CN108306288B - Micro-grid community distributed energy distribution method based on demand side response - Google Patents

Micro-grid community distributed energy distribution method based on demand side response Download PDF

Info

Publication number
CN108306288B
CN108306288B CN201810150524.7A CN201810150524A CN108306288B CN 108306288 B CN108306288 B CN 108306288B CN 201810150524 A CN201810150524 A CN 201810150524A CN 108306288 B CN108306288 B CN 108306288B
Authority
CN
China
Prior art keywords
load
grid
microgrid
micro
power
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Expired - Fee Related
Application number
CN201810150524.7A
Other languages
Chinese (zh)
Other versions
CN108306288A (en
Inventor
王思明
牛玉刚
王蓓
贾廷纲
陈蓓
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
East China University of Science and Technology
Original Assignee
East China University of Science and Technology
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by East China University of Science and Technology filed Critical East China University of Science and Technology
Priority to CN201810150524.7A priority Critical patent/CN108306288B/en
Publication of CN108306288A publication Critical patent/CN108306288A/en
Application granted granted Critical
Publication of CN108306288B publication Critical patent/CN108306288B/en
Expired - Fee Related legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/008—Circuit arrangements for ac mains or ac distribution networks involving trading of energy or energy transmission rights
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06315—Needs-based resource requirements planning or analysis
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/381—Dispersed generators
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
    • Y02E40/70—Smart grids as climate change mitigation technology in the energy generation sector
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
    • Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications

Landscapes

  • Business, Economics & Management (AREA)
  • Human Resources & Organizations (AREA)
  • Engineering & Computer Science (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Power Engineering (AREA)
  • Economics (AREA)
  • Strategic Management (AREA)
  • Marketing (AREA)
  • Game Theory and Decision Science (AREA)
  • Educational Administration (AREA)
  • Development Economics (AREA)
  • Operations Research (AREA)
  • Quality & Reliability (AREA)
  • Tourism & Hospitality (AREA)
  • Physics & Mathematics (AREA)
  • General Business, Economics & Management (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Supply And Distribution Of Alternating Current (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

The invention relates to a micro-grid community distributed energy distribution method based on demand side response. The method comprises the following steps: (1) an Operator optimization model (2) is established, a peak-to-average ratio (PAR) is introduced into a distributed optimization algorithm (3) based on demand side response to adjust a load side so as to meet the requirement of a large power grid. The demand side response technology adopts a distributed control strategy, each micro-grid is operated in parallel, the leakage of private information is effectively avoided, the problem of single-point failure cannot occur, the load demand of the whole micro-grid community can be coordinately controlled through setting of PAR, the optimal running cost of the micro-grid is guaranteed through the particle swarm intelligent optimization algorithm, and the electricity consumption cost of a user is reduced.

