as follows:

the extensive volume flow matrix is as follows:

H＝[H₁ H₂ H₃ H₄ H₅ H₆ H₇ H₈]^T

the basic loop matrix is:

the matrix of the intensity difference values at the two ends of the line is as follows:

ΔX＝[ΔX₁ ΔX₂ ΔX₃ ΔX₄ ΔX₅ ΔX₆ ΔX₇ ΔX₈]^T

in the electric energy transfer process, the intensity quantity X is electric potential, the intensity quantity difference value is electric potential difference value delta X, and the extensive quantity H is electric charge. In the process of transferring heat energy, the strength X is temperature, the strength difference value is temperature difference value delta X, and the extension H is entropy.

For the model of the multi-energy system in step 1.3, it can be seen from fig. 3 that the power branch b is removed₃Branch b in the fluid network where the pump is located₅Branch b of the electric network in which the pump is located₁And besides the electric heater branch, other branches can write a branch characteristic equation:

from fig. 2, TR is the heat exchanger at the heat source to transfer the heat energy to the hot water network, therefore, in the above formula, L is other electrical loads in the power network, R is the flow resistance of the hot water network, and S_eFor power supply, each parameter subscript line indicates the number of the branch in which it is located.

Branch b of the fluid network in which the pump is located₅And branch b in which the pump is located in the electrical network₁The energy conservation relationship therebetween, so the following relationship can be listed:

ΔX₅H₅+ΔX₁H₁＝0

based on the thermal load characteristics of the electric heater, the following relationships are listed for the heater branches:

P_2e＝ΔX₄H₄

in the formula, P_2eIs the heat load power of the electric heater.

The preferred embodimentIs the operating state at which the calculation meets the user load demand, and thus the load is known, i.e. P_ThOr P_TAre known. When P is present_ThWhen known, the heat exchange satisfies the following relational expression:

P_Th＝ρcΔTH₇

in the formula, rho is the density of the fluid, c is the specific heat capacity of the fluid, and delta T is the temperature difference between the inlet and the outlet of the heat exchanger.

And 2, based on the multi-energy system model, considering the purchase cost of natural gas and diesel oil, taking the optimal 24-hour running cost of the multi-energy system as a target function, and combining the constraint of the running limit of the multi-energy system and the constraint of the running mechanism to construct an energy utilization optimization model of the multi-energy system.

Wherein the objective function satisfies the following relation:

in the formula (I), the compound is shown in the specification,

f is the 24-hour running cost of the multi-energy system,

Q_G(t) is the natural gas quantity purchased by the multi-energy system in the tth hour,

Q_D(t) is the amount of diesel oil purchased by the multi-energy system in the tth hour,

C_G(t) is the purchase price of natural gas at the tth hour,

C_D(t) is the purchase price of the diesel oil at the t hour.

Constraints on the operating limits of the multi-energy system include energy supply device power constraints and energy storage device capacity constraints.

Further, the powered device power constraints include:

(1) the gas internal combustion engine power constraint is as follows:

P_GEmin≤P_GE≤P_GEmax

in the formula, P_GEFor power of gas internal combustion engines, P_GEminIs the minimum limit of the power of the gas internal combustion engine, P_GEmaxIs the maximum limit of the power of the gas internal combustion engine, and

(2) air conditioning power constraints are as follows:

Q_acmin≤Q_ac≤Q_acmax

in the formula, Q_acFor air conditioning system power, Q_acmin、Q_acmaxThe minimum value and the maximum value of the power of the air conditioning system. (3) The power constraint of the smoke absorption refrigerating unit is as follows:

Q_garmin≤Q_gar≤Q_garmax

in the formula, Q_garFor power, Q, of a flue gas absorption refrigerating unit_garmin、Q_garmaxThe power is the minimum value and the maximum value of the smoke absorption refrigerating unit.

(4) The power constraint of the compression type electric refrigerator is as follows:

Q_ecmin≤Q_ec≤Q_ecmax

in the formula, Q_ecFor power, Q, of compression-type electric refrigerators_ecmax、Q_ecminThe power of the compression type electric refrigerator is up and down limited.

(5) Gas boiler power constraints are as follows:

H_gbmin≤H_gb≤H_gbmax

in the formula, H_gbIs the power of the gas boiler, H_gbmax、H_gbminThe upper and lower power limits of the gas boiler.

(6) The power constraint of the waste heat boiler is as follows:

0≤H_b，out≤H_b，outmax

in the formula, H_b，outIs the maximum value of the power of the waste heat boiler, H_b，outmaxAnd the maximum value of the power of the waste heat boiler.

