CN105955931A - High-density distributed photovoltaic absorption-oriented regional energy network optimizing and scheduling method - Google Patents

Abstract

The invention discloses a high-density distributed photovoltaic absorption-oriented regional energy network optimizing and scheduling method. The method comprises the following steps: analyzing energy supply and energy storage apparatuses of a plurality of users in a zone energy network, and establishing energy supply mathematic models of the apparatuses; establishing a regional energy network optimizing and scheduling model which considers the high-density distributed photovoltaic absorption on the basis of centralized mutual connected energy exchange networks; in the regional energy network optimizing and scheduling model, analyzing the influences, caused by each user, on network energy loss, so as to enable the flow constraint of the network to be equivalent to power exchange constraint of the user and the energy network; enabling whole network energy loss, caused by the user, in a whole network loss calculation function in a target function to be equivalent to a product of the power exchange value of the user and the energy network and a network loss impact factor; and aiming at the characteristics of the model, solving the model by using an interior point method. Through scheduling the user energy supply apparatuses in the intelligent power grid zone and the operation manners and contribution of high-density distributed photovoltaics, the economic optimized operation of the energy network of the whole zone can be realized.

Description

The Regional Energy network optimization dispatching method dissolved towards high density distributed photovoltaic

Technical field

The present invention relates to technical field of power systems, be specifically related to the Regional Energy net dissolved towards high density distributed photovoltaic Network Optimization Scheduling.

Background technology

Under the pressure facing energy shortage difficulty, energy resource structure transition and energy-saving and emission-reduction, in conjunction with active distribution network technology, Flow by using network topology structure flexibly to manage the network energy, the distributed energy equipment in zones of different is entered Row actively controls and active management, and then when development cleaning, energy utilization patterns reliable, interactive, efficient become promotion For economic transition, the important means of development low-carbon economy.Micro power source network packet contains gentle four kinds of energy shapes hot and cold, electric Formula, utilizes technology of Internet of things and information technology to all powering device unified integrations in region and to implement scheduling, to reach Region cool and thermal power load is optimized energy supply, promotes the effect of efficiency of energy utilization.CCHP(Combined Cooling Heating and Power) cool and thermal power energy mix co-generation system is as the Typical Representative of micro power source net, efficient with it Efficiency of energy utilization, the energy supply pattern of flexibility and reliability become realize production of energy and consumption transition, promote the energy combine Close utilization ratio and solve the important means of energy environment issues.

At present in actual application aspect, micro power source net co-feeding system both domestic and external is still in the starting stage, wherein the most relatively For famous Demonstration Application have UNIVERSITY OF CALIFORNIA, DAVIS (UCD) project and The projects such as UNIVERSITY OF NEW MEXICO (UNM), the most famous domestic nascent state city in Tianjin that has is moved Unrestrained garden cooling-heating treatment projects etc., these researchs are mostly based on unique user type or single energy source station is optimized, not Consider the situation of Regional Energy interconnection.

Theoretical research aspect, all has certain research both at home and abroad, the most abroad has research institution face CCHP systematic research To certain large-scale Medical Zone, devise the hybrid energy supplying system of corresponding supply of cooling, heating and electrical powers, calculate its Main Economic index, Analyze its peak modulation capacity and the sensitivity to some variable economic factor, and compare with normal grid and drawn CCHP System all has the conclusion having great advantage in terms of economy or reliability, but it is only in economical index evaluation side Face is discussed, and concrete analysis is not made in operation and scheduling to the equipment in CCHP system；Scholar is had to grind aforementioned Combine tou power price on studying carefully, it is considered to trilogy supply unit operation cost function, set up and comprise production cost, Environmental costs and The economic load dispatching model of Cost for Coordination, uses quadratic programming to solve model, but the most not in CCHP system The operation of equipment and scheduling make concrete analysis.

Domestic in terms of energy resource system interconnection modeling, there is scholar to propose the energy of a kind of combined cooling and power distributed energy supply system Management optimization model, and cold, the electric load prediction data of choosing typical case's month be optimized calculating, demonstrate model can Row, but in its co-feeding system, device category is relatively limited, only accounts for miniature gas turbine, does not supply other auxiliary Corresponding model can be set up by equipment；In terms of environmental conservation factor, Environmental Factors is taken into account scheduling model by multidigit scholar, as Consider that proposing that dusty gas discharges penalty adds object function, considers operating cost and pollutant emission etc. many Factor establishes multiple target chance plan model, but lacks the consideration to low-temperature receiver；Have scholar establish consideration greenhouse gases, Pollutant emission with the microgrid economic model of the minimum object function of microgrid operating cost, and with particle swarm optimization algorithm pair Above-mentioned model solves, but the equipment of its CCHP system composition is the most single, it is impossible to adapt to the current energy mutual Contact system development trend.

Summary of the invention

Goal of the invention: in order to overcome the deficiencies in the prior art, the present invention provides and disappears towards high density distributed photovoltaic The Regional Energy network optimization dispatching method received, establishes consideration high density distribution based on the energy exchange network concentrating interconnection The Regional Energy network economy Optimal Operation Model that formula photovoltaic is dissolved, by analyzing each user to network energy damage in model The impact of consumption, it is achieved the economic optimization of whole garden energy network runs；Solve problem of the prior art.

Technical scheme: for achieving the above object, the technical solution used in the present invention is: disappear towards high density distributed photovoltaic The Regional Energy network optimization dispatching method received, it is characterised in that the Regional Energy net bag that high density distributed photovoltaic is dissolved Include powering device, energy storage device and auxiliary powering device；Described energy storage device include cold energy storage device, hot energy storage device and Electricity energy storage device；Described auxiliary powering device includes gas fired-boiler, family air-conditioning, absorption refrigeration unit and renewable energy Source generating equipment；

Powering device and the energy storage device of user in garden energy network are analyzed, set up energy supply model；Based on concentration The energy exchange network of interconnection, it is considered to the energy network economic optimization that high density distributed photovoltaic is dissolved dispatches described energy supply mould Type；Analyze the impact that energy exchange network is lost by each user, the trend of energy exchange network is retrained and is equivalent to user Retrain with the exchange of electric power of energy exchange network, by object function, in the whole network line losses management function by user cause complete Network energy loss is equivalent to exchange of electric power value and the network loss factor to affect product of user and energy exchange network, special for model Property use interior point method model is solved；By the method for operation of all types of user powering device in scheduling garden with exert oneself, The economic optimization realizing whole garden energy exchange network runs.

