CN105574681A - Multi-time-scale community energy local area network energy scheduling method - Google Patents

Abstract

The invention relates to a multi-time-scale community energy local area network energy scheduling method. The method includes following steps: step 1, performing long-term prediction energy optimization by employing a genetic algorithm, and generating prediction values of a scheduling plan of the community in the coming 24 hours according to historical data of community photovoltaic output and load demand; and step 2, comparing real-time operation data of community photovoltaic output and load demand and the prediction values of the scheduling plan obtained in step 1, calculating a community deviation value, and performing real-time short-time energy optimization of the community photovoltaic output and load demand according to the community deviation value. According to the method, uninterrupted short-term energy optimization is performed with the combination of real-time operation data of the total photovoltaic output and the total load demand of a certain community and the long-term scheduling plan, the practical operation approaches to the scheduling plan, the algorithm solution space is compressed and optimized, the algorithm efficiency is improved, and the influence of uncertainty of photovoltaic output and load demand can be reduced with the combination of long-term and short-term energy optimization.

Description

A kind of Multiple Time Scales community energy LAN (Local Area Network) energy dispatching method

Technical field

The present invention relates to electric system energy dispatching technique field, particularly a kind of Multiple Time Scales community energy LAN (Local Area Network) energy dispatching method.

Background technology

In recent years; the utilization that the huge pollution that is day by day exhausted and that use traditional energy to cause of traditional energy makes to improve efficiency of energy utilization, strengthens regenerative resource, become solve energy demand growth and energy scarcity, the inevitable choice of contradiction between energy utilization and environmental protection.One of, the most efficient Land use systems the most clean as the energy, the energy that distributed generation technology utilizes various available dispersion to exist carries out generating energy supply, contribute to making full use of the abundant clean and regenerative resource in various places, there is provided " green electric power supply " to user, its research is day by day subject to various countries and pays close attention to.Wherein, photovoltaic generation due to the theoretical foundation of its handled easily and comparatively perfect, one of important form becoming distributed power generation, and obtain wideling popularize of country.Family photovoltaic, as the small distributed generating easily of a kind of economy, has entered common community, and Residents has equipped family photovoltaic generation unit and corresponding distributed energy storage system.

Along with the access with various intelligent electric equipment that improves constantly of distributed power generation permeability, be badly in need of the platform of an efficient scheduling and each equipment of coordination.Energy dispatching system can according to the practical operation situation of electrical network, in conjunction with electricity price information, to controllable in electrical network, as distributed energy storage unit regulates and controls, provide optimum operation plan arrangement, ensure the number one of the economy of operation of power networks, stability, reliability and power consumer, provide an energy management platform.

But distributed power generation and customer charge demand have uncertainty, the long-term dispatch plan proposed according to information of forecasting and actual electrical network unit ruuning situation are not inconsistent.Energy dispatching method is in the past mostly by the mode process of uncertain factor by chance constraint, but uncertainty is still very large on the impact of operation plan, makes operation plan and actual motion produce large deviation, thus affects the economic reliability service of community's electrical network.In addition, each controllable is set to independently variable by the optimized algorithm in past.When there is a large amount of distributed power generation and energy-storage units in community, the complexity of the variation of long-term dispatch mode, energy management algorithm increases greatly, and Riming time of algorithm is elongated.

Summary of the invention

The object of the invention is to overcome the deficiencies in the prior art, provide a kind of reasonable in design, operation efficiency is high and the Multiple Time Scales community energy LAN (Local Area Network) energy dispatching method that result of calculation reliability is strong.

The present invention solves its technical matters and takes following technical scheme to realize:

A kind of Multiple Time Scales community energy LAN (Local Area Network) energy dispatching method, comprises the following steps:

Step 1, to exert oneself according to community's photovoltaic and the historical data of workload demand, calling genetic algorithm, to carry out long-term forecasting energy-optimised, generates the predicted value of the operation plan in following 24 hours of community;

Step 2, community's photovoltaic to be exerted oneself and in the real-time running data of workload demand and step 1, the predicted value of gained operation plan contrasts, calculate community's deviate and according to the size of this community's deviate community's photovoltaic to be exerted oneself and workload demand carries out real-time short-term energy optimization.

