CN104730923A - Combined cooling-heating-power based comprehensive energy optimizing and controlling method for smart power grid region - Google Patents

Abstract

The invention discloses a combined cooling-heating-power based comprehensive energy optimizing and controlling method for a smart power grid region. By aiming at the characteristics of the cooling load, the electrical load, the heating load and various loads in the smart power grid region, by combining the fundamental principle of the combined cooling-heating-power and the working characteristics of various devices, the optimal utilization of multi-class energy is achieved; during implementation, the kilowatt is utilized as the measurement unit, the equivalent calculation of the various energy is combined, and the optimal management of the energy is prompted. The energy optimizing and controlling method has the advantages of being effective, practical and scientific, and beneficial to popularization and application of energy conservation.

Description

Comprehensive energy optimization control method for smart grid parks based on combined cooling, heating and power supply

technical field

The invention relates to the technical field of power systems, in particular to a comprehensive energy optimization control method for smart grid parks based on distributed combined cooling, heating and power supply.

Background technique

As the country attaches great importance to energy conservation and emission reduction, coupled with the higher flexibility requirements of modern power systems, distributed combined cooling and heat and power (DCCHP) has been highly valued. DCCHP is a combined cooling, heating and power system based on distributed power sources. It can not only realize the supply of cooling, heating and electric loads, realize the cascade utilization of energy, but also reduce the emission of polluting gases, which has good social and economic benefits. Therefore, distributed combined cooling, heating and power supply is an effective means to achieve energy saving and emission reduction and improve energy utilization.

There are different types of users in the smart grid park. Due to the difference in user types and energy consumption habits, the energy consumption characteristic curves presented by each other are also different. At the same time as energy consumption, there are also various forms of distributed energy such as wind power, light energy, hydraulic power, and geothermal energy in the park. These energy sources often exist in the smart grid park in the form of distributed power. Some current technologies only consider the combined cooling, heating and power generation based on micro gas turbines, and insufficient consideration is given to other combined power generation equipment. How to realize the cascade utilization of energy in the smart grid park, make full use of different forms of distributed energy, and give full play to the advantages of the smart grid park in the management and control of energy and various devices is a prominent problem facing the builders of the smart grid park.

Contents of the invention

Technical problem to be solved: Aiming at the deficiencies of the existing technology, the present invention proposes a comprehensive energy optimization control method for smart grid parks based on combined cooling, heating and power supply, which is used to solve the problems of existing smart grid parks in the combined supply of cooling, heating and power. In the process, only micro gas turbines are considered and other technical issues that can be used as co-generation equipment in the smart grid park are insufficiently considered.

Technical solution: In order to solve the above-mentioned technical problems, the present invention adopts the following technical solutions:

A comprehensive energy optimization control method for smart grid parks based on combined cooling, heating, and power supply. Multiple types of energy supply equipment and energy storage devices in smart grid parks are used as distributed energy sources to provide combined cooling, heating, and power supplies to various users with different loads. ;

First select the energy supply equipment, energy storage equipment and the corresponding capacity; then determine the optimization objective function; combine the selected energy supply equipment, energy storage equipment and smart grid park operating constraints and the load curves of various users, use intelligent Optimize the genetic algorithm to solve the optimization objective function;

During the implementation process, the solution results are used to schedule the energy supply equipment and energy storage equipment to realize the optimal utilization of energy in the smart grid park.

Wherein, the optimization objective function is determined, and the steps include:

Step (1), determine the energy exchange cost function between the smart grid park and the external grid:

${\mathrm{<span\; class="notranslate">\; the\; price</span>}}_{\mathrm{<span\; class="notranslate">\; grid</span>}}<span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; twenty\; four</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; c</span>}}_{\mathrm{<span\; class="notranslate">\; grid</span>}}^{\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; \&times;</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; grid</span>}}^{\mathrm{<span\; class="notranslate">\; t</span>}}$

In the formula, is the hourly electricity price; is the hourly power exchange value between the smart grid park and the external grid.