Further, it is analyzed comprising the following steps to powering device and the energy storage device of user in Regional Energy network:

Step one) set up powering device model；

Described powering device is miniature gas turbine system, and described miniature gas turbine system includes some miniature steam turbines Machine；Described miniature steam turbine system selects unit to open number of units according to the power generation command value of scheduling, and the unit having turned on is average Sharing electric load, by fitting of a polynomial, obtain the efficiency of miniature gas turbine system and go out force function, its parameter is as follows:

${\mathrm{\η}}_{C1000}=f\left(P\right)=\underset{i=1}{\overset{16}{\Σ}}{p}_i{\left(\frac{P}{P_{max}}\right)}^{16-i}$

In formula:

η_C1000For: the miniature gas turbine system efficiency when exerting oneself as P；

P_maxFor: power-handling capability；

F (P) is: system go out force function；

p_iFor: go out the every coefficient of force function；

Step 2): set up the energy supply model of energy storage device；

$E(t+1)=E\left(t\right)\·(1-\mathrm{\μ})+\left({\mathrm{\η}}^{abs}\·P_{abs}\left(t\right)-\frac{1}{{\mathrm{\η}}^{relea}}\·P_{relea}\left(t\right)\right)\·\mathrm{\Δ}t$

In formula:

E (t) is the energy that energy storage device stored in the t period；

Δ t is the t period time interval to the t+1 period；

P_absT () is t period energy storage power；

P_releaT () is t period exoergic power；

μ is that energy storage device self dissipates the loss of energy or the energy coefficient from loss to environment；

η^absFor the energy storage efficiency of energy storage device,

η^releaFor energy storage device exergic efficiency.

Step 3): set up auxiliary equipment energy supply model:

${\mathrm{\η}}_b=\frac{H_{aux,out}}{H_{aux,in}}$

Constraints is: 0≤H_aux,out≤H_aux,outmax；

In formula:

H_aux,outFor the hot/cold amount of auxiliary powering device output, unit is kW；

H_aux,inFor the energy of auxiliary powering device input, unit is kW；

η_auxEfficiency of energy utilization or energy efficiency coefficient COP for equipment.

Further, analyze the impact that energy exchange network is lost by each user to comprise the following steps:

Step one), select need optimize garden topology；

Step 2), determine Campus Network Parameter Conditions and each node users type and average load；

Step 3), select to produce interactive energy source station access point with garden；

Step 4), analyzed the maximum power exchange power of energy source station access point and whole garden by continuous tide；

Step 5), the whole network energy loss caused by user in the whole network line losses management function in object function is equivalent to The exchange of electric power value of user and energy network and network loss factor to affect product, analyzed by continuous tide, determine factor to affect Value；The loss S computing formula of each circuit in the whole network is:

$S=\frac{\mathrm{\Δ}U\×{\mathrm{\ΔU}}^{*}}{Z^{*}}$

Wherein,

Δ U is poor for the end-point voltage of circuit i；

ΔU^*Conjugation for Δ U；

Z^*For the conjugation of the impedance of circuit i, unit is Ω；

The network loss of all circuits is overlapped, obtains the whole network network loss.

Further, the whole network effective power flow Web-based exercise and the whole network reactive power flow punishment cost function are analyzed:

For the whole network trend active power loss cost function, can be described as:

${\mathrm{pri}}_{loss}=\underset{1}{\overset{24}{\Σ}}{c}_{Pi}^t\×c_{PGrid}^t\×P_{Grid}^t$

In formula,Active power by time electricity price；It is that user exchanges power at i-th access point with public distribution The loss factor afterwards system caused,Be user and external electrical network by time exchange of electric power value.

Network loss punishment cost function idle for the whole network trend, can be described as:

In formula,Active power by time electricity price；It is that user exchanges power at i-th access point with public distribution The loss factor afterwards system caused,It is that system is made after public distribution exchange power by user at i-th access point The idle loss factor become,Be user and external electrical network by time exchange of electric power value,It it is rated power factor mark Quasi-value,It is to produce the coefficient of punishment/incentive fees based on actual power factor and the difference of rated power factor.

5, the Regional Energy network optimization dispatching method dissolved towards high density distributed photovoltaic as claimed in claim 1, It is characterized in that, the trend of described energy exchange network is constrained to:

$P_{iGrid}^{\min }\≤P_{iGrid}^t\≤P_{iGrid}^{\max },i\∈n_{customer}$

In formula,For t Campus Network user i and the active power interaction value of network, unit is kW；WithActive power minimum and maximum limit for garden user i after continuous tide calculates with the mutual electricity of external electrical network Value, unit is kW；T is time span, and unit is hour.

Beneficial effect: energy supply and the energy storage system of catergories of user in garden energy network are analyzed by the present invention, sets up each The energy supply mathematical model of equipment, sets up based on the energy exchange network concentrating interconnection and considers what high density distributed photovoltaic was dissolved Energy network economic optimization scheduling model, by analyzing each user impact on network energy loss in model, by network Trend constraint be equivalent to the exchange of electric power constraint of user and energy network, by the whole network line losses management function in object function In the whole network energy loss of being caused by user be equivalent to the exchange of electric power value of user and energy network and take advantage of with network loss factor to affect Long-pending, use interior point method that model is solved for model characteristics.By all types of user energy supply in scheduling intelligent grid garden The method of operation of equipment and exerting oneself, it is achieved the economic optimization of whole garden energy network runs.In implementation process, by adjusting The method of operation of each powering device and exerting oneself in degree supply of cooling, heating and electrical powers type micro power source net, thus it is micro-to realize supply of cooling, heating and electrical powers type The economic optimization of type energy net runs.

The present invention has also given full play to intelligent grid garden advantage in terms of data acquisition and in powering device multiformity side The advantage in face, the most also gives full play to interior point method advantage in terms of solving nonlinear optimal problem, and scheduling strategy improves The comprehensive energy efficiency of intelligent grid garden, reduces systematic running cost and uses, it is achieved the economy of supply of cooling, heating and electrical powers micro power source network is excellent Change and run.

Accompanying drawing explanation

Fig. 1 is the Capstone C200 efficiency in the present invention and power curve.

Fig. 2 is the Capstone C1000 efficiency in the present invention and power curve.

Fig. 3 is the energy supply district system topology schematic diagram of the present invention.

