CN111769543B - Regional power distribution network autonomous cooperative operation optimization method containing multiple micro-grids - Google Patents
Regional power distribution network autonomous cooperative operation optimization method containing multiple micro-grids Download PDFInfo
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
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- H—ELECTRICITY
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- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
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- 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
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Abstract
The invention discloses a regional power distribution network autonomous cooperative operation optimization method containing multiple micro-grids, which comprises the following steps in sequence: the power distribution network publishes the price information of electricity purchase and sale; a step of forming a power purchase and sale plan of the micro-grid on the next day; a step of planning power purchase and sale of the DEMS the next day; adjusting the MEMS autonomous optimization; a step of DEMS re-optimization; a step of MEMS passive cooperation; and passively cooperating the DEMS. According to the method, on the basis of considering respective economic operation requirements of the power distribution network and the micro-grid, a method for forming a day-ahead scheduling plan of the regional power distribution network with multiple micro-grids is provided, and a method for passively coordinating the power distribution network after a transaction plan of the micro-grid is changed is also provided, so that the method is beneficial to guiding the safe operation of the regional power distribution network with multiple micro-grids, and is an important basis for building an intelligent power distribution network.
Description
Technical Field
The invention belongs to the field of micro-grid and intelligent power distribution network operation control, and provides a regional power distribution network autonomous cooperative operation optimization method containing multiple micro-grids on the basis of considering respective economic operation requirements of the power distribution network and the micro-grid.
Background
With the gradual decrease of fossil fuels and the increasing severity of environmental pollution, countries around the world are striving to explore energy production modes capable of sustainable development, and strive to change the energy pattern which is currently dominated by fossil fuels. In order to effectively treat environmental pollution and inhibit fossil fuel consumption, china in 2012 established the national renewable energy center, and a series of renewable energy development policies were developed in succession to promote the rapid development and comprehensive popularization of renewable energy in China.
Under the trend of improving the reliability of a power grid and increasing the input of new energy power Generation, the Distributed Generation (DG) technology is widely regarded by countries in the world with the advantages of low investment cost, short construction period, flexible power Generation mode and the like. While the introduction of distributed generation technology brings numerous benefits to the grid, the uncertainty and complexity it brings to the grid is also non-negligible. On one hand, DGs such as wind power and photovoltaic are influenced by environmental factors, the power change randomness is high, and the unstable risk is undoubtedly brought to a power grid by a large number of networking; on the other hand, the access of the DG changes the radial characteristic of the traditional distribution network tide, and when the distribution network has a short-circuit fault, the size and the phase of the current of a short-circuit point can be changed without fail, so that the failure of a relay protection device and a reclosing device is caused, and the safety accident is brought to a power grid. Therefore, in order to reduce the adverse effect of the DG on the power grid, the micro-grid is produced.
The microgrid formed by the energy storage system, the local load and the DGs can meet the load demand of a local area by using the energy storage system and the DGs in an island mode, and can also coordinate the exchange power with a power distribution network in a networking mode to relieve the power transmission pressure of the power distribution network. The flexible switching between the two operation modes enables DGs in the micro-network to have the characteristics of low pollution and high efficiency and have the characteristics of flexibility and intelligence. The micro-grid has great application potential in improving the quality of electric energy, reducing the loss of a power grid and improving the electricity utilization problem of remote areas and islands.
Along with the access of a large number of micro-grids, a multi-micro-grid system formed by cooperation of a plurality of micro-grids gradually appears in a certain area of a distribution network. The multi-microgrid system promotes local consumption of renewable energy sources by coordinating the micro-sources and the energy storage devices of each microgrid, so that greater benefit benefits are brought to each microgrid. However, the multi-microgrid system brings benefits to each microgrid and also brings problems to a distribution network. Due to the fact that the operation targets of various micro-grids in the multi-micro-grid system are different, the operation requirements of the micro-grids are difficult to meet during operation of the multi-micro-grid. Meanwhile, due to the fact that different benefit subjects are involved in the multi-microgrid system, operation control is complex, and serious safety accidents can be caused to a power distribution network with a weak structure due to improper control.
Therefore, the invention provides an autonomous cooperative operation optimization method for a regional power distribution network with multiple micro-grids on the basis of considering respective economic operation requirements of the power distribution network and the micro-grid. The method not only provides a method for forming a day-ahead scheduling plan of the regional power distribution network with multiple micro-grids, but also provides a passive coordination method of the power distribution network after the trading plan of the micro-grid changes, is favorable for guiding the safe operation of the regional power distribution network with multiple micro-grids, and is an important basis for constructing an intelligent power distribution network.
Disclosure of Invention
The invention aims to design an autonomous cooperative operation optimization method for a regional power distribution network with multiple micro-grids.
