CN110544044B - Edge collaborative calculation method for distributed power supply to time-sharing electricity price power generation response - Google Patents
Edge collaborative calculation method for distributed power supply to time-sharing electricity price power generation response Download PDFInfo
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Abstract
The invention discloses an edge collaborative computing method for a distributed power supply to respond to time-sharing electricity price power generation. The invention realizes the coordinated operation among the distributed power supplies by using a scheduling algorithm of multiple decision makers. Meanwhile, the time-of-use electricity price power generation response is carried out by utilizing the edge calculation, so that the distributed power supply edge calculation terminal equipment has the capability of local regulation, the utilization rate of the distributed power supply and the power supply reliability of a user are effectively improved, the configuration of power resources is optimized, and the operation economy of a power grid is improved.
Description
Technical Field
The invention relates to an edge collaborative computing method for a distributed power supply to time-sharing electricity price power generation response, and belongs to the field of distributed power generation.
Background
The worldwide continuously tense energy situation promotes the application and development of clean energy. Distributed power generation technology based on renewable energy has become the key point of research for realizing energy diversification. Under the social big background that energy resources are gradually exhausted and the environment is rapidly deteriorated, the appearance of the distributed power supply provides a new clean natural energy utilization mode for people, the organic combination of the distributed power supply and a large power grid is a feasible mode for reducing energy consumption and improving the reliability and flexibility of a power system, and the distributed power supply is also a direction for realizing sustainable green development of the power industry in China. But at the same time, the distributed power supply with high permeability brings great challenges to the safe and stable operation and economic dispatch of the power grid.
When the power system is in operation, due to the influence of randomness and fluctuation of the distributed power supply, it becomes very difficult to maintain real-time frequency and voltage balance; uncertainty of the operation mode and the control characteristic of the distributed power supply can cause that the change of the size and the direction of the power flow of the power grid has certain randomness, so that the traditional power flow calculation method is not applicable any more; the distributed power supply adopts an inverter based on a power electronic technology to be connected into a power distribution network, and is different from the traditional power grid in a mode, harmonic components near the switching frequency are easily generated by frequent switching of a switching device, and high-frequency and high-order harmonic pollution is easily caused to the power grid. When the power system is in a fault period, the distributed power system does not detect a power failure state and cuts off the distributed power system, but continues to supply power and peripheral loads to form an uncontrollable self-supply power island phenomenon, the generation of the island effect may influence the power quality and even damage electrical equipment, and the personal safety of maintenance personnel may be endangered in a serious case.
Disclosure of Invention
The purpose of the invention is as follows: the invention provides an edge collaborative calculation method for time-shared electricity price power generation response of a distributed power supply, which is used for effectively monitoring a large number of distributed power supplies and implementing operation management.
The technical scheme is as follows: the invention adopts the technical scheme that the edge collaborative computing method for the distributed power supply to the time-sharing electrovalence power generation response comprises an upper distributed power supply cluster control center and a lower distributed power supply edge computing terminal device, and comprises the following steps:
1) Setting a total scheduling objective function and constraint conditions of an upper distributed power cluster control center;
2) Implementing time-of-use electricity price;
3) And performing time-sharing configuration on the power generation of the lower distributed power supply by using edge calculation.
The overall scheduling objective function in the step 1) is as follows:
where C is the total cost of the distributed power cluster, I DG Is the number of distributed power supplies, f i The cost function of the ith distributed power supply is a of a coefficient of a quadratic term, a coefficient of a linear term and a coefficient of a constant term i 、b i 、c i ；
The power is exchanged between the ith distributed power source and the distributed power source cluster.
The constraint conditions in the step 1) are as follows:
the upper and lower limits of the exchange power are restricted, and the upper and lower limits of the exchange power between the distributed power supply cluster and the distributed power supply i are restricted:
wherein, the first and the second end of the pipe are connected with each other,
the lower limit and the upper limit of the exchange power are determined by the transmission limit of the power line formed by clustering the distributed power supplies among the distributed power supplies;
total power demand constraints:
wherein, P demand For the total power demand issued by the dispatch center, the distributed power clusters need to generate corresponding power to meet the demand.
The time-sharing configuration in the step 3) takes the minimum running cost of a single distributed power supply as an objective function, and the objective function is as follows:
wherein the content of the first and second substances,
is the total operating cost of the ith distributed power supply; p i Is the planned power generation of the ith distributed power supply; c i (P i ) Is a power generation cost function of the ith distributed power supply;
is the power generation amount of the ith distributed power supply, which is equal to the power generation response function P DG (t)；
Is a factor of the distributed power supply operating and maintenance costs;
is a factor of the operating and maintenance costs of the energy storage device;
and
the values of the discharge power and the charge power of the energy storage device are positive during discharge and negative during charge respectively.
In the step 3), the time-sharing configuration takes power flow constraint, distributed power supply output constraint and energy storage equipment charging and discharging constraint as constraint conditions,
the power flow constraint is as follows:
wherein, the first and the second end of the pipe are connected with each other,
and
respectively representing the charging and discharging efficiency of the energy storage equipment in the ith distributed power supply.
Representing the load carried at the ith distributed power source;
and (3) limiting the output upper limit and the output lower limit of the ith distributed power supply:
and (3) charge and discharge constraint of the ith distributed power supply energy storage device:
wherein, the first and the second end of the pipe are connected with each other,
and
maximum and minimum charging power for the ith distributed power energy storage device,
and
maximum and minimum discharge power of the ith distributed power supply energy storage device, B i For the battery storage status of the ith distributed power energy storage device,
and
respectively, the upper and lower limits of the battery storage state.
The power generation response function P DG (t) is
In the formula, P DG (t) represents the power generation response function of the distributed power supply at time t, unit: kw · h; delta P ff (t) represents the increment of the self power generation of the power supply in the time-of-use price pre-peak and post-peak periods, and the unit is as follows: kw · h; delta P gf (t) represents the amount of power generation transfer from the flat period of the electricity prices to the peak period before and after the time of use of the electricity prices, in units of: kw · h; delta P gg (t) represents the reduction amount of the power generated by the power supply in the valley period before and after the time-of-use electricity price, and the unit is: kw · h; delta P gf (t) represents a power generation transfer amount from the electricity rate valley period to the peak period before and after the time of use of electricity rate, in units of: kw · h; t is f 、T p 、T g Respectively represent a peak period, a flat period, and a valley period of electricity rates.
Has the advantages that: the invention realizes the coordinated operation among the distributed power supplies by using a scheduling algorithm of multiple decision makers. Meanwhile, the time-of-use electricity price power generation response is carried out by utilizing the edge calculation, so that the distributed power supply edge calculation terminal equipment has the capability of local regulation, the utilization rate of the distributed power supply and the power supply reliability of a user are effectively improved, the configuration of power resources is optimized, and the operation efficiency of a power grid is improved.
Drawings
FIG. 1 is a flow chart of an edge collaborative calculation method of distributed power supply to time-of-use electricity price generation response in accordance with the present invention;
fig. 2 is a schematic structural diagram of a distributed power cluster optimization management system based on edge cooperative computing according to the present invention.
Detailed Description
The present invention is further illustrated by the following figures and specific examples, which are to be understood as illustrative only and not as limiting the scope of the invention, which is to be given the full breadth of the appended claims and any and all equivalent modifications thereof which may occur to those skilled in the art upon reading the present specification.
The distributed power supply cluster has the characteristics of centralized upper-layer scheduling targets and distributed lower-layer calculation. Therefore, aiming at the characteristic, the distributed power generation cluster edge collaborative optimization calculation method establishes a distributed power generation cluster two-layer optimization scheduling model. The upper layer is a distributed power supply cluster control center which sets up a total dispatching target according to the power grid requirement; the lower layer is distributed power supply edge computing terminal equipment, and grid-connected power of the distributed power supply is correspondingly adjusted by adopting edge computing and according to a time-of-use electricity price power generation response strategy.
The time-of-use electricity price generation response strategy is defined according to the time-of-use electricity price concept for the distributed power supplies, which is proposed herein, that is, only the distributed power supplies in the power grid implement time-of-use electricity prices, and other types of power supplies are still fixed electricity prices. Decision makers on the upper layer and the lower layer interact with each other through respective decision variables, and coordinate operation among the distributed power supplies is achieved by adopting an upper-layer and lower-layer iteration solving strategy.
And the upper-layer distributed power cluster control center sets a total scheduling objective function according to the power grid requirements, and establishes a target optimization scheduling model of the distributed power cluster by taking the minimum total power generation cost as a target and the upper and lower limits of the exchange power and the total power requirements as constraint conditions.
