CN103107558B - Multi-modal customizable green energy concentrator and method thereof - Google Patents

Abstract

The invention discloses a multi-modal customizable green energy concentrator and a method thereof. The multi-modal customizable green energy concentrator comprises a data acquisition module, a section type fuzzy prediction module, an energy management optimization module and an output control module. The concentrator collects relevant information of distributed generation and micro network load, and the section type fuzzy prediction module is set to predict wind power, photovoltaic generating capacity and load values so that a section type fuzzy prediction set can be acquired. According to a grid-connected operation modal or an isolated island operation modal, exchange power with a large power grid is customized, and a micro grid energy optimization management algorithm is set with the minimum operation cost of a micro grid as the objective function. The micro grid energy optimization management algorithm with a fuzzy prediction function is proposed, a generation scheduling with a controllable micro source reserve capacity is acquired, and therefore a micro grid system can operate reasonably and effectively.

Description

Multi-modal customizable green energy hub and method thereof

Technical field

The present invention relates to distributed power generation, micro-capacitance sensor technical field, particularly relate to a kind of multi-modal customizable green energy hub and method thereof.

Background technology

Micro-capacitance sensor can promote effective utilization of distributed power source, in the huge advantage that solution lack of energy, environmental issue etc. show, has been subject to the common concern of countries in the world.Distributed power source in micro-capacitance sensor mainly comprises wind-driven generator, solar-energy photo-voltaic cell, fuel cell, miniature gas turbine etc., also has all kinds of energy-storage travelling wave tube in addition.But dissimilar micro battery has different operation characteristics, in order to make micro-grid system run rationally and effectively, need to carry out effective energy management optimization to it.

Wind-force and photovoltaic generation depend on the natural resources such as wind, light, have very strong randomness, and microgrid energy management has larger difficulty and challenge.When micro-grid connection is run, and energetic interaction amphicheirality between bulk power grid, and also there is energy in bidirectional flow between energy-storage units, therefore need to adopt certain energy management algorithm to control the energy flow of micro-capacitance sensor, safeguards system stability and economy; During micro-capacitance sensor islet operation, for ensureing the reliable power supply of critical load, also needing the discharge and recharge taking certain energy management algorithm management energy-storage units, ensureing the energy balance of micro-capacitance sensor.

Summary of the invention

Goal of the invention: the invention provides a kind of multi-modal customizable green energy hub and method thereof, when micro-capacitance sensor be incorporated into the power networks or islet operation mode time, effectively management microgrid energy management.

Technical scheme: multi-modal customizable energy green hub, comprise data acquisition module, interval type prediction module, energy management optimization module and output control module, wherein, interval type prediction module comprises wind/light energy output prediction module and load prediction module, and prediction module comprises fuzzy device, rule base, inference machine, falls type device and defuzzifier; Module is optimized in energy management, carries out energy-optimised distribution by energy management Optimized model.Data acquisition module is by the real time environment information of collection and historical data input interval type prediction module, prediction module prediction of output data, and prediction data is inputted energy management optimization module, energy management is optimized module and is calculated exerting oneself of energy-storage units and controlled micro battery, exports control command by output control module.

Energy management Optimized model is:

1) target function, minimum as target function using cost of electricity-generating, make micro-capacitance sensor self profit maximization, expression formula is: $\min F\left(x\right)=\min \underset{t=1}{\overset{T}{\mathrm{\Σ}}}[C_F+C_{\mathrm{OM}}+C_{\mathrm{BUY}}+C_{\mathrm{SEL}}]$

Wherein, T is the time period of dispatching cycle, C _ffor fuel cost, C _oMfor operation and maintenance cost, C _bUYthe expense bought from bulk power grid, C _sELbe sold to the profit that bulk power grid obtains,

A) fuel cost is C _f, $C_F=\underset{i}{\overset{n}{\mathrm{\Σ}}}f\left(P_i^t\right)C_i,$

Wherein i represents generator unit kind, and n is t generator unit number, the energy output of generator unit i in t, the fuel used amount of t generator unit i, C _iit is the fuel used price of generator unit i.

