CN203933038U - From the grid-connected mixing photovoltaic power generation control system of net - Google Patents
From the grid-connected mixing photovoltaic power generation control system of net Download PDFInfo
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Abstract
本实用新型涉及一种离网并网混合光伏发电控制系统。该控制系统由光伏阵列，并网逆变器、蓄电池组、双向逆变器、负载、功率表、公共电网、需求响应控制系统和开关组构成。所述光伏阵列通过并网逆变器接入交流侧，蓄电池组通过双向逆变器接入交流侧；并网逆变器通过、双向逆变器相连、公共电网与负载之间通过开关组（S1-S4）相连接；整个离网并网混合发电系统通过需求响应控制系统统一管理控制。通过开关组的开闭组合，系统支持8种不同的运行模式，以实现经济效益的最优化与电网的削峰填谷，并通过限制蓄电池放电深度与充放电功率的方法延长其使用寿命。
The utility model relates to an off-grid and grid-connected hybrid photovoltaic power generation control system. The control system is composed of photovoltaic array, grid-connected inverter, battery pack, bidirectional inverter, load, power meter, public grid, demand response control system and switch group. The photovoltaic array is connected to the AC side through a grid-connected inverter, and the battery pack is connected to the AC side through a bidirectional inverter; S1-S4) are connected; the entire off-grid and grid-connected hybrid power generation system is uniformly managed and controlled by the demand response control system. Through the opening and closing combination of the switch group, the system supports 8 different operating modes to achieve the optimization of economic benefits and the peak-shaving and valley-filling of the power grid, and extend the service life of the battery by limiting the discharge depth and charging and discharging power of the battery.
Description
Technical Field
The utility model relates to a solar energy new forms of energy electricity generation and application especially relate to an off-grid and grid-connected hybrid photovoltaic power generation control system.
Background
With the increasing energy crisis, how to more reasonably utilize energy sources is also becoming a social concern while developing and utilizing renewable energy sources. In recent years, the load increase speed of the electric energy is higher than that of the electric energy, so that the load rate of the electric network is reduced, and the peak-valley difference is increased. For the power consumption enterprises, the electricity consumption cost can be greatly increased, the economic benefit is not benefited, and for the public power grid, the reliability and the stability of the power grid operation are influenced. Therefore, comprehensive utilization of photovoltaic power generation and power grid electric energy is achieved, and optimal scheduling of the distributed power system based on peak-valley electricity prices is of great significance.
Most distributed power systems can realize grid-connected and off-grid operation of the system and have an energy storage device to improve the energy utilization rate, but still have the following defects:
1. existing systems do not optimize the economic operation of the system for peak-to-valley electricity rates. Although individual systems can support peak-valley electricity prices, the scheduling schemes of the systems are simple, and mode switching is performed only according to a few thresholds or conditions, so that the operation cost can be reduced to a certain extent, but optimization control is not performed from the perspective of global optimization, and the optimization effect is limited.
2. The existing system usually lacks prediction of photovoltaic power generation amount in each time period, and although individual systems relate to power generation amount prediction, prediction algorithms are simple, and reliability of predicted values is low. And if the prediction of the power generation amount in each time interval is lacked, the scheduling scheme of the day cannot be arranged through the power generation amount, and the economic optimization is difficult to realize.
3. The service life of the storage battery is not considered in the conventional system, the discharge depth of the storage battery cannot be controlled in the operation process, and the service life of the storage battery is greatly reduced due to deep discharge.
SUMMERY OF THE UTILITY MODEL
In order to solve the not enough of existence on the prior art, the utility model provides a according to the price of electricity time of the use of time, carry out optimization control's the mixed photovoltaic power generation control system that is incorporated into power networks that leaves the net to the microgrid that photovoltaic power generation set, storage battery and public net constitute.
