CN110991753A - Electric heating internet system scheduling optimization method considering multi-energy demand response - Google Patents
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Abstract
The invention provides an electric heating internet system scheduling optimization method considering multi-energy demand response, which comprises the following steps: firstly, taking the electric load demand, the heat load demand, the available power supply output and the available heat source output as the input of an EST system; secondly, constructing an operation optimization model of the EST system, and optimizing the operation optimization model of the EST system by utilizing economic benefit maximization and output fluctuation minimization to obtain an optimal value of economic benefit, an optimal value of output fluctuation, an economic benefit value and an output fluctuation value of the EST system; and finally, weighting 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 method, economic benefits are used as benefit indexes, the load fluctuation rate is used as a risk index to optimize a scheduling model of the EST system, and an EST optimal scheduling result is obtained; the invention reduces the running risk of the EST system while giving consideration to economic benefits, 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
With the increasingly prominent energy problem, renewable energy represented by photovoltaic and wind power is gradually paid attention to the state and the world, and becomes an important direction for future development of energy, and the development and utilization of renewable energy are helpful for adjusting energy structures, perfecting power systems and realizing sustainable development of power economy. Due to the limitations of distributed power supplies such as wind power generation and photovoltaic power generation, the 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 restricted by the heat production quantity thereof, so that the energy consumption is challenged.
For EST (electric heating cooperative scheduling), the marginal cost of Power generation of WPP (wind Power prediction) is almost zero, and the method has good economic benefit and environmental benefit. Therefore, for realizing optimal operation, the WPP power generation is required to be maximally utilized to meet the electric load and the heat load in a coordinated manner. However, due to the random characteristic of the WPP power generation output, strong impact is brought to the system by high proportion of wind power generation grid connection, and the running risk of the system is high. Therefore, the significance of how to balance the benefits and risks brought by WPP and establish the optimal EST system operation strategy is highlighted.
Disclosure of Invention
Aiming at the defects in the background technology, the invention provides a scheduling optimization method of an electric heating internet system considering multi-energy demand response, and solves the technical problems of energy consumption and high EST system operation risk in the prior art.
The technical scheme of the invention is realized as follows:
a scheduling optimization method of an electric heating Internet system considering multi-energy demand response comprises the following steps:
s1, taking the electric load demand, the heat load demand, the available power supply output and the available heat source output as the input of the EST system;
s2, constructing an operation optimization model of the EST system, and obtaining the optimal value f of the economic benefit of the EST system by utilizing the operation optimization model of the EST system with the economic benefit maximized1 maxAnd the fluctuation value f of the output1 2；
S3, obtaining the output fluctuation value optimal value of the EST system by utilizing the operation optimization model of the EST system with minimized output fluctuation
And economic benefit value
S4, optimal value f for economic benefit in step S21 maxOutput fluctuation value f1 2And minimum value of fluctuation value of output in step S3
Value of economic benefit
And performing weighting processing to obtain the 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 respectively related to heat load and electric load;
the thermal loads include distributed thermal loads associated with the incentive demand response modules and centralized thermal loads associated with the price demand response model.
The available power supply output comprises a WPP output, an IBDR output and a CGT output; the CGT output is dependent on natural gas consumption; the IBDR take-off depends on load response capability; the WPP output depends on the natural wind condition, and the relation between the WPP output and the wind speed is as follows:
wherein ,gRRated power, v, for the WPP outputinCut-in wind speed, v, for WPP contributionRRated wind speed, v, for WPP outputoutCut-out wind speed, v, for WPP contributiontThe real-time wind speed of the WPP contribution at time t,
the WPP output is the output available at time T, T being 1,2, …, T being the total time.
The available heat source output comprises electric boiler output and heat storage tank output; the output of the electric boiler is as follows:
wherein ,
representing the amount of electricity consumed by the electric boiler at time t for converting thermal energy,
represents the heating output of the electric boiler at the moment t,
the electric heat conversion efficiency of the electric boiler at the moment t is shown;
the output of the heat storage tank is as follows:
wherein ,
to store the heat in the heat storage tank at time t,
the heat storage loss rate of the heat storage tank is shown,
indicating the amount of heat supplied from the heat storage tank at time t,
showing the heat release power of the heat storage tank at time t,
it is shown that the heat absorption efficiency is,
indicating the exothermic efficiency.
The economic benefits comprise WPP operation income, CGT operation income, IBDR operation income and renewable energy source operation income, and the economic benefit maximization objective function is as follows:
wherein ,f1Is an economic benefit objective function, piWPP,tFor WPP operating income, piRE,tFor RE operation income, piCGT,tFor CGT operating profits, piIB,tEarning for the IBDR operation.
The CGT operation income is as follows:
wherein ,ρCGT,tPrice of surfing the Net for CGT power gCGT,tIs the power on-line electricity quantity of the CGT,
in order to generate a fuel cost for the CGT,
the start-stop cost of the CGT output is obtained;
the IBDR operation income is as follows:
wherein ,
for energy supply price, I is 1,2, …, I is supplier, J is 1,2, …, J is unit number;
the RE operation income is as follows:
wherein ,
for the heating price at the moment t,
for the supply price at time t, QRE,tG for the heat output of the heat accumulating electric boiler at the moment tRE,tIs the amount of electricity consumed by the regenerative electric boiler at time t.
The output fluctuation minimization objective function is as follows:
in the formula ：f2As load fluctuation value of EST system, GtIs the average value of the load fluctuation of the EST system in the whole scheduling period,
net stored thermal power provided for REB.
The constraint conditions of the economic benefit maximization objective function and the output fluctuation minimization objective function comprise an energy balance constraint, an IBDR operation constraint, a regenerative electric boiler operation constraint and a system standby constraint.
The energy balance constraint is:
wherein ,
the power consumption rate of the WPP is the power consumption rate of the WPP,
service power rate g for CGT outputUEG,tAmount of power purchased to electric vehicle fleet for EST system, LtIn order to meet the electrical load requirements of the end user,
decentralized heat load demand, Δ L, for end usersPB,tAmount of load fluctuation generated for PBDR, ηdeFor the efficiency of electrothermal conversion uL,tPBDR state variable for electrical loads, PBDR being an excitatory demand response;
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 ,
the maximum output of RE at time t,
is the heat storage amount of the heat storage tank at the beginning of the dispatching cycle,
is the heat storage amount of the heat storage tank at the end of the dispatching cycle,
is the minimum heat storage capacity of the heat storage tank at the time t,
is the maximum heat storage capacity of the heat storage tank at time t, QHS,nomThe rated heat storage capacity of the heat exchange device;
the IBDR operation constraints are:
in the formula ：
indicating the minimum load reduction of the ith supplier in the energy market and the reserve market,
indicating the maximum load reduction of the ith supplier in the energy market and the reserve market,
indicating the amount of load reduction provided by the ith supplier at time t of step j,
indicates the amount of load reduction, Δ L, actually provided by the ith supplier at time t of step ji,tIndicates the cumulative amount of load reduction, Δ L, supplied by the ith supplier at time tIB,tRepresenting the IBDR time t output; and the IBDR moment t is exerted by delta LIB,tThe constraint conditions are satisfied as follows:
wherein ,
for the IBDR to participate in the power generation output of the energy market at time t,
standby market rollout for IBDR at time tThe force is applied to the inner wall of the container,
for the IBDR to contribute to the reserve market at time t,
for the maximum contribution of the IBDR at time t,
minimum output for the IBDR at time t;
the system standby constraint is:
wherein ,
the maximum force applied by the MES at time t,
minimum force, r, of MES at time t1For rotating the stand-by factor, r, on the power load2For the upper rotational stand-by coefficient of WPP, r3Is the lower rotational standby factor of WPP.
The operation optimization model of the EST system is as follows:
wherein ,α1Weight coefficient for maximizing economic efficiency, α2Minimizing the weight coefficients of the objective function for the output ripple, and α1+α2＝1。
The beneficial effect that this technical scheme can produce: according to the method, economic benefits are used as benefit indexes, the load fluctuation rate is used as a risk index to optimize a scheduling model of the EST system, and weighting processing is carried out on an economic benefit optimal value and a load fluctuation optimal value to obtain an optimal scheduling result of the EST system; the invention reduces the running risk of the EST system while giving consideration to economic benefits, and has important significance.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a block diagram of an EST system of the present invention.
FIG. 2 is a flow chart of the cooperative optimization of the electric-thermal interconnection system of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without inventive effort based on the embodiments of the present invention, are within the scope of the present invention.
