CN111244939A - A two-level optimal design method for multi-energy complementary systems 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. The comprehensive optimization of energy saving, economy and environmental protection is the goal to establish a capacity optimization model that takes into account the demand side response, use genetic algorithm to solve the load data and equipment capacity, and use the optimized load and equipment capacity 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 for 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

Translated fromChinese

一种计及需求侧响应的多能互补系统两级优化设计方法A two-level optimal design method for multi-energy complementary systems considering demand-side response

技术领域technical field

本公开属于新能源多能互补冷热电联供系统技术领域，涉及一种计及需求侧响应的多能互补系统两级优化设计方法。The present disclosure belongs to the technical field of new energy multi-energy complementary cooling, heating and power supply systems, and relates to a two-level optimization design method for a multi-energy complementary system that takes into account demand-side response.

背景技术Background technique

本部分的陈述仅仅是提供了与本公开相关的背景技术信息，不必然构成在先技术。The statements in this section merely provide background information related to the present disclosure and do not necessarily constitute prior art.

全球正面临着前所未有的能源和环境危机，大力发展以风光为主的新能源分布式供能系统是解决问题的关键途径。多能互补系统集成能量生产、转换、存储技术，包括新能源发电、冷热电联供(Combined cooling,heating and power system,CCHP)系统、电制热/冷、储能系统。该系统基于能量梯级利用原理，可满足用户电、冷、热多元化用能需求，能够大幅提高能源利用率以及新能源消纳率，同时减少污染物排放，极具发展潜力。但多能互补CCHP系统结构复杂、设备种类繁多，系统的优化设计是保障其高效经济运行的基础。然而由于新能源固有的间歇性和不确定性使得CCHP系统运行模态多变，系统容量配置与运行模式耦合关系进一步加深，导致系统优化设计极难。同时，据发明人了解，目前针对多能互补CCHP系统的优化设计方法，并未有集合需求侧响应、容量配置与运行优化的优化设计方法。The world is facing an unprecedented energy and environmental crisis, and vigorously developing a new energy distributed energy supply system based on scenery is the key way to solve the problem. Multi-energy complementary systems integrate energy production, conversion, and storage technologies, including new energy power generation, combined cooling, heating and power (CCHP) systems, electric heating/cooling, and energy storage systems. The system is based on the principle of energy cascade utilization, which can meet the diversified energy needs of users for electricity, cooling and heating, and can greatly improve the energy utilization rate and new energy consumption rate, while reducing pollutant emissions, which has great development potential. However, the multi-energy complementary CCHP system has a complex structure and a wide variety of equipment. The optimal design of the system is the basis for ensuring its efficient and economical operation. However, due to the inherent intermittency and uncertainty of new energy sources, the operating modes of the CCHP system are changeable, and the coupling relationship between the system capacity configuration and the operating mode is further deepened, which makes the optimal design of the system extremely difficult. At the same time, as far as the inventors know, there is no optimal design method for integrating demand-side response, capacity configuration and operation optimization for the current optimal design method for multi-energy complementary CCHP systems.

发明内容SUMMARY OF THE INVENTION

本公开为了解决上述问题，提出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，本公开考虑了需求侧响应问题，利用双层优化循环迭代，最终求得最佳负荷曲线、设备容量和运行计划，实现源-荷最佳匹配，进一步提高系统的综合性能。In order to solve the above problems, the present disclosure proposes a two-level optimization design method for a multi-energy complementary system that takes into account the demand-side response. The present disclosure considers the demand-side response problem, and uses the double-layer optimization loop iteration to finally obtain the optimal load curve. , equipment capacity and operation plan to achieve the best matching of source and load, and further improve the overall performance of the system.

根据一些实施例，本公开采用如下技术方案：According to some embodiments, the present disclosure adopts the following technical solutions:

一种计及需求侧响应的多能互补系统两级优化设计方法，包括以下步骤：A two-level optimization design method for a multi-energy complementary system considering demand-side response, comprising 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，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；Build a two-level optimization layer. The first level is the capacity configuration layer that takes into account the demand-side response. This layer aims at the comprehensive optimization of energy saving, economy and environmental protection considering the user's comfort. The 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 double-layer optimization loop iteration, the optimal load curve, equipment capacity and operation plan are finally obtained, and the optimal source-load matching is obtained.

作为可选择的实施方式，对多能互补冷热电联供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。As an optional embodiment, the energy flow of the multi-energy complementary cooling, heating and power cogeneration system is analyzed to determine the electricity balance, primary energy, heat balance, cooling balance and total gas consumption of the system.

作为可选择的实施方式，所述第一级优化模型，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。As an optional implementation, the first-level optimization model introduces smart home appliances into the demand-side response model, adjusts the use time of home appliances, and then optimizes the electrical load. At the same time, taking into account the thermal inertia of the building, the user's comfort is acceptable. Cooling/heating load optimization within the temperature range.