Description

Micro-grid community distributed energy distribution method based on demand side response
Technical Field
The invention relates to a distributed energy distribution method based on a demand side response technology in micro-grid community construction, in particular to a micro-grid community system containing a large number of single micro-grids, load requirements of the whole micro-grid community are optimized to meet requirements of accessing a large power grid, a traditional central controller mode is abandoned, stability and economy of the micro-grid community system are improved by adopting a distributed control method, transmission of privacy data among the micro-grids is reduced, and privacy of key load data of the single micro-grids is guaranteed.
Background
The micro-grid is used as an integrated system for integrating distributed power generation, comprises a distributed power supply, an energy storage device, a load and energy distribution system and the like, and can effectively solve the problem that new energy is connected into a power grid. The microgrid community is a microgrid group consisting of two or more single microgrids and microgrid community layer equipment (energy storage devices and diesel engines), the distributed units of each microgrid in the microgrid community system are different, and various power loads on the load side have different power demands, so that the power output of each single microgrid and the microgrid community layer equipment can be coordinated, the stability and the economy of each single microgrid can be met while the high-quality and high-efficiency operation of the microgrid community system is guaranteed, and the energy distribution technology is critical. Therefore, a novel energy distribution method and a novel control mode are explored, the problems of large calculation amount, poor stability and the like of a traditional central control mode are solved, and the method has great practical significance in the construction and popularization of the micro-grid community.
At present, research aiming at microgrid energy distribution mainly focuses on application of a demand side response technology, and the research can be divided into two categories: the first is indirect regulation and control of electricity price, and through the height of electricity price, the intelligent user can adjust own power demand to reach that the peak period (electricity price sets up highly) power demand is few, and the valley period (electricity price sets up lowly) power demand is many. The microgrid system with the adjustment mode in a small range can often obtain a better result, but when the base number of the users is too large, the unified selection of the users can cause a new load peak value, namely a peak value transfer phenomenon; the second method is remote direct regulation, the large power grid sets the optimal load requirement at the moment through peak-valley regulation, and the start-stop right of the user equipment is acquired through a signing agreement, so that the load requirement is remotely operated. The adjustment mode needs to obtain privacy information of a user (specific power consumption requirement of equipment), belongs to remote central adjustment, and when the base number of the user is too large, a central controller needs great computing capacity and communication capacity, and in addition, remote control has higher control cost, so the adjustment mode is often used in some emergency situations, such as short-circuit fault and the like.
Therefore, the novel demand side response technology serving for the microgrid community is developed, the defects of the existing demand side response technology are overcome, the computing capacity and the communication capacity required by the traditional central control mode are reduced, the privacy of the user is improved, the power generation cost of the distributed units is reduced, and the power supply cost of various intelligent users is reduced, so that the novel demand side response technology has important theoretical and practical significance.
Disclosure of Invention
The invention provides a novel demand side response microgrid community distributed energy distribution method serving a microgrid community, which is theoretically guided by reaching consistency within limited time in a multi-agent system, obtains the whole load demand information of the microgrid community through information transmission among the microgrids and aims to avoid exposure of privacy information and use of a central controller; calculating the optimal output of the micro-grid community layer equipment by taking a particle swarm algorithm as an optimization algorithm; an indirect load regulation algorithm is provided by taking PAR (peak-to-average ratio) as a reference value, and a load demand curve after regulation is calculated so as to meet the set PAR.
The demand side response technology adopts a distributed control method, each micro-grid performs parallel operation, so that the leakage of privacy information is effectively avoided, the problem of single-point failure is avoided, the load demand of the whole micro-grid community can be coordinately controlled through setting of PAR, the optimal operation cost of the micro-grid is ensured through the particle swarm intelligent optimization algorithm, and the electricity consumption cost of a user is reduced.
Micro-grid community model