Further, the energy storage device capacity constraints include:

(1) battery capacity constraints are as follows:

E_BT，min≤E_BT≤E_BT，max

the above relationship is expressed using state of charge, as follows:

30％≤SOC≤70％

the remaining capacity of the battery must be satisfied at all times:

and making an assumption that the initial value and the final value of each period of the storage battery are equal, namely in one period, the storage battery is not discharged or charged, and the following relational expression is satisfied:

(2) the heat storage tank capacity constraint is as follows:

H_h，inmin≤H_h，in≤H_h，inmax

H_h，outmin≤H_h，out≤H_h，outmax

in the formula, H_h，inAs input heat value of the heat storage tank, H_h，inmin、H_h，inmaxRespectively represent the upper and lower limits of the input heat value of the heat storage tank, H_h，outIs the output heat value of the heat storage tank, H_h，outmin、H_h，outmaxRespectively represent the upper and lower limits of the output heat value of the heat storage tank.

And an assumption is made that the initial value and the final value of each cycle of the heat storage tank are equal, i.e. in one cycle, the heat storage tank does not exert any force nor store heat.

(3) Ice storage capacity constraints are as follows:

Q_ic，inmin≤Q_ic，in≤Q_ic，inmax

Q_ic，outmin≤Q_ic，out≤Q_ic，outmax

W_ic，min≤W_ic≤W_ic，max

in the formula, Q_ic，inFor storing iceLoad of cold water consumed, Q_ic，inmax、Q_ic，inminUpper and lower cold water load limits, Q, consumed for ice storage_ic，outCold water load, Q, generated for ice storage_ic，outmax、Q_ic，outminThe upper and lower limits of the cold water load generated by ice cold accumulation.

And an assumption is made that the initial value and the final value of each period of ice cold accumulation are equal, namely in one period, the ice cold accumulation has no output and no cold accumulation.

(4) Heat pump capacity constraints, as follows:

0≤H_hp≤H_hpmax

0≤Q_hp≤Q_hpmax

in the formula, H_hpHeat power generated for heat pumps, H_hpmaxMaximum value of thermal power, Q, generated by the heat pump_hpSpace cooling load, Q, generated for heat pumps_hpmaxThe maximum space cooling load generated by the heat pump.

(5) Chiller capacity constraints are as follows:

0≤H_c≤H_cmax

0≤Q_c≤Q_cmax

in the formula, H_cThermal power, H, generated for water chilling units_cmaxMaximum value of thermal power, Q, generated by water chilling unit_cCold power, Q, produced for water chiller units_cmaxThe maximum value of the cold power generated by the water chilling unit.

Constraints on the operating mechanism of the multi-energy system include: an electrical power balance constraint, a thermal power balance constraint, a cold power balance constraint, and a power network flow constraint.

Specifically, the electric power balance constraint satisfies the following relation:

in the formula (I), the compound is shown in the specification,

P_buyin order to purchase the electric quantity,

P_sellin order to sell the electricity quantity,

P_BT，outis the output power of the storage battery,

P_BT，inis the input power of a battery, P_acFor the power consumption of the air conditioning system at the time t,

P_ecfor the power consumption of the compression-type electric refrigerator at the t-th time,

P_{hp_h}is the power consumption of the heat pump for heating at time t,

P_{hp_c}is the power consumption of the heat pump for cooling at time t,

P_loadthe electric load of the system at the t moment;

specifically, the thermal power balance constraint comprises a space thermal load thermal power balance constraint and a hot water load thermal power balance constraint, wherein the space thermal load thermal power balance constraint satisfies the following relational expression:

in the formula (I), the compound is shown in the specification,

H_gb，j1for the space heat load generated by the jth gas boiler,

H_gb，j2for the hot water load produced by the jth gas boiler,

H_h，out1is the space heat load generated by the heat storage tank,

H_h，out2hot water produced for heat storage tankThe load is applied to the workpiece to be processed,

H_hp1for the space heat load generated by the heat pump,

H_hp2for the hot water load generated by the heat pump,

H_b，out1for the space heat load generated by the waste heat boiler,

H_b，out2is the hot water load generated by the waste heat boiler,

H_load1for the space heat load of the multi-energy system at the t moment,

H_load2for the hot water load of the multi-energy system at time t,

H_h，inis the load of hot water consumed by the heat storage tank,

N_gbthe total number of the gas-fired boilers;

the heat energy consumed by the steam absorption refrigerator is smaller than the heat energy of the smoke generated by the internal combustion engine, the heat energy of the smoke generated by the internal combustion engine and the cylinder sleeve water is larger than the heat energy consumed by the steam absorption refrigerator, the smoke absorption refrigerator, the waste heat boiler and the water chilling unit, and the heat energy is specifically shown as the following two formulas:

H_gas≥H_{gar_i}

H_gas+H_water≥H_{gar_i}+H_{sar_i}+H_b，in+H_c

in the formula (I), the compound is shown in the specification,

H_gasthe available calorific value of the flue gas discharged by the internal combustion engine at the t moment,

H_wateris the available calorific value of jacket cooling water of the internal combustion engine at the time t,

H_{sar_i}the heat consumed by the vapor absorption chiller unit at time t,

H_{gar_i}the heat consumed by the smoke absorption refrigerating unit at the t moment,

H_b，inthe heat consumed by the waste heat boiler at the time tt is shown.

Specifically, the cold power balance constraint includes a space cold load cold power balance constraint and a cold water load cold power balance constraint, wherein the space cold load cold power balance constraint satisfies the following relational expression:

Q_ac+Q_sar1+Q_gar1+Q_ec1+Q_c1+Q_hp+Q_ic，out1＝Q_load1

Q_sar2+Q_gar2+Q_ec2+Q_ic，out2+Q_c2＝Q_ic，in+Q_load2

in the formula (I), the compound is shown in the specification,

Q_acfor the space cooling load generated by the air conditioning system,

Q_sar1for space cooling loads generated by vapor absorption chiller units,

Q_sar2for the cold water load generated by a vapor absorption chiller,

Q_c1is the space cold load generated by the water chilling unit,

Q_c2is the cold water load generated by the water chilling unit,

Q_hpis the space cold load generated by the heat pump,

Q_ic，out1the space cold load generated for ice cold accumulation,

Q_ic，out2the cold water load generated for ice storage,

Q_ic，inthe cold water load consumed for ice storage,

Q_load1for the space cooling load of the multi-energy system at the time t,

Q_load2the cold water load of the multi-energy system at the t moment;

specifically, the power network power flow constraints comprise non-out-of-limit constraints on the interactive power flow of the user and the distribution network and upper and lower limit constraints on the node voltage of the whole network, wherein the non-out-of-limit constraints satisfy the following relational expression:

in the formula (I), the compound is shown in the specification,

drawing the line tidal current active power from the network node l to the node n at the t-th moment,

the minimum limit value of the line tide active power between the nodes l and n of the hook network,

for line-flow reactive power of network nodes l to n at time t,

for the line-flow reactive power minimum limit from network node l to node n,

for the line tide reactive power from a network node l to a node nThe value of the large limit value is,

l，n∈n_node，n_nodethe total number of all nodes in the network;

in the formula (I), the compound is shown in the specification,

is the voltage at the node l and,

the upper and lower limits of the voltage at node l.

In particular, the amount of the solvent to be used,

as shown in fig. 4, step 3 includes:

3.3, taking the random particle swarm as input data of iterative computation, taking a target function as a fitness function, and iteratively updating an individual extreme value and a global extreme value of the random particle swarm according to preset particle swarm parameters to determine the position and the speed of the particles; obtaining a particle swarm optimization solution after multiple iterations; the preset particle swarm parameters comprise the scale of the particle swarm and the iteration times;

Further, in step 3.4, the comprehensive utilization efficiency of the cogeneration unit satisfies the following relational expression:

Further, in step 3.4, the adjustment of the thermoelectric ratio is realized by the constraint condition of the minimum thermoelectric ratio expressed by the following relation:

R′_CHPmin＝R_CHPmin+ΔR_CHP

Example 1.

In the preferred embodiment, taking an off-grid operation park as an example, the energy utilization optimization strategy of the multi-source system facing the platform area side is subjected to simulation verification.

The combined heat and power generation device arranged in the garden contains a afterburning waste heat boiler and has the characteristic of adjustable heat-power ratio. The influence of seasonal factors on the energy demand of the comprehensive energy system is analyzed by introducing a CHP containing an after-burning boiler and adjusting the heat-electricity ratio of CHPs in different seasons, the influence of cold load on the operation of the system is separately considered, and in addition, the optimization strategy also considers the optimization degree of real-time electricity price on the operation cost of the system.

