CN111244939B - Two-stage optimization design method for multi-energy complementary system considering demand side response - Google Patents
Two-stage optimization design method for multi-energy complementary system considering demand side response Download PDFInfo
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Abstract
The utility model provides a two-stage optimization design method of a multi-energy complementary system considering demand side response, which comprises the steps of constructing two-stage optimization layers, wherein the first stage is a capacity configuration layer considering the demand side response, the first stage is a capacity optimization model aiming at the comprehensive optimization of energy conservation, economy and environmental protection considering the comfort of users, the capacity optimization model considering the demand side response is established, the load data and the equipment capacity are solved by utilizing a genetic algorithm, and the optimized load and the optimized equipment capacity are used as the input of lower-layer optimization; the second level is an operation optimization layer, the lowest energy consumption, cost and emission are taken as targets, the output of the equipment is optimized, and a calculation result is output to an upper layer for optimization; and finally obtaining an optimal load curve, equipment capacity and an operation plan through double-layer optimization loop iteration to obtain the source-load optimal matching.
Description
Technical Field
The disclosure belongs to the technical field of a new energy multi-energy complementary combined cooling heating and power system, and relates to a two-stage optimization design method for a multi-energy complementary system considering demand side response.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
The world is facing unprecedented energy and environmental crises, and the vigorous development of new energy distributed energy supply systems mainly based on wind and light is a key approach for solving the problems. The multi-energy complementary system integrates energy production, conversion and storage technologies, and comprises a new energy power generation, a Combined cooling and power system (CCHP) system, an electric heating/cooling system and an energy storage system. The system is based on the energy cascade utilization principle, can meet the diversified energy utilization requirements of electricity, cold and heat of users, can greatly improve the energy utilization rate and the new energy consumption rate, reduces the pollutant emission and has great development potential. However, the multifunctional complementary CCHP system has a complex structure and various equipment types, and the optimal design of the system is the basis for guaranteeing the efficient and economic operation of the system. However, due to the inherent intermittency and uncertainty of new energy, the operation modes of the CCHP system are variable, and the coupling relation between the system capacity configuration and the operation modes is further deepened, so that the system optimization design is extremely difficult. Meanwhile, according to the knowledge of the inventor, the current optimal design method aiming at the multi-energy complementary CCHP system does not have an optimal design method integrating demand side response, capacity configuration and operation optimization.
Disclosure of Invention
The method comprises the steps of considering the response problem of the demand side, utilizing double-layer optimization loop iteration to finally obtain an optimal load curve, equipment capacity and an operation plan, realizing source-load optimal matching and further improving the comprehensive performance of the system.
According to some embodiments, the following technical scheme is adopted in the disclosure:
a two-stage optimization design method for a multi-energy complementary system considering demand side response comprises the following steps:
constructing a two-stage optimization layer, wherein the first stage is a capacity configuration layer for considering the response of the demand side, the first stage is a capacity optimization model for considering the response of the demand side by taking comprehensive optimization of energy conservation, economy and environmental protection for considering the comfort of users as a target, a genetic algorithm is utilized to solve load data and equipment capacity, and the optimized load and equipment capacity are used as the input of lower-layer optimization;
the second level is an operation optimization layer, the lowest energy consumption, cost and emission are taken as targets, the output of the equipment is optimized, and a calculation result is output to an upper layer for optimization;
and finally obtaining an optimal load curve, equipment capacity and an operation plan through double-layer optimization loop iteration to obtain the source-load optimal matching.
As an alternative embodiment, the energy flow of the multi-energy complementary combined cooling heating and power system is analyzed to determine the electric quantity balance, the primary energy source, the heat balance, the cold balance and the total gas consumption of the system.
As an alternative embodiment, the first-stage optimization model introduces intelligent household appliances into the demand-side response model, adjusts the service time of the household appliances, and further optimizes the electrical load, and simultaneously takes the thermal inertia of the building into consideration, and performs cold/heat load optimization within a comfortable temperature range acceptable by a user.
