CN110991753B - Electric heating internet system scheduling optimization method considering multi-energy demand response - Google Patents
Electric heating internet system scheduling optimization method considering multi-energy demand response Download PDFInfo
- Publication number
- CN110991753B CN110991753B CN201911246590.5A CN201911246590A CN110991753B CN 110991753 B CN110991753 B CN 110991753B CN 201911246590 A CN201911246590 A CN 201911246590A CN 110991753 B CN110991753 B CN 110991753B
- Authority
- CN
- China
- Prior art keywords
- output
- heat
- time
- load
- wpp
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 230000004044 response Effects 0.000 title claims abstract description 29
- 238000005457 optimization Methods 0.000 title claims abstract description 25
- 238000000034 method Methods 0.000 title claims abstract description 10
- 238000005485 electric heating Methods 0.000 title description 7
- 230000008901 benefit Effects 0.000 claims abstract description 70
- 238000005338 heat storage Methods 0.000 claims description 40
- 238000010248 power generation Methods 0.000 claims description 19
- 230000009467 reduction Effects 0.000 claims description 16
- 238000009825 accumulation Methods 0.000 claims description 12
- VNWKTOKETHGBQD-UHFFFAOYSA-N methane Chemical compound C VNWKTOKETHGBQD-UHFFFAOYSA-N 0.000 claims description 12
- 238000006243 chemical reaction Methods 0.000 claims description 10
- 230000005611 electricity Effects 0.000 claims description 8
- 230000005284 excitation Effects 0.000 claims description 7
- 239000007789 gas Substances 0.000 claims description 7
- 238000010438 heat treatment Methods 0.000 claims description 7
- 230000001419 dependent effect Effects 0.000 claims description 6
- 239000003345 natural gas Substances 0.000 claims description 6
- 239000000446 fuel Substances 0.000 claims description 3
- 230000001172 regenerating effect Effects 0.000 claims description 3
- 230000001186 cumulative effect Effects 0.000 claims description 2
- 230000002964 excitative effect Effects 0.000 claims description 2
- YTAHJIFKAKIKAV-XNMGPUDCSA-N [(1R)-3-morpholin-4-yl-1-phenylpropyl] N-[(3S)-2-oxo-5-phenyl-1,3-dihydro-1,4-benzodiazepin-3-yl]carbamate Chemical compound O=C1[C@H](N=C(C2=C(N1)C=CC=C2)C1=CC=CC=C1)NC(O[C@H](CCN1CCOCC1)C1=CC=CC=C1)=O YTAHJIFKAKIKAV-XNMGPUDCSA-N 0.000 description 3
- 238000003860 storage Methods 0.000 description 3
- 238000010521 absorption reaction Methods 0.000 description 2
- 238000005265 energy consumption Methods 0.000 description 2
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000009194 climbing Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000009826 distribution Methods 0.000 description 1
- 238000004146 energy storage Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000013486 operation strategy Methods 0.000 description 1
- BULVZWIRKLYCBC-UHFFFAOYSA-N phorate Chemical compound CCOP(=S)(OCC)SCSCC BULVZWIRKLYCBC-UHFFFAOYSA-N 0.000 description 1
- 239000002699 waste material Substances 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06312—Adjustment or analysis of established resource schedule, e.g. resource or task levelling, or dynamic rescheduling
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0635—Risk analysis of enterprise or organisation activities
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
Landscapes
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Engineering & Computer Science (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Entrepreneurship & Innovation (AREA)
- Theoretical Computer Science (AREA)
- Development Economics (AREA)
- Marketing (AREA)
- General Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- Tourism & Hospitality (AREA)
- Physics & Mathematics (AREA)
- Quality & Reliability (AREA)
- Operations Research (AREA)
- Game Theory and Decision Science (AREA)
- Educational Administration (AREA)
- Health & Medical Sciences (AREA)
- Public Health (AREA)
- Water Supply & Treatment (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The invention provides an electrothermal Internet system scheduling optimization method considering a multi-energy demand response, which comprises the following steps: firstly, taking an electric load demand, a heat load demand, a power available output and a heat source available output as inputs of an EST system; secondly, an operation optimization model of the EST system is built, and the operation optimization model of the EST system is optimized by utilizing maximization of economic benefit and minimization of output fluctuation, so that an optimal value, an optimal value of the output fluctuation value, an economic benefit value and an output fluctuation value of the EST system are obtained; and finally, carrying out weighting treatment on the optimal value of the economic benefit, the minimum value of the output fluctuation value, the economic benefit value and the output fluctuation value to obtain the optimal scheduling result of the EST system. According to the invention, economic benefit is used as a benefit index and load fluctuation rate is used as a risk index to optimize a scheduling model of the EST system, so as to obtain an EST optimal scheduling result; the invention reduces the running risk of the EST system while taking economic benefit into consideration, and has important significance.
Description
Technical Field
The invention relates to the technical field of electric heating Internet, in particular to an electric heating Internet system scheduling optimization method considering multi-energy demand response.
Background
Along with the increasing prominence of energy problems, renewable energy sources represented by photovoltaic and wind power are gradually valued in China and worldwide, and become an important direction of energy future development, and the development and utilization of renewable energy sources are helpful for adjusting energy structures, perfecting electric power systems and realizing sustainable development of electric power economy. Because of the limitation of distributed power sources such as wind power generation, photovoltaic power generation and the like, an electric heating internet system is rapidly developed as a form for effectively improving the energy utilization efficiency, however, in the electric heating internet system, the electric output power of a CHP (combined heat and power) unit is constrained by the heat generated by the CHP unit, so that the electric heating internet system brings challenges to the energy consumption.
For EST (electric heat cooperative scheduling), the power generation marginal cost of WPP (Wind Power Prediction) is almost zero, and the method has good economic benefit and environmental benefit. Therefore, to achieve optimal operation, it is necessary to maximize the utilization of WPP power generation in conjunction with satisfying electrical and thermal loads. However, due to the random characteristic of the WPP power generation output, high-proportion wind power generation grid connection brings strong impact to the system, and the system running risk is high. Therefore, how to balance benefits and risks brought by WPP, establish an optimal EST system operation strategy and make sense remarkable.
Disclosure of Invention
Aiming at the defects in the background technology, the invention provides an electrothermal Internet system scheduling optimization method considering the response of the multifunctional demands, and solves the technical problems of energy consumption and high running risk of an EST system in the prior art.
The technical scheme of the invention is realized as follows:
an electrothermal internet system dispatching optimization method considering multipotency demand response comprises the following steps:
s1, taking an electric load demand, a heat load demand, a power available output and a heat source available output as inputs of an EST system;
s2, constructing an operation optimization model of the EST system, and obtaining an optimal value f of the economic benefit of the EST system by using the operation optimization model of the economic benefit maximization EST system 1 max And the output fluctuation value f 1 2 ；
S3, obtaining an optimal value of the output fluctuation value of the EST system by using an operation optimization model of the output fluctuation minimized EST systemAnd economic value->
S4, optimizing the economic benefit f in the step S2 1 max Output fluctuation value f 1 2 And the minimum value of the fluctuation value of the output in the step S3Economic value->And carrying out weighting processing to obtain an optimal scheduling result of the EST system.
The EST system is connected with an external public power grid and comprises a WPP module, a heat accumulating type electric boiler module, a gas turbine power generation module and an excitation type demand response module;
the WPP module is related to wind energy, the gas turbine power generation module is related to natural gas, and the excitation type demand response module is related to thermal load and electric load respectively;
the thermal load includes a distributed thermal load associated with the incentive type demand response module and a centralized thermal load associated with the price demand response model.
The power available output comprises WPP output, IBDR output and CGT output; the CGT output is dependent on natural gas consumption; the IBDR output force is dependent on load response capabilities; the WPP output depends on the natural wind condition, and the relation between the WPP output and the wind speed is as follows:
wherein ,gR Rated power for WPP output, v in Cut-in wind speed, v, for WPP output R Rated wind speed, v, for WPP output out Cut-out wind speed, v, for WPP output t For WPP to output real-time wind speed at time t,for WPP to be available at time T, t=1, 2, …, T is the total time.
The heat source available output comprises an electric boiler output and a heat storage tank output; the electric boiler output is as follows:
wherein ,representing the amount of electricity consumed by the electric boiler for converting heat energy at time t +.>Indicating the heating output of the electric boiler at time t, < >>The electrothermal conversion efficiency of the electric boiler at the time t is shown;
the output force of the heat storage tank is as follows:
wherein ,heat storage for a heat storage tank at time tAbility to (I)>Indicates the heat storage loss rate of the heat storage tank, +.>Indicating the heat supply power consumption of the heat storage tank at time t, < >>Indicating the heat release power of the heat storage tank at time t, < + >>Indicating the efficiency of the heat absorption,indicating the heat release efficiency.
The economic benefit comprises WPP operation benefit, CGT operation benefit, IBDR operation benefit and renewable energy operation benefit, and the economic benefit maximization objective function is as follows:
wherein ,f1 As an objective function of economic benefit, pi WPP,t For WPP operation benefit, pi RE,t For RE operation benefit, pi CGT,t For CGT operation benefit, pi IB,t And operating benefits for IBDR.
The CGT operation benefits are as follows:
wherein ,ρCGT,t Internet price for CGT output g CGT,t The power of the network is supplied by the CGT,cost of fuel for CGT, +.>Start-stop cost for CGT output;
the IBDR operating benefits are:
wherein ,for energy price, i=1, 2, …, I is vendor, j=1, 2, …, J is unit number;
the RE operation benefits are:
wherein ,for the heating price at time t +.>For the power supply price at time t, Q RE,t The heat supply output of the heat accumulating type electric boiler at the time t, g RE,t The electric quantity consumed by the heat accumulating electric boiler at the time t.
The output fluctuation minimization objective function is as follows:
in the formula ：f2 G is the load fluctuation value of EST system t As an average of the load fluctuations of the EST system throughout the scheduling period,the net thermal storage power provided for the REB.
The constraint conditions of the economic benefit maximization objective function and the output fluctuation minimization objective function comprise energy balance constraint, IBDR operation constraint, heat accumulating type electric boiler operation constraint and system standby constraint.
The energy balance constraint is:
wherein ,station power for WPP output, < ->Station power consumption g for CGT output UEG,t Electric quantity L purchased by EST system to electric automobile group t For the end user electrical load demand, < >>Distributed thermal load demand, ΔL, for end users PB,t For the load fluctuation amount, eta generated by PBDR de For electrothermal conversion efficiency u L,t PBDR, which is an excitatory demand response, is a PBDR state variable of the electrical load;
the heat accumulating type electric boiler operation constraint comprises an electric boiler operation constraint, a heat accumulating tank operation constraint and an energy balance constraint, and the specific constraint formula is as follows:
wherein ,for maximum force of RE at time t, +.>For the heat accumulation of the heat accumulation tank at the beginning of the scheduling period, < >>For the heat accumulation of the heat accumulation tank at the end of the scheduling period, < > for>For the minimum heat storage capacity of the heat storage tank at time t,/-, for example>For maximum heat storage capacity of the heat storage tank at time t, Q HS,nom Is the rated heat storage capacity of the heat exchange device;
the IBDR operating constraints are:
in the formula ：indicating the minimum load reduction of the ith supplier in the energy market and in the back-up market,/->Indicating the maximum load reduction of the ith supplier in the energy market and in the back-up market,/->Indicating the load reduction amount, i.e. the load reduction amount, provided by the ith supplier at the moment t of the jth step j>Indicating the load reduction amount DeltaL actually provided by the ith supplier at time t of the jth step i,t Indicating the cumulative load reduction, ΔL, provided by the ith supplier at time t IB,t Indicating IBDR moment t-force; and IBDR time tOutlet ΔL IB,t The constraint conditions met are:
wherein ,for the IBDR to participate in the power generation of the energy market at time t, < >>Contribution to the backup market for IBDR at time t,/I>For IBDR, the contribution of the back-up market at time t>For maximum force of IBDR at time t, < >>Minimum force at time t for IBDR;
the system standby constraints:
wherein ,for maximum force of MES at time t, < +.>For the minimum force of MES at time t, r 1 For rotating reserve factor on power load, r 2 For the upper rotation reserve coefficient of WPP, r 3 Reserve coefficients for the lower rotation of WPP.
The operation optimization model of the EST system is as follows: wherein ,α1 Weight coefficient, alpha, for maximization of economic efficiency 2 Minimizing the weighting coefficient of the objective function for the force ripple, and alpha 1 +α 2 ＝1。
The technical scheme has the beneficial effects that: according to the invention, economic benefit is used as a benefit index and load fluctuation rate is used as a risk index to optimize a scheduling model of the EST system, and the economic benefit optimal value and the load fluctuation optimal value are weighted to obtain an EST system optimal scheduling result; the invention reduces the running risk of the EST system while taking economic benefit into consideration, and has important significance.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a block diagram of an EST system of the present invention.
FIG. 2 is a flow chart of the collaborative optimization of the electrothermal interconnection system of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without any inventive effort, are intended to be within the scope of the invention.
As shown in fig. 2, the embodiment of the invention provides an electrothermal internet system scheduling optimization method considering a multi-energy demand response, which comprises the following specific steps:
s1, taking an electric load demand, a heat load demand, a power available output and a heat source available output as inputs of the EST system.