In a park, when the rated power of a cogeneration unit is 3MW, the rated electric efficiency is 33%, and the heat efficiency is limited by the afterburning amount. The rated power of the diesel engine set is 10MW, and the efficiency is 40%. In order to fully utilize the capacity of the gas boiler and the ice storage unit, a 5MW gas boiler and two sets of ice storage air conditioning systems with rated power of 5MW and 4MW are configured as cold and hot energy storage equipment. In addition, the invention assumes that the capacity of the heat storage water tank configured in the park can completely collect the waste heat, considering that the installation cost of the heat storage water tank is low and the waste heat recovery is beneficial to improving the comprehensive energy efficiency. However, due to the high cost of the batteries and the limited number of the charging cars, the maximum storage capacity of the V2G power station is 900kWh, and 500kWh power is required to be supplied every day for charging cars in the park. The cold, hot and electric loads of the park throughout the year are shown in fig. 5 to 8.

Considering that the price of diesel oil is less influenced by load and is relatively low in price. Thus, the price of diesel in the preferred embodiment is only considered to be seasonally affected, and not to be time-varying. In four seasons of spring, summer, autumn and winter, the price of the diesel oil is 1.034, 0.983, 1.025 and 1.051$/L respectively. In addition, with small-scale distributed cogeneration and increase of installed capacity of a gas boiler, the consumption of natural gas is increased day by day, and finally, the phenomenon of insufficient supply of natural gas at the peak time of gas utilization occurs. For this reason, the preferred embodiment simulates time-of-use electricity prices, and optimizes the system energy cost by adopting the time-of-use natural gas prices according to different cold, heat and electricity demands every day. The natural gas prices in different seasons are shown in fig. 9.

(1) CHP thermoelectric ratio optimization

As can be seen from FIG. 10, the thermoelectric ratio R is varied_CHPIs increased, eta₀Initially, there is an increasing trend, with the energy conversion efficiency reaching a maximum of 78.2% when the thermoelectric ratio is 337%. After that, as the thermoelectric ratio continues to increase, the energy conversion efficiency gradually decreases. This is because the exhaust gas temperature of the boiler is higher and higher with the increase of the post combustion amount, so that the effect of the post combustion on improving the boiler efficiency is smaller and smaller, and further, the energy conversion efficiency of the CHP is gradually reduced.

Fig. 10 also shows the four season CHP heat-power ratio optimization results. In fig. 10, points a, b, c, and d represent the optimal thermoelectric ratios in spring, summer, fall, and winter, respectively, the four season CHP optimal thermal power ratios are 267%, 251%, 276%, and 304%, respectively, and the optimal fuel costs are 11275.9 $ 12847.6 $ 12226.9 $ 13701.2, respectively. Thus, if seasonal considerations are taken into account, the average daily energy cost for a year is $ 12512.9. In addition, the M point represents the annual energy cost optimum thermoelectric ratio and its corresponding overall CHP efficiency, regardless of seasonal factors. Studies have shown that when the thermoelectric ratio is 271%, the average daily energy cost is $ 13159.3.

From the above data it can be seen that:

first, the optimal heat-to-power ratio of the CHP can differ by up to 20% depending on the season, which indicates that seasonal factors have a greater impact on the form of energy demand;

secondly, by optimizing the heat-electricity ratio of the CHP by seasons, the daily average energy cost of the system can be remarkably reduced by 5.2% compared with that before optimization.

In conclusion, the seasonal factors have a large influence on the thermoelectric ratio of the CHP, and the thermoelectric ratio of the CHP is optimized by seasons, so that the thermoelectric load ratio of the CHP can be matched with that of the garden more, and the energy cost of the system is effectively reduced.

(2) Independent consideration of the influence of cold load on system operation

The optimization results in fig. 11 to 14 show that the individual analysis of the system cooling load demand helps to realize peak clipping and valley filling for the electrical load, and improve the system operation stability, and fig. 11, 12, 13 and 14 represent spring, summer, autumn and winter, respectively. P in FIGS. 11 to 14_C1，P_C2The running states of the two sets of ice storage air conditioning equipment are shown, and when the cold load is greater than zero, the ice storage air conditioning system is in a refrigeration working state; on the contrary, the ice storage air conditioning system is in an ice storage state. In addition, P_T1The average daily equivalent electrical load curve (the cold load is converted into the electrical load) before the cold load is considered independently, P_T2The electric load and other electric loads required by the ice storage air conditioning system after the cold load is considered independentlyAnd (4) summing.