Overall scheduling objective function:
where C is the total cost of the distributed power cluster, I DG Is the number of distributed power supplies, f i The cost function of the ith distributed power supply is usually a quadratic function, and the coefficient of the quadratic term, the coefficient of the linear term and the coefficient of the constant term of the quadratic function are respectively a i 、b i 、c i 。
For the exchange power between the ith distributed power supply and the distributed power supply cluster, if so
The distributed power cluster supplies power to the distributed power if
The distributed power source powers the distributed power cluster.
The constraint conditions include:
1) And the upper and lower limits of the exchange power between the distributed power supply cluster and the distributed power supply i are constrained as follows:
wherein the content of the first and second substances,
respectively a lower limit and an upper limit value of the exchange power,they are determined by the transmission limits of the power lines of the distributed power clusters between the distributed power sources;
2) Total power demand constraints:
wherein, P demand For the total power demand issued by the dispatch center, the distributed power clusters need to generate corresponding power to meet the demand.
And the lower-layer distributed power supply edge computing terminal equipment adopts time-of-use electricity price power generation response, and performs time-of-use configuration on the power generation of the distributed power supply by using edge computing. Meanwhile, the edge calculation is optimized and adjusted by taking the minimum total operation cost of the ith distributed power supply as a target and adding the power flow constraint, the distributed power supply output constraint and the energy storage equipment charging and discharging constraint as constraint conditions.
The concept of time-of-use electricity price is to coordinate the supply of power from a power supply end and relieve the power utilization pressure. The time of day is divided into peak time, flat time and valley time according to the number of the power loads, the electricity price sold in the peak time is high, the electricity price sold in the valley time is low, and the electricity price sold in the ordinary time is medium. Therefore, power generation can be encouraged at the peak of power utilization to help relieve power utilization tension, higher economic benefits can be obtained, and the power supply situation is more reasonable in consideration of the power supply side.
The time-of-use electricity price power generation response is based on the incentive strategy of the time-of-use electricity price dividing time period, the power generation amount is increased in the electricity price peak time period, and the power generation amount is reduced in the electricity price valley time period, so that the power supply condition in the peak time period can be relieved, the resource allocation is more reasonable, and higher economic benefit can be obtained.
In this embodiment, the power generation response function of the user at time t is defined as: driven by a time-of-use electricity price excitation mechanism, and in the process of realizing time-of-use electricity generation, the electricity generation response quantity of a single distributed power supply at the time t is obtained.
Defining the response quantity of the distributed power supply in the time-sharing power generation process in the peak period of the electricity price as the increment of the distributed power supply before and after the time-sharing power generation; defining the response quantity of the distributed power supply in the electricity price flat period in the time-sharing electricity generation process as the electricity generation transfer quantity from the valley period to the flat period; the response quantity of the distributed power supply defining the valley period in the time-sharing power generation process is composed of a reduction quantity of self power generation and a transfer quantity transferred to the peak period. According to the above definition, the power generation response functions of the peak, flat and valley periods of the power rate can be expressed as:
in the formula, P DG (t) represents the power generation response function of the distributed power supply at the time t, and the unit kw & h; delta P ff (t) represents the increment of self power generation of the power supply in the time interval of front and back peaks of the time-of-use price, and the unit kw.h; delta P gf (t) represents the amount of power generation transfer from the flat time period of the electricity prices to the peak time period before and after the time of use of electricity prices, in kw · h; delta P gg (t) represents the reduction of the self power generation of the power supply in the valley time before and after the time-of-use electricity price, and the unit kw.h; delta P gf (t) represents the amount of power generation transfer from the trough period to the peak period of the power rates before and after the time of use of electricity, in kw · h; t is a unit of f 、T p 、T g Respectively represent a peak period, a flat period, and a valley period of electricity rates.
The elastic coefficient concept is introduced, and the elastic coefficient of power generation is defined as the percentage representing the power generation amount change caused by the change of the electricity price in a certain period. Is formulated as follows:
wherein E is the elastic coefficient of power generation; Δ ρ is a change in electricity price in units: yuan/kw.h; Δ d represents a change in power generation amount due to a price change; rho 0 Representing the electricity price before the time-of-use electricity price is changed; d 0 The power generation amount before the time-of-use electricity price is shown.
The change of the generated energy before and after the implementation of the time-of-use electricity price is determined by the reduction amount and the transfer amount of the distributed power supply, wherein the division isThe amount of the distributed power supply is reduced corresponding to the self-elasticity coefficient, and the amount of the transfer is corresponding to the cross-elasticity coefficient. Note t 1 Time t to be determined for the implementation of the change in power generation after the time of use price 2 For the sampling time in the power generation curve, the elastic coefficient is expressed here as E P (t 1 ,t 2 ) If the time t is to be obtained 1 I.e. the sampling instant t 2 Then define the elastic coefficient at this time as the self-elastic coefficient, and E at this time P (t 1 ,t 2 ) Not less than 0, otherwise, if t 1 ≠t 2 Then define the elastic coefficient at this time as the cross elastic coefficient, and E at this time P (t 1 ,t 2 ) Less than or equal to 0. Assuming that the data dimension of the power generation curve is N, t is around the time-of-use price 1 The power generation change amount at the time is:
in the formula, P 0 (t 1 ) Is t 1 The power generation capacity of a single distributed power supply at a moment; rho 0 (t 2 ) To implement the time-of-use price of electricity t 2 The electricity price at the moment; ρ (t) 2 ) To implement time-of-use electricity price t 2 The electricity price at the moment.
Let lambda P (t 1 ) Showing a single distributed power supply at t after the time of day of electricity 1 The power generation amount change rate at the moment is as follows:
let k (t) 2 ) Denotes t 2 The floating ratio of the electricity prices before and after the time-of-use electricity price is defined as:
equation (7) can be rewritten as:
therefore, when t is 1 、t 2 When the electricity price peak, the flat and the valley time periods are respectively belonged to, the change rate of the generated energy is as follows:
in the formula, λ pf 、λ gf 、λ gp Represents the power generation transfer rate; lambda [ alpha ] ff Represents the increase rate of self-generation in the peak period; lambda [ alpha ] gg The reduction rate of self-generation in the valley period is represented; due to the peak period of the electricity price, k f >0, flat period, k p <0, at the electricity valley period k g <0, and cross elastic coefficient E P (t 1 ,t 2 ) Not more than 0, so λ pf 、λ gf 、λ gp Are all positive numbers; due to the coefficient of self-elasticity E P (t 1 ) Not less than 0, so ff >0，λ gg <0。
Based on the analysis of the power generation amount change at the time-of-use electricity price and the power generation response function definition at the peak, flat and valley periods of the electricity price, the specific calculation formula of the power generation response amount is as follows:
and then, optimally adjusting the constraint conditions of the minimum total operation cost of the ith distributed power supply and the load flow constraint, the distributed power supply output constraint and the energy storage equipment charging and discharging constraint.
The objective function is the minimum running cost, and the expression is as follows:
wherein the content of the first and second substances,
is the total operating cost of the ith distributed power supply; p i Is the planned power generation of the ith distributed power supply; c i (P i ) Is the power generation cost function of the ith distributed power supply;
is the power generation amount of the ith distributed power supply, which is equal to the power generation response function P DG (t)；
Is a factor of the distributed power supply operating and maintenance costs;
is a factor of the operating and maintenance costs of the energy storage device;
and
the values of the discharge power and the charge power of the energy storage device are positive during discharge and negative during charge respectively.
The power flow constraint is as follows:
wherein the content of the first and second substances,
and
respectively representing the charging and discharging efficiency of the energy storage equipment in the ith distributed power supply.
Representing the load carried at the ith distributed power source.
And (3) limiting the output upper limit and the output lower limit of the ith distributed power supply:
and (3) charge and discharge constraint of the ith distributed power supply energy storage device:
wherein the content of the first and second substances,
and
maximum and minimum charging power for the ith distributed power energy storage device,
and
maximum and minimum discharge power, B, respectively, for the ith distributed power source energy storage device i For the battery storage status of the ith distributed power energy storage device,
and
respectively, the upper and lower limits of the battery storage state.
Aiming at the established optimization model, decision makers in each layer set corresponding decision variables according to the characteristics of the decision makers. The upper layer (distributed power supply cluster layer) takes the total generated energy of the distributed power supply cluster as a decision variable; the lower layer (distributed power supply layer) takes the power generation power of each distributed power supply and the charging (generating) capacity of the storage battery as decision variables.