As shown in fig. 1, the EST system is connected to an external utility grid, and the ETS system can purchase electrical energy from an electric fleet (Electric Vehicle Group, UEG) to meet electrical and thermal load demands. And the EST system comprises a WPP module, a regenerative electric boiler module (Regenerative Electric Boiler, REB), a gas turbine power generation module (Conventional Gas Turbine, CGT) and an excitation type demand response module (IBDR); to motivate end users to participate in the system optimization schedule, electrical and thermal loads are set to participate in price-type demand response (price-based demand response, PBDR), respectively. The WPP module is related to wind energy, the gas turbine power generation module is related to natural gas, and the excitation type demand response module is related to thermal load and electric load respectively; the thermal load includes a distributed thermal load associated with the incentive type demand response module and a centralized thermal load associated with the price demand response model. When the electric energy is utilized to meet the heat load demands of the end users, the distributed heat load is generally operated by electric conversion through electric appliances such as an air conditioner, an air source heat pump and the like, so the distributed heat load is still classified as an electric load. For a centralized heat load (Centralized thermal load), the REB converts electric energy to perform centralized heat supply, and the whole form is the heat load. For ease of analysis, the present invention sets the implementation of IBDR on distributed thermal loads (Decentralized thermal load) and PBDR on electrical loads and centralized thermal loads.
The power available output comprises WPP output, IBDR output and CGT output; the CGT output is dependent on natural gas consumption; the IBDR output force is dependent on load response capabilities; the WPP output depends on the natural wind condition, and the natural wind has strong random characteristics, so that the WPP output has strong uncertainty; the relationship between WPP output and wind speed is:
wherein ,gR Rated power for WPP output, v in Cut-in wind speed, v, for WPP output R Rated wind speed, v, for WPP output out Cut-out wind speed, v, for WPP output t For WPP to output real-time wind speed at time t,for WPP to be available at time T, t=1, 2, …, T is the total time.
The heat accumulating type electric boiler mainly comprises an electric boiler module and a heat accumulating tank module. And in the power generation peak time of the WPP and the PV, the heat storage tank can convert redundant electric load into heat energy, so that the power generation space of wind power and photovoltaic is improved. The heat accumulating electric boiler mainly stores heat energy in a water storage tank and uses water as a heat medium. The heat source available output comprises an electric boiler output and a heat storage tank output; the electric boiler is an energy conversion unit, which mainly converts electric energy into heat energy. The electric boiler converts clean energy waste into heat energy, improves the electric load space of the EST system, and has the output of:
wherein ,representing the amount of electricity consumed by the electric boiler for converting heat energy at time t +.>Indicating the heating output of the electric boiler at time t, < >>The electrothermal conversion efficiency of the electric boiler at time t is shown.
The heat storage tank is a heat energy storage device for wind abandoning and light abandoning conversion, and is used for heating output when the heat load demand is higher, and the output of the heat storage tank is as follows:
wherein ,at the moment of the heat storage tankt heat storage capacity, < >>Indicates the heat storage loss rate of the heat storage tank, +.>Indicating the heat supply power consumption of the heat storage tank at time t, < >>Indicating the heat-releasing power of the heat-accumulating tank at time t, < >>Indicating the efficiency of the heat absorption,indicating the heat release efficiency.
S2, constructing an operation optimization model of the EST system, and obtaining an optimal value f of the economic benefit of the EST system by using the operation optimization model of the economic benefit maximization EST system 1 max And the output fluctuation value f 1 2 The method comprises the steps of carrying out a first treatment on the surface of the According to the invention, economic benefits are selected as benefit indexes and load fluctuation rates are selected as risk indexes, and an objective function for maximizing the economic benefits and an objective function for minimizing the load fluctuation are respectively constructed.
The economic benefit comprises WPP operation benefit, CGT operation benefit, IBDR operation benefit and renewable energy operation benefit, and the economic benefit maximization objective function is as follows:
wherein ,f1 As an objective function of economic benefit, pi WPP,t Operating benefits, pi, for WPP contribution RE,t For RE operation benefit, pi CGT,t For CGT operation benefit, pi IB,t And operating benefits for IBDR. Since WPP generation marginal cost is almost zero, WPP operation returns are equal to the product of electricity quantity and electricity price.
The CGT operation benefits are as follows:
wherein ,ρCGT,t The online price for CGT power generation g CGT,t The network-surfing electric quantity for CGT power generation,cost of generating fuel for CGT, < >>And generating starting and stopping costs for the CGT.
The IBDR then gives corresponding compensation according to the protocol when the system invokes the end user's flexible load by signing an advance protocol with the end user. In general, IBDR is mainly provided by demand response providers (Demand response provider, DRP) based on output price distribution, and the IBDR operation yields are:
wherein ,for energy price, i=1, 2, …, I is the supplier, j=1, 2, …, J is the crew number.
The RE operation benefits are:
wherein ,for the heating price at time t +.>For the moment of timet power supply price, Q RE,t The heat supply output of the heat accumulating type electric boiler at the time t, g RE,t The electric quantity consumed by the heat accumulating electric boiler at the time t.
Wind power output has stronger uncertainty, and large risk is brought to a large-scale access system. Therefore, how to measure the risk of the wind power access system is important to ensure the ETS safe operation. The invention selects the net load fluctuation rate as a risk index and takes the minimized operation risk as an ETS operation objective function, wherein the output fluctuation minimization objective function is as follows:
in the formula ：f2 G is the load fluctuation value of EST system t As an average of the load fluctuations of the EST system throughout the scheduling period,the net thermal storage power provided for the REB.
The constraint conditions of the economic benefit maximization objective function and the output fluctuation minimization objective function comprise energy balance constraint, IBDR operation constraint, heat accumulating type electric boiler operation constraint and system standby constraint.
The energy balance constraints mainly include an electric load balance constraint and a thermal load balance constraint. The invention sets ETS to ensure electric load supply preferentially, and surplus electric power is heated by REB. When the electric power or the heating power is insufficient, the ETS can purchase electricity to perform energy conversion and supply to the public power grid, and the energy balance constraint is as follows:
wherein ,station power for WPP output, < ->Station power consumption g for CGT output UEG,t L for the amount of electricity purchased by EST systems to UEG t For the end user electrical load demand, < >>Distributed thermal load demand, ΔL, for end users PB,t For the load fluctuation amount, eta generated by PBDR de For electrothermal conversion efficiency u L,t PBDR state variable, u, for electrical load L,t =0 or 1; when u is L,t When=1, it indicates that PBDR is implemented, whereas PBDR is not implemented. According to the micro-economics theory, PBDR can be elastically described by electricity price.
wherein ,a concentrated heat load demand at time t; u (u) Q,t State variables for implementing PBDR for thermal load, u Q,t =0 or 1; when u is Q,t =1, indicating that PBDR is implemented, whereas PBDR is not implemented. ΔQ PB,t Load fluctuation amount for PBDR; in PBDR, the end user load demand curve can be smoothed, but if the user responds excessively, the load curve "peak Gu Daogua" will be created, so load fluctuations due to PBDR need to be limited, for example, thermal load, with constraints as follows:
wherein ,ΔQPB,t Hill climbing constraints for fluctuating loads generated by PBDR,downhill constraint for fluctuating loads generated by PBDR, +.>Load fluctuation limit value for PBDR, +.>A maximum of load fluctuation is generated for PBDR.
The heat accumulating type electric boiler operation constraint comprises an electric boiler operation constraint, a heat accumulating tank operation constraint and an energy balance constraint, and the specific constraint formula is as follows:
wherein ,for maximum force of RE at time t, +.>For the heat accumulation of the heat accumulation tank at the beginning of the scheduling period, < >>For the heat accumulation of the heat accumulation tank at the end of the scheduling period, < > for>For the minimum heat storage capacity of the heat storage tank at time t,/-, for example>For maximum heat storage capacity of the heat storage tank at time t, Q HS,nom Is the rated heat storage capacity of HS.
The IBDR operating constraints are:
in the formula ：representing minimum load shedding of the ith DRP in the energy market and the back-up marketDecrement (S)>Indicating the maximum load reduction amount of the ith DRP in the energy market and the standby market,/->Indicating the load reduction amount provided by the ith DRP at time t of step j,/for>Indicating the load reduction amount DeltaL actually provided by the ith DRP at time t of step j i,t Indicating the load reduction amount, deltaL, provided by the ith DRP cumulatively at time t IB,t Indicating IBDR moment t-force; and IBDR time tOutlet ΔL IB,t The constraint conditions met are:
wherein ,for the IBDR to participate in the power generation of the energy market at time t, < >>Contribution to the backup market for IBDR at time t,/I>For IBDR, the contribution of the back-up market at time t>For maximum force of IBDR at time t, < >>Is the minimum force of the IBDR at time t.
To overcome the influence of WPP and PV output volatility on MES operation stability, a certain power capacity needs to be reserved, and the system reserve constraint:
wherein ,for maximum force of MES at time t, < +.>For the minimum force of MES at time t, r 1 For rotating reserve factor on power load, r 2 For the upper rotation reserve coefficient of WPP, r 3 Reserve coefficients for the lower rotation of WPP. Similarly, the backup capacity for the heat load should be reserved, with the following specific constraints:
wherein ,r4 For the upper rotational reserve factor of the thermal load, r 5 Is a coefficient of reserve for the lower rotation of the thermal load.
S3, obtaining an optimal value of the output fluctuation value of the EST system by using an operation optimization model of the output fluctuation minimized EST systemAnd economic value->
S4, optimizing the economic benefit f in the step S2 1 max Output fluctuation value f 1 2 And the minimum value of the fluctuation value of the output in the step S3Economic value->And carrying out weighting processing to obtain an optimal scheduling result of the EST system. Since the optimization directions of the objective functions are different, when the multi-objective weighting is performed as a single objective, the corresponding processing is required to be performed, and alpha is set 1 Weight coefficient, alpha, for maximization of economic efficiency 2 And (3) for minimizing the weight coefficient of the objective function for the output fluctuation, weighting the objective function, wherein the running optimization model of the EST system is as follows: />Wherein, and alpha 1 +α 2 ＝1。
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, alternatives, and improvements that fall within the spirit and scope of the invention.
Claims (1)
1. The electrothermal Internet system dispatching optimization method considering the multi-energy demand response is characterized by comprising the following steps:
s1, taking an electric load demand, a heat load demand, a power available output and a heat source available output as inputs of an EST system; the EST system is connected with an external public power grid and comprises a WPP module, a heat accumulating type electric boiler module, a gas turbine power generation module and an excitation type demand response module;
the WPP module is related to wind energy, the gas turbine power generation module is related to natural gas, and the excitation type demand response module is related to thermal load and electric load respectively;
the heat load comprises a distributed heat load and a centralized heat load, wherein the distributed heat load is related to an excitation type demand response module, and the centralized heat load is related to a price demand response model;
the power available output comprises WPP output, IBDR output and CGT output; the CGT output is dependent on natural gas consumption; the IBDR output force is dependent on load response capabilities; the WPP output depends on the natural wind condition, and the relation between the WPP output and the wind speed is as follows:
wherein ,gR Rated power for WPP output, v in Cut-in wind speed, v, for WPP output R Rated wind speed, v, for WPP output out Cut-out wind speed, v, for WPP output t For WPP to output real-time wind speed at time t,for the WPP output available at time T, t=1, 2, …, T is the total time;
the heat source available output comprises an electric boiler output and a heat storage tank output; the electric boiler output is as follows:
wherein ,representing the amount of electricity consumed by the electric boiler for converting heat energy at time t +.>Indicating the heat supply of the electric boiler at time tForce (I) of>The electrothermal conversion efficiency of the electric boiler at the time t is shown;
the output force of the heat storage tank is as follows:
wherein ,for the heat storage capacity of the heat storage tank at time t, < >>Indicates the heat storage loss rate of the heat storage tank, +.>Indicating the heat supply power consumption of the heat storage tank at time t, < >>Indicating the heat release power of the heat storage tank at time t, < + >>Indicating the endothermic efficiency, +.>Indicating the heat release efficiency;
s2, constructing an operation optimization model of the EST system, and obtaining an optimal value f of the economic benefit of the EST system by using the operation optimization model of the economic benefit maximization EST system 1 max And the output fluctuation value f 1 2 ；
The economic benefit comprises WPP operation benefit, CGT operation benefit, IBDR operation benefit and renewable energy operation benefit, and the economic benefit maximization objective function is as follows:
wherein ,f1 As an objective function of economic benefit, pi WPP,t For WPP operation benefit, pi RE,t For RE operation benefit, pi CGT,t For CGT operation benefit, pi IB,t Operating revenues for IBDR;
the CGT operation benefits are as follows:
wherein ,ρCGT,t Internet price for CGT output g CGT,t The power of the network is supplied by the CGT,cost of fuel for CGT, +.>Start-stop cost for CGT output;
the IBDR operating benefits are:
wherein ,for energy price, i=1, 2, …, I is vendor, j=1, 2, …, J is unit number; />Indicating the amount of load reduction provided by the ith supplier at the jth step time t;
the RE operation benefits are:
wherein ,for the heating price at time t +.>For the power supply price at time t, Q RE,t The heat supply output of the heat accumulating type electric boiler at the time t, g RE,t The electric quantity consumed by the heat accumulating type electric boiler at the time t;
the output fluctuation minimization objective function is as follows:
in the formula ：f2 G is the load fluctuation value of EST system t As an average of the load fluctuations of the EST system throughout the scheduling period,net regenerative power provided to REB;
constraint conditions of the economic benefit maximization objective function and the output fluctuation minimization objective function comprise energy balance constraint, IBDR operation constraint, heat accumulating type electric boiler operation constraint and system standby constraint;
the energy balance constraint is:
wherein ,station power for WPP output, < ->Station power consumption g for CGT output UEG,t Electric quantity L purchased by EST system to electric automobile group t For the end user electrical load demand, < >>Distributed thermal load demand, ΔL, for end users PB,t For the load fluctuation amount generated by PBDR, deltaL IB,t Represents the IBDR moment t-out, eta de For electrothermal conversion efficiency u L,t PBDR, which is an excitatory demand response, is a PBDR state variable of the electrical load;
the heat accumulating type electric boiler operation constraint comprises an electric boiler operation constraint, a heat accumulating tank operation constraint and an energy balance constraint, and the specific constraint formula is as follows:
wherein ,for maximum force of RE at time t, +.>For the heat accumulation of the heat accumulation tank at the beginning of the scheduling period, < >>For the heat accumulation of the heat accumulation tank at the end of the scheduling period, < > for>For the minimum heat storage capacity of the heat storage tank at time t,/-, for example>For maximum heat storage capacity of the heat storage tank at time t, Q HS,nom The rated heat storage capacity of the heat exchange unit;
the IBDR operating constraints are:
in the formula ：indicating the minimum load reduction of the ith supplier in the energy market and in the back-up market,/->Indicating the maximum load reduction of the ith supplier in the energy market and in the back-up market,/->Indicating the load reduction amount, ++provided by the ith supplier at the jth step time t>Indicating the load reduction amount, deltaL, actually provided by the ith supplier at the jth step time t i,t Indicating the cumulative load reduction, ΔL, provided by the ith supplier at time t IB,t Indicating IBDR moment t-force; and IBDR time tOutlet ΔL IB,t The constraint conditions met are:
wherein ,for the IBDR to participate in the power generation of the energy market at time t, < >>Contribution to the backup market for IBDR at time t,/I>For IBDR, the contribution of the back-up market at time t>For maximum force of IBDR at time t, < >>Minimum force at time t for IBDR;
the system standby constraints:
wherein ,r1 For rotating reserve factor on power load, r 2 For the upper rotation reserve coefficient of WPP, r 3 A lower rotation reserve factor for WPP;
the spare capacity of the heat load should also be reserved, with the following specific constraints:
wherein ,r4 For the upper rotational reserve factor of the thermal load, r 5 A lower rotation reserve factor for the thermal load;
s3, obtaining an optimal value of the output fluctuation value of the EST system by using an operation optimization model of the output fluctuation minimized EST systemAnd economic value->
S4, optimizing the economic benefit f in the step S2 1 max Output fluctuation value f 1 2 And the minimum value f of the fluctuation value of the output in the step S3 2 min Economic benefit value f 2 1 Weighting processing is carried out, and an optimal scheduling result of the EST system is obtained;
the operation optimization model of the EST system is as follows: wherein ,α1 Weight coefficient, alpha, for maximization of economic efficiency 2 Minimizing the weighting coefficient of the objective function for the force ripple, and alpha 1 +a 2 ＝1。
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911246590.5A CN110991753B (en) | 2019-12-07 | 2019-12-07 | Electric heating internet system scheduling optimization method considering multi-energy demand response |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911246590.5A CN110991753B (en) | 2019-12-07 | 2019-12-07 | Electric heating internet system scheduling optimization method considering multi-energy demand response |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110991753A CN110991753A (en) | 2020-04-10 |
| CN110991753B true CN110991753B (en) | 2023-10-31 |
Family
ID=70091001
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911246590.5A Active CN110991753B (en) | 2019-12-07 | 2019-12-07 | Electric heating internet system scheduling optimization method considering multi-energy demand response |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110991753B (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117236530B (en) * | 2023-11-16 | 2024-02-09 | 国网湖北省电力有限公司 | Power energy optimization method based on 4G/5G short sharing power wireless communication |
| CN117350410B (en) * | 2023-12-06 | 2024-02-20 | 中国科学院电工研究所 | Micro-grid group cooperative operation optimization method based on multi-agent federation reinforcement learning |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108154309A (en) * | 2017-12-30 | 2018-06-12 | 国网天津市电力公司电力科学研究院 | The energy internet economy dispatching method of meter and the more load dynamic responses of cool and thermal power |
