CN102104251B

CN102104251B - Microgrid real-time energy optimizing and scheduling method in parallel running mode

Info

Publication number: CN102104251B
Application number: CN201110045064XA
Authority: CN
Inventors: 江全元; 石庆均; 耿光超; 王云; 刘霞; 程玮; 薛美东
Original assignee: Zhejiang University ZJU
Current assignee: Zhejiang University ZJU
Priority date: 2011-02-24
Filing date: 2011-02-24
Publication date: 2013-04-24
Anticipated expiration: 2031-02-24
Also published as: CN102104251A

Abstract

The invention discloses a real-time energy optimal scheduling method for a micro-grid in a grid-connected operation mode. This method firstly divides 24 hours a day into peak period, normal period and valley period according to the load of the large power grid, and then monitors the working status of the battery in the microgrid in real time during the real-time operation of the microgrid. Different energy optimization strategies are used in different working states to determine the active power output of each adjustable micro power source in the microgrid, the charging and discharging power of the battery, the active power interacting with the grid, and the reactive power of the reactive adjustable power source Scheduling instructions. The micro-grid applicable to the present invention is composed of renewable energy power generation, dispatchable micro-power sources, and storage batteries. The present invention can not only improve the economy and reliability of micro-grid operation, but also help the large power grid to "cut peaks and fill valleys". It is beneficial to prolong the service life of the battery.

Description

A Real-time Energy Optimal Scheduling Method for Microgrid under Grid-connected Operation Mode

技术领域 technical field

本发明涉及一种微电网规划、运行与管理，尤其涉及一种并网运行模式下的微电网实时能量优化调度方法。The invention relates to planning, operation and management of a micro-grid, in particular to a real-time energy optimization scheduling method for a micro-grid in a grid-connected operation mode.

背景技术 Background technique

微电网是由各种分布式电源、储能单元、负荷以及控制保护系统组成的集合，通过相关控制装置间的协调配合，为负荷提供较高可靠性和高质量电能。微电网因其环境友好、建设成本低等因素已经成为大电网的有益补充，得到越来越多的重视和研究。在确保微电网安全稳定运行的基础上，微电网的经济优化运行是能量管理研究中的一个重要内容。A microgrid is a collection of various distributed power sources, energy storage units, loads, and control and protection systems. Through the coordination and cooperation of related control devices, it provides high reliability and high-quality power for loads. Due to its environmental friendliness and low construction cost, the microgrid has become a useful supplement to the large power grid, and has received more and more attention and research. On the basis of ensuring the safe and stable operation of the microgrid, the economically optimized operation of the microgrid is an important content in the research of energy management.

目前，国内外在微电网能量优化调度方面已经做了一些研究，在文献[1]～[5]中主要从微电网运行成本最低和环境效益最好方面建立能量优化模型，通过优化模型求解结果调度各可调度微电源的有功功率，对其无功功率输出却不做优化，且都没有考虑微电网自身的网络结构，使得优化结果欠缺实际，另外，对于微电网中的蓄电池的使用也只是无依据的循环充电放电，并没有对蓄电池充放电进行优化，从而没有充分利用蓄电池提高微电网运行的经济性。相关文献：At present, some researches have been done on micro-grid energy optimization scheduling at home and abroad. In the literature [1]-[5], the energy optimization model is mainly established from the aspects of the lowest operating cost and the best environmental benefits of the micro-grid, and the results are solved by the optimization model. The active power of each schedulable micro-power source is dispatched, but the reactive power output is not optimized, and the network structure of the micro-grid itself is not considered, so that the optimization results are not realistic. In addition, the use of batteries in the micro-grid is only The unfounded cyclic charging and discharging does not optimize the charging and discharging of the battery, so that the battery is not fully utilized to improve the economy of the microgrid operation. Related literature:

[1]丁明，张颖媛，茆美琴，杨为，刘小平.集中控制式微电网系统的稳态建模与运行优化[J].电力系统自动化，2009，33(24)：78-82。[1] Ding Ming, Zhang Yingyuan, Mao Meiqin, Yang Wei, Liu Xiaoping. Steady-state modeling and operation optimization of centralized control microgrid system [J]. Electric Power System Automation, 2009, 33(24): 78-82.

[2]陈达威，朱桂萍.微电网负荷优化分配[J].电力系统自动化，2010，34(20)：45-48。[2] Dawei Chen, Guiping Zhu. Microgrid load optimization distribution [J]. Electric Power System Automation, 2010, 34(20): 45-48.

[3]Mohamed FA，Koivo HN.MicroGrid Online Management andBalancing Using Multiobjective Optimization.2007 IEEE LAUSANNEPOWERTECH，VOLS 1-5，PP：639-644。[3] Mohamed FA, Koivo HN. MicroGrid Online Management and Balancing Using Multiobjective Optimization. 2007 IEEE LAUSANNE POWERTECH, VOLS 1-5, PP: 639-644.

[4]牛铭，黄伟，郭佳欢，苏玲.微电网并网时的经济运行研究[J].电网技术，2010，34(11)：39-42。[4] Niu Ming, Huang Wei, Guo Jiahuan, Su Ling. Research on the Economic Operation of Microgrids Connected to the Grid [J]. Power Grid Technology, 2010, 34(11): 39-42.

[5]Mohamed F.and H Koivo.Online Management of MicroGrid with BatteryStorage Using Multiobjective Optimization，”the first Interna-tional Conferenceon Power Engineering，Energy and Electrical Drives(POWERENG07)，12-14 April2007，Setubal，Portugal.231-236。[5] Mohamed F. and H Koivo. Online Management of MicroGrid with BatteryStorage Using Multiobjective Optimization," the first International Conference on Power Engineering, Energy and Electrical Drives (POWERENG07), 12-14 April2007, 362Setubal, 3Port1 .

发明内容 Contents of the invention

为了保证微电网内部蓄电池在使用过程中不处于过充电或过放电状态，同时又充分发挥微电网内部蓄电池的作用，以提高微电网运行经济性，并帮助大电网进行“削峰填谷”，区别于现有的微电网能量优化调度方法，本发明的目的是提供一种并网运行模式下的微电网实时能量优化调度方法。In order to ensure that the battery inside the micro-grid is not in an overcharge or over-discharge state during use, and at the same time give full play to the role of the battery inside the micro-grid to improve the operating economy of the micro-grid and help the large power grid to "cut peaks and fill valleys", Different from the existing micro-grid energy optimal dispatching method, the purpose of the present invention is to provide a micro-grid real-time energy optimal dispatching method in grid-connected operation mode.

本发明采用的技术方案是包括如下步骤：The technical scheme that the present invention adopts is to comprise the steps:

1)根据大电网的负荷情况将一天24小时划分为峰时段、平时段、谷时段三种时段，如果大电网采用峰谷分时电价，则就依照分时电价所确定的峰时段、平时段和谷时段划分；1) According to the load conditions of the large power grid, divide 24 hours a day into three periods: peak time, normal time, and valley time. and valley time division;

2)在微电网的实时运行过程中，在每次调度时刻确定当前所处的时段，监测微电网内的蓄电池的荷电状态SOC，测量各节点负荷的有功功率和无功功率，测量可再生能源发电的输出有功功率和无功功率；2) During the real-time operation of the microgrid, determine the current time period at each scheduling time, monitor the state of charge SOC of the storage battery in the microgrid, measure the active power and reactive power of each node load, and measure the renewable energy. Output active power and reactive power of energy generation;

3)如果当前处于谷时段或平时段，则进一步判断当前蓄电池的荷电状态SOC是否满足SOC＜SOC^max，如当前处于峰时段，则进一步判断当前蓄电池的荷电状态是否满足SOC＞SOC^min；3) If it is currently in a valley period or a normal period, it is further judged whether the current state of charge SOC of the battery satisfies SOC<SOC ^max , and if it is currently in a peak period, then it is further judged whether the current state of charge of the battery satisfies SOC>SOC ^min ;

4)如果当前处于谷时段或平时段，且不满足SOC＜SOC^max，则确定蓄电池可放电，并进行优化1，满足SOC＜SOC^max，则确定蓄电池既可充电又可放电，并进行优化2；如果当前处于峰时段，且满足SOC＞SOC^min，则确定蓄电池可放电，并进行优化3，不满足SOC＞SOC^min，则确定以恒定功率对蓄电池充电，并进行优化4；4) If it is currently in a valley period or a normal period, and SOC<SOC ^max is not satisfied, it is determined that the battery can be discharged, and optimization 1 is performed, and if SOC<SOC ^max is met, it is determined that the battery can be charged and discharged, and optimization 2 is performed ; If it is currently in peak time and SOC>SOC ^min is satisfied, then determine that the battery can be discharged and optimize 3, if not satisfy SOC>SOC ^min , then determine to charge the battery with constant power and optimize 4;

5)通过步骤4)中的优化结果得到各微电源的有功功率和无功功率输出指令，然后将其传送给各微电源，各微电源按照指令输出相应的有功功率和无功功率。5) Obtain the active power and reactive power output commands of each micro-power source through the optimization result in step 4), and then transmit it to each micro-power source, and each micro-power source outputs corresponding active power and reactive power according to the command.

所述步骤2)中的调度时刻是指微电网实时能量优化管理是在微电网实时运行过程中，以每5分钟或15分钟为一时间间隔对微电网进行一次能量优化调度。The scheduling time in the step 2) means that the real-time energy optimization management of the microgrid is during the real-time operation of the microgrid, and the energy optimal scheduling of the microgrid is performed at an interval of 5 minutes or 15 minutes.

