CN106127337A

CN106127337A - Unit Combination method based on the modeling of convertible frequency air-conditioner virtual robot arm

Info

Publication number: CN106127337A
Application number: CN201610457311.XA
Authority: CN
Inventors: 宋梦; 高赐威
Original assignee: Southeast University
Current assignee: Southeast University
Priority date: 2016-06-22
Filing date: 2016-06-22
Publication date: 2016-11-16
Anticipated expiration: 2036-06-22
Also published as: CN106127337B

Abstract

The invention discloses a unit combination method based on variable frequency air conditioner virtual unit modeling, comprising the following steps: (1) establishing a thermodynamic model and an electrical model of a single air conditioner; (3) Model the virtual unit according to the characteristics of the traditional unit, and establish the cost function of the virtual unit under the condition of uncertain user response; (4) In the day-ahead market, according to the load and temperature of the next day Forecasting, establishing a joint unit combination model of traditional units and virtual units, and arranging the dispatch plan for the next day; (5) In the actual dispatching process, comprehensively consider the economic interests of the load aggregator and the fairness and comfort of users participating in virtual unit modeling , to realize the optimal distribution of load adjustment among users. The invention realizes the centralized scheduling control of the air-conditioning group, and reduces the calculation amount and control difficulty of related scheduling departments.

Description

Unit Combination Method Based on Inverter Air Conditioning Virtual Unit Modeling

技术领域technical field

本发明涉及一种基于变频空调虚拟机组建模的机组组合方法，尤其涉及变频空调聚合建模和考虑用户不确定性的虚拟机组建模技术，属于属需求响应参与传统机组组合的应用。The invention relates to a unit combination method based on variable frequency air conditioner virtual unit modeling, in particular to variable frequency air conditioner aggregation modeling and virtual unit modeling technology considering user uncertainty, which belongs to the application of demand response participating in traditional unit combination.

背景技术Background technique

需求响应技术是智能电网的核心技术之一，通过需求响应缓解供需紧张态势、增强系统应对潮流波动能力、提高系统运行效率、减少相关企业损失并最大化经济利益已经成为业界普遍认知。在所有柔性负荷中，热控负荷因其具有热存储能力并能在一定时间内转移负荷可为系统提供多种辅助服务而受到了广泛关注。空调负荷是一种典型的热控负荷，其压缩机经历了从定频到变频的发展，目前变频空调因其较高的效率在市场中的份额正在逐渐增大。研究变频空调的建模及调控技术具有较好的应用前景。Demand response technology is one of the core technologies of the smart grid. It has become common knowledge in the industry to alleviate the tension between supply and demand, enhance the system's ability to cope with power flow fluctuations, improve system operation efficiency, reduce related enterprise losses, and maximize economic benefits through demand response. Among all flexible loads, thermal control loads have received extensive attention because of their thermal storage capacity and the ability to transfer loads within a certain period of time to provide a variety of auxiliary services for the system. The air-conditioning load is a typical heat-controlled load. Its compressor has experienced the development from fixed frequency to variable frequency. At present, the market share of variable frequency air conditioners is gradually increasing due to their high efficiency. Research on the modeling and control technology of frequency conversion air conditioner has a good application prospect.

空调负荷分布范围广、体量大，故需要一定的技术手段对其进行聚合建模，方便相关部门的统一调度和控制，负荷聚合商作为一种专门用于整合负荷侧资源的商业模式，不仅能够代表中小型负荷资源为系统提供各种服务，而且能够借助于智能电网的高级测量体系对负荷进行实时测量与控制，实现资源的高效利用和经济效益的最大化。与此同时，空调负荷受用户行为、天气情况等因素影响较大，具有较大的不确定性，因此有必要在对机组进行调度计划的过程中充分考虑用户响应不确定性，减少相关部门的经济损失。The air-conditioning load has a wide distribution range and a large volume, so certain technical means are needed to aggregate and model it to facilitate the unified scheduling and control of relevant departments. As a business model dedicated to integrating load-side resources, the load aggregator not only It can provide various services for the system on behalf of small and medium-sized load resources, and can measure and control the load in real time with the help of the advanced measurement system of the smart grid, so as to realize the efficient utilization of resources and the maximization of economic benefits. At the same time, the air-conditioning load is greatly affected by user behavior, weather conditions and other factors, and has great uncertainty. Therefore, it is necessary to fully consider the user response uncertainty in the process of scheduling the unit, and reduce the burden of relevant departments. Economic losses.

发明内容Contents of the invention

发明目的：为了克服现有技术中存在的不足，本发明提供一种基于变频空调虚拟机组建模的机组组合方法，通过充分挖掘需求侧资源，实现大规模负荷的集中统一调度控制，以减少传统机组的频繁启停控制。Purpose of the invention: In order to overcome the deficiencies in the prior art, the present invention provides a unit combination method based on the modeling of variable frequency air conditioner virtual units. By fully mining the resources on the demand side, the centralized and unified dispatching control of large-scale loads can be realized to reduce the traditional Frequent start-stop control of the unit.

技术方案：为实现上述目的，本发明采用的技术方案为：Technical scheme: in order to achieve the above object, the technical scheme adopted in the present invention is:

一种基于变频空调虚拟机组建模的机组组合方法，包括如下步骤：A unit combination method based on variable frequency air conditioner virtual unit modeling, comprising the following steps:

(1)根据能量守恒原理和空调运行特性建立单台空调的热力学模型和电气模型，即建立空调的功率P与空调的制冷量Q之间的关系；(1) Establish the thermodynamic model and electrical model of a single air conditioner according to the principle of energy conservation and the operating characteristics of the air conditioner, that is, establish the relationship between the power P of the air conditioner and the cooling capacity Q of the air conditioner;

(2)通过负荷聚合商对空调群进行集中调度与控制，建立空调群的聚合模型；(2) Centrally dispatch and control the air-conditioning group through the load aggregator, and establish the aggregation model of the air-conditioning group;

(3)在聚合模型的基础上对负荷调整量进行潜力评估，同时依据传统机组特性进行虚拟机组建模，并建立用户响应不确定情况下虚拟机组的成本函数；(3) On the basis of the aggregation model, the potential evaluation of the load adjustment is carried out, and the virtual unit is modeled according to the characteristics of the traditional unit, and the cost function of the virtual unit is established under the condition of uncertain user response;

(4)在日前市场中，根据次日负荷和温度预测，建立传统机组和虚拟机组的联合机组组合模型，安排次日调度计划；(4) In the day-ahead market, according to the load and temperature forecast of the next day, a joint unit combination model of traditional units and virtual units is established to arrange the scheduling plan for the next day;

(5)在实际调度过程中，综合考虑负荷聚合商的经济利益与参与虚拟机组建模的用户公平性和舒适度，以最大化负荷聚合商的经济利益以及最小化用户不舒适度为目标函数，实现负荷调整量在用户之间的优化分配。(5) In the actual scheduling process, the economic interests of the load aggregator and the fairness and comfort of the users participating in the modeling of the virtual unit are considered comprehensively, and the objective function is to maximize the economic interests of the load aggregator and minimize the discomfort of the users , to realize the optimal distribution of load adjustment among users.

具体的，所述步骤(1)包括如下步骤：Specifically, the step (1) includes the following steps:

(11)建立单台空调的热力学模型：(11) Establish a thermodynamic model of a single air conditioner:

${C C}_{a a} \frac{{dT dT}_{i i n no}}{d d t t} = = \frac{11}{{R R}_{11}} (({T T}_{o o u u t t} - - {T T}_{i i n no})) + + {Q Q}^{' '} - - Q Q - - - - - - ((11))$

其中：T_out为室外温度，T_in为室内温度，C_a为空调的等效热容，R₁为空调的等效阻抗，Q为空调的制冷量，Q'为室内物体的散热量，t为时间；Where: T _out is the outdoor temperature, T _in is the indoor temperature, C _a is the equivalent heat capacity of the air conditioner, R ₁ is the equivalent impedance of the air conditioner, Q is the cooling capacity of the air conditioner, Q' is the heat dissipation of indoor objects, t for time;

(12)建立单台空调的电气模型：(12) Establish the electrical model of a single air conditioner:

空调下一时刻的频率f_t+1由当前时刻的频率f_t和设定温度T_s和当前室内温度T_in,t之差ΔT_t决定，空调的频率f的递推关系如下：The frequency f _t+1 of the air conditioner at the next moment is determined by the frequency f _t at the current moment and the difference ΔT _t between the set temperature T _s and the current indoor temperature T _in,t . The recursive relationship of the frequency f of the air conditioner is as follows:

ΔT_t＝T_s-T_in,t (2)ΔT _t =T _s -T _in,t (2)

将空调的功率P和空调的频率f的关系表示为：The relationship between the power P of the air conditioner and the frequency f of the air conditioner is expressed as:

P＝k₁f+l₁ (4)P＝k ₁ f+l ₁ (4)

将空调的制冷量Q和空调的频率f的关系表示为：The relationship between the cooling capacity Q of the air conditioner and the frequency f of the air conditioner is expressed as:

Q＝k₂f+l₂ (5)Q＝k ₂ f+l ₂ (5)

建立空调的功率P和空调的制冷量Q之间的关系为：Establish the relationship between the power P of the air conditioner and the cooling capacity Q of the air conditioner as:

$Q Q = = \frac{{k k}_{22}}{{k k}_{11}} P P + + \frac{{k k}_{11} {l l}_{22} - - {l l}_{11} {k k}_{22}}{{k k}_{11}} - - - - - - ((66))$

其中：ΔT_min为设定的最小检测温度差，ΔT_max为设定的最大检测温度差，k、k₁、l₁、k₂和l₂均为常系数。Where: ΔT _min is the set minimum detection temperature difference, ΔT _max is the set maximum detection temperature difference, k, k ₁ , l ₁ , k ₂ and l ₂ are constant coefficients.

具体的，所述步骤(2)包括如下步骤：Specifically, the step (2) includes the following steps:

空调是将电能以热能的形式存储于所属建筑物中，室内温度越高储能量越小，室内温度越低储能量越大，记用户的舒适度范围为[T_min,T_max]；设室内温度为T_max时储能量为0，则室内温度为T_in时储能量O_in为：The air conditioner stores electric energy in the form of heat energy in the building. The higher the indoor temperature, the smaller the stored energy, and the lower the indoor temperature, the greater the stored energy. The user's comfort range is [T _min , T _max ]; When the temperature is T _max , the stored energy is 0, and when the indoor temperature is T _in , the stored energy O _in is:

O_in＝C_a(T_max-T_in) (7)O _in ＝C _a (T _max -T _in ) (7)

建筑物的储能容量O为：The energy storage capacity O of the building is:

O＝C_a(T_max-T_min) (8)O＝C _a (T _max -T _min ) (8)

定义空调的荷电状态SOC为储能量O_in与储能容量O的比值：The state of charge SOC of the air conditioner is defined as the ratio of the storage energy O _in to the energy storage capacity O:

$S S O o C C = = \frac{{O o}_{i i n no}}{O o} = = \frac{{T T}_{m m a a x x} - - {T T}_{i i n no}}{{T T}_{max max} - - {T T}_{min min}} - - - - - - ((99))$

当室内温度保持在T_in时，根据式(1)可得空调的制冷量Q为：When the indoor temperature is kept at T _in , according to formula (1), the cooling capacity Q of the air conditioner can be obtained as:

$Q Q = = \frac{11}{{R R}_{11}} (({T T}_{o o u u t t} - - {T T}_{i i n no})) + + {Q Q}^{' '} - - - - - - ((1010))$

将式(10)带入式(6)，得到空调的功率P为：Putting formula (10) into formula (6), the power P of the air conditioner can be obtained as:

$P P = = \frac{{k k}_{11}}{{k k}_{22} {R R}_{11}} (({T T}_{o o u u t t} - - {T T}_{i i n no})) + + \frac{{k k}_{11} {Q Q}^{' '} + + {k k}_{22} {l l}_{11} - - {k k}_{11} {l l}_{22}}{{k k}_{22}} - - - - - - ((1111))$

将式(9)带入式(11)，得到空调的功率P与空调的荷电状态SOC的关系为：Putting Equation (9) into Equation (11), the relationship between the power P of the air conditioner and the SOC of the air conditioner is obtained as:

P＝αSOC+βT_o+γ (12)P=αSOC+βT _o +γ (12)

$α α = = \frac{{k k}_{11} (({T T}_{m m a a x x} - - {T T}_{min min}))}{{k k}_{22} {R R}_{11}} - - - - - - ((1313))$

$β β = = \frac{{k k}_{11}}{{k k}_{22} {R R}_{11}} - - - - - - ((1414))$

$γ γ = = \frac{- - {k k}_{11} {T T}_{max max} + + {k k}_{11} {R R}_{11} {Q Q}^{' '} + + {k k}_{22} {R R}_{11} {l l}_{11} - - {k k}_{11} {R R}_{11} {l l}_{22}}{{k k}_{22} {R R}_{11}} - - - - - - ((1515))$

将空调的荷电状态SOC的变化范围[0,1]划分为N个小区间，根据每台空调的荷电状态SOC，将所有空调划分到各个小区间内，统计每个小区间内的空调数量分别为m₁,m₂,…,m_i,…,m_N，将第i个小区间内的空调的荷电状态统一为SOC_i：Divide the change range [0,1] of the state of charge SOC of the air conditioner into N small areas, divide all air conditioners into each small area according to the state of charge SOC of each air conditioner, and count the air conditioners in each small area The quantities are m ₁ , m ₂ ,...,m _i ,...,m _N , and the state of charge of the air conditioner in the i-th cell is unified as SOC _i :

${SOC SOC}_{i i} = = \frac{11}{N N} i i - - \frac{11}{22 N N} - - - - - - ((1616))$

建立第i个小区间内所有空调的聚合模型：Establish the aggregation model of all air conditioners in the i-th cell:

P_i＝α_iSOC_i+β_iT_o+γ_i (17)P _i =α _i SOC _i +β _i T _o +γ _i (17)

${α α}_{i i} = = {α α}_{i i__11} + + {α α}_{i i__22} + + ... ... + + {α α}_{i i__k k} + + ... ... + + {α α}_{i i__{m m}_{i i}} - - - - - - ((1818))$

${β β}_{i i} = = {β β}_{i i__11} + + {β β}_{i i__22} + + ... ... + + {β β}_{i i__k k} + + ... ... + + {β β}_{i i__{m m}_{i i}} - - - - - - ((1919))$

${γ γ}_{i i} = = {γ γ}_{i i__11} + + {γ γ}_{i i__22} + + ... ... + + {γ γ}_{i i__k k} + + ... ... + + {γ γ}_{i i__{m m}_{i i}} - - - - - - ((2020))$

整个空调群的总聚合功率P_total为：The total aggregated power P _total of the entire air conditioning group is:

${P P}_{o o t t a a l l} = = {Σ Σ}_{i i = = 11}^{N N} {P P}_{i i} - - - - - - ((21 twenty one))$

当室外温度保持不变时，第i个小区间内所有空调的状态整体调整到第j个小区内的聚合功率变化ΔP_i-j为：When the outdoor temperature remains constant, the aggregate power change ΔP _ij of all air conditioners in the i-th cell is adjusted to the j-th cell as a whole:

ΔP_i-j＝α_i(SOC_j-SOC_i) (22)ΔP _ij =α _i (SOC _j −SOC _i ) (22)

其中：P_i为第i个小区间内所有空调的聚合功率，α_{i_k}、β_{i_k}和γ_{i_k}分别为第i个小区间内第k台空调的α、β和γ。Where: P _i is the aggregate power of all air conditioners in the i-th cell, α _{i_k} , β _{i_k} and γ _{i_k} are α, β and γ of the k-th air conditioner in the i-th cell, respectively.

具体的，所述步骤(3)包括如下步骤：Specifically, the step (3) includes the following steps:

确定虚拟机组的可调度功率为：Determine the schedulable power of the virtual group as:

${P P}^{u u p p} = = {Σ Σ}_{i i = = 11}^{N N} {α α}_{i i} (({SOC SOC}_{N N} - - {SOC SOC}_{i i})) - - - - - - ((23 twenty three))$

${P P}^{d d o o w w n no} = = {Σ Σ}_{i i = = 11}^{N N} {α α}_{i i} (({SOC SOC}_{i i} - - {SOC SOC}_{11})) - - - - - - ((24 twenty four))$

其中：P^up为虚拟机组上调功率，P^down为虚拟机组下调功率；Among them: P ^up is to increase the power of the virtual group, and P ^down is to reduce the power of the virtual group;

建立虚拟机组的成本为：The cost of creating a virtual group is:

C＝λ|ΔP| (25)C＝λ|ΔP| (25)

其中：C为调度虚拟机组的成本，λ为虚拟机组单位调度成本，ΔP为虚拟机组的计划调度功率；Among them: C is the cost of dispatching the virtual group, λ is the unit scheduling cost of the virtual group, and ΔP is the planned dispatching power of the virtual group;

考虑用户响应不确定性，设虚拟机组的实际调度功率ΔP'为计划调度功率ΔP与随机变量ω之和：Considering the uncertainty of user response, the actual scheduling power ΔP' of the virtual machine set is the sum of the planned scheduling power ΔP and the random variable ω:

|ΔP'|＝|ΔP|+ω (26)|ΔP'|=|ΔP|+ω (26)

当虚拟机组的实际调度功率少于计划调度功率时，除调度部门需根据虚拟机组的实际调度功率支付给负荷聚合商费用外，负荷聚合商还需要向调度部门进行一定的经济补偿；当虚拟机组的实际调度功率大于计划调度功率时，则调度部门根据虚拟机组的计划调度功率支付给负荷聚合商费用；故考虑用户响应不确定情况下虚拟机组的成本函数C'为：When the actual dispatching power of the virtual group is less than the planned dispatching power, in addition to the dispatch department having to pay the load aggregator according to the actual dispatching power of the virtual group, the load aggregator also needs to make certain economic compensation to the dispatching department; when the virtual group When the actual dispatching power is greater than the planned dispatching power, the dispatching department will pay the load aggregator according to the planned dispatching power of the virtual group; therefore, considering the uncertain user response, the cost function C' of the virtual group is:

${C C}^{' '} = = \{\begin{matrix} λ λ | | Δ Δ P P | | + + τ τ ω ω & ω ω < < 00 \\ λ λ | | Δ Δ P P | | & ω ω &GreaterEqual; &Greater Equal; 00 \end{matrix} - - - - - - ((2727))$

其中：τ为负荷聚合商支付给调度部门的补偿单价；Among them: τ is the compensation unit price paid by the load aggregator to the dispatching department;

建立虚拟机组的期望成本C”(ΔP)为：The expected cost C”(ΔP) of establishing a virtual group is:

${C C}^{' '' '} ((Δ Δ P P)) = = λ λ | | Δ Δ P P | | + + {&Integral; &Integral;}_{- - ∞ ∞}^{00} τ τ ω ω d d f f ((ω ω)) - - - - - - ((2828))$

其中：f(ω)为ω的概率密度函数，根据历史数据统计得到。Where: f(ω) is the probability density function of ω, obtained according to historical data statistics.

具体的，所述步骤(4)包括如下步骤：Concretely, described step (4) comprises the following steps:

在日前市场中，根据次日负荷和温度预测，建立传统机组和虚拟机组的联合机组组合模型，安排次日调度计划，目标函数为：In the day-ahead market, according to the load and temperature forecast of the next day, a joint unit combination model of traditional units and virtual units is established to arrange the scheduling plan for the next day. The objective function is:

$min min F f = = {Σ Σ}_{t t = = 11}^{T T} {Σ Σ}_{i i = = 11}^{{N N}_{G G}} [[{C C}_{G G i i} (({P P}_{G G i i}^{t t})) {U u}_{G G i i}^{t t} + + {U u}_{G G i i}^{t t} ((11 - - {U u}_{G G i i}^{t t - - 11})) {S S}_{G G i i}]] + + {C C}^{' '' '} (({ΔP ΔP}^{t t})) - - - - - - ((2929))$

其中：T为总调度时段，ΔP^t为第t个调度时段虚拟机组的计划调度功率，C”(ΔP^t)为第t个调度时段虚拟机组的期望成本，N_G为传统机组台数，为第t个调度时段传统机组i的启停状态，表示开机，表示停机，S_Gi为传统机组i的启动费用，为传统机组i的出力，为传统机组i的有功生产费用函数：Among them: T is the total dispatching period, ΔP ^t is the planned dispatching power of the virtual unit in the tth dispatching period, C”(ΔP ^t ) is the expected cost of the virtual unit in the tth dispatching period, N _G is the number of traditional units, is the start-stop state of traditional unit i in the tth scheduling period, Indicates booting, Indicates shutdown, S _Gi is the start-up cost of traditional unit i, is the output of traditional unit i, is the active production cost function of traditional unit i:

${C C}_{G G i i} (({P P}_{G G i i}^{t t})) = = {a a}_{G G i i} {(({P P}_{G G i i}^{t t}))}^{22} + + {b b}_{G G i i} {P P}_{G G i i}^{t t} + + {c c}_{G G i i} - - - - - - ((3030))$

其中：a_Gi、b_Gi和c_Gi为传统机组i的成本系数；Among them: a _Gi , b _Gi and c _Gi are the cost coefficients of traditional unit i;

目标函数对应的系统约束如下：The system constraints corresponding to the objective function are as follows:

(a)系统负荷平衡(a) System load balancing

${ΔP ΔP}^{t t} + + {Σ Σ}_{i i = = 11}^{{N N}_{G G}} {P P}_{G G i i}^{t t} {U u}_{G G i i}^{t t} = = {P P}_{L L}^{t t} - - - - - - ((3131))$

其中：为第t个调度时段预测的负荷需求；in: The load demand predicted for the tth scheduling period;

(b)系统备用约束(b) System backup constraints

${ΔP ΔP}_{m m a a x x}^{t t} + + {Σ Σ}_{i i = = 11}^{{N N}_{G G}} {P P}_{G G i i__m m a a x x}^{t t} {U u}_{G G i i}^{t t} &GreaterEqual; &Greater Equal; {P P}_{L L}^{t t} + + {P P}_{R R}^{t t} - - - - - - ((3232))$

其中：为第t个调度时段虚拟机组的最大出力，为第t个调度时段传统机组i的最大出力，为第t个调度时段的备用需求；in: is the maximum output of the virtual machine group in the tth scheduling period, is the maximum output of traditional unit i in the tth scheduling period, is the spare demand of the tth scheduling period;

(c)传统机组出力上下限约束(c) Constraints on the upper and lower limits of traditional unit output

${P P}_{G G i i__min min}^{t t} \leq \leq {P P}_{G G i i}^{t t} \leq \leq {P P}_{G G i i__max max}^{t t} - - - - - - ((3333))$

其中：为第t个调度时段传统机组i的最小出力；in: is the minimum output of traditional unit i in the tth scheduling period;

(d)传统机组爬坡约束(d) Traditional unit climbing constraints

${P P}_{G G i i}^{t t} {U u}_{G G i i}^{t t} - - {P P}_{G G i i}^{t t - - 11} {U u}_{G G i i}^{t t - - 11} \leq \leq {R R}_{u u p p,, i i} - - - - - - ((3434))$

${P P}_{G G i i}^{t t - - 11} {U u}_{G G i i}^{t t - - 11} - - {P P}_{G G i i}^{t t} {U u}_{G G i i}^{t t} \leq \leq {R R}_{d d o o w w n no,, i i} - - - - - - ((3535))$

其中：R_up,i为传统机组i的最大上坡速率，R_down,i为传统机组i的最大下坡速率；Among them: R _up,i is the maximum uphill rate of traditional unit i, R _down,i is the maximum downhill rate of traditional unit i;

(e)传统机组开停机时间约束(e) Constraints on start and stop time of traditional units

$(({X x}_{G G i i}^{o o n no} ((t t - - 11)) - - {T T}_{G G i i}^{o o n no})) (({U u}_{G G i i}^{t t - - 11} - - {U u}_{G G i i}^{t t})) &GreaterEqual; &Greater Equal; 00 - - - - - - ((3636))$

$(({X x}_{G G i i}^{o o f f f f} ((t t - - 11)) - - {T T}_{G G i i}^{o o f f f f})) (({U u}_{G G i i}^{t t} - - {U u}_{G G i i}^{t t - - 11})) &GreaterEqual; &Greater Equal; 00 - - - - - - ((3737))$

其中：为传统机组i在第t-1个调度时段的累计开机时间，为传统机组i在第t-1个调度时段的累计停机时间，为传统机组i的最小开机持续时长，为传统机组i的最小停机持续时长；in: is the cumulative start-up time of the traditional unit i in the t-1th scheduling period, is the accumulative shutdown time of traditional unit i in the t-1th scheduling period, is the minimum start-up duration of the traditional unit i, is the minimum shutdown duration of traditional unit i;

(e)虚拟机组约束(e) Virtual group constraints

$- - {P P}^{d d o o w w n no} (({T T}_{o o u u t t}^{t t})) \leq \leq {ΔP ΔP}^{t t} \leq \leq {P P}^{u u p p} (({T T}_{o o u u t t}^{t t})) - - - - - - ((3838))$

其中：为第t个调度时段虚拟机组在室外温度为时的下调功率，为第t个调度时段虚拟机组在室外温度为时的上调功率。in: For the tth scheduling period, the outdoor temperature of the virtual group is When the down-regulated power, For the tth scheduling period, the outdoor temperature of the virtual group is when the power is adjusted up.

具体的，所述步骤(5)包括如下步骤：Specifically, the step (5) includes the following steps:

在实际调度过程中，最大化负荷聚合商的经济利益的目标函数为：In the actual scheduling process, the objective function to maximize the economic benefits of the load aggregator is:

其中：σ为负荷聚合商支付用户的补偿单价；Among them: σ is the compensation unit price paid by the load aggregator to the user;

考虑负荷聚合商的经济利益与参与虚拟机组建模的用户公平性和舒适度，使总功率调整量之和最小的目标函数为：Considering the economic interests of the load aggregator and the fairness and comfort of the users participating in the virtual group modeling, the objective function to minimize the sum of the total power adjustments is:

$min min | | {Σ Σ}_{i i = = 11}^{N N} {ν ν}_{i i} {ΔSOC ΔSOC}_{i i} | | - - - - - - ((4040))$

${ν ν}_{i i} = = \frac{{x x}_{i i}}{x x} - - - - - - ((4141))$

其中：x_i为第i个小区间的空调被调整的次数，x为所有空调被调整的次数。Among them: x _i is the number of times the air conditioner in the i-th cell is adjusted, and x is the number of times all air conditioners are adjusted.

有益效果：本发明针对变频空调体量大分布范围广的特点，提供一种基于变频空调虚拟机组建模的机组组合方法，提出一种空调群的聚合建模方法，实现了空调群的集中调度控制，减少了相关调度部门的计算量和控制难度；在空调群的聚合建模基础上，根据传统机组的运行特性，建立了虚拟机组模型，并考虑用户响应不确定性，建立虚拟机组不确定性情况下的调度成本，减少了调度部门的风险；在对空调群进行控制时，在最大化负荷聚合商经济利益基础上兼顾了用户舒适度和公平性，实现了负荷调整量在用户之间的优化分配。Beneficial effects: Aiming at the characteristics of large volume and wide distribution range of frequency conversion air conditioners, the present invention provides a unit combination method based on virtual unit modeling of frequency conversion air conditioners, proposes an aggregation modeling method of air conditioners, and realizes centralized scheduling of air conditioners control, which reduces the calculation amount and control difficulty of the relevant dispatching department; on the basis of the aggregation modeling of the air-conditioning group, according to the operating characteristics of the traditional unit, a virtual unit model is established, and considering the user response uncertainty, the establishment of virtual unit uncertain Dispatching costs under critical conditions reduce the risk of the dispatching department; when controlling the air-conditioning group, user comfort and fairness are taken into account on the basis of maximizing the economic interests of the load aggregator, and the load adjustment amount is distributed between users. optimal distribution.

附图说明Description of drawings

图1为本发明方法的总流程图；Fig. 1 is the general flowchart of the inventive method;

图2为空调聚合模型；Fig. 2 is the air-conditioning aggregation model;

图3机组组合调度框架。Figure 3. Unit combination scheduling framework.

具体实施方式detailed description

下面结合附图对本发明作更进一步的说明。The present invention will be further described below in conjunction with the accompanying drawings.

如图1所示为一种基于变频空调虚拟机组建模的机组组合方法，下面就各个步骤加以具体说明。As shown in Figure 1, it is a unit combination method based on the modeling of the inverter air conditioner virtual unit, and each step will be described in detail below.

步骤一：根据能量守恒原理和空调运行特性建立单台空调的热力学模型和电气模型，即建立空调的功率P与空调的制冷量Q之间的关系。Step 1: Establish the thermodynamic model and electrical model of a single air conditioner according to the principle of energy conservation and the operating characteristics of the air conditioner, that is, establish the relationship between the power P of the air conditioner and the cooling capacity Q of the air conditioner.

ΔT_t＝T_s-T_in,t (2)ΔT _t =T _s -T _in,t (2)

P＝k₁f+l₁ (4)P＝k ₁ f+l ₁ (4)

Q＝k₂f+l₂ (5)Q＝k ₂ f+l ₂ (5)

步骤二：通过负荷聚合商对空调群进行集中调度与控制，建立空调群的聚合模型。Step 2: Centrally dispatch and control the air-conditioning group through the load aggregator, and establish the aggregation model of the air-conditioning group.

O_in＝C_a(T_max-T_in) (7)O _in ＝C _a (T _max -T _in ) (7)

O＝C_a(T_max-T_min) (8)O＝C _a (T _max -T _min ) (8)

P＝αSOC+βT_o+γ (12)P=αSOC+βT _o +γ (12)

$β β = = \frac{{k k}_{11}}{{k k}_{22} {R R}_{11}} - - - - - - ((1414))$

P_i＝α_iSOC_i+β_iT_o+γ_i (17)P _i =α _i SOC _i +β _i T _o +γ _i (17)

ΔP_i-j＝α_i(SOC_j-SOC_i) (22)ΔP _ij =α _i (SOC _j −SOC _i ) (22)

步骤三：在聚合模型的基础上对负荷调整量进行潜力评估，同时依据传统机组特性进行虚拟机组建模，并建立用户响应不确定情况下虚拟机组的成本函数。Step 3: Evaluate the potential of the load adjustment based on the aggregation model, and at the same time model the virtual unit based on the characteristics of the traditional unit, and establish the cost function of the virtual unit when the user response is uncertain.

建立虚拟机组的成本为：The cost of creating a virtual group is:

C＝λ|ΔP| (25)C＝λ|ΔP| (25)

|ΔP'|＝|ΔP|+ω (26)|ΔP'|=|ΔP|+ω (26)

步骤四：在日前市场中，根据次日负荷和温度预测，建立传统机组和虚拟机组的联合机组组合模型，安排次日调度计划。Step 4: In the day-ahead market, according to the load and temperature forecast of the next day, a joint unit combination model of traditional units and virtual units is established, and the scheduling plan for the next day is arranged.

(a)系统负荷平衡(a) System load balancing

(b)系统备用约束(b) System backup constraints

(c)传统机组出力上下限约束(c) Constraints on upper and lower limits of output of traditional units

(d)传统机组爬坡约束(d) Traditional unit climbing constraints

(e)虚拟机组约束(e) Virtual group constraints

步骤五：在实际调度过程中，综合考虑负荷聚合商的经济利益与参与虚拟机组建模的用户公平性和舒适度，以最大化负荷聚合商的经济利益以及最小化用户不舒适度为目标函数，实现负荷调整量在用户之间的优化分配。Step 5: In the actual scheduling process, comprehensively consider the economic interests of the load aggregator and the fairness and comfort of the users participating in the modeling of the virtual unit, with the objective function of maximizing the economic interests of the load aggregator and minimizing the user discomfort , to realize the optimal distribution of load adjustment among users.

$m m i i n no | | {Σ Σ}_{i i = = 11}^{N N} {ν ν}_{i i} {ΔSOC ΔSOC}_{i i} | | - - - - - - ((4040))$

${ν ν}_{i i} = = \frac{{x x}_{i i}}{x x} - - - - - - ((4141))$

以上所述仅是本发明的优选实施方式，应当指出：对于本技术领域的普通技术人员来说，在不脱离本发明原理的前提下，还可以做出若干改进和润饰，这些改进和润饰也应视为本发明的保护范围。The above is only a preferred embodiment of the present invention, it should be pointed out that for those of ordinary skill in the art, without departing from the principle of the present invention, some improvements and modifications can also be made, and these improvements and modifications are also possible. It should be regarded as the protection scope of the present invention.

Claims

1. a Unit Combination method based on the modeling of convertible frequency air-conditioner virtual robot arm, it is characterised in that: comprise the steps:

(1) set up thermodynamical model and the electrical model of single air conditioner according to conservation of energy principle and operation of air conditioner characteristic, i.e. build Relation between power P and refrigerating capacity Q of air-conditioning of vertical air-conditioning；

(2) by Load aggregation business, air-conditioning group is carried out centralized dispatching and control, set up the polymerization model of air-conditioning group；

(3) load adjustment amount is carried out Potential Evaluation on the basis of polymerization model, carry out void according to conventional rack characteristic simultaneously Intend unit modeling, and set up user and respond the cost function of virtual robot arm under uncertain condition；

(4) in ahead market, according to secondary daily load and temperature prediction, the Unit erriger group of conventional rack and virtual robot arm is set up Matched moulds type, arranges time daily dispatch scheduling；

(5) during actual schedule, the economic interests of Load aggregation business and the user participating in virtual robot arm modeling are considered Fairness and comfort level, with the economic interests maximizing Load aggregation business and minimize user's non-comfort as object function, Realize load adjustment amount optimized distribution between users.

Unit Combination method based on the modeling of convertible frequency air-conditioner virtual robot arm the most according to claim 1, it is characterised in that: institute State step (1) to comprise the steps:

(11) thermodynamical model of single air conditioner is set up:

C_{a} \frac{{dT}_{i n}}{d t} = \frac{1}{R_{1}} (T_{o u t} - T_{i n}) + Q^{'} - Q - - - (1)

Wherein: T_outFor outdoor temperature, T_inFor indoor temperature, C_aFor the equivalent thermal capacitance of air-conditioning, R₁For the equiva lent impedance of air-conditioning, Q is The refrigerating capacity of air-conditioning, Q' is the heat dissipation capacity of indoor object, and t is the time；

(12) electrical model of single air conditioner is set up:

Frequency f of air-conditioning subsequent time_t+1By frequency f of current time_tWith design temperature T_sWith current indoor temperature T_in,tDifference ΔT_tDetermining, the recurrence relation of frequency f of air-conditioning is as follows:

ΔT_t=T_s-T_in,t (2)

By the relational representation of the power P of air-conditioning and frequency f of air-conditioning it is:

P=k₁f+l₁ (4)

By the relational representation of refrigerating capacity Q of air-conditioning and frequency f of air-conditioning it is:

Q=k₂f+l₂ (5)

The relation between the power P of air-conditioning and refrigerating capacity Q of air-conditioning of setting up is:

Q = \frac{k_{2}}{k_{1}} P + \frac{k_{1} l_{2} - l_{1} k_{2}}{k_{1}} - - - (6)

Wherein: Δ T_minFor the minimum detection temperature difference set, Δ T_maxFor the maximum detection temperature difference set, k, k₁、l₁、k₂And l₂ It is constant coefficient.

Unit Combination method based on the modeling of convertible frequency air-conditioner virtual robot arm the most according to claim 1, it is characterised in that: institute State step (2) to comprise the steps:

Air-conditioning is to be stored in affiliated building with the form of heat energy by electric energy, and the highest energy storage capacity of indoor temperature is the least, Indoor Temperature Spending the lowest energy storage capacity the biggest, the comfort level scope of note user is [T_min,T_max]；If indoor temperature is T_maxTime energy storage capacity be 0, then Indoor temperature is T_inTime energy storage capacity O_inFor:

O_in=C_a(T_max-T_in) (7)

The stored energy capacitance O of building is:

O=C_a(T_max-T_min) (8)

The state-of-charge SOC of definition air-conditioning is energy storage capacity O_inRatio with stored energy capacitance O:

S O C = \frac{O_{i n}}{O} = \frac{T_{m a x} - T_{i n}}{T_{\max} - T_{\min}} - - - (9)

When indoor temperature is maintained at T_inTime, refrigerating capacity Q that can obtain air-conditioning according to formula (1) is:

Q = \frac{1}{R_{1}} (T_{o u t} - T_{i n}) + Q^{'} - - - (10)

Bringing formula (10) into formula (6), the power P obtaining air-conditioning is:

P = \frac{k_{1}}{k_{2} R_{1}} (T_{o u t} - T_{i n}) + \frac{k_{1} Q^{'} + k_{2} l_{1} - k_{1} l_{2}}{k_{2}} - - - (11)

Bringing formula (9) into formula (11), the relation obtaining the power P of air-conditioning and the state-of-charge SOC of air-conditioning is:

P=α SOC+ β T_o+γ (12)

α = \frac{k_{1} (T_{m a x} - T_{\min})}{k_{2} R_{1}} - - - (13)

β = \frac{k_{1}}{k_{2} R_{1}} - - - (14)

γ = \frac{- k_{1} T_{m a x} + k_{1} R_{1} Q^{'} + k_{2} R_{1} l_{1} - k_{1} R_{1} l_{2}}{k_{2} R_{1}} - - - (15)

The excursion [0,1] of the state-of-charge SOC of air-conditioning is divided into N number of minizone, according to the state-of-charge of every air-conditioning All air-conditionings are divided in each minizone by SOC, and the air-conditioning quantity added up in each minizone is respectively m₁,m₂,…, m_i,…,m_N, by unified for the state-of-charge of the air-conditioning in i-th minizone for SOC_i:

{SOC}_{i} = \frac{1}{N} i - \frac{1}{2 N} - - - (16)

Set up air-conditioned polymerization model in i-th minizone:

P_i=α_iSOC_i+β_iT_o+γ_i (17)

α_{i} = α_{i_1} + α_{i_2} + ... + α_{i_k} + ... + α_{i_m_{i}} - - - (18)

β_{i} = β_{i_1} + β_{i_2} + ... + β_{i_k} + ... + β_{i_m_{i}} - - - (19)

γ_{i} = γ_{i_1} + γ_{i_2} + ... + γ_{i_k} + ... + γ_{i_m_{i}} - - - (20)

The total polymerization power P of whole air-conditioning group_totalFor:

P_{t o t a l} = Σ_{i = 1}^{N} P_{i} - - - (21)

When outdoor temperature keeps constant, i-th the minizone in air-conditioned state integrated regulation to gathering in jth community Close changed power Δ P_i-jFor:

ΔP_i-j=α_i(SOC_j-SOC_i) (22)

Wherein: P_iFor aggregate power air-conditioned in i-th minizone, α_{i_k}、β_{i_k}And γ_{i_k}It is respectively in i-th minizone α, β and γ of kth platform air-conditioning.

Unit Combination method based on the modeling of convertible frequency air-conditioner virtual robot arm the most according to claim 3, it is characterised in that: institute State step (3) to comprise the steps:

The schedulable power determining virtual robot arm is:

P^{u p} = Σ_{i = 1}^{N} α_{i} ({SOC}_{N} - {SOC}_{i}) - - - (23)

P^{d o w n} = Σ_{i = 1}^{N} α_{i} ({SOC}_{i} - {SOC}_{1}) - - - (24)

Wherein: P^upFor adjusting power on virtual robot arm, P^downFor adjusting power under virtual robot arm；

The cost setting up virtual robot arm is:

C=λ | Δ P | (25)

Wherein: C is the cost of schedule virtual unit, λ is that virtual robot arm unit dispatches cost, and Δ P is that the plan of virtual robot arm is adjusted Degree power；

Consider that user responds uncertainty, if the actual schedule power Δ P' of virtual robot arm is that planned dispatching power Δ P is with random Variable ω sum:

| Δ P'|=| Δ P |+ω (26)

When the actual schedule power of virtual robot arm is less than planned dispatching power, except traffic department need to be according to the reality of virtual robot arm Schedule power is paid outside Load aggregation business's expense, and Load aggregation business also needs to carry out certain economic compensation to traffic department； When the actual schedule power of virtual robot arm is more than planned dispatching power, then traffic department is according to the planned dispatching merit of virtual robot arm Rate pays Load aggregation business's expense；Therefore consider that user responds the cost function C' of virtual robot arm under uncertain condition and is:

C^{'} = \{\begin{matrix} λ | Δ P | + τ ω & ω < 0 \\ λ | Δ P | & ω &GreaterEqual; 0 \end{matrix} - - - (27)

Wherein: τ is the compensation unit price that Load aggregation business pays traffic department；

Set up the expected cost C of virtual robot arm " (Δ P) be:

C^{''} (Δ P) = λ | Δ P | + {&Integral;}_{- \infty}^{0} τ ω d f (ω) - - - (28)

Wherein: f (ω) is the probability density function of ω, obtain according to historical data statistics.

Unit Combination method based on the modeling of convertible frequency air-conditioner virtual robot arm the most according to claim 4, it is characterised in that: institute State step (4) to comprise the steps:

In ahead market, according to secondary daily load and temperature prediction, set up the Unit erriger combination of conventional rack and virtual robot arm Model, arranges time daily dispatch scheduling, and object function is:

\min F = Σ_{t = 1}^{T} Σ_{i = 1}^{N_{G}} [C_{G i} (P_{G i}^{t}) U_{G i}^{t} + U_{G i}^{t} (1 - U_{G i}^{t - 1}) S_{G i}] + C^{''} ({ΔP}^{t}) - - - (29)

Wherein: T is the total activation period, Δ P^tIt is the planned dispatching power of the t scheduling slot virtual robot arm, C " (Δ P^t) it is t The expected cost of individual scheduling slot virtual robot arm, N_GFor conventional rack number of units,It is opening of the t scheduling slot conventional rack i Stop state,Represent start,Represent and shut down, S_GiFor the payment for initiation use of conventional rack i,For conventional rack i Exert oneself,Meritorious producing cost function for conventional rack i:

C_{G i} (P_{G i}^{t}) = a_{G i} {(P_{G i}^{t})}^{2} + b_{G i} P_{G i}^{t} + c_{G i} - - - (30)

Wherein: a_Gi、b_GiAnd c_GiCost coefficient for conventional rack i；

The system restriction that object function is corresponding is as follows:

A () system loading balances

{ΔP}^{t} + Σ_{i = 1}^{N_{G}} P_{G i}^{t} U_{G i}^{t} = P_{L}^{t} - - - (31)

Wherein:It it is the workload demand of the t scheduling slot prediction；

B () system reserve retrains

{ΔP}_{m a x}^{t} + Σ_{i = 1}^{N_{G}} P_{G i_\max}^{t} U_{G i}^{t} &GreaterEqual; P_{L}^{t} + P_{R}^{t} - - - (32)

Wherein:It is the EIAJ of the t scheduling slot virtual robot arm,It is the t scheduling slot conventional rack i EIAJ,It it is the stand-by requirement of t scheduling slot；

(c) conventional rack exert oneself bound constraint

P_{G i_\min}^{t} \leq P_{G i}^{t} \leq P_{G i_m a x}^{t} - - - (33)

Wherein:It it is the minimum load of the t scheduling slot conventional rack i；

(d) conventional rack Climing constant

P_{G i}^{t} U_{G i}^{t} - P_{G i}^{t - 1} U_{G i}^{t - 1} \leq R_{u p, i} - - - (34)

P_{G i}^{t - 1} U_{G i}^{t - 1} - P_{G i}^{t} U_{G i}^{t} \leq R_{d o w n, i} - - - (35)

Wherein: R_up,iFor the maximum upward slope speed of conventional rack i, R_down,iMaximum downslope speed for conventional rack i；

(e) conventional rack startup-shutdown time-constrain

(X_{G i}^{o n} (t - 1) - T_{G i}^{o n}) (U_{G i}^{t - 1} - U_{G i}^{t}) &GreaterEqual; 0 - - - (36)

(X_{G i}^{o f f} (t - 1) - T_{G i}^{o f f}) (U_{G i}^{t} - U_{G i}^{t - 1}) &GreaterEqual; 0 - - - (37)

Wherein:For conventional rack i in the accumulative available machine time of t-1 scheduling slot,For tradition machine I is in the accumulative downtime of t-1 scheduling slot for group,For the minimum start duration of conventional rack i,For tradition The minimum of unit i shuts down duration；

E () virtual robot arm retrains

- P^{d o w n} (T_{o u t}^{t}) \leq {ΔP}^{t} \leq P^{u p} (T_{o u t}^{t}) - - - (38)

Wherein:It is that the t scheduling slot virtual robot arm is in outdoor temperatureTime lower adjusting power,For The t scheduling slot virtual robot arm in outdoor temperature isTime upper adjusting power.

Unit Combination method based on the modeling of convertible frequency air-conditioner virtual robot arm the most according to claim 5, it is characterised in that: institute State step (5) to comprise the steps:

During actual schedule, the object function of the economic interests maximizing Load aggregation business is:

Wherein: σ is the compensation unit price that Load aggregation business pays user；

Consider the economic interests of Load aggregation business and participate in user fairness and the comfort level of virtual robot arm modeling, making general power adjust The object function of whole amount sum minimum is:

m i n | Σ_{i = 1}^{N} ν_{i} {ΔSOC}_{i} | - - - (40)

ν_{i} = \frac{x_{i}}{x} - - - (41)

Wherein: x_iFor the controlled number of times of air-conditioning of i-th minizone, x is the controlled number of times of all air-conditionings.