CN111244939B - Two-stage optimization design method for multi-energy complementary system considering demand side response - Google Patents

Abstract

Translated fromChinese

本公开提供了一种计及需求侧响应的多能互补系统两级优化设计方法，构建两级优化层，第一级为计及需求侧响应的容量配置层，该层以计及用户舒适度的节能性、经济性和环保性综合最优为目标建立计及需求侧响应的容量优化模型，利用遗传算法求解负荷数据和设备容量，并将优化后的负荷和设备容量作为下层优化的输入；第二级为运行优化层，以能耗、成本、排放最低为目标，优化设备出力，并将计算结果输出给上层优化；通过双层优化循环迭代，最终求得最佳负荷曲线、设备容量和运行计划，得到源‑荷最佳匹配。

The present disclosure provides a two-level optimization design method for a multi-energy complementary system that takes into account demand-side response. A two-level optimization layer is constructed. The first level is a capacity configuration layer that takes into account demand-side response. This layer takes into account user comfort. Based on the comprehensive optimization of energy saving, economy and environmental protection, a capacity optimization model that takes into account the demand-side response is established, and the load data and equipment capacity are solved by genetic algorithm, and the optimized load and equipment capacity are used as the input of the lower layer optimization; The second level is the operation optimization layer, aiming at the lowest energy consumption, cost and emission, optimizing the equipment output, and outputting the calculation results to the upper layer optimization; through the double-layer optimization loop iteration, the optimal load curve, equipment capacity and Run the plan to get the best match of source-load.

Description

Two-stage optimization design method for multi-energy complementary system considering demand side response

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:

E_load(t)+E_p(t)＝E_pv(t)+E_wt(t)+E_grid(t)+E_pgu(t) (1)

in the formula, E_loadIs an electrical load;

E_pvoutputting electric power for the photovoltaic power generation system;

E_wtoutputting electric power for the wind power generation system;

E_pguoutputting electric power for the internal combustion generator set;

E_gridfor power interaction with the grid, purchasing electricity (E)_grid>0) Selling electricity (E)_grid<0)；

E_pThe power consumption of the heat pump is reduced.

Wherein the fuel gas consumption F required by the internal combustion generator set at the moment t_pguComprises the following steps:

in the formula eta_th,pguAnd η_e,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, PLR_pguIs the load factor of the power generation stack, expressed as,

PLR_pgu(t)＝E_pgu(t)/N_pgu (5)

in the formula, N_pguThe rated power of the generator set.

Primary energy F consumed by power grid power purchase of system at time t_gbComprises the following steps:

in the formula eta_e,gridAnd η_d,gridThe power generation efficiency and the transmission efficiency of the power grid.

The heat balance equation for the system is:

H_load(t)＝Q_he(t)+Q_b(t)+Q_s(t) (7)

in the formula, H_loadIs a thermal load;

Q_heheat exchange power for the heat exchanger;

Q_bthe heating power of the gas boiler is set;

Q_sfor 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 boiler_bComprises the following steps:

in the formula eta_bIs the thermal efficiency of the gas boiler.

Therefore, the total gas consumption of the CCHP system at time t is:

F_gas(t)＝F_pgu(t)+F_b(t) (9)

the cold balance equation of the system is as follows:

C_load(t)＝Q_ab(t)+Q_p(t)+Q_s(t) (10)

in the formula, C_loadIs a cold load;

Q_abis the output power of the absorption refrigerator;

Q_pis the refrigeration power of the heat pump.

Q_sFor input/output power, output time (Q) of the cold storage device_s>0) At input (Q)_s<0)。

Output power Q of absorption refrigerator_abComprises the following steps:

Q_ab(t)＝Q_rh(t)COP_ab (11)

in the formula, Q_rhFor recovery of power from waste heat of generator set, COP_abIs the energy efficiency ratio of the absorption chiller.

Power consumption E of heat pump at time t_pComprises the following steps:

in the formula, COP_pIs the energy efficiency ratio of the heat pump.

The energy storage equipment comprises:

Q_sta(t+1)＝η_sQ_sta(t)-Q_s(t) (13)

in the formula, Q_sta(t +1) andQ_sta(t) the energy storage states at time t +1 and t of the energy storage device, eta, respectively_sIs 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.

E_conloadIs a controllable electrical load; d represents the set of all load controllable devices; x is the number of_dRepresenting the operating power of the d-th device; y is_dE {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.