In order to achieve the above purpose, the utility model adopts the following technical scheme:
an off-grid and grid-connected hybrid photovoltaic power generation control system is composed of a photovoltaic array, a grid-connected inverter, a storage battery pack, a bidirectional inverter, a load, a power meter, a public power grid, a demand response controller, a management computer and a switch group, wherein the switch group comprises switches S1-S4; the photovoltaic array is connected to an alternating current side through a grid-connected inverter, and the storage battery pack is connected to the alternating current side through a bidirectional inverter; the grid-connected inverter is connected with a power grid through a switch S1, connected with the bidirectional inverter through a switch S3 and connected with a load through switches S3 and S4; the bidirectional inverter is connected with a load through a switch S4 and is connected with a power grid through switches S4 and S2; the power grid is connected with a load through a switch S2; the whole off-grid and grid-connected hybrid power generation system is managed and controlled in a unified way through a demand response controller and a management computer; through different on-off combinations of the switches S1-S4, the system supports the switching of a shutdown mode, an off-grid storage battery power supply mode, an off-grid photovoltaic-storage battery working mode, a power grid independent power supply mode, a power grid power supply-charging mode, a photovoltaic power supply-grid connection-charging mode, a power grid power supply-photovoltaic grid connection-charging mode and a power grid power supply-photovoltaic grid connection-discharging mode.
The management computer is provided with a historical information database, predicts a photovoltaic power generation capacity curve and an electrical load curve of the same day according to historical data of photovoltaic power generation capacity and electrical load and local meteorological information, and sends the prediction result to the demand response controller through the Ethernet.
The demand response controller consists of a microcontroller, an Ethernet communication interface and an alternating current contactor control interface, receives a power predicted value from a management computer, performs optimized scheduling according to the power predicted value, and finally controls the switch group to adjust the working mode through the opening and closing of each switch, so as to realize a scheduling scheme.
The operation modes are as follows:
1) in the shutdown mode, all switches are in an off state;
2) in the off-grid storage battery power supply mode, S1, S2 and S3 are disconnected, S4 is closed, the load is supplied with power by the storage battery alone, and energy flows to the load from the storage battery;
3) the off-grid photovoltaic-storage battery working mode is divided into two conditions due to different charging and discharging states of the storage battery: when the switches S1 and S2 are opened, and S3 and S4 are closed, when the power of the photovoltaic array is not enough to meet the load requirement, the photovoltaic array and the storage battery are supplemented by the storage battery to jointly supply power to the load, and energy flows to the load from the photovoltaic array and the storage battery;
the switches S1 and S2 are opened, S3 and S4 are closed, when the power of the photovoltaic array is enough to meet the requirement of a load, the storage battery is charged, and energy flows to the storage battery and the load from the photovoltaic array;
4) in the independent power supply mode of the power grid, the switches S1, S3 and S4 are opened, the switch S2 is closed, the public power grid independently supplies power to the load, and energy flows from the public power grid to the load;
5) in a power supply-charging mode of the power grid, S1 and S3 are disconnected, S2 and S4 are closed, the public power grid supplies power to the load and charges the storage battery, and energy flows from the public power grid to the storage battery and the load;
6) in a photovoltaic power supply-grid connection-charging mode, the switch S2 is switched off, the switches S1, S3 and S4 are switched on, the power of the photovoltaic array is enough to meet the load requirement, redundant power is transmitted to a public power grid after the storage battery is charged, and energy flows to the load, the storage battery and the public power grid from the photovoltaic array;
7) in a power grid power supply-photovoltaic grid connection-charging mode, S3 is disconnected, and S1, S2 and S4 are closed, so that on one hand, the photovoltaic array outputs power to a public power grid, and on the other hand, the public power grid supplies power to a load and charges a storage battery;
8) and in a grid power supply-photovoltaic grid connection-discharge mode, S4 is disconnected, S1, S2 and S3 are closed, the photovoltaic array and the storage battery output power to a public power grid, and the public power grid supplies power to a load.
An optimization control method of an off-grid and grid-connected hybrid photovoltaic power generation control system is characterized in that a photovoltaic power generation curve and a power load curve of the same day are predicted according to historical data of photovoltaic power generation and power load and meteorological information, the minimum operation cost is taken as an optimization target according to the predicted photovoltaic power generation and power load, the charge-discharge power of a storage battery in each hour, the exchange power with a power grid and the operation mode of the system are taken as optimized variables, the decision scheduling is performed on the operation mode of the system through a particle swarm optimization algorithm by taking the electric energy balance condition, the upper limit and the lower limit of the exchange power with the power grid, the charge-discharge power limit of the storage battery and the charge state limit as constraint conditions, so that the operation cost of the system is the lowest, and the service life of the system is prolonged by limiting the discharge.