Therefore, the technical scheme of the invention is as follows:
an autonomous cooperative operation optimization method for a regional distribution network with multiple micro-grids comprises the following steps in sequence:
1) A distribution network publishes an S1 stage of purchasing electricity selling price information: at this stage, the DEMS publishes the electricity selling price p of each time of the next day to each microgrid t MG,sell And the purchase price p of electricity from each microgrid t MG,buy Then entering the S2 stage;
2) The microgrid forms an S2 stage of a next-day electricity purchasing and selling plan: in the stage, each MEMS makes a power trading plan between the MEMS and a power distribution network 24h the next day and uploads the power trading plan to the DEMS according to load prediction data in the micro-grid and renewable energy output prediction data such as WT and PV and the like by taking the optimal economy as a target, and then the S3 stage is started;
3) And an S3 stage of the DEMS power purchase and sale plan on the next day: at this stage, after receiving the power transaction plans uploaded by each MEMS, the DEMS makes a power transaction plan of 24h of the next day and a superior power grid by taking the optimal economic performance as a target according to other power load prediction data in the power grid and output prediction data of other renewable energy sources in the power grid, and simultaneously checks the security of the 24h power grid of the next day. If the running state of the power distribution network at a certain moment causes security threat due to the power trading plan of a certain microgrid, the DEMS issues a new control instruction to the MEMS of the microgrid (for distinguishing from the MEMS of other normal microgrids, the MEMS is marked as the MEMS needing to be adjusted, and the other MEMS is marked as the normal MEMS), and then the S4 stage is started;
4) The S4 stage of MEMS autonomous optimization needs to be adjusted: after receiving the control instruction of the DEMS, the MEMS needing to be adjusted can regulate and control the internal controllable unit again, so that the MEMS can meet the scheduling instruction of the DEMS and the running target of the MEMS. If the MEMS needs to be adjusted to meet the control instruction of the DEMS, an autonomous successful signal is sent to the DEMS; if the MEMS needing to be adjusted cannot meet the control instruction of the DEMS, sending an autonomous unsuccessful signal to the DEMS, uploading the adjusted limit power trading plan to the DEMS, and entering the S5 stage;
5) And (5) an S5 stage of DEMS re-optimization: at this stage, the DEMS receives the signal which is sent by the MEMS and fails to be regulated and the limit electric power trading plan, and issues a re-optimization instruction to the normal MEMS to eliminate the operation risk of the power distribution network. After the transaction plans at all the moments pass safety verification, forming a formal transaction plan of 24h of the next day of each microgrid, issuing the formal transaction plan to each microgrid, and entering a stage S6;
6) S6 stage of MEMS passive synergy: at this stage, if the micro-grid cannot execute a pre-agreed trading plan due to internal reasons of the micro-grid, sending a trading plan adjusting request and an adjusted electric power trading plan value to the DEMS, and entering a stage S7;
7) And an S7 stage of passive cooperation of DEMS: at this stage, the DEMS receives a trading plan adjustment request sent by the MEMS to be adjusted, sends temporarily adjusted price information to the MEMS to be adjusted and other normal MEMS, and formulates a new electric power trading plan value at the moment by taking economic optimization as a target.
In one embodiment, in the step of forming the power purchase and sale plan by the microgrid, the optimization model of the power purchase and sale plan formed by the microgrid on the next day is as follows:
i) optimization objective function of microgrid
The microgrid optimization objective function is the maximum profit of power selling of a microgrid owner in one day, and comprises income or cost of power transaction with a power distribution network, fuel cost of MT and operation and maintenance cost of various distributed power supplies, and specific mathematical expressions are as follows:
wherein gamma is a binary variable, when P is t MG,n >When 0, gamma =1, the nth microgrid sells electricity to the power distribution network, and when P is t MG,n <When 0 is γ =0, it means that the nth microgrid purchased power from the power distribution network; f t MT,n Represents the fuel cost of the MT; c t OP,n The operating maintenance costs for the various distributed power sources.
P t MG,n 、F t MT,n 、C t OP,n The specific calculation formula of (2) is as follows:
wherein, P t MG,n The unit of electric power transaction power of the nth microgrid to the power distribution network at the moment t is kW and P t MG,n More than 0 represents the micro-grid electricity selling power, P t MG,n < 0 represents the micro-grid power purchase; p t PV,n The generating power of the photovoltaic equipment in the nth microgrid at the moment t is expressed in kW; p t WT,n The generating power of the fan equipment in the nth microgrid at the moment t is expressed in kW; p t MT,n The generating power of the micro gas turbine in the nth microgrid at the time t is expressed in kW; p t ES,n The discharge power of the internal energy storage equipment of the nth microgrid at the moment t is represented in unit of kW and P t ES,n > 0 denotes the discharge power, P t ES,n < 0 indicates charging power; p t L,n The power consumption of the internal load of the nth microgrid at the moment t is expressed in kW; NM n The number of the micro gas turbines in the nth microgrid is represented in unit; c nl Is the natural gas price, and has the unit $/m 3 (ii) a L is natural gas heat value and has a unit of kWh/m 3 (ii) a Eta represents the power generation efficiency of the gas turbine in%, P t MT,n,m The active power output of the mth micro gas turbine in the nth microgrid is represented; NW n The number of the fans in the nth microgrid is represented in unit; NP n The number of photovoltaic cells in the nth microgrid is expressed in unit; NE n The number of stored energy in the nth microgrid is expressed in unit; p t n,k The output of a kth unit in the nth microgrid is expressed in kW; k OP k And the operation and maintenance cost coefficient of the kth set in the nth microgrid is represented.
