CN115021406B - Microgrid controller integrating economic model predictive control - Google Patents

Microgrid controller integrating economic model predictive control Download PDF

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CN115021406B
CN115021406B CN202210744114.1A CN202210744114A CN115021406B CN 115021406 B CN115021406 B CN 115021406B CN 202210744114 A CN202210744114 A CN 202210744114A CN 115021406 B CN115021406 B CN 115021406B
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CN115021406A (en
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段志轩
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Hefei Yuanli Zhonghe Energy Technology Co ltd
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Abstract

The invention discloses a microgrid controller integrating economic model predictive control, which comprises a control function structure and a hardware structure; the control function structure comprises an algorithm layer module, a control layer module and an equipment layer module; the algorithm layer module is used for carrying out centralized processing on the load curve, the electricity price fluctuation, the power grid scheduling instruction and the photovoltaic power generation power prediction information, and obtaining an optimal internal scheduling strategy of the microgrid through an operation research optimization algorithm solver; the control layer module completes the on-line issuing of the equipment layer indexes, and the equipment data are processed in real time through high-throughput data acquisition and processing and uploaded to the algorithm layer module; in the working process of the control function structure, corresponding division is needed according to the acquisition and processing of information under different time scales; the current hardware splits the data acquisition and predictive control algorithm processing based on different time scales, and analyzes and makes decisions on coarse granularity data under the condition of slow response.

Description

Microgrid controller integrating economic model predictive control
Technical Field
The invention belongs to the technical field of micro-grids, and particularly relates to a micro-grid controller integrating economic model predictive control.
Background
The micro-grid is a novel grid, is used as a miniature version of a benchmarking traditional large grid, integrates various subsystem units such as distributed power generation, energy storage and load regulation, and can switch two operation states of grid connection and off-grid in interaction with a main grid. The micro-grid technology is a main component of a novel power system with a new energy resource occupying larger and larger ratio, and is also a main form of a future user side energy system.
However, the current solution of the model predictive control algorithm (MPC) generally aims at primary control and secondary control, lacks an economic optimization model, and therefore, has no way to reach the global economic optimum point, and the defects are mainly reflected in the cost term in the objective equation.
The electricity price model is incomplete, only the electricity price of the electric energy is considered, the demand electricity price cannot be processed, and the demand response price which randomly occurs cannot be linked according to the price fluctuation of the spot transaction market. Therefore, even if the economic optimum is achieved, the optimum is achieved in the case of the input condition defect, and the global optimum cannot be achieved.
Disclosure of Invention
In order to solve the problems of the scheme, the invention provides a microgrid controller integrating economic model predictive control.
The purpose of the invention can be realized by the following technical scheme:
a microgrid controller integrating predictive control of an economic model comprises a control function structure and a hardware structure;
the control function structure comprises an algorithm layer module, a control layer module and an equipment layer module;
the algorithm layer module is used for carrying out centralized processing on the load curve, the electricity price fluctuation, the power grid scheduling instruction and the photovoltaic power generation power prediction information, and obtaining an optimal microgrid internal scheduling strategy through an operation research optimization algorithm solver;
the control layer module completes the on-line issuing of the indexes of the equipment layer, and the equipment data is processed in real time and uploaded to the algorithm layer module through high-throughput data acquisition and processing;
in the working process of the control function structure, corresponding division is needed according to the acquisition and processing of information under different time scales;
the current hardware splits the data acquisition and predictive control algorithm processing based on different time scales, and analyzes and makes decisions on coarse granularity data under the condition of slow response, including acquiring external information; the control algorithm processes external information under the condition of being based on the quasi-steady state, obtains a target set value of the real-time control algorithm through the optimization calculation of the prediction algorithm and steady-state control parameters, and drives the execution of real-time response.
And calculating real-time response parameters, namely rapidly calculating in a second dimension based on real-time data acquisition of the SCADA system to obtain control instructions for various subsystems to complete control.
