CN107622324A - A kind of robust environmental economy dispatching method for considering more microgrid energy interactions - Google Patents
A kind of robust environmental economy dispatching method for considering more microgrid energy interactions Download PDFInfo
- Publication number
- CN107622324A CN107622324A CN201710777335.8A CN201710777335A CN107622324A CN 107622324 A CN107622324 A CN 107622324A CN 201710777335 A CN201710777335 A CN 201710777335A CN 107622324 A CN107622324 A CN 107622324A
- Authority
- CN
- China
- Prior art keywords
- microgrid
- power
- model
- robust
- formula
- 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.)
- Pending
Links
Classifications
-
- 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
- Pharmaceuticals Containing Other Organic And Inorganic Compounds (AREA)
Abstract
The invention discloses a kind of robust environmental economy dispatching method for considering more microgrid energy interactions, this method comprises the following steps：Under the fast-developing background of microgrid support active distribution network, the energetic interaction between more microgrids is taken into full account；The foundation of the model and cost model of renewable energy power generation in micro-capacitance sensor；Establish the environmental economy scheduling model for considering more microgrid energy interactions；Consider regenerative resource and the uncertainty of load, establish the robust environmental economy scheduling model for considering more microgrid energy interactions, be sampled using Latin hypercube method, robust environmental economy scheduling model is converted into robust deterministic models；Model is determined using the above-mentioned robust of multiple target chemotaxis Algorithm for Solving, finds Pareto optimal solutions.The present invention has taken into full account regenerative resource and the uncertainty of load prediction, and considers the energetic interaction between more microgrids so that result of calculation is more approached with actual conditions, and reasonability is strong, and reliable basis are provided for Economical Operation of Power Systems work.
Description
Technical Field
The invention relates to the technical field of economic dispatching of power systems, in particular to a robust environment economic dispatching method considering multi-microgrid energy interaction.
Background
With the increasing penetration degree of distributed power sources (photovoltaic power generation, wind power generation, micro gas turbines, fuel cells, cogeneration sets and the like) which rely on renewable energy sources for power generation, active power distribution networks have been produced as a concept of modern power systems. In fact, active distribution networks are a future generation of power networks that involve all the changes of the traditional distribution network from generation to transmission to distribution. An important part of the development of active power distribution networks is the microgrid, which is formed by a low-voltage network and is designed to meet the electrical, thermal and cold load requirements of a town or a residence, education, commercial park or commercial area. Therefore, the traditional power distribution network will evolve into an active power distribution network containing a microgrid and a large number of distributed power sources. It is worth paying attention to the study of energy management and control and operation optimization of a single microgrid by scholars at home and abroad at present, less attention is paid to the study of optimized scheduling of a microgrid group formed by multiple microgrids, and the multiple microgrids are connected into a system and then have influence on the aspects of system operation, stability, benefit and the like.
Robust optimization is a new optimization method for solving uncertain environments of internal structures (such as parameters) and external environments (such as disturbance). In recent years, it has been increasingly applied to power system scheduling to take into account the uncertainty of renewable energy generation.
In the current few multi-microgrid scheduling researches, power interaction among the microgrids is rarely considered, pollutant gas emission of controllable distributed power generation units in the multi-microgrid is rarely considered, and uncertainty of renewable energy power generation and load prediction in the microgrid is also rarely considered.
In summary, it is necessary to invent a robust environmental economic dispatching optimization method considering multi-microgrid energy interaction aiming at considering multi-microgrid energy interaction, renewable energy power generation and load prediction inaccuracy.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide a robust environment economic dispatching method considering multi-microgrid energy interaction, which comprehensively considers the uncertainty of energy interaction among microgrids, renewable energy power generation and load prediction.
In order to solve the technical problems, the invention is realized by the following technical scheme:
a robust environment economic dispatching method considering multi-microgrid energy interaction comprises the following steps:
the method comprises the following steps: under the condition that a microgrid supports rapid development of an Active Distribution Network (ADN), energy interaction among multiple microgrids is fully considered;
step two: establishing a renewable energy power generation model and cost models of all power generation units based on the microgrid;
step three: establishing an environmental economic dispatching model considering multi-microgrid energy interaction according to the renewable energy power generation model and the cost models of all the power generation units in the step two;
step four: according to the environmental economic dispatching model in the third step, considering the uncertainty of renewable energy sources and loads, establishing a robust environmental economic dispatching model considering multi-microgrid energy interaction, sampling by adopting a Latin hypercube method, and converting the robust environmental economic dispatching model into a robust deterministic model;
step five: and solving the robust determination model by adopting a multi-target chemotaxis algorithm to find a Pareto optimal solution.
Further, in the first step, under the condition that an Active Distribution Network (ADN) is rapidly developed, energy interaction among multiple piconets is fully considered, that is, electricity purchasing and electricity selling costs among piconets are considered;
in the method of the invention, all the piconets are connected together to the large power grid, so that each piconet is able to inject power into the other piconets or the large power grid. By selling power to other microgrids or to large grids, microgrids may increase their revenue, as opposed to reducing their cost. Similarly, if the power generation of one microgrid cannot meet the demand of its own load, it may purchase power from another microgrid or a large power grid to supplement its own demand, in which case the microgrid represents an increase in cost. The rate of electricity purchased and sold by a microgrid may be expressed by the following equation:
taking three piconets as an example, the electricity purchased by MG1 in the above formula can be represented as:
in the formula (I), the compound is shown in the specification,the cost for purchasing electric energy for the microgrid m;the cost for selling electricity for the microgrid m; m is the number of the microgrids; r is a radical of hydrogen m Selling unit price of electricity for the micro-grid m; d is the price of electricity sold by the large power grid;microgrid m slave microgrid m 2 Purchasing electric quantity;for m-direction microgrid m 2 The amount of electricity sold;purchasing electric quantity from a large power grid for the micro-grid m;selling electricity to a large power grid for the microgrid m.
Further, in the second step, the specific process of establishing the renewable energy power generation model is as follows:
1. wind power generation modeling
The wind speed is expressed by a Weibull distribution, and the formula is as follows:
converting wind speed to power, as follows:
wherein v is the wind speed; alpha and beta are proportion parameters and shape parameters of a Weibull function; v. of ci Wind turbine cut-in speed; v. of co Cutting out the speed for the wind turbine; v. of r Rated rotating speed of the wind driven generator; p is a radical of G,WT Generating electrical power for a wind turbine; p is a radical of formula WT,r Rated power is generated for wind power.
2. Photovoltaic power generation modeling
The illumination intensity and the temperature are two important influence factors of photovoltaic power generation. The photovoltaic power generation output power in the invention can be represented by the following formula:
in the formula, p pv Is photovoltaic power generation amount; p is a radical of STC Standard of meritPhotovoltaic power generation capacity under illumination intensity and temperature; g ING Actual illumination intensity; g STC Standard illumination intensity; k is the highest power temperature coefficient; t is a unit of c A standard temperature; t is r The actual temperature.