The data shows that by analyzing the system cold load alone, the equivalent maximum electrical load in summer is reduced from 4.23MW to 3.25MW, which is reduced by 23.2%; the lowest electric load of the system is increased from 0.89MW to 1.27MW, which is increased by 29.9%; in addition, the peak-to-valley difference of the power system of the park is reduced from 3.34MW to 1.98MW, which is reduced by 40.7%. Although the effect of the independent consideration of the cold load on the peak-valley difference of the power-saving system is more obvious in summer, the independent consideration of the cold load of the system can also reduce the peak-valley difference of the power system in other seasons, so that the stability of the system is improved.

By introducing the ice storage device and separately considering the system cooling load, the installed capacity of the diesel generator can be reduced in addition to the stabilization of the electric load fluctuation. In the summer with the maximum annual electric load, P in FIG. 15_DE1And P_DE2The average daily curves of the output force of the diesel generator before and after the system cold load is considered independently.

As can be seen from fig. 15, the maximum output power of the diesel generator before and after considering the cooling load alone is 3.14MW and 1.98MW, respectively, which indicates that the capacity of the diesel generator can be theoretically reduced by 1.16MW by considering the cooling load alone. Therefore, by considering the system cooling load alone, the configuration capacity of the diesel generator can be greatly reduced. Meanwhile, the data also show that the ratio of the average output to the maximum output of the diesel generator in summer after optimization reaches 78%, which is far higher than 49% before optimization, so that the power generation capacity of the diesel generator is more fully utilized.

(3) Energy cost optimization taking into account real-time gas prices

In FIGS. 16 to 19, P_WTWhen the temperature is more than zero, the heat storage water tank is in a heat release state; p_WTWhen the temperature is less than zero, the heat storage water tank is in a heat storage state. In one day, the heat energy stored in the heat storage water tank is larger than the heat energy emitted by the heat storage water tank, because the heat loss of the heat storage water tank is 8-11% (different heat losses in different seasons). Fig. 16, 17, 18 and 19 represent spring, summer, fall and winter, respectively.

The optimization results in fig. 16 to 19 show that the energy cost of the system can be reduced by adjusting the heat charging and discharging strategy of the hot water storage tank based on the real-time gas price. Through the reasonable strategy of adjusting CHP, heat production of a gas boiler and heat energy charging and discharging of a hot water storage tank, the heat energy of 7.4MWh of natural gas in a high-price period and a low-price period can be transferred to the low-price period every year on average, so that the annual daily average energy cost is reduced by 7%.

Besides the hot water storage tank, the V2G also plays a role in responding to real-time gas price by transferring electric load, and energy cost is reduced. In FIGS. 20 to 23, when P is_V2GWhen the voltage is more than zero, the V2G power changing station supplies electric load to the park; when P is present_V2GWhen the voltage is less than zero, the park power supply system charges the V2G power conversion station. Fig. 20, 21, 22 and 23 represent spring, summer, fall and winter, respectively.

As can be seen from fig. 20 to 23, during the period of low gas price, the park power supply system charges the V2G charging station; during peak gas prices, the V2G conversion station discharges the stored electric energy, and can transfer 1.3MW of electric load on average each day. In addition, the electric quantity discharged for the second time of the V2G power conversion station is smaller than that discharged for the first time in one day, because more electric vehicles need to be charged in a 17: 00-19: 00 time period in consideration of the running time of the electric vehicles. The capacity of the conventional V2G battery replacement station is small, the response real-time gas price of the battery replacement station is influenced, the energy cost of a system is reduced, and the V2G battery replacement station can play a greater role along with the rapid popularization of electric automobiles.

The research shows that: by optimizing the CHP thermoelectric ratio by seasons, the CHP thermoelectric ratio can be better matched with the thermoelectric ratio of the comprehensive energy system, so that the energy cost of the system is reduced by 5.2%. Meanwhile, for the comprehensive energy system running off the grid, the stability of the system running can be seriously influenced by a large peak-to-valley difference of the power load. And by considering the system cold load independently and using the ice storage system to participate in the power load demand response, the peak clipping and valley filling of the power system are indirectly realized. On the premise of meeting the system cold load, the peak-to-valley difference of the summer power load is reduced by 40.7%. In addition, the optimized operation strategy provided by the invention also fully utilizes the heat load transfer capacity of the heat storage water tank, improves the system stability, effectively responds to the real-time gas price and reduces the energy cost. Compared with the prior art, the method has the advantages that the method realizes multi-energy combined supply and coordinated interconnection optimization technology by constructing a complex energy system theory and an optimization method system facing regional energy planning, plays a positive role in innovating an energy service mode, guaranteeing the balance of supply and demand of a power grid, improving the economic operation level of the power grid, improving the overall efficiency of a regional energy system, reducing environmental pollution, developing an intelligent power grid, reducing the total investment and operation management cost of a social energy system and optimizing an energy structure, and provides an effective technical means for greatly reducing the energy consumption in a short period.