By way of further limitation, the controllable electrical load includes an interruptible load and a non-interruptible load, and in the load scheduling scheme, a translation scheduling of the controllable electrical load is performed within a day: assuming that the operation power x of the controllable equipment participating in the demand response is fixed and unchanged, a discrete binary variable y belongs to {0,1} to represent the start-stop state of the equipment, 1 represents operation, and 0 represents closing, and the purpose of load transfer is achieved by optimizing the value of the variable y.
As an alternative embodiment, the first level of optimization has constraints including schedulable device load, indoor temperature and device capacity constraints.
As an alternative embodiment, the second-level optimization layer takes the minimum energy consumption, the minimum running cost and the minimum carbon emission in unit time as an optimization target, and a linear weighted combination method is adopted to convert the multi-target problem into single-target optimization.
As an alternative embodiment, the second level optimization layer has constraints including energy flow balance constraints and rated capacity of the genset and rated capacity constraints of other devices.
As an alternative embodiment, the two-stage optimization model is solved based on a hybrid solution of a genetic algorithm and a nonlinear programming, and the specific process comprises the following steps:
step 1: initializing and setting system parameters, genetic algorithms and equipment parameters;
step 2: population initialization: randomly generating N individuals as an initial population P0And each individual is binary coded;
and step 3: calculating the fitness of the current population P, and calling a nonlinear programming method to solve an operation optimization model;
and 4, step 4: judging whether the current population meets the termination requirement, and if the current population reaches a preset maximum algebra, executing a step 6; otherwise, continuing to step 5;
and 5: selecting, crossing and mutating to form a new population P3And returning to execute the step 3;
step 6: and decoding to obtain a load optimization result.
A two-stage optimization design method for a multi-energy complementary system considering demand side response comprises the following steps:
the first-stage optimization layer is a capacity configuration layer for considering the demand side response, a capacity optimization model for considering the demand side response is established by taking comprehensive optimization of energy conservation, economy and environmental protection for considering the user comfort as a target, load data and equipment capacity are solved by using a genetic algorithm, and the optimized load and equipment capacity are used as input of lower-layer optimization;
the second-level optimization layer is an operation optimization layer, optimizes the output of equipment by taking the lowest energy consumption, cost and emission as targets, and outputs a calculation result to the upper-level optimization;
and the solving module is configured to finally obtain an optimal load curve, equipment capacity and an operation plan through double-layer optimization loop iteration to obtain source-load optimal matching.
A computer readable storage medium having stored therein instructions adapted to be loaded by a processor of a terminal device and to execute the method for two-stage design optimization of a multi-energy complementary system taking into account demand-side responses.
A terminal device comprising a processor and a computer readable storage medium, the processor being configured to implement instructions; the computer readable storage medium is used for storing a plurality of instructions, and the instructions are suitable for being loaded by a processor and executing the two-stage optimization design method of the multi-energy complementary system considering the response of the demand side.
Compared with the prior art, the beneficial effect of this disclosure is:
the method and the system have the advantages that the response, the capacity configuration and the operation optimization of the demand side are innovatively unified in an optimization design framework, the problem of uncertainty of new energy is effectively solved, the optimal design of the system is realized, and the comprehensive performance of the system is further improved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the disclosure, illustrate embodiments of the disclosure and together with the description serve to explain the disclosure and are not to limit the disclosure.
FIG. 1 is a diagram of a multi-energy complementary system;
FIG. 2 is a schematic diagram of a two-level optimization logic relationship;
fig. 3 is a detailed flowchart of the present embodiment.
The specific implementation mode is as follows:
the present disclosure is further described with reference to the following drawings and examples.
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present disclosure. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
The typical structure of the multi-energy complementary system is shown in fig. 1, and the system comprises a fan, a photovoltaic generator, an internal combustion generator set, an absorption refrigerator, a gas boiler, a heat pump, a cold storage device, a heat storage water tank, an electric load and a heat load. The electric load is supplied by a fan, a photovoltaic generator set, an internal combustion generator set and a superior power grid; the cold load is supplied by a heat pump, an absorption refrigerator and a cold storage device; the heat load is supplied by a gas boiler, a generator set waste heat system and heat storage equipment; the demand response of the electric, cold and heat loads flexibly participates in the dispatching.