II) microgrid operational constraints
In the formula, P t PV,n,m The generating power of the mth photovoltaic equipment in the nth microgrid at the time t is represented in kW; p t WT,n,m The generating power of the mth wind turbine equipment in the nth microgrid at the time t is represented by kW; p t MT,n,m The generating power of the mth micro gas turbine in the nth microgrid at the time t is represented by kW; p t ES,n,m The discharge power of the mth energy storage equipment in the nth microgrid at the time t is represented in unit of kW and P t ES,n,m > 0 denotes the discharge power, P t ES,n,m < 0 indicates charging power; p t L,n,m The power consumption of the mth load in the nth microgrid at the time t is represented by kW; NL n And the number of the loads in the nth microgrid is expressed in units of one load.
In the formula (I), the compound is shown in the specification,
the maximum power of the micro-grid for purchasing electricity from the power distribution network is represented in kW;
the minimum power generation power of the mth micro gas turbine in the nth microgrid at the time t is represented in kW;
the nth microgrid generates the minimum power, namely the maximum charging power, of the mth energy storage device at the time t,
the unit is kW.
In the formula (I), the compound is shown in the specification,
the maximum power of electricity sold to the power distribution network by the micro-grid is represented in kW;
the maximum power generation power of the mth micro gas turbine in the nth microgrid at the time t is represented by kW;
the maximum power generation power of the mth energy storage equipment in the nth microgrid at the moment t is kW.
In the formula, E t ES,n,m The energy of the mth energy storage device in the nth microgrid at the time t is represented in the unit of kWh; e t -1 ES,n,m The energy of the mth energy storage device in the nth microgrid at the time t-1 is represented in the unit of kWh; p t-1 ES,n,m The discharge power of the mth energy storage equipment in the nth microgrid at the time t-1 is represented in kW;
the minimum discharge power, namely the maximum charging power, of the mth energy storage device in the nth microgrid is represented in kW;
the maximum charging power of the mth energy storage equipment in the nth microgrid is represented in kW; SOC t ES,n,m The charge state of the mth energy storage equipment in the nth microgrid at the time t is represented in unit; SOC (system on chip) min ES,n,m The minimum charge state of the mth energy storage device in the nth microgrid at the time t is represented in unit; SOC max ES,n,m And the maximum charge state of the mth energy storage device in the nth microgrid at the time t is represented in percentage.
In one embodiment, in the step of the DEMS power purchase and sale plan the next day, the optimization model of the DEMS power purchase and sale plan the next day is as follows:
a) Target function for optimizing autonomous operation of power distribution network
The self-disciplined operation optimization objective function of the power distribution network is that the power distribution operator has the maximum profit of electricity sale in one day, and the self-disciplined operation optimization objective function consists of five parts, namely electricity sale income for non-microgrid users, electricity sale income for a microgrid, electricity sale income for a superior power grid, cost for purchasing electricity from the superior power grid, and cost for purchasing electricity from the microgrid, wherein the specific mathematical expression is as follows:
wherein, P t L The total load power of non-microgrid users in the whole power distribution network at the moment t is in kW; p is a radical of t L For the price of electricity sold by the power distribution network to the non-microgrid users at the time t, the unit is yuan/kWh; alpha is alpha n Representing a binary variable, when P t MG,n <At 0, α n =1, when P t MG,n >At 0 time, α n ＝0；p t MG,sell The unit is yuan/kWh for the electricity selling price of the power distribution network to the micro grid at the time t; beta represents a binary variable, when P t Grid,n <0, β =1, when P is t Grid,n >0, β =0; p is a radical of t Grid,sell The unit is yuan/kWh for the electricity selling price of the power distribution network to the superior power grid at the moment t; p is a radical of t MG,buy The price of electricity, bill of purchasing electricity from the microgrid for the power distribution network at the moment of tYuan is Yuan/kWh; p is a radical of t Grid,buy The unit is yuan/kWh for the electricity price purchased by the power distribution network from the superior power grid at the moment t; t is a scheduling time interval, which is 1 hour.
2) Self-discipline operation optimization constraint condition of power distribution network
wherein, δ (j) represents a branch head node set taking j as a tail node; ξ (j) represents a set of branch end nodes with j as the head-end node; p is t ij And Q t ij Respectively the active power and the reactive power flowing to the node j from the node i at the moment t; p t i And Q t i Respectively the active power and the reactive power of a node i at the moment t, wherein the unit is kW and kvar; r is ij And x ij Resistance and reactance, respectively, of line ijIn units of Ω; i.e. i t ij The unit of the current amplitude on the line ij at the time t is kA; u. of t i The voltage amplitude of the node i at the time t is in kV; p t Grid,i And Q t Grid,i The active power and the reactive power injected into a superior power grid at a node i at the time t are represented by kW and kvar respectively; p t load,i And Q t load,i The active load and the reactive load at the load node i at the moment t are represented by kW and kvar respectively; p t MG,i Representing the power trading power P of the microgrid and the power distribution network at the load node i at the moment t t MG,i >0 represents the power purchase of the microgrid from the power distribution network, P t MG,i <0 represents that the micro-grid sells electricity to the power distribution network, and the unit is kW; p t DG,i The active power output of DG at the node i at the moment t is in kW.