And the concrete steps of described step 1 comprise:

(1) generate photovoltaic in following 24 hours to exert oneself and workload demand predicted value;

(2) foundation maximizes with photovoltaic utilization factor, community's purchase of electricity minimizes and peak valley rate on the one is minimised as the assessment indicator system of target and runs the mathematical model of constraint;

(3) population number, genetic algebra, selection mode, crossing-over rate and aberration rate are set; Initialization chromosome, makes the chromosomal photovoltaic that consists of abandon energy storage gross capability in light zone bit and community, and in community, each energy-storage units exerts oneself according to keeping the equal strategy of each energy-storage units state-of-charge to distribute;

(4) judge whether to meet end condition, if meet, then retain optimum individual, jump out genetic algorithm circulation, if do not meet, proceed selection, intersection, mutation operation;

(5) energy storage general power in community's is set and generates the operation plan in following 24 hours of community.

And the concrete steps of described step 2 comprise:

(1) photovoltaic cells uploaded energy router, load, energy-storage units service data carry out Effective judgement, arrange after sorting out stored in backstage real-time data base;

(2) arrange new variables record photovoltaic to exert oneself and the actual value of workload demand and the deviate of predicted value, and deviation allowable value is set;

(3) the actual gross capability of community's photovoltaic and prediction gross capability, the actual aggregate demand of load and the community's total departure value predicting aggregate demand is calculated;

(4) if this community's total departure value is less than deviation allowable value, Ze Geng new communities total departure value also zooms in or out energy storage general power in operation plan after calculating each family deviate at double, more proportionally distribution energy-storage units is exerted oneself;

(5) if this community's total departure value is greater than deviation allowable value, then by community's total departure value zero also re invocation long-term optimization algorithm, generate current time to the operation plan when end of day, then the actual value of community's photovoltaic gross capability and load aggregate demand is compared again with new operation plan.

And the concrete steps of (1) step of described step 1 comprise:

1. the photovoltaic calling close period in historical data base goes out force data and workload demand data, utilizes method of interpolation to carry out data fitting, forms typical curve;

2. by weather forecast and holiday information input typical curve, generate photovoltaic in following 24 hours and exert oneself and workload demand predicted value.

And the assessment indicator system that (2) step of described step 1 is set up with the mathematical model running constraint is:

$\min \left\{\begin{array}{c}{P}_{PCC}={\mathrm{\ΣP}}_{LDi}-{\mathrm{\ΣP}}_{PVi}+{\mathrm{\ΣP}}_{ESSi}\\ {\mathrm{\η}}_{shedPV}\\ PVR=\frac{P_{PCC\max }}{P_{PCC\min }}\end{array}\right.$

$s.t\left\{\begin{array}{c}{\mathrm{\ΣP}}_{PVi}+{\mathrm{\ΣP}}_{ESSi}+P_{PCC}={\mathrm{\ΣP}}_{LDi}\\ {\mathrm{SOC}}_{\min }\≤{\mathrm{SOC}}_{ESS}\≤{\mathrm{SOC}}_{max}\\ P_{p\min }\≤P_{PCC}\≤P_{pmax}\\ {\mathrm{SOC}}_0\×(1-\mathrm{\β})\≤{\mathrm{SOC}}_{ESSfinal}\≤{\mathrm{SOC}}_0\×(1+\mathrm{\β})\end{array}\right.$

In above-mentioned expression formula: P _pCCfor and site power, P _lDibe the power of i-th controllable burden, P _pVibe i-th photovoltaic generation unit go out activity of force, P _eSSibe i-th energy-storage units charge-discharge electric power, η _shedPVfor abandoning light photovoltaic cells number, PVR is peak valley rate, P _pCCmax, P _pCCminbe respectively research period interior also site power maximal value and minimum value, P _pmax, P _pminbe respectively and the bound of site power, SOC _eSSfor the state-of-charge of energy storage, SOC ₀for the state-of-charge of energy storage at the beginning of the research period, SOC _eSSfinalfor studying the state-of-charge of period Mo energy storage, SOC _max, SOC _minbe respectively the bound of energy storage charge state, β is the variance rate of energy storage state-of-charge at the whole story.

And, the P that exerts oneself of each energy-storage units in community described in (3) step of described step 1 _eSSidistribute according to following formula:

In above-mentioned expression formula, P _eSSfor community's accumulator system general power, SOC _iit is the state-of-charge of i-th energy-storage units.

