CN114202169A - Multi-energy scheduling method for active peak shaving of high-energy-consumption load - Google Patents

Multi-energy scheduling method for active peak shaving of high-energy-consumption load Download PDF

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CN114202169A
CN114202169A CN202111374771.3A CN202111374771A CN114202169A CN 114202169 A CN114202169 A CN 114202169A CN 202111374771 A CN202111374771 A CN 202111374771A CN 114202169 A CN114202169 A CN 114202169A
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power
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consumption load
load
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梁凯
宁辽逸
刘宇
贺欢
焦振
祝湘博
杜亮
王�琦
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Anshan Power Supply Co Of State Grid Liaoning Electric Power Co
State Grid Corp of China SGCC
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State Grid Corp of China SGCC
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Abstract

The invention relates to a multi-energy scheduling method for high-energy-consumption load active peak regulation, which is characterized by collecting power consumption influence parameters of high-energy-consumption load users as a database, processing the power consumption influence parameters to uniformly form useful power and useless power samples of electric energy, establishing a power consumption control model of the high-energy-consumption load users by utilizing a neural network algorithm, calculating the pre-used power consumption of the high-energy-consumption load users according to the control model, comparing the pre-used power consumption with the reserved power generated by wind, nuclear, water and fire engine set energy sources, and establishing an overall economic optimal power generation scheme by utilizing a multi-dimensional constraint optimization method according to the operation characteristics and environmental benefits of a power grid to realize the optimal multi-energy scheduling method for active peak regulation; the power utilization cost is greatly saved, sudden power failure accidents of high-energy-consumption load users are reduced, the consumption capacity of clean energy is improved, and four unit power generation conditions are reasonably arranged.

Description

Multi-energy scheduling method for active peak shaving of high-energy-consumption load
Technical Field
The invention relates to the technical field of power system regulation and control, in particular to a multi-energy scheduling method for active peak shaving of high-energy-consumption load.
Background
In recent years, with the continuous consumption of energy resources and the increasingly prominent environmental problems, the development of wind power generation, photovoltaic power generation and nuclear power generation is increased in all countries around the world. With the increase of the scale of new energy, the influence of grid-connected operation of a large-scale new energy power plant on a power system is more and more obvious. Due to the randomness and the intermittent characteristics of wind/light energy, the variation trend of the generated output of a new energy power plant is difficult to predict, the operation scheduling of a power grid is difficult and complicated, and the safe and stable operation of the power grid is greatly influenced, so that the method becomes a key technical problem for restricting the large-scale access of clean energy. In order to solve these contradictions, the improvement of the clean energy utilization rate by the client flexible load control technology will become an important development direction in the future. Considering the development status, the operation characteristics and the role played in the Liaoning power grid of the high-energy-consumption load with representative significance in the user end load in the Liaoning power grid, the method has certain rationality when being used as the demonstration project of the user end flexible load control technology; the coupling relation between the high-energy-consumption industry and the power industry is fully developed, and continuous transformation upgrading and mutual profit development of the two industries can be realized; meanwhile, the utilization rate of clean energy is continuously improved under the background of high-proportion new energy access.
The thought must be changed for high energy consumption, and the original management level is changed to participate in peak clipping and valley filling. The power consumption during the peak time of the power grid and the wave valley of the power grid can be avoided by adjusting the time interval of equipment maintenance, improving the utilization rate of equipment and the like. The original equipment maintenance mode is generally arranged at a fixed time period or in the daytime, the original maintenance mode can be changed for enterprises with changed electricity prices, and equipment maintenance is arranged at the electricity price peak time and the power grid load peak time, so that the power grid peak can be staggered to realize peak clipping. The load curve shows the daily electricity consumption of the statistical unit by a curve, and the peak valley and the electricity consumption rule of the statistical unit can be clearly seen through the daily load curve. And a corresponding power utilization plan is made according to the peak valley and the rule of the curve, so that the power utilization cost can be effectively saved. Under the condition that the power utilization of an enterprise is not known, a power utilization plan is implemented blindly, and under the condition that the power fluctuation is large, the situations of over-power and electricity generation coexist easily, so that the production and economic benefits of the enterprise are influenced, and the efficiency and the safety of a power grid are influenced. In order to ensure the potential of power generation and supply equipment and the safe and reasonable operation of the equipment, the load of a power grid must be balanced and stable. In order to reduce the load fluctuation of the power grid and the power utilization unit, scientific scheduling is required. When the load of the power grid is low, that is, the power consumption of the user is allowed, the power grid may be in a valley period of the load of the user, and in this case, the capacity of the power generation equipment cannot be fully utilized, which causes great waste. When the load capacity of the power grid is high, the load capacity of the user suddenly increases on the contrary, and is in a load peak period, so that the load and unsafe factors of the power grid are increased. The invention provides a multi-energy scheduling method for peak shaving of high energy consumption loads, and the method has important reference value for the participation of the high energy consumption loads in dynamic peak shaving of a power grid.
Disclosure of Invention
The invention provides a multi-energy scheduling method for high-energy-consumption load active peak shaving, which is provided by utilizing a neural network and a multidimensional constraint optimization method and realizes bidirectional linkage regulation and control of high-energy-consumption load users and four generator sets for participating in execution of a power grid scheduling scheme.
In order to achieve the purpose, the invention adopts the following technical scheme:
a multi-energy scheduling method for active peak shaving of high energy consumption load is carried out according to the following steps:
step 1, acquiring the current production and operation situation and grid-connected influence of the high-energy-consumption load, including the current operation situation and technical characteristics of the high-energy-consumption load, acquiring the production mode, the power consumption level and the power consumption habit of the high-energy-consumption load, the management level of a high-energy-consumption load enterprise and the peak regulation capacity of the high-energy-consumption load enterprise in a power grid, and acquiring the influence of the grid-connected operation of the high-energy-consumption load on the Liaoning power grid.
Step 2, acquiring a category division method of a high-energy-consumption load production time sequence and operation characteristics, a management and control method of the whole high-energy-consumption load production operation process and a control process of the high-energy-consumption load with a time delay control potential;
step 3, collecting the in-day electrical external characteristics and the complementary energy recovery of high energy consumption and high load under different production operation scenes of high energy consumption load, and thus constructing a load operation control model represented by the high energy consumption load operation characteristics according to a neural network algorithm;
and 4, establishing a bidirectional linkage strategy for actively participating in the execution of the power grid dispatching scheme by the high-energy-consumption load by utilizing a multidimensional constraint optimization method, and realizing a power grid active load measurement regulation and control method.
Respectively establishing a day-ahead power consumption database under the influence of different parameters according to the collected data in the step 1 and the step 2,
the preconditions need to be satisfied:
Wforecast≤Ww+Wl+Wn+Wf
wherein W is under the same parameter conditionsforecastAs a reserve power of the system, WwFor the pre-generation of electricity, W, of wind turbinesnPre-generation of electricity for nuclear power, WlIs a photoelectric pre-generation quantity of electricity, WfThe pre-generated electricity quantity of the thermal power is used.
The method has the advantages that through the pre-scheduling of the first stage, the adjustment amount of the scheduling of the second stage can be reduced, the operation cost and the fuel loss cost of the next scheduling can be reduced, and particularly for nuclear power, the service life of the method can be prolonged by reducing the adjustment times.
The method comprises the steps of constructing a load operation control model characterized by high energy consumption load operation characteristics according to a neural network algorithm, wherein the neural network comprises an input layer, a hidden layer and an output layer, the number of nodes of the input layer is M, the number of nodes of the hidden layer is q, the number of nodes of the output layer is L, [ x, y ] is a sample p, [ x ═ x1, x2] is an active value and a reactive value of high energy consumption load electric energy, y ═ y1, y2] is an active value and a reactive value of the high energy consumption load after grid connection, reading sample p data, carrying out forward propagation, checking whether the prediction precision of the neural network model meets the preset precision requirement, if not, carrying out backward propagation, then returning to the step of carrying out forward propagation, and if so, finishing the process of learning and training.
The forward propagation includes:
the implicit layer input of the ith neuron under the action of the sample p is as follows:
Figure BDA0003363471190000031
wherein:
Figure BDA0003363471190000032
the input of the input layer node j when the sample P acts; omegaijIs the connection weight between the jth neuron of the input layer and the ith neuron of the hidden layer, thetaiA threshold for the ith neuron of the hidden layer;
the output of the ith neuron of the hidden layer after being acted by the excitation function is as follows:
Figure BDA0003363471190000033
in the formula: f (-) is the hidden layer excitation function;
Figure BDA0003363471190000034
the output of the hidden layer node i when the sample p acts;
when in use
Figure BDA0003363471190000035
When it is, then there are
Figure BDA0003363471190000036
When in use
Figure BDA0003363471190000037
When it is, then there are
Figure BDA0003363471190000038
After the output of the ith neuron of the hidden layer acts through a connection weight between the hidden layer and the neuron of the output layer, a signal is transmitted to the kth neuron of the output layer and is used as one of the inputs of the kth neuron; the total input to the kth neuron of the output layer is:
Figure BDA0003363471190000039
in the formula: omegakiIs the connection weight, σ, between the hidden layer unit i and the output layer unit kkIs the threshold for output layer neuron k; the kth neuron output of the output layer is after the action of the excitation function:
Figure BDA00033634711900000310
Figure BDA00033634711900000311
is the output of the output layer node k when the sample p acts; g (-) is the output layer excitation function;
and (3) taking the active value and the reactive value of the collected high-energy-consumption load data as input, taking the active value and the reactive value after the high-energy-consumption load is connected to the grid as output, training the neural network model, knowing that the prediction precision of the neural network model meets the requirement, and obtaining the high-energy-consumption load operation control model.
The multidimensional constraint optimization method provided by the method is characterized in that constraint conditions affecting high-energy-consumption power supply factors and power utilization factors in a power system can be formulated into different constraint domains according to power generation requirements and power utilization requirements, and the more the constraint quantity is, the more the dimensionality needs to be formulated. The comprehensive energy sources mentioned in the method are four, namely a wind power domain, a fire power domain, a photoelectric domain and a nuclear power domain, and the self electricity utilization factors of the high-energy-consumption load user have the limit influence of the economic benefit and the environmental benefit of the comprehensive self electricity consumption.
The method has the advantages that the multiple power generation systems are independent from each other, the power generation amount is adjusted only by the constraint of the power generation characteristics of the power generation systems, the power generation systems are not related to other power generation energy systems, the adjustment is only carried out on the power generation amount, and the economical and optimal power generation is achieved by distributing the power generation amount of each unit and reducing the adjustment cost. In addition, the self electricity utilization fluctuation factors of each high-energy-consumption load user are used as reverse constraint domains, and the two-way active regulation and control method is realized by carrying out prejudgment on the electricity generation amount of the four comprehensive energy sources.
When the load of a user side and the generated energy of a power generation end change, the generated energy needs to be adjusted by a power supply end, the climbing or landslide rate of a unit is considered on the scheduling rate by applying a multi-dimensional constraint optimization method, and the power constraint and the power generation cost of the unit are considered on the scheduling amount, so that the reliability of power supply and the economic cost constraint guarantee of power generation are guaranteed:
Figure BDA0003363471190000041
Wforefor high energy consumption load enterprises, the comprehensive coefficient of power consumption, WenThe power consumption fluctuates under the influence of environmental benefits.
Figure BDA0003363471190000042
Where λ is the cost per unit of electricity for each unit, WwIs the amount of photoelectric generation, WlIs the amount of photoelectric generation, WfGenerated power for thermal power, WnGenerated energy for nuclear power, CbFor spare capacity cost, WbFor reserve capacity, κ is a coefficient for determining reserve capacity based on electricity demand, γ is a unit cost of reserve capacity for each energy source, Wb,*Is the spare capacity for the participation of energy sources.
And according to the constraint of the multidimensional constraint optimization method, the generated energy lambda of each generator set can be quickly obtained through calculation. At the moment, the optimal power generation cost is only determined, but the start-stop cost and the fuel waste of the unit can be generated when the power generation energy of the unit is adjusted, and deep calculation is needed if the real optimal economy is realized:
the reduction of the light and wind abandoning consumption is as follows:
Figure BDA0003363471190000051
wherein Δ CwAnd Δ ClCost of adjusting wind-solar output, cwIs the price of the unit wind power,
Figure BDA0003363471190000052
reduced waste air volume of each wind turbine, clIs the unit of the price of the photoelectric electricity,
Figure BDA0003363471190000053
the light abandoning amount of each photoelectric unit is reduced.
Adjusting the cost generated during thermal power and nuclear power:
Figure BDA0003363471190000054
wherein Δ CfFor regulating the power of fireCost, ai、bi、ciCoefficients of a quadratic term, a primary term and a constant term respectively,
Figure BDA0003363471190000055
as instantaneous power, CpurThe purchase cost of the unit, Delta N the number of times of use of the unit, CgasFor real-time oil prices,. DELTA.ZgasFor fuel consumption in participating in peak shaving, PminAt the lower limit of the basic peak shaving stage, PmaxAt the upper limit of the basic peak shaving stage, PaLower limit of depth peaking, PbIs the lower limit of peak shaving in oil injection, Delta CnFor the cost generated when nuclear power participates in peak shaving,
Figure BDA0003363471190000056
the cost of the unit loss is high,
Figure BDA0003363471190000057
in the interest of the cost of the nuclear fuel,
Figure BDA0003363471190000058
the cost of manual treatment.
According to the predicted electricity cost of the high-energy-consumption load user and the calculated cost of the four comprehensive units, on one hand, the generated energy of the four units can be adjusted, on the other hand, the electricity utilization rule of the four units can be adjusted according to the high-energy-consumption load user parameter database, and the two-way linkage regulation and control can be participated in, so that the multi-energy scheduling method for actively adjusting the electricity utilization wave peak is realized.
Compared with the prior art, the invention has the beneficial effects that:
1) the high energy consumption load actively participates in the dynamic peak regulation of the power grid, the power consumption cost is saved, and the sudden power failure accident is reduced;
2) the consumption capability of clean energy is improved, and the power generation conditions of the four sets are reasonably arranged.
Drawings
FIG. 1 is a schematic diagram of the present invention.
FIG. 2 is a flow chart of the neural network of the present invention.
FIG. 3 is a flow chart of the multidimensional constraint optimization method of the present invention.
Detailed Description
The following further describes embodiments of the present invention with reference to the accompanying drawings:
fig. 1 is a schematic structural diagram of the present invention. The invention relates to a multi-energy scheduling method for active peak shaving of high energy consumption load, which comprises the following steps:
step 1, acquiring the current production and operation situation and grid-connected influence of the high-energy-consumption load, including the current operation situation and technical characteristics of the high-energy-consumption load, acquiring the production mode, the power consumption level and the power consumption habit of the high-energy-consumption load, the management level of a high-energy-consumption load enterprise and the peak regulation capacity of the high-energy-consumption load enterprise in a power grid, and acquiring the influence of the grid-connected operation of the high-energy-consumption load on the Liaoning power grid.
Step 2, acquiring a category division method of a high-energy-consumption load production time sequence and operation characteristics, a management and control method of the whole high-energy-consumption load production operation process and a control process of the high-energy-consumption load with a time delay control potential;
step 3, collecting the in-day electrical external characteristics and the complementary energy recovery of high energy consumption and high load under different production operation scenes of high energy consumption load, and thus constructing a load operation control model represented by the high energy consumption load operation characteristics according to a neural network algorithm;
and 4, establishing a bidirectional linkage strategy for actively participating in the execution of the power grid dispatching scheme by the high-energy-consumption load by utilizing a multidimensional constraint optimization method, and realizing a power grid active load measurement regulation and control method.
Respectively establishing a day-ahead power consumption database under the influence of different parameters according to the collected data in the step 1 and the step 2,
the preconditions need to be satisfied:
Wforecast≤Ww+Wl+Wn+Wf
wherein W is under the same parameter conditionsforecastAs a reserve power of the system, WwFor the pre-generation of electricity, W, of wind turbinesnPre-generation of electricity for nuclear power, WlIs a photoelectric pre-generation quantity of electricity, WfThe pre-generated electricity quantity of the thermal power is used.
The method has the advantages that through the pre-scheduling of the first stage, the adjustment amount of the scheduling of the second stage can be reduced, the operation cost and the fuel loss cost of the next scheduling can be reduced, and particularly for nuclear power, the service life of the method can be prolonged by reducing the adjustment times.
Introducing nuclear power to actively participate in peak regulation: in the process of active participation of nuclear power in peak shaving, the regulation frequency of the nuclear power is strictly required, the nuclear power actively participates in the peak shaving, the pre-generation quantity of the nuclear power can be calculated by determining the pre-power consumption in advance and predicting the pre-generation quantity of clean energy such as wind power, photoelectricity and the like according to weather forecast, the regulation frequency of the nuclear power within a specified number can be reduced, the cost of fuel loss can be reduced, and meanwhile, the safety of nuclear power generation energy is improved.
The method comprises the steps of constructing a load operation control model characterized by high energy consumption load operation characteristics according to a neural network algorithm, wherein the neural network comprises an input layer, a hidden layer and an output layer, the number of nodes of the input layer is M, the number of nodes of the hidden layer is q, the number of nodes of the output layer is L, [ x, y ] is a sample p, [ x ═ x1, x2] is an active value and a reactive value of high energy consumption load electric energy, y ═ y1, y2] is an active value and a reactive value of the high energy consumption load after grid connection, reading sample p data, carrying out forward propagation, checking whether the prediction precision of the neural network model meets the preset precision requirement, if not, carrying out backward propagation, then returning to the step of carrying out forward propagation, and if so, finishing the process of learning and training.
The forward propagation includes:
the implicit layer input of the ith neuron under the action of the sample p is as follows:
Figure BDA0003363471190000071
wherein:
Figure BDA0003363471190000072
the input of the input layer node j when the sample P acts; omegaijIs the connection weight between the jth neuron of the input layer and the ith neuron of the hidden layer, thetaiA threshold for the ith neuron of the hidden layer;
the output of the ith neuron of the hidden layer after being acted by the excitation function is as follows:
Figure BDA0003363471190000073
in the formula: f (-) is the hidden layer excitation function;
Figure BDA0003363471190000074
the output of the hidden layer node i when the sample p acts;
when in use
Figure BDA0003363471190000075
When it is, then there are
Figure BDA0003363471190000076
When in use
Figure BDA0003363471190000077
When it is, then there are
Figure BDA0003363471190000078
After the output of the ith neuron of the hidden layer acts through a connection weight between the hidden layer and the neuron of the output layer, a signal is transmitted to the kth neuron of the output layer and is used as one of the inputs of the kth neuron; the total input to the kth neuron of the output layer is:
Figure BDA0003363471190000079
in the formula: omegakiIs the connection weight, σ, between the hidden layer unit i and the output layer unit kkIs the threshold for output layer neuron k; first of the output layerThe k neuron outputs are, after being acted upon by the stimulus function:
Figure BDA00033634711900000710
Figure BDA00033634711900000711
is the output of the output layer node k when the sample p acts; g (-) is the output layer excitation function;
output of the system and given training output
Figure BDA00033634711900000712
If the error is inconsistent, the weight is connected through an error back-propagation process for correction, and the neural network enters the error back-propagation process, wherein an error function is as follows:
Figure BDA0003363471190000081
the weight coefficients should be adjusted in the opposite direction of the gradient of the J function. According to the steepest gradient descent method, the trimming formula of each neuron weight coefficient of the output layer is as follows:
Figure BDA0003363471190000082
and (3) taking the active value and the reactive value of the collected high-energy-consumption load data as input, taking the active value and the reactive value after the high-energy-consumption load is connected to the grid as output, training the neural network model, knowing that the prediction precision of the neural network model meets the requirement, and obtaining the high-energy-consumption load operation control model.
The multidimensional constraint optimization method provided by the method is characterized in that constraint conditions affecting high-energy-consumption power supply factors and power utilization factors in a power system can be formulated into different constraint domains according to power generation requirements and power utilization requirements, and the more the constraint quantity is, the more the dimensionality needs to be formulated. The comprehensive energy sources mentioned in the method are four, namely a wind power domain, a fire power domain, a photoelectric domain and a nuclear power domain, and the self electricity utilization factors of the high-energy-consumption load user have the limit influence of the economic benefit and the environmental benefit of the comprehensive self electricity consumption.
The method has the advantages that the multiple power generation systems are independent from each other, the power generation amount is adjusted only by the constraint of the power generation characteristics of the power generation systems, the power generation systems are not related to other power generation energy systems, the adjustment is only carried out on the power generation amount, and the economical and optimal power generation is achieved by distributing the power generation amount of each unit and reducing the adjustment cost. In addition, the self electricity utilization fluctuation factors of each high-energy-consumption load user are used as reverse constraint domains, and the two-way active regulation and control method is realized by carrying out prejudgment on the electricity generation amount of the four comprehensive energy sources.
When the load of a user side and the generated energy of a power generation end change, the generated energy needs to be adjusted by a power supply end, the climbing or landslide rate of a unit is considered on the scheduling rate by applying a multi-dimensional constraint optimization method, and the power constraint and the power generation cost of the unit are considered on the scheduling amount, so that the reliability of power supply and the economic cost constraint guarantee of power generation are guaranteed:
Figure BDA0003363471190000083
Wforefor high energy consumption load enterprises, the comprehensive coefficient of power consumption, WenThe power consumption fluctuates under the influence of environmental benefits.
Figure BDA0003363471190000084
Where λ is the cost per unit of electricity for each unit, WwIs the amount of photoelectric generation, WlIs the amount of photoelectric generation, WfGenerated power for thermal power, WnGenerated energy for nuclear power, CbFor spare capacity cost, WbFor reserve capacity, κ is a coefficient for determining reserve capacity based on electricity demand, γ is a unit cost of reserve capacity for each energy source, Wb,*Is the spare capacity for the participation of energy sources.
And according to the constraint of the multidimensional constraint optimization method, the generated energy lambda of each generator set can be quickly obtained through calculation. At the moment, the optimal power generation cost is only determined, but the start-stop cost and the fuel waste of the unit can be generated when the power generation energy of the unit is adjusted, and deep calculation is needed if the real optimal economy is realized:
the reduction of the light and wind abandoning consumption is as follows:
Figure BDA0003363471190000091
wherein Δ CwAnd Δ ClCost of adjusting wind-solar output, cwIs the price of the unit wind power,
Figure BDA0003363471190000092
reduced waste air volume of each wind turbine, clIs the unit of the price of the photoelectric electricity,
Figure BDA0003363471190000093
the light abandoning amount of each photoelectric unit is reduced.
Adjusting the cost generated during thermal power and nuclear power:
Figure BDA0003363471190000094
wherein Δ CfTo adjust the thermal power costs, ai、bi、ciCoefficients of a quadratic term, a primary term and a constant term respectively,
Figure BDA0003363471190000095
as instantaneous power, CpurThe purchase cost of the unit, Delta N the number of times of use of the unit, CgasFor real-time oil prices,. DELTA.ZgasFor fuel consumption in participating in peak shaving, PminAt the lower limit of the basic peak shaving stage, PmaxAt the upper limit of the basic peak shaving stage, PaLower limit of depth peaking, PbIs the lower limit of peak shaving in oil injection, Delta CnFor nuclear powerAnd the cost generated during peak regulation,
Figure BDA0003363471190000096
the cost of the unit loss is high,
Figure BDA0003363471190000097
in the interest of the cost of the nuclear fuel,
Figure BDA0003363471190000098
the cost of manual treatment.
Under the condition of reasonably scheduling the power generation of four sets, the scheduling principle needs to meet the following conditions:
(1) when the load capacity is larger than the pre-generation capacity, the following requirements are met:
min(△Cf+△Cn+△Cw+△Cl+Cfj) At this time, the power consumption is required to meet the load with the lowest power generation cost;
(2) when the load is less than the pre-generation quantity, the following requirements are met:
max(△Cf+△Cn+△Cw+△Cl-Cfj)>and 0, in this case, the power generation cost of the power supply end is reduced when the load power consumption is met, but the scheduling is allowed only if the cost saved by scheduling minus the additional cost in scheduling is positive, otherwise, the cost is increased when the scheduling is involved, namely, the scheduling execution is not allowed.
According to the predicted electricity cost of the high-energy-consumption load user and the calculated cost of the four comprehensive units, on one hand, the generated energy of the four units can be adjusted, on the other hand, the electricity utilization rule of the four units can be adjusted according to the high-energy-consumption load user parameter database, and the two-way linkage regulation and control can be participated in, so that the multi-energy scheduling method for actively adjusting the electricity utilization wave peak is realized.
The following examples are carried out on the premise of the technical scheme of the invention, and detailed embodiments and specific operation processes are given, but the scope of the invention is not limited to the following examples. The methods used in the following examples are conventional methods unless otherwise specified.
[ examples ] A method for producing a compound
As shown in fig. 1, a multi-energy scheduling method for active peak shaving of high energy consumption load is performed according to the following steps:
step 1, acquiring the current production and operation situation and grid-connected influence of high energy consumption load, including the current operation situation and technical characteristics of the high energy consumption load, acquiring the production mode, power consumption level and power consumption habit of the high energy consumption load, the management level of a high energy consumption load enterprise and the peak regulation capacity of the high energy consumption load enterprise in a power grid, acquiring the influence of the grid-connected operation of the high energy consumption load on the Liaoning power grid, and respectively establishing database samples of all parameters.
And 2, acquiring a category division method of a high energy consumption load production time sequence and operation characteristics, a whole high energy consumption load production operation process control method and a high energy consumption load control process with a time delay control potential, and integrally planning the database samples in the step 1 and the step 2 into active and reactive samples (x, y) of high energy consumption load users.
Step 3, collecting the external electrical characteristics and the residual energy recovery of the high energy consumption and high load in the day under different production operation scenes of the high energy consumption load, thereby constructing a load operation control model characterized by the high energy consumption load operation characteristics according to a BP neural network algorithm, wherein the BP neural network is divided into an input layer, a hidden layer and an output layer, the number of nodes of the input layer is M, the number of nodes of the hidden layer is q, the number of nodes of the output layer is L, [ x, y ] is a sample p, x is [ x1, x2] is the active value and the reactive value of the high energy consumption load, y is [ y1, y2] is the active value and the reactive value of the high energy consumption load after grid connection, reading the data of the sample p, carrying out forward propagation, checking whether the prediction accuracy of the neural network model meets the preset accuracy requirement, if not, carrying out backward propagation, then returning to the step of carrying out forward propagation, if so as to finish the process of learning training, obtaining a high energy consumption load operation control model;
and 4, using a multidimensional constraint optimization method to take the self power consumption and environmental benefit influence of the high-energy-consumption load user as the self constraint condition, using the wind, nuclear, light and fire units as a power supply constraint domain, automatically adjusting the power supply coefficient of the units in time when the power consumption of the high-energy-consumption load user exceeds or is less than 5% of the equivalent range of the unit power supply reserved quantity, automatically finding the optimal and most economical unit power supply scheduling scheme by using the multidimensional constraint optimization, automatically regulating and controlling the power consumption arrangement of the user under the condition of analyzing that the normal production of the high-energy-consumption load user is not influenced, establishing a bidirectional linkage strategy in which the high-energy-consumption load actively participates in the execution of the power grid scheduling scheme, and realizing the power grid measurement active load regulation and control method.

Claims (4)

1. A multi-energy scheduling method for active peak shaving of high energy consumption load is characterized by comprising the following steps:
step 1, acquiring the current production and operation situation and grid-connected influence of the high-energy-consumption load, including the current operation situation and technical characteristics of the high-energy-consumption load, acquiring the production mode, the power consumption level and the power consumption habit of the high-energy-consumption load, the management level of a high-energy-consumption load enterprise and the peak regulation capacity of the high-energy-consumption load enterprise in a power grid, and acquiring the influence of the grid-connected operation of the high-energy-consumption load on the Liaoning power grid.
Step 2, acquiring a category division method of a high-energy-consumption load production time sequence and operation characteristics, a management and control method of the whole high-energy-consumption load production operation process and a control process of the high-energy-consumption load with a time delay control potential;
step 3, collecting the in-day electrical external characteristics and the complementary energy recovery of high energy consumption and high load under different production operation scenes of high energy consumption load, and thus constructing a load operation control model represented by the high energy consumption load operation characteristics according to a neural network algorithm;
and 4, establishing a bidirectional linkage strategy for actively participating in the execution of the power grid dispatching scheme by the high-energy-consumption load by utilizing a multidimensional constraint optimization method, and realizing a power grid active load measurement regulation and control method.
2. The multi-energy scheduling method of active peak shaving with high energy consumption load as claimed in claim 1, wherein the database of the current power consumption under the influence of different parameters is established according to the collected data in step 1 and step 2,
the preconditions need to be satisfied:
Wforecast≤Ww+Wl+Wn+Wf
wherein W is under the same parameter conditionsforecastAs a reserve power of the system, WwFor the pre-generation of electricity, W, of wind turbinesnPre-generation of electricity for nuclear power, WlIs a photoelectric pre-generation quantity of electricity, WfThe pre-generated electricity quantity of the thermal power is used.
The method has the advantages that through the pre-scheduling of the first stage, the adjustment amount of the scheduling of the second stage can be reduced, the operation cost and the fuel loss cost of the next scheduling can be reduced, and particularly for nuclear power, the service life of the method can be prolonged by reducing the adjustment times.
3. The multi-energy scheduling method of active peak shaving with high energy consumption load according to claim 1, wherein step three is to construct a load operation control model for characterizing the operation characteristics of the high energy consumption load according to a neural network algorithm,
the neural network comprises an input layer, a hidden layer and an output layer, wherein the number of nodes of the input layer is M, the number of nodes of the hidden layer is q, the number of nodes of the output layer is L, [ x, y ] is a sample p, [ x ═ x1, x2] is an active value and a reactive value of high-energy-consumption load electric energy, y ═ y1, and y2] is an active value and a reactive value of high-energy-consumption load after grid connection, the data of the sample p are read and transmitted forwards, whether the prediction precision of the neural network model meets the preset precision requirement is checked, if not, the neural network model is transmitted backwards, then the step of transmitting forwards is returned, and if so, the process of learning and training is finished.
The forward propagation includes:
the implicit layer input of the ith neuron under the action of the sample p is as follows:
Figure FDA0003363471180000021
wherein:
Figure FDA0003363471180000022
the input of the input layer node j when the sample P acts; omegaijIs the connection weight between the jth neuron of the input layer and the ith neuron of the hidden layer, thetaiA threshold for the ith neuron of the hidden layer;
the output of the ith neuron of the hidden layer after being acted by the excitation function is as follows:
Figure FDA0003363471180000023
in the formula: f (-) is the hidden layer excitation function;
Figure FDA0003363471180000024
the output of the hidden layer node i when the sample p acts;
when in use
Figure FDA0003363471180000025
When it is, then there are
Figure FDA0003363471180000026
When in use
Figure FDA0003363471180000027
When it is, then there are
Figure FDA0003363471180000028
After the output of the ith neuron of the hidden layer acts through a connection weight between the hidden layer and the neuron of the output layer, a signal is transmitted to the kth neuron of the output layer and is used as one of the inputs of the kth neuron; the total input to the kth neuron of the output layer is:
Figure FDA0003363471180000029
in the formula: omegakiFor the hidden layer unit i and the output layerConnection weight between cells k, σkIs the threshold for output layer neuron k; the kth neuron output of the output layer is after the action of the excitation function:
Figure FDA00033634711800000210
Figure FDA00033634711800000211
is the output of the output layer node k when the sample p acts; g (-) is the output layer excitation function;
and (3) taking the active value and the reactive value of the collected high-energy-consumption load data as input, taking the active value and the reactive value after the high-energy-consumption load is connected to the grid as output, training the neural network model, knowing that the prediction precision of the neural network model meets the requirement, and obtaining the high-energy-consumption load operation control model.
4. The multi-energy scheduling method for active peak shaving of high energy consumption load according to claim 1, wherein the multi-dimensional constraint optimization method proposed in the method is to make different constraint domains according to the power generation requirement and the power consumption requirement by using constraint conditions affecting high energy consumption power supply factors and power consumption factors in the power system, and the more the constraint quantity, the more the number of dimensions to be made. The comprehensive energy sources mentioned in the method are four, namely a wind power domain, a fire power domain, a photoelectric domain and a nuclear power domain, and the self electricity utilization factors of the high-energy-consumption load user have the limit influence of the economic benefit and the environmental benefit of the comprehensive self electricity consumption.
The method has the advantages that the multiple power generation systems are independent from each other, the power generation amount is adjusted only by the constraint of the power generation characteristics of the power generation systems, the power generation systems are not related to other power generation energy systems, the adjustment is only carried out on the power generation amount, and the economical and optimal power generation is achieved by distributing the power generation amount of each unit and reducing the adjustment cost. In addition, the self electricity utilization fluctuation factors of each high-energy-consumption load user are used as reverse constraint domains, and the two-way active regulation and control method is realized by carrying out prejudgment on the electricity generation amount of the four comprehensive energy sources.
When the load of a user side and the generated energy of a power generation end change, the generated energy needs to be adjusted by a power supply end, the climbing or landslide rate of a unit is considered on the scheduling rate by applying a multi-dimensional constraint optimization method, and the power constraint and the power generation cost of the unit are considered on the scheduling amount, so that the reliability of power supply and the economic cost constraint guarantee of power generation are guaranteed:
Figure FDA0003363471180000035
Wforefor high energy consumption load enterprises, the comprehensive coefficient of power consumption, WenThe power consumption fluctuates under the influence of environmental benefits.
Figure FDA0003363471180000031
Where λ is the cost per unit of electricity for each unit, WwIs the amount of photoelectric generation, WlIs the amount of photoelectric generation, WfGenerated power for thermal power, WnGenerated energy for nuclear power, CbFor spare capacity cost, WbFor reserve capacity, κ is a coefficient for determining reserve capacity based on electricity demand, γ is a unit cost of reserve capacity for each energy source, Wb,*Is the spare capacity for the participation of energy sources.
And according to the constraint of the multidimensional constraint optimization method, the generated energy lambda of each generator set can be quickly obtained through calculation. At the moment, the optimal power generation cost is only determined, but the start-stop cost and the fuel waste of the unit can be generated when the power generation energy of the unit is adjusted, and deep calculation is needed if the real optimal economy is realized:
the reduction of the light and wind abandoning consumption is as follows:
Figure FDA0003363471180000032
wherein Δ CwAnd Δ ClCost of adjusting wind-solar output, cwIs the price of the unit wind power,
Figure FDA0003363471180000033
reduced waste air volume of each wind turbine, clIs the unit of the price of the photoelectric electricity,
Figure FDA0003363471180000034
the light abandoning amount of each photoelectric unit is reduced.
Adjusting the cost generated during thermal power and nuclear power:
Figure FDA0003363471180000041
wherein Δ CfTo adjust the thermal power costs, ai、bi、ciCoefficients of a quadratic term, a primary term and a constant term respectively,
Figure FDA0003363471180000042
as instantaneous power, CpurThe purchase cost of the unit, Delta N the number of times of use of the unit, CgasFor real-time oil prices,. DELTA.ZgasFor fuel consumption in participating in peak shaving, PminAt the lower limit of the basic peak shaving stage, PmaxAt the upper limit of the basic peak shaving stage, PaLower limit of depth peaking, PbIs the lower limit of peak shaving in oil injection, Delta CnFor the cost generated when nuclear power participates in peak shaving,
Figure FDA0003363471180000043
the cost of the unit loss is high,
Figure FDA0003363471180000044
in the interest of the cost of the nuclear fuel,
Figure FDA0003363471180000045
the cost of manual treatment.
According to the predicted electricity cost of the high-energy-consumption load user and the calculated cost of the four comprehensive units, on one hand, the generated energy of the four units can be adjusted, on the other hand, the electricity utilization rule of the four units can be adjusted according to the high-energy-consumption load user parameter database, and the two-way linkage regulation and control can be participated in, so that the multi-energy scheduling method for actively adjusting the electricity utilization wave peak is realized.
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