Fig. 4 is the energy supply district system distribution wiring diagram of the present invention.

Fig. 5 is energy source station common user group typical case day electric heating cold prediction load curve and high density distributed photovoltaic in the present invention Colony's generated output prediction curve.

Fig. 6 is data center typical case day cool and thermal power load prediction curve in the present invention.

Fig. 7 is Industry enterprise customer energy resource system typical case day cool and thermal power load prediction curve in the present invention.

Fig. 8 is that in the present invention, public organizations' group's electric load balances Optimized Operation equipment power curve a few days ago.

Fig. 9 is public organizations' group space heat load balance Optimized Operation equipment power curve a few days ago in the present invention.

Figure 10 is that in the present invention, public organizations group hot water load balances Optimized Operation equipment power curve a few days ago.

Figure 11 is that in the present invention, public organizations' group space refrigeration duty balances Optimized Operation equipment power curve a few days ago.

Figure 12 is that in the present invention, public organizations group freezing cooling load balances Optimized Operation equipment power curve a few days ago.

Figure 13 is energy storage device running status under energy source station Optimized Operation a few days ago in the present invention.

Figure 14 is that in the present invention, data center typical case day electric load balances Optimized Operation equipment power curve a few days ago.

Figure 15 is that in the present invention, data center typical case day refrigeration duty balances Optimized Operation equipment power curve a few days ago.

Figure 16 is that in the present invention, industrial user typical case's day electric load balances Optimized Operation equipment power curve a few days ago.

Figure 17 is industrial user typical case's day heat load balance Optimized Operation equipment power curve a few days ago in the present invention.

Figure 18 is that in the present invention, industrial user typical case's day refrigeration duty balances Optimized Operation equipment power curve a few days ago.

Figure 19 is the flow chart of the present invention

Table 1 is energy supply district system distribution wiring diagram line parameter circuit value in the present invention.

Table 2 is energy supply district system distribution wiring diagram load parameter in the present invention.

Table 3 is energy source station powering device parameter in the present invention.

Table 4 is energy source station energy storage device parameter in the present invention.

Table 5 is data center's powering device parameter in the present invention.

Table 6 produces and researches and develops industrial user's powering device parameter in the present invention.

Table 7 is the tou power price table set in the present invention.

Table 8 for Capstone C1000 system effectiveness in the present invention with go out the every coefficient of force function.

Detailed description of the invention

Below in conjunction with the accompanying drawings the present invention is further described.Consider the energy interconnection that high density distributed photovoltaic is dissolved System is different from single energy consumption system, stands in the whole network angle, it is contemplated that all energy consumption systems and the region at energy source station place The ruuning situation of power distribution network, the service capacity after optimization can not make region distribution the out-of-limit feelings with voltage out-of-limit of trend occur Condition, the operation result simultaneously optimized not only to make the energy within all energy consumption systems reach ladder utilization, and efficiency is the highest, The network loss of the whole network to be ensured maintains relatively low level.

After adding trend constraint, accessing as a example by three energy source stations by Regional Energy interacted system, each energy source station is each thinks highly of oneself The energy supply of one energy consumption system of duty, the most respectively to common user, industrial user and data center's energy supply, each energy source station Access a load bus in power distribution network, to carry out electricity with regional power grid by performing the dispatch command of dispatching patcher distribution Power is mutual.The impact on regional power grid that accesses of energy source station shows two aspects: one is that the electric power after energy source station performs is handed over Should not cause regional power grid Voltage Drop or raise out-of-limit mutually, two is to be wished by the network loss of region distribution as far as possible by scheduling Ground reduces.In order to Local Area Network loss be added in allocation models, an ideal solution is by Local Area Network All nodes be all numbered, and to the voltage reality imaginary part of each node, electric current reality imaginary part with inject meritorious reactive power Carrying out variable numbering, the most each node has 6 variablees, equation aspect, counts in outside power equation and bus admittance matrix equation, Two equations are given when specifying node type.So, each node has just had 6 equations and 6 changes in each moment Amount.For the regional distribution network network of 30 nodes, being a dispatching cycle by one day, scheduling spacing is 1 little Time calculate, only trend equality constraint just has 30X6X24=4320 nonlinear equation equation, adds multiple energy supplying system Powering device constraint, variable bound constraint etc., total constraints will be more than 6000, and this will be for Solve problems band Carry out difficulty greatly.

For the optimization problem of this kind of superelevation dimension, current possible in theory solution has two kinds, and one is traditional mathematics Analytic solution, the most representative has SQP method and Nonlinear Programming Method etc., but for the most high-dimensional optimization Problem is generally difficult to convergence, is just difficult to continue at the calculating initial stage；Another method is to use intelligent algorithm such as particle Group algorithm or genetic algorithm etc., start, by progressively to initial solution owing to this type of intelligent algorithm is all based on RANDOM SOLUTION search Optimization and optimizing and then find the numerical solution being closer to optimal solution, the number that there is problems of obtaining of such method Value solves simply when the optimal solution of time iteration, and the not necessarily optimal solution of the overall situation will be in the solution space of whole superelevation dimension Launch extensive search to be difficult under current computer technology.As a example by genetic algorithm, in the parameter of genetic algorithm In selection, first having to carry out the universal understanding of the selection of population scale, academic circles at present and engineering circles is to be got by experiment Hold back the meansigma methods of time and convergence times to study as evaluation index and determining that genetic algorithm parameter, population scale are with decision-making The relation of variable number n, is shown by the test of classical function in document: proper population scale should control at 4n Between 6n, the population scale that i.e. this problem is to be chosen between 24000 to 36000 by calculating each individuality Fitness function, then carries out genetic operator operation, i.e. selects, intersects and mutation operation, selects population of future generation and carries out Next round selects, intersects and make a variation.During by the calculating of each fitness function and selection, intersection and mutation operation average individual Between be 1 second/time and calculate, under the conditions of current computer, often calculate a generation and take around 10 hours, if i.e. choosing Selecting population number was 50 generations, and the calculating completing the most a few days ago to dispatch takes around 20 days, and this is to connect in actual applications It is subject to.

The present invention provides the Regional Energy network optimization dispatching method dissolved towards high density distributed photovoltaic, to including wind The polymorphic type drive energies such as energy, solar energy, bioenergy, Fossil fuel are repaiied at interior Integrated Energy Optimal Allocation Model Change, revise constraints and the object function of model from the angle of Practical so that model had both considered trend about The impact of bundle, can be with the impact of reflecting regional network loss, simultaneously again it can be avoided that there is the dimension of variable and constraints The problem of calamity.

Energy supply and the energy storage system of catergories of user in garden energy network are analyzed, set up the energy supply mathematical modulo of each equipment Type, sets up, based on the energy exchange network concentrating interconnection, the energy network economic optimization considering that high density distributed photovoltaic is dissolved Scheduling model, by analyzing each user impact on network energy loss in model, retrains the trend of network and is equivalent to The exchange of electric power constraint of user and energy network, by the whole network line losses management function in object function by user cause complete Network energy loss is equivalent to exchange of electric power value and the network loss factor to affect product of user and energy network, makes for model characteristics With interior point method, model is solved.By the method for operation of all types of user powering device in scheduling intelligent grid garden with go out Power, it is achieved the economic optimization of whole garden energy network runs.In implementation process, miniature by scheduling supply of cooling, heating and electrical powers type The method of operation of each powering device and exerting oneself in energy net, thus realize the economic optimization of supply of cooling, heating and electrical powers type micro power source net Run.

Wherein, being analyzed energy supply and the energy storage system of catergories of user in garden energy network, its step includes:

Step (1), set up micro-gas-turbine machine equipment energy supply model:

Miniature gas turbine is the nucleus equipment that micro power source net realizes supply of cooling, heating and electrical powers, as a example by Capstone C1000 Establish the miniature gas turbine model of the economic optimization scheduling being applicable to micro power source net.Capstone C1000 system The strong adaptability being made up of 5 Capstone C200 type miniature gas turbines, low emission, the generating of low-maintenance System.Capstone C200 produces high-frequency alternating current by turbine drive rotor high-speed rotation, then is filled by power electronics Putting and carry out rectification production industrial-frequency alternating current, the high temperature waste smoke that power generation process produces then is used by waste heat boiler, converts Become steam or high-temperature-hot-water for absorption refrigeration unit refrigeration or direct heating.

Capstone C1000 system selects unit to open number of units, the machine having turned on automatically according to the power generation command value of scheduling Group shares out equally electric load, substantially increases only generating efficiency and motility, pass through for more only increasing the equipment scale of construction Fitting of a polynomial, obtains the efficiency of Capstone C1000 system and goes out force function, shown in its parameter sees attached list.

${\mathrm{\η}}_{C1000}=f\left(P\right)=\underset{i=1}{\overset{16}{\Σ}}{p}_i{\left(\frac{P}{P_{max}}\right)}^{16-i}$

Assume that the high temperature waste smoke that power generation process produces is used by waste heat boiler, changes into steam or high-temperature-hot-water herein For absorption refrigeration unit refrigeration or direct heating.

Step (2), set up energy storage equipment energy supply model:

Energy storage equipment can play the effect to cool and thermal power load peak load shifting, can alleviate cool and thermal power duty ratio with miniature simultaneously The unmatched problem of gas turbine co-feeding system hotspot stress.

The characteristic of energy storage equipment can be described as equipment self capacity, maximum energy accumulating state, accumulation of energy output, energy from damaging Several parts such as consumption rate and energy storage efficiency, the energy storage equipment difference equation model of foundation is as follows:

$\begin{array}{c}E\left(t+1\right)=E\left(t\right)\·\left(1-\mathrm{\μ}\right)\\ +\left({\mathrm{\η}}^{abs}\·P_{abs}\left(t\right)-\frac{1}{{\mathrm{\η}}^{relea}}\·P_{relea}\left(t\right)\right)\·\mathrm{\Δ}t\end{array}$

In formula, E (t) is the energy that energy storage equipment stored in the t period；Δ t is the t period time interval to the t+1 period；P_abs(t) For t period accumulation of energy power, P_releaT () is t period exoergic power；μ is that energy storage equipment self dissipates the loss of energy or from damaging to environment The energy coefficient of consumption；η^absFor the energy storage efficiency of energy storage equipment, η^releaFor energy storage equipment exergic efficiency.

Step (3), set up other auxiliary powering device energy supply model:

For the sake of Jian Dan, it is believed that the work efficiency of other auxiliary powering device is constant, and its energy supply mathematical model can be attributed to defeated Go out heat (cold) and measure and input energy (fuel of consumption, electricity or waste heat amount) holding linear relationship, can be expressed as

${\mathrm{\η}}_b=\frac{H_{aux,out}}{H_{aux,in}}$

Constraints is: 0≤H_aux,out≤H_aux,outmax。

In formula, H_aux,outFor heat (cold) amount of auxiliary powering device output, unit is kW；H_aux,inDefeated for auxiliary powering device The energy entered, unit is kW；η_auxEfficiency of energy utilization or energy efficiency coefficient COP for equipment.

Wherein, analyzing each user impact on network energy loss, its step includes:

Step (1), the garden selecting needs to optimize are topological；

Step (2), determine Campus Network Parameter Conditions and each node users type and average load；

Step (3), selection to produce interactive energy source station access point with garden；

Step (4), analyzed the maximum power exchange power of energy source station access point and whole garden by continuous tide；

Step (5), the whole network energy loss caused by user in the whole network line losses management function in object function is equivalent to The exchange of electric power value of user and energy network and network loss factor to affect product, analyzed by continuous tide, determine factor to affect Value.

The loss calculation formula of every circuit of the whole network is:

$S=\frac{\mathrm{\Δ}U\×{\mathrm{\ΔU}}^{*}}{Z^{*}}$

The network loss of all circuits is overlapped, obtains the whole network network loss.

Wherein, revising constraints and the object function of model from the angle of Practical, its step includes:

Step (1), row write object function:

Object function adds trend Web-based exercise, new target on the basis of overall situation garden day operation total cost minimum Function Synthesis total cost includes that the mutual power expense of system operation and maintenance cost, fuel cost, electrical network, the whole network are gained merit Trend Web-based exercise and the whole network reactive power flow punishment cost.

Min price=min (pri_Grid+pri_fuel+pri_maintain+pri_loss+pri_punishment)

Wherein, the electric energy switching cost function computing formula of intelligent grid garden and external electrical network is as follows:

${\mathrm{pri}}_{Grid}=\underset{1}{\overset{24}{\Σ}}{c}_{Grid}^t\×P_{Grid}^t$

In formula,Be by time electricity price；Be intelligent grid garden and external electrical network by time exchange of electric power value.