作为进一步的限定，可控电负荷包括可中断负载和不可中断负载，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。As a further limitation, the controllable electrical loads include interruptible loads and non-interruptable loads. In the load scheduling scheme, the shift scheduling of the controllable electrical loads within a day is performed: it is assumed that the operating power x of the controllable equipment participating in the demand response is fixed and variable. To change, the discrete binary variable y∈{0,1} is used to represent the start-stop state of the equipment, 1 means running, 0 means off, and the purpose of load transfer is achieved by optimizing the value of variable y.

作为可选择的实施方式，第一级优化具有约束条件，包括可调度设备负荷、室内温度和设备容量约束。As an alternative implementation, the first level optimization has constraints, including schedulable equipment load, room temperature, and equipment capacity constraints.

作为可选择的实施方式，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。As an optional implementation, the second-level optimization layer takes the minimum energy consumption, operating cost and carbon emission per unit time as the optimization goal, and adopts the linear weighted combination method to convert the multi-objective problem into a single-objective optimization.

作为可选择的实施方式，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其他设备的额定容量约束。As an optional implementation, the second-level optimization layer has constraints, including energy flow balance constraints as well as the rated capacity of the generator set and the rated capacity of other equipment.

作为可选择的实施方式，基于遗传算法和非线性规划的混合求解，对两级优化模型进行求解，具体过程包括：As an optional implementation, based on the hybrid solution of genetic algorithm and nonlinear programming, the two-level optimization model is solved, and 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.

一种计及需求侧响应的多能互补系统两级优化设计方法，包括：A two-level optimization design method for a multi-energy complementary system considering demand-side response, comprising:

第一级优化层，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；The first-level optimization layer is the capacity configuration layer that takes into account the demand-side response. This 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 the user's comfort. Use 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 loop, and finally obtain the optimal load curve, equipment capacity and operation plan, and obtain the best source-load matching.

一种计算机可读存储介质，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。A computer-readable storage medium stores a plurality of instructions, wherein the instructions are adapted to be loaded by a processor of a terminal device and execute the two-level optimization design method for a multi-energy complementary system that takes into account demand-side response.

一种终端设备，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。A terminal device, comprising a processor and a computer-readable storage medium, where the processor is used to implement various instructions; the computer-readable storage medium is used to store a plurality of instructions, the instructions are suitable for being loaded by the processor and executing the described one A two-level optimal design method for multi-energy complementary systems considering demand-side response.

与现有技术相比，本公开的有益效果为：Compared with the prior art, the beneficial effects of the present disclosure are:

本公开创新性地将需求侧响应、容量配置及运行优化统一于一个优化设计框架内，有效解决了新能源不确定性问题，实现系统最佳设计，进一步提高系统的综合性能。The present disclosure innovatively unifies demand-side response, capacity configuration, and operation optimization into an optimized design framework, effectively solves the problem of new energy uncertainty, realizes the optimal design of the system, and further improves the comprehensive performance of the system.

附图说明Description of drawings

构成本公开的一部分的说明书附图用来提供对本公开的进一步理解，本公开的示意性实施例及其说明用于解释本公开，并不构成对本公开的不当限定。The accompanying drawings that constitute a part of the present disclosure are used to provide further understanding of the present disclosure, and the exemplary embodiments of the present disclosure and their descriptions are used to explain the present disclosure and do not constitute an improper limitation of the present disclosure.

图1是多能互补系统结构图；Figure 1 is a structural diagram of a multi-energy complementary system;

图2是双层优化逻辑关系示意图；Fig. 2 is a schematic diagram of a two-layer optimization logic relationship;

图3是本实施例的具体流程图。FIG. 3 is a specific flow chart of this embodiment.

具体实施方式：Detailed ways:

下面结合附图与实施例对本公开作进一步说明。The present disclosure will be further described below with reference to the accompanying drawings and embodiments.

应该指出，以下详细说明都是例示性的，旨在对本公开提供进一步的说明。除非另有指明，本实施例使用的所有技术和科学术语具有与本公开所属技术领域的普通技术人员通常理解的相同含义。It should be noted that the following detailed description is exemplary and intended to provide further explanation of the present disclosure. Unless otherwise defined, all technical and scientific terms used in the examples have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs.

需要注意的是，这里所使用的术语仅是为了描述具体实施方式，而非意图限制根据本公开的示例性实施方式。如在这里所使用的，除非上下文另外明确指出，否则单数形式也意图包括复数形式，此外，还应当理解的是，当在本说明书中使用术语“包含”和/或“包括”时，其指明存在特征、步骤、操作、器件、组件和/或它们的组合。It should be noted that the terminology used herein is for the purpose of describing specific embodiments only, and is not intended to limit the exemplary embodiments according to the present disclosure. As used herein, unless the context clearly dictates otherwise, the singular is intended to include the plural as well, furthermore, it is to be understood that when the terms "comprising" and/or "including" are used in this specification, it indicates that There are features, steps, operations, devices, components and/or combinations thereof.