A conventional Separation Production (SP) system is composed of a large power grid (conventional thermal power generation), a gas boiler, and an electric refrigerator, the electric load of a user and the electric energy consumed by the electric refrigerator are satisfied by the large power grid, and the heating and cooling loads are satisfied by the gas boiler and the electric refrigerator, respectively. The advanced property of the method is verified as a comparison system of the multi-energy complementary CCHP system.
Based on the structure, a two-stage optimization design method of the multi-energy complementary system considering the response of the demand side is provided. As shown in fig. 2, the first level is a capacity configuration layer for considering the demand side response, which establishes a capacity optimization model for considering the demand side response with the comprehensive optimization of energy saving, economy and environmental protection for considering user comfort as a target, solves load data and equipment capacity by using a genetic algorithm, and takes the optimized load and equipment capacity as the input of lower layer optimization; the second level is an operation optimization layer, the lowest energy consumption, cost and emission is taken as a target, the output of the equipment is optimized, and a calculation result is output to an upper layer for optimization. And (4) performing double-layer optimization loop iteration to finally obtain an optimal load curve, equipment capacity and an operation plan, so that source-load optimal matching is realized, and the comprehensive performance of the system is further improved.
First, an analysis of the energy flow is performed:
the system energy flow analysis is the basis for researching the system energy characteristics and carrying out system optimization design. On the basis of determining the system structure, the embodiment performs analysis on the energy flows of the cold, the heat and the electricity in the system.
The electricity balance equation of the system is as follows:
Eload(t)+Ep(t)＝Epv(t)+Ewt(t)+Egrid(t)+Epgu(t) (1)
in the formula, EloadIs an electrical load;
Epvoutputting electric power for the photovoltaic power generation system;
Ewtoutputting electric power for the wind power generation system;
Epguoutputting electric power for the internal combustion generator set;
Egridfor power interaction with the grid, purchasing electricity (E)grid>0) Selling electricity (E)grid<0)；
EpThe power consumption of the heat pump is reduced.
Wherein the fuel gas consumption F required by the internal combustion generator set at the moment tpguComprises the following steps:
in the formula etath,pguAnd ηe,pguThe thermal efficiency and the point efficiency, respectively, of the internal combustion engine-generator set at time t, can be expressed as,
in the formula, a0,a1,a2,b0,b1And b2For fitting coefficients of polynomials, PLRpguIs the load factor of the power generation stack, expressed as,
PLRpgu(t)＝Epgu(t)/Npgu (5)
in the formula, NpguThe rated power of the generator set.
Primary energy F consumed by power grid power purchase of system at time tgbComprises the following steps:
in the formula etae,gridAnd ηd,gridThe power generation efficiency and the transmission efficiency of the power grid.
The heat balance equation for the system is:
Hload(t)＝Qhe(t)+Qb(t)+Qs(t) (7)
in the formula, HloadIs a thermal load;
Qheheat exchange power for the heat exchanger;
Qbthe heating power of the gas boiler is set;
Qsfor input/output power of heat storage water tank, while outputting (Q)s>0) At input (Q)s<0)。
Wherein the gas consumption F required at time t of the gas boilerbComprises the following steps:
in the formula etabIs the thermal efficiency of the gas boiler.
Therefore, the total gas consumption of the CCHP system at time t is:
Fgas(t)＝Fpgu(t)+Fb(t) (9)
the cold balance equation of the system is as follows:
Cload(t)＝Qab(t)+Qp(t)+Qs(t) (10)
in the formula, CloadIs a cold load;
Qabis the output power of the absorption refrigerator;
Qpis the refrigeration power of the heat pump.
QsFor input/output power, output time (Q) of the cold storage devices>0) At input (Q)s<0)。
Output power Q of absorption refrigeratorabComprises the following steps:
Qab(t)＝Qrh(t)COPab (11)
in the formula, QrhFor recovery of power from waste heat of generator set, COPabIs the energy efficiency ratio of the absorption chiller.
Power consumption E of heat pump at time tpComprises the following steps:
in the formula, COPpIs the energy efficiency ratio of the heat pump.
The energy storage equipment comprises:
Qsta(t+1)＝ηsQsta(t)-Qs(t) (13)
in the formula, Qsta(t +1) andQsta(t) the energy storage states at time t +1 and t of the energy storage device, eta, respectivelysIs the efficiency of the energy storage device.