And, according to the actual gross capability of following formulae discovery community photovoltaic and prediction gross capability, the actual aggregate demand of load and the community total departure value Delta predicting aggregate demand in (3) step of described step 2:

$Delta=\underset{i=1}{\overset{5}{\Σ}}(P_{PVacti}-P_{PVforei})-\underset{i=1}{\overset{5}{\Σ}}(P_{LDacti}-P_{LDforei})$

In above-mentioned expression formula: P _pVactibe i-th photovoltaic generation unit actual go out activity of force, P _pVforeibe i-th photovoltaic generation unit dope activity of force, P _lDactibe the real power of i-th controllable burden, P _lDforeiit is the predicted power of i-th controllable burden.

And (4) step of described step 2 is according to formula zoom in or out energy storage general power in operation plan at double;

In above-mentioned expression formula: P _eSSactfor energy-storage units real power, P _pVactfor the actual gross capability power of photovoltaic generation unit, P _pVforefor the prediction gross capability power of photovoltaic generation unit, P _lDactfor the actual general power of controllable burden, P _lDforefor the prediction general power of controllable burden.

Advantage of the present invention and good effect are:

1, the community's foundation that the present invention is directed to containing distributed photovoltaic power generation unit and distributed energy storage unit maximizes with photovoltaic utilization factor, community's purchase of electricity minimizes and peak valley rate on the one is minimised as the appraisement system of index, adopt the strategy keeping energy-storage units state-of-charge balance in community, by analyzing grid operating conditions, multiple energy-storage units charge-discharge electric power variable is integrated into single variable, each energy-storage units is according to the proportional distribution power of its state-of-charge, utilize genetic algorithm to carry out chronic energy optimization to sale of electricity between accumulator system and user and formulate operation plan, continual short-term energy optimization is carried out in conjunction with real-time running data and long-term dispatch plan in actual motion, actual motion is made to press close to operation plan, ensure that operation of power networks reaches optimum way.Thus compression optimization algorithm solution space, reduce computing time, improve efficiency of algorithm.The mode that long-term and short-term energy optimization combines also can alleviate photovoltaic and exert oneself and the probabilistic impact of workload demand.

2, the present invention considers certain several energy-storage units charging in community, the situation of certain several energy-storage units electric discharge is irrational, and in order to keep the stable of each family energy-storage units state-of-charge, power is distributed according to energy-storage units state-of-charge, when namely charging, it is large that the energy-storage units that state-of-charge is low arranges charge power, and it is little to arrange discharge power when discharging.And multiple energy storage variable is simplified to a variable, and energy exponentially doubly reduces the search volume of genetic algorithm, improves efficiency of algorithm.

3, the present invention adjusts in real time in conjunction with degree plan in following 24 hours of the community of the energy-optimised generation of long-term forecasting, the deviation of global variable record reality and predicted value is set, the energy storage only changing this moment according to inclined extent decision is exerted oneself or re-starting long-term optimization generates new operation plan, thus is able to the impact effectively revising photovoltaic and negative rules with lower operation complexity.

Accompanying drawing explanation

Fig. 1 is treatment scheme schematic diagram of the present invention;

Fig. 2 is the energy-optimised algorithm flow schematic diagram of long-term forecasting of the present invention;

Fig. 3 is the algorithm flow schematic diagram that real-time short-term energy of the present invention is optimized;

Fig. 4 is the energy-optimised accumulator system scheduling result figure of long-term forecasting of the present invention;

Fig. 5 is that the accumulator system of real-time short-term energy optimization of the present invention is exerted oneself situation map.

Embodiment

Below in conjunction with accompanying drawing, the embodiment of the present invention is described in further detail:

Based on a Multiple Time Scales community energy LAN (Local Area Network) energy dispatching method for energy storage proportional distribution, as shown in Figure 1, comprise the following steps:

Step 1, to exert oneself according to community's photovoltaic and the historical data of workload demand, calling genetic algorithm, to carry out long-term forecasting energy-optimised, generates the predicted value of the operation plan in following 24 hours of community.

As shown in Figure 2, its concrete steps comprise the genetic algorithm flow process that described step 1 is called:

(1) generate photovoltaic in following 24 hours to exert oneself and workload demand predicted value, its concrete grammar is: 1. to call in historical data base nearest 7 days and the photovoltaic of phase same date in recent years goes out force data and workload demand data, utilize method of interpolation to carry out data fitting, form typical curve; 2. by weather forecast and holiday information input typical curve, generate photovoltaic in following 24 hours and exert oneself and workload demand predicted value, the predicted data time interval is 1 hour.

