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CN105117802A - Central air-conditioner energy storage characteristic-based power market optimal dispatching strategy - Google Patents

Central air-conditioner energy storage characteristic-based power market optimal dispatching strategy Download PDF

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CN105117802A
CN105117802A CN201510570675.4A CN201510570675A CN105117802A CN 105117802 A CN105117802 A CN 105117802A CN 201510570675 A CN201510570675 A CN 201510570675A CN 105117802 A CN105117802 A CN 105117802A
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宋梦
高赐威
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Southeast University
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Abstract

The invention discloses a central air-conditioner energy storage characteristic-based power market optimal dispatching strategy. According to the power market optimal dispatching strategy, a storage battery model is established according to the thermodynamic model of a building to which a central air conditioner belongs; in the day-ahead market, a load aggregator performs electricity purchase arrangement according to clearing electricity price or load prediction condition of each time section of the next day, so that minimum day-ahead market electricity purchase cost of the load aggregator can be realized; since errors exist in load prediction of the load aggregator, difference between actual electricity purchase quantity and planned electricity purchase quantity is required to be balanced by means of real-time market, and the load aggregator predicts electricity price, load and outdoor temperature at time sections in the further according to current real-time electricity price, load and outdoor temperature; and an optimal dispatching model is established with minimum electricity purchase cost adopted as a target function and charging and discharging power of a storage battery adopted as decision-making variables, so that charging and discharging can be re-arranged for the storage battery, and the behaviors of the load aggregator in the real-time market can be optimized.

Description

一种基于中央空调储能特性的电力市场优化调度策略An Optimal Dispatch Strategy of Power Market Based on Central Air Conditioning Energy Storage Characteristics

技术领域technical field

本发明属于中央空调负荷在电力市场中的应用技术,具体涉及负荷聚合商的优化调度策略和空调负荷的储能特性。The invention belongs to the application technology of central air-conditioning loads in the electric power market, and specifically relates to the optimal dispatching strategy of load aggregators and the energy storage characteristics of air-conditioning loads.

背景技术Background technique

空调负荷在电力设备终端的比重较大,调度方式灵活,参与系统运行潜力巨大,是一种重要的需求响应资源。空调负荷具有热存储能力,通过合理的控制手段,能够快速地响应系统的调度,为系统提供优良的能量储备服务。负荷聚合商是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参与电力市场,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。Air-conditioning loads account for a large proportion of power equipment terminals, with flexible dispatching methods and great potential to participate in system operation, making them an important demand response resource. The air-conditioning load has heat storage capacity, and through reasonable control methods, it can quickly respond to system scheduling and provide excellent energy storage services for the system. Load aggregator is a business model specially used to integrate load-side resources. It can not only participate in the power market on behalf of small and medium-sized load resources, but also can measure and control loads in real time with the help of advanced measurement systems of smart grids to achieve resource efficiency. Maximization of utilization and economic benefits.

随着《关于进一步深化电力体制改革的若干意见》的出台,中国电力市场改革日益深化,日前市场和实时市场运行机制将越来越成熟,为实现负荷侧资源的高效利用和相关企业经济效益的提高提供了一个有利契机。与此同时,风能、太阳能等新能源大量接入电网以及能源互联网概念的提出,对储能元件提出了越来越高的要求,但传统储能元件(如蓄电池)的造价往往比较高,经济性较差,空调负荷为电力市场中的储能元件提供了另一种可能。With the promulgation of "Several Opinions on Further Deepening the Reform of the Power System", the reform of China's power market is deepening day by day, and the operation mechanism of the day-ahead market and the real-time market will become more and more mature. Improvement provides a favorable opportunity. At the same time, a large number of new energy sources such as wind energy and solar energy are connected to the power grid and the concept of the energy Internet has put forward higher and higher requirements for energy storage components. However, the cost of traditional energy storage components (such as batteries) is often relatively high and economical. The air-conditioning load provides another possibility for energy storage components in the electricity market.

发明内容Contents of the invention

发明目的:为了减少负荷聚合商因为负荷预测存在误差等问题而增加的购电费用,本发明提供一种基于中央空调所属建筑物储能特性的电力市场优化调度策略。负荷聚合商根据中央空调所属建筑物的储能特性建立其蓄电池模型;在日前市场中,负荷聚合商根据次日出清电价及负荷预测情况在做出次日每一时段的购电安排;实时市场中,负荷聚合商通过实时电价、负荷、室外温度对未来时段的电价、负荷及室外温度,并以蓄电池的充放电功率为决策变量,以购电费用最小为目标函数,建立优化调度模型,实现负荷聚合商的实时市场最优购电计划,使负荷聚合商的经济效益最大化。Purpose of the invention: In order to reduce the increased electricity purchase costs of load aggregators due to problems such as load forecast errors, the present invention provides an optimal dispatching strategy for the power market based on the energy storage characteristics of buildings to which the central air conditioner belongs. The load aggregator establishes its battery model according to the energy storage characteristics of the building to which the central air conditioner belongs; in the day-ahead market, the load aggregator makes power purchase arrangements for each time period of the next day according to the next day's clear electricity price and load forecast; real-time In the market, load aggregators use real-time electricity prices, loads, and outdoor temperatures to determine the electricity prices, loads, and outdoor temperatures in the future, and use the charging and discharging power of the battery as the decision variable, and take the minimum power purchase cost as the objective function to establish an optimal dispatching model. Realize the real-time market optimal power purchase plan of the load aggregator, and maximize the economic benefits of the load aggregator.

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:

一种基于中央空调储能特性的电力市场优化调度策略,包括如下步骤:A power market optimal dispatch strategy based on the characteristics of central air-conditioning energy storage, including the following steps:

(1)根据中央空调所属建筑物的热力学模型,建立蓄电池模型:(1) According to the thermodynamic model of the building to which the central air conditioner belongs, the battery model is established:

B={O,SOE,Pd,Cmax,Dmax}(1)B={O,SOE,P d ,C max ,D max }(1)

式中:B表示蓄电池模型的参数集合,O表示储能容量,SOE表示荷能状态,Pd表示浮充电功率,Cmax表示最大充电功率,Dmax表示最大放电功率;In the formula: B represents the parameter set of the battery model, O represents the energy storage capacity, SOE represents the state of charge, P d represents the floating charging power, C max represents the maximum charging power, and D max represents the maximum discharge power;

(2)负荷聚合商对次日每一时段的出清价格和负荷进行预测,并通过对蓄电池模型的充放电控制安排次日每一时段的购电量,使负荷聚合商次日购电费用最小,形成日前调度计划:(2) The load aggregator predicts the clearing price and load for each period of the next day, and arranges the purchase of electricity for each period of the next day through the charge and discharge control of the battery model, so that the load aggregator will minimize the electricity purchase cost of the next day , forming a day-ahead scheduling plan:

minFminF 11 == ΣΣ ii == 11 NN [[ EE. sthe s (( ii )) pp aa dd ′′ (( ii )) ++ λλ || CC (( ii )) -- DD. (( ii )) || ]] -- -- -- (( 22 ))

Es(i)=Lad'(i)+C(i)-D(i)(3)E s (i)=L ad '(i)+C(i)-D(i)(3)

式中:F1表示负荷聚合商在日前调度市场中的预测总购电费用,Es(i)表示日前调度市场中时段i的预测购电量,p'ad(i)表示日前调度市场中时段i的预测出清价格,Lad'(i)表示日前调度市场中时段i的预测负荷;λ表示蓄电池模型的调度费用,C(i)表示时段i时蓄电池模型的充电功率,D(i)表示时段i时蓄电池模型的放电功率,N表示时段总数;In the formula: F 1 represents the forecasted total power purchase cost of the load aggregator in the day-ahead dispatch market, E s (i) represents the forecast power purchase of time period i in the day-ahead dispatch market, p' ad (i) represents the time period in the day-ahead dispatch market The forecast clearing price of i, L ad '(i) represents the forecasted load of time period i in the day-ahead dispatch market; λ represents the dispatch cost of the battery model, C(i) represents the charging power of the battery model at time period i, D(i) Indicates the discharge power of the battery model at time period i, and N represents the total number of time periods;

(3)当负荷聚合商向系统运营商提交日前调度计划后,次日每一时段的实际出清价格即可知,则:(3) After the load aggregator submits the day-ahead scheduling plan to the system operator, the actual clearing price for each period of the next day can be known, then:

Ff 22 == ΣΣ ii == 11 NN [[ EE. sthe s (( ii )) pp aa dd (( ii )) ++ λλ || CC (( ii )) -- DD. (( ii )) || ]] -- -- -- (( 44 ))

式中:F2表示负荷聚合商在日前调度市场中的实际总购电费用,pad(i)表示日前调度市场中时段i的实际出清价格;In the formula: F 2 represents the actual total electricity purchase fee of the load aggregator in the day-ahead dispatch market, and p ad (i) represents the actual clearing price of time period i in the day-ahead dispatch market;

(4)由于负荷聚合商对次日的负荷预测存在误差,需要依靠实时市场对实时购电量和预测购电量之间的偏差进行平衡;实时市场中,负荷聚合商实时获取时段i的实时购电量、实时出清价格、实时负荷和实时室外温度进行优化调度,具体过程为:(4) Since the load aggregator has errors in the next day's load forecast, it is necessary to rely on the real-time market to balance the deviation between the real-time power purchase and the predicted power purchase; in the real-time market, the load aggregator obtains the real-time power purchase of time period i in real time , real-time clearing price, real-time load and real-time outdoor temperature for optimal scheduling, the specific process is:

(41)负荷聚合商进行优化调度时,用时段i的实时购电量、实时出清价格、实时负荷和实时室外温度对实时市场中时段i+1~时段i+n的购电量、出清价格、负荷和和室外温度进行预测;(41) When the load aggregator optimizes scheduling, use the real-time power purchase, real-time clearing price, real-time load and real-time outdoor temperature of time period i to compare the power purchase and clearing price of time period i+1 to time period i+n in the real-time market , load and outdoor temperature for forecasting;

(42)负荷聚合商以蓄电池模型的充放电功率为决策变量,以购电费用最小为目标,建立优化调度模型:(42) The load aggregator takes the charging and discharging power of the battery model as the decision variable, and aims to minimize the power purchase cost, and establishes an optimal dispatching model:

minmin Ff 33 == [[ (( EE. aa (( ii )) -- EE. sthe s (( ii )) )) pp rr tt (( ii )) ++ λλ || CC (( ii )) -- DD. (( ii )) || ]] == ΣΣ kk == ii ++ 11 ii ++ nno [[ (( EE. aa ′′ (( kk )) -- EE. sthe s (( kk )) )) pp rr tt ′′ (( kk )) ++ λλ || CC (( kk )) -- DD. (( kk )) || ]] -- -- -- (( 55 ))

Ea(i)=La(i)+C(i)-D(i)(6)E a (i) = L a (i) + C (i) - D (i) (6)

Ea'(k)=La'(k)+C(k)-D(k)(7)E a '(k)=L a '(k)+C(k)-D(k)(7)

式中:F3表示负荷聚合商在实时市场中的预测总购电费用;Ea(i)表示时段i的实时购电量,prt(i)表示时段i的实时出清价格,La(i)表示时段i的实时负荷;Ea'(k)表示实时市场中时段k的预测购电量,prt'(k)表示实时市场中时段k的预测出清价格,La'(k)表示实时市场中时段i的预测负荷;n表示后退优化时段总数;In the formula: F 3 represents the forecasted total electricity purchase fee of the load aggregator in the real-time market; E a (i) represents the real-time electricity purchase in time period i, p rt (i) represents the real-time clearing price in time period i, L a ( i) represents the real-time load of time period i; E a '(k) represents the predicted power purchase of time period k in the real-time market, p rt '(k) represents the predicted clearing price of time period k in the real-time market, L a '(k) Indicates the forecast load of period i in the real-time market; n indicates the total number of backward optimization periods;

(43)采用线性规划方法对作为目标函数的式(5)进行求解,根据求解结果对时段i时的蓄电池模型进行充放电功率控制;(43) Using the linear programming method to solve the formula (5) as the objective function, according to the solution result, the charging and discharging power control is performed on the storage battery model at the time period i;

(44)i=i+1,返回步骤(41)。(44) i=i+1, return to step (41).

所述步骤(2)和步骤(42)中,均要求0≤C(i)≤Cmax且0≤D(i)≤DmaxIn both step (2) and step (42), it is required that 0≤C(i)≤C max and 0≤D(i)≤D max .

有益效果:本发明针对负荷聚合商在日前市场和实时市场的购售电行为,提出一种基于中央空调储能特性的电力市场优化调度策略,其优点是在空调所属建筑物热力学模型基础上,充分挖掘其储能特性,建立蓄电池模型,在负荷聚合商的调度与控制下参与日前市场与实时市场,不仅减少了对造价较高的传统储能元件(如蓄电池)的依赖,延缓了电力系统中储能设备的投资,提高了电力系统运营的经济性,与此同时,也增加了负荷聚合商的经济效益。Beneficial effects: the present invention aims at the purchase and sale of electricity by load aggregators in the day-ahead market and the real-time market, and proposes an optimal scheduling strategy for the power market based on the energy storage characteristics of the central air conditioner. Fully exploit its energy storage characteristics, establish a battery model, and participate in the day-ahead market and real-time market under the scheduling and control of load aggregators, which not only reduces the dependence on traditional energy storage components with high cost (such as batteries), but also delays the power system The investment in energy storage equipment improves the economics of power system operation, and at the same time increases the economic benefits of load aggregators.

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为负荷聚合商业务系统;Fig. 2 is a load aggregator business system;

图3为空调所属建筑物的蓄电池模型。Figure 3 is the battery model of the building to which the air conditioner belongs.

具体实施方式Detailed ways

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.

一种基于中央空调储能特性的电力市场优化调度策略,包括如下步骤:A power market optimal dispatch strategy based on the characteristics of central air-conditioning energy storage, including the following steps:

(1)根据中央空调所属建筑物的热力学模型,建立蓄电池模型:(1) According to the thermodynamic model of the building to which the central air conditioner belongs, the battery model is established:

B={O,SOE,Pd,Cmax,Dmax}(1)B={O,SOE,P d ,C max ,D max }(1)

式中:B表示蓄电池模型的参数集合,O表示储能容量,SOE表示荷能状态,Pd表示浮充电功率,Cmax表示最大充电功率,Dmax表示最大放电功率;In the formula: B represents the parameter set of the battery model, O represents the energy storage capacity, SOE represents the state of charge, P d represents the floating charging power, C max represents the maximum charging power, and D max represents the maximum discharge power;

(2)负荷聚合商对次日每一时段的出清价格和负荷进行预测,并通过对蓄电池模型的充放电控制安排次日每一时段的购电量,使负荷聚合商次日购电费用最小,形成日前调度计划:(2) The load aggregator predicts the clearing price and load for each period of the next day, and arranges the purchase of electricity for each period of the next day through the charge and discharge control of the battery model, so that the load aggregator will minimize the electricity purchase cost of the next day , forming a day-ahead scheduling plan:

minFminF 11 == ΣΣ ii == 11 NN [[ EE. sthe s (( ii )) pp aa dd ′′ (( ii )) ++ λλ || CC (( ii )) -- DD. (( ii )) || ]] -- -- -- (( 22 ))

Es(i)=Lad'(i)+C(i)-D(i)(3)E s (i)=L ad '(i)+C(i)-D(i)(3)

式中:F1表示负荷聚合商在日前调度市场中的预测总购电费用,Es(i)表示日前调度市场中时段i的预测购电量,p'ad(i)表示日前调度市场中时段i的预测出清价格,Lad'(i)表示日前调度市场中时段i的预测负荷;λ表示蓄电池模型的调度费用,C(i)表示时段i时蓄电池模型的充电功率,D(i)表示时段i时蓄电池模型的放电功率,N表示时段总数;0≤C(i)≤Cmax且0≤D(i)≤DmaxIn the formula: F 1 represents the forecasted total power purchase cost of the load aggregator in the day-ahead dispatch market, E s (i) represents the forecast power purchase of time period i in the day-ahead dispatch market, p' ad (i) represents the time period in the day-ahead dispatch market The forecast clearing price of i, L ad '(i) represents the forecasted load of time period i in the day-ahead dispatch market; λ represents the dispatch cost of the battery model, C(i) represents the charging power of the battery model at time period i, D(i) Indicates the discharge power of the battery model at time period i, and N represents the total number of time periods; 0≤C(i)≤C max and 0≤D(i)≤D max ;

(3)当负荷聚合商向系统运营商提交日前调度计划后,次日每一时段的实际出清价格即可知,则:(3) After the load aggregator submits the day-ahead scheduling plan to the system operator, the actual clearing price for each period of the next day can be known, then:

Ff 22 == ΣΣ ii == 11 NN [[ EE. sthe s (( ii )) pp aa dd (( ii )) ++ λλ || CC (( ii )) -- DD. (( ii )) || ]] -- -- -- (( 44 ))

式中:F2表示负荷聚合商在日前调度市场中的实际总购电费用,pad(i)表示日前调度市场中时段i的实际出清价格;In the formula: F 2 represents the actual total electricity purchase fee of the load aggregator in the day-ahead dispatch market, and p ad (i) represents the actual clearing price of time period i in the day-ahead dispatch market;

(4)由于负荷聚合商对次日的负荷预测存在误差,需要依靠实时市场对实时购电量和预测购电量之间的偏差进行平衡;实时市场中,负荷聚合商实时获取时段i的实时购电量、实时出清价格、实时负荷和实时室外温度进行优化调度,具体过程为:(4) Since the load aggregator has errors in the next day's load forecast, it is necessary to rely on the real-time market to balance the deviation between the real-time power purchase and the predicted power purchase; in the real-time market, the load aggregator obtains the real-time power purchase of time period i in real time , real-time clearing price, real-time load and real-time outdoor temperature for optimal scheduling, the specific process is:

(41)负荷聚合商进行优化调度时,用时段i的实时购电量、实时出清价格、实时负荷和实时室外温度对实时市场中时段i+1~时段i+n的购电量、出清价格、负荷和和室外温度进行预测;(41) When the load aggregator optimizes scheduling, use the real-time power purchase, real-time clearing price, real-time load and real-time outdoor temperature of time period i to compare the power purchase and clearing price of time period i+1 to time period i+n in the real-time market , load and outdoor temperature for forecasting;

(42)负荷聚合商以蓄电池模型的充放电功率为决策变量,以购电费用最小为目标,建立优化调度模型:(42) The load aggregator takes the charging and discharging power of the battery model as the decision variable, and aims to minimize the power purchase cost, and establishes an optimal dispatching model:

minmin Ff 33 == [[ (( EE. aa (( ii )) -- EE. sthe s (( ii )) )) pp rr tt (( ii )) ++ λλ || CC (( ii )) -- DD. (( ii )) || ]] == ΣΣ kk == ii ++ 11 ii ++ nno [[ (( EE. aa ′′ (( kk )) -- EE. sthe s (( kk )) )) pp rr tt ′′ (( kk )) ++ λλ || CC (( kk )) -- DD. (( kk )) || ]] -- -- -- (( 55 ))

Ea(i)=La(i)+C(i)-D(i)(6)E a (i) = L a (i) + C (i) - D (i) (6)

Ea'(k)=La'(k)+C(k)-D(k)(7)E a '(k)=L a '(k)+C(k)-D(k)(7)

式中:F3表示负荷聚合商在实时市场中的预测总购电费用;Ea(i)表示时段i的实时购电量,prt(i)表示时段i的实时出清价格,La(i)表示时段i的实时负荷;Ea'(k)表示实时市场中时段k的预测购电量,prt'(k)表示实时市场中时段k的预测出清价格,La'(k)表示实时市场中时段i的预测负荷;n表示后退优化时段总数;0≤C(i)≤Cmax且0≤D(i)≤DmaxIn the formula: F 3 represents the forecasted total electricity purchase fee of the load aggregator in the real-time market; E a (i) represents the real-time electricity purchase in time period i, p rt (i) represents the real-time clearing price in time period i, L a ( i) represents the real-time load of time period i; E a '(k) represents the predicted power purchase of time period k in the real-time market, p rt '(k) represents the predicted clearing price of time period k in the real-time market, L a '(k) Indicates the forecast load of period i in the real-time market; n indicates the total number of backward optimization periods; 0≤C(i)≤C max and 0≤D(i)≤D max ;

(43)采用线性规划方法对作为目标函数的式(5)进行求解,根据求解结果对时段i时的蓄电池模型进行充放电功率控制;(43) Using the linear programming method to solve the formula (5) as the objective function, according to the result of the solution, the charging and discharging power of the storage battery model at the time period i is controlled;

(44)i=i+1,返回步骤(41)。(44) i=i+1, return to step (41).

所述步骤(1)中,所属建筑物的热力学模型为:In described step (1), the thermodynamic model of belonging building is:

dTdT ii nno dd tt == αα (( TT oo -- TT ii nno )) ++ γγ -- μμ QQ -- -- -- (( 11 -- 11 ))

TT ii nno == (( TT ii nno (( 00 )) -- αTαT oo ++ γγ -- μμ QQ αα )) ee -- αα tt ++ αTαT oo ++ γγ -- μμ QQ αα -- -- -- (( 11 -- 22 ))

式中:Tin表示室内温度,To表示室外温度,Q表示中央空调制冷量,α、γ和μ为系数,Tin(0)表示初始时刻的室内温度;t表示时间。In the formula: T in represents the indoor temperature, T o represents the outdoor temperature, Q represents the cooling capacity of the central air conditioner, α, γ and μ are coefficients, T in (0) represents the indoor temperature at the initial moment; t represents the time.

所述步骤(1)中,蓄电池模型的建立过程如下:In the step (1), the establishment process of the storage battery model is as follows:

(1.1)储能容量O(1.1) Energy storage capacity O

设满足人体舒适度的室内温度区间为[Tmin,Tmax],中央空调所属建筑物的冷量存储于室内空气和室内固体中(类似于蓄电池将电能存储于电容),室内温度处于Tmin时储冷量最大,室内温度处于Tmax时储冷量最小;将室内温度处于Tmax时的储冷量记为0,则当室内温度处于Tmin时的储冷量为:Assuming that the indoor temperature range that satisfies human comfort is [T min , T max ], the cooling capacity of the building to which the central air conditioner belongs is stored in the indoor air and indoor solids (similar to how a battery stores electric energy in a capacitor), and the indoor temperature is at T min The cold storage capacity is the largest when the indoor temperature is at T max , and the cold storage capacity is the smallest when the indoor temperature is at T max ; the cold storage capacity when the indoor temperature is at T max is recorded as 0, then when the indoor temperature is at T min , the cold storage capacity is:

Oo ii nno == TT mm aa xx -- TT ii nno μμ -- -- -- (( 11 -- 33 ))

式中:表示建筑物每升高1℃所需要的能量;由此可知,储能容量O为:In the formula: Indicates the energy required for every 1°C increase in the building; it can be seen that the energy storage capacity O is:

Oo == TT mm aa xx -- TT mm ii nno μμ -- -- -- (( 11 -- 44 ))

(1.2)荷能状态SOE(1.2) State of Energy SOE

荷能状态表示当前储冷量与储能容量的比值,反映了蓄电池模型的储能状态:The state of charge indicates the ratio of the current cooling capacity to the energy storage capacity, which reflects the energy storage state of the battery model:

SS Oo EE. == Oo ii nno Oo == TT mm aa xx -- TT ii nno TT maxmax -- TT minmin -- -- -- (( 11 -- 55 ))

空调能够将不便于存储的电能转化为便于存储的热能,空调所属建筑物存储冷量在本质上也是在存储电量,故SOE同时也反映了蓄电池模型的存储电量情况,SOE值越大,蓄电池模型存储电量越大。The air conditioner can convert the electric energy that is not easy to store into heat energy that is easy to store. The storage of cooling capacity of the building to which the air conditioner belongs is essentially storing electricity, so the SOE also reflects the stored electricity of the battery model. The larger the SOE value, the better the battery model will be. The greater the storage capacity.

将式(1-5)带入(1-2)可得到荷能状态随时间变化的规律:Substituting formula (1-5) into (1-2) can get the law of the state of charge and energy changing with time:

SS Oo EE. (( ii )) == SS Oo EE. (( 00 )) ·· ee -- αα tt ++ αα (( TT mm aa xx -- TT )) oo -- γγ ++ μμ QQ αα (( TT mm aa xx -- TT mm ii nno )) (( 11 -- ee -- αα tt )) -- -- -- (( 11 -- 66 ))

式中:SOE(i)表示时段i期间达到稳定状态时的荷能状态,SOE(0)表示初始时刻的荷能状态。In the formula: SOE(i) represents the state of charge when the steady state is reached during period i, and SOE(0) represents the state of charge at the initial moment.

(1.3)浮充电功率Pd (1.3) Float charging power P d

中央空调所属建筑物由于室内外温差辐射、人员散热、电器散热等原因,会产生热量,使室内温度升高,荷能状态值降低,即蓄电池模型存在内阻和自放电过程;为了维持荷能状态值不变,中央空调需要从电网中吸收电量以产生同等冷量,带走建筑物增加的热量,建立中央空调的电功率为蓄电池模型的浮充电功率:The building to which the central air conditioner belongs will generate heat due to indoor and outdoor temperature difference radiation, personnel heat dissipation, electrical appliance heat dissipation, etc., which will increase the indoor temperature and reduce the energy state value, that is, the battery model has internal resistance and self-discharge process; in order to maintain the energy The state value remains unchanged, the central air conditioner needs to absorb electricity from the grid to generate the same cooling capacity, take away the increased heat of the building, and establish the electric power of the central air conditioner as the floating charging power of the battery model:

Pd=P(1-7) Pd = P(1-7)

式中:P表示中央空调的电功率,P是SOE的函数。In the formula: P represents the electric power of the central air conditioner, and P is a function of SOE.

(1.4)最大充电功率Cmax (1.4) Maximum charging power C max

设当前蓄电池模型的荷能状态为SOE(i),当由荷能状态SOE(i)增大至荷能状态SOE(i+1)时,根据式(1-1)可知,保持荷能状态SOE(i+1)所需要的冷量为:Assuming that the state of charge of the current battery model is SOE(i), when the state of charge SOE(i) increases to the state of charge SOE(i+1), according to formula (1-1), it can be known that the state of charge is maintained The cooling capacity required by SOE(i+1) is:

QQ == αα [[ TT oo -- TT mm aa xx ++ SS Oo EE. (( ii ++ 11 )) ·· (( TT mm aa xx -- TT mm ii nno )) ]] ++ γγ μμ -- -- -- (( 11 -- 88 ))

通过变频技术对冷水机组、冷冻水泵、风机进行控制,使制冷量调整为Q,根据式(1-2)计算由荷能状态SOE(i)增大至荷能状态SOE(i+1)时所需要的时间为:The chiller, chilled water pump, and fan are controlled by frequency conversion technology, so that the cooling capacity is adjusted to Q, and when the energy state SOE(i) increases to the energy state SOE(i+1) according to formula (1-2) The time required is:

tt == -- ll nno αα (( TT mm aa xx -- TT oo )) -- γγ ++ μμ QQ -- αα ·&Center Dot; SS Oo EE. (( ii ++ 11 )) ·&Center Dot; (( TT mm aa xx -- TT mm ii nno )) αα (( TT mm aa xx -- TT oo )) -- γγ ++ μμ QQ -- αα ·&Center Dot; SS Oo EE. (( ii )) ·&Center Dot; (( TT mm aa xx -- TT minmin )) αα == ∞∞ -- -- -- (( 11 -- 99 ))

式(1-9)说明,荷能状态随着时间无限接近SOE(i+1)但不会等于SOE(i+1),这是由蓄电池模型自身特性决定的,故设一个调整精度△SOE,当荷能状态增加至SOE(i+1)-△SOE时,即可认为荷能状态已经调至SOE(i+1);为了实现蓄电池模型的有效控制,需使荷能状态在一个调度时段内达到稳定,需使得:Equation (1-9) shows that the state of charge is infinitely close to SOE(i+1) over time but will not be equal to SOE(i+1), which is determined by the characteristics of the battery model itself, so an adjustment accuracy △SOE , when the state of charge increases to SOE(i+1)-△SOE, it can be considered that the state of charge has been adjusted to SOE(i+1); in order to achieve effective control of the battery model, it is necessary to make the state of charge in a dispatch To achieve stability within a certain period of time, it is necessary to make:

t≤△t(1-10)t≤△t(1-10)

求解可得:The solution can be obtained:

SS Oo EE. (( ii ++ 11 )) ≤≤ SS Oo EE. (( ii )) ++ TT mm aa xx -- ΔΔ SS Oo EE. ·&Center Dot; (( TT mm aa xx -- TT minmin )) (( TT mm aa xx -- TT minmin )) ee -- αα ΔΔ tt -- -- -- (( 11 -- 1111 ))

故设荷能状态最大可增加至同理可得荷能状态最大可降低至故一个调度时段内蓄电池模型荷能状态允许的变化范围为在该两个值的范围内。Therefore, it is assumed that the maximum energy state can be increased to Similarly, the maximum energy state can be reduced to Therefore, the allowable change range of the energy state of the battery model within a dispatch period is within the range of the two values.

为了增大蓄电池模型的荷能状态,需要对蓄电池模型进行充电,此时表现为增加蓄电池模型的浮充电功率,故蓄电池模型的最大充电功率为:In order to increase the energy state of the battery model, it is necessary to charge the battery model. At this time, it is expressed as increasing the floating charging power of the battery model, so the maximum charging power of the battery model is:

CC maxmax == PP dd (( minmin (( 11 ,, SS Oo EE. (( ii )) ++ TT mm aa xx -- ΔΔ SS Oo EE. ·· (( TT mm aa xx -- TT minmin )) (( TT mm aa xx -- TT minmin )) ee -- αα ΔΔ tt )) )) -- PP dd (( SS Oo EE. (( ii )) )) -- -- -- (( 11 -- 1212 ))

式中:△SOE表示调度精度,△t表示一个时段的时长,Pd(SOE(i))表示荷能状态为SOE(i)时的浮充电功率。In the formula: △SOE represents the scheduling accuracy, △t represents the duration of a period, and P d (SOE(i)) represents the floating charging power when the state of charge is SOE(i).

(1.5)最大放电功率Dmax (1.5) Maximum discharge power D max

同理于最大充电功率Cmax的推算过程,最大放电功率DmaxSimilar to the calculation process of the maximum charging power C max , the maximum discharging power D max is:

DD. maxmax == PP dd (( SS Oo EE. (( ii )) -- PP dd (( maxmax (( 00 ,, SS Oo EE. (( ii )) -- TT mm aa xx -- ΔΔ SS Oo EE. ·&Center Dot; (( TT mm aa xx -- TT minmin )) (( TT mm aa xx -- TT minmin )) ee -- αα ΔΔ tt )) )) -- -- -- (( 11 -- 1313 ))

式(1-3)~(1-7)、(1-12)和(1-13)共同组成中央空调所属建筑物的蓄电池模型。Formulas (1-3)~(1-7), (1-12) and (1-13) together form the battery model of the building to which the central air conditioner belongs.

当对蓄电池模型进行充电时,充电后的SOE(i+1)与当前的SOE(i)满足如下关系:When charging the battery model, the SOE(i+1) after charging and the current SOE(i) satisfy the following relationship:

Pd(SOE(i+1))=Pd(SOE(i))+C(i)(1-14)P d (SOE(i+1))=P d (SOE(i))+C(i)(1-14)

当对蓄电池模型进行放电时,放电后的SOE(i+1)与当前的SOE(i)满足如下关系:When discharging the battery model, the SOE(i+1) after discharge and the current SOE(i) satisfy the following relationship:

Pd(SOE(i+1))=Pd(SOE(i))-D(i)(1-15)P d (SOE(i+1))=P d (SOE(i))-D(i)(1-15)

对蓄电池进行充放电功率控制后,可对方程(1-14)和(1-15)进行求解,更新蓄电池模型的参数集合。After controlling the charging and discharging power of the battery, equations (1-14) and (1-15) can be solved to update the parameter set of the battery model.

对于一个含有M个蓄电池模型的群组来说,其最大充电功率为所有M个蓄电池模型的最大充电功率Cmax之和,其最大放电功率为所有M个蓄电池模型的最大放电功率Dmax之和。For a group containing M battery models, its maximum charging power is the sum of the maximum charging power C max of all M battery models, and its maximum discharge power is the sum of the maximum discharge power D max of all M battery models .

所述步骤(43)中,负荷聚合商的优化结果为C(i)或D(i),以充电功率控制为例对蓄电池模型进行充放电功率控制的策略进行说明:In the step (43), the optimization result of the load aggregator is C(i) or D(i). Taking the charging power control as an example, the strategy for controlling the charging and discharging power of the storage battery model is described:

(43.1)将M个蓄电池模型的荷能状态按值的大小进行排序,SOE1<SOE2<…<SOEz<…<SOEM,按顺序所有M个蓄电池模型当前调度时段的最大充电功率分别为 (43.1) Sort the state of charge of the M battery models according to the size of the value, SOE 1 <SOE 2 <...<SOE z <...<SOE M , the maximum charging power of all M battery models in the current scheduling period in order are respectively for

(43.2)执行如下程序:(43.2) Execute the following procedure:

CC == CC (( ii )) -- (( WW -- CC mm aa xx zz ))

SOESOE nno ee ww zz == sthe s oo ll vv ee (( &prime;&prime; PP dd (( SOESOE nno ee ww zz )) == PP dd (( SOESOE zz )) ++ CC &prime;&prime; ,, SOESOE nno ee ww zz ))

(43.3)当要求充电功率为C时,具体的充电安排为:荷能状态为SOE1、SOE2…SOEz-1的蓄电池模型调至其最大荷能状态SOEz调至其他蓄电池的荷能状态保持不变;更新充放电功率控制后的蓄电池模型的参数集合。(43.3) When the charging power is required to be C, the specific charging arrangement is as follows: the battery model whose state of charge is SOE 1 , SOE 2 ...SOE z-1 is adjusted to its maximum state of charge, SOE z is adjusted to The state of charge of other batteries remains unchanged; the parameter set of the battery model after charging and discharging power control is updated.

放电功率的控制策略类似于充电功率的控制策略,此处不再赘述。The control strategy of the discharge power is similar to the control strategy of the charge power, and will not be repeated here.

本发明提供的基于中央空调负荷所属建筑物储能特性的电力市场优化调度策略,在中央空调所属建筑物的蓄电池模型基础上,在日前市场中,负荷聚合商根据次日出清电价及负荷预测情况做出购电安排;在实时市场中,利用当前时段的电价、负荷、室外温度预测未来时段的电价、负荷及室外温度,利用蓄电池的充放电特性以购电费用最低为目标函数建立实时市场的优化调度策略,在减少对传统造价较高的储能设备依赖的同时,也提高了负荷聚合商的经济效益。The power market optimization scheduling strategy based on the energy storage characteristics of the building to which the central air-conditioning load belongs is provided by the present invention. On the basis of the storage battery model of the building to which the central air-conditioning belongs, in the day-ahead market, the load aggregator will clear the electricity price and load forecast according to the next day In the real-time market, use the electricity price, load, and outdoor temperature in the current period to predict the electricity price, load, and outdoor temperature in the future, and use the charging and discharging characteristics of the battery to establish a real-time market with the lowest power purchase cost as the objective function The optimized scheduling strategy reduces the dependence on traditional high-cost energy storage equipment, and also improves the economic benefits of load aggregators.

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 (2)

1.一种基于中央空调储能特性的电力市场优化调度策略,其特征在于:包括如下步骤:1. A power market optimal dispatching strategy based on central air-conditioning energy storage characteristics, characterized in that: comprising the steps: (1)根据中央空调所属建筑物的热力学模型,建立蓄电池模型:(1) According to the thermodynamic model of the building to which the central air conditioner belongs, the battery model is established: B={O,SOE,Pd,Cmax,Dmax}(1)B={O,SOE,P d ,C max ,D max }(1) 式中:B表示蓄电池模型的参数集合,O表示储能容量,SOE表示荷能状态,Pd表示浮充电功率,Cmax表示最大充电功率,Dmax表示最大放电功率;In the formula: B represents the parameter set of the battery model, O represents the energy storage capacity, SOE represents the state of charge, P d represents the floating charging power, C max represents the maximum charging power, and D max represents the maximum discharge power; (2)负荷聚合商对次日每一时段的出清价格和负荷进行预测,并通过对蓄电池模型的充放电控制安排次日每一时段的购电量,使负荷聚合商次日购电费用最小,形成日前调度计划:(2) The load aggregator predicts the clearing price and load for each period of the next day, and arranges the purchase of electricity for each period of the next day through the charge and discharge control of the battery model, so that the load aggregator will minimize the electricity purchase cost of the next day , forming a day-ahead scheduling plan: minFminF 11 == &Sigma;&Sigma; ii == 11 NN &lsqb;&lsqb; EE. sthe s (( ii )) pp aa dd &prime;&prime; (( ii )) ++ &lambda;&lambda; || CC (( ii )) -- DD. (( ii )) || &rsqb;&rsqb; -- -- -- (( 22 )) Es(i)=Lad'(i)+C(i)-D(i)(3)E s (i)=L ad '(i)+C(i)-D(i)(3) 式中:F1表示负荷聚合商在日前调度市场中的预测总购电费用,Es(i)表示日前调度市场中时段i的预测购电量,p'ad(i)表示日前调度市场中时段i的预测出清价格,Lad'(i)表示日前调度市场中时段i的预测负荷;λ表示蓄电池模型的调度费用,C(i)表示时段i时蓄电池模型的充电功率,D(i)表示时段i时蓄电池模型的放电功率,N表示时段总数;In the formula: F 1 represents the forecasted total power purchase cost of the load aggregator in the day-ahead dispatch market, E s (i) represents the forecast power purchase of time period i in the day-ahead dispatch market, p' ad (i) represents the time period in the day-ahead dispatch market The forecast clearing price of i, L ad '(i) represents the forecasted load of time period i in the day-ahead dispatch market; λ represents the dispatch cost of the battery model, C(i) represents the charging power of the battery model at time period i, D(i) Indicates the discharge power of the battery model at time period i, and N represents the total number of time periods; (3)当负荷聚合商向系统运营商提交日前调度计划后,次日每一时段的实际出清价格即可知,则:(3) After the load aggregator submits the day-ahead scheduling plan to the system operator, the actual clearing price for each period of the next day can be known, then: Ff 22 == &Sigma;&Sigma; ii == 11 NN &lsqb;&lsqb; EE. sthe s (( ii )) pp aa dd (( ii )) ++ &lambda;&lambda; || CC (( ii )) -- DD. (( ii )) || &rsqb;&rsqb; -- -- -- (( 44 )) 式中:F2表示负荷聚合商在日前调度市场中的实际总购电费用,pad(i)表示日前调度市场中时段i的实际出清价格;In the formula: F 2 represents the actual total electricity purchase fee of the load aggregator in the day-ahead dispatch market, and p ad (i) represents the actual clearing price of time period i in the day-ahead dispatch market; (4)由于负荷聚合商对次日的负荷预测存在误差,需要依靠实时市场对实时购电量和预测购电量之间的偏差进行平衡;实时市场中,负荷聚合商实时获取时段i的实时购电量、实时出清价格、实时负荷和实时室外温度进行优化调度,具体过程为:(4) Since the load aggregator has errors in the next day's load forecast, it is necessary to rely on the real-time market to balance the deviation between the real-time power purchase and the predicted power purchase; in the real-time market, the load aggregator obtains the real-time power purchase of time period i in real time , real-time clearing price, real-time load and real-time outdoor temperature for optimal scheduling, the specific process is: (41)负荷聚合商进行优化调度时,用时段i的实时购电量、实时出清价格、实时负荷和实时室外温度对实时市场中时段i+1~时段i+n的购电量、出清价格、负荷和和室外温度进行预测;(41) When the load aggregator optimizes scheduling, use the real-time power purchase, real-time clearing price, real-time load and real-time outdoor temperature of time period i to compare the power purchase and clearing price of time period i+1 to time period i+n in the real-time market , load and outdoor temperature for forecasting; (42)负荷聚合商以蓄电池模型的充放电功率为决策变量,以购电费用最小为目标,建立优化调度模型:(42) The load aggregator takes the charging and discharging power of the battery model as the decision variable, and aims to minimize the power purchase cost, and establishes an optimal dispatching model: minmin Ff 33 == &lsqb;&lsqb; (( EE. aa (( ii )) -- EE. sthe s (( ii )) )) pp rr tt (( ii )) ++ &lambda;&lambda; || CC (( ii )) -- DD. (( ii )) || &rsqb;&rsqb; == &Sigma;&Sigma; kk == ii ++ 11 ii ++ nno &lsqb;&lsqb; (( EE. aa &prime;&prime; (( kk )) -- EE. sthe s (( kk )) )) pp rr tt &prime;&prime; (( kk )) ++ &lambda;&lambda; || CC (( kk )) -- DD. (( kk )) || &rsqb;&rsqb; -- -- -- (( 55 )) Ea(i)=La(i)+C(i)-D(i)(6)E a (i) = L a (i) + C (i) - D (i) (6) Ea'(k)=La'(k)+C(k)-D(k)(7)E a '(k)=L a '(k)+C(k)-D(k)(7) 式中:F3表示负荷聚合商在实时市场中的预测总购电费用;Ea(i)表示时段i的实时购电量,prt(i)表示时段i的实时出清价格,La(i)表示时段i的实时负荷;Ea'(k)表示实时市场中时段k的预测购电量,prt'(k)表示实时市场中时段k的预测出清价格,La'(k)表示实时市场中时段i的预测负荷;n表示后退优化时段总数;In the formula: F 3 represents the forecasted total electricity purchase fee of the load aggregator in the real-time market; E a (i) represents the real-time electricity purchase in time period i, p rt (i) represents the real-time clearing price in time period i, L a ( i) represents the real-time load of time period i; E a '(k) represents the predicted power purchase of time period k in the real-time market, p rt '(k) represents the predicted clearing price of time period k in the real-time market, L a '(k) Indicates the forecast load of period i in the real-time market; n indicates the total number of backward optimization periods; (43)采用线性规划方法对作为目标函数的式(5)进行求解,根据求解结果对时段i时的蓄电池模型进行充放电功率控制;(43) Using the linear programming method to solve the formula (5) as the objective function, according to the solution result, the charging and discharging power control is performed on the storage battery model at the time period i; (44)i=i+1,返回步骤(41)。