In the second step, the specific process of establishing the cost model of the power generation unit is as follows:
1. wind power and photovoltaic cost modeling
Wind power and photovoltaic are renewable clean energy sources, and the power generation cost can be regarded as zero. The maintenance cost is as follows:
c om,WT ＝k om,WT ×p WT (8)
c om,PV ＝k om,PV ×p PV (9)
in the formula, p WT 、p PV Wind power generation and photovoltaic power generation are respectively adopted; k is a radical of om,WT 、k om,PV The unit power operation and maintenance costs of wind power and photovoltaic are respectively.
2. Fuel cell cost modeling
Fuel cells are an important class of distributed power sources due to their many advantages, such as high efficiency. Its efficiency is in a trivial relationship with its power output.
The power generation cost of the fuel cell is as follows:
the maintenance cost of the fuel cell is as follows:
C om,FC ＝k om,FC ×p FC (11)
in the present invention, C nl Is the natural gas price; l natural gas with low calorific value; eta FC Is the efficiency of the fuel cell; p is a radical of FC Is the power generation amount of the fuel cell; k is a radical of om,FC Maintenance costs for fuel cell unit power operation.
3. Cost modeling for micro gas turbine
The power generation cost of the micro gas turbine is as follows:
the maintenance cost of the micro gas turbine is the same as that of the fuel cell.
In the formula, p MT Generating power for the micro gas turbine; eta MT Is the micro gas turbine efficiency.
4. Cost modeling of cogeneration units
Cogeneration units may be the core of the economic operation of a microgrid. Conventional generators have an efficiency of about 35% with a large energy loss. Without a cogeneration unit, the microgrid has less efficiency than the traditional power grid, but the existence of the cogeneration unit can improve the efficiency of the microgrid by 80-85%.
The fuel cost of the micro gas turbine in the cogeneration unit is as follows:
the maintenance cost of the cogeneration unit is the same as above.
In the formula, p CHP Generating capacity of the cogeneration unit; eta CHP The efficiency of the cogeneration unit is improved.
Further, in the third step, a specific process of establishing the environmental economic dispatching model considering multi-microgrid energy interaction is as follows:
1. objective function
The method fully considers the microgrid operation cost and the environmental cost, and establishes a multi-microgrid multi-target economic scheduling model.
MinC＝{C 1 ,C 2 } (14)
In the formula, M is the number of the microgrids; c 1 The operating cost of the microgrid is reduced;the micro-grid m operation and maintenance cost comprises the operation and maintenance cost of a micro gas turbine, a fuel cell and a cogeneration unit;fuel costs of a micro gas turbine, a fuel cell and a cogeneration unit in the micro grid m are respectively saved; k is a radical of om,MT 、k om,FC 、k om,CHP 、k om,WT 、k om,PV The operating and maintaining costs of the micro gas turbine, the fuel cell, the cogeneration unit, the wind power and the photovoltaic unit power are respectively;respectively micro-grid m middle micro gasThe system comprises a turbine, a fuel cell, a cogeneration unit, wind power and photovoltaic power generation.
In the formula, C 2 The cost of the microgrid environment is reduced; c k Penalty cost for discharging 1kg of k-th class pollutants for the system; gamma ray MT,k 、γ FC,k 、γ CHP,k 、γ grid,k And the emission coefficients of the kth pollutants are respectively when the micro gas turbine, the fuel cell and the cogeneration unit output 1kwh electric energy and when the micro gas turbine, the fuel cell and the cogeneration unit purchase 1kwh electric energy to a large power grid.
2. Constraint conditions
1. Constraint of equality
(1) Power balance constraint
In the formula (I), the compound is shown in the specification,respectively is a micro-grid m wind power generation amount, a photovoltaic power generation amount and a load value.
2. Inequality constraint
(1) The operating conditions of the micro gas turbine, the fuel cell and the cogeneration unit are as follows:
in the formulaThe output upper limits of the micro gas turbine, the fuel cell and the cogeneration unit are respectively.
(2) And power exchange constraint with a large power grid and a micro grid:
in the formula (I), the compound is shown in the specification,the minimum and maximum power of the microgrid m, the large power grid and other microgrid connecting lines are respectively.
(3) The controllable unit of the micro gas turbine, the fuel cell and the cogeneration unit climbs and restricts:
in the formula (I), the compound is shown in the specification,generating capacity of the ith controllable unit in the microgrid m;the up-down climbing speed of the ith controllable unit in the micro-grid m is respectively.
Further, in the fourth step, a specific process of establishing the robust environment economic dispatching model considering multi-microgrid energy interaction is as follows:
the invention fully considers the uncertainty of renewable energy power generation prediction and load prediction, and the actual output and load value of the renewable energy are expressed as follows:
in the formula:actual values of wind power, photovoltaic and load in the micro-grid m are obtained;the predicted values of wind power, photovoltaic and load in the microgrid m are obtained;the errors of the predicted values of wind power, photovoltaic and load in the microgrid m are calculated;andthe upper limit and the lower limit of the predicted value errors of wind power, photovoltaic and load in the micro-grid m are respectively set.
And (5) substituting the above equations (30) and (31) into an objective function and a constraint condition of the environment economic dispatching model in the step three, namely the robust environment economic dispatching model considering multi-microgrid energy interaction. As can be seen from the above equations (30) and (31), the robust model is an interval model, and needs to be converted into a robust determination model to be calculated. The method comprises the following steps of sampling by adopting a Latin hypercube method, converting a robust uncertain model into a robust deterministic model, and converting the robust uncertain model into the robust deterministic model, wherein the converting steps are as follows:
step 1: determining a sampling scale H;
step 2: dividing each dimension disturbance variable interval into H equal parts;
and step 3: generating an H multiplied by n (n is a variable number) dimensional drawing Ding Chao cubic matrix A, wherein each column of A is a random combination of positive integers from 1 to H;
and 4, step 4: each column of the Latin hypercube matrix A corresponds to a variable, each row corresponds to a sampling point, and H sampling points are generated;
and 5: calculating a corresponding objective function value by using the sampling point;
step 6: an effective objective function value is calculated.
Therefore, the conversion of the proposed robust uncertain model into a robust uncertain model is specifically as follows:
1. effective objective function
Min C＝{C 1 ,C 2 } (32)
In the formula (I), the compound is shown in the specification,and predicting error values of wind power and photovoltaic in the microgrid m in the extracted sample h.
2. Constraint conditions
1. Constraint of equality
(1) And power balance constraint:
in the formula (I), the compound is shown in the specification,and (4) predicting error values of the loads in the microgrid m in the sampled sample h.