In recent years, in order to reduce the influence of new energy uncertainty on the microgrid, meet the overall operation target (economy, environment and the like) of a region where the microgrid is located, and improve the stability of a single microgrid, as shown in fig. 1, a novel microgrid system, namely a microgrid community system, is applied, the idea is to connect the microgrid to a low-voltage layer of a microgrid community, and rely on an energy distribution system of the microgrid community, the whole microgrid community can monitor the power consumption and the requirements of each microgrid of the low-voltage layer, a medium-voltage layer energy storage system of the microgrid community is distributed, intelligent adjustment is carried out on a client buying a list for peak power demand, and in an emergency situation, the microgrid community system starts a medium-voltage layer diesel engine system of the microgrid community to control and balance the energy requirements.
The micro-grid community comprises a micro-grid on a low-voltage layer and micro-grid community layer equipment on a medium-voltage layer, the community layer equipment mainly comprises an energy storage device and a diesel engine, the balance of supply and demand of the micro-grid community can be guaranteed, and the micro-grid is coordinated to achieve the overall operation target of the micro-grid community. The microgrid community environment is similar to the multi-microgrid environment, but the two environments still have the following differences:
1) the microgrid community has microgrid community layer equipment to ensure balance of supply and demand, and a multi-microgrid environment only depends on a microgrid or a large power grid.
2) The microgrid community belongs to a cooperative environment, and the low-voltage layer microgrid and community layer equipment coordinate with each other to control the whole operation target of the microgrid community. The multi-microgrid environment is mostly in a non-cooperative environment or a federated environment.
3) The microgrid community structure is time-varying, and the low-voltage layer microgrid can be selectively connected into a microgrid community or operated in an isolated island mode. The number of piconets in a multi-piconet environment is known.
Microgrid net load model
The net load model comprises a load model on the demand side and a new energy model on the power generation side. The load model comprises a non-adjustable load (BtAgent) and an adjustable load (RtAgent). BtAgent includes uninterruptible loads (refrigerators, critical lighting, etc.) that the load requirements must meet. The Ragent comprises a load capable of being translated (a charge-discharge controller, a washing machine and the like) and a load capable of being interrupted (an air conditioner, non-important illumination and the like), and has the functions of dividing the priority of the load, ensuring the power supply of the load with high priority, and translating and cutting off the load according to the actual situation. Generally, the adjustable load is roughly divided into four time periods according to the electricity utilization habits of residents in life, wherein the first time period is a morning electricity utilization period: 00: 00-08: 00, wherein the second time interval is the power utilization time interval at noon: 08: 00-13: 00, and the third time interval is an electricity utilization time interval in the afternoon: 13: 00-19: 00, wherein the fourth time period is a night electricity utilization time period: 19: 00-00: 00. The adjustable load can be adjusted only in each time period, so that the adjustable load scheduling span is prevented from being large, and the adjustable load scheduling span is contrary to the actual operation of the microgrid. Thus, the load model can be described as:
Figure BDA0001579796780000031
dmin-b(t)≤r(t)≤dmax-b(t) (2)
where y (t) represents the expected value of the adjustable load, i.e., the predicted value of the adjustable load at each time interval before the day. b (t) represents the predicted value of the non-adjustable load before the day for each period. r (t) represents the load-adjustable scheduling value, D, optimized by the superior AgentiminThe minimum value of the required power of the adjustable load in the ith time period is ensured, and the situation that all the adjustable loads are cut off is prevented. dminAnd dmaxIs the upper and lower limit constraint of the physical incoming line capacity of the demand side.
The new energy model mainly comprises a photovoltaic power generation model and a fan power generation model in the micro-grid. As a new energy power generation end, the environmental benefit is high, and when the initial investment cost is not considered, the power generation cost can be ignored, so in the actual scheduling process, the new energy power generation power should be preferentially used. The new energy power generation satisfies the following constraints:
Figure BDA0001579796780000032
in the formula, Pi(t) is upper level scheduling decision information,
Figure BDA0001579796780000033
the predicted maximum value of the new energy sources in the day ahead. When i is 1, a photovoltaic power generation output value is represented; and when i is 2, the value represents the fan power generation output value.
Since the new energy forecast and load forecast error issues are not considered herein, the new energy and load demand can be represented by a "net load" variable:
Figure BDA0001579796780000034
in the formula (I), the compound is shown in the specification,
Figure BDA0001579796780000035
indicating the presence of energy in the ith microgrid at time tThe amount is surplus, otherwise, the energy is in an energy shortage state.
Micro-grid community layer equipment model
The community layer equipment mainly comprises an energy storage device and a diesel engine, so that the balance of supply and demand of the microgrid community can be guaranteed, and the microgrid is coordinated to achieve the overall operation target of the microgrid community.