Secondly, constructing a first-level optimization model:
the first level is a capacity configuration layer considering demand side response, and the capacity configuration layer establishes a capacity optimization model considering demand side response by taking comprehensive optimization of energy conservation, economy and environmental protection considering user comfort as a target, so that the electric, cold and heat load data and equipment capacity are optimized.
Demand side response model:
introducing intelligent household appliances into a demand side response model, adjusting the service time of the household appliances, further optimizing the electric load, simultaneously considering the thermal inertia of the building, optimizing the cold/heat load within the range of the comfortable temperature acceptable by a user, and obtaining the following model:
the controllable electric load comprises an interruptible load and an uninterruptable load, the interruptible load such as an electric automobile and the like can be randomly suspended for use in the using process, and other electric appliances such as an electric cooker, a water heater and the like can be uninterruptedly used after being started. In the load scheduling scheme, the translational scheduling of the controllable electric load within one day is realized by considering the intention of resident customers. Assuming that the operation power x of the controllable equipment participating in the demand response is fixed and constant, a discrete binary variable y epsilon {0,1} is used for representing the start-stop state of the equipment, 1 represents operation, and 0 represents closing. The load transfer is achieved by optimizing the value of the variable y.
EconloadIs a controllable electrical load; d represents the set of all load controllable devices; x is the number ofdRepresenting the operating power of the d-th device; y isdE {0,1} represents the start-stop status of the d-th equipment, 1 represents operation, and 0 represents shutdown.
Secondly, because the walls of the building have certain heat insulation effect, the heat exchange process between the indoor and the outdoor is slow, different from the electric load, and the indoor temperature changes in small level. Therefore, the indoor cooling/heating load is controlled without damaging the temperature comfort according to the energy price.
Hload(t)＝((Tin(t)-Tin(t-1)e-Δt/τ)/(1-e-Δt/τ)-Tout(t))/R (15)
Cload(t)＝((Tout(t)-(Tin(t)-Tin(t-1)e-Δt/τ)/(1-e-Δt/τ))/R (16)
In the formula, Cload、HloadRespectively controllable cold load and controllable heat load; t isin(t)、Tout(t) represents indoor and outdoor temperatures at time t, respectively.
Optimizing the target:
the energy conservation, the economy and the environmental protection considering the comfort level of the user are comprehensively optimal,
maxV＝ω1PESR+ω2ACR+ω3CERR (17)
in the formula, PESR is the annual energy saving rate, ACR is the annual comprehensive cost saving rate, and CERR is annual CO2And (4) the emission reduction rate. Omega1As energy-saving rate weight factor, omega2For annual cost saving rate weight factor, omega3Is CO2And V is a comprehensive optimization target. FSP，FCCHPAnnual energy consumption of the separate supply system and the multi-energy complementary CCHP system, CSP，CCCHPRespectively, the annual integrated cost, CE, of the separate supply system and the multi-energy complementary CCHP systemSP，CECCHPAre respectively a separate supply systemAnnual CO with a multipotent complementary CCHP system2And (4) discharging the amount. The following equations were respectively obtained:
CCCHP＝CCCHP,EQ+CCCHP,OM+CCCHP,EC+CCCHP,load (23)
CSP＝CSP,EQ+CSP,OM+CSP,EC (25)
in the formula, CCCHP,EQAnnual cost of initial investment for multi-energy complementary CCHP system equipment;
CCCHP,OMannual maintenance costs for the multi-energy complementary CCHP system;
CCCHP,ECthe annual operating cost of the multi-energy complementary CCHP system;
CCCHP,ECpenalty cost generated by influencing user comfort is scheduled for the load of the multi-energy complementary CCHP system;
CSP,EQannual cost for separately supplying system equipment investment;
CSP,OMthe annual maintenance cost of the separate supply system;
CSP,ECthe annual operating cost of the distribution system.