The present applicant has described and illustrated embodiments of the present invention in detail with reference to the accompanying drawings, but it should be understood by those skilled in the art that the above embodiments are merely preferred embodiments of the present invention, and the detailed description is only for the purpose of helping the reader to better understand the spirit of the present invention, and not for limiting the scope of the present invention, and on the contrary, any improvement or modification made based on the spirit of the present invention should fall within the scope of the present invention.

Claims

1. An energy consumption optimization control method facing to a platform area side is characterized in that,

the method comprises the following steps:

wherein the objective function satisfies the following relation:

the constraints of the operation limit of the multi-energy system comprise energy supply equipment power constraints and energy storage equipment capacity constraints; the constraints of the operation mechanism of the multi-energy system comprise: electric power balance constraint, thermal power balance constraint, cold power balance constraint and power network power flow constraint;

2. The optimizing control method for energy consumption of platform side according to claim 1,

3. The optimizing control method for energy consumption of platform side according to claim 1,

in step 1, the establishing of the multi-energy system model includes:

P_T＝P_Th+P_Te

in the formula (I), the compound is shown in the specification,

a is an energy network incidence matrix,

B_fin the form of a matrix of elementary loops,

4. The optimizing control method for energy consumption of platform side according to claim 1,

in step 2, the power constraint of the energy supply equipment comprises: the method comprises the following steps of (1) gas internal combustion engine power constraint, air conditioner power constraint, flue gas absorption type refrigerating unit power constraint, compression type electric refrigerating unit power constraint, gas boiler power constraint and waste heat boiler power constraint;

the energy storage device capacity constraints include: the capacity of the storage battery is restricted, the capacity of the heat storage tank is restricted, the capacity of the ice storage is restricted, the capacity of the heat pump is restricted, and the capacity of the water chilling unit is restricted.

5. The optimizing control method for energy consumption of platform side according to claim 1,

in the formula (I), the compound is shown in the specification,

P_buyin order to purchase the electric quantity,

P_sellin order to sell the electricity quantity,

P_BT，outis the output power of the storage battery,

P_BT，inis the input power of the storage battery,

in the formula (I), the compound is shown in the specification,

H_gb，j1for the space heat load generated by the jth gas boiler,

H_gb，j2for the hot water load produced by the jth gas boiler,

H_h，out1is the space heat load generated by the heat storage tank,

H_h，out2is the load of hot water generated by the heat storage tank,

H_hp1for the space heat load generated by the heat pump,

H_hp2for the hot water load generated by the heat pump,

H_b，out1for the space heat load generated by the waste heat boiler,

H_b，out2is the hot water load generated by the waste heat boiler,

H_load1for the space heat load of the multi-energy system at the t moment,

H_load2for the hot water load of the multi-energy system at time t,

H_h，inis the load of hot water consumed by the heat storage tank,

N_gbthe total number of the gas-fired boilers;

Q_ac+Q_sar1+Q_gar1+Q_ec1+Q_c1+Q_hp+Q_ic，out1＝Q_load1

Q_sar2+Q_gar2+Q_ec2+Q_ic，out2+Q_c2＝Q_ic，in+Q_load2

in the formula (I), the compound is shown in the specification,

Q_acfor the space cooling load generated by the air conditioning system,

Q_sar1for space cooling loads generated by vapor absorption chiller units,

Q_sar2for the cold water load generated by a vapor absorption chiller,

Q_c1is the space cold load generated by the water chilling unit,

Q_c2is the cold water load generated by the water chilling unit,

Q_hpis the space cold load generated by the heat pump,

Q_ic，out1the space cold load generated for ice cold accumulation,

Q_ic，out2the cold water load generated for ice storage,

Q_ic，inthe cold water load consumed for ice storage,

Q_load1for the space cooling load of the multi-energy system at the time t,

Q_load2the cold water load of the multi-energy system at the t moment;

the power network power flow constraint comprises non-out-of-limit constraint on user and distribution network interactive power flow and upper and lower limit constraint on the whole network node voltage, wherein the non-out-of-limit constraint satisfies the following relational expression: