CN105869075A - Economic optimization scheduling method for cold, heat and electricity combined supply type miniature energy grid - Google Patents

Abstract

The invention discloses an economic optimization scheduling method for a cold, heat and electricity combined supply type miniature energy grid. Various energy supply and energy storage devices in the cold, heat and electricity combined supply type miniature energy grid are analyzed, energy flow models of the devices are established, and a framework of the cold, heat and electricity combined supply type miniature energy grid is constructed based on energy exchange networks connected in a centralized mode; an economic optimization scheduling model of the cold, heat and electricity combined supply type miniature energy grid is established centered on the framework, and the model is solved with an interior point method based on Hessian matrix iteration according to the characteristics of the model. By scheduling the operation mode and output of all the energy supply devices in a smart grid park, economic optimization operation of the cold, heat and electricity combined supply type miniature energy grid is achieved. The energy optimization scheduling method is effective, practical and scientific, and application and popularization of energy conservation are promoted.

Description

A kind of supply of cooling, heating and electrical powers type micro power source net economic optimization dispatching method

Technical field

The present invention relates to technical field of power systems, be specifically related to a kind of supply of cooling, heating and electrical powers type micro power source net economic optimization and adjust Degree method.

Background technology

Promote production of energy and consumption revolution, increasing energy-saving and emission-reduction dynamics and preventing and treating haze under the new situation in country, promote Efficiency of energy utilization, reduces pollutant emission and has become as the common recognition of government, enterprise and the common people.CCHP(Combined Cooling Heating and Power) cogeneration cooling heating system comprises three kinds of energy of cool and thermal power, utilizes technology of Internet of things and letter Breath technology is to all powering device unified integrations in region and implements scheduling, to reach to carry out excellent to region cool and thermal power load Change energy supply, the effect of improving energy efficiency.The concept of micro-capacitance sensor is expanded to micro power source net by cool and thermal power energy mix co-generation system, With its efficient efficiency of energy utilization, the energy supply pattern of flexibility and reliability become realize production of energy and consumption transition, Promote comprehensive utilization rate of energy source, become the important means solving energy environment issues.

At present, the actual application of external CCHP system is in the starting stage, wherein the most famous external having UNIVERSITY OF CALIFORNIA, DAVIS (UCD) project and UNIVERSITY OF NEW MEXICO (UNM) project such as, domestic the most famous have animation garden, nascent state city cooling-heating treatment project etc. in Tianjin.The most right CCHP systematic research has certain research, such as, give constant volume addressing in cogeneration type micro-capacitance sensor facility planning aspect Plan model, and with particle cluster algorithm and differential evolution algorithm, model is solved；Towards commercial affairs building energy supplying system, Construct the Optimized model of CCHP system with the minimum target of operating cost, and with CPLEX, it solved, Result show to apply CCHP system after these commercial affairs building can save every year 11% operating cost, and reduce the two of 8% Carbonoxide discharges, but its object of study is only for commercial affairs building, and the replicability of conclusion is the strongest.

Domestic aspect, existing study general is for by Wind turbines, photovoltaic cell, fuel cell, waste heat boiler, combustion gas The cogeneration type micro-grid system of boiler, accumulator and thermoelectricity load structure, it is contemplated that wind-powered electricity generation, photovoltaic power and thermoelectricity are negative The randomness of lotus, proposes a kind of particle group optimizing (PSO) Algorithm for Solving model based on stochastic simulation technology, to this model Solved, but its object of study is only cogeneration type micro-grid system, do not consider to dispatch cold energy source to system energy efficiency Castering action；Or classify according to electricity, flue gas, steam, 5 kinds of energy transmission forms of water and air, use and concentrate mother The mode of line has built the basic boom of CCHP micro-grid system.Rely on this framework that each equipment is carried out Independent modeling, build Found the 0-1 MILP model of CCHP micro-grid system dynamic economic dispatch a few days ago, by regulation system Each equipment method of operation and duty, it is achieved the economical operation of system.But its thermic load is subdivided into hot water load and During air thermic load, refrigeration duty is not finely divided, and the energy storage medium of cold-storage device is usually chilled water, if by sky Between refrigeration duty and cold water load control it and the optimization of model can be produced deviation as a class and with cold-storage device.

Summary of the invention

Goal of the invention: in order to overcome the deficiencies in the prior art, the present invention provides a kind of supply of cooling, heating and electrical powers micro power source Net economic optimization dispatching method, in intelligent grid garden, plurality of energy supplies and energy storage system are analyzed, and set up the energy of each equipment Amount flow model, in scheduling supply of cooling, heating and electrical powers micro power source net, multiclass powering device and energy storage device are to having multiple load User carries out supply of cooling, heating and electrical powers；Solve the deficiencies in the prior art.

Technical scheme: for achieving the above object, the technical solution used in the present invention is: a kind of miniature energy of supply of cooling, heating and electrical powers type Source net economic optimization dispatching method, it is characterised in that include in the supply of cooling, heating and electrical powers micro power source net of the method powering device, Energy storage device and auxiliary powering device；Described energy storage device includes 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 power generation equipment；

Powering device in supply of cooling, heating and electrical powers micro power source net and energy storage device are analyzed, set up the energy stream of each equipment Movable model；Build the framework of supply of cooling, heating and electrical powers type micro power source net based on energy exchange network, set up cold around described framework Cogeneration type micro power source net economic optimization scheduling model；And to model solution.