The annual operating cost of the multi-energy complementary CCHP system comprises the fuel cost and the electricity purchasing cost of a power grid, and can be expressed in the following forms:
CCCHP,EQ＝CCCHP,INR (27)
CCCHP,OM＝σCCCHP,IN (28)
in the formula, PgridThe price of power grid interaction at the time t is positive when electricity is purchased and negative when electricity is sold;
Pgasis the gas price;
CCCHP,INthe total initial investment cost of all equipment of the multifunctional complementary CCHP system;
r is the return on investment coefficient;
and sigma is a proportional coefficient of the operating and maintenance cost of the system.
The investment recovery factor R in the above equation can be expressed as:
wherein k is the equipment lifetime;
r is the reference discount rate.
CSPCan be further expressed as:
CSP,EQ＝CSP,INR (31)
CSP,OM＝σCSP,IN (32)
in the formula, ESP,gridDistributing the purchased electric quantity of the system for t time;
CSP,INthe investment cost of the distribution system.
CO of multi-energy complementary CCHP system2The annual emissions can be expressed as:
in the formula, mugridCO for burning coal to power grid2A discharge coefficient;
μgasCO as fuel gas2The discharge coefficient.
Annual CO of separate supply system2The emissions can be expressed as:
optimizing variables:
starting and stopping state y of schedulable electric equipmentd(T) indoor controllable temperature Tin(t), genset capacity Npgu. The photovoltaic generator set and the wind turbine generator set are determined by available installation area and available total amount of natural resources, and other equipment can be obtained through an energy flow relational expression.
Constraint conditions are as follows:
firstly, the schedulable device:
[Ad,Bd]the working interval of the device d can be scheduled; edRepresenting the total power consumption of device d.
The uninterruptible load device comprises:
if yd(t) 1, then yd(t+1)＝1,…,ydAnd (t + n) is 1, and n is the working time length of the device d.
Indoor temperature:
Tin_min≤Tin(t)≤Tin_max (36)
Tin_min，Tin_minfor the upper and lower limits of indoor adjustable temperature, the bigger the indoor temperature adjusting range is, the better the control effect is, but the larger the influence on the temperature comfort of the user is.
Capacity of the equipment:
0≤Npgu≤Npgu,max (37)
0≤Nb≤Nb,max (38)
in the formula, Npgu,maxFor generating electricity by internal combustionThe upper limit of the unit capacity;
Nb,maxis the upper limit of the capacity of the photovoltaic power generation system.
The above constraint guarantees NpguAnd NbIs within a reasonably feasible range.
Constructing a second-stage optimization model:
the second level is an optimization layer of the system, which takes the minimum energy consumption, operation cost and carbon emission within 24 hours a day as an optimization target, and also adopts a linear weighted combination method to convert the multi-target problem into single-target optimization, wherein the target function is defined as:
minW＝ω1Fday+ω2Cday+ω3CEday (39)
in the formula, FdayThe total energy consumption of the whole day; cdayThe total operating cost of the whole day; CEdayThe total carbon emission is the total carbon emission of the whole day; omega1Is an energy consumption weight factor; omega2Is an operating cost weighting factor; omega3Is a carbon emission weight factor; w is the comprehensive optimization objective. The weighting factors are consistent with the corresponding indexes in the first-stage optimization configuration model.
Optimizing variables:
output plan including genset at each stage { E }pgu(1),…,Epgu(24) The output plans of other devices can be obtained by the device.
Constraint conditions are as follows:
the operation optimization model needs to satisfy the energy flow balance relation, as shown in formulas (1) to (13), and also needs to satisfy the following inequality constraints:
0≤Epgu(t)≤Npgu (43)
in the formula, NpguAnd the rated capacity of the generator set is obtained by the first-stage optimization model.
Aiming at the two-stage optimization model, a hybrid solving method based on a genetic algorithm and nonlinear programming is provided. As shown in fig. 3, the solving steps are as follows:
step 1: and (5) initializing the system. Firstly, system parameters, genetic algorithms and equipment parameters are set.
Step 2: and (5) initializing a population. In this step, N individuals are randomly generated as an initial population P0And each individual is binary coded.
And step 3: calculating the fitness of the current population P, and comprising the following two steps:
a. an operating strategy is obtained. In order to calculate the objective function value of the first-stage model, the second-stage model needs to be called to obtain an optimized operation strategy.
b. And (5) calculating the fitness. The fitness value of the individual is calculated using equation (1).
And 4, step 4: and judging whether the current population meets the termination requirement, and executing the steps if the maximum algebra indicated by the user is reached. Otherwise, step 5 needs to be continued.
And 5: selecting, crossing and mutating to form a new population P3。
Step 6: executing step 3, calculating population P3The fitness of (2).
And 7: and decoding to obtain a load optimization result.
Of course, the above calculation process may be implemented in software, for example, MATLAB.
To sum up, aiming at the uncertainty of new energy in the multi-energy complementary CCHP system, demand side response, capacity configuration and operation optimization are effective ways for solving the problem, and at present, three aspects of optimization design method unification are not available, the embodiment provides a two-stage optimization design method of the multi-energy complementary system for considering demand side response, wherein the first stage is a demand response capacity configuration layer, the comprehensive optimization of energy, economy and environmental indexes is taken as a target, the comfort level of a user is taken as a constraint, the data of electric, cold and heat loads and the equipment capacity are optimized, and the optimized load and the equipment capacity are taken as the input of the second stage of optimization; the second level is an operation optimization layer, the output plan of the equipment is optimized by taking the lowest energy consumption, cost and emission as a target, and the energy consumption, cost and emission data are output to an upper layer for optimization. And performing double-layer optimization loop iteration to finally obtain an optimal load curve, equipment capacity and an operation strategy, innovatively unifying demand side response, capacity configuration and operation optimization in an optimization design framework, effectively solving the problem of uncertainty of new energy, realizing optimal design of a system and further improving the comprehensive performance of the system.
As will be appreciated by one skilled in the art, embodiments of the present disclosure may be provided as a method, system, or computer program product. Accordingly, the present disclosure may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present disclosure may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and so forth) having computer-usable program code embodied therein.
The present disclosure is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the disclosure. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above description is only a preferred embodiment of the present disclosure and is not intended to limit the present disclosure, and various modifications and changes may be made to the present disclosure by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present disclosure should be included in the protection scope of the present disclosure.
Although the present disclosure has been described with reference to specific embodiments, it should be understood that the scope of the present disclosure is not limited thereto, and those skilled in the art will appreciate that various modifications and changes can be made without departing from the spirit and scope of the present disclosure.
Claims (8)
1. A two-stage optimization design method for a multi-energy complementary system considering demand side response is characterized by comprising the following steps: the method comprises the following steps:
constructing a two-stage optimization layer, wherein the first stage is a capacity configuration layer for considering the response of the demand side, the capacity configuration layer establishes a capacity optimization model for considering the response of the demand side by taking comprehensive optimization of energy conservation, economy and environmental protection for considering the comfort of a user as a target, solves load data and equipment capacity by utilizing a genetic algorithm, and takes the optimized load and equipment capacity as the input of lower-layer optimization;
the second level is an operation optimization layer, the lowest energy consumption, cost and emission are taken as targets, the output of the equipment is optimized, and a calculation result is output to an upper layer for optimization;
finally, obtaining an optimal load curve, equipment capacity and an operation plan through double-layer optimization loop iteration to obtain source-load optimal matching;
the capacity optimization model in the first-stage optimization layer introduces intelligent household appliances into the demand side response model, adjusts the service time of the household appliances, further optimizes the electric load, and simultaneously considers the thermal inertia of the building to optimize the cold/heat load within the range of comfortable temperature acceptable by a user;
the controllable electrical loads include interruptible loads and non-interruptible loads;
the capacity optimization model in the first-level optimization layer has constraint conditions, wherein the constraint conditions comprise schedulable equipment load, indoor temperature and equipment capacity constraint;
in the load scheduling scheme, the translation scheduling of the controllable electric load within one day is carried out: assuming that the operation power x of the controllable equipment participating in the demand response is fixed and unchangeable, using a discrete binary variable y to be in the state of starting and stopping the equipment {0,1}, wherein 1 represents operation, and 0 represents closing, and achieving the purpose of load transfer by optimizing the value of the variable y;
Econloadis a controllable electrical load; d represents the set of all load controllable devices; x is the number ofdRepresenting the operating power of the d-th device; y isdE {0,1} represents the start-stop status of the d-th device.