As shown in fig. 2, an embodiment of the present invention provides a scheduling optimization method for an electric heating internet system considering multi-energy demand response, which includes the following specific steps:
and S1, taking the electric load demand, the heat load demand, the available power supply output and the available heat source output as the input of the EST system.
As shown in fig. 1, the EST system is connected to an external public power grid, and the ETS system may purchase electrical energy from an Electric Vehicle Group (UEG) to meet electrical and thermal load demands. The EST system comprises a WPP module, a Regenerative Electric Boiler module (REB), a Gas Turbine power generation module (CGT) and an excitation-based demand response module (IBDR); in order to encourage the terminal user to participate in the system optimization scheduling, the electrical load and the thermal load are respectively set to participate in price-based demand response (PBDR). 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 respectively related to heat load and electric load; the thermal loads include distributed thermal loads associated with the incentive demand response modules and centralized thermal loads associated with the price demand response model. When the heat load demand of the end user is satisfied by using the electric energy, the distributed heat load is generally subjected to electricity-to-heat operation by using electric appliances such as an air conditioner, an air source heat pump and the like, so that the distributed heat load is still classified as the electric load. For a Centralized thermal load (Centralized thermal load), the Centralized heat supply is performed by converting the electrical energy through REB, and the whole form is a thermal load. For ease of analysis, the present invention sets forth IBDR for distributed thermal loads (Decentralizedthermal load) and PBDR for electrical and concentrated thermal loads.
The available power supply output comprises a WPP output, an IBDR output and a CGT output; the CGT output is dependent on natural gas consumption; the IBDR take-off depends on load response capability; the WPP output depends on the natural incoming wind condition, the random characteristic of the natural incoming wind is strong, and the WPP output also has strong uncertainty; the relationship between WPP contribution and wind speed is:
wherein ,gRRated power, v, for the WPP outputinCut-in wind speed, v, for WPP contributionRRated wind speed, v, for WPP outputoutCut-out wind speed, v, for WPP contributiontThe real-time wind speed of the WPP contribution at time t,
the WPP output is the output available at time T, T being 1,2, …, T being the total time.
The heat accumulating type electric boiler mainly comprises an electric boiler module and a heat accumulating tank module. In the WPP and PV power generation output peak time period, the heat storage tank can convert redundant electric loads into heat energy, and the wind power and photovoltaic power generation output space is improved. The heat accumulating type electric boiler mainly stores heat energy in a water storage tank and utilizes water as a heat medium. The available heat source output comprises electric boiler output and heat storage tank output; the electric boiler is an energy conversion unit and mainly converts electric energy into heat energy. The electric boiler abandons clean energy and turns into heat energy, promotes the electric load space of EST system, and the electric boiler is exerted oneself and is:
wherein ,
representing the amount of electricity consumed by the electric boiler at time t for converting thermal energy,
represents the heating output of the electric boiler at the moment t,
indicating the electric heat conversion efficiency of the electric boiler at the time t.
The heat storage tank is a storage device for heat energy converted from abandoned wind and abandoned light, and supplies heat to the heat storage tank when the heat load demand is higher, and the heat storage tank outputs heat as follows:
wherein ,
to store the heat in the heat storage tank at time t,
the heat storage loss rate of the heat storage tank is shown,
indicating the amount of heat supplied from the heat storage tank at time t,
indicating the heat release power of the heat storage tank at the time t,
it is shown that the heat absorption efficiency is,
indicating the exothermic efficiency.
S2, constructing an operation optimization model of the EST system, and obtaining the optimal value f of the economic benefit of the EST system by utilizing the operation optimization model of the EST system with the economic benefit maximized1 maxAnd the fluctuation value f of the output1 2(ii) a According to the method, economic benefit is selected as a benefit index, and the load fluctuation rate is selected as a risk index, and a maximum economic benefit objective function and a minimum load fluctuation objective function are respectively constructed.
The economic benefits comprise WPP operation income, CGT operation income, IBDR operation income and renewable energy source operation income, and the economic benefit maximization objective function is as follows:
wherein ,f1Is an economic benefit objective function, piWPP,tOperating yield, pi, for WPP contributionRE,tFor RE operation income, piCGT,tFor CGT operating profits, piIB,tEarning for the IBDR operation. Since the marginal cost of WPP power generation is almost zero, the WPP operation income is equal to the product of electric quantity and electricity price.
The CGT operation income is as follows:
wherein ,ρCGT,tPrice of surfing the Net for CGT Power Generation, gCGT,tNetwork power supply for CGT power generationThe amount of the compound (A) is,
in order to achieve the fuel cost of CGT power generation,
the power generation starting and stopping cost of the CGT is saved.
The IBDR provides corresponding compensation according to the protocol when the system calls the flexibility load of the terminal user by signing a prior protocol with the terminal user. Generally, the IBDR is mainly provided by a Demand Response Provider (DRP) in a distribution based on the price of output, and the operational revenue of the IBDR is:
wherein ,
for energy supply price, I is 1,2, …, I is supplier, J is 1,2, …, J is unit number.
The RE operation income is as follows:
wherein ,
for the heating price at the moment t,
for the supply price at time t, QRE,tG for the heat output of the heat accumulating electric boiler at the moment tRE,tIs the amount of electricity consumed by the regenerative electric boiler at time t.
The wind power output has strong uncertainty, and the large-scale access of the system brings great risk. Therefore, how to measure the risk of the wind power access system is important for guaranteeing safe operation of the ETS. The invention selects net load fluctuation rate as risk index, and takes minimized operation risk as ETS operation objective function, the output fluctuation minimized objective function is:
in the formula ：f2As load fluctuation value of EST system, GtIs the average value of the load fluctuation of the EST system in the whole scheduling period,
net stored thermal power provided for REB.
The constraint conditions of the economic benefit maximization objective function and the output fluctuation minimization objective function comprise an energy balance constraint, an IBDR operation constraint, a regenerative electric boiler operation constraint and a system standby constraint.
The energy balance constraints mainly include an electrical (electric load) balance constraint and a thermal (thermoload) balance constraint. The invention sets ETS to preferentially ensure the supply of electric loads, and the surplus electric power is heated by REB. When electricity or heat is insufficient, the ETS can perform energy conversion and supply to the public power grid for electricity purchase, and the energy balance constraint is as follows:
wherein ,
the power consumption rate of the WPP is the power consumption rate of the WPP,
service power rate g for CGT outputUEG,tAmount of power purchased to the UEG for the EST system, LtIn order to meet the electrical load requirements of the end user,
decentralized heat load demand, Δ L, for end usersPB,tAmount of load fluctuation generated for PBDR, ηdeFor the efficiency of electrothermal conversion uL,tPBDR state variable, u, for electrical loadsL,t0 or 1; when u isL,tWhen 1, PBDR is indicated to be implemented, whereas PBDR is not implemented. According to micro-economic theory, PBDR can be described by electricity price elasticity.
wherein ,
is the concentrated heat load demand at time t; u. ofQ,tState variable, u, for PBDR for thermal load implementationQ,t0 or 1; when u isQ,t1, PBDR is shown implemented, whereas PBDR is not implemented. Delta QPB,tThe amount of load fluctuation generated for PBDR; for PBDR, the load demand curve of the end user can be smoothed, but if the user responds excessively, the load curve "hangs over from peak to valley", so the load fluctuation generated by PBDR needs to be limited, taking the thermal load as an example, the constraint conditions of the thermal load are as follows:
wherein ,ΔQPB,tThe hill climbing constraint for the fluctuating loads generated by PBDR,
the downhill constraint for the fluctuating load produced by PBDR,
the limit value of the load fluctuation generated for PBDR,
creating a maximum of load fluctuation 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 ,
the maximum output of RE at time t,
is the heat storage amount of the heat storage tank at the beginning of the dispatching cycle,
for heat storage tank at the end of scheduling periodThe amount of stored heat of (a) is,
is the minimum heat storage capacity of the heat storage tank at the time t,
is the maximum heat storage capacity of the heat storage tank at time t, QHS,nomThe rated heat storage capacity of the HS.
The IBDR operation constraints are:
in the formula ：
indicating the minimum load reduction of the ith DRP in the energy market and the backup market,
indicating the maximum load reduction of the ith DRP in the energy market and the backup market,
indicating the amount of load reduction provided by the ith DRP at time t of step j,
indicates the amount of load reduction, Δ L, actually provided by the ith DRP at time t of step ji,tIndicates the cumulative amount of load reduction, Δ L, supplied by the ith DRP at time tIB,tRepresenting the IBDR time t output; and the IBDR moment t is exerted by delta LIB,tThe constraint conditions are satisfied as follows:
wherein ,
for the IBDR to participate in the power generation output of the energy market at time t,
for the IBDR to participate in the aftermarket contribution at time t,
for the IBDR to contribute to the reserve market at time t,
for the maximum contribution of the IBDR at time t,
the minimum contribution of the IBDR at time t.
In order to overcome the influence of WPP and PV output fluctuation on MES operation stability, certain power capacity needs to be reserved, and the system is in standby constraint:
wherein ,
the maximum force applied by the MES at time t,
minimum force, r, of MES at time t1For rotating the stand-by factor, r, on the power load2For the upper rotational stand-by coefficient of WPP, r3Is the lower rotational standby factor of WPP. Similarly, the upper and lower spare capacities of the thermal load should be reserved, with the following specific constraints:
wherein ,r4Upper rotation stand-by factor, r, for thermal load5Is the lower rotational standby factor for the thermal load.
S3, obtaining the output fluctuation value optimal value of the EST system by utilizing the operation optimization model of the EST system with minimized output fluctuation
And economic benefit value
S4, optimal value f for economic benefit in step S21 maxOutput fluctuation value f1 2And minimum value of fluctuation value of output in step S3
Value of economic benefit
And performing weighting processing to obtain the optimal scheduling result of the EST system, wherein due to different optimization directions of the objective functions, when the multi-objective weighting is performed to be a single objective, corresponding processing needs to be performed, and α is set1Weight coefficient for maximizing economic efficiency, α2And weighting the objective function if the weight coefficient of the objective function is minimized for the output fluctuation, wherein the operation optimization model of the EST system is as follows:
therein, and α1+α2＝1。
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (10)
1. A scheduling optimization method of an electric heating Internet system considering multi-energy demand response is characterized by comprising the following steps:
s1, taking the electric load demand, the heat load demand, the available power supply output and the available heat source output as the input of the EST system;
s2, constructing an operation optimization model of the EST system, and obtaining the optimal value f of the economic benefit of the EST system by utilizing the operation optimization model of the EST system with the economic benefit maximized1 maxAnd the fluctuation value f of the output1 2；
S3, obtaining the output fluctuation value optimal value of the EST system by utilizing the operation optimization model of the EST system with minimized output fluctuation
And economic benefit value
S4, optimal value f for economic benefit in step S21 maxOutput fluctuation value f1 2And minimum value of fluctuation value of output in step S3
Value of economic benefit
And performing weighting processing to obtain the optimal scheduling result of the EST system.
2. The electric heating internet system scheduling optimization method considering the multi-energy demand response according to claim 1, wherein 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 respectively related to heat load and electric load;
the thermal loads include distributed thermal loads associated with the incentive demand response modules and centralized thermal loads associated with the price demand response model.
3. The method for scheduling and optimizing an electric heating internet system considering multi-energy demand response according to claim 1, wherein the available power output includes a WPP output, an IBDR output, and a CGT output; the CGT output is dependent on natural gas consumption; the IBDR take-off depends on load response capability; the WPP output depends on the natural wind condition, and the relation between the WPP output and the wind speed is as follows:
wherein ,gRRated power, v, for the WPP outputinCut-in wind speed, v, for WPP contributionRRated wind speed, v, for WPP outputoutCut-out wind speed, v, for WPP contributiontThe real-time wind speed of the WPP contribution at time t,
the WPP output is the output available at time T, T being 1,2, …, T being the total time.
4. The electric heating internet system scheduling optimization method considering multi-energy demand response of claim 1, wherein the available heat source output includes an electric boiler output and a thermal storage tank output; the output of the electric boiler is as follows:
wherein ,
representing the amount of electricity consumed by the electric boiler at time t for converting thermal energy,
represents the heating output of the electric boiler at the moment t,
the electric heat conversion efficiency of the electric boiler at the moment t is shown;
the output of the heat storage tank is as follows:
wherein ,
to store the heat in the heat storage tank at time t,
the heat storage loss rate of the heat storage tank is shown,
indicating the amount of heat supplied from the heat storage tank at time t,
showing the heat release power of the heat storage tank at time t,
it is shown that the heat absorption efficiency is,
indicating the exothermic efficiency.
5. The method for scheduling and optimizing the electric heating internet system considering the multi-energy demand response according to claim 1, wherein the economic benefits include WPP operation income, CGT operation income, IBDR operation income and renewable energy operation income, and the economic benefit maximization objective function is as follows:
wherein ,f1Is an economic benefit objective function, piWPP,tFor WPP operating income, piRE,tFor RE operation income, piCGT,tFor CGT operating profits, piIB,tEarning for the IBDR operation.
6. The method for scheduling and optimizing an electric heating internet system considering multipotential demand response as claimed in claim 5, wherein the CGT operation income is as follows:
wherein ,ρCGT,tPrice of surfing the Net for CGT power gCGT,tIs the power on-line electricity quantity of the CGT,
in order to generate a fuel cost for the CGT,
the start-stop cost of the CGT output is obtained;
the IBDR operation income is as follows:
wherein ,
for energy supply price, I is 1,2, …, I is supplier, J is 1,2, …, J is unit number;
the RE operation income is as follows:
wherein ,
for the heating price at the moment t,
for the supply price at time t, QRE,tG for the heat output of the heat accumulating electric boiler at the moment tRE,tIs the amount of electricity consumed by the regenerative electric boiler at time t.
7. The electro-thermal internet system scheduling optimization method considering multipotential demand response according to any one of claims 1 to 6, wherein the output fluctuation minimization objective function is:
in the formula ：f2As load fluctuation value of EST system, GtIs the average value of the load fluctuation of the EST system in the whole scheduling period,
net heat storage work provided for REBAnd (4) rate.
8. The method for scheduling and optimizing an electric heating internet system considering multi-energy demand response according to claim 5, 6 or 7, wherein the constraints of the economic benefit maximization objective function and the output fluctuation minimization objective function include an energy balance constraint, an IBDR operation constraint, a regenerative electric boiler operation constraint and a system standby constraint.
9. The method for scheduling optimization of an electric heating internet system considering multi-energy demand response according to claim 8, wherein the energy balance constraint is as follows:
wherein ,
the power consumption rate of the WPP is the power consumption rate of the WPP,
service power rate g for CGT outputUEG,tAmount of power purchased to electric vehicle fleet for EST system, LtIn order to meet the electrical load requirements of the end user,
decentralized heat load demand, Δ L, for end usersPB,tAmount of load fluctuation generated for PBDR, ηdeFor the efficiency of electrothermal conversion uL,tPBDR state variable for electrical loads, PBDR being an excitatory demand response;
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 ,
the maximum output of RE at time t,
is the heat storage amount of the heat storage tank at the beginning of the dispatching cycle,
is the heat storage amount of the heat storage tank at the end of the dispatching cycle,
is the minimum heat storage capacity of the heat storage tank at the time t,
is the maximum heat storage capacity of the heat storage tank at time t, QHS,nomThe rated heat storage capacity of the heat exchange unit;
the IBDR operation constraints are:
in the formula ：
indicating the minimum load reduction of the ith supplier in the energy market and the reserve market,
indicating the maximum load reduction of the ith supplier in the energy market and the reserve market,
indicating the amount of load reduction provided by the ith supplier at time t of step j,
indicates the amount of load reduction, Δ L, actually provided by the ith supplier at time t of step ji,tIndicates the cumulative amount of load reduction, Δ L, supplied by the ith supplier at time tIB,tRepresenting the IBDR time t output; and the IBDR moment t is exerted by delta LIB,tThe constraint conditions are satisfied as follows:
wherein ,
for the IBDR to participate in the power generation output of the energy market at time t,
for the IBDR to participate in the aftermarket contribution at time t,
for the IBDR to contribute to the reserve market at time t,
for the maximum contribution of the IBDR at time t,
minimum output for the IBDR at time t;
the system standby constraint is:
wherein ,
the maximum force applied by the MES at time t,
minimum force, r, of MES at time t1For rotating the stand-by factor, r, on the power load2For the upper rotational stand-by coefficient of WPP, r3Is the lower rotational standby factor of WPP.
10. The method of claim 1, wherein the method for optimizing scheduling of the electric heating internet system considering the response of the multi-energy demand is characterized in thatThe operation optimization model of the EST system is as follows:
wherein ,α1Weight coefficient for maximizing economic efficiency, α2Minimizing the weight coefficients of the objective function for the output ripple, and α1+α2＝1。
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