The hardware structure comprises a control host, a data acquisition module, an expansion interface and auxiliary equipment, wherein the control host is used for the functions of protocol conversion, scheduling optimization algorithm deployment, control instruction generation and the like, and comprises a data acquisition and lower computer protocol analysis module, an operation processor and an upper communication module; the operation processor is divided into a transient control module and a steady-state control module and is used for meeting the operation processing under different time sequences;
the expansion interface is used for butting equipment; the auxiliary equipment is set according to the corresponding application scene;
an objective function is arranged in the algorithm layer module, the optimization goal is that in each optimization period, the total electric charge expenditure of 96 control periods in the future is kept to be the lowest; the objective function is:
Figure BDA0003716407630000021
wherein, grid im [i]Grid for purchasing electric power from commercial power ex [i]For mains supply grid-connected electric quantity, Q g [i]The Cycle is the equivalent charge-discharge Cycle number of the energy storage battery C im,e [i]For the electricity price of the commercial power, C ex，e [i]For mains supply grid-connected price, C BSS [i]The eta is the equivalent charge-discharge cost of the energy storage battery, and the eta is the generating efficiency of the generator.
And the electric quantity limiting condition, the electricity price limiting condition, the power limiting condition and the SOC limiting condition are set;
electric quantity limiting conditions are as follows:
grid im [i]-grid ex [i]-BSS c [i]+BSS dc [i]+G[i]+PV f [i]＝load f [i]，G[i]for the output power of diesel generators, PV f [i]And outputting electric quantity for photovoltaic power generation.
Electricity price limiting conditions:
C im，e [i]＝C im，en [i]+C md [i]
C im，en [i]the electric energy price part in the predicted fluctuation price;
C md [i]the demand price is converted into the equivalent electricity price part in the predicted fluctuation electricity price.
C md [i]＝(demand charg *MD[i])/total_Q forecast [i]
demand charge Is electricityA demand price part in price;
total_Q forecast [i]for an estimated value of the total monthly electricity usage, a prediction is needed.
MD [ i ] is the current maximum demand. Note that the maximum demand is calculated by the steps of: the average power is calculated every 15 minutes, taking the maximum value in the month as the MD in the month, therefore
MD[i]＝max(MD[i-1]，load[i]/15min)
After the equivalent demand price is introduced, C md [i]The method can be used for adjusting the emphasis points of the strategy so as to conveniently transmit the price factors of demand management into the scheduling optimization algorithm, and can play a role in dynamically switching multiple working modes such as demand management, peak-valley arbitrage, dynamic capacity increase and the like.
C im，en [i]The fluctuation prediction of (2) while considering the spot price and the fluctuation of the electricity price brought by the solicitation demand response. During a period when a demand response offer occurs;
C im，en [i]＝C im，en [i]-C DR [i]*(grid im [i]-Q base [i])/grid im [i]
the limits of energy storage charging and discharging comprise power limits and SOC limits;
the power limiting condition is as follows:
BSS c [i]and BSS dc [i]Respectively representing the charge and discharge electric quantity of the energy storage system. In the time period of adoption, the average power corresponding to the charge and discharge electric quantity needs to meet the power limit of the energy storage.
Figure BDA0003716407630000041
SOC limiting conditions:
Figure BDA0003716407630000042
compared with the prior art, the invention has the beneficial effects that: determining an optimized peak-valley average charging and discharging strategy by combining the power consumption cost of a peak-valley and average power price mechanism in the day within the whole life cycle of the non-energy-storage system, so as to realize the best economy; carrying out charge-discharge control and flexible load management according to the instruction of the demand side response platform, and according to related policies, realizing participation in demand side response to obtain optimal economic subsidy; considering the distributed power generation and the user power consumption cost, combining the energy storage optimization control of the photovoltaic-energy storage combined income factor, and accessing flexible load response under a proper condition; meanwhile, the optimized scheduling result is completely driven by the electricity price, the current system is also adapted to the scene of spot transaction, and the seamless switching of the optimization strategy can be realized by adopting the predicted price of the spot transaction to replace a peak-valley-level electricity price mechanism on the occasion of executing the electric power spot transaction.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a schematic diagram of the information and instruction flow of the scheduling algorithm of the present invention;
FIG. 2 is a diagram of the microgrid architecture of the present invention;
FIG. 3 is a structural diagram of a control function according to the present invention;
FIG. 4 is a block diagram showing the architecture of the main functional blocks of the microgrid controller hardware according to the present invention;
FIG. 5 is a diagram of the hardware architecture of the microgrid controller according to the present invention;
FIG. 6 is a block diagram of a model predictive control framework in accordance with the present invention.