The photovoltaic output power is predicted as: counting the photovoltaic output power of each day in a research period in various weather, uniformly dividing the output power from 0 to the maximum value into a plurality of intervals, and taking the power in the same interval as a state; dividing one day into a plurality of time periods, wherein one time period is at least 1 hour, and counting the transfer times of the photovoltaic output power of each time period in the research period to obtain a state transfer matrix corresponding to the time period; after the system is formally operated, firstly, statistical data under corresponding weather is found according to information provided by a meteorological center, and then the photovoltaic output power of a whole day is predicted by a Markov chain method.
The electrical load is predicted as: counting the daily power load in a research period, uniformly dividing the output power from 0 to the maximum value into a plurality of intervals, and taking the power in the same interval as a state; dividing one day into a plurality of time periods, wherein one time period is at least 1 hour, and counting the transfer times of the electric load in each time period in the research period to obtain a state transfer matrix corresponding to the time period; and after the system is formally operated, predicting the power load of the whole day by a Markov chain method according to the statistical data.
When power prediction is performed, an initial state probability mass function of a first unit time, namely an initial distribution p, is obtained1Making the state probability corresponding to the measured power at the initial time be 1 and the other state probabilities be 0, and then calculating the distribution probability of the next time by using the state transition matrix, wherein the formula is as follows:
pm+1＝pmPm
wherein p ismAnd pm+1Distribution probability row vectors, P, representing respectively the m-th and m + 1-th time segmentsmObtaining the distribution probability p of the m +1 time period for the state transition matrix corresponding to the m time periodm+1Then, a mathematical expectation method is used to obtain a predicted value at the m +1 th time, and a calculation formula is as follows:
Fm+1＝pm+1PEXP
wherein, PEXPFor mathematical expectation matrix, Fm+1Obtaining a power predicted value F for the m +1 time periodm+1And then, repeating the process until power predicted values of all time periods of the whole day are obtained, and then sending the output power to the demand response controller through the Ethernet.
The processing flow of the decision algorithm is as follows:
(1) acquiring preset system operating parameters, cost parameters, electricity price parameters, photovoltaic power and electricity load;
(2) acquiring preset particle swarm algorithm parameters which mainly comprise a swarm scale, a maximum iteration number, a learning factor and an inertia weight coefficient, and setting a target function and a confidence level of a constraint condition containing a random variable;
wherein the objective function is:
is an objective function
Beta is a given confidence level
CiIs the operating cost of the i-th period, where Ci＝T[Jbuy,iPbuy,i+Ppv,iCpv_m+|Pbt,i|Cbt_m-Jsel,iPsel,i]
T is the time interval of the unit time interval
n is the total number of time periods in the scheduling period
Pbuy,iElectric power purchased from the utility grid for the ith period
Psel,iElectric power output to utility grid for period i
Ppv,iGenerating power of photovoltaic array for the ith period
Pbt,iThe charging and discharging power of the storage battery in the ith period is positive in discharging and negative in charging
Jbuy,iElectricity price for purchasing electricity from public power grid for ith period
Jsel,iElectricity prices sold to the public grid for the ith period
Cpv_mUnit operating cost for photovoltaic array
Cbt_mMaintenance costs for the battery;
(3) initializing a population, randomly generating charge and discharge power of a storage battery in each scheduling period and power purchased from a public power grid to form a particle, and checking the feasibility of the particle by using constraint conditions until all the particles are initialized; simultaneously, randomly generating the initial speed of each particle;
(4) calculating the fitness value of each particle, comparing the fitness value of each particle with the individual extreme value, and if the fitness value of each particle is better than the individual extreme value, updating the current individual extreme value of each particle and the optimal position of each individual; otherwise, the operation is kept unchanged;
(5) comparing all current individual extreme values with the global extreme value, and taking an optimalist to update the current global extreme value and the global optimal position of the current global extreme value;
(6) updating the speed and position of each particle, and checking the feasibility of the particles through constraint conditions until all the particles are feasible;
(7) repeating (4) to (8) until the termination condition is met;
(8) and outputting the optimal solution, namely the operation mode of the system in each hour, the charging and discharging power of the storage battery and the power exchanged with the power grid.