所述步骤3)中的蓄电池荷电状态SOC是反映蓄电池剩余电量占其总容量的比例的参数，定义为：The battery state of charge SOC in the step 3) is a parameter that reflects the ratio of the remaining power of the battery to its total capacity, and is defined as:

$SOC SOC = = \frac{{C C}_{net net}}{C C} = = 11 - - \frac{&Integral; &Integral; Idt Idt}{C C}$

C_net-蓄电池剩余电量Ah；C _net - battery remaining capacity Ah;

C-蓄电池总容量，Ah；C- total battery capacity, Ah;

I-蓄电池放电电流，A；I-battery discharge current, A;

对应的SOC^max、SOC^min为蓄电池荷电状态的上下限取值，取SOC^max＝0.9，SOC^min＝0.5。The corresponding SOC ^max and SOC ^min are the upper and lower limits of the state of charge of the storage battery, and SOC ^max =0.9 and SOC ^min =0.5.

所述步骤4)中的优化1是指求解所建立的能量优化模型1，能量优化模型1以微电网运行成本最低为目标，以微电网内各可调度微电源的开停机状态、各可调度微电源的有功输出、与大电网之间的买卖电功率、蓄电池的放电功率和各无功输出可调节电源的无功功率为优化变量，能量优化模型1为混合整数非线性规划问题，其目标函数：The optimization 1 in the step 4) refers to solving the established energy optimization model 1. The energy optimization model 1 takes the lowest operating cost of the microgrid as the goal, and takes the on-off status of each schedulable micro power supply in the microgrid, and each schedulable The active output of the micro power supply, the electric power bought and sold between the large power grid, the discharge power of the storage battery and the reactive power of each reactive output adjustable power supply are optimization variables. The energy optimization model 1 is a mixed integer nonlinear programming problem, and its objective function :

$min min {Σ Σ}_{i i = = 11}^{N N} (({U u}_{i i}^{t t} {C C}_{f f} + + {U u}_{i i}^{t t} {C C}_{OM om} + + {U u}_{i i}^{t t} ((11 - - {U u}_{i i}^{t t - - 11})) {C C}_{si the si}^{t t})) + + {λ λ}_{bat bat} + + {U u}_{P P}^{t t} {C C}_{P P} - - {U u}_{S S}^{t t} {I I}_{S S}$

式中：λ_bat＝(σP_bat)ΔtWhere: λ _bat = (σP _bat )Δt

t-系统运行时段；t-system running period;

i-系统中可调度微电源编号；i- The number of micro power sources that can be dispatched in the system;

N-系统中可调度微电源的总个数；N-the total number of schedulable micro-power sources in the system;

-在t时刻第i台可调度微电源的状态，0表示处于停运状态，1表示处于运行状态； -The state of the i-th schedulable micropower supply at time t, 0 means it is in a shutdown state, and 1 means it is in a running state;

-在t时刻微电网是否从大电网购电，0表示否，1表示是；

-Whether the microgrid purchases electricity from the large grid at time t, 0 means no, 1 means yes;

-在t时刻微电网是否向大电网售电，0表示否，1表示是；

-Whether the microgrid sells electricity to the large grid at time t, 0 means no, 1 means yes;

C_f-可调度微电源的能耗成本；C _f - the energy consumption cost of the dispatchable micropower supply;

C_OM-可调度微电源的运行维护成本；C _OM - O&M cost of dispatchable micropower;

-可调度微电源的启动成本；

- start-up costs of dispatchable micropower sources;

λ_bat-所设计的蓄电池充放电代价函数；λ _bat - the designed battery charging and discharging cost function;

C_P-微电网从大电网购电的支出；C _P - the expenditure of the microgrid to purchase electricity from the large grid;

I_S-微电网向大电网售电的收益；I _S - income from selling electricity from the microgrid to the large grid;

Δt-优化时间间隔；Δt - optimization time interval;

σ-所设计的系数；σ - designed coefficient;

P_bat-蓄电池的放电功率；P _bat - the discharge power of the battery;

其约束条件：Its constraints:

(a)潮流约束条件(a) Power flow constraints

${P P}_{i i} - - {Σ Σ}_{j j = = 11}^{j j = = n no} [[{e e}_{i i} (({G G}_{ij ij} {e e}_{j j} - - {B B}_{ij ij} {f f}_{j j})) + + {f f}_{i i} (({G G}_{ij ij} {f f}_{j j} + + {B B}_{ij ij} {e e}_{j j}))]] = = 00$

${Q Q}_{i i} - - {Σ Σ}_{j j = = 11}^{j j = = n no} [[{f f}_{i i} (({G G}_{ij ij} {e e}_{j j} - - {B B}_{ij ij} {f f}_{j j})) - - {e e}_{i i} (({G G}_{ij ij} {f f}_{j j} + + {B B}_{ij ij} {e e}_{j j}))]] = = 00$

式中：P_i、Q_i-各节点注入有功功率和无功功率；In the formula: P _i , Q _i - each node injects active power and reactive power;

e_i、f_i-用复数表示的各节点电压的实部和虚部；e _i , f _i - the real and imaginary parts of the voltages at each node represented by complex numbers;

C_ij、B_ij-i节点与j节点导纳元素的实部和虚部；C _ij , B _ij -the real part and imaginary part of the admittance elements of node i and node j;

n-微电网内总节点个数。n-the total number of nodes in the microgrid.

(b)可调度微电源容量约束(b) Schedulable micro power supply capacity constraints

${P P}_{i i}^{min min} \leq \leq {P P}_{i i}^{t t} \leq \leq {P P}_{i i}^{max max}$

式中：

-第i台可调度微电源在t时段的有功功率输出；In the formula:

- The active power output of the i-th schedulable micro-power supply during the period t;

-第i台可调度微电源的最大有功功率输出限值和最小有功功率输出限值；

-The maximum active power output limit and the minimum active power output limit of the i-th dispatchable micro-power supply;

(c)蓄电池的放电有功功率约束(c) Discharge active power constraints of the battery

$00 \leq \leq {P P}_{bat bat} \leq \leq {P P}_{dh d h__max max}^{t t}$

式中：P_bat-蓄电池的放电功率；In the formula: P _bat - the discharge power of the storage battery;

-在t时刻时蓄电池的最大可放电功率限值；

- the maximum dischargeable power limit of the battery at time t;

(d)微电网与大电网间能够交互的最大容量约束，这可能是它们之间所达成的供求协议或者联络线的物理传输容量限值(d) The maximum capacity constraint that can be interacted between the microgrid and the large grid, which may be the supply and demand agreement reached between them or the physical transmission capacity limit of the tie line

$00 \leq \leq {P P}_{Pgrid Pgrid}^{t t} \leq \leq {P P}_{Pgrid Pgrid}^{max max}$

$00 \leq \leq {P P}_{Sgrid grid}^{t t} \leq \leq {P P}_{Sgrid grid}^{max max}$

式中：

-微电网从大电网购电的有功功率；In the formula:

- The active power purchased by the microgrid from the large grid;

-微电网向大电网售电的有功功率；

-Active power sold by the microgrid to the large grid;

-微电网从大电网购电的最大有功功率限值；

- The maximum active power limit for the microgrid to purchase electricity from the large grid;

-微电网向大电网售电的最大有功功率限值；

- The maximum active power limit for the microgrid to sell electricity to the large grid;

(e)可调度微电源的最短连续运行时间和最短连续停运时间约束(e) Minimum continuous running time and minimum continuous downtime constraints of dispatchable micropower sources

$(({T T}_{i i,, on on}^{t t - - 11} - - MRT MRT)) \cdot &Center Dot; (({U u}_{i i}^{t t - - 11} - - {U u}_{i i}^{t t})) &GreaterEqual; &Greater Equal; 00$

$(({T T}_{i i,, off off}^{t t - - 11} - - MST MST)) \cdot \cdot (({U u}_{i i}^{t t} - - {U u}_{i i}^{t t - - 11})) &GreaterEqual; &Greater Equal; 00$

式中：

-在t时刻第i台可调度微电源的状态，0表示处于停运状态，1表示处于运行状态；In the formula:

-The state of the i-th schedulable micropower supply at time t, 0 means it is in a shutdown state, and 1 means it is in a running state;

-在t-1时刻第i台可调度微电源的连续运行时间；

- The continuous running time of the i-th schedulable micro-power supply at time t-1;

-在t-1时刻第i台可调度微电源的连续停运时间；

- The continuous outage time of the i-th dispatchable micropower supply at time t-1;

MRT-第i台可调度微电源的最小连续运行时间；MRT-minimum continuous running time of the i-th schedulable micro power supply;

MST-第i台可调度微电源的最小连续停运时间；MST-minimum continuous downtime of the i-th schedulable micropower supply;

(f)可调度微电源有功功率输出变化率限制(f) Limitation of rate of change of active power output of schedulable micropower sources

${P P}_{i i}^{t t} - - {P P}_{i i}^{t t - - 11} \leq \leq Δ Δ {P P}_{U u}$

${P P}_{i i}^{t t - - 11} - - {P P}_{i i}^{t t} \leq \leq Δ Δ {P P}_{D D.}$

式中：

-第i台可调度微电源在t时段的有功功率输出；In the formula:

ΔP_U-可调度微电源本调度时刻功率相对于上一时刻功率增加限制；ΔP _U - schedulable micropower supply power increase limit at this scheduling time relative to the power at the previous time;

ΔP_D-可调度微电源本调度时刻功率相对于上一时刻功率减小限制；ΔP _D - The power reduction limit of the schedulable micro power supply at this time of dispatch compared with the power at the previous time;

(g)从大电网买卖电互斥约束(g) Mutually exclusive constraints on buying and selling electricity from the large power grid

${U u}_{P P}^{t t} + + {U u}_{S S}^{t t} \leq \leq 11$

(h)无功功率输出可调节电源的无功输出约束(h) Reactive power output constraints of power supply with adjustable reactive power output

$00 \leq \leq {Q Q}_{i i}^{t t} \leq \leq {Q Q}_{i i}^{max max}$

式中：

-第i台无功功率输出可调节电源在t时刻的无功功率输出；In the formula:

- The reactive power output of the i-th reactive power output adjustable power supply at time t;

-第i台无功功率输出可调节电源的最大无功功率输出限值。

- The maximum reactive power output limit of the i-th power supply with adjustable reactive power output.