As a example by gas turbine and gas fired-boiler, row write miniature gas turbine and the fuel of gas fired-boiler in intelligent grid garden Cost function computing formula is as follows:

${\mathrm{pri}}_{fuel}=\underset{t=1}{\overset{24}{\Σ}}\underset{i=1}{\overset{n_{CHP}}{\Σ}}{c}_{Gas}^t\×f_{CHPi}\left(P_i^t\right)+\underset{t=1}{\overset{24}{\Σ}}\underset{i=1}{\overset{n_{boiler}}{\Σ}}{c}_{Gas}^t\×H_{boileri}^t/{\mathrm{\η}}_{boileri}$

In formula, f_CHPiFor miniature gas turbine about power and the function of use combustion gas, unit calculates with kW；P_iIt is The electrical power output of i platform miniature gas turbine, unit is kW；Be by time gas price；It is i-th gas fired-boiler Exert oneself；η_boileriIt it is the energy conversion efficiency of i-th gas fired-boiler；T is time span, and unit is hour.Due to intelligence In energy electrical network garden, powering device is numerous, the fuel cost function computing formula of remaining equipment and gas turbine and gas fired-boiler Similar, can increase on the basis of the model of Optimized Operation between original single user and different complete cross section user.

As a example by gas turbine, Distributed-generation equipment, energy storage device and electric heating conversion equipment, row write intelligent grid garden The maintenance cost function computing formula of energy resource system is as follows:

$\begin{array}{c}{\mathrm{pri}}_{ma\mathrm{int}ain}=\underset{t=1}{\overset{24}{\Σ}}\underset{i=1}{\overset{n_{CHP}}{\Σ}}{p}_{mCHPi}\×P_i^t+\underset{t=1}{\overset{24}{\Σ}}\underset{i=1}{\overset{n_{distri}}{\Σ}}{p}_{mdistri}\×P_{distri}^t\\ +\underset{t=1}{\overset{24}{\Σ}}{p}_{mstor}\×H_{in}^t+\underset{t=1}{\overset{24}{\Σ}}{p}_{mstor}\×H_{out}^t+\underset{t=1}{\overset{24}{\Σ}}{p}_{mEH}\×P_{EH}^t\end{array}$

In formula, p_mCHPiUnit power operation expense for miniature gas turbine；p_mdistriFor Distributed-generation equipment list Position Power operation maintenance cost；p_msstorUnit power operation expense for hot energy storage device；p_mEHChange for electric heating The unit power operation expense of equipment；P_iBeing the electrical power output of i-th miniature gas turbine, unit is kW； Being the electrical power output of i-th distributed power source, unit is kW；WithIt is respectively the charge and discharge heat of hot energy storage device Power, unit is kW；For the power of electric heating conversion equipment, unit is kW.Owing in intelligent grid garden, energy supply sets Standby numerous, the operation expense function computing formula of remaining equipment is similar with above-mentioned formula, can be in original single user And increase on the basis of the model of Optimized Operation between difference complete cross section user.

For the whole network trend active power loss cost function, can be described as:

${\mathrm{pri}}_{loss}=\underset{1}{\overset{24}{\Σ}}{c}_{Pi}^t\×c_{PGrid}^t\×P_{Grid}^t$

In formula,Active power by time electricity price；It is that user exchanges power at i-th access point with public distribution The loss factor afterwards system caused,Be user and external electrical network by time exchange of electric power value.

Network loss punishment cost function idle for the whole network trend, can be described as:

In formula,Active power by time electricity price；It is that user exchanges power at i-th access point with public distribution The loss factor afterwards system caused,It is that system is made after public distribution exchange power by user at i-th access point The idle loss factor become,Be user and external electrical network by time exchange of electric power value,It it is rated power factor mark Quasi-value,It is to produce the coefficient of punishment/incentive fees based on actual power factor and the difference of rated power factor.

Step (2), row write system constraints；

According to preceding factors, revising and set up system and run constraints on the basis of original model, system runs constraint bar Part adds user and distribution interaction tide on the basis of the overall situation garden day operation place capacity constraint, power-balance constraint etc. Flowing the most out-of-limit constraint and the constraint of garden the whole network voltage bound, the constraints that the global optimization of intelligent grid garden runs is concrete As follows:

(1) electrical power equilibrium constraint computing formula is as follows:

$\underset{i=1}{\overset{n_{CHP}}{\Σ}}{P}_i^t+\underset{i=1}{\overset{n_{distri}}{\Σ}}{P}_{distri}^t+P_{Grid}^t+P_{stor}^t=P_{Load}^t+P_{cond}^t$

In formula,Be co-feeding system and external electrical network by time exchange of electric power value；For load value；For accumulator Power；For family air-conditioning power；For Distributed-generation equipment power；It it is i-th miniature gas turbine Generated output.

(2) heating power balance constraint function:

For thermic load, literary composition being thought, the energy transmission medium of waste heat boiler, gas fired-boiler and hot energy storage device is High-temperature-hot-water, the hot water that when meeting Space Thermal load prepared by these three equipment carries out heat by radiator and surrounding space Exchange, family air-conditioning is then changed by electric heating and is met Space Thermal load, it is impossible to prepare hot water.Meeting Space Thermal During supply on load and two aspects of hot water load, due to by waste heat boiler, gas fired-boiler and the heat of hot energy storage device The each personal variable of power represents, only meets Space Thermal load and hot water load's constraint is likely to result in total hot merit Rate supply constraint is unsatisfactory for, and therefore thermic load supplies restrained deformation is the supply constraint of hot water power and total thermal power supply Constraint, meets these 2 rear space thermic load supply constraints and automatically derives satisfied.

Total thermal power supply constraint computing formula is as follows:

$\underset{i=1}{\overset{n_{CHP}}{\Σ}}{H}_i^t+\underset{i=1}{\overset{n_{boiler}}{\Σ}}{H}_{boileri}^t+{\mathrm{COP}}_{cond}\×P_{cond}^t+H_{out}^t-H_{in}^t\≥H_{Space}^t+H_{Water}^t$

Hot water supply constraints computing formula is as follows:

$\underset{i=1}{\overset{n_{CHP}}{\Σ}}{H}_i^t+\underset{i=1}{\overset{n_{boiler}}{\Σ}}{H}_{boileri}^t+H_{out}^t-H_{in}^t\≥H_{Water}^t$

In formula,It it is the calorific value that reclaimed by waste heat boiler of i-th miniature gas turbine；For i-th gas-fired boiler The heat production value of stove；WithIt is power input and the output of hot energy storage device respectively；COP_condEfficiency for air-conditioning equipment Coefficient；WithBe respectively in micro power source net by time space thermic load and hot water load.

(3) cold power-balance constraint function:

For refrigeration duty, literary composition being thought, the energy of absorption refrigeration unit, electric refrigerating machine and cold energy storage device passes Passing medium is low-temperature cold water, the low-temperature cold water that when meeting space refrigeration duty prepared by these three equipment by fan coil and Surrounding space carries out heat exchange, and family air-conditioning is then changed by electric energy and met space refrigeration duty, it is impossible to prepare cold Water.When the supply met in space refrigeration duty and freezing two aspects of cooling load, due to by Absorption Refrigerator The each personal variable of the cold power of group, electric refrigerating machine and cold energy storage device represents, only meet space refrigeration duty and Freezing cooling load constraint is likely to result in the supply constraint of total cold power and is unsatisfactory for, and therefore refrigeration duty is supplied restrained deformation Supply for freezing refrigeration work consumption supply and total cold power, meet these 2 rear space refrigeration duty supply constraints and automatically derive Meet.

Total cold power supply constraint computing formula is as follows:

$\underset{i=1}{\overset{n_{CHP}}{\Σ}}{C}_i^t+\underset{i=1}{\overset{n_{boiler}}{\Σ}}{C}_{chil}^t+{\mathrm{EER}}_{cond}\×P_{cond}^t+C_{out}^t-C_{in}^t\≥C_{Space}^t+C_{\mathrm{Re}fri}^t$

Freezing refrigeration work consumption supply constraint computing formula is as follows:

$\underset{i=1}{\overset{n_{CHP}}{\Σ}}{C}_i^t+\underset{i=1}{\overset{n_{boiler}}{\Σ}}{C}_{chil}^t+C_{out}^t-C_{in}^t\≥C_{\mathrm{Re}fri}^t$

In formula,It it is the cold that manufactured by absorption refrigeration unit of i-th miniature gas turbine；For electric refrigerating machine The cold water value produced；WithIt is power input and the output of cold energy storage device respectively；EER_condFor air-conditioning equipment Refrigeration efficiency ratio；WithBe respectively micro power source system by time space refrigeration duty and freezing cooling load.

(4) miniature gas turbine and the capacity constraint function of gas fired-boiler in intelligent grid garden:

In intelligent grid garden, facility constraints function computing formula is as follows:

For miniature gas turbine:

$P_i^{\min }\≤P_i^t\≤P_i^{\max },i\∈n_{CHP}$

For gas fired-boiler:

$0\≤H_{boileri}^t\≤H_{boileri}^{\max },i\∈n_{boiler}$

For electric heating conversion equipment:

$0\≤P_{EH}^t\≤P_{EH}^{\max }$

For hot energy storage device:

$0\≤H_{in}^t\≤H_{in}^{\max }$

$0\≤H_{out}^t\≤H_{out}^{\max }$

$S_{stor}^{\min }\≤S_{stor}^t\≤S_{stor}^{\max }$

In formula,WithHeat accumulation equipment for t inputs and output,WithDefeated for heat accumulation equipment Enter and the output limit,Lotus Warm status for heat accumulation equipment；

The charge and discharge Warm status that heat accumulation equipment describes is a dynamic process, is shown below:

$S_{stor}^t={\mathrm{\η}}_{stor}\×S_{stor}^{t-1}+{\mathrm{\η}}_{in}\×H_{in}^t-H_{out}^t$

(5) user and the distribution the most out-of-limit constraint of interaction trend and the constraint of garden the whole network node voltage bound:

For user and the distribution the most out-of-limit constraint of interaction trend, formula is as follows:

$P_{ij}^{\min }\≤P_{ij}^t\≤P_{ij}^{\max },i,j\∈n_{node}$

$Q_{ij}^{\min }\≤Q_{ij}^t\≤Q_{ij}^{\max },i,j\∈n_{node}$

In formula,For the Line Flow active power of t Campus Network node i to node j, unit is kW； WithFor the Line Flow active power minimum and maximum limit value of Campus Network node i to node j, unit is kW； For the Line Flow reactive power of t Campus Network node i to node j, unit is kVar,WithFor Campus Network node i is to the Line Flow reactive power minimum and maximum limit value of node j, and unit is kVar；n_nodeFor garden The whole nodes in district；T is time span, and unit is hour.

Retraining for garden the whole network node voltage bound, formula is as follows:

$U_i^{\min }\≤\left|U_i^t\right|\≤U_i^{max},i\∈n_{node}$

Step (3), simplification constraints；

According to aforementioned trifle, it is necessary to considering that the energy network economic optimization scheduling model that high density distributed photovoltaic is dissolved enters Row amendment, the impact of trend constraint from the angle embodiment model of Practical, the impact of reflecting regional network loss, The problem simultaneously avoiding the occurrence of again the dimension calamity of variable and constraints.Used with each typical case by the polymorphic type energy under the whole network angle In supply and demand characteristic between the energy consumption system of family user perform dispatch command in the influence on tidal flow of the whole network it can be seen that user perform After dispatch command, the impact on garden the whole network trend is mainly reflected in the Tie line Power with garden, i.e. user and outside The power interaction value of network.The civil power that user buys is the most, and the load of node equivalent is the biggest, and the Voltage Drop of network is more Substantially.For whole radial network, as long as ensureing that the node voltage of several least significant end is the most out-of-limit, it is ensured that entirely The voltage levvl of net is the most out-of-limit.

Performed during the influence on tidal flow of the whole network is calculated by dispatch command by user it can be seen that user to buy civil power the most, end Voltage is the lowest, and Line Flow load is the biggest.By concrete example topology is carried out continuous tide calculating, show that user is with outer The higher limit of the mutual electricity of portion's electrical network.Therefore, trend constraint can turn to:

$P_{iGrid}^{\min }\≤P_{iGrid}^t\≤P_{iGrid}^{\max },i\∈n_{customer}$

In formula,For t Campus Network user i and the active power interaction value of network, unit is kW；WithActive power minimum and maximum limit for garden user i after continuous tide calculates with the mutual electricity of external electrical network Value, unit is kW；T is time span, and unit is hour.

