CN115935619A - Demand response-based day-ahead low-carbon scheduling method and device for active power distribution network - Google Patents
Demand response-based day-ahead low-carbon scheduling method and device for active power distribution network Download PDFInfo
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Abstract
The invention relates to a demand response-based active power distribution network day-ahead low-carbon scheduling method and device, wherein the method comprises the steps of obtaining operation parameter data of each main body, constructing an active power distribution network day-ahead low-carbon scheduling model by taking the minimum system cost as a main target, taking the minimum system carbon emission as a first sub-target and taking the minimum user load offset as a second sub-target, iteratively optimizing until the model converges, outputting a plurality of feasible results, converting each objective function value by adopting a trapezoidal fuzzy membership function to obtain a fuzzy comprehensive decision model, and outputting an optimal scheduling plan of the active power distribution network. The method comprehensively considers the energy consumption cost, the carbon emission cost and the user energy consumption experience of the active power distribution network side, provides the source network load storage bidirectional interactive active power distribution network day-ahead low-carbon scheduling based on demand response, overcomes the defect that only the user cost scheduling is considered at present, and is more favorable for realizing the double-carbon target.
Description
Technical Field
The invention belongs to the technical field of power dispatching, and particularly relates to a demand response-based day-ahead low-carbon dispatching method and device for an active power distribution network.
Background
Under the promotion of the strategic goal of 'double carbon', china gradually turns to a novel power system mode taking new energy as a main body, and changes to low carbonization and cleaning. Under the background, the power distribution network side gradually changes the original traditional passive power system with unidirectional energy flow into an active power distribution network system with response capability, such as distributed power generation resources, energy storage, demand response users and the like. At present, distributed power generation resources, user side energy storage and demand side response are basically distributed and independently scheduled, the problem of coordinated scheduling of various resources in the same power distribution network is not considered by a system, and the coordinated optimization of various resources on the power distribution network side cannot be realized. The coordination management of the above resources mainly has the following problems: firstly, most of distributed generation resources at a user side are intermittent power supplies of distributed wind power and photovoltaic, and how to accurately predict uncertain output of wind, light and the like is a technical problem; secondly, the energy storage and demand side response at the user side is an economic behavior which is essentially responded along with the price of the electric power wholesale market, and how to synchronously realize the minimum carbon emission and the minimum energy consumption cost of the power distribution network system is a technical problem.
Therefore, a demand response-based active power distribution network day-ahead low-carbon scheduling method is urgently needed, the safe operation of a power distribution network system is guaranteed, the carbon emission of the system is reduced, and the power consumption cost of the system is reduced.
Disclosure of Invention
In view of this, the present invention provides a demand response-based active power distribution network day-ahead low-carbon scheduling method and apparatus, so as to solve the problem that in the prior art, the carbon emission of a system cannot be reduced while the safe operation of a power distribution network system is ensured, and the power consumption cost of the system cannot be reduced.
In order to achieve the purpose, the invention adopts the following technical scheme: a demand response-based active power distribution network day-ahead low-carbon scheduling method comprises the following steps:
acquiring operation parameter data of each main body; the operation parameter data of each main body comprises: the method comprises the steps of classifying information of power users, information of distributed generator sets and information of capacity of energy storage sets;
constructing an active power distribution network day-ahead low-carbon scheduling model by taking the minimum system cost as a main target, the minimum system carbon emission as a first sub-target and the minimum user load offset as a second sub-target;
performing iterative optimization on the day-ahead low-carbon scheduling model of the active power distribution network until the model converges, and outputting a plurality of feasible results;
converting each objective function value by adopting a trapezoidal fuzzy membership function to obtain a fuzzy comprehensive decision model;
outputting an optimal scheduling plan of the active power distribution network according to the fuzzy comprehensive decision model; the optimal scheduling plan of the active power distribution network comprises an output plan of the distributed wind turbine generator, an operation plan of the energy storage unit and a power utilization plan of a user.
Further, the power consumers are divided into consumers capable of performing demand response and consumers incapable of performing demand response according to whether the power consumers have response capability, and the power consumption conditions of the consumers often have different scenes, so that the typical load values of the non-adjustable consumers and the adjustable consumers in different time periods t under different scenes s need to be input
And &>
And probability rho of occurrence of different scenes s s (ii) a From period t to t', a pattern of variation in the adjustable user load is->
The distributed generator sets are divided into a conventional distributed generator set and a distributed wind generating set;
wherein the conventional distributed generator set information includes cost information and performance information, the cost information including an operating cost C YX And a starting cost C YX And cost of shutdown C TJ ；
The running cost is
Wherein, delta i 、χ i 、θ i All are the cost parameters of the gas unit i; p RQ (i, t) is the output of the gas turbine set i at the moment t; startup cost of unit i
And cost of shutdown>
Typically a fixed value;
the performance information includes a maximum output P of the unit i max Minimum force P i min Minimum continuous down time
Minimum continuous on-time->
Maximum climbing rate->
And a maximum falling rate->
The distributed wind generating set information is
Wherein, P N,FD The maximum installation capacity of the unit, V is the wind speed, V Ci 、V R 、V Co Respectively the minimum generating wind speed, the minimum full output wind speed and the cutter wind speed;
the capacity information of the energy storage unit is
Wherein the content of the first and second substances,
the charge state, the discharge power and the charge power of the stored energy at the moment t are respectively.
Further, the system cost includes: the cost of the distributed conventional generator set, the cost generated by trading with the main network and the adjacent distribution network, the operation cost of the energy storage unit and the cost paid by demand response; the main objective function of the day-ahead low-carbon scheduling model of the active power distribution network is
Wherein, mu QD (i, t) and μ TJ (i, t) 0, 1 variables representing the operating state of the distributed conventional genset (QD =1, tj = 0); p t WM 、P t WG 、
Respectively representing the outsourcing power, the outsourcing power price and the outsourcing power price at the time t; />
Respectively represents the discharge and charge power of the energy storage system at the time t>
Respectively representing the charging/generating price, K, of the energy storage system at time t d Is the price of demand response, Δ d To adjust the fluctuation ratio of the load, D d,t Total electricity consumption of the d-th load at time t, z d,t Response 0, 1 variables for the adjustable load.
Further, under the condition of lowest system cost, the first sub-objective function with the lowest system carbon emission as the first sub-objective is
In the formula, alpha i 、β i 、ε i Is a carbon emission factor of the system and is,
carbon emission factor of outsourcing electric quantity.
Further, under the condition of minimum system cost and minimum system talk emission, the second sub-target function with minimum user load deviation as the second sub-target is as follows
Wherein ρ s Probability of s-th scene, D E (s, t) is the load distribution in the s-th scenario,
and the optimal load distribution condition of the system is achieved.
Further, constraint conditions corresponding to the multi-objective function of the day-ahead low-carbon scheduling model of the active power distribution network comprise:
network constraints, including: active balance constraint and reactive balance constraint;
the active balance is constrained to
The reactive power balance is restricted to
Wherein, P gi And Q gi Is active and reactive power, P, generated by a node i generator set di And Q di Is the active and reactive demand of the node i user, V i And delta i Is the voltage value and phase angle of the voltage at node i, Y ij And theta ij Are the elements and phase angles of the admittance matrix, respectively;
a distributed genset constraint comprising: force upper and lower limit constraints, climbing constraints and minimum start-stop constraints;
the upper and lower limits of the output are constrained to
The climbing is restricted as
Minimum on-off constraint of
Wherein, I i (t) is the starting state of the ith unit at the moment t; t is i on 、T i off Respectively representing the time of starting up and the time of stopping the machine;
an energy storage constraint comprising: a maximum power generation constraint, a minimum power generation constraint, and a power storage constraint;
the constraint of the transmitted power is carried out,
wherein, the first and the second end of the pipe are connected with each other,
respectively the upper and lower limits of the transmission capacity of the tie line.
The constraint of the load balance is realized,
furthermore, the trapezoidal fuzzy membership function is adopted to convert each objective function value in the following way,
wherein the content of the first and second substances,
and &>
Respectively the maximum value and the minimum value of the ith objective function;
obtaining a fuzzy comprehensive decision model of
Wherein alpha is i Fuzzy decision weights for different objective functions.
The embodiment of the application provides a low carbon scheduling device of initiative distribution network day before based on demand response, includes:
the acquisition module is used for acquiring the operation parameter data of each main body; the operation parameter data of each main body comprises: the method comprises the steps of classifying information of power users, information of distributed generator sets and information of capacity of energy storage sets;
the construction module is used for constructing an active power distribution network day-ahead low-carbon scheduling model by taking the minimum system cost as a main target, taking the minimum system carbon emission as a first sub-target and taking the minimum user load offset as a second sub-target;
the convergence module is used for performing iterative optimization on the active power distribution network day-ahead low-carbon scheduling model until the model converges and outputting a plurality of feasible results;
the conversion module is used for converting each objective function value by adopting a trapezoidal fuzzy membership function to obtain a fuzzy comprehensive decision model;
the output module is used for outputting an optimal scheduling plan of the active power distribution network according to the fuzzy comprehensive decision model; the optimal scheduling plan of the active power distribution network comprises an output plan of the distributed wind turbine generator, an operation plan of the energy storage unit and a power utilization plan of a user.
By adopting the technical scheme, the invention can achieve the following beneficial effects:
the invention provides a demand response-based day-ahead low-carbon scheduling method and device for an active distribution network. The optimal load distribution condition of the adjustable user under different scenes is considered, the optimal load transfer model considering the actual power consumption experience of the user is provided, and the power consumption experience of the user can be enhanced while the cost of the user is reduced. The method and the device can also comprehensively consider the energy consumption cost, the carbon emission cost and the user energy experience of the active power distribution network side, provide the source network load storage bidirectional interactive active power distribution network day-ahead low-carbon scheduling based on demand response, make up the defect that only the user cost scheduling is considered at present, and are more favorable for realizing the double-carbon target.
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 schematic step diagram of a demand response-based day-ahead low-carbon scheduling method for an active power distribution network according to the present invention;
FIG. 2 is a schematic flow chart of a day-ahead low-carbon scheduling method of an active power distribution network based on demand response according to the present invention;
fig. 3 is a schematic structural diagram of the active power distribution network day-ahead low-carbon scheduling device based on demand response.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be described in detail below. It should be apparent that the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the examples given herein without any inventive step, are within the scope of the present invention.
In the prior art, control modes of different resources are mostly studied independently. In the aspect of demand-side response, researchers propose response strategies for individual subject demand response or consider demand response capability of users based on prices of wholesale markets, research propose distributed real-time pricing models based on message exchange between smart meters and energy suppliers, or quantitative analysis is carried out on demand response characteristics of different types of users. On the aspect of a dispatching mode of distributed renewable energy sources, students construct a framework of a microgrid economic emission dispatching problem, and study a day-ahead coordinated dispatching mechanism of energy storage and distributed renewable units. In the joint scheduling mechanism, the joint scheduling problem of the island type microgrid is mainly considered at present, and a joint scheduling method of a grid-connected type microgrid is not considered. Taken together, the existing technology has studied the optimal planning of micro-grids in the presence of renewable energy and uncertainties. However, the impact of customer clusters on optimal scheduling and demand-side response control was not considered in the above-described research work.
A specific demand response-based active power distribution network day-ahead low-carbon scheduling method provided in the embodiment of the present application is described below with reference to the accompanying drawings.
First, it should be noted that, in the system on the network side of the active distribution of the present invention. The distribution network operator can regulate and control the distributed wind power, the gas turbine, the energy storage system, the adjacent micro-grid and the load through the central controller. The network operator optimizes the operation mode of various resources in the network based on the day-ahead optimization method provided by the invention.
As shown in fig. 1, a demand response-based day-ahead low-carbon scheduling method for an active distribution network provided in the embodiment of the present application includes:
s101, acquiring operation parameter data of each main body; the operation parameter data of each main body comprises: the method comprises the steps of classifying information of power users, information of distributed generator sets and information of capacity of energy storage sets;
classifying information for power consumers
The power consumer can be classified as advanced according to whether the power consumer has the response capability or notThe electricity utilization conditions of users who need to respond to the demand and users who cannot respond to the demand are different, so that the typical load values of the users who cannot adjust and the users who cannot adjust in different time periods t under different scenes s are required to be input
And &>
And probability ρ of occurrence of different scenes s s 。
From time period t to t', the change rule of the adjustable user load needs to be modeled in the following way
(II) for distributed genset information
Distributed generator sets are classified into conventional distributed generator sets and distributed wind generator sets.
Wherein, the conventional distributed generator set relevant information: the information that the conventional distributed generator set needs to input includes cost information and performance information. The cost information includes an operating cost C YX And a start-up cost C QD Shutdown cost C TJ 。
The operation cost is generally a quadratic function, and for the unit i, the power generation cost satisfies the following formula:
wherein, delta i 、χ i 、θ i Is the cost parameter of the gas unit i; p RQ (i, t) is the output of the gas unit i at the moment t; startup cost of unit i
And the cost of shutdown>
Typically a fixed value.
The performance information required to be input by the gas turbine unit comprises the maximum output P of the unit i max Minimum force P i min Minimum continuous downtime
Minimum continuous on-time->
Maximum climbing rate->
Maximum falling rate->
Wherein, the related information of the distributed wind generating set and the generating power P of the distributed wind generating set FD (v) Influenced by the wind speed, the concrete formula is as follows:
in the formula, P N,FD The maximum installation capacity of the unit, V is the wind speed, V Ci 、V R 、V Co The minimum generating wind speed, the minimum full output wind speed and the cutter wind speed are respectively.
(III) for capacity information of energy storage unit
When the dispatching mechanism is used for dispatching the stored energy, the state of charge of the stored energy is required to be kept within a certain range, and the state of charge of the stored energy can be carried out by using the following formula:
in the formula (I), the compound is shown in the specification,
the charge state, the discharge power and the charge power of the stored energy at the moment t are respectively.
S102, constructing a day-ahead low-carbon scheduling model of the active power distribution network according to the operation parameter data by taking the minimum system cost as a main target, taking the minimum system carbon emission as a first sub-target and taking the minimum user load offset as a second sub-target;
it should be noted that the active power distribution network day-ahead low-carbon scheduling model provided by the application comprises a main optimization objective, two sub-optimization objectives and three optimization objectives in total. The optimization objectives and constraints can be modeled as follows:
the method aims to minimize the power consumption cost of the distribution network system, and the cost of the distribution network system comprises four links: the cost of the distributed conventional generator set, the cost generated by trading with the main network and the adjacent distribution network, the operation cost of the energy storage unit and the cost paid by demand response.
In the formula, mu QD (i, t) and μ TJ (i, t) 0, 1 variables representing the operating status of the distributed conventional genset (QD =1, tj = 0); p t WM 、P t WG 、
Respectively representing the outsourcing power, the outsourcing power price and the outsourcing power price at the time t; />
Respectively represents the discharge and charge power of the energy storage system at the time t>
Respectively representing the charging/generating price, K, of the energy storage system at time t d Is the price of demand response, Δ d To be adjustable loadedFluctuation ratio of load, D d,t Total power consumption, z, for the d-th load at time t d,t Response 0, 1 variables for the adjustable load.
And solving the main objective function, and performing initial optimization to obtain the condition of the lowest system cost.
Then, under the condition that the system cost is the lowest, the first sub-objective function which takes the system carbon emission as the lowest as the first sub-objective is required to meet the requirement of lowest carbon emission under the condition that the electricity consumption cost is the lowest.
In the formula, alpha i 、β i 、ε i Is the carbon emission factor of the system and is,
carbon emission factor of outsourcing electric quantity.
Then under the condition of minimum system cost and minimum system talking discharge amount, taking the second sub-target function with minimum user load deviation as the second sub-target as
In the formula, ρ s Probability of s-th scene, D E (s, t) is the load distribution in the s-th scenario,
the optimal load distribution condition of the system is obtained.
The optimal load distribution is calculated as follows:
the constraint condition that the multi-objective function of the active power distribution network day-ahead low-carbon scheduling model corresponds includes:
1) Network constraint, constraint that distribution network system needs active balance and reactive balance
In the formula, P gi And Q gi Is active and reactive power, P, generated by the node i generator set di And Q di Is the active and reactive demand of the node i user, V i And delta i Is the voltage value and phase angle of the voltage at node i, Y ij And theta ij Are the elements and phase angles of the admittance matrix. The following equation may ensure that the voltage is within a safe threshold.
V i min ≤V i ≤V i max (12)
2) The constraint of the distributed generator set, the operation of the distributed conventional generator set needs to meet the output upper and lower limit constraint, the climbing constraint and the minimum start and stop constraint:
in the formula I i And (t) is the starting state of the ith unit at the time t. T is i on 、T i off The time of the startup and the time of the shutdown.
3) And energy storage constraint, wherein the energy storage unit needs to meet maximum and minimum power generation constraint and electric quantity storage constraint during operation. The method comprises the following specific steps:
v e (t)+u e (t)≤1 (20)
in the formula, v e (t) and u e And (t) is a variable of 0 and 1 in the energy storage charging and discharging state.
D min d,t z d,t ≤D d,t ≤D max d,t z d,t (22)
Each adjustable load needs to be supplied with power for a specific time, the following constraints can be used:
4) And (4) transmission power constraint, wherein the connection of the power distribution network, the main network and other adjacent distribution networks needs to meet certain transmission constraint.
In the formula (I), the compound is shown in the specification,
respectively the upper and lower limits of the power transmission capacity of the tie line.
5) The load balance is restricted, and the load balance is restricted,
s103, performing iterative optimization on the day-ahead low-carbon scheduling model of the active power distribution network until the model converges, and outputting a plurality of feasible results;
in the method, the main objective function is solved to obtain the condition of minimum system cost, and then the second sub-objective function and the third sub-objective function are solved, because the main objective function is a multi-objective function in the method, the solution solved by adopting the column and equation method comprises a plurality of feasible results.
S104, converting each objective function value by adopting a trapezoidal fuzzy membership function to obtain a fuzzy comprehensive decision model;
because the solution solved by the column sum equation method contains a plurality of feasible results, in order to obtain the non-dominated pareto optimal solution, the trapezoidal fuzzy membership function is used for converting each objective function value, and then the optimal scheduling mode is selected by decision.
In the formula (f) i For the per unit value of the ith objective function,
and &>
Respectively the maximum and minimum values of the ith objective function.
The transformed values are decided using the following model:
in the formula, alpha i Fuzzy decision weights for different objective functions.
S105, outputting an optimal scheduling plan of the active power distribution network according to the fuzzy comprehensive decision model; the optimal scheduling plan of the active power distribution network comprises an output plan of the distributed wind turbine generator, an operation plan of the energy storage unit and a power utilization plan of a user.
And finally, outputting an optimal day-ahead scheduling plan including an output plan of the distributed wind turbine generator, an operation plan of the energy storage unit and a power utilization plan of a user according to the fuzzy comprehensive decision result.
The working principle of the active power distribution network day-ahead low-carbon scheduling method based on demand response is as follows: as shown in fig. 2, in the present application, first, the operation parameter data of each subject is obtained; the operational parameter data of each subject includes: the method comprises the steps of classifying power users, distributing type generator set information and energy storage unit capacity information; constructing a day-ahead low-carbon scheduling model of the active power distribution network according to the operation parameter data by taking the minimum system cost as a main target, taking the minimum system carbon emission as a first sub-target and taking the minimum user load deviation as a second sub-target; performing iterative optimization on the day-ahead low-carbon scheduling model of the active power distribution network until the model converges, and outputting a plurality of feasible results; converting each objective function value by adopting a trapezoidal fuzzy membership function to obtain a fuzzy comprehensive decision model; outputting an optimal scheduling plan of the active power distribution network according to the fuzzy comprehensive decision model; the optimal scheduling plan of the active power distribution network comprises an output plan of the distributed wind turbine generator, an operation plan of the energy storage unit and a power utilization plan of a user.
As shown in fig. 3, an embodiment of the present application provides a demand response-based active distribution network day-ahead low-carbon scheduling device, including:
an obtaining module 201, configured to obtain operation parameter data of each main body; the operation parameter data of each main body comprises: the method comprises the steps of classifying information of power users, information of distributed generator sets and information of capacity of energy storage sets;
the building module 202 is used for building a day-ahead low-carbon scheduling model of the active power distribution network by taking the minimum system cost as a main target, taking the minimum system carbon emission as a first sub-target and taking the minimum user load offset as a second sub-target;
the convergence module 203 is used for performing iterative optimization on the day-ahead low-carbon scheduling model of the active power distribution network until the model converges and outputting a plurality of feasible results;
the conversion module 204 is configured to convert each objective function value by using a trapezoidal fuzzy membership function to obtain a fuzzy comprehensive decision model;
the output module 205 is configured to output an optimal scheduling plan of the active power distribution network according to the fuzzy comprehensive decision model; the active power distribution network optimal scheduling plan comprises an output plan of the distributed wind turbine generator, an operation plan of the energy storage unit and a power utilization plan of a user.
The working principle of the active power distribution network day-ahead low-carbon scheduling device based on demand response provided by the embodiment of the application is that the acquisition module 201 acquires the operation parameter data of each main body; the operation parameter data of each main body comprises: the method comprises the steps of classifying power users, distributing type generator set information and energy storage unit capacity information; the construction module 202 constructs an active power distribution network day-ahead low-carbon scheduling model by taking the minimum system cost as a main target, the minimum system carbon emission as a first sub-target and the minimum user load offset as a second sub-target; the convergence module 203 performs iterative optimization on the day-ahead low-carbon scheduling model of the active power distribution network until the model converges, and outputs a plurality of feasible results; the conversion module 204 converts each objective function value by adopting a trapezoidal fuzzy membership function to obtain a fuzzy comprehensive decision model; the output module 205 outputs an optimal scheduling plan of the active power distribution network according to the fuzzy comprehensive decision model; the optimal scheduling plan of the active power distribution network comprises an output plan of the distributed wind turbine generator, an operation plan of the energy storage unit and a power utilization plan of a user.
In summary, the present invention provides a demand response-based day-ahead low-carbon scheduling method and apparatus for an active power distribution network, which have the following beneficial effects
1. The comprehensive energy utilization characteristic of the active distribution network side is comprehensively considered, a carbon emission evaluation model of the active distribution network is provided, and the carbon emission condition of the active distribution network can be accurately evaluated.
2. The optimal load distribution condition of the adjustable user under different scenes is considered, the optimal load transfer model considering the actual power consumption experience of the user is provided, and the power consumption experience of the user can be enhanced while the cost of the user is reduced.
3. Energy consumption cost, carbon emission cost and user energy experience of the active power distribution network side are comprehensively considered, the demand response-based source network load storage bidirectional interactive active power distribution network day-ahead low-carbon scheduling is provided, the defect that only user cost scheduling is considered at present is overcome, and the realization of a double-carbon target is facilitated.
It can be understood that the method embodiments provided above correspond to the apparatus embodiments described above, and corresponding specific contents may be referred to each other, which are not described herein again.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and all the changes or substitutions should be covered within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.
Claims (8)
1. A demand response-based day-ahead low-carbon scheduling method for an active power distribution network is characterized by comprising the following steps:
acquiring operation parameter data of each main body; the operation parameter data of each main body comprises: the method comprises the steps of classifying power users, distributing type generator set information and energy storage unit capacity information;
constructing a day-ahead low-carbon scheduling model of the active power distribution network according to the operation parameter data by taking the minimum system cost as a main target, taking the minimum system carbon emission as a first sub-target and taking the minimum user load offset as a second sub-target;
performing iterative optimization on the day-ahead low-carbon scheduling model of the active power distribution network until the model converges, and outputting a plurality of feasible results;
converting each objective function value by adopting a trapezoidal fuzzy membership function to obtain a fuzzy comprehensive decision model;
outputting an optimal scheduling plan of the active power distribution network according to the fuzzy comprehensive decision model; the optimal scheduling plan of the active power distribution network comprises an output plan of the distributed wind turbine generator, an operation plan of the energy storage unit and a power utilization plan of a user.
2. The method of claim 1,
the power consumers are divided into consumers capable of carrying out demand response and consumers incapable of carrying out demand response according to whether the power consumers have response capability, the electricity utilization conditions of the consumers often have different scenes, and therefore the typical load values of the users incapable of being adjusted and the users incapable of being adjusted in different time periods t under different scenes s need to be input
And &>
And probability ρ of occurrence of different scenes s s (ii) a From time t to t', the change rule model of the adjustable user load is
The distributed generator sets are divided into a conventional distributed generator set and a distributed wind generating set;
wherein the conventional distributed generator set information includes cost information and performance information, the cost information including an operating cost C YX Starting cost C YX And cost of shutdown C TJ ；
The running cost is
Wherein, delta i 、χ i 、θ i All are the cost parameters of the gas unit i; p is RQ (i, t) is the output of the gas turbine set i at the moment t; startup cost of unit i
And the cost of shutdown>
Typically a fixed value;
the performance information comprises the maximum output P of the unit i max Minimum force P i min Minimum continuous down time
Minimum continuous boot time M i YX Maximum climbing rate->
And a maximum rate of descent R i XJ ；
The distributed wind generating set information is
Wherein, P N,FD The maximum installation capacity of the unit, V is the wind speed, V Ci 、V R 、V Co Respectively the minimum generating wind speed, the minimum full output wind speed and the cutter wind speed;
the capacity information of the energy storage unit is
Wherein the content of the first and second substances,
the charge state, the discharge power and the charge power of the stored energy at the moment t are respectively.
3. The method of claim 2, wherein the system cost comprises: the cost of the distributed conventional generator set, the cost generated by trading with the main network and the adjacent distribution network, the operation cost of the energy storage unit and the cost paid by demand response; the main objective function of the day-ahead low-carbon scheduling model of the active power distribution network is
Wherein, mu QD (i, t) and μ TJ (i, t) 0, 1 variables representing the operating state of the distributed conventional genset (QD =1, tj = 0); p t WM 、P t WG 、
Respectively representing the outsourcing power, the outsourcing power price and the outsourcing power price at the time t; />
Respectively represents the discharge and charge power of the energy storage system at the time t>
Respectively representing the stored energy at time tCharging/generating price of the system, K d Is the price of demand response, Δ d To adjust the proportion of fluctuations in load, D d,t Total power consumption, z, for the d-th load at time t d,t Response 0, 1 variables for the adjustable load.
4. The method of claim 3, wherein the first sub-objective function with the lowest system carbon emissions as the first sub-objective is
In the formula, alpha i 、β i 、ε i Is the carbon emission factor of the system and is,
carbon emission factor of outsourcing electric quantity.
5. The method of claim 4, wherein the second sub-objective function with the least user load shift is the second sub-objective function with the least system cost and the least system talk amount
Where ρ is s Probability of s-th scene, D E (s, t) is the load distribution in the s-th scenario,
the optimal load distribution condition of the system is obtained.
6. The method according to claim 5, wherein the constraint condition corresponding to the multi-objective function of the day-ahead low-carbon scheduling model of the active power distribution network comprises:
network constraints comprising: active balance constraint and reactive balance constraint;
the active balance is constrained to
The reactive power balance is restricted to
Wherein, P gi And Q gi Is active and reactive power, P, generated by the node i generator set di And Q di Is the active and reactive demand of the node i user, V i And delta i Is the voltage value and phase angle of the voltage at node i, Y ij And theta ij Respectively, the element and phase angle of the admittance matrix;
a distributed genset constraint comprising: force upper and lower limit constraints, climbing constraints and minimum start-stop constraints;
the upper and lower limits of the output are constrained to
The climbing is restricted as
Minimum on-off constraint of
Wherein, I i (t) is the starting state of the ith unit at the moment t; t is i on 、T i off Respectively representing the time of starting up and the time of stopping the machine;
an energy storage restraint comprising: maximum power generation constraints, minimum power generation constraints and electric quantity storage constraints;
the constraint of the transmitted power is carried out,
wherein the content of the first and second substances,
respectively the upper limit and the lower limit of the transmission capacity of the tie line; p t WM And P t WG The system outgoing power and the incoming tie line power at the moment t are respectively;
the load balance is restricted, and the load balance is restricted,
7. the method of claim 6, wherein each objective function value is transformed using a trapezoidal fuzzy membership function in the following manner,
wherein f is i For the per unit value of the ith objective function,
and &>
Respectively the maximum value and the minimum value of the ith objective function;
obtain a fuzzy comprehensive decision model of
Wherein alpha is i Fuzzy decision weights for different objective functions.
8. The utility model provides an active distribution network low carbon scheduling device day ahead based on demand response which characterized in that includes:
the acquisition module is used for acquiring the operation parameter data of each main body; the operation parameter data of each main body comprises: the method comprises the steps of classifying information of power users, information of distributed generator sets and information of capacity of energy storage sets;
the construction module is used for constructing an active power distribution network day-ahead low-carbon scheduling model by taking the minimum system cost as a main target, taking the minimum system carbon emission as a first sub-target and taking the minimum user load offset as a second sub-target;
the convergence module is used for performing iterative optimization on the day-ahead low-carbon scheduling model of the active power distribution network until the model converges and outputting a plurality of feasible results;
the conversion module is used for converting each objective function value by adopting a trapezoidal fuzzy membership function to obtain a fuzzy comprehensive decision model;
the output module is used for outputting an optimal scheduling plan of the active power distribution network according to the fuzzy comprehensive decision model; the active power distribution network optimal scheduling plan comprises an output plan of the distributed wind turbine generator, an operation plan of the energy storage unit and a power utilization plan of a user.
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| CN116544955B (en) * | 2023-07-03 | 2023-11-24 | 阳光慧碳科技有限公司 | Load regulation and control method, device and system |
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