(44) i=i+1, return to step (41). 2.根据权利要求1所述的基于中央空调储能特性的电力市场优化调度策略,其特征在于:所述步骤(1)中,所属建筑物的热力学模型为:2. The electricity market optimal dispatching strategy based on central air-conditioning energy storage characteristics according to claim 1, characterized in that: in the step (1), the thermodynamic model of the belonging building is: dTdT ii nno dd tt == &alpha;&alpha; (( TT oo -- TT ii nno )) ++ &gamma;&gamma; -- &mu;&mu; QQ -- -- -- (( 11 -- 11 )) TT ii nno == (( TT ii nno (( 00 )) -- &alpha;T&alpha;T oo ++ &gamma;&gamma; -- &mu;&mu; QQ &alpha;&alpha; )) ee -- &alpha;&alpha; tt ++ &alpha;T&alpha;T oo ++ &gamma;&gamma; -- &mu;&mu; QQ &alpha;&alpha; -- -- -- (( 11 -- 22 )) 式中:Tin表示室内温度,To表示室外温度,Q表示中央空调制冷量,α、γ和μ为系数,Tin(0)表示初始时刻的室内温度;t表示时间;In the formula: T in represents the indoor temperature, T o represents the outdoor temperature, Q represents the cooling capacity of the central air conditioner, α, γ and μ are coefficients, T in (0) represents the indoor temperature at the initial moment; t represents the time; 蓄电池模型的建立过程如下:The process of building the battery model is as follows: (1.1)储能容量O(1.1) Energy storage capacity O 设满足人体舒适度的室内温度区间为[Tmin,Tmax],中央空调所属建筑物的冷量存储于室内空气和室内固体中,室内温度处于Tmin时储冷量最大,室内温度处于Tmax时储冷量最小;将室内温度处于Tmax时的储冷量记为0,则当室内温度处于Tmin时的储冷量为:Assuming that the indoor temperature range that satisfies human comfort is [T min , T max ], the cooling capacity of the building to which the central air conditioner belongs is stored in the indoor air and indoor solids . The cold storage capacity is the smallest when the temperature is max ; the cold storage capacity when the indoor temperature is at T max is recorded as 0, then when the indoor temperature is at T min , the cold storage capacity is: Oo ii nno == TT mm aa xx -- TT ii nno &mu;&mu; -- -- -- (( 11 -- 33 )) 式中:表示建筑物每升高1℃所需要的能量;由此可知,储能容量O为:In the formula: Indicates the energy required for every 1°C increase in the building; it can be seen that the energy storage capacity O is: Oo == TT mm aa xx -- TT mm ii nno &mu;&mu; -- -- -- (( 11 -- 44 )) (1.2)荷能状态SOE(1.2) State of Energy SOE 荷能状态表示当前储冷量与储能容量的比值,反映了蓄电池模型的储能状态:The state of charge indicates the ratio of the current cooling capacity to the energy storage capacity, which reflects the energy storage state of the battery model: SS Oo EE. == Oo ii nno Oo == TT mm aa xx -- TT ii nno TT maxmax -- TT minmin -- -- -- (( 11 -- 55 )) 将式(1-5)带入(1-2)可得到荷能状态随时间变化的规律:Substituting formula (1-5) into (1-2) can get the law of the state of charge and energy changing with time: SS Oo EE. (( ii )) == SS Oo EE. (( 00 )) &CenterDot;&Center Dot; ee -- &alpha;&alpha; tt ++ &alpha;&alpha; (( TT mm aa xx -- TT )) oo -- &gamma;&gamma; ++ &mu;&mu; QQ &alpha;&alpha; (( TT maxmax -- TT minmin )) (( 11 -- ee -- &alpha;&alpha; tt )) -- -- -- (( 11 -- 66 )) 式中:SOE(i)表示时段i期间达到稳定状态时的荷能状态,SOE(0)表示初始时刻的荷能状态;In the formula: SOE(i) represents the state of charge when it reaches a steady state during period i, and SOE(0) represents the state of charge at the initial moment; (1.3)浮充电功率Pd (1.3) Float charging power P d 建立中央空调的电功率为蓄电池模型的浮充电功率:Establish the electric power of the central air conditioner as the float charging power of the battery model: Pd=P(1-7) Pd = P(1-7) 式中:P表示中央空调的电功率,P是SOE的函数;In the formula: P represents the electric power of the central air conditioner, and P is a function of SOE; (1.4)最大充电功率Cmax (1.4) Maximum charging power C max CC maxmax == PP dd (( minmin (( 11 ,, SS Oo EE. (( ii )) ++ TT mm aa xx -- &Delta;&Delta; SS Oo EE. &CenterDot;&Center Dot; (( TT mm aa xx -- TT minmin )) (( TT mm aa xx -- TT minmin )) ee -- &alpha;&alpha; &Delta;&Delta; tt )) )) -- PP dd (( SS Oo EE. (( ii )) )) -- -- -- (( 11 -- 88 )) 式中:△SOE表示调度精度,△t表示一个时段的时长,Pd(SOE(i))表示荷能状态为SOE(i)时的浮充电功率;In the formula: △SOE represents the scheduling accuracy, △t represents the duration of a period, P d (SOE(i)) represents the floating charging power when the state of charge is SOE(i); 当对蓄电池模型进行充电时,充电后的SOE(i+1)与当前的SOE(i)满足如下关系:When charging the battery model, the SOE(i+1) after charging and the current SOE(i) satisfy the following relationship: Pd(SOE(i+1))=Pd(SOE(i))+C(i)(1-9)P d (SOE(i+1))=P d (SOE(i))+C(i)(1-9) (1.5)最大放电功率Dmax (1.5) Maximum discharge power D max DD. maxmax == PP dd (( SS Oo EE. (( ii )) -- PP dd (( maxmax (( 00 ,, SS Oo EE. (( ii )) -- TT mm aa xx -- &Delta;&Delta; SS Oo EE. &CenterDot;&CenterDot; (( TT mm aa xx -- TT minmin )) (( TT mm aa xx -- TT minmin )) ee -- &alpha;&alpha; &Delta;&Delta; tt )) )) -- -- -- (( 11 -- 1010 )) 当对蓄电池模型进行放电时,放电后的SOE(i+1)与当前的SOE(i)满足如下关系:When discharging the battery model, the SOE(i+1) after discharge and the current SOE(i) satisfy the following relationship: Pd(SOE(i+1))=Pd(SOE(i))-D(i)(1-11)P d (SOE(i+1))=P d (SOE(i))-D(i)(1-11) 对蓄电池进行充放电功率控制后,对方程(1-10)和(1-11)进行求解,更新蓄电池模型的参数集合。After controlling the charge and discharge power of the battery, solve the equations (1-10) and (1-11), and update the parameter set of the battery model.
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