Detailed Description
The technical solutions of the present invention will be described below clearly and completely in conjunction with the embodiments, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1 to 6, a microgrid controller integrating predictive control of an economic model relates to various energy subsystems including distributed power generation, energy storage, user load and the like for the control of a microgrid system. The distributed power generation equipment comprises user-side distributed photovoltaic and standby diesel generators, natural gas generators and the like. In the user load, also divide into key load equipment and flexible load, like fill electric pile, warm equipment of leading to etc.. Due to the randomness of photovoltaic power generation and the continuous promotion of the construction of novel power systems, various unpredictabilities also exist in the fluctuation of the price of electricity. Therefore, the dispatching use of each subsystem in the micro-grid must be uniformly coordinated, so that the overall efficiency can be optimized, and the design index of the micro-grid is achieved.
As shown in fig. 2, is a typical microgrid structure diagram; the micro-grid and the commercial power grid are connected through a transformer. Because the mains supply and the local power generation power supply exist at the same time, under the action of the coupling energy storage equipment, the characteristics of different types of power supplies need to be considered in local control so as to ensure the stable operation of the system. The photovoltaic power generation and electrochemical energy storage equipment are power electronic equipment, are direct current, are converted into alternating current through an inverter and enter a microgrid. And the diesel generator and the mains supply are alternating current. Therefore, the primary challenge of microgrid control is to ensure the requirement of stable frequency and voltage to ensure the normal operation of the electric equipment and loads. The current frequency and voltage stability control is accomplished by the primary and secondary control functions of the microgrid controller. The primary control and the secondary control of the microgrid controller essentially meet the requirement of flexible switching of the microgrid and the mains supply from 100% dependency to 0% dependency without influencing the normal work of local load equipment. Even under the condition of not depending on the mains supply, under the condition that the photovoltaic equipment and the energy storage equipment have surplus, the power grid can be fed back, and the power auxiliary service support services such as power grid peak regulation, frequency regulation and the like are provided. The process of dynamically adjusting the control target value of the degree of interaction with the power grid according to the benefit index is a three-level control part of the microgrid controller.
The control function structure comprises:
as shown in fig. 3, the device is divided into three layers, namely an algorithm layer module, a control layer module and an equipment layer module.
The algorithm layer module is an operation module of a high-level scheduling algorithm, the load curve, the electricity price fluctuation, the power grid scheduling instruction, the photovoltaic power generation power prediction and other information are processed in a centralized mode, an optimal internal scheduling strategy of the microgrid is obtained through an operation research optimization algorithm solver, and the system is automatically switched in various modes such as peak-valley arbitrage, demand management, dynamic capacity increase, photovoltaic maximum consumption and the like, so that the optimal operation benefit of the system is achieved.
And the control layer module completes the communication and transmission of the indexes of the equipment layer, and processes the equipment data in real time through high-throughput data acquisition and processing and uploads the equipment data to the scheduling algorithm layer. Meanwhile, the optimized scheduling command is converted through a protocol to form a device layer management scheduling control command, and interaction of the device layer is realized.
And the equipment layer module refers to an independent control management unit of each subsystem module, belongs to the control system of the subsystem and does not belong to the component part of the microgrid control system.
As shown in fig. 1, a schematic flow diagram of scheduling algorithm information and instructions; scheduling instructions sent by an EMS are divided into two types, one type is transient control, and the other type is steady-state control; the primary control method has higher requirement on timeliness, is used for meeting the requirement of transient auxiliary service of a temporary power grid and ensuring the stability of frequency and voltage in the micro-power grid so as to ensure the safety and stability of a system and the reliability of power supply quality, and belongs to the content of primary control and secondary control; this layer control is typically in seconds; the latter is scheduling under the optimal target, usually in hours, to achieve the optimal benefit of the whole system, which belongs to the category of triple control.
At present, energy storage equipment suppliers have mature control algorithms and solutions in the aspect of transient control, and the control is usually achieved by combining methods such as droop speed control, PID standard control and the like; in the steady state control regime, a complete solution is lacking. The main challenge lies in the software and hardware level, and corresponding division needs to be made according to the acquisition and processing of information under different time scales.
As shown in fig. 5, the current hardware splits the data acquisition and predictive control algorithm processing based on different time scales. Under the condition of slow response, analyzing and deciding the coarse granularity data, including acquiring external information such as weather forecast data, a power grid dispatching instruction and the like; the control algorithm processes external information under the condition of being based on the quasi-steady state, obtains a target set value of the real-time control algorithm through the optimization calculation of the prediction algorithm and steady-state control parameters, and drives the execution of real-time response. And calculating real-time response parameters, namely rapidly calculating in a second-level dimension based on the real-time data acquisition of the SCADA system to obtain control instructions for various types of subsystems to complete control.
Hardware structure:
as shown in fig. 4, the architecture diagram of the main functional modules of the hardware of the microgrid controller is shown, wherein the main body of the hardware module is an independent control host and a data acquisition module with expansibility.
The control host is used for protocol conversion, deployment of scheduling optimization algorithm, generation of control instruction and other functions. According to the control layering, the control host is divided into a data acquisition and lower computer protocol analysis module, an operation processor and an upper communication module; the operation processor is further divided into a transient control module and a steady-state control module to meet the operation processing under different time sequences.
The expansion interface is used for butting equipment; the number and complexity of field devices are different, and the requirements on interfaces are also different, so that the communication interface is required to be standard and flexibly expanded; the currently adapted communication interfaces comprise CAN communication, ethernet and RS-485 and are suitable for communication protocols such as Modbus RTU/TCP, DNP3 serial, DNP3 LAN/WAN, IEC 61850GOOSE, IEC 60870-5-101/104, IEC 61850 MMS and Sunspec/Mesa Modbus API.
In addition to the control host, a variety of auxiliary devices may be configured, such as:
and a longitudinal encryption authentication gateway can be configured at a gateway for interaction between the control host and the external network, so that the information security is ensured when the control host interacts with the external network. Other optional accessories comprise power carrier protocol conversion equipment, a man-machine interaction configuration screen, a UPS standby power supply and a thermal management system.
And on the occasion of higher equipment dispersion, local networking is performed by adopting a broadband power line carrier communication (HD-PLC) technology. The technology relies on a power line network, does not need to be rewired, and has the greatest advantages of high communication rate, bidirectional transmission, high transmission stability and strong expandability compared with narrow-band power line communication.
The human-computer interaction configuration screen is used for field debugging and parameter setting; the related interface can be flexibly configured.
The standby power supply is used for power failure or temporary power supply debugging; the protection is provided for the control host, the UPS standby power supply and the longitudinal encryption authentication gateway, and a thermal management system, a man-machine interaction configuration screen and a communication expansion interface are integrated.
And (3) an optimization algorithm:
model Predictive Control (MPC) is based on iterative, finite-time-domain rolling optimization for a controlled-body model, and samples the state of the controlled body at time t, predicts the parameter variation of a future time period [ t + N ] (prediction time domain), and calculates a control strategy (numerical minimization algorithm) for minimizing the [ t + N ] cost for a short period of the rolling time period [ t, t +1] (control time domain).
As shown in fig. 6, a model predictive control framework diagram, in a typical MPC controller involving three-level control, every 15 minutes (control time domain), an optimization decision is made for a future period of time (usually, 24 hours, 96 control cycles in the future) to achieve an optimal operation strategy, and a future 15-minute scheduling strategy is sent; the prediction model generally comprises photovoltaic power generation power prediction and load prediction; optimizing a target equation of the scheduling model to obtain the highest profit in 24 hours in the future; in the actual scene of the microgrid, the state estimator estimates the internal states of the energy storage equipment, the photovoltaic equipment and other equipment mainly based on the acquired real-time state, and acts on the constraint conditions in the next prediction period.
Economic Model Predictive Control (EMPC) was developed on the basis of Model Predictive Control (MPC). The EMPC has the remarkable characteristics that the performance index of the EMPC is influenced by economic factors; the method realizes the combination of the process economy optimization index and the control performance index under the environment of the same model and the same calculation frequency, and achieves the economy optimal control under different scenes and time domains; the main characteristics of the MPC are reflected by the division of a prediction algorithm and an optimization control algorithm. And the input of the electricity price fluctuation prediction and the energy storage service life prediction reflects the system improvement of the EMPC.
The fluctuation of the electricity price includes the fluctuation of the price of the electric energy and the fluctuation of the capacity electricity fee. The fluctuation of the electric energy price, namely the electric power spot price, is a function of time and place, and under a novel electric power system, the electric power spot price has extremely strong fluctuation and is a factor for ensuring the operation income of the existing micro-grid system to be considered. The fluctuation of the capacity electric charge considers the scenes of the demand electric charge of the user and the demand response income; the cost of the user for capacity expansion is converted into a part of the demand price and is introduced into a target equation; in the above parameters, the spot price is an average value of one month affected by the demand electric charge when the spot price changes once in 15 minutes, and the yield of demand response is random; the capacity cost is a scenario triggered by a change in the production behavior of the user. Therefore, the current electricity price prediction model unifies the relationship between the user behavior and the income under different time scales.
The energy storage life decays as the number of charge and discharge cycles increases. Because the energy storage device is often the most core asset in the microgrid system, the depreciation cost of the device for calling the energy storage also needs to be included in a target equation for embodying the system benefits. The current system is accessed into a real-time calculation model of the energy storage SOH, and high-precision charging and discharging cost accounting can be provided according to state parameters of the energy storage system.
The energy storage life decays as the number of charge and discharge cycles increases. Since the energy storage device is often the most core asset in the microgrid system, the depreciation cost of the device caused by calling the energy storage should also be included in the objective equation representing the system profit. The current system is accessed into a real-time calculation model of the energy storage SOH, and high-precision charging and discharging cost accounting can be provided according to state parameters of the energy storage system.
The objective function is as follows:
Figure BDA0003716407630000091
wherein, grid im [i]Grid for purchasing electric power from commercial power ex [i]For mains supply grid-connected electric quantity, Q g [i]The Cycle is the equivalent charge-discharge Cycle number of the energy storage battery, C im,e [i]For the electricity price of the commercial power, C ex，e [i]For mains supply grid-connected price, C BSS [i]And eta is the equivalent charge-discharge cost of the energy storage battery, and the electric generation efficiency of the generator.
The optimization objective is to keep the total electricity cost for the next 96 control cycles to a minimum in each optimization cycle.
In a limiting sense, a plurality of parameters are to be coupled with predictive analysis, including:
the limiting condition of the electric quantity is as follows:
grid im [i]-grid ex [i]-BSS c [i]+BSS dc [i]+G[i]+PV f [i]＝load f [i]
BSS c [i]and BSS dc [i]Respectively representing the charge and discharge electric quantity of the energy storage system.
Conditions of electricity prices:
C im，e [i]＝C im，en [i]+C md [i]
C im，en [i]the electric energy price part in the predicted fluctuation price;
C md [i]the demand price is converted into the equivalent electricity price part in the predicted fluctuation electricity price.
C md [i]＝(demand charge *MD[i])/total_Q forecast [i]
demand charge The demand price part in the electricity price;
total_Q forecast [i]for the prediction of the total monthly power consumption, a prediction is required。
MD [ i ] is the current maximum demand; note that the maximum demand is calculated by the steps of: the average power is calculated every 15 minutes, taking the maximum value in the month as the MD in the month, therefore
MD[i]＝max(MD[i-1]，load[i]/15min)
After the equivalent demand price is introduced, C md [i]The method can be used for adjusting the emphasis points of the strategy so as to conveniently transmit the price factors of demand management into the scheduling optimization algorithm, and can play a role in dynamically switching multiple working modes such as demand management, peak-valley arbitrage, dynamic capacity increase and the like.
C im，en [i]The fluctuation prediction of (2) while considering the spot price and the fluctuation of the electricity price brought by the solicitation demand response. During a period when a demand response offer occurs;
C im，en [i]＝C im，en [i]-C DR [i]*(grid im [i]-Q base [i])/grid im [i]
the limits of energy storage charging and discharging comprise power limits and SOC limits;
power limitation:
BSS c [i]and BSS dc [i]Respectively representing the charge and discharge electric quantity of the energy storage system. In the time period of adoption, the average power corresponding to the charge and discharge electric quantity needs to meet the power limit of the energy storage.
Figure BDA0003716407630000111
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SOC limitation:
Figure BDA0003716407630000112
photovoltaic prediction:
and photovoltaic power generation prediction, namely fitting based on weather forecast and historical power generation data of the equipment.
And (3) load prediction:
and load prediction, namely fitting according to the actual load state and the historical data.
In other embodiments, the operating mode may be switched manually, but not optimally.
In other embodiments, the optimized scheduling scheme of the system can be fitted not by an optimized scheduling algorithm model (operations research), but by a genetic algorithm in a big data algorithm (statistics); however, the method has high computational power requirement and is not suitable for large-area popularization.
The above formulas are all calculated by removing dimensions and taking numerical values thereof, the formula is a formula which is obtained by acquiring a large amount of data and performing software simulation to obtain the most approximate real condition, and the preset parameters and the preset threshold values in the formula are set by the technical personnel in the field according to the actual condition or obtained by simulating a large amount of data.
Although the present invention has been described in detail with reference to the preferred embodiments, it will be understood by those skilled in the art that various changes may be made and equivalents may be substituted for elements thereof without departing from the spirit and scope of the present invention.

Claims (4)

1. A microgrid controller integrating predictive control of an economic model is characterized by comprising a control function structure and a hardware structure;
the control function structure comprises an algorithm layer module, a control layer module and an equipment layer module;
the algorithm layer module is used for carrying out centralized processing on the load curve, the electricity price fluctuation, the power grid scheduling instruction and the photovoltaic power generation power prediction information, and obtaining an optimal internal scheduling strategy of the microgrid through an operation research optimization algorithm solver;
an objective function and limiting conditions in the data processing process are arranged in the algorithm layer module, the objective function is used for optimizing that the total electricity expense of 96 control cycles in the future is kept to be minimum in each optimization cycle, and the limiting conditions comprise an electricity quantity limiting condition, an electricity price limiting condition, a power limiting condition and an SOC limiting condition;
the control layer module completes the on-line issuing of the indexes of the equipment layer, and the equipment data is processed in real time and uploaded to the algorithm layer module through high-throughput data acquisition and processing;
the hardware structure comprises a control host, a data acquisition module, an expansion interface and auxiliary equipment, wherein the control host also comprises a data acquisition and lower computer protocol analysis module, an operation processor and an upper communication module; the operation processor is divided into a transient control module and a steady-state control module and is used for meeting the operation processing under different time sequences;
in the working process of the control function structure, corresponding division is needed according to the acquisition and processing of information under different time scales;
the current hardware splits the data acquisition and predictive control algorithm processing based on different time scales, and analyzes and decides coarse granularity data under the situation of slow response, including acquiring external information; the control algorithm processes external information under the condition of being based on a quasi-steady state, obtains a target set value of the real-time control algorithm through the optimal calculation of a prediction algorithm and steady-state control parameters, and drives the execution of real-time response;
the objective function is:
Figure QLYQS_1
wherein, grid im [i]Grid for purchasing electric power from commercial power ex [i]For mains supply grid-connected electric quantity, Q g [i]The cycle is the equivalent charge-discharge cycle number of the energy storage battery C im,e [i]Is the electricity price of the commercial power C ex,e [i]For mains supply grid-connected price, C BSS [i]The equivalent charge-discharge cost of the energy storage battery is obtained, and eta is the power generation efficiency of the generator;
the electric quantity limiting conditions are as follows:
grid im [i]-grid ex [i]-BSS c [i]+BSS dc [i]+G[i]+PV f [i]＝load f [i]，G[i]for the output power of diesel generators, PV f [i]For photovoltaic power generation, BSS c [i]And BSS dc [i]Respectively representing charging of energy storage systemsDischarging the electric quantity;
the electricity price limiting conditions are as follows:
C im,e [i]＝C im,en [i]+C md [i](ii) a Wherein, C im,en [i]The electric energy price part in the predicted fluctuation price; c md [i]The equivalent electricity price part converted from the demand price in the predicted fluctuation electricity price; demand charge The demand price part in the electricity price; total _ Q forecast [i]A predicted value of total electricity consumption in the whole month is obtained;
C md [i]＝(demand charge *MD[i])/total_Q forecast [i]；MD[i]for the current maximum demand, MD [ i]＝max(MD[i-1],load f [i]/15min)。
2. The microgrid controller of claim 1, wherein C is the controller of the microgrid integrating predictive control of an economic model im,en [i]The fluctuation prediction of (2) while considering the spot price and the fluctuation of the electricity price brought by the solicitation demand response; a period during which a demand response offer occurs;
C im,en [i]＝C im,e [i]-C DR [i]*(grid im [i]-Q base [i])/grid im [i]。
3. the microgrid controller of claim 2, wherein the power limiting conditions are:
in the time period of use, the average power corresponding to the charge and discharge electric quantity needs to meet the power limit of the stored energy,
Figure QLYQS_2
4. the microgrid controller of integrated economic model predictive control according to claim 3, wherein the SOC limiting condition:
Figure QLYQS_3
/>
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