The energy storage device is coordinated with other distributed power supplies to jointly maintain the stable operation of the micro-grid. Due to the fluctuation of the output of the new energy, the energy storage device can be used as a buffer device to smooth the fluctuation of the new energy. However, the installation cost of the energy storage device is relatively high, and the energy storage investment cost is reduced to the service life of the energy storage device, that is, the service life of the energy storage device is optimized, which is equivalent to the reduction of the energy storage cost. Research finds that the energy storage life is influenced by the energy storage charging and discharging times and the charging and discharging depth, so that the energy storage device life is combined with the charging and discharging power, and the charging and discharging cost of the energy storage device is obtained as follows:
Figure BDA0001579796780000041
in the formula (I), the compound is shown in the specification,
Figure BDA0001579796780000042
and scheduling power for the energy storage of the ith microgrid, wherein the power is in a charging state when the power is greater than zero, and is equivalent to a load, and the power is in a discharging state when the power is less than zero, and is equivalent to a micro source. n iscAnd ndRespectively, charge-discharge efficiency, beta, of the energy storage deviceesIs the cost coefficient of charging and discharging of the energy storage device. In order to achieve self-charging and discharging rate and capacity limitation of the energy storage device,
Figure BDA0001579796780000043
and (3) satisfying the constraint:
Figure BDA0001579796780000044
therein, SOCi(t) is the state of charge of the ith microgrid at time t. Alpha is alphabaIs the energy storage capacity and is the maximum charge and discharge of the energy storage device respectivelyThe power, and the energy storage device maximum minimum state of charge, respectively, are typically set to 0.8 and 0. To ensure independence between scheduling periods, we specify that the battery state SOC (24) at the end of a scheduling period is equal to the period start state SOCInt.
The diesel engine model generally considers a binomial form, and the cost of generating electricity is mainly related to the output power:
Figure BDA0001579796780000045
wherein k1, k2 and k3 are cost factors of diesel engine, pD(t) represents the output power of the diesel engine, which satisfies the following constraints:
Figure BDA0001579796780000046
s.t.Sd(t)∈{0,1}(8)
in the formula, SdAnd (t) represents the starting and stopping state of the diesel engine, wherein 1 represents opening, and 0 represents closing.
Operator optimization model
The main operation target of the micro-grid community is the economy and stability of system operation, and the following equation constraints are satisfied by matching with equipment of a medium-pressure layer and demand side response of a low-pressure layer in the micro-grid community:
Figure BDA0001579796780000051
in the formula, pgrid(t) represents the electric quantity purchased from a large power grid, because the new energy in the microgrid cannot always meet the demand of a user, the power shortage of the microgrid is made up by the Operator for purchasing electricity from the power grid, and the electricity purchasing cost is generated:
Figure BDA0001579796780000052
wherein, CbAnd (t) is the electricity purchase price at the time t. Valley period of 10:0015:00, flat time interval of 01: 00-09: 00 and 23: 00-00: 00, and peak time interval of 16: 00-22: 00. The invention assumes that the electricity rates of the grid in each period are as shown in table 1.
TABLE 1 time-of-use electricity price of electric network
Figure BDA0001579796780000053
Due to the constraint of the physical tie line of the micro-grid and the grid, the electricity purchasing power meets the following constraint:
Figure BDA0001579796780000054
in summary, the problems considered by the present invention are as follows:
Figure BDA0001579796780000055
subject to(1)-(5),(8),(9),(11)
distributed optimization algorithm based on demand side response
In order to ensure the privacy data of each microgrid and avoid the leakage of key load power utilization information, only net load information-is published outwards by each microgrid
Figure BDA0001579796780000056
The method is guided by the theory that consistency is achieved in a multi-agent system within a limited time, and the whole load demand information of the micro-grid community is obtained through information transmission among micro-grids:
Figure BDA0001579796780000057
and representing the information strip iterated for k times, and setting the initial information strip of the jth microgrid as:
Figure BDA0001579796780000058
the information is iterated in the following way:
Figure BDA0001579796780000059
Njand (3) expressing a neighbor microgrid set of the jth microgrid, and theoretically proving that iteration tends to be consistent in a limited time, and a final load information strip can be obtained:
Figure BDA00015797967800000510
therefore, each microgrid can obtain global load information.
In order to solve the defects of the existing demand side response technology and develop a novel demand side response technology serving a microgrid community, the invention introduces a concept of PAR (peak-to-average ratio) to adjust the load side to meet the requirement of a large power grid, as shown in the following formula:
Figure BDA0001579796780000061
a higher par indicates a higher peak in the time period, and a par of 1 indicates a horizontal line of load demand.
The novel demand side response method related by the invention is as follows:
Figure BDA0001579796780000062
based on the analysis, in the operation process of the microgrid community, the equal intelligent algorithm of the particle swarm is utilized to define the corresponding operator decision variable, such as the medium-pressure layer equipment of the microgrid community: the output of the energy storage device and the diesel engine can well macroscopically regulate and control the load demand of a low-voltage layer in the microgrid community according to the novel demand side response technology provided by the text.
The invention provides a micro-grid community energy distribution algorithm based on a demand side response technology, and provides a theoretical framework for optimal economic operation of a micro-grid community system from the perspective of an Operator. The method overcomes the defects of the existing demand side response technology, reduces the computing power and the communication power required by the traditional central control mode, improves the privacy of users, and reduces the power generation cost of distributed units.
Drawings
Fig. 1 is a schematic diagram of a microgrid community system structure.
Fig. 2 is a general flow chart of a microgrid community energy distribution algorithm based on a demand-side response technology.
Fig. 3 is a comparison diagram of microgrid information strip iteration.
Fig. 4 is a partial effect diagram.
Description of the figures
In fig. 1, a schematic diagram of a microgrid community system structure is shown. The day-ahead scheduling period T of the microgrid community is one day, the minimum scheduling time is 1 hour, the one day is divided into 24 scheduling time periods, and the output power of the power generation side and the required power of the demand side in each time period are assumed to be kept unchanged.
In fig. 2, an overall flowchart of a microgrid community energy management algorithm based on a demand-side response technology is shown. The ACA shows that the limited time reaches consistency, so that the global load information is obtained, the privacy of a user is guaranteed, and the ICLA shows that the novel demand side response method provided by the invention is provided. PSO then represents the intelligent particle swarm algorithm.
In fig. 3, a comparison diagram of microgrid information bar iteration is shown. Based on the finite time consistency theory of multi-agent, the upper iteration curve is the actual situation, and the lower iteration curve is the theoretical situation. The difference between the two is mainly caused by network packet loss and network delay in actual conditions.
In fig. 4, red is shown as the original load curve, and the effect diagram shows the effect diagram after load adjustment for three cases of setting values PAR 1,1.5, and 2.
Detailed Description
The invention is further illustrated by way of example in the following with reference to the accompanying drawings.
As shown in fig. 1, the microgrid community system adopted in the present invention includes 5 microgrid systems and medium-pressure layer devices: a set of energy storage system and diesel engine system. The technical parameters are set as follows:
Figure BDA0001579796780000071
the general flow of the microgrid community energy distribution algorithm based on the demand-side response technology is given below, as shown in fig. 2:
1. by applying the ACA theory, on the premise of fully ensuring the privacy of the user, each microgrid calculates the net load of each microgrid, and information bar iteration is carried out according to the method provided by the invention.
2. And after iteration is finished, each microgrid obtains the whole load demand information, the medium-pressure layer equipment is distributed to obtain the information, and an intelligent algorithm is used for deciding the optimal output of the medium-pressure layer equipment (the energy storage device and the diesel engine).
3. Judging the fitness function value f of the k stepfit(K) Whether or not f is satisfiedfit(K+1)-ffit(K) If yes, step 4 is carried out, and if not, step 2 is skipped.
4. By applying the novel demand side response method provided by the invention, each microgrid simultaneously calculates the load adjustment limit of the microgrid in parallel, and the iteration is carried out until PAR is satisfied.
The invention relates to a novel demand side response technology serving a micro-grid community, which takes consistency achieved in a limited time in a multi-agent system as theoretical guidance, obtains the whole load demand information of the micro-grid community through information transmission among micro-grids and aims to avoid exposure of privacy information and use of a central controller; calculating the optimal output of the micro-grid community layer equipment by taking a particle swarm algorithm as an optimization algorithm; an indirect load regulation algorithm is provided by taking PAR (peak-to-average ratio) as a reference value, and a load demand curve after regulation is calculated so as to meet the set PAR. The demand side response technology adopts a distributed control strategy, each micro-grid is operated in parallel, the leakage of private information is effectively avoided, the problem of single-point failure cannot occur, the load demand of the whole micro-grid community can be coordinately controlled through setting of PAR, the optimal running cost of the micro-grid is guaranteed through the particle swarm intelligent optimization algorithm, and the electricity consumption cost of a user is reduced.
The above description is only a preferred embodiment of the present invention, and is not intended to limit the scope of the present invention. That is, all equivalent changes and modifications made according to the contents of the claims of the present invention should be within the technical scope of the present invention.

