CN107807523A - Consider the Regional Energy internet multi-source coordination optimization operation reserve of tou power price - Google Patents

Consider the Regional Energy internet multi-source coordination optimization operation reserve of tou power price Download PDF

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CN107807523A
CN107807523A CN201710968940.3A CN201710968940A CN107807523A CN 107807523 A CN107807523 A CN 107807523A CN 201710968940 A CN201710968940 A CN 201710968940A CN 107807523 A CN107807523 A CN 107807523A
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mrow
msubsup
cold
energy
heat
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霍现旭
蒋菱
赵洪磊
赵宝国
杨宇全
严晶晶
王旭东
马世乾
王嘉庚
王凯
徐青山
孙璐
曾艾东
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State Grid Corp of China SGCC
State Grid Tianjin Electric Power Co Ltd
Electric Power Research Institute of State Grid Tianjin Electric Power Co Ltd
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State Grid Corp of China SGCC
State Grid Tianjin Electric Power Co Ltd
Electric Power Research Institute of State Grid Tianjin Electric Power Co Ltd
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    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
    • G05B13/042—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators in which a parameter or coefficient is automatically adjusted to optimise the performance

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Abstract

The present invention relates to a kind of Regional Energy internet multi-source coordination optimization operation reserve for considering tou power price, its technical characterstic is：Comprise the following steps：The primary data parameter of step 1, input area energy internet system；Cold heat/electric flexible load response under step 2, analysis tou power price, build the response model of two class cold heats/electric flexible load；Step 3, structure minimize the Regional Energy internet system Optimal Operation Model and its operation constraints of financial cost；Step 4, solved using Hessian interior point methods the step 3 minimum financial cost Regional Energy internet system Optimal Operation Model.The present invention not only contributes to lifting region energy internet regenerative resource consumption level, for realizing that systematic economy safe operation is also significant.

Description

Consider the Regional Energy internet multi-source coordination optimization operation reserve of tou power price
Technical field
The invention belongs to field of new energy technologies, is related to Regional Energy internet, especially a kind of consideration tou power price Regional Energy internet multi-source coordination optimization operation reserve.
Background technology
With the change of the social overall situation such as energy security, climate crisis, environmental protection, various countries' real-time energy resource structure one after another Change and energy-consuming changes.Energy internet is the various advanced Power Electronic Technique of an integrated use, information communication skill Art and intelligent management technology get up electric power networks, oil network, natural gas network, the interconnection of transportation network equal energy source, to realize The energy peer switch of energy in bidirectional flow and shared complication system.Its construction and development is the important of energy technology field Change, it is intended to improve efficiency of energy utilization, reduce disposal of pollutants, ensure energy supply and energy security.
The elementary cell that Regional Energy internet develops as energy Internet Strategy, promote provide multiple forms of energy to complement each other, it is renewable Good application prospect is shown in terms of energy consumption, lifting energy utilization rate.Therefore, how to realize in Regional Energy internet The collaboration optimization operation of portion's mass energy resource, increasingly become the emphasis of people's research.Existing research only accounts for mostly Various power-supply devices or the system optimization scheduling being combined with energy storage, and do not consider flexible load under energy prices excitation can Scheduling property.
The content of the invention
It is an object of the invention to overcome the deficiencies of the prior art and provide a kind of consideration point reasonable in design, science is practical When electricity price Regional Energy internet multi-source coordination optimization operation reserve.
The present invention solves its technical problem and takes following technical scheme to realize：
A kind of Regional Energy internet multi-source coordination optimization operation reserve for considering tou power price, comprises the following steps：
The primary data parameter of step 1, input area energy internet system, including all kinds of energy device configurations in system The initial predicted demand of parameter, specific tou power price and Gas Prices and typical day day part cold heat/electric load, cold/ The initial demand predicted value of heat/electric load is taken as Q respectivelyct、Qht、Eet；Cold heat/electricity under step 2, analysis tou power price is flexible Load responding, it is modified by the initial demand predicted value of cold heat/electric load to step 1, structure two class cold heats/electricity is soft Property load response model, and then establish multiple-energy-source interacted system cold heat/electricity is flexible under tou power price for energy demand side bears Lotus responding ability；
The response model of two class cold heats/electric flexible load, false under step 3, the tou power price mechanism established based on step 2 Be located at natural gas price maintenance under day yardstick is constant, only consider the influence of tou power price in the case of, structure minimizes economical The Regional Energy internet system Optimal Operation Model and its operation constraints of cost；
Step 4, solved using Hessian interior point methods the step 3 minimum financial cost Regional Energy internet System optimization scheduling model.
Moreover, the specific method of cold heat/electric flexible load response under the analysis tou power price of the step 2 is：
Flexible cold heat/electric load in Regional Energy internet is divided into following two class first：The first kind：Power consumption power is consolidated The fixed, time of running adjustable load；Second class：The time of running fixes, two class cool and thermal powers of the adjustable load of watt level are flexible Load；Then two class cold heats/electric flexible load respondent behavior and response policy are analyzed, then the side for passing through statistical forecast Method obtains the maximum allowable adjustable amount Δ Q of the cold and hot electric load of the class of day part two in Regional Energy internet1ct、ΔQ1ht、ΔE1etWith ΔQ2ct、ΔQ2ht、ΔE2et；
Wherein, Δ Q1ct、ΔQ2ctThe adjustable amount of the maximum of respectively two class flexibility refrigeration dutys；ΔQ1ht、ΔQ2htRespectively The adjustable amount of the maximum of two class flexibility thermic loads；ΔE1et、ΔE2etThe maximum allowable of respectively two class flexibility electric loads can adjust Amount.
Moreover, the specific method of the response model of the class cold heat of structure two/electric flexible load of the step 2 is：According to right The analysis of two class cold heats/electric flexible load respondent behavior and response policy, predicted by the initial demand in cold heat/electric load Corrected on the basis of value, can obtain the requirement express formula of two class cold heats/electric load response model of multi-energy system：
Q‘ct=Qct+ΔQ1ctδ1ct+ΔQ2ctδ2ct
Q’ht=Qht+ΔQ1htδ1ht+ΔQ2htδ2ht
E‘et=Eet+ΔE1etδ1et+ΔE2etδ2et
In formula, Qct、Qht、EetFor the cold and hot electrical load requirement predicted value of day part；Q‘ct、Q‘ht、E‘etTo consider flexible load The cold and hot electrical load requirement correction value of day part obtained after response tou power price；δ1ct、δ2ct、δ1ht、δ2ht、δ1et、δ2etIt is fixed respectively Justice is two class cold heats/electric flexible load response coefficient under tou power price different periods, and numerical value reflects between -1 to 1 Cold heat/schedulability of the electric flexible load under tou power price.
Moreover, the step 3 includes step in detail below：
(1) multiple-energy-source interacted system day power purchase under tou power price, purchase gas cost minimization object function are established：
minCtotal=Celc+Cgas
In formula, CtotalFor resulting cost, Celc、CgasRespectively power purchase, the expense of purchase gas；For the electricity price of t periods, Et,buyFor the purchase of electricity of t periods, Et,buy＜ 0 then represents the period system to external power grid sale of electricity, and purchases strategies now are negative Value, equivalent to income；pgasFor in a few days natural gas and coal-fired unit price；Respectively gas-fired boiler Stove, gas electricity generator the t periods heating capacity and generated energy and corresponding heating efficiency, generating efficiency；
(2) bar is constrained according to the operation of actual conditions given area energy internet on the basis of object function is established Part, is specifically divided into equipment operation constraint and energy balance constrains two classes：
1) equipment operation constraint：
A photovoltaic cell units limits：
In formula, Pt PV、The participation dispatch value of photovoltaic unit generation power respectively in the t periods, predicted value and Maximum；
B Wind turbines units limits：
In formula, Pt Wind、The dispatch value of Wind turbines generated output respectively in the t periods, predicted value and most Big value；
C gas turbine operations constrain：
In formula, Pt CT、Respectively generated output, the rated power of t period gas turbines；Cut for gas turbine Except coefficient；Respectively most increase power output and maximum drop power output of the gas turbine in continuous operation； Et,gasFor the amount of natural gas of t periods gas turbine consumption；A, b, c, d are efficiency factor.
D batteries to store energy facility constraints：
In formula,Respectively accumulator cell charging and discharging power in the t periods, and be only capable of charging in the same period or put Electricity,The respectively maximum allowable charge-discharge electric power of battery；A respectively cycle is initial and terminates Moment batteries to store energy.
E earth source heat pumps unit operation constrains：
Under refrigeration mode：
Under heating mode：
In formula,Respectively earth source heat pump unit refrigeration work consumption and heats power；Respectively To be used to supply refrigeration duty and cold energy for cold-storage under heat pump refrigerating pattern；Respectively heat pump heating mode It is lower to be used to supply thermic load and the heat energy for accumulation of heat；Respectively earth source heat pump unit is in the t periods Electric energy and its refrigeration, the heating efficiency of consumption.
The operation constraint of f gas fired-boilers：
In formula,The respectively general power of t period gas fired-boilers, maximum allowable power, heating and refrigeration work( Rate；Qt,gasFor the air consumption in the gas fired-boiler t periods；Respectively gas fired-boiler is heated for heat Load, absorption refrigeration, the heat energy of heat storage tank.
The operation constraint of g waste heat boilers：
In formula,Total waste heat is absorbed for the t periods；ηREFor to gas turbine waste heat recovery coefficient；
Respectively residual heat collection is used for absorption refrigeration, heating demand, heat storage tank accumulation of heat Heat energy.
The operation constraint of h heat storage tanks：
In formula,For the storage of t period heat storage tanks, heat release power；Be respectively heat storage tank store, The maximum allowable operation power of heat release；For a cycle is initial and the energy storage value of end time heat storage tank.
The operation constraint of i electric refrigerating machines：
In formula, Pt ECT、For electric refrigerating machine t periods power and power limit；Electricity is consumed for t periods electric refrigerating machine Energy；For electric refrigerating machine coefficient of refrigerating performance.
The operation constraint of j Absorption Refrigerators：
In formula, Pt ACT、For absorption formula refrigeration machine t periods refrigeration work consumption and its allow to run limit value；To absorb Formula refrigeration machine cooling coefficient；
The operation constraint of k cold-storage tanks：
In formula,For the storage of t period cold-storage apparatus, let cool energy power；It is cold-storage respectively The storage of equipment, let cool the maximum allowable operation power of energy；For a cycle is initial and the energy storage of end time cold-storage apparatus Value；
2) account load balancing constraints；
A refrigeration duty Constraints of Equilibrium：
In formula,For earth source heat pump unit work power in cooling mode, if supplying cold directly, take on the occasion of, if To cold-storage apparatus cold-storage, then to take negative value；Pt SCFor the power of cold-storage apparatus, taken when discharging cold energy taken when, cold-storage it is negative Value, and for cold-storage tank, because release cold energy and cold-storage can not be carried out simultaneously, therefore Pt SC、Must be with positive and negative；Pt ECT、 Pt ACTThe respectively refrigeration work consumption of electric refrigerating machine and Absorption Refrigerator；
B heat load balances constrain：
In formula,For earth source heat pump unit work power in a heating mode, if direct heating, take on the occasion of, if To heat storage tank accumulation of heat, then to take negative value；Pt SHFor the power of heat storage tank, taken when discharging heat energy and take negative value when, accumulation of heat, together Sample can not be carried out simultaneously due to discharging heat energy and accumulation of heat, therefore Pt SH、Must be with positive and negative；Respectively fire Gas boiler heat supply and the power of waste heat boiler heating；
C electric load Constraints of Equilibrium：Pt PV+Pt Wind+Pt GT+Pt buy+Pt SE-Pt pump-Pt ECT=Eet'
In formula, Pt PV、Pt WindFor system photovoltaic cell group and the operation power of blower fan, predicted value is taken in the present embodiment；Pt SE For the operation power in the battery t periods, take take negative value when, charging upon discharging；Pt pumpFor the earth source heat pump unit t periods Interior electrical power；Pt ECTFor power consumption of the electric refrigerating machine within the t periods；
The d times of running can tone category first kind cold heat/electric flexible load conservation power constraint：
∑Q1ctδ1ct=0
∑Q1htδ1ht=0
∑E1etδ1et=0
Moreover, the step 4 comprises the following specific steps that：
(1) to the variable assignments being related in Optimized model, the tune of each energy device and all kinds of cool and thermal power flexible loads is determined Spend initial value；And given maximum iteration and the iteration ends limits of error；
(2) slack variable is introduced, by the inequality constraints equation in above-mentioned Optimal Operation Model；
(3) optimization after constraining equation is calculated to the optimization meter for being converted into unconfined condition using Lagrangian Calculate；
(4) Unconstrained Optimization Problem is embodied, a series of Nonlinear System of Equations is obtained by KKT conditions；
(5) to the Hessian matrix Ls DL in Nonlinear System of EquationsTDecompose and be iterated solution；
(6) judge whether result of calculation restrains, if convergence, export result of calculation；Otherwise iteration is continued, if until super Go out maximum iteration not restrain still, then change initial guess and recalculate；
(7), should if generally meeting to optimize criterion based on the result of calculation that Hessian interior point methods solve to obtain As a result it is final Optimized Operation plan, including each energy device is contributed and all kinds of cold heats/electric flexible load response；Otherwise Respondence to the Price of Electric Power coefficient of the two class cold heats/electric flexible load in day part should be adjusted, and corrects day part cold heat/electric load accordingly Actual demand value, substitute into Optimized model and carry out the Optimized Iterative of next step and calculate, until to obtain an overall economics optimal The scheduling scheme a few days ago changed.
The advantages and positive effects of the present invention are：
1st, the present invention proposes a kind of Regional Energy internet multi-source coordination optimization operation reserve for considering tou power price, with Include gas turbine, regenerative resource (photovoltaic, wind-powered electricity generation), gas fired-boiler, refrigeration machine, energy storage device, cool and thermal power flexible load etc. The Regional Energy internet of various energy resources resource is research object, considers real-time electric power energy prices to flexible load demand With the influence of energy supplying system power consumption behavior, propose that the Regional Energy under the conditions of tou power price interconnects the scheduling plan that multi-source coordinates and optimizes Slightly；Target is turned to Regional Energy system power purchase, purchase gas cost minimization, the multipotency association established under tou power price load responding mechanism With complementary Optimized model, carried optimization problem is solved using interior point method, and by case verification carry multi-source coordinate it is excellent Change the validity and economy of scheduling strategy.The present invention provides a kind of effective, science, the Regional Energy interconnection microgrid multi-source of practicality Coordination optimization strategy, lifting region energy internet regenerative resource consumption level is not only contributed to, for realizing systematic economy Safe operation is also significant.
2nd, the Optimal Scheduling of the cost of the invention that run minimized for Regional Energy internet, propose that a kind of consideration divides When electricity price Regional Energy internet multi-source coordination optimization operation reserve, it is mutual obtaining certain Regional Energy using load prediction technology In networked system on the basis of cold and hot electrical load requirement, influence of the tou power price to flexible load demand is considered, formulate demand and ring Strategy and the accordingly cold and hot electrical load requirement of update the system are answered, in combination with the price variance pair of different time sections electric energy and natural gas The influence of system power consumption behavior, to minimize Regional Energy net purchase electricity, purchase gas cost as target, meeting cold and hot electric energy balance Under conditions of, the response coefficient of each equipment output and all kinds of flexible loads is adjusted, finally realizes that Regional Energy internet is a variety of The collaboration optimization of energy resources.
3rd, the present invention is tactful according to the Regional Energy internet multi-source coordination optimization of the consideration tou power price carried, with certain allusion quotation Type Regional Energy internet garden is example, with reference to system typical energy source device parameter, establishes operating cost and minimizes model, excellent Change solve the strategy under example system typical case's day each unit output, flexible load response and system operation economy refer to Mark, the results showed that：All kinds of cool and thermal power flexible loads can effectively participate in the Optimized Operation of system, day part load under tou power price Adjustable angle value and the electricity price correlation of the period；Under different electric energy, natural gas rate structure, the power consumption row of system To have differences, when energy value is higher, it is often higher that natural gas accounts for energy-consuming proportion；With not considering tou power price to soft Property load side influence Optimized Operation strategy compare, the present invention carry strategy in the energy resource consumption of multi-energy system more economy Property.
4th, the present invention combines the energy supplying system of certain cold and hot electric load in representative region energy internet garden, it is proposed that Yi Zhongkao Consider the Regional Energy internet multi-source coordination optimization strategy of tou power price, and utilize the supply of summer typical case's day, refrigeration duty, winter Season electricity, the supply of thermic load situation, the scheduling result of typical day system is obtained by solving-optimizing model, including in day part it is each The output of kind equipment and the regulated quantity of cool and thermal power flexible load, as a result show tou power price for the soft of Regional Energy internet Property cool and thermal power burdened resource have positive scheduling effect, while also power purchase, purchase gas behavior in system are had an impact, consider timesharing The Optimized Operation strategy of electricity price is while realization is provided multiple forms of energy to complement each other, regenerative resource dissolves with more good economy.
Brief description of the drawings
Fig. 1 is the process chart of the present invention；
Fig. 2 is that representative region energy internet energizes structure chart；
Fig. 3 is that Hessian interior point methods solve flow chart；
Fig. 4 is Regional Energy internet garden typical case's day tou power price figure；
Fig. 5 is garden typical case summer, regenerative resource day output and cool and thermal power predicted load schematic diagram；
Fig. 6 is typical case summer, flexible electrical day, refrigeration duty is adjustable angle value schematic diagram；
Fig. 7 gardens typical case winter, regenerative resource day is contributed and cool and thermal power predicted load schematic diagram；
Fig. 8 is typical case winter, flexible electrical day, thermic load is adjustable angle value schematic diagram；
Fig. 9 is summer typical case day electric energy optimizing scheduling result schematic diagram；
Figure 10 is summer typical case day cold energy Optimized Operation result schematic diagram；
Figure 11 is winter typical case day electric energy optimizing scheduling result schematic diagram；
Figure 12 is winter typical case day heat energy Optimized Operation result schematic diagram.
Embodiment
The embodiment of the present invention is described in further detail below in conjunction with accompanying drawing：
A kind of Regional Energy internet multi-source coordination optimization operation reserve for considering tou power price, it is mutual with the representative region energy Network as research object, consider influence of the tou power price to workload demand side and energy supply side, control flexible load Respond and adjust the power consumption behavior of powering device, so as to optimize the fortune that Regional Energy internet supplied, needed both sides various energy resources resource OK, with meet system minimize electric energy, natural gas purchase cost target, thus solve multipotency cooperative compensating and systematic economy fortune The problem of row.The present invention with reference to powering device by the flexible cool and thermal power burdened resource under the conditions of tou power price, carrying out comprehensive Coordination optimization scheduling is closed, to improve the economy of system operation.The main energy for determining multiple-energy-source interacted system of the invention needs Two class cold heats/electric flexible load responding ability of the side under tou power price mechanism, structure is asked to consider the Regional Energy of tou power price The analysis checking of internet Optimal Operation Model and real case.
The present invention considers flexible load and the schedulability of multiple power sources equipment under power network tou power price mechanism, proposes A kind of Regional Energy internet multi-source coordination optimization operation reserve for considering tou power price, as shown in figure 1, comprising the following steps：
The primary data parameter of step 1, input area energy internet system, including all kinds of energy device configurations in system The initial predicted demand of parameter, specific tou power price and Gas Prices and typical day day part cold heat/electric load, cold/ The initial demand predicted value of heat/electric load is taken as Q respectivelyct、Qht、Eet；Wherein, QctFor the initial demand predicted value of refrigeration duty, QhtThe initial demand predicted value of thermic load, EtThe initial demand predicted value of electric load；
Cold heat/electric flexible load response under step 2, analysis tou power price, passes through cold heat/electric load to step 1 Initial demand predicted value be modified, build two class cold heats/electric flexible load response model, and then establish multiple-energy-source it is mutual Contact the energy demand side united cold heat/electric flexible load responding ability under tou power price；
In the present embodiment, the specific side of cold heat/electric flexible load response under the analysis tou power price of the step 2 Method is：Flexible cold heat/electric load in Regional Energy internet is divided into following two class first：The first kind：Power consumption power fix, The time of running adjustable load；Second class：The time of running, which is fixed, two class cool and thermal powers of the adjustable load of watt level are flexible bears Lotus；Then two class cold heats/electric flexible load respondent behavior and response policy are analyzed, then the method for passing through statistical forecast Obtain the maximum allowable adjustable amount Δ Q of the cold and hot electric load of the class of day part two in Regional Energy internet1ct、ΔQ1ht、ΔE1etAnd Δ Q2ct、ΔQ2ht、ΔE2et；
Wherein, Δ Q1ct、ΔQ2ctThe adjustable amount of the maximum of respectively two class flexibility refrigeration dutys；ΔQ1ht、ΔQ2htRespectively The adjustable amount of the maximum of two class flexibility thermic loads；ΔE1et、ΔE2etThe maximum allowable of respectively two class flexibility electric loads can adjust Amount.
It analyzes the detailed process of cold heat/electric flexible load response under tou power price：
Flexible cold heat/electric load in Regional Energy internet can generally be divided into two classes, one kind be power consumption power fix, The time of running adjustable cold heat/electric load, it is another kind of be fix the time of running, the adjustable cold heat/electric load of watt level. In the case of tou power price, user is from the angle for safeguarding number one, for first kind cold heat/electric flexible load, user Tend to reduce in the energy value higher period and use the type load, and such is used in the relatively low period increase of energy value Load, such as the electricity price phase at a low ebb uses washing machine or water heater in one day；For the second class cold heat/electric flexible load, Such as summer air-conditioning refrigeration, in the case where not influenceing users'comfort, user is more willing to reduce operation temperature in the electricity price high period Spend to save the electricity charge.From the perspective of system operation scheduling, above-mentioned cold heat/electric flexible load as a kind of schedulable resource, The active response of flexible load is rationally utilized, system operation can be made more economical, reliable.It can obtain by the method for statistical forecast The maximum allowable adjustable amount Δ Q of the class cold heat of day part two/electric flexible load in Regional Energy internet1ct、ΔQ1ht、ΔE1etWith ΔQ2ct、ΔQ2ht、ΔE2et。
Wherein, Δ Q1ct、ΔQ2ctThe adjustable amount of the maximum of respectively two class flexibility refrigeration dutys；ΔQ1ht、ΔQ2htRespectively The adjustable amount of the maximum of two class flexibility thermic loads；ΔE1et、ΔE2etThe adjustable amount of the maximum of respectively two class flexibility electric loads.
In the present embodiment, the specific method of the response model of the class cold heat of structure two/electric flexible load of the step 2 For：According to the analysis to two class cold heats/electric flexible load respondent behavior and response policy, by the first of cold heat/electric load Corrected on the basis of beginning requirement forecasting value, can obtain the requirement express of two class cold heats/electric load response model of multi-energy system Formula：
Q‘ct=Qct+ΔQ1ctδ1ct+ΔQ2ctδ2ct
Q’ht=Qht+ΔQ1htδ1ht+ΔQ2htδ2ht
E‘et=Eet+ΔE1etδ1et+ΔE2etδ2et
In formula, Qct、Qht、EetFor the cold and hot electrical load requirement predicted value of day part；Q‘ct、Q‘ht、E‘etTo consider flexible load The cold and hot electrical load requirement correction value of day part obtained after response tou power price；δ1ct、δ2ct、δ1ht、δ2ht、δ1et、δ2etIt is fixed respectively Justice is two class cold heats/electric flexible load response coefficient under tou power price different periods, and numerical value reflects between -1 to 1 Cold heat/schedulability of the electric flexible load under tou power price.
The response model of two class cold heats/electric flexible load, false under step 3, the tou power price mechanism established based on step 2 Be located at natural gas price maintenance under day yardstick is constant, only consider the influence of tou power price in the case of, structure minimizes economical The Regional Energy internet system Optimal Operation Model and its operation constraints of cost；Including establishing optimization object function, giving Surely constraints is run, the economic sexual clorminance that strategy is carried for further checking provides theory support, including step in detail below：
(1) multiple-energy-source interacted system day power purchase under tou power price, purchase gas cost minimization object function are established：By such as Fig. 2 Shown Regional Energy internet energy supply structure chart understands actual hot and cold, electric load respectively by plurality of devices joint supply.Work as area , can be to the direct power purchase of external power grid in the energy internet of domain during electric energy insufficient supply, on the contrary also may be used when region electric energy supply surplus With to external electrical network sale of electricity.In the case of assuming that the holding of Gas Prices short-term is constant in region, to pursue cost minimization Change, tou power price will necessarily have an impact to the power consumption behavior in system day part., can if only considering day power purchase, purchase gas cost Building object function is：
minCtotal=Celc+Cgas
In formula, CtotalFor resulting cost, Celc、CgasRespectively power purchase, the expense of purchase gas；For the electricity price of t periods, Et,buyFor the purchase of electricity of t periods, Et,buy＜ 0 then represents the period system to external power grid sale of electricity, and purchases strategies now are negative Value, equivalent to income；pgasFor in a few days natural gas and coal-fired unit price；Respectively gas-fired boiler Stove, gas electricity generator the t periods heating capacity and generated energy and corresponding heating efficiency, generating efficiency；
The optimized variable of mains side includes day part combustion turbine power, gas fired-boiler power, earth source heat pump power, power purchase Amount and the cold and hot storage distribution coefficient for respectively producing hot and cold equipment, the optimized variable of load side include the cold heat of day part/electricity flexibility Load responding coefficient.
(2) bar is constrained according to the operation of actual conditions given area energy internet on the basis of object function is established Part, it is main in the present embodiment to consider that equipment operation constraint and energy balance constrain two classes, be specially：
1) equipment operation constraint：
A photovoltaic cell units limits：The utilization of solar energy has the advantages of cost is low, pollution is small, to realize to greatest extent Consumption, assume that photovoltaic cell unit participates in system coordination Optimized Operation with pre- power scale in the present embodiment：
In formula, Pt PV、The participation dispatch value of photovoltaic unit generation power respectively in the t periods, predicted value and Maximum；
B Wind turbines units limits：Identical with photovoltaic cell unit, Wind turbines are contributed and also participate in system tune with predicted value Degree：
In formula, Pt Wind、The dispatch value of Wind turbines generated output respectively in the t periods, predicted value and most Big value；
C gas turbine operations constrain：Not only generating efficiency is high for gas turbine, easy to use, and can pass through waste heat The comprehensive utilization ratio of the lifting energy is collected, gas turbine uses three stage efficiency Models in addition,
In formula, Pt CT、Respectively generated output, the rated power of t period gas turbines；Cut for gas turbine Except coefficient；Respectively most increase power output and maximum drop power output of the gas turbine in continuous operation； Et,gasFor the amount of natural gas of t periods gas turbine consumption；A, b, c, d are efficiency factor.
D batteries to store energy facility constraints：The discharge and recharge of batteries to store energy is limited by maximum charge-discharge electric power, is charged and discharged It can not carry out simultaneously；Simultaneously using day as the cycle, charge-discharge energy balances in one day, and energy storage value should keep constant；To simplify the analysis, Present embodiment assumes that accumulator cell charging and discharging efficiency is 1；
In formula,Respectively accumulator cell charging and discharging power in the t periods, and be only capable of charging in the same period or put Electricity,The respectively maximum allowable charge-discharge electric power of battery；A respectively cycle is initial and terminates Moment batteries to store energy.
E earth source heat pumps unit operation constrains：Earth source heat pump unit low-grade electric energy can be converted to high-grade heat energy or Cold energy, it can be directly used for supplying cooling and heating load, can also be stored, but energy supply and accumulation of energy can not occur simultaneously, and same unit is only It can run under heating or refrigeration mode, wherein：
Under refrigeration mode：
Under heating mode：
In formula,Respectively earth source heat pump unit refrigeration work consumption and heats power；Respectively To be used to supply refrigeration duty and cold energy for cold-storage under heat pump refrigerating pattern；Respectively heat pump heating mode It is lower to be used to supply thermic load and the heat energy for accumulation of heat；Respectively earth source heat pump unit is in the t periods Electric energy and its refrigeration, the heating efficiency of consumption.
The operation constraint of f gas fired-boilers：Using natural gas as Initial energy source, directly heat is provided to system by gas fired-boiler work Energy；
In formula,The respectively general power of t period gas fired-boilers, maximum allowable power, heating and refrigeration work( Rate；Qt,gasFor the air consumption in the gas fired-boiler t periods；Respectively gas fired-boiler is heated for heat Load, absorption refrigeration, the heat energy of heat storage tank.
The operation constraint of g waste heat boilers：
In formula,Total waste heat is absorbed for the t periods；ηREFor to gas turbine waste heat recovery coefficient；
Respectively residual heat collection is used for absorption refrigeration, heating demand, heat storage tank accumulation of heat Heat energy.
The operation constraint of h heat storage tanks：When supply exceed demand for the heat energy of system, unnecessary thermal energy storage can be got up and work as heat energy Heat energy is discharged when insufficient, realizes the equilibrium of supply and demand, similar to battery unit, with the period can not only accumulation of heat but also heat supply, and week The heat energy of heat storage tank is kept constant in phase；
In formula,For the storage of t period heat storage tanks, heat release power；It is heat storage tank respectively Store, the maximum allowable operation power of heat release；For a cycle is initial and the energy storage value of end time heat storage tank.
The operation constraint of i electric refrigerating machines：
In formula,For electric refrigerating machine t periods power and power limit；Electricity is consumed for t periods electric refrigerating machine Energy；For electric refrigerating machine coefficient of refrigerating performance.
The operation constraint of j Absorption Refrigerators：
In formula, Pt ACT、For absorption formula refrigeration machine t periods refrigeration work consumption and its allow to run limit value；To absorb Formula refrigeration machine cooling coefficient；
The operation constraint of k cold-storage tanks：It is similar to heat storage tank operation logic, two kinds of work shapes of savings cold energy and release cold energy be present State；
In formula,For the storage of t period cold-storage apparatus, let cool energy power；It is cold-storage respectively The storage of equipment, let cool the maximum allowable operation power of energy；For a cycle is initial and the energy storage of end time cold-storage apparatus Value；
2) account load balancing constraints；
A refrigeration duty Constraints of Equilibrium：
In formula,For earth source heat pump unit work power in cooling mode, if supplying cold directly, take on the occasion of, if To cold-storage apparatus cold-storage, then to take negative value；Pt SCFor the power of cold-storage apparatus, taken when discharging cold energy taken when, cold-storage it is negative Value, and for cold-storage tank, because release cold energy and cold-storage can not be carried out simultaneously, therefore Pt SC、Must be with positive and negative；Pt ECT、 Pt ACTThe respectively refrigeration work consumption of electric refrigerating machine and Absorption Refrigerator；
B heat load balances constrain：
In formula,For earth source heat pump unit work power in a heating mode, if direct heating, take on the occasion of, if To heat storage tank accumulation of heat, then to take negative value；Pt SHFor the power of heat storage tank, taken when discharging heat energy and take negative value when, accumulation of heat, together Sample can not be carried out simultaneously due to discharging heat energy and accumulation of heat, therefore Pt SH、Must be with positive and negative；Respectively fire Gas boiler heat supply and the power of waste heat boiler heating；
C electric load Constraints of Equilibrium：Pt PV+Pt Wind+Pt GT+Pt buy+Pt SE-Pt pump-Pt ECT=Eet'
In formula, Pt PV、Pt WindFor system photovoltaic cell group and the operation power of blower fan, predicted value is taken in the present embodiment；Pt SE For the operation power in the battery t periods, take take negative value when, charging upon discharging；Pt pumpFor the earth source heat pump unit t periods Interior electrical power；Pt ECTFor power consumption of the electric refrigerating machine within the t periods；
The d times of running can tone category (first kind) cold heat/electric flexible load power conservation constraint：Because the time of running is adjustable The flexible cool and thermal power load of class (first kind) cold heat/electricity be the time of running change, therefore in a cycle (the present embodiment with Day be the cycle) run that total amount is constant, and day part first kind cold heat/electric flexible load incrementss and decrement are cancelled out each other, i.e.,：
∑Q1ctδ1ct=0
∑Q1htδ1ht=0
∑E1etδ1et=0
Step 4, solved using Hessian interior point methods the step 3 minimum financial cost Regional Energy internet System optimization scheduling model, as shown in figure 3, comprising the following specific steps that：
Consider that Optimized model is related to the scheduling of the multiple-energy-source resource of multi-energy system supply and demand bilateral, model is complex, square There is sparse characteristic in battle array, therefore example will be solved from Hessian interior point methods in the present embodiment when solving, such as Fig. 3 institutes Show, its specific steps includes：
(1) to the variable assignments being related in Optimized model, the tune of each energy device and all kinds of cool and thermal power flexible loads is determined Spend initial value；And given maximum iteration and the iteration ends limits of error；
(2) slack variable is introduced, by the inequality constraints equation in above-mentioned Optimal Operation Model；
(3) optimization after constraining equation is calculated to the optimization meter for being converted into unconfined condition using Lagrangian Calculate；
(4) Unconstrained Optimization Problem is embodied, a series of Nonlinear System of Equations is obtained by KKT conditions；
(5) to the Hessian matrix Ls DL in Nonlinear System of EquationsTDecompose and be iterated solution；
(6) judge whether result of calculation restrains, if convergence, export result of calculation；Otherwise iteration is continued, if until super Go out maximum iteration not restrain still, then change initial guess and recalculate；
(7), should if generally meeting to optimize criterion based on the result of calculation that Hessian interior point methods solve to obtain As a result it is final Optimized Operation plan, including each energy device is contributed and all kinds of cold heats/electric flexible load response；Otherwise Respondence to the Price of Electric Power coefficient of the two class cold heats/electric flexible load in day part should be adjusted, and corrects day part cold heat/electric load accordingly Actual demand value, substitute into Optimized model and carry out the Optimized Iterative of next step and calculate, until to obtain an overall economics optimal The scheduling scheme a few days ago changed.
The one of the Regional Energy internet multi-source coordination optimization operation reserve for considering tou power price is given in abovementioned steps As flow, in the present embodiment, for checking carry strategy feasibility and economy, below by selection practical application scene enter Row sample calculation analysis, its step include：
Step (1), choose Regional Energy internet multi-source energy supply typical scene under tou power price
The present invention carries out Simulation Example by taking a certain representative region energy internet garden in China as an example, and checking is above-mentioned to be carried The economy and validity of strategy, device parameter needed for Regional Energy the Internet model are as shown in table 1；Assuming that the garden natural gas Price keeps constant in one day, takes 3.6 yuan/cubic metre, is converted into 0.36 yuan/kWh, and this area typical case day tou power price is as schemed Shown in 4.For simplify problem, the present invention in only for summer electric load and the energy supply problem and winter electric load and heat of refrigeration duty The energy supply problem of load carries out instance analysis.Fig. 5 give garden typical case summer, day part day regenerative resource contribute and cold/ Heat/electrical load requirement predicted value；Fig. 6 gives garden typical case winter, day part day regenerative resource output and cold heat/electricity is negative Lotus requirement forecasting value；Fig. 7 gives the adjustable amount of maximum of the class electricity of garden typical case summer, day part day two/cold flexible load； Fig. 8 gives the adjustable amount of maximum of the class electricity of garden typical case winter, day part day two/thermal flexibility load.
The representative region energy internet device parameter of table 1
Step (2), the analysis of example optimum results
1) equipment, which is contributed, analyzes：Solved by above-mentioned optimized algorithm and can obtain typical case's day summer each set optimization scheduling knot Fruit, as shown in accompanying drawing 9,10,11,12.Wherein accompanying drawing 9 for garden typical case summer, electric energy day Optimized Operation results, it can be seen that During the high electricity price of timesharing, to save cost, the less power purchase of system, opposite more purchase gas；Load side is soft when electricity price is higher simultaneously Property electric load actual participation system call load value it is bigger, it is lower to correct electrical load requirement because obtained from, and in electricity price The amendment electrical load requirement value obtained when relatively low is higher, and this embodies user side and pursues the maximized Respondence to the Price of Electric Power machine of number one System.Accompanying drawing 10 is the Optimized Operation result of garden typical case summer, cold energy day, and electric refrigerating machine is when electricity price is relatively low as seen from the figure Cold energy is supplied to system by consuming electric energy, and the cold energy obtained by Absorption Refrigerator is then relatively fewer；Pass through cold-storage simultaneously Tank carries out energy stores in the cold energy sufficient period, and is released energy in the period that electricity price is higher or cold energy is insufficient, embodies storage Beneficial effect of the cold tank in terms of load peak load shifting.Figure 11 be garden typical case winter, electric energy day Optimized Operation result, power consumption Class energy device is being concentrated mainly on low rate period operation, and is responsible for system in high rate period gas turbine, gas fired-boiler Main energy supply.Figure 12 is the Optimized Operation result of garden typical case winter, heat energy day, and gas fired-boiler exists as seen from the figure Heat energy is supplied to system by consuming natural gas when electricity price is higher, and the heat energy supplied by earth source heat pump is then relatively fewer；Simultaneously Energy stores are carried out in the heat energy sufficient period by heat storage tank, and released energy in the period that electricity price is higher or heat energy is insufficient. Optimized Operation result and Figure 11 from typical case's day summer shown in Fig. 9,10, the Optimized Operation result of typical case's day winter shown in 12, no It is difficult to see：Tou power price has guided bone regulating and controlling effect for all kinds of cold heats of user/electric flexible load, to avoid electricity as far as possible Valency peak, so as to reduce electric energy purchase cost；Meanwhile the harmonizing of load side and Demand-side various energy resources resource is in very big journey The operation of system is optimized on degree, it is significant for lifting economy level.
2) cost analysis：The economic sexual clorminance of Optimized Operation strategy is carried for the prominent present invention, calculates consider timesharing respectively Flexible load responds the day operation expense with being not responding to Regional Energy interconnection microgrid garden under two kinds of Optimized Operation strategies under electricity price, As a result it is as shown in table 2.
Regional Energy internet garden day operation cost under 2 different scheduling strategies of table
The present invention carries the influence that strategy refers to considering tou power price, meter and the class flexible load of load side two in timesharing electricity Active response under valency, contributed with reference to the optimization of multiple power sources equipment, meet the cold and hot electrical load requirement of system, transport typical case's day summer Row expense is 8172.6 yuan, and winter typical case's day operation expense is 7038.9 yuan；Do not consider that the scheduling strategy of tou power price refers to not Consider the effect of flexible load under tou power price, contributed with each power supply and auxiliary equipment of mains side as optimized variable, given The optimization that system is obtained under loading condiction is contributed, and is made systematic economy Optimized Operation, is obtained summer typical case's day operation expense 8438.3 Member, 7358.4 yuan of winter typical case's day operation expense；Contrast understand, the present invention carry strategy compared to do not consider tou power price influence The obtained scheduling result of scheduling strategy it is more economical, the day operation expense of typical case's day summer and typical case's day winter reduce respectively 3.15%, 4.34%.
In the present embodiment, the present invention is using certain representative region energy internet garden as example, with reference to system typical energy source Device parameter, establish operating cost and minimize model, under the Optimization Solution strategy example system typical case's day each unit output, The response of flexible load and system operation economic index, the present invention consider that energy prices are flexible to Regional Energy internet first Workload demand and the influence for behavior of consuming energy, propose to consider the multi-source coordination optimization operation reserve flow chart of tou power price, such as Fig. 1 It is shown；Then response method of all kinds of flexible loads for tou power price is specifically illustrated, provides representative region energy internet Energy-structure, as shown in Fig. 2 with reference to system capacity flow principles build power procurement cost minimization model, it is contemplated that institute The complexity of established model, is solved from interior point method, and algorithm basic procedure is as shown in Figure 3；Finally choose a certain actual area Verify that Fig. 4 gives the tou power price knot in this area's summer and winter to carrying strategy by scene in energy internet garden Structure, Fig. 5,7 sets forth this area's summer, the cool and thermal power predicted load of typical case winter, day part day and regenerative resource The predicted value of generating, Fig. 6,8 maximum adjustable angle value for respectively illustrating typical day day part cool and thermal power flexible load, finally will Data bring model into, optimize solution and obtain the Optimized Operation result as shown in Fig. 9,10,11,12, by system call As a result and the power procurement cost analysis of system day can be obtained to draw a conclusion：
(1) considering the Regional Energy internet multi-source collaboration Optimal Operation Strategies of tou power price has good economy, Summer, winter typical case day power procurement cost under the strategy is relative to Optimal Operation Strategies when not considering that tou power price influences 3.15%, 4.34% is reduced respectively, if the strategy is applied to large-scale Regional Energy internet area, it will bring considerable Cost savings.
(2) real case in the present invention is fallen into a trap and the regenerative resource such as wind-powered electricity generation, photovoltaic, utilizes energy storage and flexible load The Regional Energy internet of response can effectively dissolve regenerative resource, flexibly solve because it goes out system work(caused by fluctuation Rate imbalance problem, provided multiple forms of energy to complement each other for realization with positive role；
(3) the equipment group such as gas turbine, waste heat boiler, Absorption Refrigerator is indirectly contained in Regional Energy internet Into cooling heating and power generation system, the utilization rate of primary energy is remarkably improved by the cascade utilization of energy, contributes to energy-conservation to subtract Row.
It is emphasized that embodiment of the present invention is illustrative, rather than it is limited, therefore present invention bag Include and be not limited to embodiment described in embodiment, it is every by those skilled in the art's technique according to the invention scheme The other embodiment drawn, also belongs to the scope of protection of the invention.

Claims (5)

  1. A kind of 1. Regional Energy internet multi-source coordination optimization operation reserve for considering tou power price, it is characterised in that：Including with Lower step：
    The primary data parameter of step 1, input area energy internet system, including all kinds of energy device configuration ginsengs in system The initial predicted demand of several, specific tou power price and Gas Prices and typical day day part cold heat/electric load, cold/ The initial demand predicted value of heat/electric load is taken as Q respectivelyct、Qht、Eet；
    Cold heat/electric flexible load response under step 2, analysis tou power price, passes through the first of cold heat/electric load to step 1 Beginning requirement forecasting value is modified, and builds the response model of two class cold heats/electric flexible load, and then is established multiple-energy-source and mutually contacted The energy demand side of system cold heat/electric flexible load responding ability under tou power price；
    The response model of two class cold heats/electric flexible load under step 3, the tou power price mechanism established based on step 2, it is assumed that Natural gas price maintenance under day yardstick is constant, only consider the influence of tou power price in the case of, structure minimizes financial cost Regional Energy internet system Optimal Operation Model and its operation constraints；
    Step 4, solved using Hessian interior point methods the step 3 minimum financial cost Regional Energy internet system Optimal Operation Model.
  2. A kind of 2. Regional Energy internet multi-source coordination optimization operation plan for considering tou power price according to claim 1 Slightly, it is characterised in that：The specific method of cold heat under the analysis tou power price of the step 2/electric flexible load response is：It is first Flexible cold heat/electric load in Regional Energy internet is first divided into following two class：The first kind：Power consumption power is fixed, the time of running Adjustable load；Second class：The time of running fixes, two class cold heats/electric flexible load of the adjustable load of watt level；Then Two class cold heats/electric flexible load respondent behavior and response policy are analyzed, then region is obtained by the method for statistical forecast The maximum allowable adjustable amount Δ Q of the cold and hot electric load of the class of day part two in energy internet1ct、ΔQ1ht、ΔE1etWith Δ Q2ct、Δ Q2ht、ΔE2et；
    Wherein, Δ Q1ct、ΔQ2ctThe adjustable amount of the maximum of respectively two class flexibility refrigeration dutys；ΔQ1ht、ΔQ2htRespectively two classes The adjustable amount of the maximum of flexible thermic load；ΔE1et、ΔE2etThe respectively maximum allowable adjustable amount of two class flexibility electric loads.
  3. A kind of 3. Regional Energy internet multi-source coordination optimization operation for considering tou power price according to claim 1 or 2 Strategy, it is characterised in that：The specific method of the response model of the class cold heat of structure two/electric flexible load of the step 2 is：Root According to the analysis to two class cold heats/electric flexible load respondent behavior and response policy, pass through the initial demand in cold heat/electric load Corrected on the basis of predicted value, can obtain the requirement express formula of two class cold heats/electric load response model of multi-energy system：
    Qc‘t=Qct+ΔQ1ctδ1ct+ΔQ2ctδ2ct
    Qh’t=Qht+ΔQ1htδ1ht+ΔQ2htδ2ht
    Ee‘t=Eet+ΔE1etδ1et+ΔE2etδ2et
    In formula, Qct、Qht、EetFor the cold and hot electrical load requirement predicted value of day part；Q‘ct、Q‘ht、E‘etTo consider flexible load response The cold and hot electrical load requirement correction value of day part obtained after tou power price；δ1ct、δ2ct、δ1ht、δ2ht、δ1et、δ2etIt is respectively defined as Two class cold heats/electric flexible load response coefficient under tou power price different periods, numerical value between -1 to 1, reflect cold heat/ Schedulability of the electric flexible load under tou power price.
  4. A kind of 4. Regional Energy internet multi-source coordination optimization operation for considering tou power price according to claim 1 or 2 Strategy, it is characterised in that：The step 3 includes step in detail below：
    (1) multiple-energy-source interacted system day power purchase under tou power price, purchase gas cost minimization object function are established：
    min Ctotal=Celc+Cgas
    <mrow> <msub> <mi>C</mi> <mrow> <mi>e</mi> <mi>l</mi> <mi>c</mi> </mrow> </msub> <mo>=</mo> <msubsup> <mi>&amp;Sigma;p</mi> <mi>t</mi> <mrow> <mi>g</mi> <mi>r</mi> <mi>i</mi> <mi>d</mi> </mrow> </msubsup> <msub> <mi>E</mi> <mrow> <mi>t</mi> <mo>,</mo> <mi>b</mi> <mi>u</mi> <mi>y</mi> </mrow> </msub> </mrow>
    <mrow> <msub> <mi>C</mi> <mrow> <mi>g</mi> <mi>a</mi> <mi>s</mi> </mrow> </msub> <mo>=</mo> <msub> <mi>&amp;Sigma;p</mi> <mrow> <mi>g</mi> <mi>a</mi> <mi>s</mi> </mrow> </msub> <mrow> <mo>(</mo> <msubsup> <mi>Q</mi> <mi>t</mi> <mrow> <mi>B</mi> <mi>o</mi> <mi>i</mi> <mi>l</mi> </mrow> </msubsup> <mo>/</mo> <msubsup> <mi>&amp;mu;</mi> <mi>t</mi> <mrow> <mi>B</mi> <mi>o</mi> <mi>i</mi> <mi>l</mi> </mrow> </msubsup> <mo>+</mo> <msubsup> <mi>E</mi> <mi>t</mi> <mrow> <mi>G</mi> <mi>T</mi> </mrow> </msubsup> <mo>/</mo> <msubsup> <mi>&amp;mu;</mi> <mi>t</mi> <mrow> <mi>G</mi> <mi>T</mi> </mrow> </msubsup> <mo>)</mo> </mrow> </mrow>
    In formula, CtotalFor resulting cost, Celc、CgasRespectively power purchase, the expense of purchase gas；For the electricity price of t periods, Et,buyFor The purchase of electricity of t periods, Et,buy＜ 0 then represents the period system to external power grid sale of electricity, and purchases strategies now are negative value, equivalent to Income；pgasFor in a few days natural gas and coal-fired unit price；Respectively gas fired-boiler, fuel gas generation Heating capacity and generated energy and corresponding heating efficiency, generating efficiency of the machine in the t periods；
    (2) according to the operation constraints of actual conditions given area energy internet, tool on the basis of object function is established Body is divided into equipment operation constraint and energy balance constrains two classes：
    1) equipment operation constraint：
    A photovoltaic cell units limits：
    <mrow> <msubsup> <mi>P</mi> <mi>t</mi> <mrow> <mi>P</mi> <mi>V</mi> </mrow> </msubsup> <mo>=</mo> <msubsup> <mi>P</mi> <mrow> <mi>t</mi> <mo>,</mo> <mi>p</mi> <mi>r</mi> <mi>e</mi> </mrow> <mrow> <mi>P</mi> <mi>V</mi> </mrow> </msubsup> </mrow>
    <mrow> <mn>0</mn> <mo>&amp;le;</mo> <msubsup> <mi>P</mi> <mi>t</mi> <mrow> <mi>P</mi> <mi>V</mi> </mrow> </msubsup> <mo>&amp;le;</mo> <msubsup> <mi>P</mi> <mi>max</mi> <mrow> <mi>P</mi> <mi>V</mi> </mrow> </msubsup> </mrow>
    In formula, Pt PV、Participation dispatch value, predicted value and the maximum of photovoltaic unit generation power respectively in the t periods Value；
    B Wind turbines units limits：
    <mrow> <msubsup> <mi>P</mi> <mi>t</mi> <mrow> <mi>W</mi> <mi>i</mi> <mi>n</mi> <mi>d</mi> </mrow> </msubsup> <mo>=</mo> <msubsup> <mi>P</mi> <mrow> <mi>t</mi> <mo>,</mo> <mi>p</mi> <mi>r</mi> <mi>e</mi> </mrow> <mrow> <mi>W</mi> <mi>i</mi> <mi>n</mi> <mi>d</mi> </mrow> </msubsup> </mrow>
    <mrow> <mn>0</mn> <mo>&amp;le;</mo> <msubsup> <mi>P</mi> <mi>t</mi> <mrow> <mi>W</mi> <mi>i</mi> <mi>n</mi> </mrow> </msubsup> <mo>&amp;le;</mo> <msubsup> <mi>P</mi> <mi>max</mi> <mrow> <mi>W</mi> <mi>i</mi> <mi>n</mi> </mrow> </msubsup> </mrow>
    In formula, Pt Wind、Dispatch value, predicted value and the maximum of Wind turbines generated output respectively in the t periods；
    C gas turbine operations constrain：
    <mrow> <msubsup> <mi>P</mi> <mi>t</mi> <mrow> <mi>G</mi> <mi>T</mi> </mrow> </msubsup> <mo>-</mo> <msubsup> <mi>P</mi> <mrow> <mi>t</mi> <mo>-</mo> <mn>1</mn> </mrow> <mrow> <mi>G</mi> <mi>T</mi> </mrow> </msubsup> <mo>&amp;le;</mo> <msubsup> <mi>&amp;gamma;</mi> <mrow> <mi>u</mi> <mi>p</mi> </mrow> <mrow> <mi>G</mi> <mi>T</mi> </mrow> </msubsup> <mi>&amp;Delta;</mi> <mi>t</mi> </mrow>
    <mrow> <msubsup> <mi>P</mi> <mi>t</mi> <mrow> <mi>G</mi> <mi>T</mi> </mrow> </msubsup> <mo>-</mo> <msubsup> <mi>P</mi> <mrow> <mi>t</mi> <mo>-</mo> <mn>1</mn> </mrow> <mrow> <mi>G</mi> <mi>T</mi> </mrow> </msubsup> <mo>&amp;GreaterEqual;</mo> <msubsup> <mi>&amp;gamma;</mi> <mrow> <mi>d</mi> <mi>o</mi> <mi>w</mi> <mi>n</mi> </mrow> <mrow> <mi>G</mi> <mi>T</mi> </mrow> </msubsup> <mi>&amp;Delta;</mi> <mi>t</mi> </mrow>
    <mrow> <msub> <mi>E</mi> <mrow> <mi>t</mi> <mo>,</mo> <mi>g</mi> <mi>a</mi> <mi>s</mi> </mrow> </msub> <mo>=</mo> <msubsup> <mi>P</mi> <mi>t</mi> <mrow> <mi>G</mi> <mi>T</mi> </mrow> </msubsup> <mi>&amp;Delta;</mi> <mi>t</mi> <mo>/</mo> <msubsup> <mi>&amp;mu;</mi> <mi>t</mi> <mrow> <mi>G</mi> <mi>T</mi> </mrow> </msubsup> </mrow>
    <mrow> <msubsup> <mi>&amp;mu;</mi> <mi>t</mi> <mrow> <mi>G</mi> <mi>T</mi> </mrow> </msubsup> <mo>=</mo> <mi>a</mi> <mo>+</mo> <msubsup> <mi>bP</mi> <mi>t</mi> <mrow> <mi>G</mi> <mi>T</mi> </mrow> </msubsup> <mo>+</mo> <mi>c</mi> <msup> <mrow> <mo>(</mo> <msubsup> <mi>P</mi> <mi>t</mi> <mrow> <mi>G</mi> <mi>T</mi> </mrow> </msubsup> <mo>)</mo> </mrow> <mn>2</mn> </msup> <mo>+</mo> <mi>d</mi> <msup> <mrow> <mo>(</mo> <msubsup> <mi>P</mi> <mi>t</mi> <mrow> <mi>G</mi> <mi>T</mi> </mrow> </msubsup> <mo>)</mo> </mrow> <mn>3</mn> </msup> </mrow>
    In formula, Pt CT、Respectively generated output, the rated power of t period gas turbines；Cut off for gas turbine and be Number；Respectively most increase power output and maximum drop power output of the gas turbine in continuous operation；Et,gasFor The amount of natural gas of t periods gas turbine consumption；A, b, c, d are efficiency factor；
    D batteries to store energy facility constraints：
    <mrow> <mn>0</mn> <mo>&amp;le;</mo> <msubsup> <mi>P</mi> <mrow> <mi>t</mi> <mo>,</mo> <mi>c</mi> <mi>h</mi> <mi>a</mi> </mrow> <mrow> <mi>S</mi> <mi>E</mi> </mrow> </msubsup> <mo>&amp;le;</mo> <msubsup> <mi>P</mi> <mrow> <mi>c</mi> <mi>h</mi> <mi>a</mi> <mo>,</mo> <mi>m</mi> <mi>a</mi> <mi>x</mi> </mrow> <mrow> <mi>S</mi> <mi>E</mi> </mrow> </msubsup> </mrow>
    <mrow> <mn>0</mn> <mo>&amp;le;</mo> <msubsup> <mi>P</mi> <mrow> <mi>t</mi> <mo>,</mo> <mi>d</mi> <mi>i</mi> <mi>s</mi> </mrow> <mrow> <mi>S</mi> <mi>E</mi> </mrow> </msubsup> <mo>&amp;le;</mo> <msubsup> <mi>P</mi> <mrow> <mi>d</mi> <mi>i</mi> <mi>s</mi> <mo>,</mo> <mi>m</mi> <mi>a</mi> <mi>x</mi> </mrow> <mrow> <mi>S</mi> <mi>E</mi> </mrow> </msubsup> </mrow>
    <mrow> <msubsup> <mi>P</mi> <mrow> <mi>t</mi> <mo>,</mo> <mi>c</mi> <mi>h</mi> <mi>a</mi> </mrow> <mrow> <mi>S</mi> <mi>E</mi> </mrow> </msubsup> <msubsup> <mi>P</mi> <mrow> <mi>t</mi> <mo>,</mo> <mi>d</mi> <mi>i</mi> <mi>s</mi> </mrow> <mrow> <mi>S</mi> <mi>E</mi> </mrow> </msubsup> <mo>=</mo> <mn>0</mn> </mrow>
    <mrow> <msubsup> <mi>E</mi> <mi>t</mi> <mrow> <mi>S</mi> <mi>E</mi> </mrow> </msubsup> <mo>=</mo> <msubsup> <mi>E</mi> <mrow> <mi>t</mi> <mo>+</mo> <mi>T</mi> </mrow> <mrow> <mi>S</mi> <mi>E</mi> </mrow> </msubsup> </mrow>
    In formula,Respectively accumulator cell charging and discharging power in the t periods, and charge or discharge are only capable of in the same period,The respectively maximum allowable charge-discharge electric power of battery；When a respectively cycle is initial and terminates Carve batteries to store energy；
    E earth source heat pumps unit operation constrains：
    Under refrigeration mode：
    Under heating mode：
    In formula,Respectively earth source heat pump unit refrigeration work consumption and heats power；Respectively It is used to supply refrigeration duty and the cold energy for cold-storage under the heat pump refrigerating pattern of source；Respectively earth source heat pump heats It is used to supply thermic load and the heat energy for accumulation of heat under pattern； Respectively earth source heat pump unit is in t The electric energy and its refrigeration, heating efficiency of section consumption；
    The operation constraint of f gas fired-boilers：
    <mrow> <mn>0</mn> <mo>&amp;le;</mo> <msubsup> <mi>P</mi> <mi>t</mi> <mrow> <mi>B</mi> <mi>o</mi> <mi>i</mi> <mi>l</mi> </mrow> </msubsup> <mo>&amp;le;</mo> <msubsup> <mi>P</mi> <mi>max</mi> <mrow> <mi>B</mi> <mi>o</mi> <mi>i</mi> <mi>l</mi> </mrow> </msubsup> </mrow>
    <mrow> <msubsup> <mi>Q</mi> <mi>t</mi> <mrow> <mi>B</mi> <mi>o</mi> <mi>i</mi> <mi>l</mi> </mrow> </msubsup> <mo>=</mo> <msubsup> <mi>P</mi> <mi>t</mi> <mrow> <mi>B</mi> <mi>o</mi> <mi>i</mi> <mi>l</mi> </mrow> </msubsup> <mi>&amp;Delta;</mi> <mi>t</mi> </mrow>
    <mrow> <msub> <mi>Q</mi> <mrow> <mi>t</mi> <mo>,</mo> <mi>g</mi> <mi>a</mi> <mi>s</mi> </mrow> </msub> <mo>=</mo> <msubsup> <mi>Q</mi> <mi>t</mi> <mrow> <mi>B</mi> <mi>o</mi> <mi>i</mi> <mi>l</mi> </mrow> </msubsup> <mo>/</mo> <msubsup> <mi>&amp;mu;</mi> <mi>t</mi> <mrow> <mi>B</mi> <mi>o</mi> <mi>i</mi> <mi>l</mi> </mrow> </msubsup> </mrow>
    <mrow> <msubsup> <mi>Q</mi> <mi>t</mi> <mrow> <mi>B</mi> <mi>o</mi> <mi>i</mi> <mi>l</mi> </mrow> </msubsup> <mo>=</mo> <msubsup> <mi>Q</mi> <mrow> <mi>t</mi> <mo>,</mo> <mi>h</mi> <mi>e</mi> <mi>a</mi> <mi>t</mi> </mrow> <mrow> <mi>B</mi> <mi>o</mi> <mi>i</mi> <mi>l</mi> </mrow> </msubsup> <mo>+</mo> <msubsup> <mi>Q</mi> <mrow> <mi>t</mi> <mo>,</mo> <mi>c</mi> <mi>o</mi> <mi>o</mi> <mi>l</mi> </mrow> <mrow> <mi>B</mi> <mi>o</mi> <mi>i</mi> <mi>l</mi> </mrow> </msubsup> <mo>+</mo> <msubsup> <mi>Q</mi> <mrow> <mi>t</mi> <mo>,</mo> <mi>s</mi> <mi>t</mi> <mi>o</mi> <mi>r</mi> <mi>e</mi> </mrow> <mrow> <mi>B</mi> <mi>o</mi> <mi>i</mi> <mi>l</mi> </mrow> </msubsup> </mrow>
    In formula,The respectively general power of t period gas fired-boilers, maximum allowable operation power, heating and refrigeration work( Rate；Qt,gasFor the air consumption in the gas fired-boiler t periods；Respectively gas fired-boiler is heated for heat Load, absorption refrigeration, the heat energy of heat storage tank；
    The operation constraint of g waste heat boilers：
    <mrow> <msubsup> <mi>Q</mi> <mi>t</mi> <mrow> <mi>R</mi> <mi>E</mi> </mrow> </msubsup> <mo>=</mo> <msub> <mi>E</mi> <mrow> <mi>t</mi> <mo>,</mo> <mi>g</mi> <mi>a</mi> <mi>s</mi> </mrow> </msub> <msub> <mi>&amp;eta;</mi> <mrow> <mi>R</mi> <mi>E</mi> </mrow> </msub> </mrow>
    <mrow> <msubsup> <mi>Q</mi> <mi>t</mi> <mrow> <mi>R</mi> <mi>E</mi> </mrow> </msubsup> <mo>=</mo> <msubsup> <mi>Q</mi> <mrow> <mi>t</mi> <mo>,</mo> <mi>c</mi> <mi>o</mi> <mi>o</mi> <mi>l</mi> </mrow> <mrow> <mi>R</mi> <mi>E</mi> </mrow> </msubsup> <mo>+</mo> <msubsup> <mi>Q</mi> <mrow> <mi>t</mi> <mo>,</mo> <mi>h</mi> <mi>e</mi> <mi>a</mi> <mi>t</mi> </mrow> <mrow> <mi>R</mi> <mi>E</mi> </mrow> </msubsup> <mo>+</mo> <msubsup> <mi>Q</mi> <mrow> <mi>t</mi> <mo>,</mo> <mi>s</mi> <mi>t</mi> <mi>o</mi> <mi>r</mi> <mi>e</mi> </mrow> <mrow> <mi>R</mi> <mi>E</mi> </mrow> </msubsup> </mrow>
    In formula,Total waste heat is absorbed for the t periods；ηREFor to gas turbine waste heat recovery coefficient；
    Respectively residual heat collection is used for absorption refrigeration, heating demand, the heat energy of heat storage tank accumulation of heat；
    The operation constraint of h heat storage tanks：
    <mrow> <mn>0</mn> <mo>&amp;le;</mo> <msubsup> <mi>P</mi> <mrow> <mi>t</mi> <mo>,</mo> <mi>c</mi> <mi>h</mi> <mi>a</mi> </mrow> <mrow> <mi>S</mi> <mi>H</mi> </mrow> </msubsup> <mo>&amp;le;</mo> <msubsup> <mi>P</mi> <mrow> <mi>c</mi> <mi>h</mi> <mi>a</mi> <mo>,</mo> <mi>m</mi> <mi>a</mi> <mi>x</mi> </mrow> <mrow> <mi>S</mi> <mi>H</mi> </mrow> </msubsup> </mrow>
    <mrow> <mn>0</mn> <mo>&amp;le;</mo> <msubsup> <mi>P</mi> <mrow> <mi>t</mi> <mo>,</mo> <mi>d</mi> <mi>i</mi> <mi>s</mi> </mrow> <mrow> <mi>S</mi> <mi>H</mi> </mrow> </msubsup> <mo>&amp;le;</mo> <msubsup> <mi>P</mi> <mrow> <mi>d</mi> <mi>i</mi> <mi>s</mi> <mo>,</mo> <mi>m</mi> <mi>a</mi> <mi>x</mi> </mrow> <mrow> <mi>S</mi> <mi>H</mi> </mrow> </msubsup> </mrow>
    <mrow> <msubsup> <mi>P</mi> <mrow> <mi>t</mi> <mo>,</mo> <mi>c</mi> <mi>h</mi> <mi>a</mi> </mrow> <mrow> <mi>S</mi> <mi>H</mi> </mrow> </msubsup> <msubsup> <mi>P</mi> <mrow> <mi>t</mi> <mo>,</mo> <mi>d</mi> <mi>i</mi> <mi>s</mi> </mrow> <mrow> <mi>S</mi> <mi>H</mi> </mrow> </msubsup> <mo>=</mo> <mn>0</mn> </mrow>
    <mrow> <msubsup> <mi>Q</mi> <mi>t</mi> <mrow> <mi>S</mi> <mi>H</mi> </mrow> </msubsup> <mo>=</mo> <msubsup> <mi>Q</mi> <mrow> <mi>t</mi> <mo>+</mo> <mi>T</mi> </mrow> <mrow> <mi>S</mi> <mi>H</mi> </mrow> </msubsup> </mrow>
    In formula,For the storage of t period heat storage tanks, heat release power；It is heat storage tank storage, heat release respectively Maximum allowable operation power；For a cycle is initial and the energy storage value of end time heat storage tank；
    The operation constraint of i electric refrigerating machines：
    <mrow> <mn>0</mn> <mo>&amp;le;</mo> <msubsup> <mi>P</mi> <mi>t</mi> <mrow> <mi>E</mi> <mi>C</mi> <mi>T</mi> </mrow> </msubsup> <mo>&amp;le;</mo> <msubsup> <mi>P</mi> <mi>max</mi> <mi>max</mi> </msubsup> </mrow>
    <mrow> <msubsup> <mi>P</mi> <mrow> <mi>t</mi> <mo>,</mo> <mi>c</mi> <mi>o</mi> <mi>o</mi> <mi>l</mi> </mrow> <mrow> <mi>E</mi> <mi>C</mi> <mi>T</mi> </mrow> </msubsup> <mo>=</mo> <msubsup> <mi>E</mi> <mi>t</mi> <mrow> <mi>E</mi> <mi>C</mi> <mi>T</mi> </mrow> </msubsup> <msubsup> <mi>&amp;lambda;</mi> <mrow> <mi>c</mi> <mi>o</mi> <mi>o</mi> <mi>l</mi> </mrow> <mrow> <mi>E</mi> <mi>C</mi> <mi>T</mi> </mrow> </msubsup> <mo>/</mo> <mi>&amp;Delta;</mi> <mi>t</mi> </mrow>
    In formula, Pt ECT、For electric refrigerating machine t periods power and power limit；Electric energy is consumed for t periods electric refrigerating machine；For electric refrigerating machine coefficient of refrigerating performance；
    The operation constraint of j Absorption Refrigerators：
    <mrow> <mn>0</mn> <mo>&amp;le;</mo> <msubsup> <mi>P</mi> <mi>t</mi> <mrow> <mi>A</mi> <mi>C</mi> <mi>T</mi> </mrow> </msubsup> <mo>&amp;le;</mo> <msubsup> <mi>P</mi> <mi>max</mi> <mrow> <mi>A</mi> <mi>C</mi> <mi>T</mi> </mrow> </msubsup> </mrow>
    <mrow> <msubsup> <mi>P</mi> <mi>t</mi> <mrow> <mi>A</mi> <mi>C</mi> <mi>T</mi> </mrow> </msubsup> <mo>=</mo> <mrow> <mo>(</mo> <msubsup> <mi>Q</mi> <mrow> <mi>t</mi> <mo>,</mo> <mi>c</mi> <mi>o</mi> <mi>o</mi> <mi>l</mi> </mrow> <mrow> <mi>R</mi> <mi>E</mi> </mrow> </msubsup> <mo>+</mo> <msubsup> <mi>Q</mi> <mrow> <mi>t</mi> <mo>,</mo> <mi>c</mi> <mi>o</mi> <mi>o</mi> <mi>l</mi> </mrow> <mrow> <mi>B</mi> <mi>o</mi> <mi>i</mi> <mi>l</mi> </mrow> </msubsup> <mo>)</mo> </mrow> <msubsup> <mi>&amp;lambda;</mi> <mrow> <mi>c</mi> <mi>o</mi> <mi>o</mi> <mi>l</mi> </mrow> <mrow> <mi>A</mi> <mi>C</mi> <mi>T</mi> </mrow> </msubsup> <mo>/</mo> <mi>&amp;Delta;</mi> <mi>t</mi> </mrow>
    In formula, Pt ACT、For absorption formula refrigeration machine t periods refrigeration work consumption and its allow to run limit value；For absorption system Cold coefficient of refrigerating performance；
    The operation constraint of k cold-storage tanks：
    <mrow> <mn>0</mn> <mo>&amp;le;</mo> <msubsup> <mi>P</mi> <mrow> <mi>t</mi> <mo>,</mo> <mi>c</mi> <mi>h</mi> <mi>a</mi> </mrow> <mrow> <mi>S</mi> <mi>C</mi> </mrow> </msubsup> <mo>&amp;le;</mo> <msubsup> <mi>P</mi> <mrow> <mi>c</mi> <mi>h</mi> <mi>a</mi> <mo>,</mo> <mi>m</mi> <mi>a</mi> <mi>x</mi> </mrow> <mrow> <mi>S</mi> <mi>C</mi> </mrow> </msubsup> </mrow>
    <mrow> <mn>0</mn> <mo>&amp;le;</mo> <msubsup> <mi>P</mi> <mrow> <mi>t</mi> <mo>,</mo> <mi>d</mi> <mi>i</mi> <mi>s</mi> </mrow> <mrow> <mi>S</mi> <mi>C</mi> </mrow> </msubsup> <mo>&amp;le;</mo> <msubsup> <mi>P</mi> <mrow> <mi>d</mi> <mi>i</mi> <mi>s</mi> <mo>,</mo> <mi>m</mi> <mi>a</mi> <mi>x</mi> </mrow> <mrow> <mi>S</mi> <mi>C</mi> </mrow> </msubsup> </mrow>
    <mrow> <msubsup> <mi>P</mi> <mrow> <mi>t</mi> <mo>,</mo> <mi>c</mi> <mi>h</mi> <mi>a</mi> </mrow> <mrow> <mi>S</mi> <mi>C</mi> </mrow> </msubsup> <msubsup> <mi>P</mi> <mrow> <mi>t</mi> <mo>,</mo> <mi>d</mi> <mi>i</mi> <mi>s</mi> </mrow> <mrow> <mi>S</mi> <mi>C</mi> </mrow> </msubsup> <mo>=</mo> <mn>0</mn> </mrow>
    <mrow> <msubsup> <mi>Q</mi> <mi>t</mi> <mrow> <mi>S</mi> <mi>C</mi> </mrow> </msubsup> <mo>=</mo> <msubsup> <mi>Q</mi> <mrow> <mi>t</mi> <mo>+</mo> <mi>T</mi> </mrow> <mrow> <mi>S</mi> <mi>C</mi> </mrow> </msubsup> </mrow>
    In formula,For the storage of t period cold-storage apparatus, let cool energy power；It is cold-storage apparatus respectively Storage, let cool can maximum allowable operation power；For a cycle is initial and the energy storage value of end time cold-storage apparatus；
    2) account load balancing constraints；
    A refrigeration duty Constraints of Equilibrium：
    In formula,For earth source heat pump unit work power in cooling mode, if supplying cold directly, take on the occasion of, if to Cold-storage apparatus cold-storage, then take negative value；Pt SCFor the power of cold-storage apparatus, taken when discharging cold energy and take negative value when, cold-storage, and For cold-storage tank, because release cold energy and cold-storage can not be carried out simultaneously, thereforeMust be with positive and negative；Pt ECT、Pt ACT The respectively refrigeration work consumption of electric refrigerating machine and Absorption Refrigerator；
    B heat load balances constrain：
    In formula,For earth source heat pump unit work power in a heating mode, if direct heating, take on the occasion of, if to Heat storage tank accumulation of heat, then take negative value；Pt SHFor the power of heat storage tank, taken when discharging heat energy and take negative value when, accumulation of heat, same Because release heat energy and accumulation of heat can not be carried out simultaneously, therefore Pt SH、Must be with positive and negative；Respectively gas-fired boiler Stove heat supply and the power of waste heat boiler heating；
    C electric load Constraints of Equilibrium：
    In formula, Pt PV、Pt WindFor system photovoltaic cell group and the operation power of blower fan, predicted value is taken in the present embodiment；Pt SETo store Operation power in the battery t periods, take take negative value when, charging upon discharging；Pt pumpFor in the earth source heat pump unit t periods Electrical power；Pt ECTFor power consumption of the electric refrigerating machine within the t periods；
    The d times of running can tone category the constraint of first kind cold heat/electric flexible load power conservation：
    ∑Q1ctδ1ct=0
    ∑Q1htδ1ht=0
    ∑E1etδ1et=0
  5. A kind of 5. Regional Energy internet multi-source coordination optimization operation for considering tou power price according to claim 1 or 2 Strategy, it is characterised in that：The step 4 comprises the following specific steps that：
    (1) to the variable assignments being related in Optimized model, at the beginning of the scheduling for determining each energy device and all kinds of cool and thermal power flexible loads Initial value；And given maximum iteration and the iteration ends limits of error；
    (2) slack variable is introduced, by the inequality constraints equation in above-mentioned Optimal Operation Model；
    (3) optimization after constraining equation is calculated to the optimization calculating for being converted into unconfined condition using Lagrangian；
    (4) Unconstrained Optimization Problem is embodied, a series of Nonlinear System of Equations is obtained by KKT conditions；
    (5) to the Hessian matrix Ls DL in Nonlinear System of EquationsTDecompose and be iterated solution；
    (6) judge whether result of calculation restrains, if convergence, export result of calculation；Otherwise iteration is continued, if until beyond most Big iterations is not restrained still, then changes initial guess and recalculate；
    (7) if generally meeting to optimize criterion, the result based on the result of calculation that Hessian interior point methods solve to obtain For final Optimized Operation plan, including each energy device output and all kinds of cold heats/electric flexible load response；Otherwise should adjust Whole two classes cold heat/electric flexible load and corrects the reality of day part cold heat/electric load in the Respondence to the Price of Electric Power coefficient of day part accordingly Border requirements, substitute into Optimized model and carry out the Optimized Iterative calculating of next step, until obtaining what an overall economics optimized Scheduling scheme a few days ago.
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Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103246263A (en) * 2013-04-22 2013-08-14 天津大学 General optimized dispatching strategy for combined supply of cooling, heating and power microgrid system
CN103346562A (en) * 2013-07-11 2013-10-09 江苏省电力设计院 Multi-time scale microgrid energy control method considering demand response
CN104392286A (en) * 2014-12-02 2015-03-04 山东大学 Microgrid operation optimizing method by considering combined supply of cooling, heating and power with stored energy operation strategy
CN105634024A (en) * 2016-02-23 2016-06-01 国网江苏省电力公司电力科学研究院 Price demand response-based intraday economic scheduling model and linear solving method
CN105869075A (en) * 2016-04-19 2016-08-17 东南大学 Economic optimization scheduling method for cold, heat and electricity combined supply type miniature energy grid
CN106505634A (en) * 2016-12-14 2017-03-15 东南大学 Based on two benches coordination optimization and the supply of cooling, heating and electrical powers type microgrid operation method for controlling
CN106709610A (en) * 2017-01-12 2017-05-24 浙江大学 Micro-grid electricity energy storage and ice storage combined optimization scheduling method
CN107025519A (en) * 2017-03-30 2017-08-08 厦门大学 Area distribution formula cool and thermal power polygenerations systeme MIXED INTEGER nonlinear model optimization method

Patent Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103246263A (en) * 2013-04-22 2013-08-14 天津大学 General optimized dispatching strategy for combined supply of cooling, heating and power microgrid system
CN103346562A (en) * 2013-07-11 2013-10-09 江苏省电力设计院 Multi-time scale microgrid energy control method considering demand response
CN104392286A (en) * 2014-12-02 2015-03-04 山东大学 Microgrid operation optimizing method by considering combined supply of cooling, heating and power with stored energy operation strategy
CN105634024A (en) * 2016-02-23 2016-06-01 国网江苏省电力公司电力科学研究院 Price demand response-based intraday economic scheduling model and linear solving method
CN105869075A (en) * 2016-04-19 2016-08-17 东南大学 Economic optimization scheduling method for cold, heat and electricity combined supply type miniature energy grid
CN106505634A (en) * 2016-12-14 2017-03-15 东南大学 Based on two benches coordination optimization and the supply of cooling, heating and electrical powers type microgrid operation method for controlling
CN106709610A (en) * 2017-01-12 2017-05-24 浙江大学 Micro-grid electricity energy storage and ice storage combined optimization scheduling method
CN107025519A (en) * 2017-03-30 2017-08-08 厦门大学 Area distribution formula cool and thermal power polygenerations systeme MIXED INTEGER nonlinear model optimization method

Non-Patent Citations (7)

* Cited by examiner, † Cited by third party
Title
B.ALUISIO 等: "An optimization procedure for Microgrid day-ahead operation in the presence of CHP facilities", 《SUSTAINABLE ENERGY, GRIDS AND NETWORKS》 *
XU QINGSHAN 等: "Configuration optimization model of multi-energy distributed generation system", 《JOURNAL OF SOUTHEAST UNIVERSITY (ENGLISH EDITION)》 *
刘楚晖: "基于分布式预测控制的智能能源网经济性优化", 《中国优秀硕士学位论文全文数据库 工程科技Ⅱ辑》 *
徐青山 等: "基于Hessian内点法的微型能源网日前冷热电联供经济优化调度", 《电网技术》 *
曾博: "面向低碳经济的主动配电网综合资源规划与决策理论", 《中国博士学位论文全文数据库 工程科技Ⅱ辑》 *
林世平: "分布式能源系统中能源与环境耦合特性及优化集成模型研究", 《中国博士学位论文全文数据库 工程科技Ⅰ辑》 *
蒲天骄 等: "基于主动配电网的城市能源互联网体系架构及其关键技术", 《中国电机工程学报》 *

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