所述步骤4)中的优化2是指求解所建立的能量优化模型2，能量优化模型2以微电网运行成本最低为目标，以微电网内各可调度微电源的开停机状态、各可调度微电源的有功输出、与大电网之间的买卖电功率、蓄电池的放电功率和各无功输出可调节电源的无功功率为优化变量，能量优化模型2为混合整数非线性规划问题，其目标函数与能量优化模型1相同，其约束条件与能量优化模型1基本相同，只是约束条件中的“(c)蓄电池的放电有功功率约束”有变化，能量优化模型2的约束条件中的“(c)蓄电池的放电有功功率约束”为：The optimization 2 in the step 4) refers to solving the established energy optimization model 2. The energy optimization model 2 takes the lowest operating cost of the microgrid as the goal, and takes the on-off status of each schedulable micro power supply in the microgrid, and each schedulable The active output of the micro power supply, the electric power purchased and sold between the large power grid, the discharge power of the storage battery and the reactive power of each reactive output adjustable power supply are optimization variables. The energy optimization model 2 is a mixed integer nonlinear programming problem, and its objective function The same as energy optimization model 1, its constraints are basically the same as energy optimization model 1, except that "(c) battery discharge active power constraint" in the constraints has changed, and in the constraints of energy optimization model 2, "(c) The discharge active power constraint of the battery" is:

$- - {P P}_{ch ch__max max}^{t t} \leq \leq {P P}_{bat bat} \leq \leq {P P}_{dh d h__max max}^{t t}$

式中：P_bat-蓄电池的放电功率；P_bat＜0，表示对蓄电池充电，P_bat＞0，表示对蓄电池放电；In the formula: P _bat - the discharge power of the battery; P _bat <0 means charging the battery, P _bat >0 means discharging the battery;

-蓄电池最大可充电功率；

- The maximum chargeable power of the battery;

-蓄电池最大可放电功率。 - The maximum dischargeable power of the battery.

所述步骤4)中的优化3是指求解所建立的能量优化模型3，能量优化模型3以微电网运行成本最低为目标，以微电网内各可调度微电源的开停机状态、各可调度微电源的有功输出、与大电网之间的买卖电功率、蓄电池的放电功率和各无功输出可调节电源的无功功率为优化变量，能量优化模型3为混合整数非线性规划问题，其约束条件与能量优化模型1相同，其目标函数与能量优化模型1略微不同，不同之处在于能量优化模型3的目标函数中的λ_bat项与能量优化模型1的目标函数中的λ_bat项设计得不一样，能量优化模型3的目标函数为：The optimization 3 in the step 4) refers to solving the established energy optimization model 3. The energy optimization model 3 takes the lowest operating cost of the microgrid as the goal, and takes the on-off status of each schedulable micro power supply in the microgrid, and each schedulable The active output of the micro power supply, the electric power bought and sold between the large power grid, the discharge power of the storage battery and the reactive power of each reactive output adjustable power supply are optimization variables. The energy optimization model 3 is a mixed integer nonlinear programming problem, and its constraints The same as the energy optimization model 1, its objective function is slightly different from the energy optimization model 1, the difference is that the λ _bat item in the objective function of the energy optimization model 3 and the λ _bat item in the objective function of the energy optimization model 1 are not well designed Similarly, the objective function of the energy optimization model 3 is:

式中：λ_bat＝δ·P_dh·ΔtWhere: λ _bat = δ · P _dh · Δt

δ＝a₁+a₂·dSOC+a₃·P_dh+a₄·dSOC·P_dh+a₅·dSOC² δ＝a ₁ +a ₂ dSOC+a ₃ P _dh +a ₄ dSOC P _dh +a ₅ dSOC ²

dSOC＝SOC-SOC^min dSOC＝SOC- ^SOCmin

t-系统运行时段；t-system running period;

i-系统中可调度微电源编号；i - the number of micro power sources that can be dispatched in the system;

-在t时刻第i台可调度微电源的状态，0表示处于停运状态，1表示处于运行状态；

-在t时刻微电网是否从大电网购电，0表示否，1表示是； -Whether the microgrid purchases electricity from the large grid at time t, 0 means no, 1 means yes;

-在t时刻微电网是否向大电网售电，0表示否，1表示是；

-可调度微电源的启动成本；

- start-up costs of dispatchable micropower sources;

Δt-优化时间间隔；Δt - optimization time interval;

P_bat-蓄电池的放电功率；P _bat - the discharge power of the battery;

SOC-蓄电池的荷电状态；SOC-the state of charge of the battery;

SOC^min-蓄电池的荷电状态下限值；SOC ^min - the lower limit of the state of charge of the battery;

a₁、a₂、a₃、a₄、a₅-所设计的系数。a ₁ , a ₂ , a ₃ , a ₄ , a ₅ - designed coefficients.

所述步骤4)中的优化4是指求解所建立的能量优化模型4，能量优化模型4以微电网运行成本最低为目标，以微电网内各可调度微电源的开停机状态、各可调度微电源的有功输出、与大电网之间的买卖电功率、蓄电池的放电功率和各无功输出可调节电源的无功功率为优化变量，能量优化模型4为混合整数非线性规划问题，相对于能量优化模型1，能量优化模型4的目标函数中没有λ_bat项，能量优化模型4的目标函数为：The optimization 4 in the step 4) refers to solving the established energy optimization model 4. The energy optimization model 4 takes the lowest operating cost of the microgrid as the goal, and takes the on-off status of each schedulable micro power supply in the microgrid, and each schedulable The active output of the micro power supply, the electric power bought and sold between the large power grid, the discharge power of the storage battery, and the reactive power of each reactive output adjustable power supply are optimization variables, and the energy optimization model 4 is a mixed integer nonlinear programming problem. There is no λ _bat item in the objective function of optimization model 1 and energy optimization model 4, and the objective function of energy optimization model 4 is:

$min min {Σ Σ}_{i i = = 11}^{N N} (({U u}_{i i}^{t t} {C C}_{f f} + + {U u}_{i i}^{t t} {C C}_{OM om} + + {U u}_{i i}^{t t} ((11 - - {U u}_{i i}^{t t - - 11})) {C C}_{si the si}^{t t})) + + {U u}_{P P}^{t t} {C C}_{P P} - - {U u}_{S S}^{t t} {I I}_{S S}$

式中：t-系统运行时段；In the formula: t-system running period;

-在t时刻微电网是否从大电网购电，0表示否，1表示是；

-在t时刻微电网是否向大电网售电，0表示否，1表示是；

-可调度微电源的启动成本；

- start-up costs of dispatchable micropower sources;

能量优化模型4的约束条件与能量优化模型1的约束条件基本相同，只是能量优化模型4的约束条件中没有“(c)蓄电池的放电有功功率约束”这一项约束。The constraints of energy optimization model 4 are basically the same as those of energy optimization model 1, except that the constraint of "(c) active power of battery discharge" is not included in the constraints of energy optimization model 4.

所述步骤4)中的以恒定功率对蓄电池充电，并进行优化4是指在优化4中蓄电池的充放电功率不参与优化，而以恒定功率对蓄电池充电，此时蓄电池相当于一恒定的负荷，且将对蓄电池充电的恒定功率计入到蓄电池所在的微电网网络节点处的负荷中去，对蓄电池的恒定充电功率为Charging the storage battery with constant power in said step 4) and performing optimization 4 means that the charging and discharging power of the storage battery does not participate in optimization in optimization 4, but charges the storage battery with a constant power, and the storage battery is equivalent to a constant load at this time , and the constant power for charging the battery is included in the load at the node of the microgrid network where the battery is located, the constant charging power for the battery is

${P P}_{bat bat}^{t t} = = {I I}_{bat bat__max max__ch ch} \cdot &Center Dot; {U u}_{batt batt}^{t t}$

式中：I_{bat_max_ch}-蓄电池最大允许充电电流，由生产说明书给定；In the formula: I _{bat_max_ch} - the maximum allowable charging current of the battery, given by the production manual;

-蓄电池端电压。

- battery terminal voltage.

所述步骤5)中的优化结果是指通过求解步骤4)中的优化1、优化2、优化3和优化4所涉及的能量优化模型1、能量优化模型2、能量优化模型3和能量优化模型4而得到的结果，包括微电网内各可调度微电源的开停机状态、各可调度微电源的有功输出值、与大电网之间的买卖电功率值、蓄电池的放电功率值和各无功输出可调节电源的无功功率值。The optimization result in said step 5) refers to the energy optimization model 1, energy optimization model 2, energy optimization model 3 and energy optimization model involved in solving step 4) in optimization 1, optimization 2, optimization 3 and optimization 4 4 The obtained results include the on-off status of each schedulable micro-power source in the micro-grid, the active power output value of each schedulable micro-power source, the value of the electric power traded with the large power grid, the discharge power value of the battery, and each reactive power output value. The reactive power value of the power supply can be adjusted.