2. Constraint of inequality
(1) The operating conditions of the micro gas turbine, the fuel cell and the cogeneration unit are as follows:
(2) And power exchange constraint with a large power grid and a micro-grid:
(3) The controllable unit of the micro gas turbine, the fuel cell and the cogeneration unit climbs and restricts:
further, in the fifth step, the robust determination model is solved by using a multi-objective chemotaxis algorithm to find a Pareto optimal solution, and the specific process is as follows:
(1) Starting; preparing system data, and determining a target function, a decision variable, an uncertain variable and each constraint condition of an algorithm model;
(2) Setting parameters; calculating basic parameters of the algorithm according to the following formula; setting parameters such as the size of the algorithm bacterial population, the algorithm precision, the iteration times and the like according to the algorithm model in the step (1); setting t =1 between scheduling cells, and entering model solution between first cells;
in the formula, epsilon is the optimizing precision; t is 0 Is the minimum mean time for bacteria to move; b is a dimension independent parameter; tau. c Is the correlation time;
(3) Initializing the position of bacteria; initializing a bacteria position according to a feasible range of decision variables in system data in an initial interval t =1; initializing the bacteria position at the t moment in a climbing constraint mode of the optimal solution and decision variables of t-1 at the t >1 moment; setting the iteration number i =1;
(4) Optimizing the bacterial individuals; calculating the bacterial individual optimization, carrying out solution feasibility inspection and adjustment according to a constraint formula after the bacterial individual optimization, and then updating the position;
(5) Optimizing the bacterial population; calculating bacterial population optimization, carrying out solution feasibility inspection and adjustment according to a constraint formula after the bacterial population optimization, and then updating the position;
(6) Updating the position of the bacterial population; generating a new generation of bacterial population according to the average effective objective function value and the bacterial positions of the Pareto-domination comparison steps (4) and (5);
(7) Judging a termination condition at the time t; judging whether a termination condition at the time t is reached, and executing the step (8) if the termination condition is reached; otherwise, i = i +1 goes to step (4) for the next iteration;
(8) Selecting a satisfactory solution from TOPSIS; selecting a satisfactory solution at the time t by the TOPSIS method; judging whether t reaches the final time or not, and executing the step (9) when the t reaches the final time; otherwise, t = t +1, and the step (3) is carried out to solve at the next moment;
(9) Saving the optimal solution; and (5) ending the solution in the whole scheduling period T and storing the optimal solution.
Compared with the prior art, the invention has the following beneficial effects: according to the invention, power interaction among micro-grids is fully considered, so that the cost of the micro-grids can be reduced; the inaccuracy of renewable energy power generation prediction and load prediction is considered, so that the result is more accurate; and the environment cost is also considered, the economic cost is not only pursued but the environment problem is not considered, and the optimal scheduling solution which takes the economic cost and the environment cost into consideration is obtained by adopting a multi-target chemotaxis algorithm.
Drawings
Fig. 1 is a diagram of a multi-piconet architecture;
FIG. 2 is a graph comparing Latin hypercube and Monte Carlo sampling uniformity;
fig. 3 is a schematic diagram of a multi-microgrid robust environment economic dispatching solution concept considering power interaction;
fig. 4 is a mocbc algorithm flow chart.
Detailed Description
The invention is further described below with reference to the accompanying drawings.
As shown in fig. 1 and 3, the method of the present invention comprises the following steps:
the method comprises the following steps: under the condition that Active Distribution Networks (ADNs) are rapidly developed by a microgrid, energy interaction among multiple microgrids is fully considered.
In the method of the present invention, all the piconets are connected together to a large power grid, so that each piconet is able to inject power into the other piconets or the large power grid. By selling power to other microgrids or to large grids, microgrids may increase their revenue, as opposed to reducing their cost. Similarly, if the power generation of one microgrid cannot meet the demand of its own load, it may purchase power from another microgrid or a large power grid to supplement its own demand, in which case the microgrid represents an increase in cost. The cost of electricity purchased and sold by a microgrid may be expressed by the following equation:
taking three piconets as an example, the electricity purchased and sold by the MG1 in the formulas (1) and (2) can be expressed as:
in the formulaThe cost for purchasing electric energy for the microgrid m;the cost for selling electricity for the micro-grid m; m is the number of the micro-grids; r is m Selling unit price of electricity for the micro-grid m; d is the price of electricity sold by the large power grid;for m slave microgrid m 2 Purchasing electric quantity;for m-direction microgrid m 2 The amount of electricity sold;purchasing electric quantity from a large power grid for the micro-grid m;selling electricity to a large power grid for the microgrid m.
In the second step, the distributed power supply and the cost modeling are as follows:
1. wind power generation modeling
The wind speed is expressed by a Weibull distribution, and the formula is as follows:
converting wind speed to power, as follows:
wherein v is the wind speed; alpha and beta are proportion parameters and shape parameters of a Weibull function; v. of ci Wind turbine cut-in speed; v. of co Cutting out a speed for the wind turbine; v. of r Rated rotating speed of the wind driven generator; p is a radical of G,WT Generating electrical power for a wind turbine; p is a radical of WT,r Rated power is generated for wind power.
2. Photovoltaic power generation modeling
The illumination intensity and the temperature are two important influence factors of photovoltaic power generation. The photovoltaic power generation output power in the invention can be represented by the following formula:
in the formula, p pv Is photovoltaic power generation; p is a radical of STC Photovoltaic power generation under standard illumination intensity and temperature; g ING Actual illumination intensity; g STC Standard illumination intensity; k is the highest power temperature coefficient; t is c A standard temperature; t is r The actual temperature.
3. Cost modeling
1. Wind power and photovoltaic cost modeling
Wind power and photovoltaic are renewable clean energy sources, and the power generation cost can be regarded as zero. The maintenance cost is as follows:
c om,WT ＝k om,WT ×p WT (55)
c om,PV ＝k om,PV ×p PV (56)
in the formula, p WT 、p PV Wind power generation and photovoltaic power generation are respectively adopted; k is a radical of om,WT 、k om,PV The operation and maintenance costs of wind power and photovoltaic unit power are respectively.
2. Fuel cell cost modeling
Fuel cells are an important class of distributed power sources due to their many advantages, such as high efficiency. Its efficiency is strongly linked to its power output.
The power generation cost of the fuel cell is as follows:
the maintenance cost of the fuel cell is as follows:
C om,FC ＝k om,FC ×p FC (58)
in the present invention, C nl Is the natural gas price; l daysLow heating value of natural gas; eta FC Is the efficiency of the fuel cell; p is a radical of formula FC Is the power generation amount of the fuel cell; k is a radical of om,FC The maintenance cost for the unit power operation of the fuel cell.
3. Micro gas turbine cost modeling
The power generation cost of the micro gas turbine is as follows:
the maintenance cost of the micro gas turbine is the same as that of the fuel cell.
In the formula, p MT Generating power for the micro gas turbine; eta MT Is the micro gas turbine efficiency.
4. Cost modeling of cogeneration units
Cogeneration units may be the core of the economic operation of a microgrid. Conventional generators have an efficiency of about 35% with a large energy loss. Without a cogeneration unit, the microgrid has higher efficiency than that of the traditional power grid, but the existence of the cogeneration stage group can improve the efficiency of the microgrid by 80-85%.
The fuel cost of the micro gas turbine in the cogeneration unit is as follows:
the maintenance cost of the cogeneration unit is the same as above.
In the formula, p CHP Generating capacity of the cogeneration unit; eta CHP The efficiency of the cogeneration unit.
In the third step, an environment economic dispatching model considering multi-microgrid energy interaction is established according to the second step, and the steps are as follows:
1. objective function
The method fully considers the microgrid operation cost and the environmental cost, and establishes a multi-microgrid multi-target economic scheduling model.
Min C＝{C 1 ,C 2 } (61)
Wherein:
in the formula, M is the number of the microgrids; c 1 The operating cost of the microgrid is reduced;the micro-grid m operation and maintenance cost comprises the operation and maintenance cost of a micro gas turbine, a fuel cell and a cogeneration unit;fuel costs of a micro gas turbine, a fuel cell and a cogeneration unit in the micro grid m are respectively saved; k is a radical of om,MT 、k om,FC 、k om,CHP 、k om,WT 、k om,PV The operating and maintaining costs of the micro gas turbine, the fuel cell, the cogeneration unit, the wind power and the photovoltaic unit power are respectively;the micro gas turbine, the fuel cell, the cogeneration unit, the wind power generation and the photovoltaic power generation in the micro grid m are respectively.
In the formula, C 2 The cost of the microgrid environment is reduced; c k Penalty cost for discharging 1kg of k-th class pollutants for the system; gamma ray MT,k 、γ FC,k 、γ CHP,k 、γ grid,k And the emission coefficients of the kth pollutants are respectively when the micro gas turbine, the fuel cell and the cogeneration unit output 1kwh electric energy and when the micro gas turbine, the fuel cell and the cogeneration unit purchase 1kwh electric energy to a large power grid.
3. Constraint conditions
1. Constraint of equality
(1) Constraint of power balance
In the formula (I), the compound is shown in the specification,respectively a micro-grid m wind power generation amount, a photovoltaic power generation amount and a load value.
2. Constraint of inequality
(1) Operating conditions of micro gas turbine, fuel cell and cogeneration unit
In the formulaThe output upper limits of the micro gas turbine, the fuel cell and the cogeneration unit are respectively.
(2) Power exchange constraint with large power grid and microgrid
In the formula (I), the compound is shown in the specification,the minimum and maximum power of the microgrid m, the large power grid and other microgrid connecting lines are respectively.
(3) Controllable unit climbing restraint of micro gas turbine, fuel cell and cogeneration unit
In the formula (I), the compound is shown in the specification,generating capacity of the ith controllable unit in the microgrid m;the up-down climbing speed of the ith controllable unit in the micro-grid m is respectively.
According to the figures 1, 2 and 3, on the basis of the third step, renewable energy sources and load prediction uncertainty are fully considered in the fourth step, a robust environmental economic dispatching model considering multi-microgrid energy interaction is established, a latin hypercube method is adopted for sampling, and the uncertain model is converted into a robust determined model, wherein the specific steps are as follows:
the invention fully considers the uncertainty of renewable energy power generation prediction and load prediction, and the concrete expression is as follows:
in the formula:actual values of wind power, photovoltaic and load in the micro-grid m are obtained;the predicted values of wind power, photovoltaic and load in the micro-grid m are obtained;the errors of the predicted values of wind power, photovoltaic and load in the micro-grid m are obtained;andthe upper limit and the lower limit of the predicted value errors of wind power, photovoltaic and load in the micro-grid m are respectively set.
And (5) substituting the above equations (77) and (78) into the environment economic dispatching model in the third step, namely the robust environment economic dispatching model considering multi-microgrid energy interaction. From the above equations (77), (78), it can be seen that the robust model is an interval model, which needs to be converted into a deterministic model for calculation. The method comprises the following steps of sampling by adopting a Latin hypercube method, converting an uncertain model into a deterministic model, and converting the uncertain model into the deterministic model by adopting the Latin hypercube method as follows:
step 1: determining a sampling scale H;
step 2: dividing each dimension disturbance variable interval into H equal parts;
and step 3: generating an H multiplied by n (n is a variable number) dimensional drawing Ding Chao cubic matrix A, wherein each column of A is a random combination of positive integers from 1 to H;
and 4, step 4: each column of the Latin hypercube matrix A corresponds to a variable, each row corresponds to a sampling point, and H sampling points are generated;
and 5: and calculating the corresponding objective function value by using the sampling points.
Step 6: an effective objective function value is calculated.
Based on this, the conversion of the proposed robust uncertain model into a robust uncertain model is specifically as follows:
1. effective objective function
Min C＝{C 1 ,C 2 } (79)
In the formula (I), the compound is shown in the specification,and (4) predicting error values of wind power and photovoltaic in the microgrid m in the extracted sample h.
2. Constraint conditions
1. Constraint of equality
(1) Power balance constraint
In the formula (I), the compound is shown in the specification,and (4) predicting error values of the loads in the microgrid m in the sampled sample h.
2. Constraint of inequality
(1) Operating conditions of micro gas turbine, fuel cell and cogeneration unit
(2) Power exchange constraint with large power grid and microgrid
(3) Controllable unit climbing restraint of micro gas turbine, fuel cell and cogeneration unit
Step five: according to the fourth step, solving the robust determination model by using an MOBCC algorithm to obtain an optimal Proreo solution, and the steps are as follows:
(1) Starting; preparing system data, and determining a target function, a decision variable, an uncertain variable and each constraint condition of an algorithm model;
(2) Setting parameters; calculating basic parameters of the algorithm according to the following formula; setting parameters such as the size of the algorithm bacterial population, the algorithm precision, the iteration times and the like according to the algorithm model in the step (1); setting t =1 between scheduling cells, and entering model solution between first cells;
in the formula, epsilon is the optimizing precision; t is 0 Is the minimum mean time for bacteria to move; b is a dimension independent parameter; tau is c Is the correlation time.
