CN116780644A - Method for cooperatively participating in response of peak shaving demands of power grid by micro-grid source and storage - Google Patents
Method for cooperatively participating in response of peak shaving demands of power grid by micro-grid source and storage Download PDFInfo
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Abstract
The invention relates to the technical field of power systems, in particular to a method for cooperatively participating in responding to power grid peak shaving demands by micro-grid source loads, which comprises the steps of analyzing adjustable source load characteristics in a micro-grid; acquiring power grid dispatching based on a flow of the micro power grid participating in the power grid peak shaving dispatching, wherein the demand of reducing peak load or filling valley load is solved in 1 day; establishing a micro-grid source-storage cooperative participation response power grid peak regulation demand model considering flexible load based on source storage characteristics and demands; the invention comprehensively considers three layers of source storage to analyze the characteristic of schedulable resources in the micro-grid, reduces the peak-valley-difference load of the system, reduces the running cost of the system and solves the problem of high running cost of the existing energy storage system participating in peak regulation mode.
Description
Technical Field
The invention relates to the technical field of power systems, in particular to a method for responding to power grid peak shaving demands by cooperative participation of micro-grid source loads.
Background
The peak regulation of the power system requires that the power supply and demand keep dynamic balance, and the peak regulation performance is poor, so that a series of problems such as frequency fluctuation, voltage fluctuation and the like of the power system are caused, and the power generator output power is required to have enough upper and lower limit margins, so that the load increasing and decreasing changing requirements are met. The traditional power supply side mainly based on thermal power has poor flexibility and lower social benefit in participating in peak shaving auxiliary service market. Compared with the traditional coal-fired thermal power generating unit, the peak shaving of the gas turbine has the advantages of small occupied area, simple and convenient installation and water resource saving. However, the operation cost is high, and the method is not suitable for long-term operation of the generator set. The energy storage system takes part in peak regulation, which is to store electric energy when the power generation amount is higher than the power consumption amount of the load and release the electric energy when the power consumption amount of the load is higher than the power generation amount, so as to achieve the purposes of balancing supply and demand, peak clipping and valley filling in time sequence, but the single investment cost is larger. Thereby increasing the operating costs of the system.
Disclosure of Invention
The invention aims to provide a method for cooperatively participating in response of grid peak shaving demands by micro-grid source and storage, and aims to solve the problem of high operation cost of the existing energy storage system participating in peak shaving.
In order to achieve the above purpose, the invention provides a method for responding to power grid peak shaving demands by cooperative participation of micro-grid source storage, which comprises the following steps:
analyzing adjustable source storage characteristics within the microgrid;
acquiring power grid dispatching based on a flow of the micro power grid participating in the power grid peak shaving dispatching, wherein the demand of reducing peak load or filling valley load is solved in 1 day;
establishing a micro-grid source-storage cooperative participation response power grid peak regulation demand model considering flexible load based on the source storage characteristics and the demand;
and solving a mixed integer linear programming problem of the micro-grid source and load cooperative participation response power grid peak regulation demand model to obtain a micro-grid source and load cooperative response peak regulation demand scheme.
Wherein analyzing the adjustable source storage characteristics within the microgrid comprises:
dividing the flexible load participating in the interaction into a load-reducible load, a load-transferable load and a load-translatable load according to the response characteristics of the user;
and respectively analyzing the load capable of being reduced, the load capable of being transferred and the load capable of being translated to obtain source storage characteristics.
The micro-grid participates in a power grid peak shaving scheduling process, and the method comprises the following steps of:
carrying out load prediction by a power grid company, carrying out normalization processing on a predicted system load curve to obtain scheduling information, generating a scheduling instruction based on the scheduling information, and sending the scheduling instruction to a micro-grid comprehensive operator;
after receiving the scheduling instruction, the comprehensive operator of the micro-grid adjusts and optimizes internal resources to participate in peak regulation and pressure relief of the main network;
and the power grid company compensates the micro-grid comprehensive operation business according to the actual peak regulation capacity of the micro-grid.
The establishing a micro-grid source load cooperative participation response power grid peak regulation demand model considering flexible load based on the source load characteristics and the demand comprises the following steps:
based on the source storage characteristics and the requirements, various resources in the micro-grid are considered to optimally allocate and operate strategies, and an objective function can be obtained by taking the minimum system operation cost, minimum wind and light abandon punishment cost and the maximum total amount of the system participating in the peak regulation requirement response of the grid as targets;
carrying out normalization processing on each objective function value by adopting a min-max normalization method to obtain a processing result;
converting the multi-objective optimization problem into a single-objective optimization problem by adopting a linear weighted summation method based on the processing result;
based on the single-objective optimization problem, new energy, energy storage and flexible load characteristics in the micro-grid and the running characteristics of the micro-grid are considered, so that constraint conditions are obtained;
and generating a micro-grid source and storage cooperative participation response power grid peak regulation demand model considering the flexible load based on the constraint condition.
Wherein the constraints include power supply system constraints and flexible load constraints.
According to the method for the cooperative participation of the source charges of the micro-grid in responding to the peak shaving demand of the grid, the adjustable source charge characteristics in the micro-grid are analyzed; acquiring power grid dispatching based on a flow of the micro power grid participating in the power grid peak shaving dispatching, wherein the demand of reducing peak load or filling valley load is solved in 1 day; establishing a micro-grid source-storage cooperative participation response power grid peak regulation demand model considering flexible load based on the source storage characteristics and the demand; the micro-grid source-storage-load cooperative participation response power grid peak regulation demand model considering the flexible load is comprehensively considered to analyze the characteristic of schedulable resources in the micro-grid from three layers of source storage, so that the peak-valley-difference load of a system is reduced, the running cost of the system is reduced, and the peak regulation demand of the response power grid is participated. The decision variables and the constraint conditions in the model comprehensively consider the source-storage-load characteristics, fully consider the electricity utilization characteristics of different users, and provide references for relieving the peak regulation pressure of the power grid under the novel power system. The problem of the running cost of the mode that current energy storage system participated in peak shaving is high is solved.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic diagram of a micro-grid structure.
Fig. 2 is a schematic diagram of load prediction in a certain area.
Fig. 3 is a schematic diagram of the microgrid operation results of scheme 2.
Fig. 4 is a schematic diagram of the flexible load scheduling result of scheme 2.
Fig. 5 is a flowchart of a method for the cooperative participation of micro-grid source and storage in responding to the peak shaving demand of a grid.
Detailed Description
Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements or elements having like or similar functions throughout. The embodiments described below by referring to the drawings are illustrative and intended to explain the present invention and should not be construed as limiting the invention.
Referring to fig. 1 to 5, the invention provides a method for responding to power grid peak shaving demands by cooperative participation of micro-grid source storage, which comprises the following steps:
s1, analyzing adjustable source storage characteristics in a micro-grid;
the specific method is as follows:
s11, dividing the flexible load participating in interaction into a load-reducible load, a load-transferable load and a load-translatable load according to the response characteristics of the user;
s12, analyzing the load capable of being reduced, the load capable of being transferred and the load capable of being translated respectively to obtain source storage characteristics.
In particular. The micro-grid is a small power generation and distribution system formed by integrating a distributed power supply, an energy storage device, an energy conversion device, related loads, a monitoring device and a protection device, and the whole micro-grid comprises the distributed micro-power supply, the loads, the energy storage system and a matched control system. The internal sensing layer of the micro-grid mainly comprises physical equipment such as an intelligent electric energy meter, a mutual inductor, a magnetic field sensor and the like, and senses and collects information such as running states, energy data and the like of a distributed power supply, energy storage and the like in the micro-grid. The data sources are collected efficiently, and the terminal intelligence and edge calculation level is improved; the network layer equipment mainly comprises a chip, an optical fiber, a positioning system, a data center and the like, so that wide data integration and sharing of an Internet platform in the micro-network are realized; the platform layer comprises a micro-grid control cloud, a micro-grid internal management cloud and the like, and the micro-grid participates in an external market according to a decision of a platform layer data processing center; the application layer develops intelligent comprehensive energy service by utilizing intelligent technologies such as AI and the like, and promotes the promotion of micro-network management and service transformation.
The operator is a manager of the community micro-grid, has a power generation unit such as wind and light in a public area, and has a public energy storage device (i.e., an energy storage device built by investment of the operator, hereinafter also referred to as an operator-side energy storage device). The operators directly regulate the flexible load of the users, aggregate the community public areas and the renewable energy sources of the users, are connected with the distribution network through the public connection point PCC, and the users directly purchase electricity to the operators in the electric energy transaction. The diesel engine is composed of a machine body, two large mechanisms (a crank connecting rod mechanism and a valve mechanism) and four large systems (a fuel supply system, a lubricating system, a cooling system and a starting system). The engine body is a frame for forming a diesel engine and consists of a cylinder body and a crankcase. The crank connecting rod mechanism consists of a piston connecting rod group, a crankshaft flywheel group and the like, and is one of key elements of the micro-grid.
