CN110544044A - edge collaborative calculation method for distributed power supply to time-sharing electricity price power generation response - Google Patents
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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 power flow and the direction of the power grid has certain randomness, so that the traditional power flow calculation method is not applicable any more; the distributed power supply is connected to a power distribution network by an inverter based on a power electronic technology, and is different from the traditional power grid in a mode, and the frequent switching of a switching device easily generates harmonic components near the switching frequency, so that the high-frequency and higher harmonic pollution to the power grid is easily caused. 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 response of a distributed power supply to time-sharing electrovalence power generation, 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-layer distributed power supply by using edge calculation.
the overall scheduling objective function in the step 1) is as follows:
Wherein C is the total cost of the distributed power supply cluster, IDG is the number of the distributed power supplies, fi is the cost function of the ith distributed power supply, and the quadratic term coefficient, the linear term coefficient and the constant term coefficient are ai, bi and ci respectively; 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:
The lower limit and the upper limit of the exchange power are respectively 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:
The Pdemand is a total power demand issued by the dispatching center, and corresponding power needs to be generated by the distributed power supply cluster 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, is the total operating cost of the ith distributed power supply; pi is the planned power generation amount of the ith distributed power supply; ci (Pi) is a power generation cost function of the ith distributed power source; is the power generation amount of the ith distributed power supply, which is equal to the power generation response function PDG (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 device respectively, wherein the numerical value is positive during discharging and negative 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 sum respectively represents 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:
the sum is respectively the maximum charging power and the minimum charging power of the ith distributed power supply energy storage device, and is respectively the maximum discharging power and the minimum discharging power of the ith distributed power supply energy storage device, and Bi is the battery storage state of the ith distributed power supply energy storage device, and is respectively the upper limit and the lower limit of the battery storage state.
The electricity generation response function PDG (t) is
Pdg (t) represents a power generation response function of the distributed power supply at time t, and has the unit: kw · h; Δ pff (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; Δ pgf (t) represents the amount of power generation transfer from the flat period of time of power rate to the peak period before and after time of power rate, in units of: kw · h; Δ pgg (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, unit: kw · h; Δ pgf (t) represents the amount of power generation transfer from the trough period to the peak period before and after the time of use of electricity, in units: kw · h; tf, Tp, Tg denote a power rate peak period, a flat period, and a valley period, respectively.
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 generation cluster edge collaborative optimization calculation method establishes a distributed power source 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:
wherein, C is the total cost of the distributed power supply cluster, IDG is the number of distributed power supplies, fi is the cost function of the ith distributed power supply, and is usually a quadratic function, and its quadratic term coefficient, linear term coefficient and constant term coefficient are ai, bi and ci, respectively. And for exchanging power between the ith distributed power supply and the distributed power supply cluster, if so, the distributed power supply cluster supplies power to the distributed power supply, and if not, the distributed power supply supplies power to the distributed power supply 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:
the lower limit and the upper limit of the exchange power are respectively determined by the transmission limit of the power line formed by clustering the distributed power supplies among the distributed power supplies;
2) Total power demand constraints:
The Pdemand is a total power demand issued by the dispatching center, and corresponding power needs to be generated by the distributed power supply cluster to meet the demand.
and the lower 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 price of electricity sold in the peak time is high, the price of electricity sold in the valley time is low, and the price of electricity sold in the flat 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:
PDG (t) represents a power generation response function of the distributed power supply at time t and has a unit kw · h; delta Pff (t) represents the increment of self power generation of the power supply in the time-of-use price front and rear peak periods, and the unit kw.h; Δ pgf (t) represents the amount of power generation transition from the flat time period to the peak time period of the power rates before and after the time of use, in kw · h; delta Pgg (t) represents the reduction amount of the self power generation of the power supply in the valley period before and after the time-of-use electricity price, and the unit kw.h; Δ pgf (t) represents the amount of power generation transfer from the trough period to the peak period before and after the time of use of electricity, in kw · h; tf, Tp, Tg denote a power rate peak period, a flat period, and a valley period, respectively.
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; ρ 0 represents the electricity price before the time-of-use electricity price change; d0 represents the amount of generated electricity before the time-of-use electricity price is implemented.
The change of the generated energy before and after implementation of the time-of-use electricity price is determined by the own reduction amount and the transfer amount of the distributed power supply, wherein the own reduction amount of the distributed power supply corresponds to the self-elasticity coefficient, and the transfer amount corresponds to the cross-elasticity coefficient. Let t1 be the time at which the power generation change amount is to be obtained after the implementation of the time-of-use electricity price, t2 be the sampling time in the power generation curve, where the expression of the elastic coefficient here is EP (t1, t2), if the time t1 to be obtained is the sampling time t2, the elastic coefficient at this time is defined as the self elastic coefficient, and EP (t1, t2) at this time is greater than or equal to 0, otherwise, if t1 is not equal to t2, the elastic coefficient at this time is defined as the cross elastic coefficient, and EP (t1, t2) at this time is less than or equal to 0. Assuming that the data dimension of the power generation curve is N, the power generation change amount at time t1 before and after the time-of-use power price is:
In the formula, P0(t1) is the power generation amount of a single distributed power supply at time t 1; ρ 0(t2) is the electricity rate at time t2 before the time of day electricity rate implementation; ρ (t2) is the electricity rate at time t2 after the time-of-use electricity rate is implemented.
Let λ P (t1) denote the rate of change in the amount of power generated by a single distributed power supply at time t1 after time-of-use electricity prices have been implemented, then:
Let k (t2) represent the floating ratio of electricity prices before and after the time-of-use electricity price at time t2, and is defined as:
Equation (7) can be rewritten as:
therefore, when t1 and t2 belong to the peak, flat and valley periods of the electricity price, respectively, the rate of change of the amount of electricity generation is:
Wherein λ pf, λ gf and λ gp represent the power generation transition rate; λ ff represents an increase rate of self-generation in a peak period; λ gg represents the reduction rate of self-generation in the valley period; because kf is greater than 0 in the peak period of the electrovalence, kp is less than 0 in the flat period, kg is less than 0 in the valley period of the electrovalence, and the cross elasticity coefficient EP (t1, t2) is less than or equal to 0, the lambda pf, the lambda gf and the lambda gp are positive numbers; since the self-elastic coefficient EP (t1) ≧ 0, λ ff >0 and 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, is the total operating cost of the ith distributed power supply; pi is the planned power generation amount of the ith distributed power supply; ci (Pi) is a power generation cost function of the ith distributed power source; is the power generation amount of the ith distributed power supply, which is equal to the power generation response function PDG (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 device respectively, wherein the numerical value is positive during discharging and negative during charging.
The power flow constraint is as follows:
wherein, the sum respectively represents 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:
The sum is respectively the maximum charging power and the minimum charging power of the ith distributed power supply energy storage device, and is respectively the maximum discharging power and the minimum discharging power of the ith distributed power supply energy storage device, and Bi is the battery storage state of the ith distributed power supply energy storage device, and is respectively the upper limit and the lower limit 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 a 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 to obtain a correction solution according to a time-of-use electricity price electricity generation response strategy, and returns to the upper layer, wherein the time-of-use electricity price electricity generation response strategy is designed according to the time-of-use electricity price strategy for the distributed power supplies 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 (6)
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) and performing time-sharing configuration on the power generation of the lower-layer distributed power supply by using edge calculation.
2. the edge collaborative calculation method for the distributed power supply to time-of-use power price power generation response according to claim 1, characterized in that: the overall scheduling objective function in the step 1) is as follows:
Wherein C is the total cost of the distributed power supply cluster, IDG is the number of the distributed power supplies, fi is the cost function of the ith distributed power supply, and the quadratic term coefficient, the linear term coefficient and the constant term coefficient are ai, bi and ci respectively; the power is exchanged between the ith distributed power source and the distributed power source cluster.
3. the edge collaborative calculation method for the distributed power supply to time-of-use power price power generation response according to claim 1, characterized in that: 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:
The lower limit and the upper limit of the exchange power are respectively 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:
The Pdemand is a total power demand issued by the dispatching center, and corresponding power needs to be generated by the distributed power supply cluster to meet the demand.
4. The edge collaborative calculation method for the distributed power supply to time-of-use power price power generation response according to claim 1, characterized in that: 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, is the total operating cost of the ith distributed power supply; pi is the planned power generation amount of the ith distributed power supply; ci (Pi) is a power generation cost function of the ith distributed power source; is the power generation amount of the ith distributed power supply, which is equal to the power generation response function PDG (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 device respectively, wherein the numerical value is positive during discharging and negative during charging.
5. The edge collaborative calculation method for the distributed power supply to time-of-use power price power generation response according to claim 1, characterized in that: 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 sum respectively represents 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:
The sum is respectively the maximum charging power and the minimum charging power of the ith distributed power supply energy storage device, and is respectively the maximum discharging power and the minimum discharging power of the ith distributed power supply energy storage device, and Bi is the battery storage state of the ith distributed power supply energy storage device, and is respectively the upper limit and the lower limit of the battery storage state.
6. the edge collaborative calculation method for the distributed power supply to time-of-use power price power generation response according to claim 4, characterized in that: the electricity generation response function PDG (t) is
pdg (t) represents a power generation response function of the distributed power supply at time t, and has the unit: kw · h; Δ pff (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; Δ pgf (t) represents the amount of power generation transfer from the flat period of time of power rate to the peak period before and after time of power rate, in units of: kw · h; Δ pgg (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, unit: kw · h; Δ pgf (t) represents the amount of power generation transfer from the trough period to the peak period before and after the time of use of electricity, in units: kw · h; tf, Tp, Tg denote a power rate peak period, a flat period, and a valley period, respectively.
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