Step (4), row write method for solving；

Optimized model owing to setting up is a numerous and jumbled nonlinear model containing multidimensional variable, therefore uses for non-linear Optimized model is solved by the interior point method of optimization problem, is specifically expressed as:

$\left\{\begin{array}{cc}\min & f\left(x\right)\\ s.t.& c\left(x\right)\≤0\\ & ceq\left(x\right)=0\\ & Ax\≤b\\ & A_{eq}x=b_{eq}\\ & lb\≤x\≤ub\end{array}\right.$

Finally, obtain operation plan a few days ago according to solving result, carry out energy scheduling according to above-mentioned plan.

Embodiment: such as the flow chart of Figure 19, first energy supply and the energy storage system of catergories of user in garden energy network are entered Row is analyzed, and sets up the energy supply mathematical model of each equipment, such as Fig. 1, shown in 2；Energy supply district system topology schematic diagram and district Territory energy supplying system distribution wiring diagram, such as Fig. 3, shown in 4.Consideration is set up highly dense based on the energy exchange network concentrating interconnection The energy network economic optimization scheduling model that degree distributed photovoltaic is dissolved, uses interior point method to carry out model for model characteristics Solve.By the method for operation of each powering device in dispatcher-controlled territory energy internet and exerting oneself, thus realize cold and hot Electricity Federation Economic optimization for type micro power source net runs；Said process is referring specifically to Summary.

Fig. 5-Fig. 7 is all types of user typical case day electric heating cold prediction load curve in the present invention, carries out model based on interior point method Solving, obtain such as the simulation result of Fig. 8-Figure 18, from Fig. 8-Figure 18, the energization schemes after optimization can meet Whole energy demands of Regional Energy internet, do not have in system and abandon light, abandon heat, abandon situation that is cold and that abandon useless cigarette Occurring, the Regional Energy internet energization schemes of optimized mistake has the feature that

(1) energy source station carries out energy supply to public organizations group, and photovoltaic exerts oneself full according to prediction, to reduce miniature energy Supply of electric power demand in the net of source；Accumulator is higher due to use cost, although there being the effect of peak load shifting, but from entirely Being unfavorable for the economic load dispatching of micro power source net from the point of view of in office, therefore be not engaged in actual motion, Space Thermal load is by air conditioning system Supply system combined with miniature gas turbine, is all provided by air-conditioning in low ebb electricity price time space thermic load, part crest segment electricity The valency moment is provided by the waste heat boiler in miniature gas turbine system, owing to using natural gas higher for level Waste Heat Price, and gas-fired boiler Stove operating scheme the most on the schedule, hot water load is met by waste heat boiler and hot energy storage device, hot storage energy operation cost Relatively low, it is suitable for substituting accumulator storage and undertakes the effect of peak load shifting, similarly, since use natural gas supplying hot water price higher, Gas fired-boiler also excludes hot water supply prioritization scheme, space refrigeration duty by air conditioning system, miniature gas turbine system and Compression electric refrigerating machine joint supply, is all carried by air-conditioning and compression electric refrigerating machine in low ebb electricity price time space refrigeration duty Confession, remaining moment is provided by the lithium bromide absorption refrigerating set in miniature gas turbine system, when suction-type lithium bromide system During cold group refrigerating capacity deficiency, family air conditioning system serves as space refrigeration peak regulation equipment, and freezing cooling load is inhaled by lithium bromide Receipts formula refrigeration unit, compression electric refrigerating machine and cold energy storage device meet, and the moment gas turbine at the non-low ebb of electricity price goes out Power is relatively big, and useless cigarette is more, and cooling and heating load is limited, therefore utilizes cold energy storage device to store cold water, at refrigeration duty liter Carry out released cold quantity time high, play the effect of peak load shifting.Cold and hot energy storage device energy storage capacity within the whole Optimized Operation cycle All not less than limit value, due to the reason of cost, accumulator is not considered into Optimized Operation scheme, and cold and hot energy storage device runs Cost is relatively low, is especially suitable for substituting accumulator and undertakes the task of peak load shifting, to reduce the purpose of systematic running cost.

(2) refrigeration duty of data center has greatly by the heat pump generation cold supply of energy source station, typical case's day 1:00 The electric power not enough to 9:00,16:00 and 17:00 and these time periods of 22:00 to 24:00 is bought from bulk power grid, and And certain several time period accumulator is in the state of charging, this is relevant with Spot Price.In several periods that electricity price valency is high, Do not buy electric power from electrical network.Wherein, heat pump and electric refrigerating machine consume electric energy, for negative value.Owing to typical case arrives day 1:00 The electricity price of 9:00,16:00 and 17:00 and these time periods of 22:00 to 24:00 is relatively low, and electric refrigerating machine assume responsibility for this Most of refrigeration duty of several time periods.Owing to the output cold of electric refrigerating machine has the upper limit, so another part refrigeration duty Exported cold by the heat pump of energy source station to undertake.In other periods, electricity price is higher, so for the economy run, cold negative Lotus major part is undertaken by the heat pump of energy source station.

(3) for Industry enterprise customer, within 1 to 7 period and 23 to 24 periods, owing to network load is in The low ebb phase, inexpensively, miniature gas turbine is in suspended state to electricity price, is not providing three kinds of workload demands of cool and thermal power, Section at this moment, electric energy mainly buys from public network, and thermic load is mainly provided by air-conditioning, refrigeration duty mainly by energy source station energy storage and Electric refrigerating machine provides.Electric load balance aspect, within 8 to 22 periods, the electrical load requirement of public network is relatively big, and electricity price is relatively For costliness, section miniature gas turbine is substantially at rated operation the most at this moment, and not enough electric load is bought to public network.

Heat load balance aspect, thermic load is mainly provided by miniature gas turbine and air conditioning system, and refrigeration duty is mainly by combustion gas Turbine provides, and electric refrigerating machine meets the cold demand of part in 2 to 7 periods.Energy storage device is in energy storage state by day, Night is in energised state, so that it is guaranteed that keep high efficiency when miniature gas turbine works by day.Owing to gas turbine produces Raw waste heat is insufficient for the thermal load demands of user, therefore gas fired-boiler is in the state that do not enables.This industrial development and life Produce enterprise's major part energy and broadly fall into self-sufficiency through productive labour form, after participating in energy global optimization scheduling, it is possible to effectively utilize The energy storage device of energy source station around, strengthens the conversion space utilisation of the energy, thus reaches the target of improving energy efficiency.