The single flexible load has the flexible characteristics of adjustability, translational capability, interruptibility and the like, and the flexible load clusters formed by different levels and different forms of aggregation of the flexible load groups such as agents, load aggregators, energy efficiency power plants, virtual power plants and the like reach a certain magnitude, namely the capacity of participating in micro-grid dispatching is achieved, and the micro-grid can be influenced through reasonable arrangement. The flexible load can be used as a novel and important scheduling resource except for a micro source in the micro power grid to participate in the operation optimization scheduling of the micro power grid, the cooperative control of the flexible load and the energy storage system can fully exert the adjustment potential of the flexible load, the renewable energy fluctuation can be stabilized, the peak clipping and valley filling can be realized, and the safe and stable operation of the power grid is facilitated. The common scheduling modes of flexible loads mainly comprise a contract-based mode, an electricity price-based mode and a demand bidding mode, and the flexible loads are divided into load shedding loads, load shifting loads and load transferring loads according to the time distribution characteristics and the autonomous response characteristics of users.
The contract content capable of interrupting the load is a measure that an electric company makes a valid contract with a user in advance, and a request signal is sent to the user to interrupt part of electricity consumption in the peak period of the load, and meanwhile, a certain compensation fee is given to the user. The load to be reduced is typically lighting, air conditioning, home entertainment equipment, or the like. The translatable load needs to be translated as a whole, and the electricity duration spans a plurality of scheduling periods, if the translatable load is scheduled by taking the scheduling period as a unit, the continuous working production flow is often split, the electricity continuity is violated, and even the electricity time sequence is disturbed, so that the translated load curve cannot be implemented. Transferable loads generally mean that a user can change the load during a period of electricity usage according to electricity rates or other motivational measures, such loads typically having a sensitivity to electricity rates or motivations, and can effect transfer of the load during that period, but with the total amount of electricity used unchanged during a scheduling period. Such loads are mainly electric kettles, dish washers, washing machines, water heaters and the like.
S2, acquiring power grid dispatching based on a flow of the micro power grid participating in the power grid peak shaving dispatching, wherein the demand of reducing peak load or filling valley load is solved in 1 day;
the micro-grid participates in a flow of grid peak shaving scheduling, which comprises the following steps:
s21, carrying out load prediction by a power grid company, carrying out normalization processing on a predicted system load curve to obtain scheduling information, generating a scheduling instruction based on the scheduling information, and transmitting the scheduling instruction to a micro-grid comprehensive operator;
s22, after receiving a scheduling instruction, the comprehensive operator of the micro-grid adjusts and optimizes internal resources to participate in peak regulation and pressure relief of the main network;
and S23, the grid company compensates the micro-grid comprehensive operator according to the actual peak shaving capacity of the micro-grid.
S3, establishing a micro-grid source load cooperative participation response power grid peak regulation demand model considering flexible load based on the source load characteristics and the demand;
the specific method is as follows:
s31, based on the source storage characteristics and the requirements, various resources in the micro-grid are considered to optimally allocate and operate strategies, and an objective function can be obtained by taking the minimum system operation cost, minimum wind and light abandoning punishment cost and the maximum total amount of the system participating in the peak shaving requirement response of the grid as targets;
specifically, the objective function is:
min F 1 ＝{f 1 ,-f 2 }
f 1 ＝C DE +C user +C bat +C ex +C punish
objective function f 1 The total cost of one day for the system to operate. Including the cost of diesel engine operation C DE Paying for user cost C user Energy storage operation cost C bat Cost of interaction with the grid C ex Wind and light punishment cost C is abandoned punish . The expressions are respectively as follows:
1) Diesel engine running cost:
wherein P is DE And (t) is the operating power of the diesel engine in the period t, and a and b are the secondary coefficient and the primary coefficient of the operating cost of the diesel engine respectively.
2) Payment of user cost:
wherein C is cut 、C shift 、C trans Load shedding, load translation and load transferring costs are respectively carried out; n (N) cut 、N shift 、N trans The load can be reduced, the load can be translated and the number of the loads can be transferred respectively; c (C) cut,a,price 、C shift,b,price 、C trans,c,price Load can be reduced, load can be translated, and load compensation unit price can be transferred.
3) Energy storage operation cost:
wherein C is E Planning the total cost of investment for energy storage by the planning quantity e E Determining; c E Cost per capacity of energy storage;is the reference capacity; r is the discount rate.
4) Cost of interaction with the grid
Wherein P is buy,t 、P sell,t Purchasing and selling electric power to the power grid by the micro-grid at t time intervals respectively, C buy,t 、C sell,t And purchasing electricity price for the period t.
5) Wind and light abandoning punishment cost
Wherein P is fore,t 、P wind,t C is wind power predicted power and fan actual power punish The price of the unit power waste wind is shown.
Objective function f 2 The total peak load is adjusted for the system through the connecting line. Wherein T is 1 For the upper power grid to consume the power demand period set, T 2 A set of power demand periods is supported for the upper grid. Δt is the unit scheduling time.
S32, carrying out normalization processing on each objective function value by adopting a min-max normalization method to obtain a processing result;
specifically, in order to eliminate the dimension between the system cost and the peak shaving capacity, a min-max standardization method is adopted to normalize each objective function value:
f i '＝(f i -f imin )/(f imax -f imin )
wherein: f (f) i 、f i ' is the true value and the normalized value of the objective function respectively; f (f) imax 、f imin Respectively, the maximum and minimum values of the corresponding objective function.
S33, converting the multi-objective optimization problem into a single-objective optimization problem by adopting a linear weighted summation method based on the processing result;
specifically, after normalization, a linear weighted summation method is adopted to convert the multi-objective optimization problem into a single-objective optimization problem. The weight coefficient of the objective function can be adjusted according to the actual scheduling requirement, and the weight coefficients of the objective function are respectively 0.5 and 0.5 for solving.
S34, based on the single-objective optimization problem, new energy, energy storage and flexible load characteristics in the micro-grid and operation characteristics of the micro-grid are considered, so that constraint conditions are obtained;
the expression is:
1) Power supply system constraints
Electric power balance constraint:
wherein P is dis,t 、P buy,t 、P fix,t 、P cut,t The energy storage discharge, the charging, the traditional load power and the reducible power are respectively carried out in the t period.
Diesel unit constraint:
-R down Δt＜＝P DE,t -P DE,t-1 ＜＝R up Δt
in the method, in the process of the invention,the upper limit and the lower limit of the power generation power of the diesel engine are respectively; r is R up 、R down The up-down climbing rates of the micro-combustion engine are respectively; />Is firewoodMaximum start-stop times in the dispatching cycle of the oil engine; t (T) t on 、T t off Minimum continuous running time and minimum continuous stop time of the diesel engine respectively; />Is the time of continuous running and continuous stopping of the diesel engine in the period t. G t Is a start-stop variable of the diesel engine.
Wind power constraint:
(1-θ)＜＝P wind,t ＜＝P fore,t
in the formula, θ is the maximum wind curtailment rate.
Energy storage constraint:
λ min W＜＝E t ＜＝λ max W
0＜＝P ch,t ＜＝P ch,max
0＜＝P dis,t ＜＝P dis,max
P ch,t P dis,t ＝0
wherein: lambda (lambda) min And lambda (lambda) max Respectively the maximum charge state and the minimum charge state of the energy storage device; w is the capacity of the energy storage device; p (P) ch,max And P dis,max And the maximum charging and discharging power of the energy storage equipment are respectively. E (E) t And storing the charge state for the period t.
Tie line constraint:
0＜＝P buy,t ,P sell,t ＜＝P TL,max
wherein: p (P) TL,max Is the link power maximum.
2) Flexible load restraint
Interruptible load constraint:
S i,t ＜＝S i,max
T i,d ＜＝T i,dmax
wherein S is i,max An upper limit for the allowable interrupt capacity for each user; t (T) i,dmax Allowing an upper limit of interrupt duration for each time for the first user; y is i A total number of interruptions allowed per day for the ith user; n is n i The minimum time interval required by the load interruption twice for the ith user; v (V) i,t A 0-1 variable, indicates whether the ith user starts an interrupted state at the t period.