B) operation and maintenance cost C _oM,

$C_{\mathrm{OM}}=P_{\mathrm{Mt}}^t\·C_{\mathrm{MT}\_\mathrm{om}}+{\tilde{P}}_{\mathrm{PV}}^t\·C_{\mathrm{PV}\_\mathrm{om}}+{\tilde{P}}_{\mathrm{WT}}^t\·C_{\mathrm{WT}\_\mathrm{om}}+\left|P_{\mathrm{BT}}^t\right|\·C_{\mathrm{BT}\_\mathrm{om}}+P_{\mathrm{FC}}^t\·C_{\mathrm{FC}\_\mathrm{om}}$

Wherein C _{mT_om}, C _{pV_om}, C _{wT_om}, C _{bT_om}, C _{fC_om}be respectively the unit operation and maintenance cost of miniature gas turbine, photovoltaic cell, wind-driven generator, storage battery and fuel cell; for the energy output of miniature gas turbine and fuel cell t; for the power of storage battery t, represent discharge power, represent charge power; for the energy output of photovoltaic cell and wind-driven generator t, obtain its Interval Fuzzy set respectively by interval type fuzzy prediction, wherein be respectively the bound of photovoltaic power generation quantity forecast interval, be respectively the bound of wind power generation amount forecast interval;

C) C _bUYthe expense bought from bulk power grid, C _sELbe be sold to the profit that bulk power grid obtains, be specially

$C_{\mathrm{BUY}}=C_{\mathrm{buy}}^t\·P_{\mathrm{ex}}^t,(P_{\mathrm{ex}}^t>0)$

$C_{\mathrm{SEL}}=C_{\mathrm{sel}}^t\&CenterDot;P_{\mathrm{ex}}^t,(P_{\mathrm{ex}}^t<0)$

Wherein the t period buy the price of electric energy from bulk power grid, sell electric energy the t period to the price of bulk power grid, represent the Power Exchange between t period micro-capacitance sensor and bulk power grid, represent to bulk power grid sale of electricity, represent and buy electricity from bulk power grid;

2) constraints, comprises power-balance constraint, micro-capacitance sensor and the maximum mutual capacity of major network retrains, the power output upper and lower limit of miniature gas turbine and fuel cell retrains and accumulator cell charging and discharging power upper and lower limit retrains;

A) power-balance constraint: $P_{\mathrm{MT}}^t+P_{\mathrm{FC}}^t+P_{\mathrm{BT}\_\mathrm{disch}}^t\&CenterDot;{\mathrm{\&eta;}}_{\mathrm{BT}}^{\mathrm{disch}}+P_{\mathrm{ex}}^t={\tilde{P}}_d^t-{\tilde{P}}_{\mathrm{PV}}^t-{\tilde{P}}_{\mathrm{WT}}^t$

$P_{\mathrm{MT}}^t+P_{\mathrm{FC}}^t+P_{\mathrm{BT}\_\mathrm{ch}}^t\&CenterDot;{\mathrm{\&eta;}}_{\mathrm{BT}}^{\mathrm{ch}}+P_{\mathrm{ex}}^t={\tilde{P}}_d^t-{\tilde{P}}_{\mathrm{PV}}^t-{\tilde{P}}_{\mathrm{WT}}^t$

Wherein it is all as described above, represent the charge and discharge power of t storage battery respectively; for the charge and discharge efficiency of storage battery; for micro-grid load forecast interval value be respectively the bound in load prediction interval;

B) micro-capacitance sensor and the maximum mutual capacity of major network retrain, i.e. the physical transfer capacity limit value of interconnection:

$P_{\mathrm{ex}}^{\min }\&le;{P^t}_{\mathrm{ex}}\&le;P_{\mathrm{ex}}^{\max }$

Wherein, micro-capacitance sensor and bulk power grid mutual capacity lower limit; micro-capacitance sensor and the bulk power grid mutual capacity upper limit;

C) the power output upper and lower limit constraint of miniature gas turbine and fuel cell:

$P_{\mathrm{FC}}^{\min }\&le;P_{\mathrm{FC}}^t\&le;P_{\mathrm{FC}}^{\max }$

$P_{\mathrm{MT}}^{\min }\&le;P_{\mathrm{MT}}^t\&le;P_{\mathrm{MT}}^{\max }$

Wherein represent the minimum load of fuel cell, miniature gas turbine, represent the maximum output of fuel cell, miniature gas turbine;

D) accumulator cell charging and discharging power upper and lower limit constraint:

$P_{\mathrm{BT}\_\mathrm{ch}}^{\min }\&le;P_{\mathrm{BT}\_\mathrm{ch}}^t\&le;P_{\mathrm{BT}\_\mathrm{ch}}^{\max }$

$P_{\mathrm{BT}\_\mathrm{disch}}^{\min }\&le;P_{\mathrm{BT}\_\mathrm{disch}}^t\&le;P_{\mathrm{BT}\_\mathrm{disch}}^{\max }$

SOC _min≤SOC ^t≤SOC _max

Wherein for storage battery t charge and discharge power; for the minimum charge and discharge power of storage battery; for the maximum charge and discharge power of storage battery; SOC ^tfor the memory capacity of storage battery t, SOC _minfor accumulators store capacity minimum value, SOC _maxfor accumulators store maximum capacity.

The method of work of described multi-modal customizable green energy hub is:

1) data collecting module collected wind, light energy output and load prediction relevant information;

2) set up the interval type fuzzy logic forecast model of wind/light energy output and load prediction respectively, export wind, the fuzzy interval set of light energy output prediction and the fuzzy interval set of load prediction;

3) by 2) in the fuzzy interval of gained each premeasuring set input energy management Optimized model, customization energy exchange power P _ex, be optimized calculating, obtain the generation schedule interval set of controlled micro battery and storage battery, its bound is controlled micro battery and storage battery reserve capacity, and its central value is deterministic type generation schedule;

4) 3 are judged) whether the generation schedule of gained reasonable, if unreasonable, returns 3) reset energy exchange power P _ex, recalculate; If rationally, it exports control command by output control module.

The present invention adopts technique scheme, has following beneficial effect: the present invention proposes a kind of microgrid energy management algorithm based on Interval Fuzzy prediction, can obtain the generation schedule with reserve capacity.Predicted and load prediction by wind-force and photovoltaic power generation quantity, obtain the Interval Fuzzy set of each premeasuring, inputted energy-optimised administration module, finally obtain the energy output planned value interval set of controlled micro battery and energy-storage units.The Lower and upper bounds of interval set is the upper and lower reserve capacity of controlled micro battery and energy-storage units, and the interval central value of set and the deterministic type plan of controlled micro battery are exerted oneself.

Accompanying drawing explanation

Fig. 1 is the structural representation of the embodiment of the present invention;

Fig. 2 is the method flow diagram of the embodiment of the present invention.

Embodiment

Below in conjunction with specific embodiment, illustrate the present invention further, these embodiments should be understood only be not used in for illustration of the present invention and limit the scope of the invention, after having read the present invention, the amendment of those skilled in the art to the various equivalent form of value of the present invention has all fallen within the application's claims limited range.

Fig. 1 is the structural representation of the embodiment of the present invention, this kind of hub comprises Fuzzy prediction module 112 between data acquisition module, wind/light energy output interval type fuzzy prediction module 111, loading zone, energy management optimization module 12 and output control module 13, the microgrid energy optimum management containing wind power generation, photovoltaic generation, miniature gas turbine, fuel cell distributed power supply and storage battery can be realized, the energetic optimum of micro-capacitance sensor under different operational modal (grid-connected or isolated island) can be realized and distribute.Specifically:

(1) data acquisition module, for gathering the relevant information of wind-force and photovoltaic power generation quantity prediction, comprise wind speed, intensity of illumination, ambient humidity and temperature, historical load data, comprise in addition the fuel price of generator unit, sale electricity price from micro-capacitance sensor to bulk power grid and buy electricity price etc.