Step (2), determine the fuel cost function of micro gas turbines and gas boilers in the smart grid park:

${\mathrm{<span\; class="notranslate">\; the\; price</span>}}_{\mathrm{<span\; class="notranslate">\; fuel</span>}}<span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; twenty\; four</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{{\mathrm{<span\; class="notranslate">\; no</span>}}_{\mathrm{<span\; class="notranslate">\; CHP</span>}}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; c</span>}}_{\mathrm{<span\; class="notranslate">\; Gas</span>}}^{\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; \&times;</span>{\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; CHpi</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}^{\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; +</span>\underset{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; twenty\; four</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{{\mathrm{<span\; class="notranslate">\; no</span>}}_{\mathrm{<span\; class="notranslate">\; boiler</span>}}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; c</span>}}_{\mathrm{<span\; class="notranslate">\; Gas</span>}}^{\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; \&times;</span>{\mathrm{<span\; class="notranslate">\; h</span>}}_{\mathrm{<span\; class="notranslate">\; boileri</span>}}^{\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; /</span>{\mathrm{<span\; class="notranslate">\; \&eta;</span>}}_{\mathrm{<span\; class="notranslate">\; boileri</span>}}<span\; class="notranslate">\; ;</span>$

In the formula, f _CHPi is the function of the power and the gas used by the micro gas turbine, the unit is kW; P _i is the electric power output of the i-th micro gas turbine, the unit is kW; n _CHP is the number of micro gas turbines; is the hourly gas price; is the output of the i-th gas-fired boiler; η _boileri is the energy conversion efficiency of the i-th gas-fired boiler; t is the length of time in hours; n _boiler is the number of gas-fired boilers.

Step (3), determine the maintenance cost function of the smart grid park energy system:

$\begin{array}{c}{\mathrm{<span\; class="notranslate">\; the\; price</span>}}_{\mathrm{<span\; class="notranslate">\; maintain</span>}}<span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; twenty\; four</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{{\mathrm{<span\; class="notranslate">\; no</span>}}_{\mathrm{<span\; class="notranslate">\; CHP</span>}}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; wxya</span>}}<span\; class="notranslate">\; \&times;</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}^{\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; +</span>\underset{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; twenty\; four</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{{\mathrm{<span\; class="notranslate">\; no</span>}}_{\mathrm{<span\; class="notranslate">\; Distributor</span>}}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; mdistri</span>}}<span\; class="notranslate">\; \&times;</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; Distributor</span>}}^{\mathrm{<span\; class="notranslate">\; t</span>}}\\ <span\; class="notranslate">\; +</span>\underset{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; twenty\; four</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; mstor</span>}}<span\; class="notranslate">\; \&times;</span>{\mathrm{<span\; class="notranslate">\; h</span>}}_{\mathrm{<span\; class="notranslate">\; in</span>}}^{\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; +</span>\underset{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; twenty\; four</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; mstor</span>}}<span\; class="notranslate">\; \&times;</span>{\mathrm{<span\; class="notranslate">\; h</span>}}_{\mathrm{<span\; class="notranslate">\; out</span>}}^{\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; +</span>\underset{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; twenty\; four</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; mH</span>}}<span\; class="notranslate">\; \&times;</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; EH</span>}}^{\mathrm{<span\; class="notranslate">\; t</span>}}\end{array}$

In the formula, p _mCHPi is the operation and maintenance cost per unit power of micro gas turbine; p _mdistri is the operation and maintenance cost per unit power of renewable energy power generation equipment; p _msstor is the operation and maintenance cost per unit power of thermal energy storage equipment; p _mEH is the electrothermal conversion equipment Operation and maintenance cost per unit power; is the electric power output of the i-th renewable energy power generation equipment, the unit is kW; and Respectively, the charging and discharging power of thermal energy storage equipment, the unit is kW; is the power of electrothermal conversion equipment in kW; n _distri is the number of renewable energy power generation equipment. Since gas boilers generally do not need maintenance during use, the related items of gas boilers are not involved in the maintenance cost function.

In step (4), the three types of costs are superimposed to obtain the optimization objective function:

min price＝min(pri _Grid +pri _fuel +pri _maintain )

After obtaining the optimized objective function, write down the constraints on the operation of the selected energy supply equipment, energy storage equipment, and smart grid park. The steps include:

Step (1), determine the electric power balance constraint function:

${\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; grid</span>}}^{\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; +</span>\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{{\mathrm{<span\; class="notranslate">\; no</span>}}_{\mathrm{<span\; class="notranslate">\; CHP</span>}}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}^{\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; +</span>\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{{\mathrm{<span\; class="notranslate">\; no</span>}}_{\mathrm{<span\; class="notranslate">\; Distributor</span>}}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; Distributor</span>}}^{\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; load</span>}}^{\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; EH</span>}}^{\mathrm{<span\; class="notranslate">\; t</span>}}$

In the formula, is the hourly power exchange value between the smart grid park and the external grid, in kW; is the load value, the unit is kW.

Step (2), determining the thermal power balance constraint function;

$\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{{\mathrm{<span\; class="notranslate">\; no</span>}}_{\mathrm{<span\; class="notranslate">\; CHP</span>}}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; h</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}^{\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; +</span>\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{{\mathrm{<span\; class="notranslate">\; no</span>}}_{\mathrm{<span\; class="notranslate">\; boiler</span>}}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; h</span>}}_{\mathrm{<span\; class="notranslate">\; boileri</span>}}^{\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; \&eta;</span>}}_{\mathrm{<span\; class="notranslate">\; EH</span>}}<span\; class="notranslate">\; \&times;</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; EH</span>}}^{\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; \&eta;</span>}}_{\mathrm{<span\; class="notranslate">\; out</span>}}<span\; class="notranslate">\; \&times;</span>{\mathrm{<span\; class="notranslate">\; h</span>}}_{\mathrm{<span\; class="notranslate">\; out</span>}}^{\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; h</span>}}_{\mathrm{<span\; class="notranslate">\; in</span>}}^{\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; \&Greater\; Equal;</span>{\mathrm{<span\; class="notranslate">\; h</span>}}_{\mathrm{<span\; class="notranslate">\; load</span>}}^{\mathrm{<span\; class="notranslate">\; t</span>}}$

In the formula, is the calorific value of the i-th micro gas turbine; is the heat production value of the i-th gas-fired boiler; η _EH and η _out are the efficiency of the electrothermal conversion equipment and the heat release efficiency of thermal energy storage, respectively; is the hourly heat load of the smart grid park.

Step (3), determining the cooling power balance constraint function;

$\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{{\mathrm{<span\; class="notranslate">\; no</span>}}_{\mathrm{<span\; class="notranslate">\; CHP</span>}}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}^{\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; cond</span>}}^{\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; \&Greater\; Equal;</span>{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; load</span>}}^{\mathrm{<span\; class="notranslate">\; t</span>}}$

In the formula, is the exhaust cooling power of the i-th micro gas turbine passing through the absorption refrigerator; Cooling power for the air conditioner; is the hourly cooling load of the smart grid park.

Step (4), determine the capacity constraint functions of the distributed energy resources in the smart grid park, these capacity constraint functions are easy to obtain according to the specific equipment;

For micro gas turbines: $P_i^{\min }\&le;P_i^t\&le;P_i^{\max },i\&Element;{\mathrm{no}}_{\mathrm{CHP}}$

For gas boilers: $0\&le;h_{\mathrm{boileri}}^t\&le;h_{\mathrm{boileri}}^{\max },i\&Element;{\mathrm{no}}_{\mathrm{boiler}}$

For electrothermal conversion equipment:

For thermal energy storage devices: $0\&le;h_{\mathrm{in}}^t\&le;h_{\mathrm{in}}^{\max },0\&le;h_{\mathrm{out}}^t\&le;h_{\mathrm{out}}^{\max },S_{\mathrm{store}}^{\min }\&le;S_{\mathrm{store}}^t\&le;S_{\mathrm{store}}^{\max };$

For renewable energy generation equipment:

In the formula, and are the input power and output power of the thermal energy storage device at time t, respectively, and are the input power limit and output power limit of thermal energy storage equipment, is the thermal load state of the thermal energy storage equipment, and the thermal energy storage equipment satisfies η _in is the thermal charging efficiency of the thermal energy storage device, and η _stor is the energy storage efficiency of the thermal energy storage device; the charging and discharging state described by this formula is a dynamic process.

Finally, the optimization objective function is solved by using the intelligent optimization genetic algorithm combined with the load curves of various users, so as to obtain the day-ahead scheduling plan, and carry out energy scheduling according to the above-mentioned plan.

Beneficial effect:

The present invention combines the characteristics of multi-energy utilization in the smart grid park, comprehensively considers the multi-energy energy consumption of various users, and utilizes the distributed combined cooling, heating and power supply technology to optimize control and manage the multi-energy in the smart grid park, and can make full use of All kinds of distributed energy in the park realize cascade utilization of energy.

Specifically, the present invention comprehensively considers the characteristics of various types of energy supply equipment and energy storage equipment such as micro gas turbines, gas boilers, electrothermal conversion equipment, thermal energy storage equipment, and renewable energy power generation equipment, and controls the heating and cooling of users in the smart grid park. The characteristics of different loads of electricity were investigated, and the operation of various distributed energy supply equipment was optimized through algorithms to realize the cascade utilization of energy in the smart grid park;

The invention also gives full play to the advantages of the smart grid park in terms of data collection and the diversity of energy supply equipment, and at the same time gives full play to the advantages of the intelligent optimization genetic algorithm in solving optimization problems, realizing the regionalization of energy optimization, Automation and intelligence have improved the comprehensive energy efficiency of the smart grid park, and the improvement effect is good.

Description of drawings

Fig. 1 is a flowchart of the present invention.

Fig. 2 is the cooling, heating and electric load curve of the smart grid park in the present invention.

Fig. 3 is a flow chart of solving the comprehensive energy optimization problem of the smart grid park by the genetic algorithm provided by the present invention.

Fig. 4 is the operating electric power curves of various energy supply equipment when the day-ahead scheduling plan formulated according to the genetic algorithm is adjusted according to the present invention.

Fig. 5 is the operating thermal power curves of various energy supply equipment when the day-ahead scheduling plan formulated according to the genetic algorithm is adjusted according to the present invention.

Fig. 6 is the operating cold power curves of various energy supply equipment when the day-ahead scheduling plan formulated according to the genetic algorithm is adjusted according to the present invention.

Detailed ways

The present invention will be further described below in conjunction with the accompanying drawings.

The present invention proposes a method for optimizing the control and management of multiple energy sources in the smart grid park by using the distributed combined cooling, heating and power supply technology. The specific method of the invention is as follows: equipment, thermal energy storage equipment, and renewable energy power generation equipment. Three types of loads), the load characteristics of various types of distributed energy supply systems are reflected in the load curve, as shown in Figure 2, where the heat load of the smart grid park is the difference between the hot water load curve and the space heat load curve in the figure and, the cooling load of the smart grid park is the sum of the cooling load curve and the freezing load curve in the figure, and the electric load of the smart grid park is the pure electric load curve in the figure; then determine the optimization objective function; And the constraints on the operation of the smart grid park; for the above process, please refer to the content of the invention.

Then solve the optimization objective function according to the genetic algorithm process shown in Figure 3, and obtain the optimization results as shown in Figure 4, Figure 5 and Figure 6, and finally schedule various energy supply and energy storage equipment through the optimization results obtained from the solution , to achieve optimal utilization of park energy.

The above is only a preferred embodiment of the present invention, it should be pointed out that for those of ordinary skill in the art, without departing from the principle of the present invention, some improvements and modifications can also be made, and these improvements and modifications are also possible. It should be regarded as the protection scope of the present invention.

Claims (9)

1. based on an intelligent grid garden comprehensive energy optimal control method for cold, heat and electricity triple supply, it is characterized in that: multiclass powering device in intelligent grid garden and energy storage device are carried out cold, heat and electricity triple supply as distributed energy to the multiple user with different load;

First powering device and energy storage device and corresponding capacity is selected; Then optimization object function is determined; The constraint condition run in conjunction with selected powering device, energy storage device and intelligent grid garden three and the load curve of all types of user, utilize genetic algorithm to solve optimization object function and obtain operation plan a few days ago;

In implementation process, the operation plan a few days ago solving acquisition is utilized to dispatch powering device and energy storage device.

2. the intelligent grid garden comprehensive energy optimal control method based on cold, heat and electricity triple supply according to claim 1, is characterized in that: described distributed energy comprises miniature gas turbine, gas fired-boiler, electric heating conversion equipment, hot energy storage device and renewable energy power generation equipment; Determine that optimization object function comprises the following steps: the electric energy switching cost function pri determining intelligent grid garden and external electrical network _grid; Determine the fuel cost function pri of miniature gas turbine and gas fired-boiler in intelligent grid garden _fuel; Determine the maintenance cost function pri of intelligent grid garden energy resource system _maintain; Then superposed by above-mentioned three class costs, be optimized objective function min price=min (pri _grid+ pri _fuel+ pri _maintain).

3. the intelligent grid garden comprehensive energy optimal control method based on cold, heat and electricity triple supply according to claim 1, is characterized in that: described constraint condition comprises satisfied following 4 class constraint functions: electric power Constraints of Equilibrium function; Heating power balance constraint function; Cold power-balance constraint function; In intelligent grid garden distributed energy capacity constraint function.

4. the intelligent grid garden comprehensive energy optimal control method based on cold, heat and electricity triple supply according to claim 2, is characterized in that: described intelligent grid garden and external electrical network electric energy switching cost function computing formula as follows:

${\mathrm{pri}}_{\mathrm{Grid}}=\underset{1}{\overset{24}{\mathrm{\&Sigma;}}}{c}_{\mathrm{Grid}}^t\&times;P_{\mathrm{Grid}}^t---\left(1\right)$

(1) in formula, be by time electricity price; be intelligent grid garden and external electrical network by time exchange of electric power value.

5. the intelligent grid garden comprehensive energy optimal control method based on cold, heat and electricity triple supply according to claim 2, is characterized in that: the fuel cost function computing formula of described miniature gas turbine and gas fired-boiler is as follows:

${\mathrm{pri}}_{\mathrm{fuel}}=\underset{t=1}{\overset{24}{\mathrm{\&Sigma;}}}\underset{i=1}{\overset{n_{\mathrm{CHP}}}{\mathrm{\&Sigma;}}}{c}_{\mathrm{Gas}}^t\&times;f_{\mathrm{CHPi}}\left({P_i}^t\right)+\underset{t=1}{\overset{24}{\mathrm{\&Sigma;}}}\underset{i=1}{\overset{n_{\mathrm{boiler}}}{\mathrm{\&Sigma;}}}{c}_{\mathrm{Gas}}^t\&times;H_{\mathrm{boileri}}^t/{\mathrm{\&eta;}}_{\mathrm{boileri}}---\left(2\right)$

(2) in formula, f _cHPifor miniature gas turbine is about power and the function using combustion gas, unit is kW; P _ibe the electric power output of i-th miniature gas turbine, unit is kW; n _cHPfor the quantity of miniature gas turbine; be by time gas price; be exerting oneself of i-th gas fired-boiler; η _boileriit is the energy conversion efficiency of i-th gas fired-boiler; T is time span, and unit is hour; n _boilerfor the quantity of gas fired-boiler.

6. the intelligent grid garden comprehensive energy optimal control method based on cold, heat and electricity triple supply according to claim 2, is characterized in that: the maintenance cost function computing method of described intelligent grid garden energy resource system are as follows:

$\begin{array}{c}{\mathrm{pri}}_{\mathrm{maintain}}=\underset{t=1}{\overset{24}{\mathrm{\&Sigma;}}}\underset{i=1}{\overset{n_{\mathrm{CHP}}}{\mathrm{\&Sigma;}}}{p}_{\mathrm{mCHPi}}\&times;{P_i}^t+\underset{t=1}{\overset{24}{\mathrm{\&Sigma;}}}\underset{i=1}{\overset{n_{\mathrm{distri}}}{\mathrm{\&Sigma;}}}{p}_{\mathrm{mdistri}}\&times;P_{\mathrm{distri}}^t\\ +\underset{t=1}{\overset{24}{\mathrm{\&Sigma;}}}{p}_{\mathrm{mstor}}\&times;H_{\mathrm{in}}^t+\underset{t=1}{\overset{24}{\mathrm{\&Sigma;}}}{p}_{\mathrm{mstor}}\&times;H_{\mathrm{out}}^t+\underset{t=1}{\overset{24}{\mathrm{\&Sigma;}}}{p}_{\mathrm{mEH}}\&times;P_{\mathrm{EH}}^t\end{array}---\left(3\right)$