Translatable load constraint:
in the method, in the process of the invention,a set of start periods acceptable for translatable load; />And->The initial starting period, the energy using duration period, the earliest starting period and the latest starting period of the translatable load b are respectively;the electric power distribution for each period of translatable load. Start state variable v for translatable load b after scheduling b,τ Assuming that the translatable load b starts to be translated from period b to period τ, then v b,τ And the original power distribution->The power profile of the translatory load b can be deduced +.>
Load constraints can be transferred:
wherein:load power upper and lower limit values allowed by the transferable load respectively; />Is a minimum continuous run time limit; />Is an acceptable load transfer interval; />Is the total amount of power that needs to be consumed during the scheduling period. />Power at time period t after scheduling for transferable load c.
Specifically, the constraint conditions include a power supply system constraint and a flexible load constraint.
And S35, generating a micro-grid source storage cooperative participation response power grid peak shaving demand model considering the flexible load based on the constraint condition.
And S4, solving a mixed integer linear programming problem of the micro-grid source load cooperative participation response power grid peak regulation demand model to obtain a micro-grid source load cooperative response peak regulation demand scheme.
For further understanding of the present invention, the actual application of the present invention will be explained below by taking a certain exemplary engineering of a micro grid as shown in fig. 1. The model of the present invention is solved by CPLEX. The user contract data are shown in tables 1, 2 and 3. The optimal scheduling period is 24 hours.
Table 1 interruptible load contract data
TABLE 2 translatable load contract data
TABLE 3 transferable load contract data
The traditional micro-grid economic operation model (without considering flexible load and with the minimum micro-grid operation cost single-objective optimization) is taken as a scheme 1, and the micro-grid source storage collaborative multi-objective optimization operation model with the grid peak shaving requirement considered is taken as a scheme 2. According to the regional load prediction in fig. 2, scheme 2 is as required by the upper power grid dispatching center: the micro-grid is required to consume as much power as possible from the large power grid in 2-5 time periods in 1 day while ensuring the self economy, and the 11-12 time periods provide peak load support for returning power to the large power grid as much as possible.
Table 4 comparison of the results of the different protocols
Table 5 scheme 2 interruptible user scheduling case
Table 6 scheme 2 translatable user scheduling case
Table 7 scheme 2 transferable user scheduling case
As can be seen from table 4, the operating cost of scheme 2 is increased compared to scheme 1, but the peak shaving capacity provided to the grid is significantly increased. If the compensation amount of the grid peak regulation 1 kW.h is unchanged through the connecting transmission line, the upper-level grid only needs to give 0.19 yuan/kW.h to the micro-grid as compensation, and the micro-grid can actively participate in the dispatching plan of the grid on the premise of not losing the self economy. The time of load interruption is mainly concentrated at 11:00 and 20:00. in connection with tables 5, 6, 7 and fig. 4, the translational load and the translational load are mainly concentrated in the load trough period in the early morning or afternoon.
As can be seen from fig. 3, the generation of electricity is preferred because of the low running cost of wind power and photovoltaic in the micro-grid. The diesel engine is maintained at the minimum output for most of the time during the entire scheduling period due to the high operating cost, and the diesel engine handles all increases during the peak at noon and peak at night. The storage battery is charged in the load low valley period, and is discharged in the load peak period, so that peak clipping and valley filling are facilitated. According to the power curve of the connecting line, the power transmitted by the 2-5 time period large power grid to the micro power grid through the connecting line reaches the maximum value, and the power transmitted by the 10-12 time period micro power grid to the large power grid through the connecting line reaches the maximum value, so that the whole micro power grid plays a role in peak clipping and valley filling in the large power grid.
The micro-grid source-storage-load cooperative participation response power grid peak regulation demand model which takes the flexible load into account, comprehensively considers three layers of source storage to analyze the characteristic of schedulable resources in the micro-grid, reduces the peak-valley load of the system, reduces the running cost of the system and participates in the peak regulation demand of the response power grid. The decision variables and the constraint conditions in the model comprehensively consider the source-storage-load characteristics, fully consider the electricity utilization characteristics of different users, and provide references for relieving the peak regulation pressure of the power grid under the novel power system.
The beneficial effects are that:
1. the flexible load is taken as a virtual power supply, and the flexible load is considered to participate in the optimized operation of the micro-grid in the same way as other micro-sources in the micro-grid.
2. And the user electricity utilization characteristic is fully considered, and compared with a traditional micro-grid optimization scheme, the micro-grid dispatching potential can be further excavated, so that more sufficient response resources are provided for peak clipping and valley filling of the grid.