H_load(t)＝((T_in(t)-T_in(t-1)e^-Δt/τ)/(1-e^-Δt/τ)-T_out(t))/R (15)

C_load(t)＝((T_out(t)-(T_in(t)-T_in(t-1)e^-Δt/τ)/(1-e^-Δt/τ))/R (16)

In the formula, C_load、H_loadRespectively controllable cold load and controllable heat load; t is_in(t)、T_out(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. F_SP，F_CCHPAnnual energy consumption of the separate supply system and the multi-energy complementary CCHP system, C_SP，C_CCHPRespectively, the annual integrated cost, CE, of the separate supply system and the multi-energy complementary CCHP system_SP，CE_CCHPAre respectively a separate supply systemAnnual CO with a multipotent complementary CCHP system₂And (4) discharging the amount. The following equations were respectively obtained:

C_CCHP＝C_CCHP,EQ+C_CCHP,OM+C_CCHP,EC+C_CCHP,load (23)

C_SP＝C_SP,EQ+C_SP,OM+C_SP,EC (25)

in the formula, C_CCHP,EQAnnual cost of initial investment for multi-energy complementary CCHP system equipment;

C_CCHP,OMannual maintenance costs for the multi-energy complementary CCHP system;

C_CCHP,ECthe annual operating cost of the multi-energy complementary CCHP system;

C_CCHP,ECpenalty cost generated by influencing user comfort is scheduled for the load of the multi-energy complementary CCHP system;

C_SP,EQannual cost for separately supplying system equipment investment;

C_SP,OMthe annual maintenance cost of the separate supply system;

C_SP,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:

C_CCHP,EQ＝C_CCHP,INR (27)

C_CCHP,OM＝σC_CCHP,IN (28)

in the formula, P_gridThe price of power grid interaction at the time t is positive when electricity is purchased and negative when electricity is sold;

P_gasis the gas price;

C_CCHP,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.

C_SPCan be further expressed as:

C_SP,EQ＝C_SP,INR (31)

C_SP,OM＝σC_SP,IN (32)

in the formula, E_SP,gridDistributing the purchased electric quantity of the system for t time;

C_SP,INthe investment cost of the distribution system.

CO of multi-energy complementary CCHP system₂The annual emissions can be expressed as:

in the formula, mu_gridCO for burning coal to power grid₂A discharge coefficient;

μ_gasCO 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 equipment_d(T) indoor controllable temperature T_in(t), genset capacity N_pgu. 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:

[A_d,B_d]the working interval of the device d can be scheduled; e_dRepresenting the total power consumption of device d.

The uninterruptible load device comprises:

if y_d(t) 1, then y_d(t+1)＝1,…,y_dAnd (t + n) is 1, and n is the working time length of the device d.

Indoor temperature:

T_{in_min}≤T_in(t)≤T_{in_max} (36)

T_{in_min}，T_{in_min}for 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≤N_pgu≤N_pgu,max (37)

0≤N_b≤N_b,max (38)

in the formula, N_pgu,maxFor generating electricity by internal combustionThe upper limit of the unit capacity;

N_b,maxis the upper limit of the capacity of the photovoltaic power generation system.

The above constraint guarantees N_pguAnd N_bIs 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＝ω₁F_day+ω₂C_day+ω₃CE_day (39)

in the formula, F_dayThe total energy consumption of the whole day; c_dayThe total operating cost of the whole day; CE_dayThe 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),…,E_pgu(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≤E_pgu(t)≤N_pgu (43)

in the formula, N_pguAnd 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)

Translated fromChinese

1.一种计及需求侧响应的多能互补系统两级优化设计方法，其特征是：包括以下步骤：1. a multi-energy complementary system two-level optimization design method taking into account demand side response, is characterized in that: comprise the following steps:构建两级优化层，第一级为计及需求侧响应的容量配置层，容量配置层以计及用户舒适度的节能性、经济性和环保性综合最优为目标建立计及需求侧响应的容量优化模型，利用遗传算法求解负荷数据和设备容量，并将优化后的负荷和设备容量作为下层优化的输入；A two-level optimization layer is constructed. The first level is the capacity configuration layer that takes into account the demand-side response. The capacity configuration layer aims at the comprehensive optimization of energy saving, economy and environmental protection considering the user's comfort. The capacity optimization model uses genetic algorithm to solve the load data and equipment capacity, and uses the optimized load and equipment capacity as the input of the lower layer optimization;第二级为运行优化层，以能耗、成本、排放最低为目标，优化设备出力，并将计算结果输出给上层优化；The second level is the operation optimization layer, aiming at the lowest energy consumption, cost and emission, optimizing the output of the equipment, and outputting the calculation results to the upper layer for optimization;通过双层优化循环迭代，最终求得最佳负荷曲线、设备容量和运行计划，得到源-荷最佳匹配；Through the double-layer optimization cycle iteration, the optimal load curve, equipment capacity and operation plan are finally obtained, and the optimal source-load matching is obtained;第一级优化层中的容量优化模型，在需求侧响应模型中引入智能家电，调整家电使用时间，进而优化电负荷，同时考虑到建筑物的热惯性，在用户可接受的舒适温度范围内，进行冷/热负荷优化；The capacity optimization model in the first-level optimization layer introduces smart home appliances into the demand-side response model, adjusts the use time of home appliances, and then optimizes the electrical load, while taking into account the thermal inertia of the building, within the user's acceptable comfortable temperature range, Perform cooling/heating load optimization;可控电负荷包括可中断负载和不可中断负载；Controllable electrical loads include interruptible loads and uninterruptible loads;第一级优化层中的容量优化模型具有约束条件，约束条件包括可调度设备负荷、室内温度和设备容量约束；The capacity optimization model in the first-level optimization layer has constraints, including schedulable equipment load, indoor temperature and equipment capacity constraints;在负荷调度方案中，进行可控电负荷在一天内的平移调度：假定参与需求响应的可控设备运行功率x是固定不变的，使用离散二进制变量y∈{0,1}表示设备的启停状态，1表示运行，0表示关闭，通过优化变量y的值，来达到负荷转移的目的；In the load scheduling scheme, the shift scheduling of the controllable electrical load in one day is carried out: it is assumed that the operating power x of the controllable equipment participating in the demand response is fixed, and the discrete binary variable y∈{0,1} is used to represent the start-up of the equipment. Stop state, 1 means running, 0 means off, the purpose of load transfer is achieved by optimizing the value of variable y;

E_conload为可控电负荷；D表示所有负荷可控设备的集合；x_d表示第d个设备的工作功率；y_d∈{0,1}表示第d个设备的启停状态。E_conload is the controllable electrical load; D represents the set of all load-controllable devices; x_d represents the working power of the d-th device; y_d ∈ {0,1} represents the start-stop state of the d-th device.2.如权利要求1所述的一种计及需求侧响应的多能互补系统两级优化设计方法，其特征是：对多能互补CCHP系统的能量流进行分析，确定系统的电量平衡、一次能源、热平衡、冷量平衡和燃气消耗总量。2. a kind of multi-energy complementary system two-level optimization design method taking into account demand side response as claimed in claim 1, it is characterized in that: the energy flow of multi-energy complementary CCHP system is analyzed, determine the power balance of the system, a Total energy, heat balance, cooling balance and gas consumption.3.如权利要求1所述的一种计及需求侧响应的多能互补系统两级优化设计方法，其特征是：第二级优化层具有约束条件，包括能量流平衡约束以及发电机组的额定容量和燃气锅炉的额定容量约束。3. The two-level optimization design method for a multi-energy complementary system considering demand side response as claimed in claim 1, wherein the second-level optimization layer has constraints, including energy flow balance constraints and the rated value of the generator set. Capacity and rated capacity constraints of gas boilers.4.如权利要求1所述的一种计及需求侧响应的多能互补系统两级优化设计方法，其特征是：第二级优化层以单位时间内能源消耗、运行成本与碳排放量最小为优化目标，采用线性加权组合法将多目标问题转化为单目标优化。4. A two-level optimization design method for a multi-energy complementary system considering demand-side response as claimed in claim 1, wherein the second-level optimization layer minimizes energy consumption, operating cost and carbon emission per unit time To optimize the objective, a linear weighted combination method is used to transform the multi-objective problem into a single-objective optimization.5.如权利要求1所述的一种计及需求侧响应的多能互补系统两级优化设计方法，其特征是：基于遗传算法和非线性规划的混合求解，对两级优化层进行求解，具体过程包括：5. a kind of multi-energy complementary system two-level optimization design method considering demand side response as claimed in claim 1 is characterized in that: based on the mixed solution of genetic algorithm and nonlinear programming, the two-level optimization layer is solved, The specific process includes:步骤1：对系统参数、遗传算法和设备参数进行初始化设置；Step 1: Initialize the system parameters, genetic algorithm and equipment parameters;步骤2：种群初始化：随机生成N个个体，作为初始种群P₀，并对每个个体进行二进制编码；Step 2: Population initialization: randomly generate N individuals as the initial population P₀ , and perform binary coding on each individual;步骤3：计算当前种群P的适应度，调用非线性规划方法求解运行优化模型；Step 3: Calculate the fitness of the current population P, and call the nonlinear programming method to solve the operation optimization model;步骤4：判断当前种群是否满足终止要求，若达到了预设的最大代数，则执行步骤6；否则，需要继续步骤5；Step 4: Determine whether the current population meets the termination requirements, if the preset maximum number of generations is reached, then perform Step 6; otherwise, proceed to Step 5;步骤5：选择、交叉和变异，形成新种群P₃，返回执行步骤3；Step 5: Select, cross and mutate to form a new population P₃ , and return to step 3;步骤6：解码，得到负荷优化结果。Step 6: Decoding to obtain load optimization results.6.一种计及需求侧响应的多能互补系统两级优化设计方法，其特征是：包括：6. A two-level optimization design method for a multi-energy complementary system taking into account demand side response, characterized in that: comprising:第一级优化层，为计及需求侧响应的容量配置层，容量配置层以计及用户舒适度的节能性、经济性和环保性综合最优为目标建立计及需求侧响应的容量优化模型，利用遗传算法求解负荷数据和设备容量，并将优化后的负荷和设备容量作为下层优化的输入；The first-level optimization layer is the capacity configuration layer that takes into account the demand-side response. The capacity configuration layer establishes a capacity optimization model that takes into account the demand-side response, aiming at the comprehensive optimization of energy saving, economy and environmental protection considering user comfort. , using the genetic algorithm to solve the load data and equipment capacity, and take the optimized load and equipment capacity as the input of the lower optimization;第二级优化层，为运行优化层，以能耗、成本、排放最低为目标，优化设备出力，并将计算结果输出给上层优化；The second-level optimization layer is the operation optimization layer, aiming at the lowest energy consumption, cost and emission, optimizes the output of the equipment, and outputs the calculation results to the upper layer for optimization;求解模块，被配置为通过双层优化循环迭代，最终求得最佳负荷曲线、设备容量和运行计划，得到源-荷最佳匹配；The solving module is configured to iterate through a two-layer optimization cycle, and finally obtain the optimal load curve, equipment capacity and operation plan, and obtain the best source-load matching;所述第一级优化层中的容量优化模型，在需求侧响应模型中引入智能家电，调整家电使用时间，进而优化电负荷，同时考虑到建筑物的热惯性，在用户可接受的舒适温度范围内，进行冷/热负荷优化；The capacity optimization model in the first-level optimization layer introduces smart home appliances into the demand-side response model, adjusts the use time of home appliances, and then optimizes the electrical load, while taking into account the thermal inertia of the building, within the user's acceptable comfortable temperature range within the cooling/heating load optimization;可控电负荷包括可中断负载和不可中断负载；Controllable electrical loads include interruptible loads and uninterruptible loads;第一级优化层中的容量优化模型具有约束条件，约束条件包括可调度设备负荷、室内温度和设备容量约束；The capacity optimization model in the first-level optimization layer has constraints, including schedulable equipment load, indoor temperature and equipment capacity constraints;在负荷调度方案中，进行可控电负荷在一天内的平移调度：假定参与需求响应的可控设备运行功率x是固定不变的，使用离散二进制变量y∈{0,1}表示设备的启停状态，1表示运行，0表示关闭，通过优化变量y的值，来达到负荷转移的目的；In the load scheduling scheme, the shift scheduling of the controllable electrical load in one day is carried out: it is assumed that the operating power x of the controllable equipment participating in the demand response is fixed, and the discrete binary variable y∈{0,1} is used to represent the start-up of the equipment. Stop state, 1 means running, 0 means off, the purpose of load transfer is achieved by optimizing the value of variable y;

E_conload为可控电负荷；D表示所有负荷可控设备的集合；x_d表示第d个设备的工作功率；y_d∈{0,1}表示第d个设备的启停状态。E_conload is the controllable electrical load; D represents the set of all load-controllable devices; x_d represents the working power of the d-th device; y_d ∈ {0,1} represents the start-stop state of the d-th device.7.一种计算机可读存储介质，其特征是：其中存储有多条指令，所述指令适于由终端设备的处理器加载并执行权利要求1-5中任一项所述的一种计及需求侧响应的多能互补系统两级优化设计方法。7. A computer-readable storage medium, characterized in that: a plurality of instructions are stored therein, and the instructions are adapted to be loaded by a processor of a terminal device and execute a computer according to any one of claims 1-5. and a two-level optimal design method for multi-energy complementary systems with demand-side response.8.一种终端设备，其特征是：包括处理器和计算机可读存储介质，处理器用于实现各指令；计算机可读存储介质用于存储多条指令，所述指令适于由处理器加载并执行权利要求1-5中任一项所述的一种计及需求侧响应的多能互补系统两级优化设计方法。8. A terminal device, characterized in that it comprises a processor and a computer-readable storage medium, wherein the processor is used to implement each instruction; the computer-readable storage medium is used to store a plurality of instructions, and the instructions are suitable for being loaded by the processor and storing the instructions. A two-level optimal design method for a multi-energy complementary system considering demand-side response according to any one of claims 1-5 is implemented.

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