Claims (1)

1. A micro-grid community distributed energy distribution method based on demand side response comprises the following steps:
(1) microgrid net load model
The net load model comprises a load model on a demand side and a new energy model on a power generation side; the load model comprises an unadjustable load and an adjustable load; the load model can be described as:
Figure FDA0002820061080000011
dmin-b(t)≤r(t)≤dmax-b(t) (2)
where y (t) represents the expected value of the adjustable load, i.e., the predicted value of the adjustable load before the day for each time interval; b (t) represents the predicted value of the non-adjustable load before the day for each period; r (t) represents the load-adjustable scheduling value, D, optimized by the superior AgentiminThe minimum value of the required power of the adjustable load in the ith time period is ensured, and the situation that all the adjustable loads are cut off is prevented; dminAnd dmaxThe upper and lower limits of the physical incoming line capacity of the demand side are restricted;
the new energy model on the power generation side has the following constraint that the new energy power generation meets the following constraint:
0≤Pi(t)≤Pi max(t) (3)
in the formula, Pi(t) is upper scheduling decision information, Pi maxThe predicted maximum value of the new energy sources in the day ahead; when i is 1, a photovoltaic power generation output value is represented; when i is 2, representing the fan power generation output value;
when the new energy prediction and load prediction error problem is not considered, the new energy and load demand are expressed by a net load variable:
Figure FDA0002820061080000012
in the formula (I), the compound is shown in the specification,
Figure FDA0002820061080000013
the energy surplus of the ith microgrid occurs at the time t, otherwise, the ith microgrid is in an energy shortage state;
(2) micro-grid community layer equipment model
The community layer equipment comprises an energy storage device and a diesel engine;
the energy storage device is coordinated with other distributed power supplies to jointly maintain the stable operation of the microgrid; the service life of the energy storage device is combined with the charging and discharging power, and the charging and discharging cost of the energy storage device is obtained as follows:
Figure FDA0002820061080000021
in the formula (I), the compound is shown in the specification,
Figure FDA0002820061080000022
the power is scheduled for the energy storage of the ith microgrid, when the power is greater than zero, the state is a charging state, which is equivalent to a load, and when the power is less than zero, the state is a discharging state, which is equivalent to a micro source; n iscAnd ndRespectively, charge-discharge efficiency, beta, of the energy storage deviceesIs the energy storage device charge-discharge cost coefficient; in order to achieve self-charging and discharging rate and capacity limitation of the energy storage device,
Figure FDA0002820061080000023
and (3) satisfying the constraint:
Figure FDA0002820061080000024
therein, SOCi(t) is the state of charge of the ith microgrid at time t; alpha is alphabaThe energy storage capacity, the maximum charge-discharge power of the energy storage device and the maximum minimum charge state of the energy storage device are respectively set to be 0.8 and 0; in order to ensure the independence between the scheduling periods, the battery state SOC (24) at the end of the scheduling period is set to be equal to the period starting state SOCInt;
the diesel engine model considers a binomial form, and the cost of generating electricity is mainly related to output power:
Figure FDA0002820061080000025
wherein k1, k2 and k3 are cost factors of diesel engine, pD(t) represents the output power of the diesel engine, which satisfies the following constraints:
Figure FDA0002820061080000026
s.t.Sd(t)∈{0,1} (8)
in the formula, Sd(t) is the start-stop state of the diesel engine, wherein 1 represents opening, and 0 represents closing;
(3) establishing Operator optimization model
The main operation target of the micro-grid community is the economy and stability of system operation, and the following equation constraints are satisfied by matching with equipment of a medium-pressure layer and demand side response of a low-pressure layer in the micro-grid community:
Figure FDA0002820061080000027
in the formula, pgrid(t) represents the electric quantity purchased from a large power grid, because the new energy in the microgrid cannot always meet the demand of a user, the power shortage of the microgrid is made up by the Operator for purchasing electricity from the power grid, and the electricity purchasing cost is generated:
Figure FDA0002820061080000028
wherein, Cb(t) the electricity purchase price at time t;
the valley time period is 10: 00-15: 00, the flat time period is 01: 00-09: 00, 23: 00-00: 00, and the peak time period is 16: 00-22: 00; the electricity prices of the power grid in each period are shown in table 1:
TABLE 1 time-of-use electricity price of electric network
Figure FDA0002820061080000031
Due to the constraint of the physical tie line of the micro-grid and the grid, the electricity purchasing power meets the following constraint:
Figure FDA0002820061080000032
obtaining:
Figure FDA0002820061080000033
(4) distributed optimization algorithm based on demand side response
The method is characterized in that consistency achieved within a limited time in a multi-agent system is taken as a basis, and the whole load demand information of the micro-grid community is acquired through information transmission among micro-grids:
Figure FDA0002820061080000034
and representing the information strip iterated for k times, and setting the initial information strip of the jth microgrid as:
Figure FDA0002820061080000035
Figure FDA0002820061080000036
representing payload information; the information is iterated in the following way:
Figure FDA0002820061080000037
Njand (3) representing a neighbor microgrid set of the jth microgrid, wherein iteration tends to be consistent within a limited time to obtain a final load information strip:
Figure FDA0002820061080000038
therefore, each microgrid can obtain global load information;
(5) a peak-to-average power ratio (PAR) is introduced to adjust a load side to meet the requirement of a large power grid, and the following formula is shown:
Figure FDA0002820061080000039
a higher par indicates a higher peak in the time period, and if par is 1, it indicates that the load demand is a horizontal straight line;
(6) par-based demand-side response
And (6.1) initializing, namely calculating par at the moment according to the formula (15), acquiring a standard value PAR set by the large power grid, and setting an additional variable
Figure FDA00028200610800000310
(6.2) if PAR ≦ PAR, then
Figure FDA00028200610800000311
(6.3) otherwise, iterating the loop:
(6.4):for t<＝24 do
t＝t+1
if
Figure FDA0002820061080000041
then
Figure FDA0002820061080000042
end if
end for
(6.5) load adjustment-finding the smallest four P's in the original datanet(t) in the order of Plow(1),Plow(2),Plow(3),Plow(4).
if Pnet(t)＝Plow(1),
then
Figure FDA0002820061080000043
else if Pnet(t)＝Plow(2),
then
Figure FDA0002820061080000044
else if Pnet(t)＝Plow(3),
then
Figure FDA0002820061080000045
else if Pnet(t)＝Plow(4),
then
Figure FDA0002820061080000046
end if
(6.6) calculating the adjusted
Figure FDA0002820061080000047
And (4) whether PAR is met or not, and continuing to the step (6.3) if the PAR is not met.
CN201810150524.7A 2018-02-13 2018-02-13 Micro-grid community distributed energy distribution method based on demand side response Expired - Fee Related CN108306288B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201810150524.7A CN108306288B (en) 2018-02-13 2018-02-13 Micro-grid community distributed energy distribution method based on demand side response

