CN112465305B - Method, device and equipment for optimizing scheduling and evaluating clean energy - Google Patents
Method, device and equipment for optimizing scheduling and evaluating clean energy Download PDFInfo
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Abstract
The invention relates to a method, a device and equipment for evaluating clean energy optimization scheduling, belonging to the technical field of power scheduling; obtaining the loss amount of the clean energy according to the optimal scheduling model of the actual data; respectively constructing a scheduling decision optimal scheduling model and a boundary factor optimal scheduling model; obtaining an evaluation result of the scheduling decision ideality according to the scheduling decision optimal scheduling model; obtaining a boundary factor ideality evaluation result according to the boundary factor optimal scheduling model; and acquiring a comprehensive evaluation result of the clean energy optimization scheduling. According to the method, the influence of the scheduling operation service on the consumption of the clean energy can be quantified by comparing the effect difference of the optimal scheduling model and the actual scheme in different scenes, the influence degree of each key link in the scheduling service can be analyzed in detail, and the method has a remarkable effect of improving the accuracy and comprehensiveness of the evaluation result of the optimal scheduling benefit of the clean energy.
Description
Technical Field
The invention belongs to the technical field of power dispatching, and particularly relates to a method, a device and equipment for optimizing dispatching evaluation of clean energy.
Background
Clean energy mainly comprises hydropower, wind power, photovoltaic and the like. Ensuring clean energy consumption is an important target for the operation of the current power grid. In the prior art, the optimized scheduling evaluation of the clean energy is to analyze and evaluate the actual effect of the scheduling operation of the power grid in the aspect of clean energy consumption from the viewpoint of post-statistical analysis.
At present, indexes commonly used for optimizing, scheduling and evaluating the clean energy comprise water abandoning electric quantity, wind abandoning electric quantity and light abandoning electric quantity. The three indexes are based on actual operation results, and the amount of unconsumed clean energy such as water abandonment, wind abandonment and light abandonment in actual operation is counted so as to evaluate the implementation benefits of clean energy optimal scheduling in different provinces.
However, the reasons for the unconsumed amount of clean energy such as water abandoning, wind abandoning, light abandoning and the like in actual operation are complex, and include factors exceeding the bearing capacity achieved by the planning and construction of the power supply and power grid and factors that the power grid cannot be optimally scheduled. Therefore, how to accurately evaluate the optimal scheduling of the clean energy, identify key factors causing water abandonment, wind abandonment and light abandonment, and accurately quantify the influence degree of different factors in the key factors becomes a technical problem to be solved urgently in the prior art.
Disclosure of Invention
In order to at least solve the problems in the prior art, the invention provides a method, a device and equipment for optimizing and scheduling evaluation of clean energy, so that the influence degrees of different factors in water abandonment, wind abandonment and light abandonment are accurately quantized through the comprehensive evaluation result of optimizing and scheduling of clean energy.
The technical scheme provided by the invention is as follows:
in one aspect, a method for optimizing, scheduling and evaluating clean energy includes:
constructing an actual data optimal scheduling model according to actual data and a preset rule of a target power grid;
evaluating the benefit of scheduling and operating clean energy according to the actual data optimal scheduling model to obtain the loss amount of the clean energy;
respectively constructing a scheduling decision optimal scheduling model and a boundary factor optimal scheduling model;
obtaining an evaluation result of the scheduling decision ideality according to the scheduling decision optimal scheduling model;
obtaining a boundary factor ideality evaluation result according to the boundary factor optimal scheduling model;
and acquiring a comprehensive evaluation result of the optimized scheduling of the clean energy according to the scheduling decision ideality evaluation result and the boundary factor ideality evaluation result based on the loss amount of the clean energy.
Optionally, the target power grid comprises a wind, light and fire mutual aid mode power grid;
the actual data of the target power grid are boundary data of the actual data optimal scheduling model;
the optimal scheduling model of the actual data comprises the following steps:
wherein, the formula (1) is an optimization target,
wind curtailment power and light curtailment power of a wind power plant w and a photovoltaic power station p time period T are respectively, NT is the number of scheduling operation decision optimization time periods, delta T is a corresponding time interval, NW and NP are the number of a power grid wind power plant and a photovoltaic power station respectively; the formula (2) is a constraint condition,
sequentially generating planned power of a wind power plant w, a photovoltaic power plant p and a thermal power plant c in a time period t,
the actual load power of the node b in the time period t, NC and NB are the number of thermal power generating units of the power grid and the number of load nodes respectively,
the upper limit and the lower limit of the power flow of the operation section s; GSDF (Global System for function & data function) s,w 、GSDF s,p 、GSDF s,c 、GSDF s,b Sequentially comprises a wind power plant w, a photovoltaic power plant p, a thermal power plant c, a load node b and a load transfer distribution factor of the operation section,
respectively the generated power in actual operating conditions of the wind farm w and of the photovoltaic power station p for a period t,
respectively is the wind abandoning rate and the light abandoning rate in the time period t,
respectively the maximum output and the minimum output of the thermal power generating unit c,
the maximum and minimum climbing capacities of the thermal power generating unit c are respectively.
Optionally, the loss amount of clean energy is expressed as:
wherein CVA is the loss of clean energy generated by comprehensive action, ER, W, A, ER, P and A are respectively the total abandoned wind power and the total abandoned light power actually generated in a scheduling decision period,
and solving the wind and light curtailment electric quantity under the actual data optimal scheduling model.
Optionally, the boundary data of the scheduling decision optimal scheduling model is a predicted value of the scheduling decision stage.
Optionally, the scheduling decision ideality evaluation result includes: scheduling decision ideality index PD D ；
PD D ＝(E D,W,A +E D,P,A )-(E PD,W,A +E PD,P,A ) (4)
In the formula, PD D For scheduling decisions ideality index, E D,W,A 、E D,P,A Wind power curtailment quantity and light power curtailment quantity in decision period in scheduling operation scheme actually issued by scheduling operation personnel respectively, E PD,W,A 、E PD,P,A And respectively solving the obtained abandoned wind electric quantity and abandoned light electric quantity for the optimal scheduling model of the scheduling decision.
Optionally, the evaluation result of the ideality of the boundary factor includes: boundary factor b i Index of degree of ideality
In the formula (I), the compound is shown in the specification,
is a boundary factor b i The index of the degree of ideality is,
solving the obtained abandoned wind electric quantity and abandoned light electric quantity respectively for a single boundary factor optimal scheduling model corresponding to the boundary factors;
the ideality of the boundary factors of the whole network is the sum of the idealities of all the boundary factors, and is as follows:
in the formula, PD B Is the ideality of the boundary factor of the whole network, b i E B represents all the boundary factor items belonging to the boundary factor set B.
Optionally, the comprehensive evaluation result of the clean energy optimization scheduling includes:
if the value of the boundary factor ideality of the whole network is positive and the value of the scheduling decision ideality is negative, the scheduling operator is indicated to pre-judge the boundary data prediction deviation, the reason for causing the scheduling operation clean energy loss is mainly attributed to the boundary data prediction deviation of each type, and the clean energy loss caused by each type of boundary factor is distributed according to the boundary factor ideality proportion of the boundary factor ideality of the whole network, and can be expressed as:
in the formula (I), the compound is shown in the specification,
i.e. boundary factor b i The loss amount of the clean energy to be born;
if the value of the boundary factor ideality of the whole network is negative and the value of the scheduling decision ideality is positive, the result shows that the prediction deviation of scheduling operation personnel on the boundary data is opposite to the actual result, and the reason for causing the loss amount of the clean energy in scheduling operation is mainly attributed to the fact that the scheduling decision is not accurate enough, and the loss amount of the clean energy caused by the inaccurate scheduling decision is the whole network, and can be expressed as follows:
CV D ＝CV A (8)
in the formula, CV D The loss amount of the clean energy which is supposed to be born by the scheduling decision;
if the values of the boundary factor ideality of the whole network and the scheduling decision ideality are the same, the scheduling decision and the boundary data deviation are shown to influence the operation condition, the loss amount of clean energy in scheduling operation is shared according to the ideality proportion, and can be expressed as:
in another aspect, a device for optimizing, scheduling and evaluating clean energy includes: the system comprises a first construction module, an acquisition module, a second construction module, a first evaluation module, a second evaluation module and a comprehensive evaluation module;
the first construction module is used for constructing an actual data optimal scheduling model according to actual data and a preset rule of a target power grid;
the obtaining module is used for evaluating the efficiency of the scheduled and operated clean energy according to the actual data optimal scheduling model and obtaining the loss amount of the clean energy;
the second construction module is used for respectively constructing a scheduling decision optimal scheduling model and a boundary factor optimal scheduling model;
the first evaluation module is used for obtaining a scheduling decision ideality evaluation result according to the scheduling decision optimal scheduling model;
the second evaluation module is used for obtaining a boundary factor ideality evaluation result according to the boundary factor optimal scheduling model;
and the comprehensive evaluation module is used for obtaining a comprehensive evaluation result of the optimized scheduling of the clean energy according to the scheduling decision ideality evaluation result and the boundary factor ideality evaluation result based on the loss amount of the clean energy.
Optionally, the target power grid comprises a wind, light and fire mutual aid mode power grid; the actual data of the target power grid are boundary data of the actual data optimal scheduling model; the actual data optimal scheduling model constructed by the first construction module is as follows:
wherein, the formula (1) is an optimization target,
wind curtailment power and light curtailment power of a wind power plant w and a photovoltaic power station p time period T are respectively, NT is the number of scheduling operation decision optimization time periods, delta T is a corresponding time interval, NW and NP are the number of a power grid wind power plant and a photovoltaic power station respectively; the formula (2) is a constraint condition,
sequentially generating planned power of a wind power plant w, a photovoltaic power station p and a thermal power plant c time period t,
the actual load power of the node b in the time period t, NC and NB are the number of thermal power generating units of the power grid and the number of load nodes respectively,
the upper limit and the lower limit of the power flow of the operation section s are set; GSDF (Global System for function & data function) s,w 、GSDF s,p 、GSDF s,c 、GSDF s,b Sequentially comprises a wind power plant w, a photovoltaic power station p, a thermal power plant c, a load node b and a power flow transfer distribution factor of the operation section,
respectively the electricity generatable power in actual operating situations of the wind farm w and of the photovoltaic power station p for a period t,
respectively, the air abandon rate of the time interval t andthe light rejection rate of the light-emitting diode,
respectively the maximum output and the minimum output of the thermal power generating unit c,
the maximum and minimum climbing capacities of the thermal power generating unit c are respectively.
In another aspect, a device for optimizing, scheduling and evaluating clean energy includes: a processor, and a memory coupled to the processor;
the memory is used for storing a computer program, and the computer program is at least used for executing the clean energy optimization scheduling evaluation method;
the processor is used for calling and executing the computer program in the memory.
The invention has the beneficial effects that:
according to the method, the device and the equipment for the optimized scheduling evaluation of the clean energy, which are provided by the embodiment of the invention, an optimal scheduling model of actual data is constructed according to the actual data and the preset rule of a target power grid; according to the actual data optimal scheduling model, evaluating the scheduling operation clean energy benefits to obtain the loss amount of the clean energy; respectively constructing a scheduling decision optimal scheduling model and a boundary factor optimal scheduling model; obtaining a scheduling decision ideality evaluation result according to the scheduling decision optimal scheduling model; obtaining a boundary factor ideality evaluation result according to the boundary factor optimal scheduling model; and acquiring a comprehensive evaluation result of the optimized scheduling of the clean energy according to the scheduling decision ideality evaluation result and the boundary factor ideality evaluation result based on the loss amount of the clean energy. According to the method, the influence of the scheduling operation service on the consumption of the clean energy can be quantified by comparing the effect difference of the optimal scheduling model and the actual scheme under different scenes, the influence degree of each key link in the scheduling service can be analyzed in detail, and the method has an obvious effect of improving the accuracy and comprehensiveness of the evaluation result of the optimal scheduling benefit of the clean energy.
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 flow chart of a clean energy optimization scheduling evaluation method according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a clean energy optimal scheduling evaluation apparatus according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of a clean energy optimization scheduling evaluation device according to an embodiment of the present invention.
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 order to at least solve the technical problem provided by the invention, the embodiment of the invention provides a clean energy optimization scheduling evaluation method.
Fig. 1 is a schematic flow chart of a method for optimizing scheduling and evaluating clean energy according to an embodiment of the present invention, referring to fig. 1, the method according to the embodiment of the present invention may include the following steps:
s11, constructing an actual data optimal scheduling model according to actual data and preset rules of the target power grid.
In a specific implementation process, any one power grid can be defined as a target power grid, and the clean energy optimal scheduling evaluation method provided by the application is applied to evaluate the clean energy optimal scheduling of the target power grid.
In the embodiment of the invention, the actual data optimal scheduling model is an optimal scheduling model constructed on the basis of actual load, actual operation conditions of the power transmission and transformation equipment and an actual new energy power generation curve. The optimal scheduling model takes actual data as boundary data and maximum consumption of clean energy as an optimization target, eliminates decision deviation possibly caused by manual decision of scheduling operation personnel, and the result actually reflects the highest level of consumption of clean energy which can be achieved by scheduling operation.
Optionally, the target grid comprises a wind, solar and fire cross-economy mode grid.
For example, the optimal scheduling models of actual data under different power grid power supply structures are different, and in this embodiment, the optimal scheduling models of actual data under the wind, light and fire mutual aid mode are introduced and explained by combining the actual power supply structures of most provinces in china:
wherein, the formula (1) is an optimization target,
wind curtailment power and light curtailment power of a wind power plant w and a photovoltaic power plant p time period T are respectively, NT is the number of scheduling operation decision optimization time periods, delta T is a corresponding time interval, and NW and NP are the number of a power grid wind power plant and a photovoltaic power plant respectively. The formula (2) is a constraint condition,
sequentially generating planned power of a wind power plant w, a photovoltaic power plant p and a thermal power plant c in a time period t,
the actual load power of the node b in the time period t, NC and NB are the number of the thermal power generating units of the power grid and the number of the load nodes respectively,
GSDF being upper and lower limits of the power flow of the running section s s,w 、GSDF s,p 、GSDF s,c 、GSDF s,b Sequentially comprises a wind power plant w, a photovoltaic power station p, a thermal power plant c, a load node b and a power flow transfer distribution factor of the operation section,
respectively the electricity generatable power in actual operating situations of the wind farm w and of the photovoltaic power station p for a period t,
respectively is the wind abandoning rate and the light abandoning rate in the time period t,
respectively the maximum output and the minimum output of the thermal power generating unit c,
the maximum and minimum climbing capacities of the thermal power generating unit c are respectively.
The method is characterized in that the formula (1) takes the minimum wind power curtailment and light power curtailment in the whole decision period as the optimization target, and is equivalent to the maximization of the clean energy consumption; the constraint conditions of the formula (2) are power balance constraint, operation section constraint, wind power plant power constraint, wind abandoning rate consistency constraint, photovoltaic power station power constraint, light abandoning rate consistency constraint, thermal power unit output range constraint and thermal power unit climbing capacity constraint in sequence. The actual data optimal scheduling model essentially belongs to a linear programming problem, can be solved by adopting a simplex method and other traditional mathematical programming methods, does not influence the main innovative content of the invention, and is not repeated in the solving process. And solving the actual data optimal scheduling model to obtain the wind curtailment electric quantity and the light curtailment electric quantity of the actual data optimal scheduling model.
And S12, evaluating the clean energy efficiency of the scheduled operation according to the actual data optimal scheduling model, and obtaining the loss of the clean energy.
And after the actual data optimal scheduling model is established, solving the actual data optimal scheduling model. The solving result of the actual data optimal scheduling model actually reflects the highest level of clean energy consumption which can be achieved by scheduling operation, and the solved wind curtailment electric quantity and light curtailment electric quantity actually represent the clean energy consumption amplitude which exceeds the resolution of scheduling operation optimization. The difference between the actually generated abandoned wind electric quantity and abandoned light electric quantity and the difference between the abandoned wind electric quantity and abandoned light electric quantity obtained by solving the optimal scheduling model of the actual data are the loss quantity of clean energy generated by the comprehensive action of factors such as scheduling decision, boundary factor deviation and the like, and can be expressed as:
in the formula, CV A The loss amount of clean energy generated by the comprehensive action of the scheduling decision, the boundary factor deviation and other factors may be referred to as total loss amount of clean energy, E R,W,A 、E R,P,A Respectively the total abandoned wind electric quantity and the total abandoned light electric quantity which actually occur in the scheduling decision period,
namely, the wind curtailment and light curtailment electric quantity under the actual data optimal scheduling model obtained by solving.
In the embodiment of the invention, the total loss of the clean energy can only be used for evaluating the overall level of the clean energy optimal scheduling, the advantages and the disadvantages of various factors in the optimal scheduling are not disclosed, and the evaluation result is rough.
In a specific matter process, optimization scheduling factors influencing clean energy consumption can be divided into two categories, namely scheduling decisions and boundary data, the scheduling decisions are actually unreasonable scheduling operation schemes caused by insufficient decision-making capability of scheduling operators, and the boundary data refer to the problem that actual operation benefits are reduced due to fluctuation or deviation of boundary data on which the scheduling operators make decisions such as load prediction deviation and new energy prediction deviation. The boundary data can be further refined, the core innovation content of the invention is not influenced, the boundary data which needs to be considered is included in the boundary factor set B, and the influence of the boundary factors is evaluated.
And S13, respectively constructing a scheduling decision optimal scheduling model and a boundary factor optimal scheduling model.
And S14, obtaining a scheduling decision ideality evaluation result according to the scheduling decision optimal scheduling model.
And S15, obtaining a boundary factor ideality evaluation result according to the boundary factor optimal scheduling model.
The optimal scheduling model for scheduling decision refers to an optimal scheduling scheme for optimizing the clean energy which can be achieved by scheduling operators according to the boundary data prediction results mastered by the scheduling operators, and the scheme actually reflects the scheduling decision capability of the scheduling operators. Referring to the construction mode of the actual data optimal scheduling model in the above embodiment, the scheduling decision optimal scheduling model under different power grid power structures is different, and the wind, light and fire mutual aid system is still used as a research object, the scheduling decision optimal scheduling model expression is the same as the construction of the actual data optimal scheduling model, the difference is that boundary parameters such as wind power prediction, photovoltaic power prediction, load prediction, grid structure and power grid operation control requirements all adopt the predicted values of the scheduling decision stage, while in the construction of the actual data optimal scheduling model, according to the actual occurrence value, namely in the scheduling decision optimal scheduling model,
for the load power of node b for time period t,
the upper limit and the lower limit of the power flow of the operation section s, GSDF, which need to be considered in the scheduling decision stage s,w 、GSDF s,p 、GSDF s,c 、GSDF s,b The power flow transfer distribution factors of the operation section, the wind power field w, the photovoltaic power station p, the thermal power plant c and the load node b are considered in turn in the scheduling decision stage,
respectively wind farm w and lightWind power prediction and photovoltaic power prediction of a photovoltaic power station p time period t. The main innovation content of the invention is not influenced, and the scheduling decision optimal scheduling model is not described again. And (3) solving the problem by adopting conventional solving algorithms such as a simplex method and the like by referring to the actual data optimal scheduling model, wherein the problems are linear mixed integer programming problems.
The difference between the scheduling operation scheme obtained by the optimal scheduling model of the scheduling decision and the scheduling operation scheme formed by actual scheduling operators actually reflects the degree of influence of the scheduling decision on the optimal scheduling of the clean energy. Based on this, the scheduling decision ideality index PD is introduced in the embodiment of the present application D And in order to quantify the difference between the optimal scheduling model of the scheduling decision and the actual scheduling operation scheme, the index is essentially the difference between the loss electric quantity of the clean energy under the two conditions, namely:
PD D ＝(E D,W,A +E D,P,A )-(E PD,W,A +E PD,P,A ) (4)
in the formula, PD D For scheduling decisions ideality index, E D,W,A 、E D,P,A Respectively obtaining the wind curtailment electric quantity and the light curtailment electric quantity at decision time intervals in a dispatching operation scheme actually issued by dispatching operation personnel, wherein the data can be obtained from the actual dispatching operation scheme, E PD,W,A 、E PD,P,A And respectively solving the obtained abandoned wind electric quantity and abandoned light electric quantity for the optimal scheduling model of the scheduling decision.
In the embodiment of the invention, in the compilation process of the actual scheduling operation scheme, because the scheduling operation personnel decide different safety margins and the like, the abandoned wind electric quantity and the abandoned light electric quantity do not necessarily exceed the abandoned wind electric quantity and the abandoned light electric quantity obtained by calculation of the optimal scheduling model of the scheduling decision, the ideality result in the formula (4) may be a positive value, a negative value or 0, and when the value is the positive value, the scheduling operation personnel are more conservative to consider, and a larger margin is reserved, so that the abandoned wind electric quantity and the abandoned light electric quantity are increased; the value is 0, which indicates that the scheduling operator has proper decision analysis; and the value is a negative value, which indicates that the scheduling operator judges boundary data in advance to have errors, and the reservation margin is small, so that the wind and light abandonment quantity is smaller than that of the optimal scheduling model scene of the scheduling decision.
In this embodiment, the boundary factor optimal scheduling model refers to a comparison and comparison model of the influence of deviation or fluctuation of each boundary factor on the clean energy optimal scheduling benefit. In order to analyze the influence of different types of boundary factors in detail, a single boundary factor optimal scheduling model is constructed by adopting a control variable method, and the influence of different boundary factors on the optimal scheduling of the clean energy is analyzed one by one.
For any boundary factor, the optimal scheduling model of the boundary factor is the optimal scheduling model under the condition that other boundary factors are used for predicting data in a scheduling decision stage and only the boundary factor is actual data, namely for any boundary factor B in the boundary factor set B i And the single boundary factor optimal scheduling model is the optimal scheduling model when the corresponding parameter value of the boundary factor is the actual occurrence value, namely the prediction deviation does not exist.
The core innovation point is not influenced, the optimal scheduling model of the single boundary factor and the solving method thereof are not repeated, the optimal scheduling model of the single boundary factor to be analyzed can be obtained by referring to the optimal scheduling model of the actual data and replacing all the non-to-be-evaluated boundary factors with the prediction data according to the scheduling decision stage.
The boundary factor ideality is an evaluation index introduced in the embodiment of the invention to quantify the influence degree of different boundary factors on the optimal scheduling of clean energy, is essentially the difference between the solution result of the optimal scheduling model of the single boundary factor and the solution result of the optimal scheduling model of the scheduling decision, and can be represented as follows:
in the formula (I), the compound is shown in the specification,
is a boundary factor b i The index of the degree of ideality is,
respectively corresponding to the boundary factors, and a single boundary factor optimal scheduling modelAnd solving the obtained abandoned wind electric quantity and abandoned light electric quantity.
The ideality of the boundary factors of the whole network is the sum of the idealities of the boundary factors, and can be expressed as:
in the formula, PD B Is the ideality of the boundary factor of the whole network, b i E B represents all the boundary factor items belonging to the boundary factor set B.
In the embodiment of the invention, the ideality of the boundary factor in the whole network is the same as that of the scheduling decision obtained by the optimal scheduling scheme of the scheduling decision, and the ideality value of the boundary factor in the whole network can be a positive value, a negative value or 0; the value is 0, which indicates that the comprehensive action of all boundary factors causes the wind and light abandoning power to be unchanged; and the value is a negative value, which indicates that the boundary factor prediction deviation causes the wind abandonment and the light abandonment electric quantity to be reduced.
And S16, acquiring a comprehensive evaluation result of the optimized scheduling of the clean energy according to the scheduling decision ideality evaluation result and the boundary factor ideality evaluation result based on the loss amount of the clean energy.
In this embodiment, the boundary factor ideality and the scheduling decision ideality act together to generate the amount of clean energy lost in scheduling operation. In the decision process of the scheduling operator, the reasonability of the boundary data can be evaluated according to the operation experience of the scheduling operator, and in order to accurately evaluate the condition that the scheduling operator researches and judges the boundary data, the evaluation process of the clean energy optimization scheduling benefit in the embodiment of the invention is stated as follows:
(1) If the value of the boundary factor ideality of the whole network is positive and the value of the scheduling decision ideality is negative, it is indicated that scheduling operators prejudge the boundary data prediction deviation, and the reason for causing the scheduling operation clean energy loss is mainly attributed to the prediction deviation of various types of boundary data, and the clean energy loss caused by various types of boundary factors is distributed according to the boundary factor ideality proportion of the boundary factors in the whole network, and can be expressed as follows:
in the formula (I), the compound is shown in the specification,
i.e. boundary factor b i The amount of clean energy loss to be borne.
(2) If the value of the boundary factor ideality of the whole network is negative and the value of the scheduling decision ideality is positive, the result shows that the prediction deviation of scheduling operation personnel on the boundary data is opposite to the actual result, and the reason for causing the loss amount of the clean energy in scheduling operation is mainly attributed to the fact that the scheduling decision is not accurate enough, and the loss amount of the clean energy caused by the inaccurate scheduling decision is the whole network, and can be expressed as follows:
CV D ＝CV A (8)
in the formula, CV D I.e. the amount of clean energy lost due to the scheduling decision.
(3) If the values of the boundary factor ideality of the whole network and the scheduling decision ideality are the same, the scheduling decision and the boundary data deviation are shown to influence the operation condition, the loss amount of clean energy in scheduling operation is shared according to the ideality proportion, and can be expressed as:
in the embodiment of the invention, clean energy loss CV is scheduled to run A The method can be used for evaluating the benefit of the whole scheduling operation in the clean energy optimization scheduling, and the larger the numerical value of the method is, the worse the scheduling operation is in the aspect of promoting the clean energy consumption; and the influence of internal scheduling decision and boundary factors is determined according to the clean energy loss borne by the internal scheduling decisionQuantitative assessment analysis of the loss.
The optimal scheduling model in the embodiment of the invention refers to an optimal scheduling operation scheme which can be realized in a certain scene. It should be noted that the optimal scheduling operation schemes that can be realized under different scenarios are different, and theoretically, if the manual decision of the scheduling operator is completely accurate and the boundary data on which the decision analysis is based is completely accurate, the obtained scheduling operation scheme should have the optimal clean energy consumption effect, i.e., the optimal scheduling model in the present invention. However, due to the deviation of the manual decision of the scheduling operator and the prediction deviation of each item of boundary data, the scheduling operation scheme issued by the actual execution of the scheduling operation generally has a difference from the optimal scheduling model.
The method for optimizing, scheduling and evaluating the clean energy provided by the embodiment of the invention comprises the following steps: constructing an actual data optimal scheduling model according to actual data and a preset rule of a target power grid; evaluating the benefit of the scheduled and operated clean energy according to the actual data optimal scheduling model to obtain the loss amount of the clean energy; respectively constructing a scheduling decision optimal scheduling model and a boundary factor optimal scheduling model; obtaining a scheduling decision ideality evaluation result according to the scheduling decision optimal scheduling model; obtaining a boundary factor ideality evaluation result according to the boundary factor optimal scheduling model; and acquiring a comprehensive evaluation result of the optimized scheduling of the clean energy according to the scheduling decision ideality evaluation result and the boundary factor ideality evaluation result based on the loss amount of the clean energy. According to the method, the influence of the scheduling operation service on the consumption of the clean energy can be quantified by comparing the effect difference of the optimal scheduling model and the actual scheme under different scenes, the influence degree of each key link in the scheduling service can be analyzed in detail, and the method has an obvious effect of improving the accuracy and comprehensiveness of the evaluation result of the optimal scheduling benefit of the clean energy.
Based on a general inventive concept, the embodiment of the invention also provides a device for optimizing, scheduling and evaluating the clean energy.
Fig. 2 is a schematic structural diagram of a device for optimizing scheduling and evaluating clean energy according to an embodiment of the present invention, and referring to fig. 2, the device according to the embodiment of the present invention may include the following structures: a first construction module 21, an acquisition module 22, a second construction module 23, a first evaluation module 24, a second evaluation module 25 and a comprehensive evaluation module 26;
the first construction module 21 is configured to construct an actual data optimal scheduling model according to actual data of the target power grid and a preset rule;
the obtaining module 22 is configured to evaluate the benefit of the scheduled clean energy according to the optimal scheduling model of the actual data, and obtain the loss amount of the clean energy;
a second constructing module 23, configured to respectively construct a scheduling decision optimal scheduling model and a boundary factor optimal scheduling model;
the first evaluation module 24 is configured to obtain a scheduling decision ideality evaluation result according to the scheduling decision optimal scheduling model;
the second evaluation module 25 is configured to obtain a boundary factor ideality evaluation result according to the boundary factor optimal scheduling model;
and the comprehensive evaluation module 26 is used for obtaining a comprehensive evaluation result of the optimized dispatching of the clean energy according to the evaluation result of the ideality of the dispatching decision and the evaluation result of the ideality of the boundary factor based on the loss amount of the clean energy.
Optionally, the target power grid comprises a wind, light and fire mutual aid mode power grid; the actual data of the target power grid are boundary data of an actual data optimal scheduling model; the actual data optimal scheduling model constructed by the first construction module is as follows:
wherein the formula (1) is an optimization target,
wind curtailment power and light curtailment power of a wind power plant w and a photovoltaic power plant p time period t respectively, and NT is dispatching operationDeciding the number of optimization time sections, wherein delta T is a corresponding time interval, NW and NP are the number of a power grid wind power plant and a photovoltaic power plant respectively; the formula (2) is a constraint condition,
sequentially generating planned power of a wind power plant w, a photovoltaic power station p and a thermal power plant c time period t,
the actual load power of the node b in the time period t, NC and NB are the number of thermal power generating units of the power grid and the number of load nodes respectively,
the upper limit and the lower limit of the power flow of the operation section s; GSDF s,w 、GSDF s,p 、GSDF s,c 、GSDF s,b Sequentially comprises a wind power plant w, a photovoltaic power station p, a thermal power plant c, a load node b and a power flow transfer distribution factor of the operation section,
respectively the electricity generatable power in actual operating situations of the wind farm w and of the photovoltaic power station p for a period t,
respectively is the wind abandoning rate and the light abandoning rate in the time period t,
respectively the maximum output and the minimum output of the thermal power generating unit c,
the maximum and minimum climbing capacities of the thermal power generating unit c are respectively.
With regard to the apparatus in the above embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment related to the method, and will not be described in detail here.
According to the device for optimizing, scheduling and evaluating the clean energy, the actual data optimal scheduling model is constructed according to the actual data and the preset rule of the target power grid; evaluating the benefit of the scheduled and operated clean energy according to the actual data optimal scheduling model to obtain the loss amount of the clean energy; respectively constructing a scheduling decision optimal scheduling model and a boundary factor optimal scheduling model; obtaining an evaluation result of the scheduling decision ideality according to the scheduling decision optimal scheduling model; obtaining a boundary factor ideality evaluation result according to the boundary factor optimal scheduling model; and acquiring a comprehensive evaluation result of the optimized scheduling of the clean energy according to the scheduling decision ideality evaluation result and the boundary factor ideality evaluation result based on the loss amount of the clean energy. According to the method, the influence of the scheduling operation service on the consumption of the clean energy can be quantified by comparing the effect difference of the optimal scheduling model and the actual scheme in different scenes, the influence degree of each key link in the scheduling service can be analyzed in detail, and the method has a remarkable effect of improving the accuracy and comprehensiveness of the evaluation result of the optimal scheduling benefit of the clean energy.
Based on a general inventive concept, the embodiment of the invention also provides a device for optimizing, scheduling and evaluating the clean energy.
Fig. 3 is a schematic structural diagram of an apparatus for optimizing scheduling and evaluating clean energy according to an embodiment of the present invention, and referring to fig. 3, the apparatus for optimizing scheduling and evaluating clean energy according to an embodiment of the present invention includes: a processor 31 and a memory 32 connected to the processor.
The memory 32 is used for storing a computer program, and the computer program is at least used for the method for optimizing, scheduling and evaluating the clean energy according to any one of the above embodiments;
the processor 31 is used to invoke and execute the computer program in the memory.
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.
It is understood that the same or similar parts in the above embodiments may be mutually referred to, and the same or similar parts in other embodiments may be referred to for the content which is not described in detail in some embodiments.
It should be noted that the terms "first," "second," and the like in the description of the present invention are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. In addition, in the description of the present invention, the meaning of "a plurality" means at least two unless otherwise specified.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps in the process, and alternate implementations are included within the scope of the preferred embodiment of the present invention in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present invention.
It should be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or combination of the following technologies, which are well known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
It will be understood by those skilled in the art that all or part of the steps carried by the method for implementing the above embodiments may be implemented by hardware related to instructions of a program, which may be stored in a computer readable storage medium, and when the program is executed, the program includes one or a combination of the steps of the method embodiments.
In addition, functional units in the embodiments of the present invention may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a separate product, may also be stored in a computer-readable storage medium.
The storage medium mentioned above may be a read-only memory, a magnetic or optical disk, etc.
In the description of the specification, reference to the description of "one embodiment," "some embodiments," "an example," "a specific example," or "some examples" or the like means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made to the above embodiments by those of ordinary skill in the art within the scope of the present invention.
Claims (8)
1. A method for optimizing, scheduling and evaluating clean energy is characterized by comprising the following steps:
constructing an actual data optimal scheduling model according to actual data and a preset rule of a target power grid;
evaluating the benefit of scheduling and operating clean energy according to the actual data optimal scheduling model to obtain the loss of the clean energy;
respectively constructing a scheduling decision optimal scheduling model and a boundary factor optimal scheduling model;
obtaining a scheduling decision ideality evaluation result according to the scheduling decision optimal scheduling model;
obtaining a boundary factor ideality evaluation result according to the boundary factor optimal scheduling model;
based on the loss amount of the clean energy, obtaining a comprehensive evaluation result of the optimized scheduling of the clean energy according to the scheduling decision ideality evaluation result and the boundary factor ideality evaluation result;
the target power grid comprises a wind, light and fire mutual aid mode power grid;
the actual data of the target power grid are boundary data of the actual data optimal scheduling model;
the optimal scheduling model of the actual data comprises the following steps:
wherein the formula (1) is an optimization target,
wind curtailment power and light curtailment power of a wind power plant w and a photovoltaic power station p time period T are respectively, NT is the number of scheduling operation decision optimization time periods, delta T is a corresponding time interval, NW and NP are the number of a power grid wind power plant and a photovoltaic power station respectively; the formula (2) is a constraint condition,
sequentially generating planned power of a wind power plant w, a photovoltaic power station p and a thermal power plant c time period t,
the actual load power of the node b in the time period t, NC and NB are the number of the thermal power generating units of the power grid and the number of the load nodes respectively,
the upper limit and the lower limit of the power flow of the operation section s; GSDF (Global System for function & data function) s,w 、GSDF s,p 、GSDF s,c 、GSDF s,b Sequentially comprises a wind power plant w, a photovoltaic power station p, a thermal power plant c, a load node b and a power flow transfer distribution factor of the operation section,
respectively the electricity generatable power in actual operating situations of the wind farm w and of the photovoltaic power station p for a period t,
respectively is the wind abandoning rate and the light abandoning rate in the time period t,
respectively the maximum output and the minimum output of the thermal power generating unit c,
the maximum and minimum climbing capacities of the thermal power generating unit c are respectively.
2. The method of claim 1, wherein the amount of clean energy lost is expressed as:
wherein, CV is A The amount of loss of clean energy, E, for combined action R,W,A 、E R,P,A
Respectively the total abandoned wind electric quantity and the total abandoned light electric quantity which actually occur in the scheduling decision period,
and solving the wind and light abandoning electric quantity under the actual data optimal scheduling model.
3. The method of claim 2, wherein the boundary data of the scheduling decision optimal scheduling model is a predicted value of a scheduling decision stage.
4. The method of claim 3, wherein the scheduling decision desirability evaluation result comprises: scheduling decision ideality index PD D ；
PD D ＝(E D,W,A +E D,P,A )-(E PD,W,A +E PD,P,A ) (4)
In the formula, PD D For scheduling decisions ideality index, E D,W,A 、E D,P,A Wind power curtailment quantity and light power curtailment quantity in decision period in scheduling operation scheme actually issued by scheduling operation personnel respectively, E PD,W,A 、E PD,P,A And solving the obtained abandoned wind electric quantity and abandoned light electric quantity for the optimal scheduling model of the scheduling decision.
5. The method of claim 4, wherein the boundary factor desirability assessment result comprises: boundary factor b i Index of degree of ideality
In the formula (I), the compound is shown in the specification,
is a boundary factor b i The index of the degree of ideality is,
solving the obtained abandoned wind electric quantity and abandoned light electric quantity respectively for a single boundary factor optimal scheduling model corresponding to the boundary factors;
the ideality of the boundary factors of the whole network is the sum of the idealities of all the boundary factors, and is as follows:
in the formula, PD B Is the ideality of the boundary factor of the whole network, b i E B represents all the boundary factor items belonging to the boundary factor set B.
6. The method of claim 5, wherein the optimal scheduling of the clean energy comprehensive evaluation result comprises:
if the value of the boundary factor ideality of the whole network is positive and the value of the scheduling decision ideality is negative, it is indicated that scheduling operators prejudge the boundary data prediction deviation, and the reason for causing the scheduling operation clean energy loss is mainly attributed to the prediction deviation of various types of boundary data, and the clean energy loss caused by various types of boundary factors is distributed according to the boundary factor ideality proportion of the boundary factors in the whole network, and can be expressed as follows:
in the formula (I), the compound is shown in the specification,
i.e. boundary factor b i The amount of clean energy loss to be borne;
if the value of the boundary factor ideality of the whole network is negative and the value of the scheduling decision ideality is positive, the result shows that the prediction deviation of scheduling operation personnel on the boundary data is opposite to the actual result, and the reason for causing the loss amount of the clean energy in scheduling operation is mainly attributed to the fact that the scheduling decision is not accurate enough, and the loss amount of the clean energy caused by the inaccurate scheduling decision is the whole network, and can be expressed as follows:
CV D ＝CV A (8)
in the formula, CV D The loss amount of the clean energy which is supposed to be born by the scheduling decision;
if the value mathematics of the boundary factor ideality of the whole network is the same as that of the scheduling decision ideality, the scheduling decision and the boundary data deviation both affect the operation condition, and the loss of clean energy in scheduling operation is apportioned according to the ideality proportion, which can be expressed as:
7. a device for optimizing, scheduling and evaluating clean energy is characterized by comprising: the system comprises a first construction module, an acquisition module, a second construction module, a first evaluation module, a second evaluation module and a comprehensive evaluation module;
the first construction module is used for constructing an actual data optimal scheduling model according to actual data and a preset rule of a target power grid;
the obtaining module is used for evaluating the efficiency of the scheduled and operated clean energy according to the actual data optimal scheduling model and obtaining the loss amount of the clean energy;
the second construction module is used for respectively constructing a scheduling decision optimal scheduling model and a boundary factor optimal scheduling model;
the first evaluation module is used for obtaining an evaluation result of the scheduling decision ideality according to the scheduling decision optimal scheduling model;
the second evaluation module is used for obtaining a boundary factor ideality evaluation result according to the boundary factor optimal scheduling model;
the comprehensive evaluation module is used for obtaining a comprehensive evaluation result of the optimized dispatching of the clean energy according to the evaluation result of the ideality of the dispatching decision and the evaluation result of the ideality of the boundary factor based on the loss amount of the clean energy;
the target power grid comprises a wind-light-fire mutual aid mode power grid; the actual data of the target power grid are boundary data of the actual data optimal scheduling model; the actual data optimal scheduling model constructed by the first construction module is as follows:
wherein the formula (1) is an optimization target,
wind curtailment power and light curtailment power of a wind power plant w and a photovoltaic power station p time period T are respectively, NT is the number of scheduling operation decision optimization time periods, delta T is a corresponding time interval, NW and NP are the number of a power grid wind power plant and a photovoltaic power station respectively; the formula (2) is a constraint condition,
sequentially generating planned power of a wind power plant w, a photovoltaic power station p and a thermal power plant c time period t,
the actual load power of the node b in the time period t, NC and NB are the number of the thermal power generating units of the power grid and the number of the load nodes respectively,
the upper limit and the lower limit of the power flow of the operation section s are set; GSDF s,w 、GSDF s,p 、GSDF s,c 、GSDF s,b Sequentially comprises a wind power plant w, a photovoltaic power station p, a thermal power plant c, a load node b and a power flow transfer distribution factor of the operation section,
respectively the generated power in actual operating conditions of the wind farm w and of the photovoltaic power station p for a period t,
respectively a wind curtailment rate and a light curtailment rate in a time period t,
respectively the maximum output and the minimum output of the thermal power generating unit c,
the maximum and minimum climbing capacities of the thermal power generating unit c are respectively.
8. A device for optimizing, scheduling and evaluating clean energy is characterized by comprising: a processor, and a memory coupled to the processor;
the memory is used for storing a computer program, and the computer program is at least used for executing the clean energy optimization scheduling evaluation method of any one of claims 1 to 6;
the processor is used for calling and executing the computer program in the memory.
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Citations (3)
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| CN105429184A (en) * | 2015-12-11 | 2016-03-23 | 国电南瑞科技股份有限公司 | Overall process quantification assessment method of wind power priority scheduling evaluation |
| CN109802447A (en) * | 2019-01-30 | 2019-05-24 | 广东工业大学 | A kind of CCHP system call evaluation method |
| CN111327046A (en) * | 2018-12-14 | 2020-06-23 | 国网能源研究院有限公司 | Economic dispatching method for power system containing large-scale wind power scene |
Family Cites Families (2)
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| CN103577892A (en) * | 2013-10-30 | 2014-02-12 | 河海大学 | Progressive intelligent power distribution system scheduling method |
| US10816235B2 (en) * | 2017-04-27 | 2020-10-27 | Johnson Controls Technology Company | Building energy system with predictive control of battery and green energy resources |
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Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105429184A (en) * | 2015-12-11 | 2016-03-23 | 国电南瑞科技股份有限公司 | Overall process quantification assessment method of wind power priority scheduling evaluation |
| CN111327046A (en) * | 2018-12-14 | 2020-06-23 | 国网能源研究院有限公司 | Economic dispatching method for power system containing large-scale wind power scene |
| CN109802447A (en) * | 2019-01-30 | 2019-05-24 | 广东工业大学 | A kind of CCHP system call evaluation method |
Non-Patent Citations (1)
| Title |
|---|
| 计及多能源多需求响应手段的园区综合能源系统优化调度模型;李鹏等;《电力建设》;20200531;全文 * |
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