Table 1

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The above is only the preferred embodiment of the present invention, it should be pointed out that: for those skilled in the art For, under the premise without departing from the principles of the invention, it is also possible to make some improvements and modifications, these improvements and modifications are also Should be regarded as protection scope of the present invention.

Claims (5)

1. the Regional Energy network optimization dispatching method dissolved towards high density distributed photovoltaic, it is characterised in that highly dense The Regional Energy net that degree distributed photovoltaic is dissolved includes powering device, energy storage device and auxiliary powering device；Described energy storage sets For including cold energy storage device, hot energy storage device and electricity energy storage device；Described auxiliary powering device includes that gas fired-boiler, family are used Air-conditioning, absorption refrigeration unit and renewable energy power generation equipment；

Powering device and the energy storage device of user in garden energy network are analyzed, set up energy supply model；Based on concentration The energy exchange network of interconnection, it is considered to the energy network economic optimization that high density distributed photovoltaic is dissolved dispatches described energy supply mould Type；Analyze the impact that energy exchange network is lost by each user, the trend of energy exchange network is retrained and is equivalent to user Retrain with the exchange of electric power of energy exchange network, by object function, in the whole network line losses management function by user cause complete Network energy loss is equivalent to exchange of electric power value and the network loss factor to affect product of user and energy exchange network, special for model Property use interior point method model is solved；By the method for operation of all types of user powering device in scheduling garden with exert oneself, The economic optimization realizing whole garden energy exchange network runs.

2. the Regional Energy network optimization dispatching method dissolved towards high density distributed photovoltaic as claimed in claim 1, It is characterized in that, be analyzed comprising the following steps to powering device and the energy storage device of user in Regional Energy network:

Step one) set up powering device model；

Described powering device is miniature gas turbine system, and described miniature gas turbine system includes some miniature steam turbines Machine；Described miniature steam turbine system selects unit to open number of units according to the power generation command value of scheduling, and the unit having turned on is average Sharing electric load, by fitting of a polynomial, obtain the efficiency of miniature gas turbine system and go out force function, its parameter is as follows:

${\mathrm{\η}}_{C1000}=f\left(P\right)=\underset{i=1}{\overset{16}{\Σ}}{p}_i{\left(\frac{P}{P_{max}}\right)}^{16-i}$

In formula:

η_C1000For: the miniature gas turbine system efficiency when exerting oneself as P；

P_maxFor: power-handling capability；

F (P) is: system go out force function；

p_iFor: go out the every coefficient of force function；

Step 2): set up the energy supply model of energy storage device；

$E(t+1)=E\left(t\right)\·(1-\mathrm{\μ})+({\mathrm{\η}}^{abs}\·P_{abs}(t)-\frac{1}{{\mathrm{\η}}^{relea}}\·P_{relea}(t\left)\right)\·\mathrm{\Δ}t$

In formula:

E (t) is the energy that energy storage device stored in the t period；

Δ t is the t period time interval to the t+1 period；

P_absT () is t period energy storage power；

P_releaT () is t period exoergic power；

μ is that energy storage device self dissipates the loss of energy or the energy coefficient from loss to environment；

η^absFor the energy storage efficiency of energy storage device,

η^releaFor energy storage device exergic efficiency.

Step 3): set up auxiliary equipment energy supply model:

${\mathrm{\η}}_b=\frac{H_{aux,out}}{H_{aux,in}}$

Constraints is: 0≤H_aux,out≤H_aux,outmax；

In formula:

H_aux,outFor the hot/cold amount of auxiliary powering device output, unit is kW；

H_aux,inFor the energy of auxiliary powering device input, unit is kW；

η_auxEfficiency of energy utilization or energy efficiency coefficient COP for equipment.

3. the Regional Energy network optimization dispatching method dissolved towards high density distributed photovoltaic as claimed in claim 1, It is characterized in that, analyze the impact that energy exchange network is lost by each user and comprise the following steps:

Step one), select need optimize garden topology；

Step 2), determine Campus Network Parameter Conditions and each node users type and average load；

Step 3), select to produce interactive energy source station access point with garden；

Step 4), analyzed the maximum power exchange power of energy source station access point and whole garden by continuous tide；

Step 5), the whole network energy loss caused by user in the whole network line losses management function in object function is equivalent to The exchange of electric power value of user and energy network and network loss factor to affect product, analyzed by continuous tide, determine factor to affect Value；The loss S computing formula of each circuit in the whole network is:

$S=\frac{\mathrm{\Δ}U\×{\mathrm{\ΔU}}^{*}}{Z^{*}}$

Wherein,

Δ U is poor for the end-point voltage of circuit i；

ΔU^*Conjugation for Δ U；

Z^*For the conjugation of the impedance of circuit i, unit is Ω；

The network loss of all circuits is overlapped, obtains the whole network network loss.

4. the Regional Energy network optimization dispatching method dissolved towards high density distributed photovoltaic as claimed in claim 1, It is characterized in that, the whole network effective power flow Web-based exercise and the whole network reactive power flow punishment cost function are analyzed:

For the whole network trend active power loss cost function, can be described as:

${\mathrm{pri}}_{loss}=\underset{1}{\overset{24}{\Σ}}{c}_{Pi}^t\×c_{PGrid}^t\×P_{Grid}^t$

In formula,Active power by time electricity price；It is that user exchanges power at i-th access point with public distribution The loss factor afterwards system caused,Be user and external electrical network by time exchange of electric power value.

Network loss punishment cost function idle for the whole network trend, can be described as:

In formula,Active power by time electricity price；It is that user exchanges power at i-th access point with public distribution The loss factor afterwards system caused,It is that system is made after public distribution exchange power by user at i-th access point The idle loss factor become,Be user and external electrical network by time exchange of electric power value,It it is rated power factor mark Quasi-value,It is to produce the coefficient of punishment/incentive fees based on actual power factor and the difference of rated power factor.

5. the Regional Energy network optimization dispatching method dissolved towards high density distributed photovoltaic as claimed in claim 1, It is characterized in that, the trend of described energy exchange network is constrained to:

$P_{iGrid}^{\min }\≤P_{iGrid}^t\≤P_{iGrid}^{\max },i\∈n_{customer}$

In formula,For t Campus Network user i and the active power interaction value of network, unit is kW；WithActive power minimum and maximum limit for garden user i after continuous tide calculates with the mutual electricity of external electrical network Value, unit is kW；T is time span, and unit is hour.