3. The method comprehensively considers that the micro-grid source and the storage are cooperated to participate in responding to the grid peak shaving, reduces the peak-valley-difference load in the system, relieves the grid peak shaving pressure, and achieves the mutual benefits and win-win of the grid side and the micro-grid side.
The above disclosure is only a preferred embodiment of the method for cooperatively participating in the peak shaving request response of the micro grid source according to the present invention, but it should be understood that the scope of the present invention is not limited thereto, and those skilled in the art will understand that all or part of the procedures for implementing the above embodiments are equivalent and still fall within the scope of the present invention.
Claims (5)
1. The method for the cooperative participation of the micro-grid source and the storage to respond to the grid peak shaving demand is characterized by comprising the following steps of:
analyzing adjustable source storage characteristics within the microgrid;
acquiring power grid dispatching based on a flow of the micro power grid participating in the power grid peak shaving dispatching, wherein the demand of reducing peak load or filling valley load is solved in 1 day;
establishing a micro-grid source-storage cooperative participation response power grid peak regulation demand model considering flexible load based on the source storage characteristics and the demand;
and solving a mixed integer linear programming problem of the micro-grid source and load cooperative participation response power grid peak regulation demand model to obtain a micro-grid source and load cooperative response peak regulation demand scheme.
2. A method for collaborative participation in responding to grid peak shaver demands by micro-grid source storage as set forth in claim 1, wherein,
the analyzing adjustable source storage characteristics within the microgrid comprises:
dividing the flexible load participating in the interaction into a load-reducible load, a load-transferable load and a load-translatable load according to the response characteristics of the user;
and respectively analyzing the load capable of being reduced, the load capable of being transferred and the load capable of being translated to obtain source storage characteristics.
3. A method for the collaborative participation in the response of grid peak shaver demands by a micro-grid source load according to claim 2, wherein,
the micro-grid participates in a flow of grid peak shaving scheduling, which comprises the following steps:
carrying out load prediction by a power grid company, carrying out normalization processing on a predicted system load curve to obtain scheduling information, generating a scheduling instruction based on the scheduling information, and sending the scheduling instruction to a micro-grid comprehensive operator;
after receiving the scheduling instruction, the comprehensive operator of the micro-grid adjusts and optimizes internal resources to participate in peak regulation and pressure relief of the main network;
and the power grid company compensates the micro-grid comprehensive operation business according to the actual peak regulation capacity of the micro-grid.
4. A method for collaborative participation in responding to grid peak shaver demands by micro-grid source storage as set forth in claim 3, wherein,
the establishing a micro-grid source load cooperative participation response power grid peak shaving demand model considering flexible load based on the source load characteristics and the demand comprises the following steps:
based on the source storage characteristics and the requirements, various resources in the micro-grid are considered to optimally allocate and operate strategies, and an objective function can be obtained by taking the minimum system operation cost, minimum wind and light abandon punishment cost and the maximum total amount of the system participating in the peak regulation requirement response of the grid as targets;
by using min -normalizing each objective function value by a max normalization method to obtain a processing result;
converting the multi-objective optimization problem into a single-objective optimization problem by adopting a linear weighted summation method based on the processing result;
based on the single-objective optimization problem, new energy, energy storage and flexible load characteristics in the micro-grid and the running characteristics of the micro-grid are considered, so that constraint conditions are obtained;
and generating a micro-grid source and storage cooperative participation response power grid peak regulation demand model considering the flexible load based on the constraint condition.
5. The method for the collaborative participation in the response of the peak shaver demand of the power grid by the micro-grid source load according to claim 4,
the constraints include power supply system constraints and flexible load constraints.
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| CN117175609A (en) * | 2023-11-01 | 2023-12-05 | 南方电网数字电网研究院有限公司 | Flexible regulation and control terminal of power load |
| CN117977812A (en) * | 2024-03-28 | 2024-05-03 | 中电装备山东电子有限公司 | Intelligent data monitoring and management system and method for energy concentrator |
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| CN117175609A (en) * | 2023-11-01 | 2023-12-05 | 南方电网数字电网研究院有限公司 | Flexible regulation and control terminal of power load |
| CN117175609B (en) * | 2023-11-01 | 2024-02-23 | 南方电网数字电网研究院有限公司 | Flexible regulation and control terminal of power load |
| CN117977812A (en) * | 2024-03-28 | 2024-05-03 | 中电装备山东电子有限公司 | Intelligent data monitoring and management system and method for energy concentrator |
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