In the step (3), the constraint conditions comprise an electric power balance constraint, an exchange power constraint with a power grid and a storage battery constraint;
the electric power balance constraint formula is as follows:
Pbuy,i+Ppv,iηinv+Pbt,iηdis-Psel,i-Pld,i＝0
in the formula, Ppv,iPhotovoltaic power generation power for the i-th period, Pbuy,iAnd Psel,iPower bought and sold from the grid, P, for the ith period, respectivelybt,iFor charging and discharging power of the accumulator during the i-th period, Pld,iIs the load of the i-th period, ηinvFor the efficiency of the inverter, ηchFor the charging efficiency of the accumulator, etadisThe discharge efficiency of the storage battery;
the formula for exchanging power with the grid is:
in the formula,the maximum power values of electricity purchasing and electricity selling are respectively;
the constraint formula of the storage battery is as follows:
SOCmin≤SOCi≤SOCmax
wherein,maximum power, SOC, for charging and discharging of the battery at the i-th time periodiIs the state of charge, SOC, of the battery at the i-th time periodmin，SOCmaxThe minimum value and the maximum value of the state of charge of the storage battery are respectively, and the capacity of the storage battery is assumed to be unchanged, which means that the energy storage capacity of the storage battery at the initial time and the end time of the scheduling period is equal.
The beneficial effects of the utility model
1. Accurate power prediction: the existing system is usually lack of prediction of photovoltaic power generation power, although individual systems relate to power prediction, the algorithm is simple, if the weather of the day is compared with the weather of a certain historical day, and the result is matched, the power generation amount of each time period of the certain historical day is used as the predicted value of the power generation amount of each time period of the day. And the utility model discloses a prediction method of Markov chain, this method are in the initial probability of different states and the transition probability between each state through the thing, judge the general trend of change of state to the realization is to the prediction of future state, so the credibility of prediction result is higher.
2. Optimizing economic operation: the economic operation optimization scheme of the existing system is simple, mode switching is generally performed according to a plurality of threshold values or conditions, the actual situation is variable, and the system contains a random variable of photovoltaic power, so that the method cannot realize global optimization, namely the operation cost of the whole day cannot be minimized. And the utility model discloses a particle swarm optimization algorithm, the influence of fully considering peak valley price of electricity during the optimization, with the whole day cost minimum as objective function, so its optimization result is close the most genuine optimization more.
3. Electric network 'peak clipping and valley filling': the utility model discloses an economic operation is optimized, is through the millet stage from the electric wire netting purchase the electricity or to battery charging, and the peak period is sold the electricity or the electric energy of preferred utilization battery to the electric wire netting realizes, has just so realized that the "peak clipping of electric wire netting fills in millet", helps the electric wire netting steady, efficient operation.
4. Prolonging the service life of the storage battery: the utility model discloses when solving objective function through the particle swarm algorithm, can add battery charge and discharge power and battery state of charge's restrictive condition, consequently can restrict the battery maximum depth of discharge to battery life has been prolonged.
Drawings
Fig. 1 is a block diagram of the system architecture of the present invention;
FIG. 2 is a schematic illustration of a shutdown mode;
FIG. 3 is a schematic diagram of an off-grid battery supply mode;
FIG. 4 is a schematic diagram of battery discharge in an off-grid photovoltaic-battery operating mode;
FIG. 5 is a schematic diagram of the battery charging in an off-grid photovoltaic-battery mode of operation;
FIG. 6 is a schematic diagram of a grid isolated power mode;
FIG. 7 is a schematic diagram of a grid power-charging mode;
fig. 8 is a schematic diagram of a photovoltaic power supply-grid connection-charging mode;
fig. 9 is a schematic diagram of grid power supply-photovoltaic grid connection-charging mode;
fig. 10 is a schematic diagram of grid power supply-photovoltaic grid connection-discharge mode;
FIG. 11 is a process flow diagram of a decision algorithm.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. The specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Fig. 1 is a block diagram of the system structure of the present invention. The utility model discloses constructed a system based on timesharing price of electricity carries out optimal control to the microgrid, it comprises photovoltaic array, grid-connected inverter, storage battery, two-way inverter, load, power meter, interchange generating line, public electric wire netting, demand response controller, management computer and switch block.
The demand response controller can control four switches S1-S4, each switch has a closed state and an open state, and the system can work in 8 different operation modes by changing the states of S1-S4:
1) shutdown mode. All switches are off and the entire system operates in shutdown mode with no energy flow, as shown in fig. 2.