Further, described powering device and energy storage device are analyzed, and the energy Flow model setting up each equipment specifically wraps Include following steps:

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:

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

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.

Further, the method includes that row write optimization object function and system constraints, specifically includes:

Step one): row write optimization object function: described optimization aim includes co-feeding system miniature gas turbine and gas-fired boiler The electric energy switching cost of the fuel cost of stove, co-feeding system and electrical network and the operation expense of co-feeding system；Function is as follows Formula:

Minprice=min (pri_fuel+pri_grid+pri_maintain)

Wherein, the fuel cost function computing formula of co-feeding system miniature gas turbine and gas fired-boiler is as follows:

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

In formula:

f_CHPiFor miniature gas turbine consumption characteristic curve function, unit is all converted as kW；

P_iBeing the electrical power output of i-th miniature gas turbine, unit is kW；

Be by time gas price, unit by heating value of natural gas conversion be；

Being the consumption of i-th gas fired-boiler t period, unit by heating value of natural gas conversion is；

T is time period sequence number, and unit is hour；

Co-feeding system is as follows with the electric energy switching cost function computing formula of electrical network:

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

In formula:

Be by time electricity price；

Be co-feeding system and external electrical network by time exchange of electric power value.

The operation expense function computing formula of co-feeding system is as follows:

$\begin{array}{c}{\mathrm{pri}}_{ma\mathrm{int}ain}=\underset{t=1}{\overset{24}{\mathrm{\Σ}}}\underset{i=1}{\overset{n_{CHP}}{\mathrm{\Σ}}}{p}_{mCHPi}\×P_i^t\\ +\underset{t=1}{\overset{24}{\mathrm{\Σ}}}\underset{i=1}{\overset{n_{distri}}{\mathrm{\Σ}}}{p}_{mdistri}\×P_{distri}^t+\underset{t=1}{\overset{24}{\mathrm{\Σ}}}{p}_{mstor}\×H_{in}^t\\ +\underset{t=1}{\overset{24}{\mathrm{\Σ}}}{p}_{mstor}\×H_{out}^t+\underset{t=1}{\overset{24}{\mathrm{\Σ}}}{p}_{mGB}\×Q_{GB}^t\\ +\underset{t=1}{\overset{24}{\mathrm{\Σ}}}{p}_{mEB}\×Q_{EB}^t+\underset{t=1}{\overset{24}{\mathrm{\Σ}}}{p}_{mAC}\×P_{AC}^t\\ +\underset{t=1}{\overset{24}{\mathrm{\Σ}}}{p}_{mEC}\×P_{chil}^t+\underset{t=1}{\overset{24}{\mathrm{\Σ}}}{p}_{mAir}\×P_{cond}^t\end{array}$

In formula, p_mCHPiUnit power operation expense for gas turbine；p_mdistriFor Distributed-generation equipment specific work Rate operation expense；p_mstorRefer to the unit power operation expense of energy storage device；p_mGBList for gas fired-boiler Position Power operation maintenance cost；p_mEBUnit power operation expense for waste heat boiler；p_mACFor Absorption Refrigerator Unit power operation expense；p_mECUnit power operation expense for compression electric refrigerating machine；p_mAirFor The unit power operation expense of air conditioning system；P_iBeing the electrical power output of i-th miniature gas turbine, unit is kW；Being exerting oneself of i-th distributed generation system, unit is kW；WithRefer to three class energy storage devices respectively Charge and discharge energy power, unit is kW；For the power that heats of gas fired-boiler, unit has been scaled to kW；For waste heat pot Stove heat power, unit has been scaled to kW；For the refrigeration work consumption of Absorption Refrigerator, unit has been scaled to kW；For the operation power of compression electric refrigerating machine, unit is kW；Running power for air conditioning system, unit is kW.

Step 2), row write system constraints: described system constraints includes power-balance constraint, place capacity about Bundle and equipment run constraint；The constraint of described power-balance includes electrical power Constraints of Equilibrium, hot general power Constraints of Equilibrium, hot water Constraints of Equilibrium, cold total power constraint, freezing refrigeration work consumption Constraints of Equilibrium, place capacity constraint and equipment run constraint；

Described electrical power equilibrium constraint computing formula is as follows:

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

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

Described total thermal power equilibrium constraint computing formula is as follows:

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

Heat-water balance's constraints computing formula is as follows:

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

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；COP_cond、η_in、η_houtIt is respectively energy efficiency coefficient and the charge and discharge thermal efficiency of hot energy storage of air-conditioning equipment；WithBe respectively co-feeding system by time space thermic load and hot water load；

Total cold power-balance constraints computing formula is as follows:

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

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

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

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, EER_cond、η_in、η_coutIt is respectively the refrigeration efficiency of air-conditioning equipment than the cold effect of charge and discharge with cold energy storage Rate；WithBe respectively micro power source system by time space refrigeration duty and freezing cooling load；

In system, place capacity constraint computing formula is as follows:

For miniature gas turbine:

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

For gas fired-boiler:

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

For air-conditioning equipment:

$0\≤P_{cond}^t\≤P_{cond}^{\max }$

For hot/cold 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,WithHot/cold energy storage device for t inputs and output,WithFor heat/ The input of cold energy storage device and the output limit,Lotus Warm status for hot/cold energy storage device；

The charge and discharge hot/cold state that hot/cold energy storage device describes is a dynamic process, is shown below:

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

Further, the method includes the method for solving of model:

The canonical form of solving model is

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

Interior point method based on Hessian matrix iteration constructs during solving and meets Karush-Kuhn-Tucker bar The Lagrangian auxiliary function of part:

L (x, λ)=f (x)+∑ λ_c,ic_i(x)+∑λ_A,i(Ax-b)

+∑λ_ceq,iceq_i(x)+∑λ_Aeq,i(A_eqx-b_eq)

Second order derived function Hessian matrix in its update equation formula is:

$\begin{array}{c}{\▿}_{xx}^{2}L(x,\mathrm{\λ})={\▿}^{2}f\left(x\right)+{\mathrm{\Σ\λ}}_i{\▿}^2{c}_i\left(x\right)\\ +{\mathrm{\Σ\λ}}_i{\▿}^2{\mathrm{ceq}}_i\left(x\right)\end{array}$

By matrix in update equation group is converted, LDL^TDecompose and back substitution revising, it is achieved iterative, It is automatically stopped iteration after meeting the condition of convergence；

In formula, variable x to be optimized is that n ties up variable, including gas turbine even for the exerting oneself by a hour cool and thermal power of system, Auxiliary type powering device by hour exerting oneself, all kinds of energy storage device by hour exerting oneself, the SOC state of energy storage and with What electrical network was mutual purchases electricity sales amount；In constraints, inequality constraints is the function constraint of exerting oneself of miniature gas turbine, linear portions Divide and have p line style inequality constraints and q line style equality constraint, including powering device units limits, cool and thermal power load about Bundle and the constraint of energy storage SOC bound；λ_c,iλ_A,iλ_ceq,iλ_Aeq,iFor Lagrange multiplier, dimension and respective constraint Condition number is identical；X, lb, ub are n dimensional vector, and b is p dimensional vector, b_eqFor q dimensional vector, A is P × n ties up matrix, A_eqTieing up matrix for q × n, Hessian matrix is that n × n ties up matrix；

Energy scheduling is carried out according to solving result.

Beneficial effect: the feature of polynary utilization of energy in combined with intelligent electrical network garden of the present invention, by intelligent grid garden Cool and thermal power load decomposition is pure electric load, hot water load, Space Thermal load, freezing cooling load and space refrigeration duty 5 class, By the method for operation of each powering device in Optimized Operation intelligent grid garden and exerting oneself, thus it is miniature to realize supply of cooling, heating and electrical powers The economic optimization of energy net runs.

Concrete, plurality of energy supplies and energy storage device in intelligent grid garden are analyzed by the present invention, set up the energy of each equipment Amount flow model, has built the framework of supply of cooling, heating and electrical powers micro power source net based on the energy exchange network concentrating interconnection.Around This framework, establishes supply of cooling, heating and electrical powers micro power source net economic optimization scheduling model, for model characteristics use based on Model is solved by the interior point method of Hessian matrix iteration, thus obtains operation plan a few days ago, according to above-mentioned plan into Row energy scheduling.

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 the interior point method based on Hessian matrix iteration advantage in terms of solving-optimizing problem, Scheduling strategy improves the comprehensive energy efficiency of intelligent grid garden, reduces systematic running cost and uses, it is achieved the miniature energy of supply of cooling, heating and electrical powers The economic optimization of source net runs.

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 supply of cooling, heating and electrical powers micro power source net energy supply structure of the present invention.

Fig. 4 is the economic optimization scheduling model framework of supply of cooling, heating and electrical powers micro power source net of the present invention.

Fig. 5 is the day electric heating cold prediction load curve of certain typical user in the present invention.

Fig. 6 is that in the present invention, electric load balances Optimized Operation equipment power curve a few days ago.

Fig. 7 is heat load balance Optimized Operation equipment power curve a few days ago in space in the present invention.

Fig. 8 is that in the present invention, hot water load balances Optimized Operation equipment power curve a few days ago.

Fig. 9 is that in the present invention, space refrigeration duty balances Optimized Operation equipment power curve a few days ago.

Figure 10 is that in the present invention, freezing cooling load balances Optimized Operation equipment power curve a few days ago.

Figure 11 is energy storage device running status under Optimized Operation a few days ago in the present invention.

Figure 12 is system system day operation expense under different scheduling strategies in the present invention.

Detailed description of the invention

Below in conjunction with the accompanying drawings the present invention is further described.

The present invention proposes a kind of economic optimization dispatching method towards supply of cooling, heating and electrical powers micro power source net, by intelligent grid garden Interior cool and thermal power load decomposition is pure electric load, hot water load, Space Thermal load, freezing cooling load and space refrigeration duty 5 classes, are analyzed plurality of energy supplies and energy storage device in intelligent grid garden, set up the energy Flow model of each equipment, The framework of supply of cooling, heating and electrical powers type micro power source net has been built based on the energy exchange network concentrating interconnection.Around this framework, build Found supply of cooling, heating and electrical powers type micro power source net economic optimization scheduling model, used based on Hessian matrix for model characteristics Model is solved by the interior point method of iteration.By the method for operation of each powering device in scheduling intelligent grid garden with go out Power, thus the economic optimization realizing supply of cooling, heating and electrical powers type micro power source net runs.

The concrete grammar of the present invention is as follows: first carry out plurality of energy supplies and energy storage device in supply of cooling, heating and electrical powers micro power source net Analyze, set up the energy Flow model of each equipment, such as Fig. 1, shown in 2, take based on the energy exchange network concentrating interconnection Build the framework of supply of cooling, heating and electrical powers type micro power source net, such as Fig. 3, shown in 4.Around this framework, establish cold and hot Electricity Federation For micro power source net economic optimization scheduling model, use interior point method pair based on Hessian matrix iteration for model characteristics Model solves.By the method for operation of each powering device in scheduling intelligent grid garden with exert oneself, thus realize cold and hot The economic optimization of CCHP type micro power source net runs；Said process is referring specifically to Summary.

In implementation process, the powering device in supply of cooling, heating and electrical powers micro power source net and energy storage device are analyzed, set up each The energy Flow model of equipment；The framework of supply of cooling, heating and electrical powers type micro power source net is built, around institute based on energy exchange network State framework and set up supply of cooling, heating and electrical powers type micro power source net economic optimization scheduling model；And to model solution.

Described powering device and energy storage device are analyzed, and the energy Flow model setting up each equipment specifically includes following step Rapid:

Step one): set up powering device model:

Described powering device is miniature gas turbine system, and miniature gas turbine is that micro power source net realizes supply of cooling, heating and electrical powers Nucleus equipment, establishes the miniature combustion of the economic optimization scheduling being applicable to micro power source net as a example by Capstone C1000 Gas-turbine model.Capstone C1000 system be made up of 5 Capstone C200 type miniature gas turbines one Individual strong adaptability, low emission, the electricity generation system of low-maintenance.Capstone C200 passes through turbine drive rotor high-speed rotation Produce high-frequency alternating current, then carry out rectification production industrial-frequency alternating current, the high temperature that power generation process produces by power electronic equipment Useless cigarette is then used by waste heat boiler, changes into steam or high-temperature-hot-water for absorption refrigeration unit refrigeration or direct Heat supply.Described miniature gas turbine system includes some miniature steam turbines；Described miniature steam turbine system is according to scheduling Power generation command value selects unit to open number of units, and the unit having turned on shares out equally electric load, by fitting of a polynomial, obtains The efficiency of miniature gas turbine system with go out force function, its parameter is as follows:

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

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；

Table 1 Capstone C1000 system effectiveness with go out the every coefficient of force function

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 the energy supply model of energy storage device；Energy storage device can play the work to cool and thermal power load peak load shifting With, cool and thermal power duty ratio and the miniature gas turbine unmatched problem of co-feeding system hotspot stress can be alleviated simultaneously.

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

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

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: for the sake of Jian Dan, it is believed that the work efficiency of other auxiliary powering device Constant, its energy supply mathematical model can be attributed to heat outputting (cold) and measures and input energy (fuel of consumption, electricity or remaining Heat) keep 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 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.

The method includes that row write optimization object function and system constraints, specifically includes:

Step one): row write optimization object function: described optimization aim includes co-feeding system miniature gas turbine and gas-fired boiler The electric energy switching cost of the fuel cost of stove, co-feeding system and electrical network and the operation expense of co-feeding system；Function is as follows Formula:

Min price=min (pri_fuel+pri_grid+pri_maintain)

In formula, supply of cooling, heating and electrical powers is miniature the day operation comprehensive cost of system can mainly include three below aspect: fuel cost, Power expense that electrical network is mutual and system operation and maintenance cost:

Wherein, the fuel cost function computing formula of co-feeding system miniature gas turbine and gas fired-boiler is as follows:

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

In formula:

f_CHPiFor miniature gas turbine consumption characteristic curve function, unit is all converted as kW；

P_iBeing the electrical power output of i-th miniature gas turbine, unit is kW；

Be by time gas price, unit by heating value of natural gas conversion be；

Being the consumption of i-th gas fired-boiler t period, unit by heating value of natural gas conversion is；

T is time period sequence number, and unit is hour；

Co-feeding system is as follows with the electric energy switching cost function computing formula of electrical network:

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

In formula:

Be by time electricity price；

Be co-feeding system and external electrical network by time exchange of electric power value.

The operation expense function computing formula of co-feeding system is as follows:

$\begin{array}{c}{\mathrm{pri}}_{ma\mathrm{int}ain}=\underset{t=1}{\overset{24}{\mathrm{\Σ}}}\underset{i=1}{\overset{n_{CHP}}{\mathrm{\Σ}}}{p}_{mCHPi}\×P_i^t\\ +\underset{t=1}{\overset{24}{\mathrm{\Σ}}}\underset{i=1}{\overset{n_{distri}}{\mathrm{\Σ}}}{p}_{mdistri}\×P_{distri}^t+\underset{t=1}{\overset{24}{\mathrm{\Σ}}}{p}_{mstor}\×H_{in}^t\\ +\underset{t=1}{\overset{24}{\mathrm{\Σ}}}{p}_{mstor}\×H_{out}^t+\underset{t=1}{\overset{24}{\mathrm{\Σ}}}{p}_{mGB}\×Q_{GB}^t\\ +\underset{t=1}{\overset{24}{\mathrm{\Σ}}}{p}_{mEB}\×Q_{EB}^t+\underset{t=1}{\overset{24}{\mathrm{\Σ}}}{p}_{mAC}\×P_{AC}^t\\ +\underset{t=1}{\overset{24}{\mathrm{\Σ}}}{p}_{mEC}\×P_{chil}^t+\underset{t=1}{\overset{24}{\mathrm{\Σ}}}{p}_{mAir}\×P_{cond}^t\end{array}$

In formula, p_mCHPiUnit power operation expense for gas turbine；p_mdistriFor Distributed-generation equipment specific work Rate operation expense；p_mstorRefer to the unit power operation expense of energy storage device；p_mGBList for gas fired-boiler Position Power operation maintenance cost；p_mEBUnit power operation expense for waste heat boiler；p_mACFor Absorption Refrigerator Unit power operation expense；p_mECUnit power operation expense for compression electric refrigerating machine；p_mAirFor The unit power operation expense of air conditioning system；P_iBeing the electrical power output of i-th miniature gas turbine, unit is kW；Being exerting oneself of i-th distributed generation system, unit is kW；WithRefer to three class energy storage devices respectively Charge and discharge energy power, unit is kW；For the power that heats of gas fired-boiler, unit has been scaled to kW；For waste heat pot Stove heat power, unit has been scaled to kW；For the refrigeration work consumption of Absorption Refrigerator, unit has been scaled to kW；For the operation power of compression electric refrigerating machine, unit is kW；Running power for air conditioning system, unit is kW.

Step 2), row write system constraints: described system constraints includes power-balance constraint, place capacity about Bundle and equipment run constraint；The constraint of described power-balance includes electrical power Constraints of Equilibrium, hot general power Constraints of Equilibrium, hot water Constraints of Equilibrium, cold total power constraint, freezing refrigeration work consumption Constraints of Equilibrium, place capacity constraint and equipment run constraint；

Described electrical power equilibrium constraint computing formula is as follows:

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

In formula,Be co-feeding system and external electrical network by time exchange of electric power value；For load value；For storing The power of battery；For family air-conditioning power；For Distributed-generation equipment power；P_i ^tIt it is i-th miniature gas The generated output of turbine；

For thermic load, it is ensured that in system, Space Thermal load and hot water load meet Constraints of Equilibrium, here will It is expressed as the constraint of hot water power-balance and total thermal power Constraints of Equilibrium, meets these 2 rear space heat load balances about Bundle automatically derives satisfied.

Described total thermal power equilibrium constraint computing formula is as follows:

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

Heat-water balance's constraints computing formula is as follows:

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

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；COP_cond、η_in、η_houtIt is respectively energy efficiency coefficient and the charge and discharge thermal efficiency of hot energy storage of air-conditioning equipment；WithBe respectively co-feeding system by time space thermic load and hot water load；

For refrigeration duty, it is ensured that in system, space refrigeration duty and freezing cooling load all meet Constraints of Equilibrium, this In be denoted as freezing refrigeration work consumption Constraints of Equilibrium and the constraint of total cold power-balance, meet these 2 rear spaces cold negative Lotus Constraints of Equilibrium automatically derives satisfied.

Total cold power-balance constraints computing formula is as follows:

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

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

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

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, EER_cond、η_in、η_coutIt is respectively the refrigeration efficiency of air-conditioning equipment than the cold effect of charge and discharge with cold energy storage Rate；WithBe respectively micro power source system by time space refrigeration duty and freezing cooling load；

In supply of cooling, heating and electrical powers micro power source system, place capacity and operation constraints computing formula are as follows:

For miniature gas turbine:

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

For gas fired-boiler:

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

For air-conditioning equipment:

$0\≤P_{cond}^t\≤P_{cond}^{\max }$

For hot/cold 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,WithHot/cold energy storage device for t inputs and output,WithFor heat/ The input of cold energy storage device and the output limit,Lotus Warm status for hot/cold energy storage device；

The charge and discharge hot/cold state that hot/cold energy storage device describes is a dynamic process, is shown below:

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

4, a kind of supply of cooling, heating and electrical powers type micro power source net economic optimization dispatching method as claimed in claim 2, its feature Being, the method includes the method for solving of model:

Owing to the equipment in Optimized model is numerous, a large amount of sparse matrix element can be produced when row write constraint matrix, when Solution procedure can be made to become the veryest long when using global solution space search class algorithm such as genetic algorithm or particle cluster algorithm, Therefore use interior point method based on Hessian matrix iteration that supply of cooling, heating and electrical powers micro power source net economic optimization scheduling model is entered Row solves, and the canonical form of solving model is:

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

Interior point method based on Hessian matrix iteration constructs during solving and meets Caro need-Ku En-Plutarch (Karush-Kuhn-Tucker) the Lagrangian auxiliary function of condition:

L (x, λ)=f (x)+∑ λ_c,ic_i(x)+∑λ_A,i(Ax-b)

+∑λ_ceq,iceq_i(x)+Σλ_Aeq,i(A_eqx-b_eq)

Second order derived function Hessian matrix in its update equation formula is:

$\begin{array}{c}{\▿}_{xx}^{2}L(x,\mathrm{\λ})={\▿}^{2}f\left(x\right)+{\mathrm{\Σ\λ}}_i{\▿}^2{c}_i\left(x\right)\\ +{\mathrm{\Σ\λ}}_i{\▿}^2{\mathrm{ceq}}_i\left(x\right)\end{array}$

By matrix in update equation group is converted, LDL^TDecompose and back substitution revising, it is achieved iterative, It is automatically stopped iteration after meeting the condition of convergence；

In formula, variable x to be optimized is that n ties up variable, including gas turbine even for the exerting oneself by a hour cool and thermal power of system, Auxiliary type powering device by hour exerting oneself, all kinds of energy storage device by hour exerting oneself, the SOC state of energy storage and with electricity Net and mutual purchase electricity sales amount；In constraints, inequality constraints is the function constraint of exerting oneself of miniature gas turbine, line style part There are p line style inequality constraints and q line style equality constraint, including powering device units limits, the constraint of cool and thermal power load And the constraint of energy storage SOC bound；λ_c,iλ_A,iλ_ceq,iλ_Aeq,iFor Lagrange multiplier, dimension and respective constraint bar Part number is identical；X, lb, ub are n dimensional vector, and b is p dimensional vector, b_eqFor q dimensional vector, A is p × n Dimension matrix, A_eqTieing up matrix for q × n, Hessian matrix is that n × n ties up matrix.

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

Fig. 5 is the day electric heating cold prediction load curve of certain typical user in the present invention, according to based on Hessian matrix iteration Interior point method model is solved, obtain such as the simulation result of Fig. 6-Figure 12, from Fig. 6-Figure 12, after optimization Energization schemes can meet whole energy demands of micro power source net, system does not has and abandons light, abandon heat, abandon cold and The situation abandoning useless cigarette occurs, the micro power source net energization schemes of optimized mistake has the feature that

(1) system buys power to public electric wire net in electricity price relatively low 23 o'clock to 8 o'clock period and meets electric refrigerating machine, sky Adjust and the demand of pure electricity type load；When entering crest segment electricity price, miniature gas turbine is started working, miniature to meet The electrical load requirement of energy net；Electric refrigerating machine only enables when low ebb electricity price, to reduce the operating cost of system；Air-conditioning is also Exert oneself when low ebb electricity price the significantly greater than crest segment electricity price moment, mainly limited by electricity price and cooling and heating load Changing Pattern； When enter photovoltaic system can generate electricity the moment time, photovoltaic is exerted oneself full sending out, to reduce the electric power in micro power source net according to prediction Supply requirement；Accumulator is higher due to use cost, although there being the effect of peak load shifting, but unfavorable from the overall situation In the economic load dispatching of micro power source net, therefore it is not engaged in actual motion.

(2) Space Thermal load is by the system combined supply of air conditioning system and miniature gas turbine, in low ebb electricity price time space heat Load is all provided by air-conditioning, and the part crest segment electricity price moment is provided by the waste heat boiler in miniature gas turbine system, due to Use natural gas higher for level Waste Heat Price, gas fired-boiler operating scheme the most on the schedule.

(3) Space Thermal workload demand can only be met due to family air conditioning system, the hot water load in this micro power source net by Waste heat boiler and hot energy storage device meet, and hot storage energy operation cost is relatively low, are suitable for substituting accumulator storage and undertake peak load shifting Effect, similarly, since use natural gas supplying hot water price higher, gas fired-boiler also excludes hot water supply prioritization scheme.

(4) space refrigeration duty is by air conditioning system, miniature gas turbine system and compression electric refrigerating machine joint supply, All being provided by air-conditioning and compression electric refrigerating machine in low ebb electricity price time space refrigeration duty, remaining moment is by miniature gas turbine Lithium bromide absorption refrigerating set in system provides, and when lithium bromide absorption refrigerating set refrigerating capacity deficiency, family is empty Adjusting system serves as space refrigeration peak regulation equipment.

(5) the space refrigeration duty demand of system, the freezing in this micro power source net can only be met due to family air conditioning system Cooling load is met by lithium bromide absorption refrigerating set, compression electric refrigerating machine and cold energy storage device, non-low in electricity price The moment Gas Turbine Output of paddy is relatively big, and useless cigarette is more, and cooling and heating load is limited, therefore utilizes cold energy storage device to enter cold water Row storage, carries out released cold quantity, plays the effect of peak load shifting when refrigeration duty raises.

(6) within the whole Optimized Operation cycle, cold and hot energy storage device energy storage capacity is 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 operating cost is relatively low, is especially suitable for substituting accumulator and holds The task of load peak load shifting, to reduce the purpose of systematic running cost.Eventually through solving the optimum results obtained Various energy supplies and energy storage device are scheduling, it is achieved the Optimum utilization of the garden energy.

(7) system after wherein optimizing have employed the scheduling strategy carried herein, and day operation expense is 24048.7 yuan；Not The electric load optimizing the system that refers to all is bought by public network, and space cooling and heating load is met by family air-conditioning, and hot water is born Lotus is met by gas fired-boiler, and freezing cooling load is met by electric refrigerating machine, and day operation expense is 30388.4 yuan；Fixed with heat Under the electricity method of operation, gas turbine co-feeding system preferentially meets the total heat duties in micro power source net, not enough by heating peak regulation Equipment gas fired-boiler and family air-conditioning provide, and refrigeration duty, by peak regulation equipment family air-conditioning and the electric refrigerating machine offer of freezing, is transported day Row expense is 26164.8 yuan；With under the fixed hot method of operation of electricity, gas turbine co-feeding system preferentially meets in micro power source net Total electric load, not enough bought by electrical network, thermic load is by heating peak regulation equipment gas fired-boiler and family air-conditioning provides, cold negative Lotus is by peak regulation equipment family air-conditioning and the electric refrigerating machine offer of freezing, and day operation expense is 27116.3 yuan.System is employing Day operation expense can be made after Optimized Operation strategy to reduce by 20.86%, and relatively other two kinds of methods of operation reduce 8.09% respectively With 11.31%.Therefore, by the method for operation of each powering device in scheduling micro power source net with exert oneself, can significantly drop The day operation expense of low system, it is achieved the economic optimization of supply of cooling, heating and electrical powers micro power source net runs.

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 (4)

1. a supply of cooling, heating and electrical powers type micro power source net economic optimization dispatching method, it is characterised in that the method cold and hot Powering device, energy storage device and auxiliary powering device is included in CCHP micro power source net；Described energy storage device includes cold storage Energy equipment, hot energy storage device and electricity energy storage device；Described auxiliary powering device includes gas fired-boiler, family air-conditioning, absorption Formula refrigeration unit and renewable energy power generation equipment；

Powering device in supply of cooling, heating and electrical powers micro power source net and energy storage device are analyzed, set up the energy stream of each equipment Movable model；Build the framework of supply of cooling, heating and electrical powers type micro power source net based on energy exchange network, set up cold around described framework Cogeneration type micro power source net economic optimization scheduling model；And to model solution.

2. a kind of supply of cooling, heating and electrical powers type micro power source net economic optimization dispatching method as claimed in claim 1, its feature Being, described powering device and energy storage device are analyzed, and the energy Flow model setting up each equipment specifically includes following step Rapid:

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. a kind of supply of cooling, heating and electrical powers type micro power source net economic optimization dispatching method as claimed in claim 2, its feature Being, the method includes that row write optimization object function and system constraints, specifically includes:

Step one): row write optimization object function: described optimization aim includes co-feeding system miniature gas turbine and gas-fired boiler The electric energy switching cost of the fuel cost of stove, co-feeding system and electrical network and the operation expense of co-feeding system；Function is as follows Formula:

Minprice=min (pri_fuel+pri_grid+pri_maintain)

Wherein, the fuel cost function computing formula of co-feeding system miniature gas turbine and gas fired-boiler is as follows:

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

In formula:

f_CHPiFor miniature gas turbine consumption characteristic curve function, unit is all converted as kW；

P_iBeing the electrical power output of i-th miniature gas turbine, unit is kW；

Be by time gas price, unit by heating value of natural gas conversion be；

Being the consumption of i-th gas fired-boiler t period, unit by heating value of natural gas conversion is；

T is time period sequence number, and unit is hour；

Co-feeding system is as follows with the electric energy switching cost function computing formula of electrical network:

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

In formula:

Be by time electricity price；

Be co-feeding system and external electrical network by time exchange of electric power value.

The operation expense function computing formula of co-feeding system is as follows:

$\begin{array}{c}{\mathrm{pri}}_{ma\mathrm{int}ain}=\underset{t=1}{\overset{24}{\Σ}}\underset{i=1}{\overset{n_{CHP}}{\Σ}}{p}_{mCHPi}\×P_i^t\\ +\underset{t=1}{\overset{24}{\Σ}}\underset{i=1}{\overset{n_{distri}}{\Σ}}{p}_{mdistri}\×P_{distri}^t+\underset{t=1}{\overset{24}{\Σ}}{p}_{mstor}\×H_{in}^t\\ +\underset{t=1}{\overset{24}{\Σ}}{p}_{mstor}\×H_{out}^t+\underset{t=1}{\overset{24}{\Σ}}{p}_{mGB}\×Q_{GB}^t\\ +\underset{t=1}{\overset{24}{\Σ}}{p}_{mEB}\×Q_{EB}^t+\underset{t=1}{\overset{24}{\Σ}}{p}_{mAC}\×P_{AC}^t\\ +\underset{t=1}{\overset{24}{\Σ}}{p}_{mEC}\×P_{chil}^t+\underset{t=1}{\overset{24}{\Σ}}{p}_{mAir}\×P_{cond}^t\end{array}$

In formula, p_mCHPiUnit power operation expense for gas turbine；p_mdistriFor Distributed-generation equipment specific work Rate operation expense；p_mstorRefer to the unit power operation expense of energy storage device；p_mGBList for gas fired-boiler Position Power operation maintenance cost；p_mEBUnit power operation expense for waste heat boiler；p_mACFor Absorption Refrigerator Unit power operation expense；p_mECUnit power operation expense for compression electric refrigerating machine；p_mAirFor The unit power operation expense of air conditioning system；P_iBeing the electrical power output of i-th miniature gas turbine, unit is kW；Being exerting oneself of i-th distributed generation system, unit is kW；WithRefer to three class energy storage devices respectively Charge and discharge energy power, unit is kW；For the power that heats of gas fired-boiler, unit has been scaled to kW；For waste heat pot Stove heat power, unit has been scaled to kW；For the refrigeration work consumption of Absorption Refrigerator, unit has been scaled to kW；For the operation power of compression electric refrigerating machine, unit is kW；Running power for air conditioning system, unit is kW.

Step 2), row write system constraints: described system constraints includes power-balance constraint, place capacity about Bundle and equipment run constraint；The constraint of described power-balance includes electrical power Constraints of Equilibrium, hot general power Constraints of Equilibrium, hot water Constraints of Equilibrium, cold total power constraint, freezing refrigeration work consumption Constraints of Equilibrium, place capacity constraint and equipment run constraint；

Described 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 storing The power of battery；For family air-conditioning power；For Distributed-generation equipment power；P_i ^tIt it is i-th miniature gas The generated output of turbine；

Described total thermal power equilibrium constraint computing formula is as follows:

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

Heat-water balance's constraints computing formula is as follows:

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

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；COP_cond、η_in、η_houtIt is respectively energy efficiency coefficient and the charge and discharge thermal efficiency of hot energy storage of air-conditioning equipment；WithBe respectively co-feeding system by time space thermic load and hot water load；

Total cold power-balance constraints computing formula is as follows:

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

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

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

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, EER_cond、η_in、η_coutIt is respectively the refrigeration efficiency of air-conditioning equipment than the cold effect of charge and discharge with cold energy storage Rate；WithBe respectively micro power source system by time space refrigeration duty and freezing cooling load；

In system, place capacity constraint 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 air-conditioning equipment:

$0\≤P_{cond}^t\≤P_{cond}^{\max }$

For hot/cold 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,WithHot/cold energy storage device for t inputs and output,WithFor heat/ The input of cold energy storage device and the output limit,Lotus Warm status for hot/cold energy storage device；

The charge and discharge hot/cold state that hot/cold energy storage device describes is a dynamic process, is shown below:

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

4. a kind of supply of cooling, heating and electrical powers type micro power source net economic optimization dispatching method as claimed in claim 2, its feature Being, the method includes the method for solving of model:

The canonical form of solving model is

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

Interior point method based on Hessian matrix iteration constructs during solving and meets Karush-Kuhn-Tucker bar The Lagrangian auxiliary function of part:

$\begin{array}{c}L(x,\mathrm{\λ})=f\left(x\right)+\Σ{\mathrm{\λ}}_{c,i}{c}_i\left(x\right)+\Σ{\mathrm{\λ}}_{A,i}(Ax-b)\\ +\Σ{\mathrm{\λ}}_{ceq,i}{\mathrm{ceq}}_i\left(x\right)+\Σ{\mathrm{\λ}}_{Aeq,i}(A_{eq}x-b_{eq})\end{array}$

Second order derived function Hessian matrix in its update equation formula is:

$\begin{array}{c}{\▿}_{xx}^{2}L(x,\mathrm{\λ})={\▿}^{2}f\left(x\right)+{\mathrm{\Σ\λ}}_i{\▿}^2{c}_i\left(x\right)\\ +\Σ{\mathrm{\λ}}_i{\▿}^2{\mathrm{ceq}}_i\left(x\right)\end{array}$

By matrix in update equation group is converted, LDL^TDecompose and back substitution revising, it is achieved iterative, It is automatically stopped iteration after meeting the condition of convergence；

In formula, variable x to be optimized is that n ties up variable, including gas turbine even for the exerting oneself by a hour cool and thermal power of system, Auxiliary type powering device by hour exerting oneself, all kinds of energy storage device by hour exerting oneself, the SOC state of energy storage and with What electrical network was mutual purchases electricity sales amount；In constraints, inequality constraints is the function constraint of exerting oneself of miniature gas turbine, linear portions Divide and have p line style inequality constraints and q line style equality constraint, including powering device units limits, cool and thermal power load about Bundle and the constraint of energy storage SOC bound；λ_c,iλ_A,iλ_ceq,iλ_Aeq,iFor Lagrange multiplier, dimension and respective constraint Condition number is identical；X, lb, ub are n dimensional vector, and b is p dimensional vector, b_eqFor q dimensional vector, A is P × n ties up matrix, A_eqTieing up matrix for q × n, Hessian matrix is that n × n ties up matrix；

Energy scheduling is carried out according to solving result.