多能互补系统典型结构如图1所示，系统由风机、光伏、内燃发电机组、吸收式制冷机、燃气锅炉、热泵、储冷装置、储热水箱、电负荷以及热负荷构成。电负荷由风机、光伏、内燃发电机组及上级电网供给；冷负荷由热泵、吸收式制冷机和储冷设备供给；热负荷由燃气锅炉、发电机组余热系统和储热设备供给；电、冷、热负荷需求响应灵活参与调度。The typical structure of the multi-energy complementary system is shown in Figure 1. The system consists of fans, photovoltaics, internal combustion generator sets, absorption chillers, gas boilers, heat pumps, cold storage devices, hot water storage tanks, electrical loads and thermal loads. The electrical load is supplied by fans, photovoltaics, internal combustion generator sets and the upper-level power grid; the cooling load is supplied by heat pumps, absorption chillers and cold storage equipment; the heat load is supplied by gas boilers, generator set waste heat systems and heat storage equipment; electricity, cooling, The heat load demand responds flexibly and participates in scheduling.

传统分工系统(Separated Production，SP)由大电网(传统的火力发电)、燃气锅炉和电制冷机构成，用户的电负荷及电制冷机消耗电能由大电网满足，供暖和供冷负荷分别由燃气锅炉和电制冷机满足。作为多能互补CCHP系统的对比系统，验证所提方法的先进性。The traditional division of labor system (Separated Production, SP) is composed of a large power grid (traditional thermal power generation), gas boilers and electric refrigerators. The user's electrical load and the power consumption of electric refrigerators are met by the large power grid, and the heating and cooling loads are separately supplied by gas. Boilers and electric refrigerators meet. As a comparison system for the multi-energy complementary CCHP system, the advanced nature of the proposed method is verified.

基于此结构，提出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。如图2所示，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、花费、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。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，实现源-荷最佳匹配，进一步提高系统的综合性能。Based on this structure, a two-level optimal design method for multi-energy complementary systems considering demand-side response is proposed. As shown in Figure 2, the first level is the capacity configuration layer that takes into account the demand-side response. This layer establishes a capacity that takes into account the demand-side response for the comprehensive optimization of energy saving, economy and environmental protection taking into account user comfort. The optimization model uses the 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; the second level is the operation optimization layer, which aims at the lowest energy consumption, cost and emission, and optimizes the output of the equipment. , and output the calculation results to the upper layer optimization. The two-layer optimization cycle is iterated, and the optimal load curve, equipment capacity and operation plan are finally obtained, so as to realize the optimal matching of source and load, and further improve the comprehensive performance of the system.

首先，进行能量流的分析：First, analyze the energy flow:

系统能量流分析是研究系统能量特性，进行系统优化设计的基础。在确定了系统结构的基础上，本实施例针对系统内部冷、热、电三种形式的能量流展开分析。System energy flow analysis is the basis for studying system energy characteristics and optimizing system design. On the basis of determining the system structure, this embodiment analyzes the three forms of energy flow in the system: cold, heat, and electricity.

系统的电量平衡方程式为:The power balance equation of the system is:

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

式中，E_load为电负荷；In the formula, E_load is the electrical load;

E_pv为光伏发电系统输出电功率；E_pv is the output electric power of the photovoltaic power generation system;

E_wt为风力发电系统输出电功率；E_wt is the output electric power of the wind power generation system;

E_pgu为内燃发电机组输出电功率；E_pgu is the output electric power of the internal combustion generator set;

E_grid为与电网交互功率，购电(E_grid>0)，售电(E_grid<0)；E_grid is the power interacting with the grid, purchasing electricity (E_grid >0) and selling electricity (E_grid <0);

E_p为热泵耗电量。_Ep is the power consumption of the heat pump.

其中内燃发电机组t时刻所需的燃气消耗量F_pgu为：Among them, the gas consumption F_pgu required by the internal combustion generator set at time t is:

式中，η_th,pgu和η_e,pgu分别为t时刻内燃发电机组的热效率和点效率，可表示为，In the formula, η_th,pgu and η_e,pgu are the thermal efficiency and point efficiency of the internal combustion generator set at time t, respectively, which can be expressed as,

式中，a₀,a₁,a₂,b₀,b₁和b₂为拟合多项式的系数，PLR_pgu为发电组的负载率，表示为，In the formula, a₀ , a₁ , a₂ , b₀ , b₁ and b₂ are the coefficients of the fitting polynomial, and PLR_pgu is the load rate of the generating set, expressed as,

PLR_pgu(t)＝E_pgu(t)/N_pgu (5)PLR_pgu (t)=E_pgu (t)/N_pgu (5)

式中，N_pgu为发电机组额定功率。In the formula, N_pgu is the rated power of the generator set.

t时刻系统由电网购电所消耗的一次能源F_gb为：The primary energy F_gb consumed by the power grid purchased by the system at time t is:

式中，η_e,grid和η_d,grid为电网的发电效率和传输效率。In the formula, η_e,grid and η_d,grid are the power generation efficiency and transmission efficiency of the grid.

系统的热平衡方程式为:The heat balance equation of the system is:

H_load(t)＝Q_he(t)+Q_b(t)+Q_s(t) (7)H_load (t)=Q_he (t)+Q_b (t)+Q_s (t) (7)

式中，H_load为热负荷；In the formula, H_load is the heat load;

Q_he为换热器换热功率；Q_he is the heat exchange power of the heat exchanger;

Q_b为燃气锅炉加热功率；Q_b is the heating power of the gas boiler;

Q_s为储热水箱输入/出功率，输出时(Q_s>0)，输入时(Q_s<0)。Q_s is the input/output power of the hot water storage tank, when it is output (Q_s >0), when it is input (Q_s <0).

其中燃气锅炉t时刻所需的燃气消耗量F_b为：Among them, the gas consumption F_b required by the gas boiler at time t is:

式中，η_b为燃气锅炉的热效率。In the formula, η_b is the thermal efficiency of the gas boiler.

因此，CCHP系统t时刻的燃气消耗总量为：Therefore, the total gas consumption of the CCHP system at time t is:

F_gas(t)＝F_pgu(t)+F_b(t) (9)F_gas (t)=F_pgu (t)+F_b (t) (9)

系统的冷量平衡方程式为:The cooling balance equation of the system is:

C_load(t)＝Q_ab(t)+Q_p(t)+Q_s(t) (10)C_load (t)=Q_ab (t)+Q_p (t)+Q_s (t) (10)

式中，C_load为冷负荷；In the formula, C_load is the cooling load;

Q_ab为吸收式制冷机的输出功率；Q_ab is the output power of the absorption chiller;

Q_p为热泵的制冷功率。Q_p is the cooling power of the heat pump.

Q_s为储冷装置输入/出功率，输出时(Q_s>0)，输入时(Q_s<0)。Q_s is the input/output power of the cold storage device, when it is output (Q_s >0), and when it is input (Q_s <0).

吸收式制冷机的输出功率Q_ab为：The output power Q_ab of the absorption chiller is:

Q_ab(t)＝Q_rh(t)COP_ab (11)Q_ab (t) = Q_rh (t) COP_ab (11)

式中，Q_rh为发电机组余热回收功率，COP_ab为吸收式制冷机的能效比。In the formula, Q_rh is the waste heat recovery power of the generator set, and COP_ab is the energy efficiency ratio of the absorption chiller.

t时刻热泵的耗电量E_p为：The power consumption E_p of the heat pump at time t is:

式中，COP_p为热泵的能效比。In the formula, COP_p is the energy efficiency ratio of the heat pump.

对储能设备有：For energy storage devices:

Q_sta(t+1)＝η_sQ_sta(t)-Q_s(t) (13)Q_sta (t+1)=η_s Q_sta (t)-Q_s (t) (13)

式中，Q_sta(t+1)和Q_sta(t)分别为储能设备t+1时刻和t时刻的储能状态，η_s为储能设备的效率。In the formula, Q_sta (t+1) and Q_sta (t) are the energy storage states of the energy storage device at time t+1 and time t, respectively, and η_s is the efficiency of the energy storage device.

其次，构建第一级优化模型：Second, build the first-level optimization model:

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、经济行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，从而优化电、冷、热负荷数据和设备容量。The first level is the capacity configuration layer that takes into account the demand-side response. This layer aims to establish a capacity optimization model that takes into account the demand-side response to optimize energy efficiency, economy, and environmental protection, taking into account user comfort. , cooling and heating load data and equipment capacity.

需求侧响应模型：Demand side response model:

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，从得到以下模型：Introduce smart home appliances into the demand-side response model, adjust the use time of home appliances, and then optimize the electrical load. At the same time, taking into account the thermal inertia of the building, the cooling/heating load optimization is carried out within the user's acceptable comfortable temperature range, and the following model is obtained. :

①可控电负荷包括可中断负载和不可中断负载，电动汽车等可中断负荷在使用过程中可以任意暂停使用，其他电器如电饭煲、热水器等，启动后不间断使用。在负荷调度方案中，考虑到居民客户的意愿，实现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。① Controllable electrical loads include interruptible loads and uninterruptible loads. Interruptible loads such as electric vehicles can be suspended arbitrarily during use. Other electrical appliances such as rice cookers and water heaters can be used without interruption after starting. In the load scheduling scheme, taking into account the wishes of residential customers, the translational scheduling of controllable electrical loads within a day is realized. Assuming that the operating power x of the controllable equipment participating in the demand response is fixed, the discrete binary variable y∈{0,1} is used to represent the on-off state of the equipment, 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，1表示运行，0表示关闭。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, and 1 represents running , 0 means off.

②由于建筑物的墙体均具有一定的隔热效果，室内与室外的热交换过程较慢，不同于电负荷，室内的温度呈小时级的变化。因此，根据能源价格，在不破坏温度舒适度的前提下控制室内冷/热负荷。②Because the walls of the building have a certain thermal insulation effect, the heat exchange process between indoor and outdoor is slow, different from the electrical load, the indoor temperature changes in an hourly level. Therefore, depending on energy prices, indoor cooling/heating loads can be controlled without compromising temperature comfort.

H_load(t)＝((T_in(t)-T_in(t-1)e^-Δt/τ)/(1-e^-Δt/τ)-T_out(t))/R (15)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)C_load (t)=((T_out (t)-(T_in (t)-T_in (t-1)e-Δt^/τ )/(1-e-^Δt/τ ))/R (16)

式中，C_load、H_load分别为可控冷、热负荷；T_in(t)、T_out(t)分别代表t时刻室内和室外温度。In the formula, C_load and H_load are the controllable cooling and heating loads, respectively; T_in (t) and T_out (t) represent the indoor and outdoor temperatures at time t, respectively.

优化目标：optimize the target:

计及用户舒适度的节能性、经济性和环保性综合最优，Taking into account the user's comfort, the comprehensive optimization of energy saving, economy and environmental protection,

maxV＝ω₁PESR+ω₂ACR+ω₃CERR (17)maxV=ω₁ PESR+ω₂ ACR+ω₃ CERR (17)

式中，PESR为一次能源年节约率，ACR为年综合成本节约率，CERR为年CO₂减排率。ω₁为节能率权重因子，ω₂为年成本节约率权重因子，ω₃为CO₂减排率权重因子，V为综合优化目标。F_SP，F_CCHP分别为分供系统和多能互补CCHP系统的年一次能源消耗量，C_SP，C_CCHP分别为分供系统和多能互补CCHP系统的年综合成本，CE_SP，CE_CCHP分别为分供系统和多能互补CCHP系统的年CO₂排放量。分别由下式求得：In the formula, PESR is the annual primary energy saving rate, ACR is the annual comprehensive cost saving rate, and CERR is the annual CO₂ emission reduction rate._ω1 is the weighting factor of the energy saving rate,_ω2 is the weighting factor of the annual cost saving rate, ω3 is the weighting factor_of the_CO2 emission reduction rate, and V is the comprehensive optimization objective. F_SP and F_CCHP are the annual primary energy consumption of the sub-supply system and the multi-energy complementary CCHP system respectively, C_SP and C_CCHP are the annual comprehensive costs of the sub-supply system and the multi-energy complementary CCHP system respectively, CE_SP and CE_CCHP are respectively is the annual CO₂ emissions of the sub-supply system and the multi-energy complementary CCHP system. are obtained by the following formulas:

C_CCHP＝C_CCHP,EQ+C_CCHP,OM+C_CCHP,EC+C_CCHP,load (23)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)C_SP = C_{SP, EQ} + C_{SP, OM} + C_{SP, EC} (25)

式中，C_CCHP,EQ为多能互补CCHP系统设备初始投资的年化成本；In the formula, C_{CCHP, EQ} is the annualized cost of the initial investment of the multi-energy complementary CCHP system equipment;

C_CCHP,OM为多能互补CCHP系统的年维护成本；C_{CCHP, OM} is the annual maintenance cost of the multi-energy complementary CCHP system;

C_CCHP,EC为多能互补CCHP系统的年运行成本；C_{CCHP, EC} is the annual operating cost of the multi-energy complementary CCHP system;

C_CCHP,EC为多能互补CCHP系统负荷调度影响用户舒适度产生的惩罚成本；C_{CCHP, EC} is the penalty cost of multi-energy complementary CCHP system load scheduling affecting user comfort;

C_SP,EQ为分供系统设备投资的年化成本；C_{SP, EQ} is the annualized cost of equipment investment in the sub-supply system;

C_SP,OM为分供系统的年维护成本；C_{SP, OM} is the annual maintenance cost of the sub-supply system;

C_SP,EC为分供系统的年运行成本。C_{SP, EC} are the annual operating costs of the sub-supply system.