2. The two-stage optimization design method of the multi-energy complementary system considering the demand side response as claimed in claim 1, characterized by: and analyzing the energy flow of the multi-energy complementary CCHP system, and determining the electric quantity balance, the primary energy, the heat balance, the cold quantity balance and the total gas consumption of the system.
3. The two-stage optimization design method of the multi-energy complementary system considering the demand side response as claimed in claim 1, characterized by: the second level optimization layer has constraints including energy flow balance constraints and generator set and gas boiler rated capacity constraints.
4. The two-stage optimization design method of the multi-energy complementary system considering the demand side response as claimed in claim 1, characterized by: the second-level optimization layer takes the minimum energy consumption, the minimum running cost and the minimum carbon emission in unit time as an optimization target, and converts a multi-target problem into single-target optimization by adopting a linear weighted combination method.
5. The two-stage optimization design method of the multi-energy complementary system considering the demand side response as claimed in claim 1, characterized by: solving the two-stage optimization layer based on the hybrid solution of the genetic algorithm and the nonlinear programming, wherein the specific process comprises the following steps:
step 1: initializing and setting system parameters, genetic algorithms and equipment parameters;
step 2: population initialization: randomly generating N individuals as an initial population P0And each individual is binary coded;
and step 3: calculating the fitness of the current population P, and calling a nonlinear programming method to solve an operation optimization model;
and 4, step 4: judging whether the current population meets the termination requirement, and if the current population reaches a preset maximum algebra, executing a step 6; otherwise, continuing to step 5;
and 5: selecting, crossing and mutating to form a new population P3And returning to execute the step 3;
step 6: and decoding to obtain a load optimization result.
6. A two-stage optimization design method for a multi-energy complementary system considering demand side response is characterized by comprising the following steps: the method comprises the following steps:
the first-stage optimization layer is a capacity configuration layer for considering the demand side response, the capacity configuration layer establishes a capacity optimization model for considering the demand side response by taking comprehensive optimization of energy conservation, economy and environmental protection for considering the user comfort as a target, solves load data and equipment capacity by utilizing a genetic algorithm, and takes the optimized load and equipment capacity as the input of lower-layer optimization;
the second-level optimization layer is an operation optimization layer, optimizes the output of equipment by taking the lowest energy consumption, cost and emission as targets, and outputs a calculation result to the upper-level optimization;
the solving module is configured to finally obtain an optimal load curve, equipment capacity and an operation plan through double-layer optimization loop iteration to obtain source-load optimal matching;
the capacity optimization model in the first-stage optimization layer introduces intelligent household appliances into the demand side response model, adjusts the service time of the household appliances, further optimizes the electric load, and simultaneously considers the thermal inertia of the building to optimize the cold/heat load within the range of comfortable temperature acceptable by a user;
the controllable electrical loads include interruptible loads and non-interruptible loads;
the capacity optimization model in the first-level optimization layer has constraint conditions, wherein the constraint conditions comprise schedulable equipment load, indoor temperature and equipment capacity constraint;
in the load scheduling scheme, the translation scheduling of the controllable electric load within one day is carried out: assuming that the operation power x of the controllable equipment participating in the demand response is fixed and unchangeable, using a discrete binary variable y to be in the state of starting and stopping the equipment {0,1}, wherein 1 represents operation, and 0 represents closing, and achieving the purpose of load transfer by optimizing the value of the variable y;
Econloadis a controllable electrical load; d represents the set of all load controllable devices; x is the number ofdRepresenting the operating power of the d-th device; y isdE {0,1} represents the start-stop status of the d-th device.
7. A computer-readable storage medium characterized by: the method comprises the steps of storing a plurality of instructions, wherein the instructions are suitable for being loaded by a processor of the terminal equipment and executing the method for designing the two-stage optimization of the multi-energy complementary system considering the response of the demand side in any one of claims 1 to 5.
8. A terminal device is characterized in that: the system comprises a processor and a computer readable storage medium, wherein the processor is used for realizing instructions; the computer readable storage medium is used for storing a plurality of instructions, and the instructions are suitable for being loaded by a processor and executing the multi-energy complementary system two-stage optimization design method considering the demand side response, wherein the method is as set forth in any one of claims 1-5.
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