(2) interval type fuzzy prediction module, for setting up wind-force, photovoltaic power generation quantity prediction and the interval type fuzzy logic module of load prediction respectively, comprising fuzzy device, rule base, inference machine, falling type device and defuzzifier five part.The input of energy output fuzzy prediction module wind speed, intensity of illumination, ambient humidity and temperature, export wind-powered electricity generation and the set of photovoltaic power generation quantity predictive fuzzy interval; Load obscurity prediction module input micro-capacitance sensor historical load data, the set of output load predictive fuzzy interval.

(3) module is optimized in energy management.Wind power generation and photovoltaic generation are to a great extent by the impact of weather conditions, and the energy output forecast interval set obtained by fuzzy prediction, as initial input, carries out energy-optimised distribution to controlled micro battery and storage battery, sets up following energy management Optimized model:

1) target function, minimum as target function using cost of electricity-generating, make micro-capacitance sensor self profit maximization, expression formula is:

$\min F\left(x\right)=\min \underset{t=1}{\overset{T}{\mathrm{\&Sigma;}}}[C_F+C_{\mathrm{OM}}+C_{\mathrm{BUY}}+C_{\mathrm{SEL}}]$ Wherein T is the time period of dispatching cycle, and respectively amount expression is as follows for F (x),

A) fuel cost C _f, $C_F=\underset{n}{\overset{n}{\mathrm{\&Sigma;}}}f\left(P_i^t\right)C_i$

Wherein i represents generator unit kind, and n is t generator unit number, the energy output of generator unit i in t, the fuel used amount of t generator unit i, C _iit is the fuel used price of generator unit i.Only have the generator unit of the consumption of fossil fuels energy just need take into account fuel cost, in this model, have miniature gas turbine, fuel cell; And the utilization of photovoltaic generation, wind power generation is free of contamination renewable natural resources, without the need to taking into account fuel cost.

B) operation and maintenance cost C _oM,

$C_{\mathrm{OM}}=P_{\mathrm{Mt}}^t\&CenterDot;C_{\mathrm{MT}\_\mathrm{om}}+{\tilde{P}}_{\mathrm{PV}}^t\&CenterDot;C_{\mathrm{PV}\_\mathrm{om}}+{\tilde{P}}_{\mathrm{WT}}^t\&CenterDot;C_{\mathrm{WT}\_\mathrm{om}}+\left|P_{\mathrm{BT}}^t\right|\&CenterDot;C_{\mathrm{BT}\_\mathrm{om}}+P_{\mathrm{FC}}^t\&CenterDot;C_{\mathrm{FC}\_\mathrm{om}}$

Wherein C _{mT_om}, C _{pV_om}, C _{wT_om}, C _{bT_om}, C _{fC_om}be respectively the unit operation and maintenance cost of miniature gas turbine, photovoltaic cell, wind-driven generator, storage battery and fuel cell; for the energy output of miniature gas turbine and fuel cell t; for the power of storage battery t, represent discharge power, represent charge power; for the energy output of photovoltaic cell and wind-driven generator t, obtain its Interval Fuzzy set respectively by interval type fuzzy prediction, wherein be respectively the bound of photovoltaic power generation quantity forecast interval, be respectively the bound of wind power generation amount forecast interval.

C) C _bUYthe expense bought from bulk power grid, C _sELbe be sold to the profit that bulk power grid obtains, be specially

$C_{\mathrm{BUY}}=C_{\mathrm{buy}}^t\&le;P_{\mathrm{ex}}^t,(P_{\mathrm{ex}}^t>0)$

$C_{\mathrm{SEL}}=C_{\mathrm{sel}}^t\&le;P_{\mathrm{ex}}^t,(P_{\mathrm{ex}}^t>0)$

Wherein the t period buy the price of electric energy from bulk power grid, sell electric energy the t period to the price of bulk power grid. represent the Power Exchange between t period micro-capacitance sensor and bulk power grid. represent to bulk power grid sale of electricity, represent and buy electricity from bulk power grid.

2) constraints

A) power-balance constraint: $P_{\mathrm{MT}}^t+P_{\mathrm{FC}}^t+P_{\mathrm{BT}\_\mathrm{disch}}^t\&CenterDot;{\mathrm{\&eta;}}_{\mathrm{BT}}^{\mathrm{disch}}+P_{\mathrm{ex}}^t={\tilde{P}}_d^t-{\tilde{P}}_{\mathrm{PV}}^t-{\tilde{P}}_{\mathrm{WT}}^t$

$P_{\mathrm{MT}}^t+P_{\mathrm{FC}}^t+P_{\mathrm{BT}\_\mathrm{ch}}^t\&CenterDot;{\mathrm{\&eta;}}_{\mathrm{BT}}^{\mathrm{ch}}+P_{\mathrm{ex}}^t={\tilde{P}}_d^t-{\tilde{P}}_{\mathrm{PV}}^t-{\tilde{P}}_{\mathrm{WT}}^t$

Wherein it is all as described above, represent the charge and discharge power of t storage battery respectively; for the charge and discharge efficiency of storage battery; for micro-grid load forecast interval value be respectively the bound in load prediction interval.

B) micro-capacitance sensor and the maximum mutual capacity of major network retrain, i.e. the physical transfer capacity limit value of interconnection:

$P_{\mathrm{ex}}^{\min }\&le;{P^t}_{\mathrm{ex}}\&le;P_{\mathrm{ex}}^{\max }$

Wherein, micro-capacitance sensor and bulk power grid mutual capacity lower limit; micro-capacitance sensor and the bulk power grid mutual capacity upper limit.

C) the power output upper and lower limit constraint of miniature gas turbine and fuel cell:

$P_{\mathrm{FC}}^{\min }\&le;P_{\mathrm{FC}}^t\&le;P_{\mathrm{FC}}^{\max }$

$P_{\mathrm{MT}}^{\min }\&le;P_{\mathrm{MT}}^t\&le;P_{\mathrm{MT}}^{\max }$

Wherein represent the minimum load of fuel cell, miniature gas turbine, represent the maximum output of fuel cell, miniature gas turbine.

D) accumulator cell charging and discharging power upper and lower limit constraint:

$P_{\mathrm{BT}\_\mathrm{ch}}^{\min }\&le;P_{\mathrm{BT}\_\mathrm{ch}}^t\&le;P_{\mathrm{BT}\_\mathrm{ch}}^{\max }$

$P_{\mathrm{BT}\_\mathrm{disch}}^{\min }\&le;P_{\mathrm{BT}\_\mathrm{disch}}^t\&le;P_{\mathrm{BT}\_\mathrm{disch}}^{\max }$

SOC _min≤SOC ^t≤SOC _max

Wherein for storage battery t charge and discharge power; for the minimum charge and discharge power of storage battery; for the maximum charge and discharge power of storage battery; SOC ^tfor the memory capacity of storage battery t, SOC _minfor accumulators store capacity minimum value, SOC _maxfor accumulators store maximum capacity.

(4) output control module, is optimized the generating optimization allocation result of module to each controlled micro battery and storage battery based on energy management, control command is exported, realize the management to controlled micro battery and energy-storage units by output module.

Fig. 2 is the method flow diagram of the embodiment of the present invention, and concrete steps comprise:

1) data collecting module collected wind, light energy output and load prediction relevant information, comprises wind speed, intensity of illumination, ambient humidity, temperature and micro-capacitance sensor historical load data;

2) set up the interval type fuzzy logic forecast model of wind, light energy output and load prediction respectively, by wind speed, intensity of illumination, ambient humidity and temperature input prediction module, export the fuzzy interval set of wind, the prediction of light energy output; By in demand history data input load prediction module, the fuzzy interval set of output load prediction;

3) by 2) in the fuzzy interval of gained each premeasuring set input interval type energy management Optimized model, customization energy exchange power P _ex, be optimized calculating, obtain the generation schedule interval set of controlled micro battery and storage battery, its bound is controlled micro battery and the upper and lower adjustable reserve capacity of storage battery, and its central value is deterministic type generation schedule;

4) 3 are judged) whether the generation schedule of gained reasonable, if unreasonable, returns 3) reset energy exchange power P _ex, recalculate; If rationally, it is exported by output control module.