(2) set up maximize with photovoltaic utilization factor, community's purchase of electricity minimizes and peak valley rate on the one is minimised as target assessment indicator system and the mathematical model of running constraint be:

$\min \left\{\begin{array}{c}{P}_{PCC}={\mathrm{\ΣP}}_{LDi}-{\mathrm{\ΣP}}_{PVi}+{\mathrm{\ΣP}}_{ESSi}\\ {\mathrm{\η}}_{shedPV}\\ PVR=\frac{P_{PCC\max }}{P_{PCC\min }}\end{array}\right.$

$s.t\left\{\begin{array}{c}{\mathrm{\ΣP}}_{PVi}+{\mathrm{\ΣP}}_{ESSi}+P_{PCC}={\mathrm{\ΣP}}_{LDi}\\ {\mathrm{SOC}}_{\min }\≤{\mathrm{SOC}}_{ESS}\≤{\mathrm{SOC}}_{max}\\ P_{p\min }\≤P_{PCC}\≤P_{pmax}\\ {\mathrm{SOC}}_0\×(1-\mathrm{\β})\≤{\mathrm{SOC}}_{ESSfinal}\≤{\mathrm{SOC}}_0\×(1+\mathrm{\β})\end{array}\right.$

In above-mentioned expression formula: P _pCCfor and site power, P _lDibe the power of i-th controllable burden, P _pVibe i-th photovoltaic generation unit go out activity of force, P _eSSibe i-th energy-storage units charge-discharge electric power, η _shedPVfor abandoning light photovoltaic cells number, PVR is peak valley rate, P _pCCmax, P _pCCminbe respectively research period interior also site power maximal value and minimum value, P _pmax, P _pminbe respectively and the bound of site power, SOC _eSSfor the state-of-charge of energy storage, SOC ₀for the state-of-charge of energy storage at the beginning of the research period, SOC _eSSfinalfor studying the state-of-charge of period Mo energy storage, SOC _max, SOC _minbe respectively the bound of energy storage charge state, β is the variance rate of energy storage state-of-charge at the whole story.

(3) arrange that population number is 100, genetic algebra is 500, selection mode is roulette, crossing-over rate is 0.9, aberration rate is 0.1; Initialization chromosome, makes the chromosomal 5*24 of consisting of position photovoltaic abandon energy storage gross capability in light zone bit and 24 communities; The P that exerts oneself of each energy-storage units in community _eSSiaccording to keeping the equal strategy of each energy-storage units state-of-charge to distribute, that is:

In above-mentioned expression formula, P _eSSfor community's accumulator system general power, SOC _iit is the state-of-charge of i-th energy-storage units.

(4) judge whether to meet end condition, if meet, then retain optimum individual, jump out genetic algorithm circulation, if do not meet, proceed selection, intersection, mutation operation;

(5) energy storage general power in community's is set and arranges net result and generate operation plan in following 24 hours of community.

In the present embodiment, be described for a villa quarter, this villa quarter has 5 users and is configured with family photovoltaic generation unit, wherein has 3 families to be equipped with energy-storage units.As shown in Figure 4, in 5 family photovoltaics, user 2 photovoltaic abandons light at 14 to the accumulator system scheduling result of this villa, and user 4 photovoltaic abandons light at 15.

Step 2, community's photovoltaic to be exerted oneself and in the real-time running data of workload demand and step 1, the predicted value of gained operation plan contrasts, calculate community's deviate and according to the size of this community's deviate community's photovoltaic to be exerted oneself and workload demand carries out real-time short-term energy optimization.

The specific algorithm flow process of described step 2 as shown in Figure 3, comprises the following steps:

(1) photovoltaic cells uploaded energy router, load, energy-storage units service data carry out Effective judgement, and arrange stored in backstage real-time data base after sorting out, data break is 1 hour.

(2) arrange new variables record photovoltaic to exert oneself and the actual value of workload demand and the deviate Delta of predicted value are double type, be initialized as 0, and deviation allowable value is set is set to 2kW.

(3) according to the actual gross capability of following formulae discovery community photovoltaic and prediction gross capability, the actual aggregate demand of load and the community total departure value Delta predicting aggregate demand:

$Delta=\underset{i=1}{\overset{5}{\Σ}}(P_{PVacti}-P_{PVforei})-\underset{i=1}{\overset{5}{\Σ}}(P_{LDacti}-P_{LDforei})$

In above-mentioned expression formula: P _pVactibe i-th photovoltaic generation unit actual go out activity of force, P _pVforeibe i-th photovoltaic generation unit dope activity of force, P _lDactibe the real power of i-th controllable burden, P _lDforeiit is the predicted power of i-th controllable burden.

(4) if this community's total departure value is less than deviation allowable value, Ze Geng new communities total departure value also to zoom in or out in operation plan energy storage general power at double and proportionally distributes energy-storage units again and exert oneself after calculating each family deviate;

(5) if this community's total departure value is greater than deviation allowable value, then by community's total departure value zero also re invocation long-term optimization algorithm, generate current time to the operation plan when end of day, then the actual value of community's photovoltaic gross capability and load aggregate demand is compared again with new operation plan.

In the present embodiment, for 8 and 12, calculate photovoltaic and exert oneself and the actual value of workload demand and the deviate of predicted value, its result of calculation is;

As seen from the above table, when 8, deviate is-1kW, is less than permissible value, then press formula

zoom in or out energy storage general power in operation plan at double, more proportionally distribution energy-storage units is exerted oneself.

In above-mentioned expression formula: P _eSSactfor energy-storage units real power, P _pVactfor the actual gross capability power of photovoltaic generation unit, P _pVforefor the prediction gross capability power of photovoltaic generation unit, P _lDactfor the actual general power of controllable burden, P _lDforefor the prediction general power of controllable burden.

When 12, deviate is-2.7kW, is greater than permissible value, then call long-term optimization algorithm, generates the new operation plan of 12 o'clock to 23 o'clock, then the actual value of community's photovoltaic gross capability and load aggregate demand is again compared with new operation plan.

During actual motion, accumulator system exerts oneself situation as shown in Figure 5, and in 5 family photovoltaics, user 4 photovoltaic abandons light at 15.

It is emphasized that; embodiment of the present invention is illustrative; instead of it is determinate; therefore the present invention includes the embodiment be not limited to described in embodiment; every other embodiments drawn by those skilled in the art's technical scheme according to the present invention, belong to the scope of protection of the invention equally.

Claims (8)

1. a Multiple Time Scales community energy LAN (Local Area Network) energy dispatching method, is characterized in that: comprise the following steps:

Step 1, to exert oneself according to community's photovoltaic and the historical data of workload demand, calling genetic algorithm, to carry out long-term forecasting energy-optimised, generates the predicted value of the operation plan in following 24 hours of community;

Step 2, community's photovoltaic to be exerted oneself and in the real-time running data of workload demand and step 1, the predicted value of gained operation plan contrasts, calculate community's deviate and according to the size of this community's deviate community's photovoltaic to be exerted oneself and workload demand carries out real-time short-term energy optimization.

2. a kind of Multiple Time Scales community energy LAN (Local Area Network) energy dispatching method according to claim 1, is characterized in that: the concrete steps of described step 1 comprise:

(1) generate photovoltaic in following 24 hours to exert oneself and workload demand predicted value;

(2) foundation maximizes with photovoltaic utilization factor, community's purchase of electricity minimizes and peak valley rate on the one is minimised as the assessment indicator system of target and runs the mathematical model of constraint;

(3) population number, genetic algebra, selection mode, crossing-over rate and aberration rate are set; Initialization chromosome, makes the chromosomal photovoltaic that consists of abandon energy storage gross capability in light zone bit and community, and in community, each energy-storage units exerts oneself according to keeping the equal strategy of each energy-storage units state-of-charge to distribute;

(4) judge whether to meet end condition, if meet, then retain optimum individual, jump out genetic algorithm circulation, if do not meet, proceed selection, intersection, mutation operation;

(5) energy storage general power in community's is set and generates the operation plan in following 24 hours of community.

3. a kind of Multiple Time Scales community energy LAN (Local Area Network) energy dispatching method according to claim 1, is characterized in that: the concrete steps of described step 2 comprise:

(1) photovoltaic cells uploaded energy router, load, energy-storage units service data carry out Effective judgement, arrange after sorting out stored in backstage real-time data base;

(2) arrange new variables record photovoltaic to exert oneself and the actual value of workload demand and the deviate of predicted value, and deviation allowable value is set;

(3) the actual gross capability of community's photovoltaic and prediction gross capability, the actual aggregate demand of load and the community's total departure value predicting aggregate demand is calculated;

(4) if this community's total departure value is less than deviation allowable value, Ze Geng new communities total departure value also zooms in or out energy storage general power in operation plan after calculating each family deviate at double, more proportionally distribution energy-storage units is exerted oneself;

(5) if this community's total departure value is greater than deviation allowable value, then by community's total departure value zero also re invocation long-term optimization algorithm, generate current time to the operation plan when end of day, then the actual value of community's photovoltaic gross capability and load aggregate demand is compared again with new operation plan.

4. a kind of Multiple Time Scales community energy LAN (Local Area Network) energy dispatching method according to claim 2, is characterized in that: the concrete steps of described step (1) comprising:

1. the photovoltaic calling close period in historical data base goes out force data and workload demand data, utilizes method of interpolation to carry out data fitting, forms typical curve;

2. by weather forecast and holiday information input typical curve, generate photovoltaic in following 24 hours and exert oneself and workload demand predicted value.

5. a kind of Multiple Time Scales community energy LAN (Local Area Network) energy dispatching method according to claim 2, is characterized in that: the mathematical model of the assessment indicator system that described step (2) is set up and operation constraint is:

$\min \left\{\begin{array}{c}{P}_{PCC}=\ΣP_{LDi}-\ΣP_{PVi}+\ΣP_{ESSi}\\ {\mathrm{\η}}_{shedPV}\\ PVR=\frac{P_{PCC\max }}{P_{PCC\min }}\end{array}\right.$

$\begin{array}{cc}s.t& \left\{\begin{array}{c}\ΣP_{PVi}+\ΣP_{ESSi}+P_{PCC}=\ΣP_{LDi}\\ {\mathrm{SOC}}_{\min }\≤{\mathrm{SOC}}_{ESS}\≤{\mathrm{SOC}}_{max}\\ P_{p\min }\≤P_{PCC}\≤P_{pmax}\\ {\mathrm{SOC}}_0\×(1-\mathrm{\β})\≤{\mathrm{SOC}}_{ESSfinal}\≤{\mathrm{SOC}}_0\×(1+\mathrm{\β})\end{array}\right.\end{array}$

In above-mentioned expression formula, P _pCCfor and site power, P _lDibe the power of i-th controllable burden, P _pVibe i-th photovoltaic generation unit go out activity of force, P _eSSibe i-th energy-storage units charge-discharge electric power, η _shedPVfor abandoning light photovoltaic cells number, PVR is peak valley rate, P _pCCmax, P _pCCminbe respectively research period interior also site power maximal value and minimum value, P _pmax, P _pminbe respectively and the bound of site power, SOC _eSSfor the state-of-charge of energy storage, SOC ₀for the state-of-charge of energy storage at the beginning of the research period, SOC _eSSfinalfor studying the state-of-charge of period Mo energy storage, SOC _max, SOC _minbe respectively the bound of energy storage charge state, β is the variance rate of energy storage state-of-charge at the whole story.

6. a kind of Multiple Time Scales community energy LAN (Local Area Network) energy dispatching method according to claim 2, is characterized in that: the P that exerts oneself of each energy-storage units in described step (3) community _eSSidistribute according to following formula:

In above-mentioned expression formula, P _eSSfor community's accumulator system general power, SOC _iit is the state-of-charge of i-th energy-storage units.

7. a kind of Multiple Time Scales community energy LAN (Local Area Network) energy dispatching method according to claim 3, is characterized in that: described step (3) is according to the actual gross capability of following formulae discovery community photovoltaic and prediction gross capability, the actual aggregate demand of load and the community total departure value Delta predicting aggregate demand:

$Delta=\underset{i=1}{\overset{5}{\Σ}}(P_{PVacti}-P_{PVforei})-\underset{i=1}{\overset{5}{\Σ}}(P_{LDacti}-P_{LDforei})$

In above-mentioned expression formula: P _pVactibe i-th photovoltaic generation unit actual go out activity of force, P _pVforeibe i-th photovoltaic generation unit dope activity of force, P _lDactibe the real power of i-th controllable burden, P _lDforeiit is the predicted power of i-th controllable burden.

8. a kind of Multiple Time Scales community energy LAN (Local Area Network) energy dispatching method according to claim 3, is characterized in that: described step (4) is according to formula zoom in or out energy storage general power in operation plan at double;

In above-mentioned expression formula: P _eSSactfor energy-storage units real power, P _pVactfor the actual gross capability power of photovoltaic generation unit, P _pVforefor the prediction gross capability power of photovoltaic generation unit, P _lDactfor the actual general power of controllable burden, P _lDforefor the prediction general power of controllable burden.