| CN108229025A (en) * | 2018-01-04 | 2018-06-29 | 东南大学 | A kind of more microgrid active distribution system economic optimization dispatching methods of supply of cooling, heating and electrical powers type |
| CN109409600A (en) * | 2018-10-24 | 2019-03-01 | 华北电力大学 | Wind-powered electricity generation, heat storage electric boiler combined heating system Optimization Scheduling |
| CN109727158A (en) * | 2019-01-25 | 2019-05-07 | 燕山大学 | A kind of electric heating integrated energy system dispatching method based on the weak robust optimization of improvement |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106998079B (en) * | 2017-04-28 | 2020-05-05 | 东南大学 | Modeling method of combined heat and power optimization scheduling model |
-
2019
- 2019-12-07 CN CN201911246590.5A patent/CN110991753B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108154309A (en) * | 2017-12-30 | 2018-06-12 | 国网天津市电力公司电力科学研究院 | The energy internet economy dispatching method of meter and the more load dynamic responses of cool and thermal power |
| CN108229025A (en) * | 2018-01-04 | 2018-06-29 | 东南大学 | A kind of more microgrid active distribution system economic optimization dispatching methods of supply of cooling, heating and electrical powers type |
| CN109409600A (en) * | 2018-10-24 | 2019-03-01 | 华北电力大学 | Wind-powered electricity generation, heat storage electric boiler combined heating system Optimization Scheduling |
| CN109727158A (en) * | 2019-01-25 | 2019-05-07 | 燕山大学 | A kind of electric heating integrated energy system dispatching method based on the weak robust optimization of improvement |
Non-Patent Citations (1)
| Title |
|---|
| 考虑不确定性的风光燃蓄多目标随机调度优化模型;赵东来;牛东晓;杨尚东;梁才;;湖南大学学报(自然科学版);第45卷(第04期);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110991753A (en) | 2020-04-10 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108154309B (en) | Energy internet economic dispatching method considering multi-load dynamic response of cold, heat and electricity | |
| CN111738502B (en) | Multi-energy complementary system demand response operation optimization method for promoting surplus wind power consumption | |
| CN110826815B (en) | Regional comprehensive energy system operation optimization method considering comprehensive demand response | |
| CN107702079B (en) | A kind of photo-thermal power station containing electric heater unit and its modeling and optimizing operation method | |
| CN110910047A (en) | Random scheduling optimization method for electrothermal coupling micro-energy source station | |
| CN108321837A (en) | A kind of wind-powered electricity generation-photo-thermal combined generating system and its operation method | |
| CN110808615A (en) | Gas-electric virtual power plant scheduling optimization method considering uncertainty | |
| CN110829503B (en) | Wind, light, water and fire storage multi-energy complementary micro-grid joint optimization scheduling method and system | |
| CN110991753B (en) | Electric heating internet system scheduling optimization method considering multi-energy demand response | |
| CN111899125B (en) | Optimized modeling operation method, device and medium of comprehensive energy system | |
| CN112488363A (en) | Generalized energy storage based optimal scheduling method for multi-energy power system | |
| Zhang et al. | Economic and Optimal Dispatch Model of Electricity, Heat and Gas for Virtual Power Plants in Parks Considering Low Carbon Targets. | |
| CN115360706A (en) | Source load storage combined scheduling method and system considering DR and flexibility supply and demand balance | |
| CN111030101A (en) | Clean energy consumption linkage regulation and control method and system based on diversified big data | |
| Mohseni et al. | A multi-agent approach to optimal sizing of a combined heating and power microgrid | |
| CN113822480A (en) | Multi-layer collaborative optimization method and system for rural comprehensive energy system | |
| Wei et al. | Optimal generation planning in a micro-grid for supplying electrical and thermal loads in order to reduce pollutant emissions | |
| CN115986812A (en) | Micro-grid economic planning method and device considering energy storage and demand response | |
| CN115204705A (en) | Regional comprehensive energy system operation optimization method considering electricity-to-gas storage and application | |
| CN110990793B (en) | Scheduling optimization method for electric heating gas coupling micro energy station | |
| CN112884191A (en) | Thermoelectric day-ahead scheduling model based on network source coordination and calculation method | |
| Wang et al. | Source-load-storage coordinated optimal scheduling of virtual power plant participating demand response | |
| Ma et al. | Optimization of Integrated Energy System Scheduling Considering Stepped Carbon Emission Trading Mechanism and Electricity Heat Demand Response | |
| Sha et al. | Robust economic dispatching of high renewable energy penetrated system with concentrating solar power providing reserve capacity | |
| CN117077368B (en) | Comprehensive energy system crowd target planning method considering industrial comprehensive demand response |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |