CN106651473B - Method for promoting wind power acceptance level by considering day-ahead hour electricity price and various demand responses - Google Patents

Method for promoting wind power acceptance level by considering day-ahead hour electricity price and various demand responses Download PDF

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CN106651473B
CN106651473B CN201710062477.6A CN201710062477A CN106651473B CN 106651473 B CN106651473 B CN 106651473B CN 201710062477 A CN201710062477 A CN 201710062477A CN 106651473 B CN106651473 B CN 106651473B
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方日升
刘文彬
王其瑜
王良缘
王颖帆
杨首晖
江岳文
温步瀛
林建新
张艺渊
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State Grid Fujian Electric Power Co Ltd
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Abstract

The invention relates to a method for promoting wind power acceptance level by considering day-ahead hour electricity price and various demand responses, which is characterized by considering the influence of the day-ahead hour electricity price on price type demand response users and incentive type demand response users under the condition of large-scale wind power access, and establishing a wind power system scheduling two-layer planning model which considers the day-ahead hour electricity price optimization under the condition of wind power uncertainty and considers the incentive type demand responses; generating a large number of wind power scenes by utilizing Latin hypercube sampling, reducing the scenes by adopting a synchronous back substitution method, and solving the model by a quantum particle group algorithm; and obtaining the optimal day-previous hour electricity price, the optimal wind power receiving capacity, the optimal thermal power generating unit dispatching output and the optimal excitation type demand response dispatching amount under the constraint condition. The method can guide the user to peak clipping and valley filling, effectively promote wind power acceptance, reduce the thermal power generation cost and increase the benefit of the user.

Description

Method for promoting wind power acceptance level by considering day-ahead hour electricity price and various demand responses
Technical Field
The invention relates to the field of electric power market analysis, in particular to a method for promoting wind power acceptance level by considering day-ahead hour electricity price and various demand responses.
Background
Wind power is the new energy which is developed fastest and applied most widely at present. However, new energy power generation generally has randomness and volatility, and wind power also has typical anti-peak-shaving characteristics. The main factors for restricting wind power consumption in China at present are system peak regulation capacity and power grid transmission capacity, and the fundamental reason influencing the system peak regulation capacity is aggravation of load peak-valley difference and insufficient capacity of a system peak regulation unit after large-scale wind power access.
The demand response item generally classifies loads into electricity price type load responses, incentive type load responses, and electricity price uncontrolled loads. In the peak-valley electricity price, the electricity price is divided into three periods of peak and valley, and the adjustment of the load is lack of flexibility; in the real-time electricity prices, the electricity prices fluctuate with market changes, and the adjustment of the electricity utilization plan of the user is greatly influenced. In view of this, scholars have proposed the concept of day-ahead hour electricity rate (quasi-real-time electricity rate), which divides the electricity rate into 24 time intervals, makes 24-hour electricity rate in the next day in advance through the load prediction and wind power prediction results of 24 hours in the second day, makes users have a buffering time, makes their own electricity utilization plan according to the electricity rate, and shifts peak time load or increases other time interval load.
Disclosure of Invention
The invention aims to provide a method for promoting wind power acceptance level by considering day-ahead hour electricity price and various demand responses, which can guide various demand response users to peak clipping and valley filling, effectively promote wind power acceptance, reduce thermal power generation cost and increase benefits of the users.
In order to achieve the purpose, the technical scheme of the invention is as follows: a method for promoting wind power acceptance level by considering day-ahead hour electricity price and various demand responses is characterized in that under the condition of large-scale wind power access, the influence of the day-ahead hour electricity price on price type demand response users and incentive type demand response users are considered, and a wind power system scheduling two-layer planning model which is used for optimizing the day-ahead hour electricity price and considering the incentive type demand responses under the condition of considering wind power uncertainty is established; generating a large number of wind power scenes by utilizing Latin hypercube sampling, reducing the scenes by adopting a synchronous back substitution method, and solving the model by a quantum particle group algorithm; and obtaining the optimal day-previous hour electricity price, the optimal wind power receiving capacity, the optimal thermal power generating unit dispatching output and the optimal excitation type demand response dispatching amount under the constraint condition.
In an embodiment of the present invention, the method is implemented as follows,
s1: extracting multi-scene power generation data, thermal power unit data, initial power price type demand response users, incentive type demand response user data and day-previous hour power price data of a wind power plant;
s2: establishing a demand response user model under the day-ahead hour electricity price, and respectively establishing respective models for price type demand response users and incentive type demand response users;
s3: establishing a wind power uncertainty scheduling two-layer planning model considering day-ahead hour electricity price optimization; defining the model: the outer layer is a day-ahead hour electricity price optimization model, and the inner layer is a day-inside power system economic dispatching model; the outer layer model is: the sum of the equivalent load difference of the system in 24 hours and the wind curtailment force of the system is minimum, and is expressed by a mathematical function as follows:
Figure BDA0001217565820000021
wherein, PDE,s,max、PDE,s,minRespectively representing the maximum value and the minimum value of the daily equivalent load of the system under the scene s, and calculating the equivalent load as shown in a formula (5); pcur,k,t,sThe abandoned wind power of the wind power plant k at the moment t under the scene s is obtained; WB is the number of wind power plants;
the inner layer is an economic dispatching model of the power system in the day, and is expressed by a mathematical function as follows:
Figure BDA0001217565820000022
in the formula: a isG,iP2G,i,t,s+bG,iPG,i,t,s+cG,iAs a function of the thermal power generation cost, aG,i、bG,i、cG,iThe power generation cost coefficient is the power generation cost coefficient of the thermal power generating unit i; pG,i,t,sThe output of the thermal power generating unit i at the moment t under the scene s is obtained; beta is aW,kThe wind abandon cost of the wind power plant k; GS is the number of thermal power generating units;
s4: and solving the output and power generation cost of the thermal power generating unit, the calling electric quantity and cost of the excitation type demand response user, the abandoned wind electric quantity, the optimal day-ahead hour electricity price and the electric quantity for promoting wind power admission.
In an embodiment of the present invention, the step S2 specifically includes the following steps:
s21: modeling electricity price type demand response users under electricity prices of day-ahead hours: a certain functional relation exists between the price and the demand of the electric energy, and the user response modeling based on the statistical principle mode is to fit a demand price curve function of a user according to the relation between the historical load data of the user and the electricity price; the relationship between the load capacity and the electricity price of the user at a certain moment can be expressed as follows:
Pact,j,t＝fj(ρt)
wherein, Pact，j，tResponding to the load quantity of the user j at the time t for the electricity price type demand; f. ofj(ρt) A demand price curve function for electricity price type demand response user j; rhotThe price of electricity at time t.
S22: intra-day incentive demand response user modeling: in consideration of promoting wind power consumption, a bidirectional load participation project is considered for modeling of an excitation type demand response user, the bidirectional load can reduce power consumption and increase the load of the power consumption, and the user can obtain compensation when increasing or decreasing the power consumption; in order to more flexibly mobilize the incentive type demand response users to promote wind power consumption, the incentive type demand response users do not require load balancing; the compensation of the user is divided into capacity compensation and electric quantity compensation, wherein the capacity compensation is fixed, the electric quantity compensation is calculated according to a user quotation curve and actual regulating electric quantity, and the user quotates according to a step-type curve;
the mathematical function of the dispatch model for the incentivized demand response is expressed as follows:
Figure BDA0001217565820000031
wherein, CIBDRScheduling costs for incentive demand response users; rho + d, m and rho-d, m are respectively the m-th section of the section price increasing and decreasing price of the incentive demand response user d; p + d, m, t and P-d, m, t are respectively the increasing and decreasing electric quantity of the excitation type demand response user d at the time m; cdResponding to the capacity cost of user d for an incentive type demand; the PIBDR d is the capacity which can be called by the incentive type demand response user d; n is a radical ofdAnd M is the total number of the sections of the reported electric quantity of the incentive type demand response users.
In an embodiment of the present invention, the step S3 specifically includes the following steps:
the outer layer changes the load capacity of a power price type demand response user through the day-ahead power price, and introduces the changed load curve and the wind power scene into the inner layer economic dispatching model; the inner layer solves the abandoned wind power, the motivation type demand response user calling capacity and cost, the thermal power unit output condition and the thermal power unit cost, then the dispatching result is returned to the outer layer, and the day-ahead hour electricity price is updated; the optimal day-ahead hour electricity price, the optimal economic dispatching result, the calling capacity of the incentive type demand response user and the abandoned wind electricity quantity are obtained through repeated optimization;
s31: model for promoting wind power acceptance by optimizing day-ahead hour electricity price
In the system scheduling considering wind power admission, the equivalent load can more intuitively reflect the load and wind power change trend in the system. The wind power is regarded as a negative load, and the equivalent load is the difference value between the load and the wind power; defining the model: the sum of the equivalent load peak-valley difference and the abandoned wind output is minimum:
Figure BDA0001217565820000032
wherein, PDE,s max、PDE,s minRespectively under scene sMaximum and minimum equivalent load per system day; pcur,k,t,sThe abandoned wind power of the wind power plant k at the moment t under the scene s is obtained; wBThe number of wind power plants;
wherein the content of the first and second substances,
Figure BDA0001217565820000033
wherein, PDE,t,sThe equivalent load of the system at the moment t under the scene s is obtained; pDG,t,sThe fixed load capacity of the system at the moment t under the scene s is obtained; pact,j,t,sThe load quantity of the user j after response at the moment t under the scene s is obtained; dMResponding to the number of users for the electricity price type demand; pW,k,t,sThe output of the wind power plant k at the moment t under the scene s is obtained;
under the condition of multiple wind power scenes, multiple day-ahead hour electricity price curves can be obtained, and the final day-ahead hour electricity price is obtained by adopting a comprehensive scene probability method:
Figure BDA0001217565820000041
where ρ isF,tThe final day-ahead hour electricity price; p is a radical ofsIs the probability of scene s; sNIs the total number of scenes; rhotThe day-ahead hour electricity price at the moment t under the scene s;
step S32: considering a scheduling model of a wind power system with uncertainty of demand response; defining the model: the power generation cost of the thermal power generating unit and the wind power plant wind curtailment cost are minimum, and the mathematical function is expressed as follows:
Figure BDA0001217565820000042
wherein, aG,iP2g,i,t,s+bG,iPg,i,t,s+cG,iAs a function of the thermal power generation cost, aG,i、bG,i、cG,iThe power generation cost coefficient is the power generation cost coefficient of the thermal power generating unit i; pG,i,t,sThe output of a thermal power generating unit i at the moment t under a scene s；βW,kThe wind abandon cost of the wind power plant k; gSThe number of thermal power generating units.
In an embodiment of the present invention, the constraint conditions of step S31 include: the method comprises the following steps of power rate upper and lower limit constraint, maximum and minimum demand response constraint per hour, power rate adjustment rate constraint of a power rate type demand response user, power purchase cost constraint of a power rate type demand response user and power consumption balance constraint of a power rate type demand response user;
the upper and lower price limits are expressed by mathematical functions as follows:
ρt,min<ρt,s<ρt,max
where ρ ist,min、ρt,maxThe upper limit and the lower limit of the price of electricity at the time t;
the maximum minimum demand response per hour constraint is expressed as a mathematical function as follows:
Pact,j,min,t<Pact,j,t,s<Pact,j,max,t
wherein, Pact,j,min,t、Pact,j,max,tThe upper and lower limits of the load capacity of the user j at the time t are set;
the power price and power price type demand response user power utilization adjustment rate constraint is expressed by a mathematical function as follows
Figure BDA0001217565820000043
Wherein, Δ Pact,j,upAnd Δ Pact,j,downUpper and lower hill climbing limits for user j, respectively;
the power price type demand response user electricity purchasing cost constraint is expressed by a mathematical function as follows:
Figure BDA0001217565820000051
wherein alpha isjThe electricity rate saved for electricity rate type demand response user j; rhobefore_sell,tOptimizing the electricity price at the previous t moment; p0,j,tResponding to demand of electricity price type when user j is before optimization tLoad at time t under the electricity price;
the electricity price type demand response user electricity balance constraint is expressed by a mathematical function as follows:
Figure BDA0001217565820000052
in an embodiment of the present invention, the constraint conditions of step S32 include: the method comprises the following steps of power balance constraint, thermal power unit output power upper and lower limit constraint, thermal power unit climbing constraint, positive and negative rotation standby constraint and excitation type demand response user power utilization constraint;
the power balance constraint is expressed as a mathematical function as follows:
Figure BDA0001217565820000053
the upper and lower limits of the output power of the thermal power generating unit are represented by mathematical functions as follows:
PG,i,min<PG,i,t,s<PG,i,max
wherein, PG,i,min、PG,i,maxThe output upper and lower limits of the thermal power generating unit i are obtained;
the ramp constraint of the thermal power generating unit is expressed by a mathematical function as follows:
Figure BDA0001217565820000054
wherein, Δ PG,i,upAnd Δ PG,i,downRespectively limiting the up-down climbing of the thermal power generating unit i;
the positive and negative rotation alternate constraints are expressed as mathematical functions as follows:
Figure BDA0001217565820000055
wherein R isup,i,t,sAnd Rdown,i,t,sRespectively serving as positive and negative rotation standby of thermal power generating unit i at t moment under scene sCapacity;
the incentive type demand response user electricity utilization constraint is expressed by a mathematical function as follows:
Figure BDA0001217565820000061
wherein, P + d, m, t,maxAnd P-d, m, t,maxAnd respectively increasing and decreasing the upper limit of the mth section of the electric quantity of the segmented quotation curve of the incentive type demand response user.
In an embodiment of the present invention, in step S4, with the day-ahead hourly electricity price optimization and the excitation-type demand response-based wind power system scheduling two-layer planning model taken into consideration, a large number of wind power scenes are generated and reduced, and then the two-layer planning model is solved; the outer optimization variable is day-ahead hour electricity price, the inner optimization variable is output of a thermal power generating unit and calling electricity quantity of an excitation type demand response user, and the inner layer and the outer layer are optimized by adopting a quantum particle swarm algorithm. And finally, the output and power generation cost of the thermal power generating unit, the calling electric quantity and cost of the excitation type demand response user, the wind power abandoning electric quantity and the optimal day-ahead hour electricity price under the wind power uncertainty condition in multiple scenes are obtained.
Compared with the prior art, the invention has the following beneficial effects: under the condition of wind power uncertainty of large-scale wind power access, the influence of the day-ahead hour electricity price on price type demand response users and incentive type demand response users are considered, and a wind power system dispatching two-layer planning model which considers the day-ahead hour electricity price optimization under the condition of wind power uncertainty and takes incentive type demand response into account is established; generating a large number of wind power scenes by utilizing Latin hypercube sampling, reducing the scenes by adopting a synchronous back substitution method, and solving the model by a quantum particle group algorithm; obtaining the optimal day-previous hour electricity price, the optimal wind power receiving capacity, the optimal thermal power generating unit dispatching output and the optimal incentive type demand response user dispatching amount under the constraint condition; the method can guide the user to peak load shifting, effectively promote wind power acceptance, reduce the thermal power generation cost and increase the benefit of the user.
Drawings
FIG. 1 is a graph of an incentive demand response customer bid in accordance with the present invention.
Detailed Description
The technical scheme of the invention is specifically explained below with reference to the accompanying drawings.
According to the method for promoting the wind power acceptance level by considering the day-ahead hour electricity price and various demand responses, under the condition of large-scale wind power access, the influence of the day-ahead hour electricity price on price type demand response users and incentive type demand response users are considered, and a wind power system scheduling two-layer planning model which considers the day-ahead hour electricity price optimization under the wind power uncertainty condition and considers the incentive type demand responses is established; generating a large number of wind power scenes by utilizing Latin hypercube sampling, reducing the scenes by adopting a synchronous back substitution method, and solving the model by a quantum particle group algorithm; and obtaining the optimal day-previous hour electricity price, the optimal wind power receiving capacity, the optimal thermal power generating unit dispatching output and the optimal excitation type demand response dispatching amount under the constraint condition.
In the embodiment, the method is implemented as follows,
s1: extracting multi-scene power generation data, thermal power unit data, initial power price type demand response users, incentive type demand response user data and day-previous hour power price data of a wind power plant;
s2: establishing a demand response user model under the day-ahead hour electricity price, and respectively establishing respective models for price type demand response users and incentive type demand response users;
s3: establishing a wind power uncertainty scheduling two-layer planning model considering day-ahead hour electricity price optimization; defining the model: the outer layer is a day-ahead hour electricity price optimization model, and the inner layer is a day-inside power system economic dispatching model; the outer layer model is: the sum of the equivalent load difference of the system in 24 hours and the wind curtailment force of the system is minimum, and is expressed by a mathematical function as follows:
Figure BDA0001217565820000071
wherein, PDE,s,max、PDE,s,minRespectively under scene sCalculating the maximum value and the minimum value of the equivalent load of the system day according to the formula (5); pcur,k,t,sThe abandoned wind power of the wind power plant k at the moment t under the scene s is obtained; WB is the number of wind power plants;
the inner layer is an economic dispatching model of the power system in the day, and is expressed by a mathematical function as follows:
Figure BDA0001217565820000072
in the formula: a isG,iP2G,i,t,s+bG,iPG,i,t,s+cG,iAs a function of the thermal power generation cost, aG,i、bG,i、cG,iThe power generation cost coefficient is the power generation cost coefficient of the thermal power generating unit i; pG,i,t,sThe output of the thermal power generating unit i at the moment t under the scene s is obtained; beta is aW,kThe wind abandon cost of the wind power plant k; GS is the number of thermal power generating units;
s4: and solving the output and power generation cost of the thermal power generating unit, the calling electric quantity and cost of the excitation type demand response user, the abandoned wind electric quantity, the optimal day-ahead hour electricity price and the electric quantity for promoting wind power admission.
In this embodiment, the step S2 specifically includes the following steps:
s21: modeling electricity price type demand response users under electricity prices of day-ahead hours: the electric energy is a commodity, the requirement theorem in economics is met, the quantity of the commodities purchased by people is closely related to the price, the higher the price is, the people tend to purchase less commodities, and the lower the price is, the contrary is true; the price and the demand of the commodity have a certain functional relationship, and the user response modeling based on the statistical principle mode is to fit a demand price curve function of the user according to the relationship between the historical load data of the user and the electricity price; the relationship between the load capacity and the electricity price of the user at a certain moment can be expressed as follows:
Pact,j,t＝fj(ρt)
wherein, Pact，j，tResponding to the load quantity of the user j at the time t for the electricity price type demand; f. ofj(ρt) A demand price curve function for electricity price type demand response user j; rhotAt time tAnd (4) price.
S22: intra-day incentive demand response user modeling: in consideration of promoting wind power consumption, modeling of an incentive demand response user is considered to be a bidirectional load participation project, the bidirectional load can reduce the power consumption and increase the load of the power consumption, such as an energy storage device, an electric automobile and a load capable of flexibly adjusting the power consumption of the user, and the user can obtain compensation when increasing or decreasing the power consumption; in order to more flexibly mobilize the incentive type demand response users to promote wind power consumption, the incentive type demand response users do not require load balancing; the compensation of the user is divided into capacity compensation and electric quantity compensation, wherein the capacity compensation is fixed, the electric quantity compensation is calculated according to a user quotation curve and actual regulating electric quantity, and the user quotates according to a step-type curve; as shown in fig. 1.
The mathematical function of the dispatch model for the incentivized demand response is expressed as follows:
Figure BDA0001217565820000081
wherein, CIBDRScheduling costs for incentive demand response users; rho + d, m and rho-d, m are respectively the m-th section of the section price increasing and decreasing price of the incentive demand response user d; p + d, m, t and P-d, m, t are respectively the increasing and decreasing electric quantity of the excitation type demand response user d at the time m; cdResponding to the capacity cost of user d for an incentive type demand; the PIBDR d is the capacity which can be called by the incentive type demand response user d; n is a radical ofdAnd M is the total number of the sections of the reported electric quantity of the incentive type demand response users.
In this embodiment, the step S3 specifically includes the following steps:
the outer layer changes the load capacity of a power price type demand response user through the day-ahead power price, and introduces the changed load curve and the wind power scene into the inner layer economic dispatching model; the inner layer solves the abandoned wind power, the motivation type demand response user calling capacity and cost, the thermal power unit output condition and the thermal power unit cost, then the dispatching result is returned to the outer layer, and the day-ahead hour electricity price is updated; the optimal day-ahead hour electricity price, the optimal economic dispatching result, the calling capacity of the incentive type demand response user and the abandoned wind electricity quantity are obtained through repeated optimization;
s31: model for promoting wind power acceptance by optimizing day-ahead hour electricity price
In the system scheduling considering wind power admission, the equivalent load can more intuitively reflect the load and wind power change trend in the system. The wind power is regarded as a negative load, and the equivalent load is the difference value between the load and the wind power; defining the model: the sum of the equivalent load peak-valley difference and the abandoned wind output is minimum:
Figure BDA0001217565820000082
wherein, PDE,s max、PDE,s minRespectively representing the maximum value and the minimum value of the daily equivalent load of the system under the scene s; pcur,k,t,sThe abandoned wind power of the wind power plant k at the moment t under the scene s is obtained; wBThe number of wind power plants;
wherein the content of the first and second substances,
Figure BDA0001217565820000091
wherein, PDE,t,sThe equivalent load of the system at the moment t under the scene s is obtained; pDG,t,sThe fixed load capacity of the system at the moment t under the scene s is obtained; pact,j,t,sThe load quantity of the user j after response at the moment t under the scene s is obtained; dMResponding to the number of users for the electricity price type demand; pW,k,t,sThe output of the wind power plant k at the moment t under the scene s is obtained;
under the condition of multiple wind power scenes, multiple day-ahead hour electricity price curves can be obtained, and the final day-ahead hour electricity price is obtained by adopting a comprehensive scene probability method:
Figure BDA0001217565820000092
where ρ isF,tThe final day-ahead hour electricity price; p is a radical ofsIs the probability of scene s; sNIs the total number of scenes; rhotThe day-ahead hour electricity price at the moment t under the scene s;
the constraints of step S31 include: the method comprises the following steps of power rate upper and lower limit constraint, maximum and minimum demand response constraint per hour, power rate adjustment rate constraint of a power rate type demand response user, power purchase cost constraint of a power rate type demand response user and power consumption balance constraint of a power rate type demand response user;
the upper and lower price limits are expressed by mathematical functions as follows:
ρt,min<ρt,s<ρt,max
where ρ ist,min、ρt,maxThe upper limit and the lower limit of the price of electricity at the time t;
the maximum minimum demand response per hour constraint is expressed as a mathematical function as follows:
Pact,j,min,t<Pact,j,t,s<Pact,j,max,t
wherein, Pact,j,min,t、Pact,j,max,tThe upper and lower limits of the load capacity of the user j at the time t are set;
the power price and power price type demand response user power utilization adjustment rate constraint is expressed by a mathematical function as follows
Figure BDA0001217565820000093
Wherein, Δ Pact,j,upAnd Δ Pact,j,downUpper and lower hill climbing limits for user j, respectively;
the power price type demand response user electricity purchasing cost constraint is expressed by a mathematical function as follows:
Figure BDA0001217565820000101
wherein alpha isjThe electricity rate saved for electricity rate type demand response user j; rhobefore_sell,tOptimizing the electricity price at the previous t moment; p0,j,tResponding to the load of the user j at the time t before optimization for the electricity price type demand;
the electricity price type demand response user electricity balance constraint is expressed by a mathematical function as follows:
Figure BDA0001217565820000102
step S32: considering a scheduling model of a wind power system with uncertainty of demand response; defining the model: the power generation cost of the thermal power generating unit and the wind power plant wind curtailment cost are minimum, and the mathematical function is expressed as follows:
Figure BDA0001217565820000103
wherein, aG,iP2g,i,t,s+bG,iPg,i,t,s+cG,iAs a function of the thermal power generation cost, aG,i、bG,i、cG,iThe power generation cost coefficient is the power generation cost coefficient of the thermal power generating unit i; pG,i,t,sThe output of the thermal power generating unit i at the moment t under the scene s is obtained; beta is aW,kThe wind abandon cost of the wind power plant k; gSThe number of thermal power generating units;
the constraints of step S32 include: the method comprises the following steps of power balance constraint, thermal power unit output power upper and lower limit constraint, thermal power unit climbing constraint, positive and negative rotation standby constraint and excitation type demand response user power utilization constraint;
the power balance constraint is expressed as a mathematical function as follows:
Figure BDA0001217565820000104
the upper and lower limits of the output power of the thermal power generating unit are represented by mathematical functions as follows:
PG,i,min<PG,i,t,s<PG,i,max
wherein, PG,i,min、PG,i,maxThe output upper and lower limits of the thermal power generating unit i are obtained;
the ramp constraint of the thermal power generating unit is expressed by a mathematical function as follows:
Figure BDA0001217565820000105
wherein, Δ PG,i,upAnd Δ PG,i,downRespectively limiting the up-down climbing of the thermal power generating unit i;
the positive and negative rotation alternate constraints are expressed as mathematical functions as follows:
Figure BDA0001217565820000111
wherein R isup,i,t,sAnd Rdown,i,t,sRespectively obtaining positive and negative rotation reserve capacities of the thermal power generating unit i at the moment t under the scene s;
the incentive type demand response user electricity utilization constraint is expressed by a mathematical function as follows:
Figure BDA0001217565820000112
wherein, P + d, m, t,maxAnd P-d, m, t,maxAnd respectively increasing and decreasing the upper limit of the mth section of the electric quantity of the segmented quotation curve of the incentive type demand response user.
Preferably, in this embodiment, in step S4, by optimizing the day-ahead hourly electricity price and taking into account the excitation-type demand response wind power system scheduling two-layer planning model, firstly, a large number of wind power scenes are generated and the wind power scenes are reduced, and then, the two-layer planning model is solved; the outer optimization variable is day-ahead hour electricity price, the inner optimization variable is output of a thermal power generating unit and calling electricity quantity of an excitation type demand response user, and the inner layer and the outer layer are optimized by adopting a quantum particle swarm algorithm. And finally, the output and power generation cost of the thermal power generating unit, the calling electric quantity and cost of the excitation type demand response user, the wind power abandoning electric quantity and the optimal day-ahead hour electricity price under the wind power uncertainty condition in multiple scenes are obtained.
The above are preferred embodiments of the present invention, and all changes made according to the technical scheme of the present invention that produce functional effects do not exceed the scope of the technical scheme of the present invention belong to the protection scope of the present invention.

Claims (1)

1. A method of facilitating wind power acceptance levels in view of day-ahead hour electricity prices and multiple demand responses, characterized by: under the condition of large-scale wind power access, considering the influence of the day-ahead hour electricity price on price type demand response users and incentive type demand response users, and establishing a wind power system scheduling two-layer planning model which considers the day-ahead hour electricity price optimization and incentive type demand response under the condition of wind power uncertainty; generating a large number of wind power scenes by utilizing Latin hypercube sampling, reducing the scenes by adopting a synchronous back substitution method, and solving a wind power uncertainty scheduling two-layer programming model through a quantum particle swarm algorithm; obtaining the optimal day-previous hour electricity price, the optimal wind power receiving capacity, the optimal thermal power generating unit dispatching output and the optimal excitation type demand response dispatching amount under the constraint condition; the method is implemented by the following steps of,
s1: extracting multi-scene power generation data, thermal power unit data, initial power price type demand response users, incentive type demand response user data and day-previous hour power price data of a wind power plant;
s2: establishing a demand response user model under the day-ahead hour electricity price, and respectively establishing respective models for price type demand response users and incentive type demand response users;
s3: establishing a wind power uncertainty scheduling two-layer planning model considering day-ahead hour electricity price optimization; defining the model: the outer layer is a day-ahead hour electricity price optimization model, and the inner layer is a day-inside power system economic dispatching model; the outer layer model is: the sum of the equivalent load difference of the system in 24 hours and the wind curtailment force of the system is minimum, and is expressed by a mathematical function as follows:
Figure FDA0002800332370000011
wherein, PDE,s,max、PDE,s,minRespectively representing the maximum value and the minimum value of the daily equivalent load of the system under the scene s; pcur,k,t,sFor wind abandon of wind power plant k at t moment under scene sPower; WB is the number of wind power plants; the equivalent load calculation is shown as follows:
Figure FDA0002800332370000012
wherein, PDE,t,sThe equivalent load of the system at the moment t under the scene s is obtained; pDG,t,sThe fixed load capacity of the system at the moment t under the scene s is obtained; pact,j,t,sThe load quantity of the user j after response at the moment t under the scene s is obtained; dMResponding to the number of users for the electricity price type demand; pW,k,t,sThe output of the wind power plant k at the moment t under the scene s is obtained;
the inner layer is an economic dispatching model of the power system in the day, and is expressed by a mathematical function as follows:
Figure FDA0002800332370000013
in the formula: a isG,iP2 G,i,t,s+bG,iPG,i,t,s+cG,iAs a function of the thermal power generation cost, aG,i、bG,i、cG,iThe power generation cost coefficient is the power generation cost coefficient of the thermal power generating unit i; pG,i,t,sThe output of the thermal power generating unit i at the moment t under the scene s is obtained; beta is aW,kThe wind abandon cost of the wind power plant k; GS is the number of thermal power generating units; cIBDRScheduling costs for incentive demand response users;
s4: solving the output and power generation cost of the thermal power generating unit, the calling electric quantity and cost of an excitation type demand response user, the abandoned wind electric quantity, the optimal day-ahead hour electricity price and the electric quantity for promoting wind power admission;
the step S2 specifically includes the following steps:
s21: modeling electricity price type demand response users under electricity prices of day-ahead hours: a certain functional relation exists between the price and the demand of the electric energy, and the user response modeling based on the statistical principle mode is to fit a demand price curve function of a user according to the relation between the historical load data of the user and the electricity price; the relationship between the load capacity and the electricity price of the user at a certain moment can be expressed as follows:
Pact,j,t＝fj(ρt)
wherein, Pact，j，tResponding to the load quantity of the user j at the time t for the electricity price type demand; f. ofj(ρt) A demand price curve function for electricity price type demand response user j; rhotThe price of electricity at the time t;
s22: intra-day incentive demand response user modeling: based on the promotion of wind power consumption, bidirectional load participation projects are considered for modeling of an incentive type demand response user, the bidirectional load can reduce power consumption and increase the load of the power consumption, and the user can obtain compensation when increasing or decreasing the power consumption; in order to more flexibly mobilize the incentive type demand response users to promote wind power consumption, the incentive type demand response users do not require load balancing; the compensation of the user is divided into capacity compensation and electric quantity compensation, wherein the capacity compensation is fixed, the electric quantity compensation is calculated according to a user quotation curve and actual regulating electric quantity, and the user quotates according to a step-type curve;
the mathematical function of the dispatch model for the incentivized demand response is expressed as follows:
Figure FDA0002800332370000021
wherein, CIBDRScheduling costs for incentive demand response users; rho + d, m and rho-d, m are respectively the m-th section of the section price increasing and decreasing price of the incentive demand response user d; p + d, m, t and P-d, m, t are respectively the increasing and decreasing electric quantity of the excitation type demand response user d at the time m; cdResponding to the capacity cost of user d for an incentive type demand; the PIBDR d is the capacity which can be called by the incentive type demand response user d; n is a radical ofdThe total number of users responding to the incentive type demand is M, and the total number of sections of reported electric quantity of the incentive type demand response users is M;
the step S3 specifically includes the following steps:
the outer layer changes the load capacity of a power price type demand response user through the day-ahead power price, and introduces the changed load curve and the wind power scene into the inner layer economic dispatching model; the inner layer solves the abandoned wind power, the motivation type demand response user calling capacity and cost, the thermal power unit output condition and the thermal power unit cost, then the dispatching result is returned to the outer layer, and the day-ahead hour electricity price is updated; the optimal day-ahead hour electricity price, the optimal economic dispatching result, the calling capacity of the incentive type demand response user and the abandoned wind electricity quantity are obtained through repeated optimization;
s31: model for promoting wind power acceptance by optimizing day-ahead hour electricity price
In the system scheduling considering wind power admission, the equivalent load can more intuitively reflect the load and wind power change trend in the system; the wind power is regarded as a negative load, and the equivalent load is the difference value between the load and the wind power; defining the model: the sum of the equivalent load peak-valley difference and the abandoned wind output is minimum:
Figure FDA0002800332370000031
wherein, PDE,smax、PDE,sminRespectively representing the maximum value and the minimum value of the daily equivalent load of the system under the scene s; pcur,k,t,sThe abandoned wind power of the wind power plant k at the moment t under the scene s is obtained; wBThe number of wind power plants;
wherein the content of the first and second substances,
Figure FDA0002800332370000032
wherein, PDE,t,sThe equivalent load of the system at the moment t under the scene s is obtained; pDG,t,sThe fixed load capacity of the system at the moment t under the scene s is obtained; pact,j,t,sThe load quantity of the user j after response at the moment t under the scene s is obtained; dMResponding to the number of users for the electricity price type demand; pW,k,t,sThe output of the wind power plant k at the moment t under the scene s is obtained;
under the condition of multiple wind power scenes, obtaining multiple day-ahead hour electricity price curves, and obtaining the final day-ahead hour electricity price by adopting a comprehensive scene probability method:
Figure FDA0002800332370000033
where ρ isF,tThe final day-ahead hour electricity price; p is a radical ofsIs the probability of scene s; sNIs the total number of scenes; rhotThe day-ahead hour electricity price at the moment t under the scene s;
step S32: considering a scheduling model of a wind power system with uncertainty of demand response; defining the model: the power generation cost of the thermal power generating unit and the wind power plant wind curtailment cost are minimum, and the mathematical function is expressed as follows:
Figure FDA0002800332370000034
wherein, aG,iP2 g,i,t,s+bG,iPg,i,t,s+cG,iAs a function of the thermal power generation cost, aG,i、bG,i、cG,iThe power generation cost coefficient is the power generation cost coefficient of the thermal power generating unit i; pG,i,t,sThe output of the thermal power generating unit i at the moment t under the scene s is obtained; beta is aW,kThe wind abandon cost of the wind power plant k; gSThe number of thermal power generating units;
the constraints of step S31 include: the method comprises the following steps of power rate upper and lower limit constraint, maximum and minimum demand response constraint per hour, power rate adjustment rate constraint of a power rate type demand response user, power purchase cost constraint of a power rate type demand response user and power consumption balance constraint of a power rate type demand response user;
the upper and lower price limits are expressed by mathematical functions as follows:
ρt,min＜ρt,s＜ρt,max
where ρ ist,min、ρt,maxThe upper limit and the lower limit of the price of electricity at the time t;
the maximum minimum demand response per hour constraint is expressed as a mathematical function as follows:
Pact,j,min,t＜Pact,j,t,s＜Pact,j,max,t
wherein, Pact,j,min,t、Pact,j,max,tThe upper and lower limits of the load capacity of the user j at the time t are set;
the power price and power price type demand response user power utilization adjustment rate constraint is expressed by a mathematical function as follows
Figure FDA0002800332370000041
Wherein, Δ Pact,j,upAnd Δ Pact,j,downUpper and lower hill climbing limits for user j, respectively;
the power price type demand response user electricity purchasing cost constraint is expressed by a mathematical function as follows:
Figure FDA0002800332370000042
wherein alpha isjThe electricity rate saved for electricity rate type demand response user j; rhobefore_sell,tOptimizing the electricity price at the previous t moment; p0,j,tResponding to the load of the user j at the time t before optimization for the electricity price type demand;
the electricity price type demand response user electricity balance constraint is expressed by a mathematical function as follows:
Figure FDA0002800332370000043
the constraints of step S32 include: the method comprises the following steps of power balance constraint, thermal power unit output power upper and lower limit constraint, thermal power unit climbing constraint, positive and negative rotation standby constraint and excitation type demand response user power utilization constraint;
the power balance constraint is expressed as a mathematical function as follows:
Figure FDA0002800332370000051
the upper and lower limits of the output power of the thermal power generating unit are represented by mathematical functions as follows:
PG,i,min＜PG,i,t,s＜PG,i,max
wherein, PG,i,min、PG,i,maxThe output upper and lower limits of the thermal power generating unit i are obtained;
the ramp constraint of the thermal power generating unit is expressed by a mathematical function as follows:
Figure FDA0002800332370000052
wherein, Δ PG,i,upAnd Δ PG,i,downRespectively limiting the up-down climbing of the thermal power generating unit i;
the positive and negative rotation alternate constraints are expressed as mathematical functions as follows:
Figure FDA0002800332370000053
wherein R isup,i,t,sAnd Rdown,i,t,sRespectively obtaining positive and negative rotation reserve capacities of the thermal power generating unit i at the moment t under the scene s;
the incentive type demand response user electricity utilization constraint is expressed by a mathematical function as follows:
Figure FDA0002800332370000054
wherein, P + d, m, t,maxAnd P-d, m, t,maxIncreasing and decreasing the upper limit of the mth section of the electric quantity of the segmented quotation curve of the incentive demand response user respectively;
in the step S4, a large number of wind power scenes are generated and reduced by taking the day-ahead hour electricity price optimization and the incentive type demand response wind power system scheduling two-layer planning model into consideration, and then the two-layer planning model is solved; the outer-layer optimization variable is day-ahead hour electricity price, the inner-layer optimization variable is output of a thermal power generating unit and calling electricity quantity of an excitation type demand response user, and the wind power uncertainty scheduling two-layer planning model is optimized by adopting a quantum particle swarm algorithm; and finally, the output and power generation cost of the thermal power generating unit, the calling electric quantity and cost of the excitation type demand response user, the wind power abandoning electric quantity and the optimal day-ahead hour electricity price under the wind power uncertainty condition in multiple scenes are obtained.
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CN108460482A (en) * 2018-01-31 2018-08-28 南京理工大学 Consider interruptible load and a few days ago the wind electricity digestion method of hour electricity price
CN108288132B (en) * 2018-03-19 2021-10-08 云南电网有限责任公司电力科学研究院 Modeling method based on source-load interactive power system scheduling
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CN109858711B (en) * 2019-03-07 2022-05-24 东北电力大学 Wind power consumption day-ahead scheduling method considering price type demand response and participation of CSP power station
CN110516851B (en) * 2019-08-05 2023-09-29 华北电力大学 Source-load double-side thermoelectric combined random optimization scheduling method based on virtual power plant
CN111291942B (en) * 2020-03-05 2023-09-26 燕山大学 Two-stage scheduling method and system for comprehensive energy system considering demand response
CN111667142B (en) * 2020-04-29 2023-09-01 国网浙江省电力有限公司湖州供电公司 Optimal daily preload scheduling method based on total demand flexibility
CN112465181A (en) * 2020-09-11 2021-03-09 国网天津市电力公司电力科学研究院 Two-stage optimization scheduling method supporting source-network-load-storage multi-element ubiquitous coordination
CN112529256B (en) * 2020-11-24 2024-03-22 华中科技大学 Multi-uncertainty-considered distributed power supply cluster day-ahead scheduling method and system
CN112653198A (en) * 2020-12-17 2021-04-13 山东大学 Wind power output scene generation method and system based on prediction box

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103475013A (en) * 2013-09-27 2013-12-25 清华大学 Method and system for comprehensively optimizing energy storing power station planning and operating
CN103762589A (en) * 2014-01-08 2014-04-30 河海大学 Method for optimizing new energy capacity ratio in layers in power grid

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8247915B2 (en) * 2010-03-24 2012-08-21 Lightsail Energy, Inc. Energy storage system utilizing compressed gas

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103475013A (en) * 2013-09-27 2013-12-25 清华大学 Method and system for comprehensively optimizing energy storing power station planning and operating
CN103762589A (en) * 2014-01-08 2014-04-30 河海大学 Method for optimizing new energy capacity ratio in layers in power grid

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
考虑日前小时电价的含风电电力系统调度模型研究;何星晔;《电力需求侧管》;20140131;第16卷(第1期);第15-20页 *
考虑风电出力偏差和调度经济性的风电接纳水平优化;林小雨，江岳文;《福州大学学报( 自然科学版)》;20161031;第44卷(第5期);第680-687页 *

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