And adopting upper and lower layer interactive iteration to solve the two-layer optimized scheduling model. The distributed power source cluster control center (upper layer) firstly calculates an initial solution according to the global target and the considered constraint condition, and inputs the initial solution into the distributed power source edge computing terminal equipment (lower layer); the edge calculation terminal equipment of each distributed power supply takes upper-layer input as an initial value to carry out edge calculation, calculates a correction solution according to a local target and a considered constraint condition, and returns the correction solution to a cluster control center (an upper layer); the cluster control center takes the returned correction solution as an initial value, calculates the optimization solution which accords with the global target again, and inputs the optimization solution into each distributed power source edge computing terminal device at the lower layer; each distributed power supply edge calculation terminal device takes the initial value as well, carries out edge calculation according to a time-of-use electricity price generation response strategy to obtain a correction solution, and returns to the upper layer, wherein the time-of-use electricity price generation response strategy is designed according to the time-of-use electricity price strategy for the distributed power supplies, which is conceived in the text; the upper layer and the lower layer are iterated repeatedly until the iteration termination conditions of each layer are met, the global and local targets are considered, and coordinated operation among a plurality of distributed power supplies is achieved.
Claims (1)
1. The edge collaborative computing method for the distributed power supply to the time-sharing electricity price power generation response comprises an upper distributed power supply cluster control center and a lower distributed power supply edge computing terminal device, and is characterized in that: comprises the following steps:
1) Setting a total scheduling objective function and constraint conditions of an upper distributed power cluster control center;
2) Implementing time-of-use electricity price;
3) Performing time-sharing configuration on the power generation of the lower distributed power supply by utilizing edge calculation;
the overall scheduling objective function in the step 1) is as follows:
where C is the total cost of the distributed power cluster, I DG Is the number of distributed power supplies, f i The cost function of the ith distributed power supply is a of a coefficient of a quadratic term, a coefficient of a linear term and a coefficient of a constant term i 、b i 、c i ；
The power is exchanged between the ith distributed power supply and the distributed power supply cluster;
the constraint conditions in the step 1) are as follows:
the upper and lower limits of the exchange power are restricted, and the upper and lower limits of the exchange power between the distributed power supply cluster and the distributed power supply i are restricted:
wherein the content of the first and second substances,
the lower limit and the upper limit of the exchange power are determined by the transmission limit of the power line formed by clustering the distributed power supplies among the distributed power supplies;
total power demand constraints:
wherein, P demand For the total power demand issued by the dispatching center, the distributed power supply cluster needs to send out corresponding powerForce to meet demand;
the time-sharing configuration in the step 3) takes the minimum running cost of a single distributed power supply as an objective function, and the objective function is as follows:
wherein, the first and the second end of the pipe are connected with each other,
is the total operating cost of the ith distributed power supply; p i Is the planned power generation of the ith distributed power supply; c i (P i ) Is a power generation cost function of the ith distributed power supply;
is the power generation amount of the ith distributed power supply, which is equal to the power generation response function P DG (t)；
Is a factor of the distributed power supply operating and maintenance costs;
is a factor of the operating and maintenance costs of the energy storage device;
and
the power for discharging and charging the energy storage equipment respectively has a positive value during discharging and a negative value during charging;
in the step 3), the time-sharing configuration takes power flow constraint, distributed power supply output constraint and energy storage equipment charging and discharging constraint as constraint conditions,
the power flow constraint is as follows:
wherein the content of the first and second substances,
and
respectively showing the charging and discharging efficiency of the energy storage device in the ith distributed power supply,
representing the load carried at the ith distributed power source;
and (3) limiting the output upper limit and the output lower limit of the ith distributed power supply:
and (3) charge and discharge constraint of the ith distributed power supply energy storage device:
wherein the content of the first and second substances,
and
maximum and minimum charging power for the ith distributed power energy storage device,
and
maximum and minimum discharge power of the ith distributed power supply energy storage device, B i For the battery storage status of the ith distributed power energy storage device,
and
upper and lower limits of the battery storage state, respectively;
the power generation response function P DG (t) is
In the formula, P DG (t) represents the power generation response function of the distributed power supply at time t, unit: kw · h; delta P ff (t) represents the increment of the self power generation of the power supply in the time-of-use price pre-peak and post-peak periods, and the unit is as follows: kwh; delta P gf (t) represents the amount of power generation transfer from the flat period of the electricity prices to the peak period before and after the time of use of the electricity prices, in units of: kw · h; delta P gg (t) represents the reduction amount of the power generated by the power supply in the valley period before and after the time-of-use electricity price, and the unit is: kw · h; delta P gf (t) represents a power generation transfer amount from the electricity price valley period to the peak period before and after the time of use of electricity, unit: kw · h; t is f 、T p 、T g Respectively represent a peak period, a flat period, and a valley period of electricity rates.
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