Claims (4)

1. a multi-modal customizable green energy hub, is characterized in that, comprise data acquisition module and output control module, and module is optimized in interval type prediction module and energy management, carries out energy-optimised distribution by energy management Optimized model;

The data of collection are inputted described interval type prediction module by described data acquisition module, described interval type prediction module prediction of output data and again by these data input energy management optimize module, finally by output control module export control command;

Described interval type prediction module comprises wind/light energy output prediction module and load prediction module; Wind/light energy output prediction module exports wind/light energy output predictive fuzzy interval set; The set of load prediction module output load predictive fuzzy interval;

Described energy management Optimized model is:

1) target function, minimum as target function using cost of electricity-generating, make micro-capacitance sensor self profit maximization, expression formula is: $\min F\left(x\right)=\min \underset{t=1}{\overset{T}{\mathrm{\&Sigma;}}}[C_F+C_{\mathrm{OM}}+C_{\mathrm{BUY}}+C_{\mathrm{SEL}}]$

Wherein, T is the time period of dispatching cycle, C _ffor fuel cost, C _oMfor operation and maintenance cost, C _bUYthe expense bought from bulk power grid, C _sELbe sold to the profit that bulk power grid obtains;

2) constraints, comprises power-balance constraint, micro-capacitance sensor and the maximum mutual capacity of bulk power grid retrains, the power output upper and lower limit of miniature gas turbine and fuel cell retrains and accumulator cell charging and discharging power upper and lower limit retrains.

2. multi-modal customizable green energy hub according to claim 1, is characterized in that,

C _fcomputational methods be $C_F=\underset{i}{\overset{n}{\mathrm{\&Sigma;}}}f\left(P_i^t\right)C_i,$

Wherein i represents generator unit kind, and n is t generator unit number, the energy output of generator unit i in t, the fuel used amount of t generator unit i, C _iit is the fuel used price of generator unit i;

C _oMcomputational methods be

$C_{\mathrm{OM}}=P_{\mathrm{MT}}^t\&CenterDot;C_{\mathrm{MT}\_\mathrm{om}}+{\tilde{P}}_{\mathrm{PV}}^t\&CenterDot;C_{\mathrm{PV}\_\mathrm{om}}+{\tilde{P}}_{\mathrm{WT}}^t\&CenterDot;C_{\mathrm{WT}\_\mathrm{om}}+\left|P_{\mathrm{BT}}^t\right|\&CenterDot;C_{\mathrm{BT}\_\mathrm{om}}+P_{\mathrm{FC}}^t\&CenterDot;C_{\mathrm{FC}\_\mathrm{om}}$

Wherein C _{mT_om}, C _{pV_om}, C _{wT_om}, C _{bT_om}, C _{fC_om}be respectively the unit operation and maintenance cost of miniature gas turbine, photovoltaic cell, wind-driven generator, storage battery and fuel cell; for the energy output of miniature gas turbine and fuel cell t; for the power of storage battery t, represent discharge power, represent charge power; for the energy output of photovoltaic cell and wind-driven generator t, obtain its Interval Fuzzy set respectively by interval type fuzzy prediction, wherein be respectively the bound of photovoltaic power generation quantity forecast interval, be respectively the bound of wind power generation amount forecast interval;

C _bUY, C _sELcomputational methods be

$\begin{array}{cc}{C}_{\mathrm{BUY}}=C_{\mathrm{buy}}^t\&CenterDot;P_{\mathrm{ex}}^t& (P_{\mathrm{ex}}^t>0)\end{array}$

$\begin{array}{cc}{C}_{\mathrm{SEL}}=C_{\mathrm{sel}}^t\&CenterDot;P_{\mathrm{ex}}^t& (P_{\mathrm{ex}}^t<0)\end{array}$

Wherein the t period buy the price of electric energy from bulk power grid, sell electric energy the t period to the price of bulk power grid, represent the Power Exchange between t period micro-capacitance sensor and bulk power grid, represent to bulk power grid sale of electricity, represent and buy electricity from bulk power grid;

Described power-balance constraint: $P_{\mathrm{MT}}^t+P_{\mathrm{FC}}^t+P_{\mathrm{BT}\_\mathrm{disch}}^t\&CenterDot;{\mathrm{\&eta;}}_{\mathrm{BT}}^{\mathrm{disch}}+P_{\mathrm{ex}}^t={\tilde{P}}_d^t-{\tilde{P}}_{\mathrm{PV}}^t-{\tilde{P}}_{\mathrm{WT}}^t$

$P_{\mathrm{MT}}^t+P_{\mathrm{FC}}^t+P_{\mathrm{BT}\_\mathrm{ch}}^t\&CenterDot;{\mathrm{\&eta;}}_{\mathrm{BT}}^{\mathrm{ch}}+P_{\mathrm{ex}}^t={\tilde{P}}_d^t-{\tilde{P}}_{\mathrm{PV}}^t-{\tilde{P}}_{\mathrm{WT}}^t$

Wherein it is all as described above, represent the charge and discharge power of t storage battery respectively; for the charge and discharge efficiency of storage battery; for micro-grid load forecast interval value be respectively the bound in load prediction interval;

Described micro-capacitance sensor and the maximum mutual capacity of bulk power grid retrain, i.e. the physical transfer capacity limit value of interconnection:

$P_{\mathrm{ex}}^{\min }\&le;{P^t}_{\mathrm{ex}}\&le;P_{\mathrm{ex}}^{\max }$

Wherein, represent micro-capacitance sensor and bulk power grid mutual capacity lower limit; micro-capacitance sensor and the bulk power grid mutual capacity upper limit;

The power output upper and lower limit constraint of described miniature gas turbine and fuel cell:

$P_{\mathrm{FC}}^{\min }\&le;P_{\mathrm{FC}}^t\&le;P_{\mathrm{FC}}^{\max }$

$P_{\mathrm{MT}}^{\min }\&le;P_{\mathrm{MT}}^t\&le;P_{\mathrm{MT}}^{\max }$

Wherein represent the minimum load of fuel cell, miniature gas turbine, represent the maximum output of fuel cell, miniature gas turbine;

Described accumulator cell charging and discharging power upper and lower limit constraint:

$P_{\mathrm{BT}\_\mathrm{ch}}^{\min }\&le;P_{\mathrm{BT}\_\mathrm{ch}}^t\&le;P_{\mathrm{BT}\_\mathrm{ch}}^{\max }$

$P_{\mathrm{BT}\_\mathrm{disch}}^{\min }\&le;P_{\mathrm{BT}\_\mathrm{disch}}^t\&le;P_{\mathrm{BT}\_\mathrm{disch}}^{\max }$

SOC _min≤SOC ^t≤SOC _max

Wherein for storage battery t charge and discharge power; for the minimum charge and discharge power of storage battery; for the maximum charge and discharge power of storage battery; SOC ^tfor the memory capacity of storage battery t, SOC _minfor accumulators store capacity minimum value, SOC _maxfor accumulators store maximum capacity.

3. multi-modal customizable green energy hub according to claim 1, it is characterized in that, wind/light energy output prediction module and load prediction module comprise fuzzy device, rule base, inference machine, fall type device and defuzzifier.

4. realize the method for multi-modal customizable green energy hub as claimed in claim 1, it is characterized in that, comprise the steps:

1) data collecting module collected wind speed, illumination, temperature and humidity environmental information and historical load relevant information;

2) set up the interval type fuzzy logic model of wind/light energy output prediction and load prediction respectively, export wind, the fuzzy interval set of light energy output prediction and the fuzzy interval set of load prediction;

3) by 2) in the fuzzy interval of gained each premeasuring set input energy management Optimized model, customization energy exchange power P _ex, be optimized calculating, obtain the generation schedule interval set of controlled micro battery and storage battery, its bound is the bound of controlled micro battery and storage battery reserve capacity, and its central value is deterministic type generation schedule;

4) 3 are judged) whether the generation schedule of gained reasonable, if unreasonable, returns 3) reset energy exchange power P _ex, recalculate; If rationally, it exports control command by output control module.