本发明与背景技术相比，具有的有益效果是：Compared with the background technology, the present invention has the beneficial effects of:

1)传统的微电网能量优化调度没有对蓄电池的充放电进行优化，而本发明通过在不同时段对蓄电池设计不同的充放电代价函数计入优化模型的目标函数中，使得在大电网的谷平时段微电网以较低电价从大电网买电来对蓄电池充电，而在峰时段微电网又让蓄电池放电并以较高电价卖给大电网，由此既提高了微电网的运行效益，又有助于对大电网进行“削峰填谷”。1) The traditional microgrid energy optimization scheduling does not optimize the charge and discharge of the storage battery, but the present invention designs different charge and discharge cost functions for the storage battery at different time intervals and includes them in the objective function of the optimization model, so that in the valley of the large power grid The segment microgrid buys electricity from the large grid at a lower electricity price to charge the storage battery, and during the peak hours the microgrid discharges the battery and sells it to the large grid at a higher electricity price, which not only improves the operating efficiency of the microgrid, but also Contribute to the "peak shaving and valley filling" of the large power grid.

2)传统的微电网能量优化调度只优化了各可调度微电源的有功功率输出，且不考虑网络损耗，本发明考虑了微电网自身的网络结构，使得优化中考虑了网络损耗，并将无功输出可调节电源(通过电力电子变换器与交流电网连接的电源，其无功功率输出也是可调节的)的无功功率也作为优化变量，使得各电源输出的有功功率输出和无功功率输出达到整体最优，优化结果更实际，使微电网运行更加经济。2) The traditional micro-grid energy optimization dispatch only optimizes the active power output of each schedulable micro-power source, and does not consider the network loss. The present invention considers the network structure of the micro-grid itself, so that the network loss is considered in the optimization, and no The reactive power of the power output adjustable power supply (the power supply connected to the AC grid through the power electronic converter, whose reactive power output is also adjustable) is also used as an optimization variable, so that the active power output and reactive power output of each power supply output To achieve the overall optimal, the optimization results are more realistic, making the operation of the microgrid more economical.

3)在峰时段时能量优化模型的目标函数中的蓄电池放电代价函数设计为蓄电池荷电状态与放电功率的函数，当蓄电池荷电状态越小(但大于蓄电池荷电状态下限SOC^min)时该代价函数值越大，当放电功率越大时，该代价函数值越大，从而引导蓄电池在荷电状态较小时减小放电功率，使蓄电池随时保持一定蓄电量，能为微电网转为孤网运行时提供紧急功率支撑，提高了微电网运行可靠性，同时又可使蓄电池的荷电状态始终维持在SOC^min和SOC^max之间，防止了蓄电池过充电或过放电，有利于延长蓄电池的使用寿命。3) The battery discharge cost function in the objective function of the energy optimization model during the peak period is designed as a function of the battery state of charge and the discharge ^power . The greater the value of the cost function, the greater the value of the cost function when the discharge power is greater, thereby guiding the battery to reduce the discharge power when the state of charge is small, so that the battery can maintain a certain amount of storage at any time, which can transform the microgrid into an isolated grid It provides emergency power support during operation, which improves the reliability of the microgrid operation, and at the same time keeps the state of charge of the battery between SOC ^min and SOC ^max , preventing overcharging or overdischarging of the battery, which is beneficial to prolonging the use of the battery life.

附图说明 Description of drawings

图1是微电网实时能量优化调度流程图。Figure 1 is a flowchart of real-time energy optimal scheduling of microgrid.

图2是实施例微电网示例图。Fig. 2 is an example diagram of the microgrid of the embodiment.

图3是采用本发明对微电网进行实时能量优化调度得到的各电源在一天内的有功功率分配图。Fig. 3 is a diagram of the active power distribution of each power source within a day obtained by using the present invention to perform real-time energy optimal scheduling on the microgrid.

图4是采用本发明对微电网进行实时能量优化调度得到的各电源在一天内的无功功率分配图。Fig. 4 is a reactive power distribution diagram of each power source within a day obtained by using the present invention to perform real-time energy optimal scheduling on the microgrid.

图5是采用本发明对微电网进行实时能量优化调度得到的蓄电池在一天内的荷电状态SOC变化图。Fig. 5 is a graph showing the SOC variation of the battery state of charge within a day obtained by using the present invention to perform real-time energy optimal scheduling on the microgrid.

具体实施方式 Detailed ways

下面结合附图和实施例对本发明作进一步说明。The present invention will be further described below in conjunction with drawings and embodiments.

如图1所示，本发明包括如下步骤：As shown in Figure 1, the present invention comprises the following steps:

2)在微电网的实时运行过程中，在每次调度时刻确定当前所处的时段，监测微电网内的蓄电池的荷电状态SOC，测量各节点负荷的有功功率和无功功率，测量可再生能源发电(光伏发电和风力发电)的输出有功功率和无功功率；2) During the real-time operation of the microgrid, determine the current time period at each scheduling time, monitor the state of charge SOC of the storage battery in the microgrid, measure the active power and reactive power of each node load, and measure the renewable energy. Output active power and reactive power of energy generation (photovoltaic power generation and wind power generation);

C_net-蓄电池剩余电量Ah；C _net - battery remaining capacity Ah;

C-蓄电池总容量，Ah；C- total battery capacity, Ah;

I-蓄电池放电电流，A；I-battery discharge current, A;

对应的SOC^max、SOC^min为蓄电池荷电状态的上下限取值，取SOC^max＝0.9，SOC^min＝0.5。蓄电池过充电或过放电均会缩短其使用寿命，通过判断蓄电池荷电状态与SOC^max、SOC^min之间的关系确定下一环节，是为了让蓄电池在充放电过程中始终保持其荷电状态SOC在SOC^min与SOC^max之间，以防止蓄电池过充电或过放电。蓄电池充电至其荷电状态达0.9时将产生析气反映，继续充电将折损蓄电池使用寿命，为此可以令SOC^max＝0.9；DOD是厂家提供的一项蓄电池技术参数，是指蓄电池的放电深度，即蓄电池最大可放电量占其总容量的比例，通常为0.6～0.7，可有SOC^min＝1-DOD，但并网运行的微电网要预防在大电网故障时转为孤网自治运行的情况，在这种情况下需要蓄电池以及其它参与压频控制的微电源为孤网提供紧急功率支撑，因此要求在并网运行时蓄电池应随时维持一定的蓄电量，以便在转为孤网时蓄电池能及时放电，弥补因与大电网失电引起的功率缺额，为此，将SOC^min设定得比1-DOD稍大，取SOC^min＝0.5。The corresponding SOC ^max and SOC ^min are the upper and lower limits of the state of charge of the storage battery, and SOC ^max =0.9 and SOC ^min =0.5. Overcharging or overdischarging of the battery will shorten its service life. The next link is determined by judging the relationship between the battery state of charge and SOC ^max and SOC ^min , so that the battery can always maintain its state of charge SOC during charging and discharging. Between SOC ^min and SOC ^max to prevent the battery from overcharging or over-discharging. When the battery is charged until its state of charge reaches 0.9, gassing reaction will occur, and continuing to charge will damage the service life of the battery. Therefore, SOC ^max = 0.9 can be set; DOD is a technical parameter of the battery provided by the manufacturer, which refers to the discharge of the battery. Depth, that is, the ratio of the maximum dischargeable capacity of the battery to its total capacity, usually 0.6 to 0.7, may have SOC ^min = 1-DOD, but the grid-connected microgrid should prevent the autonomous operation of the isolated grid when a large grid fails In this case, the battery and other micro power sources involved in voltage and frequency control are required to provide emergency power support for the isolated grid. Therefore, it is required that the battery should maintain a certain amount of storage capacity at any time during grid-connected operation, so that when it is converted to an isolated grid The storage battery can be discharged in time to make up for the power shortage caused by the power loss with the large power grid. Therefore, the SOC ^min is set to be slightly larger than 1-DOD, and SOC ^min = 0.5.

所述步骤4)中的优化1是指求解所建立的能量优化模型1，能量优化模型1以微电网运行成本最低为目标，以微电网内各可调度微电源(如微型燃气轮机、燃料电池、柴油发电机等)的开停机状态、各可调度微电源的有功输出、与大电网之间的买卖电功率、蓄电池的放电功率和各无功输出可调节电源(通过电力电子变换器与交流电网连接的电源，其输出到交流电网的无功功率也可以调节，如大电网、燃料电池、微型燃气轮机、蓄电池、光伏电池、风力发电机等)的无功功率为优化变量，能量优化模型1为混合整数非线性规划问题，其目标函数：The optimization 1 in the step 4) refers to solving the established energy optimization model 1. The energy optimization model 1 takes the lowest operating cost of the microgrid as the goal, and takes each schedulable micropower source (such as a micro gas turbine, a fuel cell, Diesel generators, etc.), the active output of each adjustable micro power supply, the electric power bought and sold between the large power grid, the discharge power of the storage battery, and the adjustable power supply of each reactive output (connected to the AC grid through a power electronic converter power supply, the reactive power output to the AC grid can also be adjusted, such as large power grids, fuel cells, micro gas turbines, storage batteries, photovoltaic cells, wind turbines, etc.) The reactive power is the optimization variable, and the energy optimization model 1 is a hybrid Integer nonlinear programming problem with objective function:

式中：λ_bat＝(σP_bat)ΔtWhere: λ _bat = (σP _bat )Δt

t-系统运行时段；t-system running period;

-在t时刻微电网是否从大电网购电，0表示否，1表示是；

-在t时刻微电网是否向大电网售电，0表示否，1表示是；

-可调度微电源的启动成本；

- start-up costs of dispatchable micropower sources;

Δt-优化时间间隔；Δt - optimization time interval;

σ-所设计的系数；σ - designed coefficient;

P_bat-蓄电池的放电功率；P _bat - the discharge power of the battery;

其约束条件：Its constraints:

(a)潮流约束条件(a) Power flow constraints

G_ij、B_ij-i节点与j节点导纳元素的实部和虚部；G _ij , B _ij -the real part and imaginary part of the admittance elements of node i and node j;

n-微电网内总节点个数。n-the total number of nodes in the microgrid.

式中：

-第i台可调度微电源在t时段的有功功率输出；In the formula:

$00 \leq \leq {P P}_{bat bat} \leq \leq {P P}_{dh d h__max max}^{t t}$

式中： $P_{dh_\max}^{t} = \min (\frac{(SO C_{t} - SO C^{\min}) \cdot C_{batt}^{'}}{Δt}, I_{bat_\max_dh}) \cdot U_{batt}^{t}$ In the formula: $P_{d h_\max}^{t} = \min (\frac{(SO C_{t} - SO C^{\min}) \cdot C_{batt}^{'}}{Δt}, I_{bat_\max_d h}) \cdot u_{batt}^{t}$

-在t时刻时蓄电池的最大允许放电功率值；

- The maximum allowable discharge power value of the battery at time t;

SOC_t-调度时刻监测到的蓄电池的荷电状态；SOC _t - the state of charge of the battery monitored at the dispatching time;

SOC^min-设定的蓄电池荷电状态下限；SOC ^min - set the battery state of charge lower limit;

C′_batt-蓄电池容量；C′ _batt - battery capacity;

I_{bat_max_dh}-蓄电池最大允许放电电流，由生产说明书给定；I _{bat_max_dh} - the maximum allowable discharge current of the battery, given by the production manual;

-蓄电池端电压；

- battery terminal voltage;

Δt-调度时间间隔；Δt-scheduling time interval;

P_bat-蓄电池的放电功率；P _bat - the discharge power of the battery;

(d)微电网与大电网间能够交互的最大容量约束，这可能是它们之间所达成的供求协议或者联络线的物理传输容量限值(d) The maximum capacity constraint that can interact between the microgrid and the large grid, which may be the supply and demand agreement reached between them or the physical transmission capacity limit of the tie line

$00 \leq \leq {P P}_{Sgrid grid}^{t t} \leq \leq {P P}_{Sgrid grid}^{max max}$

式中：

-微电网从大电网购电的有功功率；In the formula:

- The active power purchased by the microgrid from the large grid;

-微电网向大电网售电的有功功率； -Active power sold by the microgrid to the large grid;

-微电网从大电网购电的最大有功功率限值；

-微电网向大电网售电的最大有功功率限值； - The maximum active power limit for the microgrid to sell electricity to the large grid;

$(({T T}_{i i,, off off}^{t t - - 11} - - MST MST)) \cdot &Center Dot; (({U u}_{i i}^{t t} - - {U u}_{i i}^{t t - - 11})) &GreaterEqual; &Greater Equal; 00$

式中：

-在t-1时刻第i台可调度微电源的连续运行时间；

-在t-1时刻第i台可调度微电源的连续停运时间；

${P P}_{i i}^{t t} - - {P P}_{i i}^{t t - - 11} \leq \leq Δ Δ {P P}_{U u}$

${P P}_{i i}^{t t - - 11} - - {P P}_{i i}^{t t} \leq \leq Δ Δ {P P}_{D D.}$

式中：

-第i台可调度微电源在t时段的有功功率输出；In the formula:

${U u}_{P P}^{t t} + + {U u}_{S S}^{t t} \leq \leq 11$

$00 \leq \leq {Q Q}_{i i}^{t t} \leq \leq {Q Q}_{i i}^{max max}$

式中：

-第i台无功功率输出可调节电源的最大无功功率输出限值。

式中： $P_{ch_\max}^{t} = \min (\frac{({SOC}^{\max} - {SOC}_{t}) \cdot C_{batt}^{'}}{Δt}, I_{bat_\max_ch}) \cdot U_{batt}^{t}$ In the formula: $P_{ch_\max}^{t} = \min (\frac{({SOC}^{\max} - {SOC}_{t}) \cdot C_{batt}^{'}}{Δt}, I_{bat_\max_ch}) &Center Dot; u_{batt}^{t}$

${P P}_{dh d h__max max}^{t t} = = max max ((00,, min min ((\frac{(({SOC SOC}_{t t} - - {SOC SOC}^{min min})) \cdot \cdot {C C}_{batt batt}^{' '}}{Δt Δt},, {I I}_{bat bat__max max__dh d h})))) \cdot &Center Dot; {U u}_{batt batt}^{t t}$

P_bat-蓄电池的放电功率；P_bat＜0，表示对蓄电池充电，P_bat＞0，表示对蓄电池放电；P _bat - the discharge power of the battery; P _bat <0 means charging the battery, P _bat >0 means discharging the battery;

-蓄电池的最大允许充电功率；

- the maximum allowable charging power of the battery;

-蓄电池的最大允许放电功率；

- the maximum allowable discharge power of the battery;

SOC^max-设定的蓄电池荷电状态上限；SOC ^max - set upper limit of battery state of charge;

C′_batt-蓄电池容量；C′ _batt - battery capacity;

I_{bat_max_ch}-蓄电池最大允许充电电流，由生产说明书给定；I _{bat_max_ch} - the maximum allowable charging current of the battery, given by the production manual;

-蓄电池端电压；

- battery terminal voltage;

Δt-调度时间间隔。Δt - scheduling time interval.

式中：λ_bat＝δ·P_dh·ΔtWhere: λ _bat = δ · P _dh · Δt

dSOC＝SOC-SOC^min dSOC＝SOC- ^SOCmin

t-系统运行时段；t-system running period;

-在t时刻微电网是否从大电网购电，0表示否，1表示是；

-在t时刻微电网是否向大电网售电，0表示否，1表示是；

-可调度微电源的启动成本；

- start-up costs of dispatchable micropower sources;

Δt-优化时间间隔；Δt - optimization time interval;

P_bat-蓄电池的放电功率；P _bat - the discharge power of the battery;

SOC-蓄电池的荷电状态；SOC-the state of charge of the battery;

a₁、a₂、a₃、a₄、a₅-所设计的系数，a ₁ , a ₂ , a ₃ , a ₄ , a ₅ - designed coefficients,

优化3处于峰时段，在该时段蓄电池主要处于放电状态，考虑到并网运行的微电网要预防在大电网故障时转为孤网自治运行的情况，在这种情况下需要蓄电池以及其它参与压频控制的微电源为孤网提供紧急支撑，因此要求在并网运行时蓄电池应随时维持一定的蓄电量，所以希望蓄电池在荷电量充足时可以多放电，而当荷电量较少时少放电，所设计的计入能量优化模型3的目标函数中的蓄电池充放电代价函数具有如下特性：当荷电状态越小，其函数值越大；当输出功率越大时，其函数值越大，又因为优化目标是使目标函数值最小，因此采用本函数表示蓄电池放电代价函数将引导蓄电池在荷电状态较小时减小放电功率，使蓄电池总能维持一定的蓄电量，能为微电网转为孤网运行时提供紧急功率支撑，提高了微电网运行可靠性。Optimization 3 is in the peak period, during which the battery is mainly in the discharge state. Considering that the grid-connected microgrid should prevent the autonomous operation of the isolated grid when the large grid fails. In this case, the battery and other participating voltage The frequency-controlled micro-power supply provides emergency support for the isolated grid. Therefore, it is required that the battery should maintain a certain storage capacity at any time during grid-connected operation. Therefore, it is hoped that the battery can discharge more when the charge is sufficient, and discharge less when the charge is low. The designed battery charging and discharging cost function included in the objective function of energy optimization model 3 has the following characteristics: when the state of charge is smaller, the function value is larger; when the output power is larger, the function value is larger, and Because the optimization goal is to minimize the value of the objective function, this function is used to indicate that the battery discharge cost function will guide the battery to reduce the discharge power when the state of charge is small, so that the battery can always maintain a certain amount of storage capacity, which can transform the microgrid into an isolated grid. Emergency power support is provided during the operation of the microgrid, which improves the reliability of the microgrid operation.

式中：t-系统运行时段；In the formula: t-system running period;

-在t时刻微电网是否从大电网购电，0表示否，1表示是；

-在t时刻微电网是否向大电网售电，0表示否，1表示是；

-可调度微电源的启动成本； - start-up costs of dispatchable micropower sources;

-蓄电池端电压。

- battery terminal voltage.

以下结合附图，对本发明的实施例作详细说明，该发明的流程图如图1所示。The embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings, and the flow chart of the present invention is shown in FIG. 1 .

实施例：Example:

考虑如图2所示的微电网，微电网内含柴油发电机、微型燃气轮机、燃料电池、风力发电机、光伏电池及蓄电池，微电网通过公共耦合点(PCC)与大电网连接而并网运行。假定以每15分钟为一调度时段，当前时间为13点30分，采用本发明对微电网进行实时能量优化调度，各步骤分述如下：Consider the microgrid shown in Figure 2. The microgrid contains diesel generators, micro gas turbines, fuel cells, wind generators, photovoltaic cells, and storage batteries. The microgrid is connected to the large power grid through the point of public coupling (PCC) and runs in parallel. . Assuming that every 15 minutes is a scheduling period, and the current time is 13:30, the present invention is used to perform real-time energy optimal scheduling of the microgrid, and each step is described as follows:

1)将一天24小时划分为峰时段、平时段和谷时段：谷时段为00:00-07:00，平时段为08:00-10:00、16:00-18:00和22:00-23:00，峰时段为11:00-15:00和19:00-21:00。1) Divide 24 hours a day into peak period, normal period and valley period: valley period is 00:00-07:00, and normal period is 08:00-10:00, 16:00-18:00 and 22:00 -23:00, peak hours are 11:00-15:00 and 19:00-21:00.

2)在微电网的实时运行过程中，在每次调度时刻确定当前所处的时段，监测蓄电池的荷电状态SOC，测量各节点负荷的有功功率和无功功率，测量可再生能源发电(光伏发电和风力发电)的输出有功功率和无功功率；。当前时间为13点30分，根据所划分的时段可知，当前处于峰时段，假定测得的蓄电池的荷电状态SOC＝0.67。2) During the real-time operation of the microgrid, determine the current time period at each scheduling time, monitor the SOC of the battery, measure the active power and reactive power of each node load, and measure the renewable energy power generation (photovoltaic Power generation and wind power) output active power and reactive power;. The current time is 13:30, and according to the divided time period, it is known that it is currently in a peak time period, assuming that the measured state of charge of the storage battery is SOC=0.67.

3)当前处于峰时段，进一步判断当前蓄电池的荷电状态是否满足SOC＞SOC^min，由于SOC^min＝0.5，所以满足SOC＞SOC^min。3) It is currently in a peak time period, further judging whether the current state of charge of the battery satisfies SOC>SOC ^min , and since SOC ^min =0.5, it satisfies SOC>SOC ^min .

4)当前处于峰时段，且满足SOC＞SOC^min，则进行优化3，其所建立的能量优化模型3以微电网运行成本最低为目标，以微电网内各可调度微电源(微型燃气轮机、燃料电池和柴油发电机)的开停机状态、各可调度微电源(微型燃气轮机、燃料电池、柴油发电机)的有功输出、与大电网之间的买卖电功率、蓄电池的放电功率和各无功输出可调节电源(通过电力电子变换器与交流电网连接的电源，其输出到交流电网的无功功率也可以调节，本实施例包括大电网、燃料电池、微型燃气轮机、蓄电池和风力发电机)的无功功率为优化变量，能量优化模型3为混合整数非线性规划问题，其所建立的能量优化模型3为：4) It is currently in peak hours, and if SOC>SOC ^min is satisfied, then optimization 3 will be performed. The energy optimization model 3 established by it will aim at the lowest operating cost of the microgrid, and use the schedulable micropower sources (micro gas turbines, fuel battery and diesel generator), the active power output of each adjustable micro power source (micro gas turbine, fuel cell, diesel generator), the trading power with the large power grid, the discharge power of the battery and the reactive power output can be Adjust the reactive power of the power supply (the power supply connected to the AC grid through the power electronic converter, the reactive power output to the AC grid can also be adjusted, this embodiment includes a large grid, fuel cells, micro gas turbines, storage batteries and wind power generators) Power is the optimization variable, and the energy optimization model 3 is a mixed integer nonlinear programming problem, and the energy optimization model 3 established by it is:

目标函数：Objective function:

式中： $C_{OM} (P_{i}^{t}) = K_{OM} P_{i}^{t} \cdot Δt$ In the formula: $C_{om} (P_{i}^{t}) = K_{om} P_{i}^{t} \cdot Δt$

${C C}_{P P} (({P P}_{Pgrid Pgrid}^{t t})) = = {c c}_{p p}^{t t} {P P}_{Pgrid Pgrid}^{t t} \cdot \cdot Δt Δt$

${I I}_{S S} (({P P}_{Sgrid grid}^{t t})) = = {c c}_{s the s}^{t t} {P P}_{Sgrid grid}^{t t} \cdot &Center Dot; Δt Δt$

t-系统运行时段；t-system running period;

-在t时刻微电网是否从大电网购电，0表示否，1表示是；

-在t时刻微电网是否向大电网售电，0表示否，1表示是；

-可调度微电源的启动成本；

- start-up costs of dispatchable micropower sources;

Δt-优化时间间隔；Δt - optimization time interval;

K_OMi-可调度微电源运行维护成本的比例系数；K _OMi - scaling factor for dispatchable micropower operation and maintenance costs;

-微电网从大电网购电的有功功率；

- The active power purchased by the microgrid from the large grid;

-微电网向大电网售电的有功功率；

-Active power sold by the microgrid to the large grid;

-微电网从大电网购电的电价，元/千瓦时；

- Electricity price for the microgrid to purchase electricity from the large grid, yuan/kWh;

-微电网向大电网售电的电价，元/千瓦时；

- The price of electricity sold by the microgrid to the large grid, yuan/kWh;

对于柴油发电机(DG，Diesel Generator)，其能耗成本为其有功功率输出的函数，可以用二次多项式表示如下：For a diesel generator (DG, Diesel Generator), its energy consumption cost is a function of its active power output, which can be expressed by a quadratic polynomial as follows:

${C C}_{f f__DG DG} (({P P}_{i i}^{t t})) = = ((a a {(({P P}_{i i}^{t t}))}^{22} + + b b {P P}_{i i}^{t t} + + c c)) Δt Δt$

式中：a，b，c-二次多项式相关系数。In the formula: a, b, c - quadratic polynomial correlation coefficient.

对于燃料电池(FC，Fuel Cell)和微型燃汽轮机(MT，Microturbine)，其有功功率输出与燃料输入量成比例，对应的能耗成本可表示为：For a fuel cell (FC, Fuel Cell) and a micro gas turbine (MT, Microturbine), the active power output is proportional to the fuel input, and the corresponding energy consumption cost can be expressed as:

${C C}_{f f__FC FC} (({P P}_{i i}^{t t})) = = (({c c}_{ng ng} \frac{{P P}_{i i}^{t t}}{{η η}_{i i}^{t t}})) Δt Δt$

式中：-有功输出功率；In the formula: - active output power;

c_ng为-体燃料价格；c _ng is - body fuel price;

-燃料利用效率，是输出电能与输入燃料的热值的比值；

- Fuel utilization efficiency, which is the ratio of the output electric energy to the calorific value of the input fuel;

蓄电池放电代价函数设计为：The battery discharge cost function is designed as:

dSOC＝SOC-SOC^min dSOC＝SOC- ^SOCmin

λ_bat＝δ·P_dh λ _bat = δ·P _dh

SOC-蓄电池的荷电状态；SOC-the state of charge of the battery;

a₁、a₂、a₃、a₄、a₅-所设计的系数；a ₁ , a ₂ , a ₃ , a ₄ , a ₅ - designed coefficients;

约束条件：Restrictions:

(a)潮流约束条件(a) Power flow constraints

n-微电网内总节点个数。n-the total number of nodes in the microgrid.

式中：-第i台可调度微电源在t时段的有功功率输出；In the formula: - The active power output of the i-th schedulable micro-power supply during the period t;

$00 \leq \leq {P P}_{bat bat} \leq \leq {P P}_{dh d h__max max}^{t t}$

式中： $P_{dh_\max}^{t} = \min (\frac{(SO C_{t} - SO C^{\min}) \cdot C_{batt}^{'}}{Δt}, I_{bat_\max_dh}) \cdot U_{batt}^{t}$ In the formula: $P_{d h_\max}^{t} = \min (\frac{(SO C_{t} - SO C^{\min}) &Center Dot; C_{batt}^{'}}{Δt}, I_{bat_\max_d h}) \cdot u_{batt}^{t}$

-在t时刻时蓄电池的最大允许放电功率值； - The maximum allowable discharge power value of the battery at time t;

SOG_t-调度时刻监测到的蓄电池的荷电状态；SOG _t - the state of charge of the battery monitored at the dispatching time;

C′_batt-蓄电池容量；C′ _batt - battery capacity;

-蓄电池端电压； - battery terminal voltage;

Δt-调度时间间隔；Δt-scheduling time interval;

P_bat-蓄电池的放电功率；P _bat - the discharge power of the battery;

$00 \leq \leq {P P}_{Sgrid grid}^{t t} \leq \leq {P P}_{Sgrid grid}^{max max}$

式中：

-微电网从大电网购电的有功功率；In the formula:

- The active power purchased by the microgrid from the large grid;

-微电网从大电网购电的最大有功功率限值；

-微电网向大电网售电的最大有功功率限值；

式中：

-在t-1时刻第i台可调度微电源的连续运行时间；

-在t-1时刻第i台可调度微电源的连续停运时间；

${P P}_{i i}^{t t} - - {P P}_{i i}^{t t - - 11} \leq \leq Δ Δ {P P}_{U u}$

${P P}_{i i}^{t t - - 11} - - {P P}_{i i}^{t t} \leq \leq Δ Δ {P P}_{D D.}$

${U u}_{P P}^{t t} + + {U u}_{S S}^{t t} \leq \leq 11$

$00 \leq \leq {Q Q}_{i i}^{t t} \leq \leq {Q Q}_{i i}^{max max}$

式中：

-第i台无功功率输出可调节电源的最大无功功率输出限值；

- The maximum reactive power output limit of the i-th adjustable power supply with reactive power output;

5)通过求解上述能量优化模型3得到各微电源的有功功率和无功功率输出指令，然后将其传送给各微电源，各微电源按照指令输出相应的有功功率和无功功率。5) By solving the above energy optimization model 3, the active power and reactive power output commands of each micro-power source are obtained, and then sent to each micro-power source, and each micro-power source outputs corresponding active power and reactive power according to the command.

图3为采用本发明进行微电网实时能量优化调度得到的各电源在一天内各时刻的有功功率(P_Grid为与大电网交互的有功功率，其值大于0表示从大电网购电，小于0表示向大电网售电；P_MT为微型燃气轮机发电输出的有功功率；P_FC为燃料电池发电输出的有功功率；P_DE为柴油发电机发电输出的有功功率；P_Bat为蓄电池的充放电功率，其值大于0表示蓄电池放电，小于0表示对蓄电池充电)，图4为采用本发明进行微电网实时能量优化调度得到的各无功可调节电源在一天内各时刻的无功功率输出(Q_Grid为大电网向微电网输出的无功功率；Q_MT为微型燃气轮机发电输出的无功功率；Q_FC为燃料电池发电输出的无功功率；Q_Bat为蓄电池输出的无功功率；Q_MT为风力发电机发电输出的无功功率)，图5为蓄电池荷电状态在一天内的变化情况。由图3和图5可知蓄电池的放电功率P_Bat在0～4h(谷时段)、16～18h(平时段)和22～23h(平时段)小于0(蓄电池充电)，而在11～16h(峰时段)和19～22h(峰时段)大于0(蓄电池放电)，而与大电网交互的功率P_Grid在0～10h(谷、平时段)和16～18h(平时段)大于0(从大电网购电)，而在11～15h(峰时段)和19～21h(峰时段)小于0(向大电网售电)，这样微电网在平谷时段以较低电价从大电网买电对蓄电池充电，而在峰时段蓄电池放电并以较高价格卖给电网，既提高了微电网运行的经济性，又有助于对大电网进行“削峰填谷”。由图5可知蓄电池荷电状态始终维持在0.5(SOC^min)与0.9(SOC^max)之间，同时蓄电池放电至其荷电状态越接近0.5时其放电功率越小，说明采用本发明进行的微电网能量管理始终让蓄电池运行在安全范围内，防止了蓄电池过充电或过放电，且蓄电池随时保持了一定蓄电量(因为从图5知SOC始终在0.6以上，比0.5大)，能为在大电网发生故障情况下微电网转为孤网运行时提供紧急功率支撑。Fig. 3 is the active power of each power source at each moment in a day obtained by adopting the present invention to carry out micro-grid real-time energy optimization scheduling (P_Grid is the active power interacting with the large power grid, and its value greater than 0 means purchasing electricity from the large power grid, and less than 0 means Sell electricity to the large grid; P_MT is the active power output by the micro gas turbine; P_FC is the active power output by the fuel cell; P_DE is the active power output by the diesel generator; P_Bat is the charging and discharging power of the battery, and its value is greater than 0. Battery discharge, less than 0 means charging the battery), Fig. 4 is the reactive power output of each reactive adjustable power supply obtained by using the present invention to carry out micro-grid real-time energy optimization scheduling at each moment in a day (Q_Grid is the output of the large grid to the micro grid output reactive power; Q_MT is the reactive power output by the micro gas turbine; Q_FC is the reactive power output by the fuel cell; Q_Bat is the reactive power output by the battery; Q_MT is the reactive power output by the wind generator), Figure 5 shows the change of battery state of charge in one day. It can be seen from Figure 3 and Figure 5 that the discharge power P_Bat of the battery is less than 0 (battery charging) at 0-4h (valley period), 16-18h (normal period) and 22-23h (normal period), while it is less than 0 (battery charging) at 11-16h (peak period). period) and 19~22h (peak period) are greater than 0 (battery discharge), while the power P_Grid interacting with the large power grid is greater than 0 (purchased from the large power grid) electricity), and less than 0 during 11-15h (peak time) and 19-21h (peak time) (sell electricity to the large grid), so that the micro-grid buys electricity from the large grid at a lower price during the flat valley period to charge the battery, while During the peak hours, the battery is discharged and sold to the power grid at a higher price, which not only improves the economy of the operation of the micro-grid, but also helps to "shave the peak and fill the valley" of the large power grid. It can be seen from Figure 5 that the state of charge of the battery is always maintained between 0.5 (SOC ^min ) and 0.9 (SOC ^max ), and the discharge power of the battery is smaller when the state of charge is closer to 0.5. Grid energy management always keeps the battery running within a safe range, prevents the battery from overcharging or overdischarging, and maintains a certain storage capacity at any time (because the SOC is always above 0.6, which is greater than 0.5 from Figure 5), and can be used in large In the event of a grid failure, the microgrid provides emergency power support when it switches to isolated grid operation.

Claims

1. A micro-grid real-time energy optimal dispatching method under grid-connected operation mode, is characterized in that comprising the steps:

1) According to the load conditions of the large power grid, 24 hours a day is divided into three periods: peak period, normal period and valley period;

2) During the real-time operation of the microgrid, determine the current time period at each scheduling time, monitor the state of charge SOC of the battery in the microgrid, measure the active power and reactive power of each node load, and measure the regenerative Output active power and reactive power of energy generation;

3) If it is currently in a valley period or a normal period, it is further judged whether the current state of charge SOC of the battery satisfies SOC<SOC ^max , and if it is currently in a peak period, then it is further judged whether the current state of charge of the battery satisfies SOC>SOC ^min ;

4) If it is currently in a valley period or a normal period, and SOC<SOC ^max is not satisfied, it is determined that the battery can be discharged, and optimization 1 is performed, and if SOC<SOC ^max is met, it is determined that the battery can be charged and discharged, and optimization 2 is performed ; If it is currently in peak time and SOC>SOC ^min is satisfied, then determine that the battery can be discharged and optimize 3, if not satisfy SOC>SOC ^min , then determine to charge the battery with constant power and optimize 4;

The optimization 1 in the step 4) refers to solving the established energy optimization model 1. The energy optimization model 1 takes the lowest operating cost of the microgrid as the goal, and takes the on-off status of each schedulable micro power supply in the microgrid, and each schedulable The active output of the micro power supply, the electric power bought and sold between the large power grid, the discharge power of the storage battery and the reactive power of each reactive output adjustable power supply are optimization variables. The energy optimization model 1 is a mixed integer nonlinear programming problem, and its objective function :

min min {Σ Σ}_{i i = = 11}^{N N} (({U u}_{i i}^{t t} {C C}_{f f} + + {U u}_{i i}^{t t} {C C}_{OM om} + + {U u}_{i i}^{t t} ((11 - - {U u}_{i i}^{t t - - 11})) {C C}_{si the si}^{t t})) + + {λ λ}_{bat bat} + + {U u}_{P P}^{t t} {C C}_{P P} - - {U u}_{S S}^{t t} {I I}_{S S}

Where: λ _bat = (σP _bat )Δt

t-system running period;

i - the number of micro power sources that can be dispatched in the system;

N-the total number of schedulable micro-power sources in the system;

C _f - the energy consumption cost of the dispatchable micropower supply;

C _OM - O&M cost of dispatchable micropower;

- start-up costs of dispatchable micropower sources;

λ _bat - the designed battery charging and discharging cost function;

C _P - the expenditure of the microgrid to purchase electricity from the large grid;

I _S - income from selling electricity from the microgrid to the large grid;

Δt - optimization time interval;

σ - designed coefficient;

P _bat - the discharge power of the battery;

Its constraints:

(a) Power flow constraints

{P P}_{i i} - - {Σ Σ}_{j j = = 11}^{j j = = n no} [[{e e}_{i i} (({G G}_{ij ij} {e e}_{j j} - - {B B}_{ij ij} {f f}_{j j})) + + {f f}_{i i} (({G G}_{ij ij} {f f}_{j j} + + {B B}_{ij ij} {e e}_{j j}))]] = = 00

{Q Q}_{i i} - - {Σ Σ}_{j j = = 11}^{j j = = n no} [[{f f}_{i i} (({G G}_{ij ij} {e e}_{j j} - - {B B}_{ij ij} {f f}_{j j})) - - {e e}_{i i} (({G G}_{ij ij} {f f}_{j j} + + {B B}_{ij ij} {e e}_{j j}))]] = = 00

In the formula: P _i , Q _i - each node injects active power and reactive power;

e _i , f _i - the real and imaginary parts of the voltages at each node represented by complex numbers;

G _ij , B _ij -the real part and imaginary part of the admittance elements of node i and node j;

n-the total number of nodes in the microgrid;

(b) Schedulable micro power supply capacity constraints

{P P}_{i i}^{min min} \leq \leq {P P}_{i i}^{t t} \leq \leq {P P}_{i i}^{max max}

In the formula:

(c) Discharge active power constraints of the battery

00 \leq \leq {P P}_{bat bat} \leq \leq {P P}_{dh d h__max max}^{t t}

In the formula: P _bat - the discharge power of the storage battery;

- the maximum dischargeable power limit of the battery at time t;

(d) The maximum capacity constraint that can interact between the microgrid and the large grid, which is the supply and demand agreement reached between them or the physical transmission capacity limit of the tie line

11 \leq \leq {P P}_{Pgrid Pgrid}^{t t} \leq \leq {P P}_{Pgrid Pgrid}^{max max}

00 \leq \leq {P P}_{Sgrid grid}^{t t} \leq \leq {P P}_{Sgrid grid}^{max max}

In the formula:

- The active power purchased by the microgrid from the large grid;

-Active power sold by the microgrid to the large grid;

(e) Minimum continuous running time and minimum continuous downtime constraints of dispatchable micropower sources

(({T T}_{i i,, on on}^{t t - - 11} - - MRT MRT)) \cdot \cdot (({U u}_{i i}^{t t - - 11} - - {U u}_{i i}^{t t})) &GreaterEqual; &Greater Equal; 00

(({T T}_{i i,, off off}^{t t - - 11} - - MST MST)) \cdot \cdot (({U u}_{i i}^{t t} - - {U u}_{i i}^{t t - - 11})) &GreaterEqual; &Greater Equal; 00

In the formula:

MRT-minimum continuous running time of the i-th schedulable micro power supply;

MST-minimum continuous downtime of the i-th schedulable micropower supply;

(f) Limitation of rate of change of active power output of schedulable micropower sources

{P P}_{i i}^{t t} - - {P P}_{i i}^{t t - - 11} \leq \leq {ΔP ΔP}_{U u}

{P P}_{i i}^{t t - - 11} - - {P P}_{i i}^{t t} \leq \leq {ΔP ΔP}_{D D.}

In the formula:

ΔP _U - schedulable micropower supply power increase limit at this scheduling time relative to the power at the previous time;

ΔP _D - The power reduction limit of the schedulable micro power supply at this time of dispatch compared with the power at the previous time;

(g) Mutually exclusive constraints on buying and selling electricity from the large power grid

{U u}_{P P}^{t t} + + {U u}_{S S}^{t t} \leq \leq 11

(h) Reactive power output constraints of power supply with adjustable reactive power output

00 \leq \leq {Q Q}_{i i}^{t t} \leq \leq {Q Q}_{i i}^{max max}

In the formula:

The optimization 2 in the step 4) refers to solving the established energy optimization model 2. The energy optimization model 2 takes the lowest operating cost of the microgrid as the goal, and takes the on-off status of each schedulable micro power supply in the microgrid, and each schedulable The active output of the micro power supply, the electric power bought and sold between the large power grid, the discharge power of the storage battery, and the reactive power of each reactive output adjustable power supply are optimization variables. The energy optimization model 2 is a mixed integer nonlinear programming problem, and its objective function The same as energy optimization model 1, its constraints are basically the same as energy optimization model 1, except that "(c) battery discharge active power constraint" in the constraints has changed, and in the constraints of energy optimization model 2, "(c) The discharge active power constraint of the battery" is:

{- - P P}_{ch ch__max max}^{t t} \leq \leq {P P}_{bat bat} \leq \leq {P P}_{dh d h__max max}^{t t}

In the formula: P _bat - the discharge power of the battery; P _bat <0 means charging the battery, P _bat >0 means discharging the battery;

- The maximum chargeable power of the battery;

- The maximum dischargeable power of the battery;

The optimization 3 in the step 4) refers to solving the established energy optimization model 3. The energy optimization model 3 takes the lowest operating cost of the microgrid as the goal, and takes the on-off status of each schedulable micro power supply in the microgrid, and each schedulable The active output of the micro power supply, the electric power bought and sold between the large power grid, the discharge power of the storage battery, and the reactive power of each reactive output adjustable power supply are optimization variables. Energy optimization model 3 is a mixed integer nonlinear programming problem, and its constraints The same as the energy optimization model 1, its objective function is slightly different from the energy optimization model 1, the difference is that the λ _bat term in the objective function of the energy optimization model 3 and the λ _bat term in the objective function of the energy optimization model 1 are not well designed Similarly, the objective function of the energy optimization model 3 is:

min min {Σ Σ}_{i i = = 11}^{N N} (({U u}_{i i}^{t t} {C C}_{f f} + + {U u}_{i i}^{t t} {C C}_{OM om} + + {U u}_{i i}^{t t} ((11 - - {U u}_{i i}^{t t - - 11})) {C C}_{si the si}^{t t})) + + {λ λ}_{bat bat} + + {U u}_{P P}^{t t} {C C}_{P P} - - {U u}_{S S}^{t t} {I I}_{S S}

Where: λ _bat ＝ δ·P _bat ∆t

δ＝a ₁ +a ₂ dSOC+a ₃ P _bat +a ₄ dSOC P _bat +a ₅ dSO ^C2

dSOC＝SOC- ^SOCmin

t-system running period;

i - the number of micro power sources that can be dispatched in the system;

N-the total number of schedulable micro-power sources in the system;

C _f - the energy consumption cost of the dispatchable micropower supply;

C _OM - O&M cost of dispatchable micropower;

- start-up costs of dispatchable micropower sources;

λ _bat - the designed battery charging and discharging cost function;

I _S - income from selling electricity from the microgrid to the large grid;

Δt - optimization time interval;

P _bat - the discharge power of the battery;

SOC-the state of charge of the battery;

SOC ^min - the lower limit of the state of charge of the battery;

a ₁ , a ₂ , a ₃ , a ₄ , a ₅ - designed coefficients;

The optimization 4 in the step 4) refers to solving the established energy optimization model 4. The energy optimization model 4 takes the lowest operating cost of the microgrid as the goal, and takes the on-off status of each schedulable micro power supply in the microgrid, and each schedulable The active output of the micro power supply, the electric power bought and sold between the large power grid and the reactive power of each reactive output adjustable power supply are optimization variables, and the energy optimization model 4 is a mixed integer nonlinear programming problem. Compared with the energy optimization model 1, the energy There is no λ _bat item in the objective function of optimization model 4, and the objective function of energy optimization model 4 is:

min min {Σ Σ}_{i i = = 11}^{N N} (({U u}_{i i}^{t t} {C C}_{f f} + + {U u}_{i i}^{t t} {C C}_{OM om} + + {U u}_{i i}^{t t} ((11 - - {U u}_{i i}^{t t - - 11})) {C C}_{si the si}^{t t})) + + {λ λ}_{bat bat} + + {U u}_{P P}^{t t} {C C}_{P P} - - {U u}_{S S}^{t t} {I I}_{S S}

In the formula: t-system running period;

i - the number of micro power sources that can be dispatched in the system;

N-the total number of schedulable micro-power sources in the system;

C _f - the energy consumption cost of the dispatchable micropower supply;

C _OM - O&M cost of dispatchable micropower;

- start-up costs of dispatchable micropower sources;

λ _bat - the designed battery charging and discharging cost function;

I _S - income from selling electricity from the microgrid to the large grid;

The constraints of the energy optimization model 4 are basically the same as those of the energy optimization model 1, except that the constraint of "(c) the discharge active power of the battery" is not included in the constraints of the energy optimization model 4;

5) Obtain the active power and reactive power output commands of each micro-power source through the optimization result in step 4), and then transmit it to each micro-power source, and each micro-power source outputs corresponding active power and reactive power according to the command.

2. The real-time energy optimization method for micro-grids in a grid-connected operation mode according to claim 1, characterized in that: if the large power grid adopts peak-valley time-of-use electricity prices, then the peak period determined according to the time-of-use electricity prices, The three time periods are divided into the normal time period and the valley time period.

3. The micro-grid real-time energy optimization method in a grid-connected operation mode according to claim 1 or 2, characterized in that: the dispatching time of the step 2) means that the real-time energy optimization management of the micro-grid is in the micro-grid During the real-time operation, the microgrid is dispatched for energy optimization every 5 minutes or 15 minutes.

4. The real-time energy optimization method for a microgrid in a grid-connected operation mode according to claim 1 or 2, characterized in that: the battery state of charge SOC in the step 3) reflects the remaining power of the battery in its total The scale parameter of capacity, defined as:

SOC SOC = = \frac{{C C}_{net net}}{C C} = = 11 - - \frac{&Integral; &Integral; Idt Idt}{C C}

C _net - battery remaining capacity, Ah;

C- total battery capacity, Ah;

I-battery discharge current, A;

The corresponding SOC ^max and SOC ^min are the upper and lower limits of the state of charge of the storage battery, and SOC ^max =0.9 and SOC ^min =0.5.

5. The micro-grid real-time energy optimization method under a grid-connected operation mode according to claim 1 or 2, characterized in that: charging the battery with a constant power and optimizing 4 in the step 4) means In optimization 4, the charging and discharging power of the battery does not participate in the optimization, but charges the battery with a constant power. At this time, the battery is equivalent to a constant load, and the constant power charged to the battery is included in the microgrid network node where the battery is located In the load, the constant charging power of the battery is

{P P}_{bat bat}^{t t} = = {I I}_{bat bat__max max__ch ch} \cdot \cdot {U u}_{batt batt}^{t t}

In the formula: I _{bat_max_ch} - the maximum allowable charging current of the battery, given by the production manual;

- battery terminal voltage.

6. The microgrid real-time energy optimization method in a grid-connected operation mode according to claim 1 or 2, characterized in that: the optimization result in the step 5) refers to the solution of the optimization 1 in the step 4). , optimization 2, optimization 3 and optimization 4 involved in the energy optimization model 1, energy optimization model 2, energy optimization model 3 and energy optimization model 4, including the start and stop status of each schedulable micro power supply in the microgrid, The active output value of each adjustable micro-power source, the value of the electric power purchased and sold with the large power grid, the discharge power value of the storage battery and the reactive power value of each reactive output adjustable power source.