多能互补CCHP系统的年运行成本包括燃料成本、电网购电成本，可表示为如下形式：The annual operating cost of the multi-energy complementary CCHP system includes fuel cost and grid power purchase cost, which can be expressed as follows:

C_CCHP,EQ＝C_CCHP,INR (27)C_CCHP,EQ =C_CCHP,IN R (27)

C_CCHP,OM＝σC_CCHP,IN (28)C_CCHP,OM =σC_CCHP,IN (28)

式中，P_grid为t时刻电网交互价格，购电时为正，售电时为负；In the formula, P_grid is the grid interactive price at time t, which is positive when purchasing electricity and negative when selling electricity;

P_gas为燃气价格；P_gas is the gas price;

C_CCHP,IN为多能互补CCHP系统所有设备的总初期投资成本；C_{CCHP, IN} is the total initial investment cost of all equipment of the multi-energy complementary CCHP system;

R为投资回报系数；R is the return on investment coefficient;

σ为系统运行维护费用比例系数。σ is the proportional coefficient of system operation and maintenance costs.

上述式子中的投资回收系数R可表示为：The investment recovery coefficient R in the above formula can be expressed as:

式中，k为设备寿命；In the formula, k is the equipment life;

r为基准折现率。r is the benchmark discount rate.

C_SP可以进一步表示为：C_SP can be further expressed as:

C_SP,EQ＝C_SP,INR (31)C_SP,EQ =C_SP,IN R (31)

C_SP,OM＝σC_SP,IN (32)C_SP,OM =σC_SP,IN (32)

式中，E_SP,grid为t时刻分供系统的购电量；In the formula, E_{SP, grid} is the electricity purchased by the distributed supply system at time t;

C_SP,IN为分供系统的投资成本。C_{SP, IN} is the investment cost of the sub-supply system.

多能互补CCHP系统的CO₂年排放量可表示为：The annual_CO2 emissions from the multi-energy complementary CCHP system can be expressed as:

式中，μ_grid为电网燃煤的CO₂排放系数；In the formula, μ_grid is the CO₂ emission coefficient of coal-fired power grid;

μ_gas为燃气的CO₂排放系数。μ_gas is the CO₂ emission coefficient of the gas.

分供系统年CO₂排放量可表示为：The annual CO₂ emissions of the sub-supply system can be expressed as:

优化变量：Optimization variables:

可调度用电设备的启停状态y_d(t)、室内可控温度T_in(t)，发电机组容量N_pgu。光伏、风电机组均由可利用安装面积、可利用自然资源总量确定，其他设备可通过能量流关系式获得。The start-stop state y_d (t) of the dispatchable electrical equipment, the indoor controllable temperature T_in (t), and the generator set capacity N_pgu . Photovoltaic and wind turbines are determined by the available installation area and the total amount of available natural resources, and other equipment can be obtained through the energy flow relationship.

约束条件：Restrictions:

①可调度设备：①Scheduled equipment:

[A_d,B_d]为设备d的可调度工作区间；E_d表示设备d的总耗电量。[A_d , B_d ] is the schedulable working interval of equipment d; E_d represents the total power consumption of equipment d.

对不可中断负荷设备有：For uninterruptible load equipment:

若y_d(t)＝1，则y_d(t+1)＝1,…,y_d(t+n)＝1，n为设备d的工作时长。If y_d (t)=1, then y_d (t+1)=1,...,y_d (t+n)=1, where n is the working time of the device d.

②室内温度：②Indoor temperature:

T_{in_min}≤T_in(t)≤T_{in_max} (36)T_{in_min} ≤T_in (t)≤T_{in_max} (36)

T_{in_min}，T_{in_min}为室内可调温度的上下限，室内温度调节范围越大，控制效果越好，但同时用户的温度舒适度受到的影响也越大。T_{in_min} , T_{in_min} are the upper and lower limits of the indoor adjustable temperature. The larger the indoor temperature adjustment range, the better the control effect, but at the same time, the user's temperature comfort is also more affected.

③设备容量：③ Equipment capacity:

0≤N_pgu≤N_pgu,max (37)_{0≤N pgu≤N pgu,max} (37)

0≤N_b≤N_b,max (38)0≤N_b ≤N_b,max (38)

式中，N_pgu,max为内燃发电机组容量的上限；In the formula, N_pgu,max is the upper limit of the capacity of the internal combustion generator;

N_b,max为光伏发电系统容量的上限。N_b,max is the upper limit of the capacity of the photovoltaic power generation system.

以上约束保证N_pgu和N_b的优化值在合理可行的范围内。The above constraints guarantee that the optimized values of N_pgu and N_b are within reasonable and feasible ranges.