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201810150524.7A CN108306288B (en) 2018-02-13 2018-02-13 Micro-grid community distributed energy distribution method based on demand side response

Publications (2)

Publication Number Publication Date
CN108306288A CN108306288A (en) 2018-07-20
CN108306288B true CN108306288B (en) 2021-03-02

Family

ID=62865432

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201810150524.7A Expired - Fee Related CN108306288B (en) 2018-02-13 2018-02-13 Micro-grid community distributed energy distribution method based on demand side response

Country Status (1)

Country Link
CN (1) CN108306288B (en)

Families Citing this family (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112086978B (en) * 2019-06-13 2023-07-21 苏州震旦科技有限公司 Energy scheduling and control system based on demand side response
CN110518570B (en) * 2019-07-03 2021-06-18 浙江工业大学 Household multi-microgrid system optimization control method based on event-driven automatic demand response
CN110533311B (en) * 2019-08-21 2022-07-15 三峡大学 Intelligent community coordination scheduling system and method based on energy router
CN112700084A (en) * 2020-12-08 2021-04-23 珠海格力电器股份有限公司 Competitive type energy storage distribution method, device, controller and community energy storage distribution system
CN112634076B (en) * 2020-12-09 2023-04-28 上海电力大学 Distributed regulation and control method for wind power-containing multi-microgrid system considering flexibility reserve
CN112821470B (en) * 2021-03-10 2023-10-27 江南大学 Micro-grid group optimization scheduling strategy based on niche chaotic particle swarm algorithm
CN113991684A (en) * 2021-10-26 2022-01-28 广东电网有限责任公司 Multi-microgrid load recovery method and device
CN116565882B (en) * 2023-06-29 2023-09-19 北京大学 Distributed demand response method, device, system and medium

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103972929A (en) * 2014-05-20 2014-08-06 上海电气集团股份有限公司 Microgrid power distribution optimal control method
CN104915725A (en) * 2015-05-06 2015-09-16 浙江大学 Method for optimized mutual-aid trading of electricity among micro-grid user group based on real-time price
CN105591469A (en) * 2015-08-18 2016-05-18 济南大陆机电股份有限公司 Community microgrid distributed control system
CN106356888A (en) * 2016-09-28 2017-01-25 华中科技大学 High-rise building pumped storage scenery intelligent micro-grid system and control method
CN107220889A (en) * 2017-06-30 2017-09-29 四川大学 The distributed resource method of commerce of microgrid community under a kind of many agent frameworks

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103972929A (en) * 2014-05-20 2014-08-06 上海电气集团股份有限公司 Microgrid power distribution optimal control method
CN104915725A (en) * 2015-05-06 2015-09-16 浙江大学 Method for optimized mutual-aid trading of electricity among micro-grid user group based on real-time price
CN105591469A (en) * 2015-08-18 2016-05-18 济南大陆机电股份有限公司 Community microgrid distributed control system
CN106356888A (en) * 2016-09-28 2017-01-25 华中科技大学 High-rise building pumped storage scenery intelligent micro-grid system and control method
CN107220889A (en) * 2017-06-30 2017-09-29 四川大学 The distributed resource method of commerce of microgrid community under a kind of many agent frameworks

Non-Patent Citations (4)

* Cited by examiner, † Cited by third party
Title
Demand Response in Smart Distribution System With Multiple Microgrids;H. S. V. S. Kumar Nunna等;《IEEE Transactions on Smart Grid 》;20120904;第3卷(第4期);全文 *
Hierarchical nested predictive control for energy management of multi-microgrids system;Yuanyuan Zou等;《2017 11th Asian Control Conference (ASCC)》;20180208;全文 *
基于改进粒子群算法的微网多目标经济运行策略研究;祖其武等;《电力系统保护与控制》;20170716;第45卷(第14期);全文 *
并网模式下基于多代理技术的微电网多目标优化;王思明等;《华东理工大学学报（自然科学版）》;20171231;第43卷(第6期);全文 *

Also Published As

Publication number Publication date
CN108306288A (en) 2018-07-20

Similar Documents

Publication Publication Date Title
CN108306288B (en) Micro-grid community distributed energy distribution method based on demand side response
CN103606967B (en) A kind of dispatching method realizing electric power system robust and run
US9489701B2 (en) Adaptive energy management system
CN102694391B (en) Day-ahead optimal scheduling method for wind-solar storage integrated power generation system
CN102055215B (en) Control method and operation monitoring system of charging and exchanging station of electric vehicle
US20110082598A1 (en) Electrical Power Time Shifting
CN108321796B (en) Household energy management system and method
WO2019148689A1 (en) Grid-connected tie-line power control system of micro-grid and method thereof
Wang et al. Load curve smoothing strategy based on unified state model of different demand side resources
US11843271B2 (en) Power reception control method for power storage element and power reception control device
CN106992544A (en) Microgrid energy real-time management control method
CN112234638A (en) Power grid peak regulation system and method based on load side intelligent charging pile cluster control
KR101544463B1 (en) A control method for controlling energy of building based ob microgrid and system for same
JP2014096866A (en) Energy management system, energy management method, program, and server device
CN110912200B (en) Cascade hydropower station optimal scheduling system and method and safety power grid system
CN115765015A (en) Source network load storage cooperative interaction scheme making method oriented to power grid practical application scene
US20170102677A1 (en) Power management system, power management method, and controller
Anderson et al. Simulating integrated volt/var control and distributed demand response using GridSpice
Rossi et al. Real-time optimization of the battery banks lifetime in hybrid residential electrical systems
CN114825388A (en) New energy comprehensive consumption scheduling method based on source network load-storage cooperation
Melatti et al. A two-layer near-optimal strategy for substation constraint management via home batteries
CN116780644A (en) Method for cooperatively participating in response of peak shaving demands of power grid by micro-grid source and storage
CN104794557B (en) The load dispatching method of end-user system based on Spot Price under uncertain
CN113746105A (en) Optimal control method, device, equipment and storage medium for power demand response
CN104281984A (en) Power supply method for microgrid economical operation

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant
CF01 Termination of patent right due to non-payment of annual fee
CF01 Termination of patent right due to non-payment of annual fee

Granted publication date: 20210302

Termination date: 20220213