2) And an off-grid storage battery power supply mode. S1, S2 and S3 are opened, S4 is closed, the load is separately powered by the storage battery, and energy flows to the load from the storage battery, as shown in FIG. 3.
3) And an off-grid photovoltaic-storage battery working mode. The mode is divided into two conditions due to different charging and discharging states of the storage battery: when the power of the photovoltaic array is not enough to meet the load requirement, the storage battery supplements the power to supply power to the load together, and the energy flows to the load from the photovoltaic array and the storage battery, as shown in fig. 4; s1, S2 are open and S3, S4 are closed, and when the photovoltaic array power is sufficient to meet the load demand, the battery is charged and energy flows from the photovoltaic array to the battery and the load, as shown in fig. 5.
4) And (3) a power grid independent power supply mode. S1, S3, S4 are open and S2 is closed, the utility grid alone supplies power to the load, and energy flows from the utility grid to the load, as shown in fig. 6.
5) Grid supply-charging mode. S1, S3 are open, S2, S4 are closed, the utility grid supplies power to the load and charges the battery, and energy flows from the utility grid to the battery and the load, as shown in fig. 7.
6) Photovoltaic power supply-grid connection-charging mode. S2 is opened, S1, S3 and S4 are closed, the power of the photovoltaic array is enough to meet the load demand, and after the storage battery is charged, the surplus power is transmitted to the public power grid. Energy flows from the photovoltaic array to the load, storage battery and utility grid as shown in fig. 8.
7) And a power grid power supply-photovoltaic grid connection-charging mode. S3 is opened and S1, S2 and S4 are closed, so that the photovoltaic array outputs power to the utility grid on the one hand, and the utility grid supplies power to the load and charges the storage battery on the other hand, as shown in fig. 9.
8) Grid power supply-photovoltaic grid connection-discharge mode. S4 is open, S1, S2, S3 are closed, and the photovoltaic array and battery output power to the utility grid, which powers the load, as shown in fig. 10.
When the system runs, the management computer predicts the photovoltaic output power and the electric load of the same day by a Markov chain method. Therefore, before the system is formally operated, historical data of the photovoltaic output power and the electric load need to be counted and stored in a historical information database. The specific statistical method of the photovoltaic output power comprises the following steps:
1) and counting the photovoltaic output power of each day in various weathers in a research period.
2) The output power is evenly divided into a plurality of intervals from 0 to the maximum value, and the power in the same interval is taken as a state.
3) Dividing one day into a plurality of time periods (one time period is at least 1 hour), taking 1 hour as the minimum time interval, and counting the transfer times of the photovoltaic output power of each time period in the research time period to obtain a state transfer matrix corresponding to the time period.
The statistical method of the power load is basically the same as that of the photovoltaic output power, and weather factors are not required to be considered during statistics.
After the system is formally operated, power prediction software on a management computer firstly acquires weather information provided by a meteorological center, finds statistical data in a database under corresponding weather, and then predicts the photovoltaic output power of the whole day by a Markov chain method.
When power prediction is carried out, an initial state probability mass function of a first unit time, namely an initial distribution p, is obtained firstly1The state probability corresponding to the actual measurement power at the initial time is 1, and the other state probabilities are 0. Then, the state transition matrix is used for calculating the distribution probability of each state at the next moment, and the formula is shown as follows:
pm+1＝pmPm
wherein p ismAnd pm+1Distribution probability row vectors, P, representing respectively the m-th and m + 1-th time segmentsmAnd the state transition matrix is corresponding to the mth time period. Obtaining the distribution probability p of the m +1 time periodm+1Then, a predicted value at the m +1 th moment is obtained by a mathematical expectation methodThe calculation formula is as follows:
Fm+1＝pm+1PEXP
wherein P isEXPFor mathematical expectation matrix, Fm+1Is the predicted value of the power of the (m + 1) th time segment.
To obtain Fm+1And then repeating the process until power predicted values of all time periods of the whole day are obtained.