构建第二级优化模型：Build the second-level optimization model:

第二级为系统的运行优化层，该层以一天24小时内的能源消耗、运行成本与碳排放量最小为优化目标，同样采用线性加权组合法将多目标问题转化为单目标优化，该目标函数被定义为：The second level is the operation optimization layer of the system. This layer takes the minimum energy consumption, operating cost and carbon emissions within 24 hours of the day as the optimization goal. The linear weighted combination method is also used to convert the multi-objective problem into a single-objective optimization. The function is defined as:

minW＝ω₁F_day+ω₂C_day+ω₃CE_day (39)minW=ω₁ F_day +ω₂ C_day +ω₃ CE_day (39)

式中，F_day为全天能源消耗总量；C_day为全天总运行成本；CE_day为全天碳排放总量；ω₁为能源消耗权重因子；ω₂为运行成本权重因子；ω₃为碳排放量权重因子；W为综合优化目标。此处权重因子与第一级优化配置模型中对应的指标保持一致。In the formula, F_day is the total energy consumption of the whole day; C_day is the total operation cost of the whole day; CE_day is the total carbon emission of the whole day; ω₁ is the energy consumption weight factor; ω₂ is the operation cost weight factor; ω₃ is the carbon emission weight factor; W is the comprehensive optimization goal. The weight factor here is consistent with the corresponding index in the first-level optimal configuration model.

优化变量：Optimization variables:

包括每个阶段发电机组的出力计划{E_pgu(1),…,E_pgu(24)}，其他设备的出力计划均可通过此设备求得。Including the output plan {E_pgu (1),...,E_pgu (24)} of the generator set in each stage, the output plans of other equipment can be obtained through this equipment.

约束条件：Restrictions:

运行优化模型需满足能量流平衡关系，如式(1)-(13)，还需满足如下不等式约束：To run the optimization model, the energy flow balance relationship must be satisfied, such as equations (1)-(13), and the following inequality constraints must also be satisfied:

0≤E_pgu(t)≤N_pgu (43)0≤E_pgu (t)_{≤N pgu} (43)

式中，N_pgu为发电机组的额定容量，由第一级优化模型求得。In the formula, N_pgu is the rated capacity of the generator set, which is obtained from the first-level optimization model.

针对上述两级优化模型，提出了基于遗传算法和非线性规划的混合求解方法。如图3所示，求解步骤如下：For the above two-level optimization model, a hybrid solution method based on genetic algorithm and nonlinear programming is proposed. As shown in Figure 3, the solution steps are as follows:

步骤1：系统初始化。首先对系统参数、遗传算法和设备参数进行设置。Step 1: System initialization. Firstly, set system parameters, genetic algorithm and equipment parameters.

步骤2：种群初始化。在这一步中，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. In this step, N individuals are randomly generated as the initial population P₀ , and each individual is binary coded.

步骤3：计算当前种群P的适应度，分为以下两步：Step 3: Calculate the fitness of the current population P, which is divided into the following two steps:

a.获得运行策略。为了计算第一阶段模型的目标函数值，需要调用第二级模型获得优化运行策略。a. Obtain the operating strategy. In order to calculate the objective function value of the first-stage model, it is necessary to call the second-stage model to obtain the optimal operation strategy.

b.适应度计算。利用公式(1)计算个体的适应度值。b. Fitness calculation. Use formula (1) to calculate the fitness value of the individual.

步骤4：判断当前种群是否满足终止要求，若达到了用户指示的最大代数，则执行步骤。否则，需要继续步骤5。Step 4: Determine whether the current population meets the termination requirements, and if the maximum number of generations indicated by the user is reached, execute the step. Otherwise, proceed to step 5.

步骤5：选择、交叉和变异，形成新种群P₃。Step 5: Selection, crossover and mutation to form a new population P₃ .

步骤6：执行步骤3，计算种群P₃的适应度。Step 6: Execute Step₃ to calculate the fitness of the population P3.

步骤7：解码，得到负荷优化结果。Step 7: Decoding to obtain the load optimization result.

当然，上述计算过程可以在软件中实现，例如MATLAB中实现。Of course, the above calculation process can be implemented in software, such as MATLAB.

综上，针对多能互补CCHP系统存在新能源的不确定性，需求侧响应、容量配置及运行优化是应对该问题的有效途径，目前尚无优化设计方法统一三个方面，本实施例提出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，第一级为计及需求响应容量配置层，以能源、经济、环境指标综合最优为目标，用户舒适度为约束，优化电、冷、热负荷数据和设备容量，并将优化后的负荷和设备容量作为第二级优化的输入；第二级为运行优化层，以能耗、花费、排放最低为目标，优化设备出力计划，并将能耗、花费、排放数据输出给上层优化。双层优化循环迭代，最终求得最佳负荷曲线、设备容量及运行策略，创新性地将需求侧响应、容量配置及运行优化统一于一个优化设计框架内，有效解决了新能源不确定性问题，实现系统最佳设计，进一步提高系统的综合性能。To sum up, in view of the uncertainty of new energy in the multi-energy complementary CCHP system, demand-side response, capacity allocation and operation optimization are effective ways to deal with this problem. At present, there is no unified optimization design method in three aspects. A two-level optimization design method of multi-energy complementary system considering demand side response is proposed. The first level is the capacity configuration layer considering demand response. The goal is to comprehensively optimize energy, economic and environmental indicators, and user comfort is the constraint. Optimize electricity, cooling and heating load data and equipment capacity, and use the optimized load and equipment capacity as the input of the second-level optimization; the second-level is the operation optimization layer, which optimizes equipment with the goal of minimizing energy consumption, cost, and emissions Output plan, and output energy consumption, cost, and emission data to the upper layer for optimization. Double-layer optimization cycle iteration, and finally obtain the optimal load curve, equipment capacity and operation strategy, innovatively unify demand-side response, capacity allocation and operation optimization in one optimization design framework, effectively solving the problem of new energy uncertainty , to achieve the best design of the system and further improve the overall performance of the system.

本领域内的技术人员应明白，本公开的实施例可提供为方法、系统、或计算机程序产品。因此，本公开可采用完全硬件实施例、完全软件实施例、或结合软件和硬件方面的实施例的形式。而且，本公开可采用在一个或多个其中包含有计算机可用程序代码的计算机可用存储介质(包括但不限于磁盘存储器、CD-ROM、光学存储器等)上实施的计算机程序产品的形式。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, etc.) having computer-usable program code embodied therein.

本公开是参照根据本公开实施例的方法、设备(系统)、和计算机程序产品的流程图和/或方框图来描述的。应理解可由计算机程序指令实现流程图和/或方框图中的每一流程和/或方框、以及流程图和/或方框图中的流程和/或方框的结合。可提供这些计算机程序指令到通用计算机、专用计算机、嵌入式处理机或其他可编程数据处理设备的处理器以产生一个机器，使得通过计算机或其他可编程数据处理设备的处理器执行的指令产生用于实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能的装置。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 in the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to the processor of a general purpose computer, special purpose computer, embedded processor or other programmable data processing device to produce a machine such that the instructions executed by the processor of the computer or other programmable data processing device produce Means for implementing the functions specified in a flow or flow of a flowchart and/or a block or blocks of a block diagram.

这些计算机程序指令也可存储在能引导计算机或其他可编程数据处理设备以特定方式工作的计算机可读存储器中，使得存储在该计算机可读存储器中的指令产生包括指令装置的制造品，该指令装置实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能。These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory result in an article of manufacture comprising instruction means, the instructions The apparatus implements the functions specified in the flow or flow of the flowcharts and/or the block or blocks of the block diagrams.

这些计算机程序指令也可装载到计算机或其他可编程数据处理设备上，使得在计算机或其他可编程设备上执行一系列操作步骤以产生计算机实现的处理，从而在计算机或其他可编程设备上执行的指令提供用于实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能的步骤。These computer program instructions can also be loaded on a computer or other programmable data processing device to cause a series of operational steps to be performed on the computer or other programmable device to produce a computer-implemented process such that The instructions provide steps for implementing the functions specified in the flow or blocks of the flowcharts and/or the block or blocks of the block diagrams.

以上所述仅为本公开的优选实施例而已，并不用于限制本公开，对于本领域的技术人员来说，本公开可以有各种更改和变化。凡在本公开的精神和原则之内，所作的任何修改、等同替换、改进等，均应包含在本公开的保护范围之内。The above descriptions are only preferred embodiments of the present disclosure, and are not intended to limit the present disclosure. For those skilled in the art, the present disclosure may have various modifications and changes. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present disclosure shall be included within the protection scope of the present disclosure.

上述虽然结合附图对本公开的具体实施方式进行了描述，但并非对本公开保护范围的限制，所属领域技术人员应该明白，在本公开的技术方案的基础上，本领域技术人员不需要付出创造性劳动即可做出的各种修改或变形仍在本公开的保护范围以内。Although the specific embodiments of the present disclosure have been described above in conjunction with the accompanying drawings, they do not limit the protection scope of the present disclosure. Those skilled in the art should understand that on the basis of the technical solutions of the present disclosure, those skilled in the art do not need to pay creative efforts. Various modifications or variations that can be made are still within the protection scope of the present disclosure.

Claims (10)

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 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.

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 first-stage optimization model 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.

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 controllable electric load comprises an interruptible load and an uninterruptable load, and 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 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.

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: the first level of optimization has constraints including schedulable equipment load, indoor temperature and equipment capacity constraints;

or, the second level optimization layer has constraints including energy flow balance constraints and generator set and gas boiler rated capacity constraints.

6. 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.

7. 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.

8. 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, 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.

9. A computer-readable storage medium characterized by: a plurality of instructions are stored, 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 claim 1 to 7.

10. 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 to 7.

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