CN105207205A - Distributed energy system energy optimization regulation and control method fusing demand side response - Google Patents

Abstract

The invention discloses a distributed energy system energy optimization regulation and control method fusing demand side response. The method includes the steps that a distributed energy system optimization regulation and control model is established, and the response capacity of user refrigeration equipment is introduced to participate in distributed energy system optimization operation; through considering the heat balance equation of an intra-system building, a mathematical relation between obtained user indoor temperature and output of the refrigeration equipment is described in a quantified mode. On the basis of meeting the requirement for comfort of a user in energy using, dependency on a battery energy storage system is reduced, and economy of distributed energy system operation is effectively improved; the distributed energy system optimization regulation and control model is established, the response capacity of the user side refrigeration equipment is introduced to participate in distributed energy system optimization operation, meanwhile, through considering the heat balance equation of the intra-system building, the mathematical relation between the obtained user indoor temperature and output of the refrigeration equipment is described in the quantified mode, and the comfort of the user is guaranteed. The result shows that through introduction of the user demand response, the energy-using cost can be effectively lowered.

Description

A kind of energy-optimised regulate and control method of distributed energy resource system merging Demand Side Response

Technical field

The invention belongs to distributed energy technical field, particularly relate to a kind of energy-optimised regulate and control method of distributed energy resource system merging Demand Side Response.

Background technology

Distributed energy resource system refers to the energy comprehensive utilization system being distributed in user side, by the integrated management of wind/light/storage distributed energy and Optimized Operation are realized user cold/cascade utilization of heat/electric various energy resources, effectively to improve comprehensive energy utilization ratio, reduce user and use energy cost.But in current distributed energy resource system, dissolving of intermittent distributed power source needs to configure jumbo battery energy storage system at high proportion, to ensure customer power supply quality and reliability.Current energy storage device cost is high, and Large Copacity is dropped in economically and technically all compares and is difficult to realize, and brings certain difficulty to the safe and highly efficient operation of distributed energy resource system.While ensureing performance driving economy, how realize fully dissolving to intermittent distributed power source at high proportion, be the major technology bottleneck that the propagation and employment of current distributed energy resource system faces.For this problem, some scholars has carried out deep research: due to the shortcoming of current technology, and the cost of battery energy storage system is still high; Meanwhile, the power stage of distributed power source has stochastic uncertainty, can cause the frequent impulse electricity of battery energy storage system thus, accelerate the ageing process of storage battery.

Present battery energy storage device cost is high, and Large Copacity is dropped in economically and technically all compares and is difficult to realize, and brings certain difficulty to the safe and highly efficient operation of distributed energy resource system.

Summary of the invention

The object of the present invention is to provide a kind of energy-optimised regulate and control method of distributed energy resource system merging Demand Side Response, be intended to solve present battery energy storage device cost high, Large Copacity is dropped in economically and technically all compares and is difficult to realize, and brings the problem of difficulty to the safe and highly efficient operation of distributed energy resource system.

The present invention realizes like this, a kind of energy-optimised regulate and control method of distributed energy resource system merging Demand Side Response, the energy-optimised regulate and control method of distributed energy resource system of described fusion Demand Side Response sets up distributed energy resource system Optimum Regulation model, participates in distributed energy resource system optimizing operation by the responding ability introducing user's refrigeration plant; Simultaneously by the equation of heat balance of building in consideration system, come quantitative description obtain user indoor temperature and refrigeration plant exert oneself between mathematical relationship.

Further, the energy-optimised regulate and control method of distributed energy resource system of described fusion Demand Side Response specifically comprises:

Step one, based on major network Power Market, the network formation of distributed energy resource system self, the characteristic of Demand Side Response resource, distributed power source, energy storage device that distributed energy resource system accesses, set up the energy management Optimal Operation Model of distributed energy resource system, constraint and target, obtain relevant parameter information;

Step 2, for the distributed power source in distributed energy resource system and load, residing external environment carries out the prediction in energy management dispatching cycle;

Step 3, based on the energy management Optimal Operation Model of set up target distribution formula energy resource system, carries out energy management Optimized Operation and solves;

Step 4, according to Optimized model, the scheduling scheme of exerting oneself within the operating scheme one day being obtained distributed energy resource system energy management each stage in execution cycle by interior point method;

Step 5, based on optimization gained scheduling scheme, utilizes EMS to issue control command to each controlled object.

Further, described structure Optimum Regulation model comprise users'comfort and refrigeration plant exert oneself between Mathematical Modeling, concrete construction method is as follows: P _exfor the exchange power of distributed energy resource system and external power grid, when being timing, distributed energy resource system obtains electricity from electrical network, and for time negative, distributed energy resource system is to external power grid sale of electricity; P _wt, P _pvrepresent exerting oneself of wind power generation and photovoltaic power generation equipment respectively; P _elfor the electric loading in distributed energy resource system; P _btrepresent exerting oneself of storage battery in distributed energy resource system, when it is timing, charge in batteries, for time negative, battery discharging; P _ecfor the input electric power of electric refrigerating machine; Q _ecfor the output refrigeration work consumption of electric refrigerating machine; Q _clfor the refrigeration duty in distributed energy resource system, be all that to reduce room temperature summer used;

Step one, obtain user indoor temperature and refrigeration plant exert oneself between mathematical relationship, the principle according to the conservation of energy obtains the equation of heat balance built, shown in (1):

$\mathrm{\Δ}Q=\mathrm{\ρ}\·C\·V\·\frac{{\mathrm{dT}}_{in}}{d\mathrm{\τ}}---\left(1\right)$

Wherein dT _in/ d τ is the rate of change of indoor temperature; ρ V is the quality of room air; C is specific heat capacity; Δ Q is the variable quantity of indoor heat;

Step 2, formula (1) is further converted to formula (2):

$k_{wall}\·F_{wall}\·(T_{out}-T_{in})+k_{win}\·F_{win}\·(T_{out}-T_{in})+\stackrel{\·}{I}\·F_{win}\·SC+Q-Q_{cl}=\mathrm{\ρ}\·C\·V\·\frac{{\mathrm{dT}}_{in}}{d\mathrm{\τ}}---\left(2\right)$

Wherein, k _wallf _wall(T _out-T _in) be the heat of external wall and outdoor transmission; k _wallfor the thermal transmission coefficient of external wall; F _wallfor external wall area; (T _out-T _in) for indoor and outdoor temperature poor; k _winf _win(T _out-T _in) be the heat of external window of building and outdoor transmission; k _winfor the thermal transmission coefficient of external window of building; F _winfor the area of external window of building; represent the heat that sun heat radiation transmits; for solar radiation power, the heat of every square metre of acceptance per second when representing vertical with illumination; Whether SC is shading coefficient, and have sunshading board, glass material relevant; Q is the heating power of indoor airflow; Q _clfor the refrigeration work consumption of refrigeration plant;

Step 3, constraints and Optimal Decision-making variable:

The energy-balance equation of goddess of lightning's line is as follows:

P _ex+P _WT+P _PV＝P _el+P _bt+P _ec(3)

Cold bus energy-balance equation:

Q _ec＝Q _cl(4)

Electricity refrigeration plant power conversion equation, wherein EER is the refrigeration efficiency ratio of electric refrigeration plant:

Q _ec＝P _ec·EER(5)

Formula (4) and (5) are combined into formula (6):

Q _cl＝P _ec·EER(6)

Formula (1) and (6) are the energy-balance equation that this distributed energy resource system utilizes bus-type structure to obtain;

Step 4, brings formula (6) into equation of heat balance (2), obtains the equation of heat balance of expressing with electric refrigeration plant power:

$\begin{array}{c}{k}_{wall}\·F_{wall}\·(T_{out}-T_{in})+k_{win}\·F_{win}\·(T_{out}-T_{in})+\stackrel{\·}{I}\·F_{win}\·SC+Q\\ -P_{ec}\·EER=\mathrm{\ρ}\·C\·V\·\frac{{\mathrm{dT}}_{in}}{d\mathrm{\τ}}\end{array}---\left(7\right)$

In distributed energy resource system, the power bound constraint of each equipment is such as formula shown in (8):

$\left\{\begin{array}{c}\underset{\&OverBar;}{P_{ex}}<P_{ex}<\stackrel{\&OverBar;}{P_{ex}}\\ \underset{\&OverBar;}{P_{bt}}<P_{bt}<\stackrel{\&OverBar;}{P_{bt}}\\ \underset{\&OverBar;}{P_{ec}}<P_{ec}<\stackrel{\&OverBar;}{P_{ec}}\end{array}\right.---\left(8\right)$

Step 5, the constraint of storage battery charge state, shown in (9):

$\underset{\&OverBar;}{W_{bt}}<W_{bt}<\stackrel{\&OverBar;}{W_{bt}}---\left(9\right)$

Wherein, W _btrepresent the electricity in storage battery a certain moment, such as, during moment t:

$\underset{\&OverBar;}{W_{bt}}<W_{bt\left(t\right)}=W_{bt\left(0\right)}+{\&Sigma;}_1^t{P}_{bt\left(t\right)}<\stackrel{\&OverBar;}{W_{bt}}---\left(10\right)$

Wherein, W _{bt (0)}for the initial quantity of electricity of storage battery;

Step 6, distributed energy resource system Optimum Regulation target function:

$\begin{array}{c}\begin{array}{cc}\min & C=\underset{i}{\&Sigma;}\end{array}\&lsqb;\left(\frac{C_{ph,i}+C_{se,i}}{2}\&CenterDot;P_{ex,i}+\frac{C_{ph,i}-C_{se,i}}{2}\&CenterDot;\left|P_{ex,i}\right|\right)\\ +\left(P_{WT,i}\&CenterDot;C_{{\mathrm{WT}}_{om}}+P_{PV,i}\&CenterDot;C_{{\mathrm{PV}}_{om}}+\left|P_{bt,i}\right|\&CenterDot;C_{{\mathrm{bt}}_{om}}+P_{ec,i}\&CenterDot;C_{{\mathrm{ec}}_{om}}\right)\\ +\mathrm{\&gamma;}\&CenterDot;\left|T_{in,i}-T_{set}\right|\&rsqb;\end{array}---\left(13\right)$

Section 1 in formula for distributed energy resource system and power grid energy exchange the net disbursement brought; C _{ph, i}for the i moment is from the electricity price of electrical network power purchase; C _{se, i}for the i moment is to the electricity price of electrical network sale of electricity;

Section 2 in formula represent the working service cost of each equipment in distributed energy resource system. represent the working service cost of blower fan, photovoltaic, storage battery and electric refrigerating machine unit interval section, unit power respectively;

Section 3 r|T in formula _{in, i-}t _set| for affecting the penalty function item that user's comfortableness is established, γ is penalty factor, for user is to the sensitivity of comfortableness, unit be unit/DEG C; γ selects according to different user's sensitiveness, is called user's coefficient of sensitivity;

Step 7, constraint and target function constitute a MILP model jointly, and be a time point with every 15 minutes, whole day is totally 96 moment, and the fusion Demand Side Response Optimum Regulation model of distributed energy resource system is as shown in (14):

$\left\{\begin{array}{cc}\min & C=\underset{i=1}{\overset{96}{\&Sigma;}}\&lsqb;\left(\frac{C_{ph,i}+C_{se,i}}{2}\&CenterDot;P_{ex,i}\frac{C_{ph,i}-C_{se,i}}{2}\&CenterDot;\left|P_{ex,i}\right|\right)\\ & +\left(P_{WT,i}\&CenterDot;C_{{\mathrm{WT}}_{om}}+P_{PV,i}\&CenterDot;C_{{\mathrm{PV}}_{om}}+\left|P_{bt,i}\right|\&CenterDot;C_{{\mathrm{bt}}_{om}}+P_{ec,i}\&CenterDot;C_{{\mathrm{ec}}_{om}}\right)\\ & +\mathrm{\&gamma;}\&CenterDot;\left|T_{in,i}-T_{set}\right|\&rsqb;\\ s.t.& P_{ex,t}+P_{WT,t}+P_{PV,t}-P_{el,t}-P_{bt,t}-P_{ec,t}=0\\ & k_{wall}\&CenterDot;F_{wall}\&CenterDot;\left(T_{out,t}-T_{in,t}\right)+k_{win}\&CenterDot;F_{win}\&CenterDot;\left(T_{out,t}-T_{in,t}\right)\\ & +{\stackrel{\&CenterDot;}{I}}_t\&CenterDot;F_{win}\&CenterDot;SC+Q_t-P_{ec,t}\&CenterDot;EER-\mathrm{\&rho;}\&CenterDot;C\&CenterDot;V\left(T_{in,t+1}-T_{in,t}\right)=0\\ & \underset{i=1}{\overset{96}{\&Sigma;}}{P}_{bt,t}=0\\ & \underset{\&OverBar;}{P_{ex}}<P_{ex,t}<\stackrel{\&OverBar;}{P_{ex}}\\ & \underset{\&OverBar;}{P_{bt}}<P_{bt,t}<\stackrel{\&OverBar;}{P_{bt}}\\ & \underset{\&OverBar;}{P_{ec}}<P_{ec,t}<\stackrel{\&OverBar;}{P_{ec}}\\ & \underset{\&OverBar;}{W_{bt}}<W_{bt\left(t\right)}=W_{bt\left(0\right)}+\underset{1}{\overset{t}{\&Sigma;}}{P}_{bt\left(t\right)}<\stackrel{\&OverBar;}{W_{bt}}\\ & \underset{\&OverBar;}{T_{in,t}<T_{in,t}<\frac{1}{T_{in,t}}}\end{array}\right.---\left(14\right)$

Wherein t=1,2 ..., 96.

Further, in Optimized model, the end of day moment is equal with the battery charge capacity of initial time, i.e. P _btalso need to meet following formula constraint:

∑P _bt＝0(11)

Finally, need to consider the constraint of architecture indoor temperature bound:

$\underset{\&OverBar;}{T_{in}}-<T_{in}<\stackrel{\&OverBar;}{T_{in}}---\left(12\right)$

P in this Optimized model _wt, P _pv, P _elwith be known premeasuring, the variable in optimizing process comprises P _ex, P _bt, P _ecand T _in, find out T _invalue can according to heat balance equation (2) by above-mentioned known premeasuring and P _ectry to achieve, therefore T _infor non-independent variable, independent decision-making variable only has three: P _ex, P _bt, P _ec.

The energy-optimised regulate and control method of distributed energy resource system of fusion Demand Side Response provided by the invention, user side electricity cold-hot conversion device (equipment such as electric refrigerating machine or heat pump) is included in the regulation process of distributed energy resource system, energy resource system comprehensive regulation cost minimization is target in a distributed manner, establish the Optimized model of distributed energy resource system regulate and control method, and carry out Optimization Solution, can meet on the basis of user by energy comfortableness, reduce the dependence to battery energy storage system, effectively improve the economy that distributed energy resource system runs; Establish distributed energy resource system Optimum Regulation model, distributed energy resource system optimizing operation is participated in by the responding ability introducing user side refrigeration plant, simultaneously by the equation of heat balance of building in consideration system, come quantitative description obtain user indoor temperature and refrigeration plant exert oneself between mathematical relationship, ensure user's comfortableness.Result shows that introducing user's request response effectively can economize on energy cost.The present invention, by regulating building room temperature within the scope of temperature pleasant degree, realizes the optimization response management of Demand-side resource, thus reduces the operating cost of microgrid.Meanwhile, the present invention, by can reduce the capacity configuration of energy-storage system to the optimization response management of Demand-side resource, reduces the cost of investment of microgrid to a certain extent.In addition, because user is different to the requirement of comfortableness, therefore invention introduces user's coefficient of sensitivity, and in microgrid regulation goal, consider the penalty term added because affecting user's comfortableness, make Optimized Operation can make corresponding adjustment to the different coefficient of sensitivity of user, meeting user individual can demand.

Accompanying drawing explanation

Fig. 1 is the energy-optimised regulate and control method flow chart of distributed energy resource system of the fusion Demand Side Response that the embodiment of the present invention provides.

Fig. 2 is the distributed energy resource system bus-type structural representation that the embodiment of the present invention provides.

Fig. 3 is the distributed energy resource system daily power consumption curve synoptic diagram that the embodiment of the present invention provides.

Fig. 4 is the building endogenous pyrogen heating curve schematic diagram that the embodiment of the present invention provides.

Fig. 5 is the Spot Price curve synoptic diagram that the embodiment of the present invention provides.

Fig. 6 is the ambient temperature curve synoptic diagram that the embodiment of the present invention provides.

Fig. 7 is the intensity of solar radiation curve synoptic diagram that the embodiment of the present invention provides.

Fig. 8 is the blower fan power curve schematic diagram that the embodiment of the present invention provides.

Fig. 9 is the photovoltaic power curve schematic diagram that the embodiment of the present invention provides.

Figure 10 is the Tie line Power curve synoptic diagram that the embodiment of the present invention provides.

Figure 11 is the accumulator cell charging and discharging curve synoptic diagram that the embodiment of the present invention provides.

Figure 12 is the electric refrigerating machine power curve schematic diagram that the embodiment of the present invention provides.

Figure 13 is the indoor temperature change generated in case curve synoptic diagram that the embodiment of the present invention provides.

Figure 14 is that electric refrigerating machine that the embodiment of the present invention provides is exerted oneself contrast schematic diagram.

Figure 15 is the Demand Side Response effect schematic diagram that the embodiment of the present invention provides.

Figure 16 is the execution mode flow chart that the embodiment of the present invention provides.

Embodiment

In order to make object of the present invention, technical scheme and advantage clearly understand, below in conjunction with embodiment, the present invention is further elaborated.Should be appreciated that specific embodiment described herein only in order to explain the present invention, be not intended to limit the present invention.

The present invention proposes a kind of energy-optimised regulating strategy of distributed energy resource system merging Demand Side Response, establish distributed energy resource system Optimum Regulation model, distributed energy resource system optimizing operation is participated in by the responding ability introducing user's refrigeration plant, simultaneously by the equation of heat balance of building in consideration system, come quantitative description obtain user indoor temperature and refrigeration plant exert oneself between mathematical relationship, ensure user's comfortableness.Result shows that introducing user's request response effectively can economize on energy cost.The present invention also can be applicable in the building energy supplying system of supply of cooling, heating and electrical powers unit, electric refrigerating machine, Absorption Refrigerator, electric energy storage, by regulating Indoor environment temperature within the scope of users'comfort, realize the optimization response management of Demand-side resource, thus the use of reduction building in whole dispatching cycle can cost.

Below in conjunction with accompanying drawing, application principle of the present invention is explained in detail.

S101: the major network Power Market, the network formation of distributed energy resource system self, the characteristic of Demand Side Response resource, distributed power source, energy storage device that access based on distributed energy resource system, set up the energy management Optimal Operation Model of distributed energy resource system, constraint and target, obtain relevant parameter information;

S102: for the distributed power source in distributed energy resource system and load, residing external environment carries out the premeasuring in energy management dispatching cycle, P in Optimized model of the present invention _wt, P _pv, P _elwith be known premeasuring;

S103: based on the energy management Optimal Operation Model of set up target distribution formula energy resource system, with P _ex, P _bt, P _ecas decision variable, carry out energy management Optimized Operation and solve;

S104: according to Optimized model, obtains the P in distributed energy resource system energy management each stage in execution cycle by interior point method _ex, P _bt, P _ecscheduling scheme of exerting oneself within operating scheme one day;

S105: based on optimization gained scheduling scheme, utilize EMS to issue control command to each controlled object.

Below in conjunction with accompanying drawing 16 and specific embodiment, application principle of the present invention is further described.

For typical distributed energy resource system a kind of shown in Fig. 2, build its Optimum Regulation model.In system, distributed power source comprises the distributed energy resource system bus-type structure of blower fan and photovoltaic generation as shown in Figure 2.P _exfor the exchange power of distributed energy resource system and external power grid, when it is timing, distributed energy resource system obtains electricity from electrical network, and for time negative, distributed energy resource system is to external power grid sale of electricity; P _wt, P _pvrepresent exerting oneself of wind power generation and photovoltaic power generation equipment respectively; P _elfor the general electric loading in distributed energy resource system; P _btrepresent exerting oneself of storage battery in distributed energy resource system, when it is timing, charge in batteries, for time negative, battery discharging; P _ecfor the input electric power of electric refrigerating machine; Q _ecfor the output refrigeration work consumption of electric refrigerating machine; Q _clfor the refrigeration duty in distributed energy resource system, be all that to reduce room temperature summer used.Electric refrigerating machine can be regarded as central air conditioner main machine, for handpiece Water Chilling Units, be made up of parts such as compressor, evaporator, condenser, expansion valves, can directly control its watt level consecutive variations by distributed energy resource system EMS, to realize the function participating in system optimized operation.

Users'comfort and refrigeration plant exert oneself between Mathematical Modeling

Utilize user's refrigeration plant to participate in system optimization regulation and control to run, must be based upon on the basis of guarantee user comfortableness.For user's refrigeration plant, the index weighing user's comfortableness is indoor temperature.Therefore, first to obtain user indoor temperature and refrigeration plant exert oneself between mathematical relationship.

Want accurate analog to build the situation of change of interior temperature, each factor affecting temperature must be determined.The diabatic process of building masonry wall is comparatively complicated, and will consider the impact of the factors such as convection current.For this reason when ensureing that its accuracy is in tolerance interval, here building masonry wall steady state heat transfer is supposed, and do not consider the impact of building enclosure accumulation of heat and the convection current factor of building, the principle according to the conservation of energy obtains the equation of heat balance built, shown in (1):

$\mathrm{\&Delta;}Q=\mathrm{\&rho;}\&CenterDot;C\&CenterDot;V\&CenterDot;\frac{{\mathrm{dT}}_{in}}{d\mathrm{\&tau;}}---\left(1\right)$

Wherein dT _in/ d τ is the rate of change of indoor temperature; ρ V is the quality of room air; C is specific heat capacity; Δ Q is the variable quantity of indoor heat.

The principal element affecting building interior heat has: indoor/outdoor temperature-difference cause cold/heat dissipation, sun heat radiation, human body and equipment heating in building, and the effect of cooling/heating equipment.For cooling in summer, formula (1) can be further converted to formula (2):

$k_{wall}\&CenterDot;F_{wall}\&CenterDot;(T_{out}-T_{in})+k_{win}\&CenterDot;F_{win}\&CenterDot;(T_{out}-T_{in})+\stackrel{\&CenterDot;}{I}\&CenterDot;F_{win}\&CenterDot;SC+Q-Q_{cl}=\mathrm{\&rho;}\&CenterDot;C\&CenterDot;V\&CenterDot;\frac{{\mathrm{dT}}_{in}}{d\mathrm{\&tau;}}---\left(2\right)$

Wherein, k _wallf _wall(T _out-T _in) be the heat of external wall and outdoor transmission; k _wallfor the thermal transmission coefficient of external wall; F _wallfor external wall area; (T _out-T _in) for indoor and outdoor temperature poor; k _winf _win(T _out-T _in) be the heat of external window of building and outdoor transmission; k _winfor the thermal transmission coefficient of external window of building; F _winfor the area of external window of building; represent the heat that sun heat radiation transmits; for solar radiation power, the heat of every square metre of acceptance per second when representing vertical with illumination; Whether SC is shading coefficient, and have sunshading board, glass material etc. relevant; Q is the heating power of indoor airflow, as the heating of human body and power consumption equipment; Q _clfor the refrigeration work consumption of refrigeration plant.

Constraints and Optimal Decision-making variable

The energy-balance equation of goddess of lightning's line is as follows:

P _ex+P _WT+P _PV＝P _el+P _bt+P _ec(3)

Cold bus energy-balance equation:

Q _ec＝Q _cl(4)

Electricity refrigeration plant power conversion equation, wherein EER is the refrigeration efficiency ratio of electric refrigeration plant:

Q _ec＝P _ec·EER(5)

Formula (4) and (5) can be combined into formula (6):

Q _cl＝P _ec·EER(6)

Formula (1) and (6) are the energy-balance equation that this distributed energy resource system utilizes bus-type structure to obtain.

Bring formula (6) into equation of heat balance (2), obtain the equation of heat balance of expressing with electric refrigeration plant power:

$\begin{array}{c}{k}_{wall}\&CenterDot;F_{wall}\&CenterDot;(T_{out}-T_{in})+k_{win}\&CenterDot;F_{win}\&CenterDot;(T_{out}-T_{in})+\stackrel{\&CenterDot;}{I}\&CenterDot;F_{win}\&CenterDot;SC+Q\\ -P_{ec}\&CenterDot;EER=\mathrm{\&rho;}\&CenterDot;C\&CenterDot;V\&CenterDot;\frac{{\mathrm{dT}}_{in}}{d\mathrm{\&tau;}}\end{array}---\left(7\right)$

In distributed energy resource system, the power bound constraint of each equipment is such as formula shown in (8):

$\left\{\begin{array}{c}\underset{\&OverBar;}{P_{ex}}<P_{ex}<\stackrel{\&OverBar;}{P_{ex}}\\ \underset{\&OverBar;}{P_{bt}}<P_{bt}<\stackrel{\&OverBar;}{P_{bt}}\\ \underset{\&OverBar;}{P_{ec}}<P_{ec}<\stackrel{\&OverBar;}{P_{ec}}\end{array}\right.---\left(8\right)$

In addition, storage battery, in the course of the work except needing to consider the constraint of its maximum charge-discharge electric power, also needs the constraint considering storage battery charge state, shown in (9):

$\underset{\&OverBar;}{W_{bt}}<W_{bt}<\stackrel{\&OverBar;}{W_{bt}}---\left(9\right)$

Wherein, W _btrepresent the electricity in storage battery a certain moment, such as, during moment t,

$\underset{\&OverBar;}{W_{bt}}<W_{bt\left(t\right)}=W_{bt\left(0\right)}+{\&Sigma;}_1^t{P}_{bt\left(t\right)}<\stackrel{\&OverBar;}{W_{bt}}---\left(10\right)$

Wherein, W _{bt (0)}for the initial quantity of electricity of storage battery.

Because this Optimized model carries out calculating for the situation of whole day, for ensureing continuity, need this end of day moment another equal with the battery charge capacity of initial time, i.e. P _btalso need to meet following formula constraint:

∑P _bt＝0(11)

Finally, need to consider the constraint of architecture indoor temperature bound:

$\underset{\&OverBar;}{T_{in}}-<T_{in}<\stackrel{\&OverBar;}{T_{in}}---\left(12\right)$

P in this Optimized model _wt, P _pv, P _elwith be known premeasuring.Variable in optimizing process comprises P _ex, P _bt, P _ecand T _in, can T be found out _invalue can according to heat balance equation (2) by above-mentioned known premeasuring and P _ectry to achieve, therefore T _infor non-independent variable, independent decision-making variable only has three: P _ex, P _bt, P _ec.

Distributed energy resource system Optimum Regulation target function

$\begin{array}{c}\begin{array}{cc}\min & C=\underset{i}{\&Sigma;}\end{array}\&lsqb;\left(\frac{C_{ph,i}+C_{se,i}}{2}\&CenterDot;P_{ex,i}+\frac{C_{ph,i}-C_{se,i}}{2}\&CenterDot;\left|P_{ex,i}\right|\right)\\ +\left(P_{WT,i}\&CenterDot;C_{{\mathrm{WT}}_{om}}+P_{PV,i}\&CenterDot;C_{{\mathrm{PV}}_{om}}+\left|P_{bt,i}\right|\&CenterDot;C_{{\mathrm{bt}}_{om}}+P_{ec,i}\&CenterDot;C_{{\mathrm{ec}}_{om}}\right)\\ +\mathrm{\&gamma;}\&CenterDot;\left|T_{in,i}-T_{set}\right|\&rsqb;\end{array}---\left(13\right)$

The main target of Optimum Regulation is on the basis ensureing user's comfortableness, minimizes the integrated operation cost of distributed energy resource system.Therefore its target function should have two parts to form, and one is Financial cost, and two is the punishment that user brings because comfortableness is not satisfied, and wherein Financial cost comprises again the working service cost of purchases strategies and each equipment.Distributed energy resource system shown in Fig. 2 merges the target function of the Optimum Regulation model after the response of user's request side such as formula shown in (13).

Section 1 in formula for distributed energy resource system and power grid energy exchange the net disbursement brought; C _{ph, i}for the i moment is from the electricity price of electrical network power purchase; C _{se, i}for the i moment is to the electricity price of electrical network sale of electricity.

Section 2 in formula represent the working service cost of each equipment in distributed energy resource system. represent the working service cost of blower fan, photovoltaic, storage battery and electric refrigerating machine unit interval section, unit power respectively.

Section 3 r|T in formula _{in, i}-T _set| for affecting the penalty function item that user's comfortableness is established, γ is penalty factor, can be regarded as the sensitivity of user to comfortableness, unit be unit/DEG C.γ can select according to different user's sensitiveness, is called user's coefficient of sensitivity.Can find out that γ is larger, the punishment that Demand Side Response is brought will be larger, otherwise the punishment that Demand Side Response is brought is less.

Above-mentioned a series of constraint and target function constitute a MILP model (MILP) jointly.Be a time point with every 15 minutes, whole day is totally 96 moment, and the fusion Demand Side Response Optimum Regulation model of distributed energy resource system shown in Fig. 2 is as shown in (15).

$\left\{\begin{array}{cc}\min & C=\underset{i=1}{\overset{96}{\&Sigma;}}\&lsqb;\left(\frac{C_{ph,i}+C_{se,i}}{2}\&CenterDot;P_{ex,i}\frac{C_{ph,i}-C_{se,i}}{2}\&CenterDot;\left|P_{ex,i}\right|\right)\\ & +\left(P_{WT,i}\&CenterDot;C_{{\mathrm{WT}}_{om}}+P_{PV,i}\&CenterDot;C_{{\mathrm{PV}}_{om}}+\left|P_{bt,i}\right|\&CenterDot;C_{{\mathrm{bt}}_{om}}+P_{ec,i}\&CenterDot;C_{{\mathrm{ec}}_{om}}\right)\\ & +\mathrm{\&gamma;}\&CenterDot;\left|T_{in,i}-T_{set}\right|\&rsqb;\\ s.t.& P_{ex,t}+P_{WT,t}+P_{PV,t}-P_{el,t}-P_{bt,t}-P_{ec,t}=0\\ & k_{wall}\&CenterDot;F_{wall}\&CenterDot;\left(T_{out,t}-T_{in,t}\right)+k_{win}\&CenterDot;F_{win}\&CenterDot;\left(T_{out,t}-T_{in,t}\right)\\ & +{\stackrel{\&CenterDot;}{I}}_t\&CenterDot;F_{win}\&CenterDot;SC+Q_t-P_{ec,t}\&CenterDot;EER-\mathrm{\&rho;}\&CenterDot;C\&CenterDot;V\left(T_{in,t+1}-T_{in,t}\right)=0\\ & \underset{i=1}{\overset{96}{\&Sigma;}}{P}_{bt,t}=0\\ & \underset{\&OverBar;}{P_{ex}}<P_{ex,t}<\stackrel{\&OverBar;}{P_{ex}}\\ & \underset{\&OverBar;}{P_{bt}}<P_{bt,t}<\stackrel{\&OverBar;}{P_{bt}}\\ & \underset{\&OverBar;}{P_{ec}}<P_{ec,t}<\stackrel{\&OverBar;}{P_{ec}}\\ & \underset{\&OverBar;}{W_{bt}}<W_{bt\left(t\right)}=W_{bt\left(0\right)}+\underset{1}{\overset{t}{\&Sigma;}}{P}_{bt\left(t\right)}<\stackrel{\&OverBar;}{W_{bt}}\\ & \underset{\&OverBar;}{T_{in,t}<T_{in,t}<\frac{1}{T_{in,t}}}\end{array}\right.---\left(14\right)$

Wherein t=1,2 ..., 96.

By following emulation, effect of the present invention is further described.

By the distributed energy resource system shown in Fig. 2 verify put forward the validity of the energy-optimised regulating strategy of distributed energy resource system merging Demand Side Response.What participate in Demand Side Response in system is set as small-sized only office building, long 30m, wide 20m, floor height 3m, totally three layers containing refrigerator system building.External wall adopts 190mm single row of holes to lay bricks, the adiabatic mortar of inside and outside 25mm; Window is PVC material plastic window, and glass is ordinary insulating glass, and establishes window areas to account for 50% of side exterior wall area.Relevant parameter is in table 1.

Table 1 participate in Demand Side Response containing refrigerator system architectural modulus information table

If the building office hours is 8:00 to 20:00, curve as shown in Figure 3 in this building day conventional electricity consumption (not containing refrigeration electricity consumption).Building endogenous pyrogen heating primarily of equipment and human body generate heat two parts composition.Equipment heating can be similar to be thought and to be directly proportional to its electricity consumption.Office hours is 8:00 to 20:00, adds human body heating at this moment in section again.Endogenous pyrogen heating curve can be obtained, as shown in Figure 4.The electricity price that electricity price adopts the electricity price of New York, United States typical case's day summer to convert to RMB.As shown in Figure 5.Shown in upper figure is price from electrical network power purchase, and during sale of electricity, be multiplied by a certain coefficient for sale of electricity price with this price, getting this coefficient is 0.8.For North China's typical case's day August, outdoor temperature change is as Fig. 6.

The intensity of solar radiation curve of northern China typical case's day summer can be obtained, as shown in Figure 7 from pertinent literature.The intensity of solar radiation of this value for accepting time vertical with sun direct projection direction, considers the angular relationship of sun direct projection direction and external window of building, the factor such as shading coefficient that part exterior window carries on the back sun and glass, approximately gets for 0.45*F _win* I _t.Atmospheric density ρ and air ratio thermal capacitance C gets 1.2kg/m respectively ³with 1000j/ (kg DEG C).Wind power generation and solar power generation are exerted oneself with also weather condition is relevant, predict the following Fig. 8 of its power curve, shown in 9.Select battery capacity to be 300kWh, but accumulator super-charge cross that put can cause extreme influence to the life of storage battery, therefore establishes storage battery charge capacity to be no more than 240kWh, is not less than 60kWh.Maximum charge-discharge electric power is 10kW.If storage battery initial time electricity is 100kWh.Distributed energy resource system and external power grid contact point exchange that the restriction of power is two-way is 400kW.Electricity refrigeration plant power is limited to 0 to 120kWh up and down, and refrigeration efficiency gets 3 than EER.Blower fan, photovoltaic, storage battery, electric refrigeration plant working service cost get 0.11,0.08,0.02,0.01 yuan/kWh respectively.Add Demand Side Response, if user operationally between can accept temperature and fluctuate in the scope of positive and negative 2.5 DEG C of design temperature, the design temperature of user is still 22.5 degree.Coefficient of sensitivity γ=0.1 of user.Other condition is constant, and optimum results is as shown in Figure 10 ~ 13.

The Power Exchange of distributed energy resource system and external power grid and the working condition of storage battery, when having a Demand Side Response, the two does not have significant change.The electricity working condition of refrigeration plant and indoor temperature operationally between (8:00 to 20:00) have obvious difference, there is obvious fluctuation.In this situation, total operating cost is 912.9 yuan.

First contrast introduces Demand Side Response successively, electric refrigeration plant power curve, as shown in figure 14.Can find out after adding Demand Side Response, exerting oneself of electric refrigeration plant is fluctuated for benchmark up and down with the power curve without electric refrigerating machine during Demand Side Response.Its part exceeding benchmark of exerting oneself is cold-storage, i.e. " charging ", lower than the part of benchmark for letting cool, i.e. " electric discharge ".The difference of being exerted oneself by refrigeration plant is in both cases depicted as the form of curve will be similar with battery-operated situation.With electric refrigerating machine benchmark exert oneself deduct add Demand Side Response after exert oneself, obtain curve as shown in figure 15.

The foregoing is only preferred embodiment of the present invention, not in order to limit the present invention, all any amendments done within the spirit and principles in the present invention, equivalent replacement and improvement etc., all should be included within protection scope of the present invention.

Claims (8)

1. one kind merges the energy-optimised regulate and control method of distributed energy resource system of Demand Side Response, it is characterized in that, the energy-optimised regulate and control method of distributed energy resource system of described fusion Demand Side Response sets up distributed energy resource system Optimum Regulation model, participates in distributed energy resource system optimizing operation by the responding ability introducing user's refrigeration plant; Simultaneously by the equation of heat balance of building in consideration system, quantitative description obtain user indoor temperature and refrigeration plant exert oneself between mathematical relationship.

2. the energy-optimised regulate and control method of distributed energy resource system merging Demand Side Response as claimed in claim 1, it is characterized in that, the energy-optimised regulate and control method of distributed energy resource system of described fusion Demand Side Response specifically comprises:

Step one, based on major network Power Market, the network formation of distributed energy resource system self, the characteristic of Demand Side Response resource, distributed power source, energy storage device that distributed energy resource system accesses, set up the energy management Optimal Operation Model of distributed energy resource system, obtain relevant parameter information;

Step 2, for the distributed power source in distributed energy resource system and load, residing external environment carries out the prediction in energy management dispatching cycle;

Step 3, based on the energy management Optimal Operation Model of set up target distribution formula energy resource system, carries out energy management Optimized Operation and solves;

Step 4, according to Optimized model, the scheduling scheme of exerting oneself within the operating scheme one day being obtained distributed energy resource system energy management each stage in execution cycle by interior point method.

3. the as claimed in claim 2 energy-optimised regulate and control method of distributed energy resource system merging Demand Side Response, is characterized in that, described structure Optimum Regulation model comprise users'comfort and refrigeration plant exert oneself between Mathematical Modeling, concrete construction method is as follows: P _exfor the exchange power of distributed energy resource system and external power grid, when being timing, distributed energy resource system obtains electricity from electrical network, and for time negative, distributed energy resource system is to external power grid sale of electricity; P _wt, P _pvrepresent exerting oneself of wind power generation and photovoltaic power generation equipment respectively; P _elfor the electric loading in distributed energy resource system; P _btrepresent exerting oneself of storage battery in distributed energy resource system, when it is timing, charge in batteries, for time negative, battery discharging; P _ecfor the input electric power of electric refrigerating machine; Q _ecfor the output refrigeration work consumption of electric refrigerating machine; Q _clfor the refrigeration duty in distributed energy resource system, be all that to reduce room temperature summer used;

Step one, obtain user indoor temperature and refrigeration plant exert oneself between mathematical relationship, keep according to energy

Permanent principle obtains the equation of heat balance built, shown in (1):

$\mathrm{\&Delta;}Q=\mathrm{\&rho;}\&CenterDot;C\&CenterDot;V\&CenterDot;\frac{{\mathrm{dT}}_{in}}{d\mathrm{\&tau;}}---\left(1\right)$

Wherein dT _in/ d τ is the rate of change of indoor temperature; ρ V is the quality of room air; C is specific heat capacity; Δ Q is the variable quantity of indoor heat;

Step 2, the cooling/heating power stage of human body and equipment heating and cooling/heating equipment in building, formula (1) is further converted to formula (2):

$k_{wall}\&CenterDot;F_{wall}\&CenterDot;(T_{out}-T_{in})+k_{win}\&CenterDot;F_{win}\&CenterDot;(T_{out}-T_{in})+\stackrel{\&CenterDot;}{I}\&CenterDot;F_{win}\&CenterDot;SC+Q-Q_{cl}=\mathrm{\&rho;}\&CenterDot;C\&CenterDot;V\&CenterDot;\frac{{\mathrm{dT}}_{in}}{d\mathrm{\&tau;}}---\left(2\right)$

Wherein, k _wallf _wall(T _out-T _in) be the heat of external wall and outdoor transmission; k _wallfor the thermal transmission coefficient of external wall; F _wallfor external wall area; (T _out-T _in) for indoor and outdoor temperature poor; k _winf _win(T _out-T _in) be the heat of external window of building and outdoor transmission; k _winfor the thermal transmission coefficient of external window of building; F _winfor the area of external window of building; represent the heat that sun heat radiation transmits; for solar radiation power, the heat of every square metre of acceptance per second when representing vertical with illumination; Whether SC is shading coefficient, and have sunshading board, glass material relevant; Q is the heating power of indoor airflow; Q _clfor the refrigeration work consumption of refrigeration plant;

Step 3, constraints and Optimal Decision-making variable:

The energy-balance equation of goddess of lightning's line is as follows:

P _ex+P _WT+P _PV＝P _el+P _bt+P _ec(3)

Cold bus energy-balance equation:

Q _ec＝Q _cl(4)

Electricity refrigeration plant power conversion equation, wherein EER is the refrigeration efficiency ratio of electric refrigeration plant:

Q _ec＝P _ec·EER(5)

Formula (4) and (5) are combined into formula (6):

Q _cl＝P _ec·EER(6)

Formula (1) and (6) are the energy-balance equation that this distributed energy resource system utilizes bus-type structure to obtain;

Step 4, brings formula (6) into equation of heat balance (2), obtains the equation of heat balance of expressing with electric refrigeration plant power:

$k_{wall}\&CenterDot;F_{wall}\&CenterDot;(T_{out}-T_{in})+k_{win}\&CenterDot;F_{win}\&CenterDot;(T_{out}-T_{in})+\stackrel{\&CenterDot;}{I}\&CenterDot;F_{win}\&CenterDot;SC+Q$

$-P_{ec}\&CenterDot;EER=\mathrm{\&rho;}\&CenterDot;C\&CenterDot;V\&CenterDot;\frac{{\mathrm{dT}}_{in}}{d\mathrm{\&tau;}}---\left(7\right)$

In distributed energy resource system, the power bound constraint of each equipment is such as formula shown in (8):

$\left\{\begin{array}{c}\underset{\&OverBar;}{P_{\mathrm{ex}}}<P_{\mathrm{ex}}<\stackrel{\&OverBar;}{P_{\mathrm{ex}}}\\ \underset{\&OverBar;}{P_{\mathrm{bt}}}<P_{\mathrm{bt}}<\stackrel{\&OverBar;}{P_{\mathrm{bt}}}\\ \underset{\&OverBar;}{P_{ec}}<P_{ec}<\stackrel{\&OverBar;}{P_{ec}}\end{array}\right.---\left(8\right)$

Step 5, the constraint of storage battery charge state, shown in (9):

$\underset{\&OverBar;}{W_{bt}}<W_{bt}<\stackrel{\&OverBar;}{W_{bt}}---\left(9\right)$

Wherein, W _btrepresent the electricity in storage battery a certain moment, such as, during moment t:

$\underset{\&OverBar;}{W_{bt}}<W_{bt\left(t\right)}=W_{bt\left(0\right)}+{\mathrm{\&Sigma;}}_1^t{P}_{bt\left(t\right)}<\stackrel{\&OverBar;}{W_{bt}}---\left(10\right)$

Wherein, W _{bt (0)}for the initial quantity of electricity of storage battery;

Step 6, distributed energy resource system Optimum Regulation target function:

$\begin{array}{c}\min C=\underset{i}{\mathrm{\&Sigma;}}\&lsqb;(\frac{C_{ph,i}+C_{se,i}}{2}\&CenterDot;P_{ex,i}+\frac{C_{ph,i}-C_{se,i}}{2}\&CenterDot;|P_{ex,i}\left|\right)\\ +(P_{WT,i}\&CenterDot;C_{{\mathrm{WT}}_{om}}+P_{PV,i}\&CenterDot;C_{{\mathrm{PV}}_{om}}+|P_{bt,i}|\&CenterDot;C_{{\mathrm{bt}}_{om}}+P_{ec,i}\&CenterDot;C_{{\mathrm{ec}}_{om}})\\ +\mathrm{\&gamma;}\&CenterDot;|T_{in,i}-T_{set}|\&rsqb;\end{array}---\left(13\right)$

Section 1 in formula for distributed energy resource system and power grid energy exchange the net disbursement brought; C _{ph, i}for the i moment is from the electricity price of electrical network power purchase; C _{se, i}for the i moment is to the electricity price of electrical network sale of electricity;

Section 2 in formula represent the working service cost of each equipment in distributed energy resource system; represent the working service cost of blower fan, photovoltaic, storage battery and electric refrigerating machine unit interval section, unit power respectively;

Section 3 γ in formula | T _{in, i}-T _set| for affecting the penalty function item that user's comfortableness is established, γ is penalty factor, for user is to the sensitivity of comfortableness, unit be unit/DEG C; γ selects according to different user's sensitiveness, is called user's coefficient of sensitivity;

Step 7, constraint and target function constitute a MILP model jointly, and be a time point with every 15 minutes, whole day is totally 96 moment, and the fusion Demand Side Response Optimum Regulation model of distributed energy resource system is as shown in (14):

Wherein t=1,2 ..., 96.

4. the energy-optimised regulate and control method of distributed energy resource system merging Demand Side Response as claimed in claim 3, it is characterized in that, in Optimized model, the end of day moment is equal with the battery charge capacity of initial time, i.e. P _btalso need to meet following formula constraint:

∑P _bt＝0(11)

Finally, need to consider the constraint of architecture indoor temperature bound:

$\underset{\&OverBar;}{T_{in}}<T_{in}<\stackrel{\&OverBar;}{T_{in}}---\left(12\right)$

P in this Optimized model _wt, P _pv, P _elwith be known premeasuring, the variable in optimizing process comprises P _ex, P _bt, P _ecand T _in, find out T _invalue according to heat balance equation (2) by above-mentioned known premeasuring and P _ectry to achieve, therefore T _infor non-independent variable, independent decision-making variable only has three: P _ex, P _bt, P _ec.

5. one kind uses the supply of cooling, heating and electrical powers unit of the energy-optimised regulate and control method of the distributed energy resource system merging Demand Side Response described in claim 1-4 any one.

6. one kind uses the electric refrigerating machine of the energy-optimised regulate and control method of the distributed energy resource system merging Demand Side Response described in claim 1-4 any one.

7. one kind uses the Absorption Refrigerator of the energy-optimised regulate and control method of the distributed energy resource system merging Demand Side Response described in claim 1-4 any one.

8. one kind uses the building energy supplying system of the electric energy storage of the energy-optimised regulate and control method of the distributed energy resource system merging Demand Side Response described in claim 1-4 any one.