(3) in formula, p _mCHPifor the specific power operation expense of miniature gas turbine; p _mdistrifor the specific power operation expense of renewable energy power generation equipment; p _msstorfor the specific power operation expense of hot energy storage device; p _mEHfor the specific power operation expense of electric heating conversion equipment; P _ibe the electric power output of i-th miniature gas turbine, unit is kW; be the electric power output of i-th renewable energy power generation equipment, unit is kW; with be respectively the charge and discharge thermal power of hot energy storage device, unit is kW; for the power of electric heating conversion equipment, unit is kW; n _cHPfor the quantity of miniature gas turbine; n _distrifor the quantity of renewable energy power generation equipment.

7. the intelligent grid garden comprehensive energy optimal control method based on cold, heat and electricity triple supply according to claim 3, is characterized in that: described electric power Constraints of Equilibrium function computing formula is as follows:

$P_{\mathrm{Grid}}^t+\underset{i=1}{\overset{n_{\mathrm{CHP}}}{\mathrm{\&Sigma;}}}{P_i}^t+\underset{i=1}{\overset{n_{\mathrm{distri}}}{\mathrm{\&Sigma;}}}{P}_{\mathrm{distri}}^t=P_{\mathrm{Load}}^t+P_{\mathrm{EH}}^t---\left(4\right)$

(4) in formula, be intelligent grid garden and external electrical network by time exchange of electric power value, unit is kW; P _ibe the electric power output of i-th miniature gas turbine, unit is kW; be the electric power output of i-th renewable energy power generation equipment, unit is kW; for load value, unit is kW; for the power of electric heating conversion equipment, unit is kW; n _cHPfor the quantity of miniature gas turbine; n _distrifor the quantity of renewable energy power generation equipment.

8. the intelligent grid garden comprehensive energy optimal control method based on cold, heat and electricity triple supply according to claim 3, is characterized in that: described heating power balance constraint function computing formula is as follows:

$\underset{i=1}{\overset{n_{\mathrm{CHP}}}{\mathrm{\&Sigma;}}}{H}_i^t+\underset{i=1}{\overset{n_{\mathrm{boiler}}}{\mathrm{\&Sigma;}}}{H}_{\mathrm{boileri}}^t+{\mathrm{\&eta;}}_{\mathrm{EH}}\&times;P_{\mathrm{EH}}^t+{\mathrm{\&eta;}}_{\mathrm{out}}\&times;H_{\mathrm{out}}^t-H_{\mathrm{in}}^t\&GreaterEqual;H_{\mathrm{Load}}^t---\left(5\right)$

(5) in formula, it is the heat production value of i-th miniature gas turbine; it is the heat production value of i-th gas fired-boiler; η _eH, η _outbe respectively the exothermal efficiency of the efficiency of electric heating conversion equipment, hot energy storage; for the heat load by time of intelligent grid garden; n _cHPfor the quantity of miniature gas turbine; n _boilerfor the quantity of gas fired-boiler.

9. the intelligent grid garden comprehensive energy optimal control method based on cold, heat and electricity triple supply according to claim 3, is characterized in that: described cold power-balance constraint function computing formula is as follows:

$\underset{i=1}{\overset{n_{\mathrm{CHP}}}{\mathrm{\&Sigma;}}}{C}_i^t+C_{\mathrm{cond}}^t\&GreaterEqual;C_{\mathrm{Load}}^t---\left(6\right)$

(6) in formula, the tail gas refrigeration work consumption of i-th miniature gas turbine by Absorption Refrigerator; n _cHPfor the quantity of miniature gas turbine; for air conditioner refrigerating power; for the hourly cooling load of intelligent grid garden.