in the formula u max 、u min Respectively representing the upper limit and the lower limit of voltage allowed by the node, wherein the unit is kV; i.e. i max The maximum current allowed for the line is given in kA.
in the formula: p t Grid The unit of power exchange between a power distribution network and a superior power grid at the moment t is kW;
the unit is kW, which is the upper limit of the exchange power between the power distribution network and the superior power grid.
in the formula: p t MG,n The unit of power is kw for power exchange between the power distribution network and the nth microgrid at the time t;
the unit of the upper limit of the power exchange between the power distribution network and the nth microgrid is kW;
the lower limit of the power exchange between the power distribution network and the nth microgrid is kW.
Advantageous effects
The technical scheme provided by the embodiment of the invention has the following beneficial effects: with the massive access of distributed power generation equipment and a micro-grid to a power distribution network, the consumption of renewable energy resources is greatly promoted, but a huge challenge is brought to the operation control of a power distribution system. The method considers the respective economic operation requirements of the power distribution network and the micro-grid, provides the self-discipline cooperative operation optimization method of the regional power distribution network containing multiple micro-grids, provides a method for forming a day-ahead scheduling plan of the regional power distribution network containing multiple micro-grids, also provides a passive cooperative method of the power distribution network after the transaction plan of the micro-grid is changed, is favorable for guiding the safe operation of the regional power distribution network containing multiple micro-grids, and is an important basis for constructing the intelligent power distribution network.
Drawings
FIG. 1 is a diagram of a network architecture for an IEEE-33 node power distribution system including three micro-grids;
FIG. 2 is a diagram of a microgrid architecture;
FIG. 3 is a daily load curve for a microgrid;
fig. 4 is a microgrid solar irradiance prediction curve;
FIG. 5 is a microgrid wind speed prediction curve;
FIG. 6 is a daily load curve for the distribution network;
fig. 7 is a microgrid 1 autonomous operation optimized scheduling plan;
fig. 8 is a microgrid 2 autonomous operation optimized scheduling plan;
fig. 9 is a microgrid 3 autonomous operation optimization scheduling plan;
FIG. 10-1 is a schematic diagram of branch current in an autonomous operation optimization scheduling plan of a power distribution network;
FIG. 10-2 is a schematic diagram of node voltages in a power distribution network autonomous operation optimization scheduling plan;
fig. 10-3 is a schematic diagram of the power purchased by the large power grid in the power distribution network autonomous operation optimization scheduling plan;
fig. 11 is a microgrid 2 passive cooperative operation optimized scheduling plan;
FIG. 12-1 is a schematic node voltage diagram of a power distribution network passive cooperative operation optimization scheduling plan;
FIG. 12-2 is a schematic branch current diagram of a power distribution network passive cooperative operation optimized dispatch plan;
FIG. 12-3 is a schematic diagram of the power purchased by the large power grid of the power distribution network passive cooperative operation optimized dispatching plan;
fig. 13 is a flowchart of steps of a method for optimizing autonomous cooperative operation of a regional distribution network including multiple micro-grids;
Detailed Description
The invention will be further described with reference to the following figures and specific examples, which are not intended to limit the invention in any way.
The method is established on a regional power Distribution network comprising a plurality of Micro-grids, each Micro-grid comprises one or more distributed power supplies of wind power (WT), photovoltaic (PV), a Micro gas turbine (MT) and a battery Energy storage System (ES), each Micro-grid optimizes the output plan of a controllable power generation unit in the Micro-grid through a Micro-grid Energy Management System (MEMS), and performs information interaction with a power Distribution Management mechanism of the power Distribution network, and the power Distribution Management mechanism of the power Distribution network is served by the power Distribution Energy Management System (DEMS) and is responsible for completing cooperation and regulation with each Micro-grid and processing temporary conflict of power exchange plans between the Micro-grids and the power Distribution network.
FIG. 1 is a block diagram of an IEEE-33 node power distribution system including three micro-grids.
Fig. 2 is a structural diagram of each microgrid.
And a stage S1: setting the time-sharing transaction electricity prices between the micro-grid and the distribution network and between the distribution network and the superior grid as shown in table 1;
TABLE 1 time-of-use electricity price
And S2 stage: the daily load curves of the three micro-grids are set as shown in fig. 3, the solar irradiance prediction curve of the location of the micro-grid is set as shown in fig. 4, the wind speed prediction curve of the location of the micro-grid is set as shown in fig. 5, the cut-in wind speed, the cut-out wind speed and the rated wind speed of the fan are the same and are respectively set as 2m/s, 24m/s and 13.5m/s, the price of natural gas is 3.05 mm/m 3, and the equipment parameters of the micro-grid are shown in table 2. Each micro-grid takes the optimal economy as a target, a power trading plan between the micro-grid and a power distribution network is made 24h the day, and the power trading plan is reported to the DEMS and enters the S3 stage;
TABLE 2 microgrid device parameters
And a stage S3: the daily reference load curve of the power distribution network is set as shown in fig. 6, the IEEE-33 node load value is set as shown in table 3, the upper limit value and the lower limit value of the node voltage of the power distribution network are 11.394kV (0.9p.u.), 13.926kV (1.1p.u.), and the maximum current-carrying capacity of the line is 0.353kA (0.44p.u.). The DEMS obtains a next-day power dispatching plan through calculation, the operation safety of the system is checked, if the operation state of the power distribution network at a certain moment is unsafe, the DEMS sends a new control instruction to the MEMS of the microgrid, and then the S4 stage is started;
TABLE 3 IEEE-33 node load values
And S4 stage: after receiving the control instruction of the DEMS, the MEMS needs to be adjusted, and then the internal controllable units of the MEMS are regulated again, so that the MEMS can meet the scheduling instruction of the DEMS and the running target of the MEMS. If the MEMS needs to be adjusted to meet the control instruction of the DEMS, an autonomous successful signal is sent to the DEMS; if the MEMS needing to be adjusted cannot meet the control instruction of the DEMS, sending a signal of no-discipline success to the DEMS, uploading the adjusted limit electric power trading plan to the DEMS, and entering the S5 stage;
and a stage S5: the DEMS receives the signal which is sent by the MEMS and unsuccessful in self-discipline and the limit power trading plan, and issues a re-optimization instruction to the normal MEMS to eliminate the operation risk of the power distribution network. After the transaction plans at all the moments pass safety verification, forming a formal transaction plan of 24h of the next day of each microgrid, issuing the formal transaction plan to each microgrid, and entering a stage S6;
and S6 stage: if the micro-grid cannot execute a pre-agreed trading plan at a certain moment due to internal reasons of the micro-grid, sending a trading plan adjusting request and an adjusted electric power trading plan value to the DEMS, and entering a stage S7;
and a stage S7: the DEMS receives a trading plan adjustment request sent by the MEMS to be adjusted, sends temporarily adjusted price information to the MEMS to be adjusted and other normal MEMS, and makes a new electric power trading plan value at the moment by taking economic optimality as a target.
The first embodiment is as follows: regional power distribution network autonomous operation optimization scheduling containing multiple micro-grids
The model according to fig. 1 provides the following example:
in the day-ahead scheduling stage, each MEMS makes a power trading plan between the three MGs and the power distribution network in the next 24h by taking optimal economic operation as a target according to load prediction data and renewable energy output prediction data in the own microgrid and uploads the power trading plan to the DEMS. The output plans and power trading plans of the three micro-grids obtained according to the day-ahead optimization scheduling calculation are shown in fig. 7-9, and the trading electric quantity and the trading cost are shown in table 4.
TABLE 4 transaction amount of electricity and cost
After receiving the power trading plan independently formulated by each MEMS, the DEMS performs the power distribution network autonomous operation optimization scheduling according to the load prediction data of the non-microgrid loads in the distribution network and the output prediction data of the non-microgrid renewable energy sources in the distribution network by taking the most economic operation of the power distribution network as a target, and the result is shown in FIG. 10.
And according to the optimization result, the safety of the 24h power distribution system is checked, and the running states of all the nodes and the branches are found to be normal without the out-of-limit condition. Therefore, the scheduling plan before the next day can be applied to the operation of the microgrid and the power distribution network in the next day. The daily power grid electricity purchasing cost of the power distribution system is 43496.525 yuan, the daily micro-grid electricity purchasing cost is 3342.002 yuan, and the daily operation total cost is 46838.527 yuan.
Example two: passive cooperative optimization scheduling method for regional power distribution network comprising multiple micro-grids
In order to simulate passive cooperative optimization scheduling of a regional power distribution network with multiple micro-grids, it is assumed that in the day operation stage, starting at the time when t =0, a micro-combustion engine of the micro-grid 2 breaks down, the fault lasts for 24 hours, and other micro-grid devices can normally operate. The MEMS of the microgrid 2 is marked as non-autonomous at this time and a new power trading value is communicated to the DEMS at time t = 0. The operation scheduling situation of the microgrid 2 in the trading day is shown in fig. 11.
Since the day-ahead scheduling plans of the microgrid 1 and the microgrid 3 achieve the maximization of benefits of the microgrid, the self power transaction condition is not changed. After the DEMS receives the new power trading value transmitted by the MEMS of the non-autonomous micro-grid 2, the power distribution network punishs the behavior of the non-autonomous micro-grid 2 to reduce the difference value between the new power trading value and the 'autonomous' value. And the electricity purchase price of the microgrid 2 is increased by 0.1 yuan/kWh on the basis of the table 1, and the electricity purchase prices of the microgrid 1 and the microgrid 3 are decreased by 0.1 yuan/kWh on the basis of the table 1. The power distribution network performs power distribution network optimization scheduling with the objective of economic optimization, and the result is shown in fig. 12.
After adjustment, the three micro-grids can execute the current scheduling instruction under the current condition, and the regulation and control strategy is formulated. The power distribution system has 46800.300 yuan for large power grid electricity purchasing cost on a transaction day, 2274.339 yuan for micro power grid electricity purchasing cost on the transaction day, and 49074.639 yuan for total operation cost on the transaction day. The microgrid 2 has a trade daily electricity purchase fee of 1999.028 yuan, a trade daily gas purchase fee of 0 yuan, a trade daily operation and maintenance fee of 25.286 yuan and a trade daily total fee of 2024.314 yuan.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice in the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
It will be understood that the present disclosure is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure should be limited only by the attached claims.
Claims (5)
1. An autonomous cooperative operation optimization method for a regional distribution network with multiple micro-grids is characterized by comprising the following steps in sequence:
step of publishing electricity purchasing and selling price information by power distribution networkA step of; specifically, the DEMS publishes the electricity selling price p of each time of the next day to each microgrid t MG,sell And the purchase price p of electricity from each microgrid t MG,buy Then entering a micro-grid to form a power purchase and sale plan of the next day;
a step of forming a power purchase and sale plan of the micro-grid on the next day; specifically, each MEMS makes a power transaction plan of 24h and a power distribution network on the next day of the MEMS according to load prediction data and WT and PV renewable energy output prediction data in the microgrid of the MEMS and by taking the optimal economy of the MEMS as a target, uploads the power transaction plan to the DEMS, and then enters a power purchase and sale plan on the next day of the DEMS;
a step of planning power purchase and sale of the DEMS the next day; specifically, after receiving the power transaction plans uploaded by each MEMS, the DEMS makes a power transaction plan of 24h of the next day and a superior power grid by taking the optimal economy as a target according to other power load prediction data in the power grid and output prediction data of other renewable energy sources in the power grid, and simultaneously checks the security of the 24h power grid of the next day; if the running state of the power distribution network at a certain moment causes security threat due to the power trading plan of a certain microgrid, the DEMS issues a new control instruction to the MEMS of the microgrid, and then the step of adjusting the MEMS autonomous optimization is carried out;
adjusting the MEMS autonomous optimization;
a step of DEMS re-optimization;
a step of MEMS passive cooperation;
a step of passive cooperation of DEMS;
in the step of forming the power purchase and sale plan next day by the microgrid, an optimization model of forming the power purchase and sale plan next day by the microgrid is as follows:
i) optimization objective function of microgrid
The microgrid optimization objective function is the maximum profit of power selling of a microgrid owner in one day, and comprises income or cost of power transaction with a power distribution network, fuel cost of MT and operation and maintenance cost of various distributed power supplies, and specific mathematical expressions are as follows:
wherein gamma is a binary variable, when P is t MG,n >When 0, gamma =1, the nth microgrid sells electricity to the power distribution network, and when P is t MG,n <When 0 is γ =0, it means that the nth microgrid purchased power from the power distribution network; f t MT,n Represents the fuel cost of the MT; c t OP,n Operating and maintaining costs for various distributed power sources;
P t MG,n 、F t MT,n 、C t OP,n the specific calculation formula of (2) is as follows:
wherein, P t MG,n The unit of electric power transaction power of the nth microgrid to the power distribution network at the moment t is kW and P t MG,n More than 0 represents the micro-grid electricity selling power, P t MG,n The power purchased by the microgrid is less than 0; p t PV,n The generating power of the photovoltaic equipment in the nth microgrid at the moment t is expressed in kW; p t WT,n The generating power of the fan equipment in the nth microgrid at the moment t is expressed in kW; p is t MT,n The generating power of the micro gas turbine in the nth microgrid at the time t is expressed in kW; p t ES,n The discharge power of the internal energy storage equipment of the nth microgrid at the moment t is represented in unit of kW and P t ES,n > 0 denotes the discharge power, P t ES,n < 0 indicates charging power；P t L,n The power consumption of the internal load of the nth microgrid at the moment t is expressed in kW; NM n The number of the micro gas turbines in the nth microgrid is represented in unit; c nl Is the natural gas price, and has the unit of $/m 3 (ii) a L is natural gas heat value and has a unit of kWh/m 3 (ii) a Eta represents the power generation efficiency of the gas turbine in%, P t MT,n,m The active power output of the mth micro gas turbine in the nth microgrid is represented; NW n The number of the fans in the nth microgrid is represented in unit; NP n The number of photovoltaic cells in the nth microgrid is expressed in units of one; NE n The energy storage number in the nth microgrid is represented in units of one microgrid; p t n,k The output of a kth unit in the nth microgrid is expressed in kW; k OP k Representing the operation and maintenance cost coefficient of the kth set in the nth microgrid;
II) microgrid operational constraints
Micro-grid internal power balance constraint
In the formula, P t PV,n,m The generating power of the mth photovoltaic equipment in the nth microgrid at the time t is represented in kW; p t WT,n,m The generating power of the mth wind turbine equipment in the nth microgrid at the time t is represented by kW; p t MT,n,m The generating power of the mth micro gas turbine in the nth microgrid at the time t is represented by kW; p t ES,n,m The discharge power of the mth energy storage equipment in the nth microgrid at the moment t is represented in unit of kW and P t ES,n,m > 0 denotes the discharge power, P t ES,n,m < 0 indicates charging power; p t L,n,m The power consumption of the mth load in the nth microgrid at the time t is represented by kW; NL n The number of loads in the nth microgrid is represented in unit of one load;
power trading power constraints for microgrid and power distribution network
Absolute value of maximum power of power purchase of micro-grid from power distribution network
In the formula (I), the compound is shown in the specification,
the maximum power of the micro-grid for purchasing electricity from the power distribution network is represented in kW;
the minimum power generation power of the mth micro gas turbine in the nth microgrid at the time t is represented in kW;
the nth microgrid generates the minimum power, namely the maximum charging power, of the mth energy storage device at the time t,
the unit is kW;
maximum power of electricity sold by micro-grid to distribution network
In the formula (I), the compound is shown in the specification,
the maximum power of electricity sold by the microgrid to the power distribution network is represented by kW;
the maximum power generation power of the mth micro gas turbine in the nth microgrid at the time t is represented by kW;
the maximum power generation power of the mth energy storage equipment in the nth microgrid at the time t is in kW;
restraint of stored energy
In the formula, E t ES,n,m The energy of the mth energy storage device in the nth microgrid at the time t is represented in the unit of kWh; e t-1 ES,n,m The energy of the mth energy storage device in the nth microgrid at the time t-1 is represented in the unit of kWh; p t-1 ES,n,m The discharge power of the mth energy storage equipment in the nth microgrid at the time of t-1 is represented by kW;
the minimum discharge power, namely the maximum charging power, of the mth energy storage device in the nth microgrid is represented in kW;
the maximum charging power of the mth energy storage equipment in the nth microgrid is represented in kW; SOC t ES,n,m The charge state of the mth energy storage equipment in the nth microgrid at the time t is represented in unit; SOC min ES,n,m Indicating that the nth microgrid is within the time tThe minimum charge state of the mth energy storage device is expressed in units of percent; SOC max ES,n,m The maximum charge state of the mth energy storage device in the nth microgrid at the time t is represented in unit;
in the step of the DEMS power purchase and sale plan the next day, the optimization model of the DEMS power purchase and sale plan the next day is as follows:
a) Self-discipline operation optimization objective function of power distribution network
The self-discipline operation optimization objective function of the power distribution network is that the power distribution operator has the maximum profit for electricity sale in one day, and the self-discipline operation optimization objective function consists of five parts, for electricity sale income of non-microgrid users, electricity sale income of a microgrid, electricity sale income of a superior power grid, cost for purchasing electricity from the superior power grid and cost for purchasing electricity from the microgrid, and a specific mathematical expression is as follows:
wherein, P t L The total load power of non-microgrid users in the whole power distribution network at the moment t is in kW; p is a radical of t L The unit is yuan/kWh for the electricity selling price of the power distribution network to the non-microgrid users at the time t; alpha is alpha n Representing a binary variable, when P t MG,n <At 0 time, α n =1, when P t MG,n >At 0 time, α n ＝0；p t MG,sell The unit is yuan/kWh for the electricity selling price of the power distribution network to the micro grid at the time t; beta represents a binary variable, when P t Grid,n <When 0, β =1, when P t Grid,n >0, β =0; p is a radical of t Grid,sell The unit is yuan/kWh for the electricity selling price of the power distribution network to the superior power grid at the moment t; p is a radical of t MG,buy The unit is yuan/kWh for the electricity price of the power distribution network for purchasing electricity from the microgrid at the moment t; p is a radical of t Grid,buy The unit is yuan/kWh for the electricity price of the power distribution network for purchasing electricity from a superior power grid at the moment t; t is a scheduling time interval which is 1 hour;
2) Self-discipline operation optimization constraint condition of power distribution network
And (3) power flow constraint:
wherein δ (j) represents a branch head-end node set taking j as a tail-end node; ξ (j) represents a set of branch end nodes with j as the head-end node; p t ij And Q t ij Respectively the active power and the reactive power flowing to the node j at the node i at the moment t; p t i And Q t i Respectively the active power and the reactive power of a node i at the moment t, wherein the unit is kW and kvar; r is ij And x ij The resistance and reactance of the line ij are respectively, and the unit is omega; i.e. i t ij The unit of the current amplitude on the line ij at the time t is kA; u. of t i The voltage amplitude of the node i at the time t is in kV; p t Grid,i And Q t Grid,i The active power and the reactive power injected into a superior power grid at a node i at the time t are represented by kW and kvar respectively;P t load,i and Q t load,i The active load and the reactive load at the load node i at the moment t are represented by kW and kvar respectively; p t MG,i Representing the power trading power P of the microgrid and the power distribution network at the load node i at the moment t t MG,i >0 represents that the microgrid purchases electricity from the power distribution network, P t MG,i <0 represents that the microgrid sells electricity to the power distribution network, and the unit is kW; p t DG,i The active output of DG at a node i at the time t is in kW;
voltage and branch transmission power constraints:
in the formula u max 、u min Respectively representing the upper limit and the lower limit of voltage allowed by the node, wherein the unit is kV; i.e. i max The maximum current allowed by the line is represented by kA;
and (3) power exchange constraint between the power distribution network and a superior power grid:
in the formula: p t Grid The unit of power exchange between a power distribution network and a superior power grid at the moment t is kW;
the unit is kW, which is the upper limit of the exchange power between the power distribution network and the superior power grid;
and (3) power exchange constraint between the power distribution network and the microgrid:
in the formula: p t MG,n The unit of power is kw for power exchange between the power distribution network and the nth microgrid at the time t;
the unit of the upper limit of the power exchange between the power distribution network and the nth microgrid is kW;
the lower limit of the power exchange between the power distribution network and the nth microgrid is kW.
2. The method for optimizing the autonomous cooperative operation of the regional power distribution network with multiple micro grids as claimed in claim 1, wherein the step of the autonomous optimization of the MEMS to be adjusted is to adjust the controllable unit in the MEMS after receiving the control instruction of the DEMS, so that the MEMS can satisfy both the scheduling instruction of the DEMS and the operation target of the MEMS; if the MEMS needs to be adjusted to meet the control instruction of the DEMS, an autonomous successful signal is sent to the DEMS; and if the MEMS needing to be adjusted cannot meet the control instruction of the DEMS, sending a signal of unsuccessful self-discipline to the DEMS, uploading the adjusted limit power trading plan to the DEMS, and entering the step of re-optimization of the DEMS.
3. The method for optimizing the autonomous cooperative operation of the regional power distribution network with multiple micro grids according to claim 1, wherein the step of re-optimizing the DEMS comprises the steps of receiving an unsuccessful autonomous signal and a limit power trading plan which are sent by an MEMS (micro electro mechanical system) to be adjusted, and issuing a re-optimizing instruction to a normal MEMS to eliminate the operation risk of the power distribution network; and after the transaction plans at all the moments pass safety verification, forming a formal transaction plan of 24h of the next day of each micro-grid, issuing the formal transaction plan to each micro-grid, and entering the step of MEMS passive cooperation.
4. The method for optimizing autonomous cooperative operation of the regional power distribution network including multiple micro grids according to claim 1, wherein the step of MEMS passive cooperation is specifically a step of sending a trade plan adjustment request and an adjusted power trade plan value to the DEMS and entering DEMS passive cooperation if the micro grids cannot execute a pre-agreed trade plan due to internal reasons of the micro grids.
5. The method for optimizing autonomous cooperative operation of the regional power distribution network including multiple micro grids according to claim 1, wherein the DEMS passively cooperates with each other by receiving a trade plan adjustment request sent by the MEMS to be adjusted, sending temporarily adjusted price information to the MEMS to be adjusted and other normal MEMS, and making a new electric trade plan value with economic optimization as a target.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103824235A (en) * | 2014-03-18 | 2014-05-28 | 国家电网公司 | Method for making power generation absorption scheme for new energy power stations based on price bidding |
| CN105515005A (en) * | 2015-12-14 | 2016-04-20 | 天津大学 | Multi-microgrid system optimization method based on open market environment |
| CN107292449A (en) * | 2017-07-18 | 2017-10-24 | 广东双新电气科技有限公司 | One kind is containing the scattered collaboration economic load dispatching method of many microgrid active distribution systems |
| CN107392395A (en) * | 2017-08-23 | 2017-11-24 | 天津大学 | A kind of power distribution network and micro electric network coordination optimization method based on price competition mechanism |
| CN109274099A (en) * | 2018-11-26 | 2019-01-25 | 国网河南省电力公司电力科学研究院 | Active distribution network layering and zoning coordinated scheduling method and device |
-
2020
- 2020-03-24 CN CN202010215336.5A patent/CN111769543B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103824235A (en) * | 2014-03-18 | 2014-05-28 | 国家电网公司 | Method for making power generation absorption scheme for new energy power stations based on price bidding |
| CN105515005A (en) * | 2015-12-14 | 2016-04-20 | 天津大学 | Multi-microgrid system optimization method based on open market environment |
| CN107292449A (en) * | 2017-07-18 | 2017-10-24 | 广东双新电气科技有限公司 | One kind is containing the scattered collaboration economic load dispatching method of many microgrid active distribution systems |
| CN107392395A (en) * | 2017-08-23 | 2017-11-24 | 天津大学 | A kind of power distribution network and micro electric network coordination optimization method based on price competition mechanism |
| CN109274099A (en) * | 2018-11-26 | 2019-01-25 | 国网河南省电力公司电力科学研究院 | Active distribution network layering and zoning coordinated scheduling method and device |
Non-Patent Citations (1)
| Title |
|---|
| 多微网智能配电***协同自律运控模式初探;赵毅等;《电网技术》;20180212;第42卷(第04期);1200-1209 * |
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