(3) Initializing the position of bacteria; initializing a bacteria position according to a feasible range of decision variables in system data in an initial interval t =1; initializing the bacteria position at the t moment in a climbing constraint mode of the optimal solution and decision variables of t-1 at the t >1 moment; setting the iteration number i =1;
(4) Optimizing the bacterial individuals; calculating the bacterial individual optimization, carrying out solution feasibility inspection and adjustment according to a constraint formula after the bacterial individual optimization, and then updating the position;
(5) Optimizing the bacterial population; calculating bacterial colony optimization, performing solution feasibility inspection and adjustment according to a constraint formula after the bacterial colony optimization, and then updating the position;
(6) Updating the position of the bacterial population; generating a new generation of bacterial population according to the average effective objective function value and the bacterial positions of the Pareto-domination comparison steps (4) and (5);
(7) Judging a termination condition at the time t; judging whether a termination condition at the time t is reached, and executing the step (8) if the termination condition is reached; otherwise, i = i +1 goes to step (4) for the next iteration;
(8) Selecting a satisfactory solution from TOPSIS; selecting a satisfactory solution at the time t by the TOPSIS method; judging whether t reaches the final time or not, and executing the step (9) when the t reaches the final time; otherwise, t = t +1, and the step (3) is carried out to solve at the next moment;
(9) Saving the optimal solution; and (5) ending the solution in the whole scheduling period T and storing the optimal solution.
The above-mentioned embodiments are merely illustrative of the preferred embodiments of the present invention, and do not limit the scope of the present invention, and various modifications and improvements of the technical solution of the present invention by those skilled in the art should fall within the protection scope defined by the claims of the present invention without departing from the spirit of the present invention.
Claims (6)
1. A robust environment economic dispatching method considering multi-microgrid energy interaction is characterized by comprising the following steps: the method comprises the following steps:
the method comprises the following steps: under the condition that the micro-grid supports rapid development of the active power distribution network, energy interaction among multiple micro-grids is fully considered;
step two: establishing a renewable energy power generation model and cost models of all power generation units based on the microgrid;
step three: establishing an environmental economic dispatching model considering multi-microgrid energy interaction according to the renewable energy power generation model and the cost models of all the power generation units in the step two;
step four: according to the environmental economic dispatching model in the third step, considering the uncertainty of renewable energy sources and loads, establishing a robust environmental economic dispatching model considering multi-microgrid energy interaction, sampling by adopting a Latin hypercube method, and converting the robust environmental economic dispatching model into a robust deterministic model;
step five: and solving the robust determination model by adopting a multi-target chemotaxis algorithm to find a Pareto optimal solution.
2. The robust environmental economic dispatching method considering multi-microgrid energy interaction as recited in claim 1, wherein: in the first step, under the condition that the micro-grid supports the rapid development of the active power distribution network, energy interaction among multiple micro-grids is fully considered, namely electricity purchasing and electricity selling costs among the micro-grids are calculated;
the rate of electricity purchased and sold by a microgrid may be expressed by the following equation:
in the formula (I), the compound is shown in the specification,the cost for purchasing electric energy for the microgrid m;the cost for selling electricity for the micro-grid m; m is the number of the microgrids; r is a radical of hydrogen m For microgrid m selling electricity unitsA price; d is the price of electricity sold by the large power grid;microgrid m slave microgrid m 2 Purchasing electric quantity;for m-direction microgrid m 2 The amount of electricity sold;purchasing electric quantity for the microgrid m from a large power grid;selling electricity to a large power grid for the microgrid m.
3. The robust environmental economic dispatching method considering multi-microgrid energy interaction as recited in claim 1, wherein: in the second step, the specific process of establishing the renewable energy power generation model is as follows:
1. wind power generation modeling
The wind speed is expressed by a Weibull distribution, and the formula is as follows:
converting wind speed to power, as follows:
wherein v is the wind speed; alpha and beta are proportion parameters and shape parameters of a Weibull function; v. of ci Wind turbine cut-in speed; v. of co Cutting out the speed for the wind turbine; v. of r Rated rotating speed of the wind driven generator; p is a radical of G,WT Generating electrical power for a wind turbine; p is a radical of formula WT,r Rated power for wind power generation;
2. photovoltaic power generation modeling
The illumination intensity and the temperature are two important influence factors of photovoltaic power generation; the photovoltaic power generation output power in the invention can be represented by the following formula:
in the formula, p pv Is photovoltaic power generation; p is a radical of formula STC Photovoltaic power generation under standard illumination intensity and temperature; g ING Actual illumination intensity; g STC Standard illumination intensity; k is the highest power temperature coefficient; t is c A standard temperature; t is r The actual temperature;
the specific process of establishing the cost model of the power generation unit is as follows:
1. wind power and photovoltaic cost modeling
Wind power and photovoltaic are renewable clean energy sources, and the power generation cost can be regarded as zero; the maintenance cost is as follows:
c om,WT ＝k om,WT ×p WT (6)
c om,PV ＝k om,PV ×p PV (7)
in the formula, p WT 、p PV Wind power generation and photovoltaic power generation are respectively adopted; k is a radical of om,WT 、k om,PV Respectively representing the operation and maintenance costs of wind power and photovoltaic unit power;
2. fuel cell cost modeling
The power generation cost of the fuel cell is as follows:
the maintenance cost of the fuel cell is as follows:
C om,FC ＝k om,FC ×p FC (9)
C nl is the natural gas price; l natural gas with low calorific value; eta FC Is the efficiency of the fuel cell; p is a radical of FC Is the power generation amount of the fuel cell; k is a radical of formula om,FC As fuel electricityOperating and maintaining cost of unit power of the pool;
3. cost modeling for micro gas turbine
The power generation cost of the micro gas turbine is as follows:
the maintenance cost of the micro gas turbine is the same as that of the fuel cell;
in the formula, p MT Generating power for the micro gas turbine; eta MT Micro gas turbine efficiency;
4. cost modeling of cogeneration units
The fuel cost of the micro gas turbine in the cogeneration unit is as follows:
the maintenance cost of the cogeneration unit is the same as the above;
in the formula, p CHP Generating capacity of the cogeneration unit; eta CHP The efficiency of the cogeneration unit.
4. The robust environmental economic dispatching method considering multi-microgrid energy interaction as recited in claim 1, wherein: in the third step, a specific process of establishing an environmental economic dispatching model considering multi-microgrid energy interaction is as follows:
1. objective function
Fully considering the microgrid operation cost and the environmental cost, establishing a multi-microgrid multi-target economic scheduling model:
MinC＝{C 1 ,C 2 } (12)
in the formula, M is the number of the microgrids; c 1 The operating cost of the microgrid is reduced;the micro-grid m operation and maintenance cost comprises the operation and maintenance cost of a micro gas turbine, a fuel cell and a cogeneration unit;fuel costs of a micro gas turbine, a fuel cell and a cogeneration unit in the micro grid m are respectively saved; k is a radical of om,MT 、k om,FC 、k om,CHP 、k om,WT 、k om,PV The operating and maintaining costs of the micro gas turbine, the fuel cell, the cogeneration unit, the wind power and the photovoltaic unit power are respectively;respectively a micro gas turbine in the micro-grid m,Fuel cell, cogeneration unit, wind power, photovoltaic generated energy:
in the formula, C 2 The cost of the microgrid environment is reduced; c k Penalty cost for discharging 1kg of k-th class pollutants for the system; gamma ray MT,k 、γ FC,k 、γ CHP,k 、γ grid,k The emission coefficients of the kth pollutants are respectively used when the micro gas turbine, the fuel cell and the cogeneration unit output 1kwh electric energy and when the micro gas turbine, the fuel cell and the cogeneration unit purchase 1kwh electric energy in a large power grid;
2. constraint conditions
1. Constraint of equality
(1) Power balance constraint
In the formula (I), the compound is shown in the specification,respectively representing the wind power generation amount, the photovoltaic power generation amount and the load value of the microgrid m;
2. constraint of inequality
(1) The operating conditions of the micro gas turbine, the fuel cell and the cogeneration unit are as follows:
in the formulaRespectively the output upper limits of the micro gas turbine, the fuel cell and the cogeneration unit;
(2) And power exchange constraint with a large power grid and a micro-grid:
in the formula (I), the compound is shown in the specification,the minimum and maximum power of the microgrid m, a large power grid and other microgrid connecting lines are respectively set;
(3) The controllable unit of the micro gas turbine, the fuel cell and the cogeneration unit climbs and restricts:
in the formula (I), the compound is shown in the specification,generating capacity of the ith controllable unit in the microgrid m;the up-down climbing speed of the ith controllable unit in the micro-grid m is respectively.
5. The robust economic dispatching method considering multi-microgrid energy interaction as recited in claim 1, wherein: in the fourth step, a specific process for establishing the robust environment economic dispatching model considering the multi-microgrid energy interaction is as follows:
the uncertainty of renewable energy power generation prediction and load prediction is fully considered, and the actual output and load value of the renewable energy are expressed as follows:
in the formula:actual values of wind power, photovoltaic and load in the microgrid m are obtained;the predicted values of wind power, photovoltaic and load in the micro-grid m are obtained;the errors of the predicted values of wind power, photovoltaic and load in the micro-grid m are obtained;and withRespectively representing the upper limit and the lower limit of predicted value errors of wind power, photovoltaic and load in the microgrid m;
substituting the above equations (28) and (29) into the objective function and the constraint condition of the environment economic dispatching model in the step three, namely the robust environment economic dispatching model considering multi-microgrid energy interaction;
as shown in the above formulas (28) and (29), the robust model is an interval model, and needs to be converted into a robust determination model for calculation; sampling by adopting a pull Ding Chao cubic method, converting the robust uncertain model into a robust uncertain model, wherein the conversion steps are as follows:
step 1: determining a sampling scale H;
step 2: dividing each dimension disturbance variable interval into H equal parts;
and step 3: generating an H multiplied by n (n is a variable number) dimensional drawing Ding Chao cubic matrix A, wherein each column of A is a random combination of positive integers from 1 to H;
and 4, step 4: each column of the Latin hypercube matrix A corresponds to a variable, each row corresponds to a sampling point, and H sampling points are generated;
and 5: calculating a corresponding objective function value by using a sampling point;
step 6: calculating an effective objective function value;
the conversion of the robust uncertain model into the robust determined model is specifically as follows:
1. effective objective function
Min C＝{C 1 ,C 2 } (30)
In the formula (I), the compound is shown in the specification,predicting error values of wind power and photovoltaic in the microgrid m in the extracted sample h;
2. constraint conditions
1. Constraint of equality
(1) And power balance constraint:
in the formula (I), the compound is shown in the specification,predicting error values of the loads in the microgrid m in the sampled book h;
2. constraint of inequality
(1) The operating conditions of the micro gas turbine, the fuel cell and the cogeneration unit are as follows:
(2) And power exchange constraint with a large power grid and a micro grid:
(3) The controllable unit of the micro gas turbine, the fuel cell and the cogeneration unit climbs and restricts:
6. the robust environmental economic dispatching method considering multi-microgrid energy interaction as recited in claim 1, wherein: in the fifth step, the robust determination model is solved by adopting a multi-target chemotaxis algorithm to find a Pareto optimal solution, and the specific process is as follows:
(1) Starting; preparing system data, and determining a target function, a decision variable, an uncertain variable and each constraint condition of an algorithm model;
(2) Setting parameters; calculating basic parameters of the algorithm according to the following formula; setting parameters such as the size of the algorithm bacterial population, the algorithm precision, the iteration times and the like according to the algorithm model in the step (1); setting t =1 between scheduling cells, and entering model solution between first cells;
in the formula, epsilon is the optimizing precision; t is 0 Is the minimum mean time for bacteria to move; b is a dimension independent parameter; tau is c Is the correlation time;
(3) Initializing the position of bacteria; initializing a bacteria position according to a feasible range of decision variables in system data in an initial interval t =1; initializing the bacteria position at the t moment in a climbing constraint mode of the optimal solution and decision variables of t-1 at the t >1 moment; setting the iteration number i =1;
(4) Optimizing the bacterial individuals; calculating the bacterial individual optimization, carrying out solution feasibility inspection and adjustment according to a constraint formula after the bacterial individual optimization, and then updating the position;
(5) Optimizing the bacterial population; calculating bacterial colony optimization, performing solution feasibility inspection and adjustment according to a constraint formula after the bacterial colony optimization, and then updating the position;
(6) Updating the position of the bacterial population; generating a new generation of bacterial population according to the average effective objective function value and the bacterial positions of the Pareto-domination comparison steps (4) and (5);
(7) Judging a termination condition at the time t; judging whether a termination condition at the time t is reached, and executing the step (8) if the termination condition is reached; otherwise, i = i +1 goes to step (4) for the next iteration;
(8) Selecting a satisfactory solution from TOPSIS; selecting a satisfactory solution at the time t by the TOPSIS method; judging whether t reaches the final time or not, and executing the step (9) when the t reaches the final time; otherwise, t = t +1, carrying out the step (3) and solving at the next moment;
(9) Saving the optimal solution; and (5) ending the solution in the whole scheduling period T, and saving the optimal solution.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710777335.8A CN107622324A (en) | 2017-09-01 | 2017-09-01 | A kind of robust environmental economy dispatching method for considering more microgrid energy interactions |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710777335.8A CN107622324A (en) | 2017-09-01 | 2017-09-01 | A kind of robust environmental economy dispatching method for considering more microgrid energy interactions |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN107622324A true CN107622324A (en) | 2018-01-23 |
Family
ID=61088383
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710777335.8A Pending CN107622324A (en) | 2017-09-01 | 2017-09-01 | A kind of robust environmental economy dispatching method for considering more microgrid energy interactions |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN107622324A (en) |
Cited By (22)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108510404A (en) * | 2018-03-28 | 2018-09-07 | 山东大学 | A kind of more microgrids orderly grid-connected Optimization Scheduling, apparatus and system |
| CN108596525A (en) * | 2018-06-29 | 2018-09-28 | 国家电网有限公司 | The cold and hot micro- energy net robust Optimization Scheduling electrically provided multiple forms of energy to complement each other |
| CN109193636A (en) * | 2018-10-08 | 2019-01-11 | 华东交通大学 | A kind of economic Robust Scheduling method of power system environment based on the uncertain collection of classification |
| CN109327042A (en) * | 2018-09-27 | 2019-02-12 | 南京邮电大学 | A kind of micro-grid multi-energy joint optimal operation method |
| CN109462256A (en) * | 2018-11-28 | 2019-03-12 | 燕山大学 | A kind of photovoltaic power system Optimization Scheduling and system based on flexible load |
| CN109687456A (en) * | 2019-01-25 | 2019-04-26 | 燕山大学 | A kind of dispatching method and system of electric power natural gas system |
| CN109842158A (en) * | 2019-03-28 | 2019-06-04 | 广东工业大学 | A kind of micro-capacitance sensor Optimal Configuration Method |
| CN109861309A (en) * | 2019-02-27 | 2019-06-07 | 华北电力大学 | A kind of isolated operation micro-capacitance sensor group energy amount scheduling distributed optimization method |
| CN109888840A (en) * | 2019-02-28 | 2019-06-14 | 内蒙古科技大学 | Scene-garbage power energy comprehensive utilization system method for optimizing scheduling and device |
| CN110112728A (en) * | 2019-05-10 | 2019-08-09 | 四川大学 | A kind of probabilistic more garden microgrid cooperative game methods of consideration wind-powered electricity generation robust |
| WO2019165702A1 (en) * | 2018-02-28 | 2019-09-06 | 东南大学 | Double-layer coordinated robust optimized scheduling method for multi-microgrids system |
| CN110571789A (en) * | 2018-06-06 | 2019-12-13 | 南京工程学院 | Electric heating air network three-stage scheduling method based on wind power uncertainty under data driving |
| CN111428909A (en) * | 2020-02-25 | 2020-07-17 | 浙江工业大学 | Multi-micro-grid energy management method based on multi-objective collaborative optimization |
| CN112101756A (en) * | 2020-09-02 | 2020-12-18 | 合肥工业大学 | Data-driven multi-energy complementary energy hub load optimization scheduling method and system |
| CN112257229A (en) * | 2020-09-18 | 2021-01-22 | 西安理工大学 | Two-stage robust scheduling method for microgrid |
| CN112381263A (en) * | 2020-09-23 | 2021-02-19 | 四川大学 | Block chain distributed data storage based multi-microgrid day-ahead robust electric energy transaction method |
| CN112613671A (en) * | 2020-12-28 | 2021-04-06 | 上海电力大学 | Microgrid distribution robust optimization scheduling method considering phase change water tank and building energy storage |
| CN112633575A (en) * | 2020-12-22 | 2021-04-09 | 山东大学 | Robust optimization-based capacity configuration method and system for multi-energy complementary comprehensive energy system |
| CN112952908A (en) * | 2021-03-29 | 2021-06-11 | 四川大学 | Multi-cooperation micro-grid main body distributed coordination transaction method |
| CN109103929B (en) * | 2018-09-11 | 2021-07-20 | 燕山大学 | Power distribution network economic optimization scheduling method based on improved dynamic kriging model |
| CN113673129A (en) * | 2021-08-25 | 2021-11-19 | 合肥工业大学 | Multi-objective robust optimization design method for ultra-high efficiency induction motor |
| CN110334366B (en) * | 2019-03-14 | 2023-07-14 | 华电电力科学研究院有限公司 | Building instantaneous cold load prediction method based on Monte Carlo method using Latin hypercube sampling |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110255371A1 (en) * | 2009-01-09 | 2011-10-20 | Charlie Jing | Hydrocarbon Detection With Passive Seismic Data |
| CN105550766A (en) * | 2015-12-04 | 2016-05-04 | 山东大学 | Micro-grid robustness multi-target operation optimization method containing renewable energy resources |
| JP2019513000A (en) * | 2016-03-04 | 2019-05-16 | アティジオ コーポレーション | Controlling power output using battery DC characteristics |
-
2017
- 2017-09-01 CN CN201710777335.8A patent/CN107622324A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110255371A1 (en) * | 2009-01-09 | 2011-10-20 | Charlie Jing | Hydrocarbon Detection With Passive Seismic Data |
| CN105550766A (en) * | 2015-12-04 | 2016-05-04 | 山东大学 | Micro-grid robustness multi-target operation optimization method containing renewable energy resources |
| JP2019513000A (en) * | 2016-03-04 | 2019-05-16 | アティジオ コーポレーション | Controlling power output using battery DC characteristics |
Cited By (34)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2019165702A1 (en) * | 2018-02-28 | 2019-09-06 | 东南大学 | Double-layer coordinated robust optimized scheduling method for multi-microgrids system |
| CN108510404A (en) * | 2018-03-28 | 2018-09-07 | 山东大学 | A kind of more microgrids orderly grid-connected Optimization Scheduling, apparatus and system |
| CN110571789B (en) * | 2018-06-06 | 2020-10-20 | 南京工程学院 | Electric heating air network three-stage scheduling method based on wind power uncertainty under data driving |
| CN110571789A (en) * | 2018-06-06 | 2019-12-13 | 南京工程学院 | Electric heating air network three-stage scheduling method based on wind power uncertainty under data driving |
| CN108596525A (en) * | 2018-06-29 | 2018-09-28 | 国家电网有限公司 | The cold and hot micro- energy net robust Optimization Scheduling electrically provided multiple forms of energy to complement each other |
| CN108596525B (en) * | 2018-06-29 | 2022-07-22 | 国家电网有限公司 | Robust optimization scheduling method for micro-energy network with complementary cold-heat-electricity multi-energy |
| CN109103929B (en) * | 2018-09-11 | 2021-07-20 | 燕山大学 | Power distribution network economic optimization scheduling method based on improved dynamic kriging model |
| CN109327042A (en) * | 2018-09-27 | 2019-02-12 | 南京邮电大学 | A kind of micro-grid multi-energy joint optimal operation method |
| CN109327042B (en) * | 2018-09-27 | 2023-02-10 | 南京邮电大学 | Multi-energy joint optimization scheduling method for micro-grid |
| CN109193636A (en) * | 2018-10-08 | 2019-01-11 | 华东交通大学 | A kind of economic Robust Scheduling method of power system environment based on the uncertain collection of classification |
| CN109462256A (en) * | 2018-11-28 | 2019-03-12 | 燕山大学 | A kind of photovoltaic power system Optimization Scheduling and system based on flexible load |
| CN109687456A (en) * | 2019-01-25 | 2019-04-26 | 燕山大学 | A kind of dispatching method and system of electric power natural gas system |
| CN109861309A (en) * | 2019-02-27 | 2019-06-07 | 华北电力大学 | A kind of isolated operation micro-capacitance sensor group energy amount scheduling distributed optimization method |
| CN109861309B (en) * | 2019-02-27 | 2020-09-11 | 华北电力大学 | Distributed optimization method for island operation microgrid group energy scheduling |
| CN109888840A (en) * | 2019-02-28 | 2019-06-14 | 内蒙古科技大学 | Scene-garbage power energy comprehensive utilization system method for optimizing scheduling and device |
| CN110334366B (en) * | 2019-03-14 | 2023-07-14 | 华电电力科学研究院有限公司 | Building instantaneous cold load prediction method based on Monte Carlo method using Latin hypercube sampling |
| CN109842158A (en) * | 2019-03-28 | 2019-06-04 | 广东工业大学 | A kind of micro-capacitance sensor Optimal Configuration Method |
| CN110112728A (en) * | 2019-05-10 | 2019-08-09 | 四川大学 | A kind of probabilistic more garden microgrid cooperative game methods of consideration wind-powered electricity generation robust |
| CN111428909B (en) * | 2020-02-25 | 2022-12-06 | 杭州盛星能源技术有限公司 | Multi-micro-grid energy management method based on multi-objective collaborative optimization |
| CN111428909A (en) * | 2020-02-25 | 2020-07-17 | 浙江工业大学 | Multi-micro-grid energy management method based on multi-objective collaborative optimization |
| CN112101756A (en) * | 2020-09-02 | 2020-12-18 | 合肥工业大学 | Data-driven multi-energy complementary energy hub load optimization scheduling method and system |
| CN112101756B (en) * | 2020-09-02 | 2022-09-30 | 合肥工业大学 | Data-driven multi-energy complementary energy hub load optimization scheduling method and system |
| CN112257229A (en) * | 2020-09-18 | 2021-01-22 | 西安理工大学 | Two-stage robust scheduling method for microgrid |
| CN112257229B (en) * | 2020-09-18 | 2024-04-16 | 西安理工大学 | Micro-grid two-stage robust scheduling method |
| CN112381263A (en) * | 2020-09-23 | 2021-02-19 | 四川大学 | Block chain distributed data storage based multi-microgrid day-ahead robust electric energy transaction method |
| CN112381263B (en) * | 2020-09-23 | 2023-07-14 | 四川大学 | Block chain-based distributed data storage multi-microgrid pre-day robust electric energy transaction method |
| CN112633575B (en) * | 2020-12-22 | 2023-01-31 | 山东大学 | Robust optimization-based capacity configuration method and system for multi-energy complementary comprehensive energy system |
| CN112633575A (en) * | 2020-12-22 | 2021-04-09 | 山东大学 | Robust optimization-based capacity configuration method and system for multi-energy complementary comprehensive energy system |
| CN112613671B (en) * | 2020-12-28 | 2022-04-01 | 上海电力大学 | Microgrid distribution robust optimization scheduling method considering phase change water tank and building energy storage |
| CN112613671A (en) * | 2020-12-28 | 2021-04-06 | 上海电力大学 | Microgrid distribution robust optimization scheduling method considering phase change water tank and building energy storage |
| CN112952908A (en) * | 2021-03-29 | 2021-06-11 | 四川大学 | Multi-cooperation micro-grid main body distributed coordination transaction method |
| CN112952908B (en) * | 2021-03-29 | 2024-02-02 | 四川大学 | Distributed coordination transaction method for multi-cooperation micro-grid main body |
| CN113673129A (en) * | 2021-08-25 | 2021-11-19 | 合肥工业大学 | Multi-objective robust optimization design method for ultra-high efficiency induction motor |
| CN113673129B (en) * | 2021-08-25 | 2024-02-09 | 合肥工业大学 | Multi-objective robust optimization design method for ultra-efficient induction motor |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107622324A (en) | A kind of robust environmental economy dispatching method for considering more microgrid energy interactions | |
| Guo et al. | Multi‐objective stochastic optimal planning method for stand‐alone microgrid system | |
| Yang et al. | Optimal sizing of a wind/solar/battery/diesel hybrid microgrid based on typical scenarios considering meteorological variability | |
| CN110689189A (en) | Combined cooling heating and power supply and demand balance optimization scheduling method considering energy supply side and demand side | |
| Tafreshi et al. | Optimal unit sizing of distributed energy resources in microgrid using genetic algorithm | |
| Jiao et al. | Multi-objective mean-semi-entropy model for optimal standalone micro-grid planning with uncertain renewable energy resources | |
| Hong et al. | Interactive multi-objective active power scheduling considering uncertain renewable energies using adaptive chaos clonal evolutionary programming | |
| CN110635518A (en) | Source network load and storage optimization method based on photovoltaic high permeability | |
| Semero et al. | Optimal energy management strategy in microgrids with mixed energy resources and energy storage system | |
| CN114301081B (en) | Micro-grid optimization method considering storage battery energy storage life loss and demand response | |
| Xiao et al. | A multi‐energy complementary coordinated dispatch method for integrated system of wind‐photovoltaic‐hydro‐thermal‐energy storage | |
| Saha | Adaptive model-based receding horizon control of interconnected renewable-based power micro-grids for effective control and optimal power exchanges | |
| Suresh et al. | Optimal power flow in microgrids using genetic algorithm | |
| Zhang et al. | Distributed model predictive control for hybrid energy resource system with large-scale decomposition coordination approach | |
| Zhang et al. | Optimized scheduling model for isolated microgrid of wind-photovoltaic-thermal-energy storage system with demand response | |
| CN108491977B (en) | Weak robust optimization scheduling method for micro-energy network | |
| CN111932025B (en) | Comprehensive energy system construction multi-stage planning method considering photovoltaic randomness | |
| CN112580897A (en) | Multi-energy power system optimal scheduling method based on parrot algorithm | |
| Perez et al. | Soft-islanding a group of houses through scheduling of CHP, PV and storage | |
| CN112101987A (en) | Multi-microgrid random prediction control method | |
| CN116540545A (en) | Photovoltaic power generation hydrogen production cluster random optimization scheduling method based on ember process | |
| CN113013881B (en) | Power distribution network distributed photovoltaic grid-connected admission capacity calculation method considering energy stations | |
| CN115693793A (en) | Energy optimization control method for regional micro-grid | |
| Ranjbar et al. | Economic aspect of fuel cell power as distributed generation | |
| Luo et al. | The dynamic multi-objective optimal dispatch of grid-connected micro grid |
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 | ||
| WD01 | Invention patent application deemed withdrawn after publication | ||
| WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20180123 |