The output power is then sent to the demand response controller via the ethernet. The demand response controller carries out scheduling decision through a particle swarm optimization algorithm according to predicted photovoltaic power generation and power load, by taking minimum operation cost as an optimization target, taking charge and discharge power of a storage battery in each hour, exchange power with a power grid and an operation mode of a system as optimized variables, and taking electric energy balance conditions, upper and lower limits of the exchange power with the power grid, storage battery charge and discharge power limits and charge state limits as constraint conditions, wherein the processing flow of the decision algorithm is shown in fig. 11, and the specific process is as follows:
(1) and acquiring preset operation parameters, cost parameters, electricity price parameters, photovoltaic power and electricity load of the system.
(2) Acquiring preset particle swarm algorithm parameters which mainly comprise a population scale, a maximum iteration number and a learning factor c1、c2And an inertia weight coefficient ω, etc., and sets the confidence level of the objective function and the constraint condition containing the random variable.
Wherein the objective function is:
in the formula,is an objective function, β is a given confidence level, CiIs the operating cost of the i-th session. Wherein C isi＝T[Jbuy,iPbuy,i+Ppv,iCpv_m+|Pbt,i|Cbt_m-Jsel,iPsel,i]T is the time interval of the unit time interval, n is the total number of time intervals in the scheduling period, Pbuy,iElectric power purchased from the public power grid for the i-th period, Psel,iElectric power output to the utility grid for the i-th period, Ppv,iIs the generated power of the photovoltaic array in the ith period, Pbt,iThe charging and discharging power of the storage battery in the ith period is positive, negative and Jbuy,iPrice of electricity purchased from the public power grid for the ith period, Jsel,iPrice of electricity sold to the public grid for the ith period, Cpv_mFor the unit operating cost of the photovoltaic array, Cbt_mThe maintenance cost of the storage battery.
(3) And initializing the population. And randomly generating the charge and discharge power of the storage battery and the power purchased from the public power grid in each scheduling period to form a particle, and checking the feasibility of the particle by using constraint conditions until all the particles are initialized. At the same time, the initial velocity of each particle is randomly generated.
Wherein the constraints include electric power balance constraints, power exchange with the grid constraints, and battery constraints.
The electric power balance constraint formula is as follows:
Pbuy,i+Ppv,iηinv+Pbt,iηdis-Psel,i-Pld,i＝0
in the formula, Ppv,iPhotovoltaic power generation power for the i-th period, Pbuy,iAnd Psel,iPower bought and sold from the grid, P, for the ith period, respectivelybt,iFor charging and discharging power of the accumulator during the i-th period, Pld,iIs the load of the i-th period, ηinvFor the efficiency of the inverter, ηchFor the charging efficiency of the accumulator, etadisThe discharge efficiency of the storage battery;
the formula for exchanging power with the grid is:
in the formula,the maximum power values for electricity purchase and sale, respectively.
The constraint formula of the storage battery is as follows:
SOCmin≤SOCi≤SOCmax
wherein,maximum power, SOC of charging and discharging of the accumulator at the i-th time periodiIs the state of charge, SOC, of the battery at the i-th time periodmin，SOCmaxThe minimum value and the maximum value of the state of charge of the storage battery are respectively, and the capacity of the storage battery is assumed to be unchanged, which means that the energy storage capacity of the storage battery at the initial time and the end time of the scheduling period is equal.
Since the photovoltaic output power is random, the presence of this random variable makes certain constraints no longer deterministic. Therefore, inequality constraints containing random variables are described in a probability form, and the inequality constraints can be established under a certain confidence level, so that the constraint conditions are processed.
(4) Calculating the fitness value of each particle, comparing the fitness value of each particle with the individual extreme value, and if the fitness value of each particle is better than the individual extreme value, updating the current individual extreme value of each particle and the optimal position of each individual; otherwise, the process is kept unchanged.
(5) And comparing all current individual extreme values with the global extreme value, and taking the optimalist to update the current global extreme value and the global optimal position thereof.
(6) The velocity and position of each particle is updated and the feasibility of the particles is verified by constraints until all particles are feasible.
(7) And (4) repeating (4) to (7) until a termination condition is met (the maximum iteration number is reached, the best solution does not change for a plurality of continuous times or the difference value between the best solution and the average adaptive value is less than a certain set constant).
(8) And outputting the optimal solution, namely the operation mode of the system in each hour, the charging and discharging power of the storage battery and the power exchanged with the power grid.
After the decision is completed, the demand response controller controls the on-off of the switch group according to the decision information, so that the system operates in a specified mode and implements a scheduling scheme.
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