CN114819402A - Micro-grid optimization scheduling method - Google Patents
Micro-grid optimization scheduling method Download PDFInfo
- Publication number
- CN114819402A CN114819402A CN202210590492.9A CN202210590492A CN114819402A CN 114819402 A CN114819402 A CN 114819402A CN 202210590492 A CN202210590492 A CN 202210590492A CN 114819402 A CN114819402 A CN 114819402A
- Authority
- CN
- China
- Prior art keywords
- cost
- microgrid
- power
- model
- grid
- 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
- 238000005457 optimization Methods 0.000 title claims abstract description 44
- 238000000034 method Methods 0.000 title claims abstract description 41
- 239000002245 particle Substances 0.000 claims abstract description 86
- 238000010248 power generation Methods 0.000 claims abstract description 24
- 238000004146 energy storage Methods 0.000 claims abstract description 22
- 230000007613 environmental effect Effects 0.000 claims abstract description 21
- 238000013178 mathematical model Methods 0.000 claims description 46
- 239000003344 environmental pollutant Substances 0.000 claims description 22
- 231100000719 pollutant Toxicity 0.000 claims description 22
- 238000012423 maintenance Methods 0.000 claims description 15
- 238000012360 testing method Methods 0.000 claims description 9
- 239000000446 fuel Substances 0.000 claims description 8
- 230000005540 biological transmission Effects 0.000 claims description 6
- 239000000356 contaminant Substances 0.000 claims description 6
- 238000007599 discharging Methods 0.000 claims description 6
- 230000005855 radiation Effects 0.000 claims description 6
- 230000014509 gene expression Effects 0.000 claims description 5
- 230000005611 electricity Effects 0.000 claims description 4
- 230000009194 climbing Effects 0.000 claims description 3
- 230000003993 interaction Effects 0.000 claims description 3
- 230000007246 mechanism Effects 0.000 claims description 3
- 238000012545 processing Methods 0.000 claims description 3
- 238000012163 sequencing technique Methods 0.000 claims description 3
- 239000000126 substance Substances 0.000 claims description 2
- 230000007547 defect Effects 0.000 abstract description 5
- 238000003912 environmental pollution Methods 0.000 abstract description 3
- 230000001737 promoting effect Effects 0.000 abstract description 3
- 230000006870 function Effects 0.000 description 7
- 230000035772 mutation Effects 0.000 description 4
- 238000013461 design Methods 0.000 description 3
- 230000008569 process Effects 0.000 description 3
- 230000000694 effects Effects 0.000 description 2
- 230000013016 learning Effects 0.000 description 2
- 238000004088 simulation Methods 0.000 description 2
- 230000003044 adaptive effect Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000007812 deficiency Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000009977 dual effect Effects 0.000 description 1
- 230000002068 genetic effect Effects 0.000 description 1
- 230000009326 social learning Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
- G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/004—Artificial life, i.e. computing arrangements simulating life
- G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06312—Adjustment or analysis of established resource schedule, e.g. resource or task levelling, or dynamic rescheduling
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2111/00—Details relating to CAD techniques
- G06F2111/04—Constraint-based CAD
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2113/00—Details relating to the application field
- G06F2113/04—Power grid distribution networks
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
-
- 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
- H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
- H02J2300/20—The dispersed energy generation being of renewable origin
- H02J2300/28—The renewable source being wind energy
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- General Physics & Mathematics (AREA)
- Entrepreneurship & Innovation (AREA)
- Marketing (AREA)
- Health & Medical Sciences (AREA)
- General Business, Economics & Management (AREA)
- Tourism & Hospitality (AREA)
- Evolutionary Computation (AREA)
- General Health & Medical Sciences (AREA)
- Artificial Intelligence (AREA)
- Operations Research (AREA)
- Game Theory and Decision Science (AREA)
- General Engineering & Computer Science (AREA)
- Software Systems (AREA)
- Development Economics (AREA)
- Quality & Reliability (AREA)
- Primary Health Care (AREA)
- Life Sciences & Earth Sciences (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Educational Administration (AREA)
- Medical Informatics (AREA)
- Water Supply & Treatment (AREA)
- Computer Hardware Design (AREA)
- Geometry (AREA)
- Public Health (AREA)
- Power Engineering (AREA)
- Biomedical Technology (AREA)
- Biophysics (AREA)
- Computational Linguistics (AREA)
- Data Mining & Analysis (AREA)
- Molecular Biology (AREA)
- Computing Systems (AREA)
- Mathematical Physics (AREA)
- Feedback Control In General (AREA)
Abstract
The invention relates to a micro-grid optimization scheduling method, which comprises the following steps: 1) constructing a microgrid environment-friendly and economic dispatching model considering operation cost and environment protection cost according to the power generation characteristics of distributed power sources and energy storage in the microgrid; 2) and solving the environmental protection and economic dispatching model of the microgrid by adopting an improved particle swarm algorithm to obtain an optimal dispatching result. Compared with the prior art, the improved particle swarm optimization is adopted to solve the provided optimization model, the defects of the original method are overcome, the performance of the algorithm is improved, and the aims of effectively reducing the power consumption cost of users and environmental pollution and promoting the optimized operation of the micro-grid are fulfilled.
Description
Technical Field
The invention relates to the field of micro-grid energy scheduling optimization, in particular to a micro-grid optimization scheduling method.
Background
At present, most of the design of the optimal scheduling method for the micro-grid is to achieve the purpose, such as the lowest running cost of the micro-grid, abstracts the design into a result of a mathematical model, the mathematical model in the method is also established by abstracting and reasoning various distributed power supplies and loads, and finally, an intelligent optimization algorithm is used for simulating the result of the mathematical model so as to judge whether the purpose is achieved.
In the prior art, starting from a built mathematical model, parameters and expressions contained in the mathematical model are more comprehensive, and the real situation can be better simulated, for example, a micro-grid day-ahead scheduling method which takes the sum of the power generation cost and the operation cost of each power generation unit as a target is provided by taking photovoltaic power generation and wind power generation into consideration.
However, the above prior art has the following disadvantages that firstly, the considered target is not comprehensive, some consider the operation cost but neglect the environmental protection cost, and the goal of the microgrid optimization scheduling requires to consider multiple aspects to achieve better effect; secondly, the defects of the intelligent optimization algorithm can influence the mathematical model solution in the method, so that the overdue purpose cannot be achieved.
Disclosure of Invention
The invention aims to overcome the defects in the prior art and provide a microgrid optimization scheduling method, which takes the microgrid operation cost and the environmental protection cost as optimization targets, and obtains the relative optimal values of the two targets, thereby playing the role of reducing the power consumption cost and the environmental pollution of a microgrid system and promoting the optimization operation of the microgrid.
The purpose of the invention can be realized by the following technical scheme:
a microgrid optimization scheduling method comprises the following steps:
1) constructing a microgrid environment-friendly and economic dispatching model considering operation cost and environment protection cost according to the power generation characteristics of distributed power sources and energy storage in the microgrid;
2) and solving the environmental protection and economic dispatching model of the micro-grid by adopting an improved particle swarm algorithm to obtain an optimal dispatching result.
In the step 1), the power generation characteristics of the distributed power supply and the stored energy in the microgrid are expressed by mathematical models, and the mathematical models specifically comprise a wind driven generator mathematical model, a photovoltaic power generation mathematical model, a diesel generator model, a micro gas turbine model and a storage battery model.
The wind driven generator mathematical model is specifically as follows:
wherein, P' WT Is the output power of a wind turbine, P' r Is rated power, v 'of wind power generator' ci 、v' r And v' co The wind speed parameters are represented by cut-in wind speed, rated wind speed and cut-out wind speed of the wind driven generator, a ', b', c 'and d' are wind speed parameters, and v is wind speed;
the photovoltaic power generation mathematical model specifically comprises the following steps:
wherein, P' pv Is the output active power, R 'of the photovoltaic cell' pv Is the photovoltaic output power, q 'under standard test conditions' pv Is the derating coefficient of the photovoltaic, I' T Is the actual solar radiation intensity, I' STC Is the intensity of solar radiation, alpha ', under standard test conditions' p Is the temperature coefficient, T ', of the photovoltaic cell panel' c Is the photovoltaic cell temperature at the current time, T' stc Is the photovoltaic cell temperature under standard test;
the diesel generator model is as follows:
wherein, C DE.OM (t)、C DE.F (t)、C DE.EN (t) the operating maintenance cost, fuel cost, pollutant disposal cost, P, respectively, of the diesel engine at time t DE (t) is the power generation of the diesel engine at time t, K DE.OM For the operating maintenance cost factor, gamma, of a diesel engine de,k Emission of pollutants of the k-type, C, for diesel engine operation k For the cost coefficient of processing k-type pollutants, alpha, beta and gamma are respectively the coefficients of a diesel engine;
the micro gas turbine model specifically comprises:
wherein, P MT (t) is the active output power, η, of the micro gas turbine MT (t) operating efficiency of the micro gas turbine, C MT.OM (t)、C MT.F (t)、C MT.EN (t) the operation and maintenance cost, the fuel cost, the pollutant treatment cost and P of the diesel engine at the moment t respectively MT (t) is the power generation of the diesel engine at time t, K MT.OM For the operating maintenance cost factor, gamma, of a diesel engine mt,k Emission of pollutants of the k-type, C, for diesel engine operation k Cost factor for treating class k contaminants;
the storage battery model specifically comprises:
where SOC (t) is the residual capacity of the battery at time t, P bess (t) the charging and discharging power of the battery at time t, positive for charging, negative for discharging, eta + 、η - The charge and discharge efficiencies are respectively.
The objective function of the micro-grid environment-friendly and economic dispatching model is as follows:
f(x)=min[f 1 (x),f 2 (x)]
wherein f is 1 、f 2 Respectively representing an operation cost target and an environmental protection cost target, and x is an optimization variable of the model.
The expression of the operation cost of the microgrid in the grid-connected mode is as follows:
wherein, C grid (t)、C bess (t)、C MT (t)、C DE (t) Total cost of interaction of the microgrid with the Main grid, maintenance cost of stored energy, Total operating cost of the micro gas turbine and Total operating cost of the Diesel Generator, P, respectively, at time t bess (t) power of stored energy at time t, P sell (t)、P buy (t) selling power and purchasing power of the microgrid and the main grid at the moment t respectively, c buy (t)、c sell And (t) the purchase and sale electricity prices of the micro-grid and the main grid at the moment t respectively.
The expression of the environmental protection cost of the microgrid is as follows:
wherein, C GRID.EN (t) pollutant treatment cost, gamma, of large power grids grid,k Emission of k-type pollutants for large grid operation, C k Cost factor for treating class k contaminants.
The constraint conditions of the microgrid environment-friendly and economic dispatching model comprise:
(1) and power balance constraint:
P' PV (t)+P' WT (t)+P grid (t)+P DE (t)+P MT (t)+P bess (t)=P L (t)
(2) the output constraint of the diesel generator is as follows:
(3) output constraint of the micro gas turbine:
(4) tie line transmission power constraint:
(5) and (4) energy storage device restraint:
wherein the content of the first and second substances,the upper and lower limits of the diesel engine output and the micro gas turbine output, r DE 、r MT The upper limit of the climbing power of the diesel engine and the micro gas turbine respectively,for the upper and lower limits of the transmission power of the tie line,the upper and lower limits of the energy storage device output are positive indicating power input and negative indicating power output, SOC max (t)、SOC min And (t) is the upper and lower limits of the energy storage capacity at the moment t.
In the step 2), solving the microgrid environment-friendly and economic dispatching model by adopting an improved particle swarm algorithm specifically comprises the following steps:
21) data initialization, namely inputting system composition, structural parameters, model parameters and particle swarm algorithm parameters of a micro-grid, and initializing a particle population;
22) calculating the fitness value of the initialized particle population, namely the running cost and the environmental protection cost;
23) updating the particle speed and the particle position according to the fitness value to obtain a filial generation population, and setting a dynamic inertia weight factor;
24) determining individual extreme values pbest, taking pbest as initial individual extreme values of the particles, if the current particles dominate pbest, taking the current particles as pbest individual extreme values, if the current particles and the pbest cannot be compared, calculating the number of other particles dominated by the current particles and the pbest dominated by more particles in a group;
25) carrying out layered sequencing on the population, storing the optimal non-dominated solution Pareto into an external archiving set, removing the non-Pareto solution, judging whether the external archiving set exceeds the specified capacity, and if so, selecting m particles according to the crowding distance;
26) determining a global optimal value gbest, adopting a Pareto optimal solution stored in an external archive set, and selecting the gbest from the external set by using a roulette method according to the crowding distance of the optimal solution;
27) performing small probability variation, introducing a small probability random variation mechanism to prevent the particle swarm algorithm from prematurely converging to a local optimal front edge instead of a global optimal front edge, generating +/-30% small probability disturbance on the position of the particle at the original position, and increasing the optimization capability of the global optimal front edge of the particle;
28) and returning to the step 23) until a termination condition, namely the maximum iteration number, is met, and outputting a final optimized scheduling result.
In the step 2), inertia weight factors in the improved particle swarm algorithm are improved to prevent from falling into a local optimal value, and the following steps are performed:
wherein gen is the current iteration number, gen max As total number of iterations, ω s And ω e The initial and final values of the inertial weight factor.
A microgrid optimization scheduling method is used for realizing the microgrid optimization scheduling method, and a microgrid optimization scheduling system for realizing the microgrid optimization scheduling method comprises the following steps:
an input module: the method comprises the steps of obtaining parameters of a micro-grid environment-friendly and economic dispatching model and an improved particle swarm algorithm;
a mathematical model module: the micro-grid environment-friendly and economic dispatching method comprises a wind driven generator mathematical model, a photovoltaic power generation mathematical model, a diesel generator mathematical model, a micro gas turbine mathematical model and an energy storage and load mathematical model, wherein a micro-grid environment-friendly and economic dispatching model is constructed through the mathematical models;
a scheduling module: solving the environmental protection and economic dispatching model of the micro-grid by adopting an improved particle swarm algorithm;
an output module: and the system is used for outputting the solving result of the scheduling module, including the optimal value after the operation cost and the environmental protection cost are taken into consideration, and the optimal configuration result among all distributed power supplies, the energy storage units and the loads, namely the scheduling result.
Compared with the prior art, the invention has the following advantages:
the improved particle swarm algorithm is adopted to solve the provided optimization model, so that the defects of the original method are overcome, the performance of the algorithm is improved, and the aims of effectively reducing the power consumption cost of users and environmental pollution and promoting the optimized operation of a micro-grid are fulfilled.
Drawings
FIG. 1 is a flow chart of the optimized scheduling of the present invention.
Fig. 2 is a schematic block diagram of the system of the present invention.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments.
Examples
As shown in fig. 2, in order to implement optimal scheduling of a microgrid, the present invention first designs a microgrid optimal scheduling system, which includes:
an input module: the module mainly has the function of acquiring specific parameters of a mathematical model and an intelligent optimization algorithm and preparing for the overall simulation solution.
A mathematical model module: the mathematical model specifically comprises: the system comprises a wind driven generator mathematical model, a photovoltaic power generation mathematical model, a diesel generator mathematical model, a micro gas turbine mathematical model and an energy storage and load mathematical model. Finally, a scheduling mathematical model capable of simulating real conditions is constructed through the mathematical model
A scheduling module: this module acts as a simulated solution to the scheduling mathematical model. The microgrid optimal scheduling model is a nonlinear, multi-model and multi-target complex scheduling model. The Particle Swarm Optimization (PSO) has strong optimization capability. Meanwhile, the method is easier to apply when solving the optimization problem. The method considers the dual targets of operation cost and environmental protection cost. Therefore, the scheduling module adopts a particle swarm optimization algorithm in an intelligent optimization algorithm to carry out simulation solution.
An output module: the output result not only includes an optimal value after the operation cost and the environmental protection cost are taken into consideration, but also includes an optimal configuration result among all the distributed power supplies, the energy storage units and the loads, namely a scheduling result.
Based on the microgrid optimal scheduling system, the invention provides a microgrid optimal scheduling method which is used for determining the processing distribution of each distributed power supply, realizing the optimal configuration among each distributed power supply, an energy storage unit and a load in a microgrid, enabling the output power of each distributed power supply and an energy storage device in the microgrid to meet the load requirement of the microgrid, ensuring the safety and stability of the microgrid and realizing the economic optimal operation of the microgrid.
As shown in fig. 1, the method comprises the steps of:
firstly, building a model. The established mathematical model is finally expressed in the form of a function, and the result of the function is the purpose to be achieved and is called as an objective function.
1. Distributed power supply in microgrid and energy storage power generation characteristic
(1) The wind driven generator mathematical model is as follows:
wherein: p' WT Is the output power of the wind turbine, P' r Is rated power, v 'of wind power generator' ci 、v' r And v' co Representative are the cut-in wind speed, the rated wind speed and the cut-out wind speed of the wind turbine; a ', b', c 'and d' are wind speed parameters, and v is a wind speed;
(2) photovoltaic power generation mathematical model:
wherein: p' pv Is the output active power, R 'of the photovoltaic cell' pv Is the photovoltaic output power, q 'under standard test conditions' pv Is the derating coefficient of the photovoltaic, I' T Is the actual solar radiation intensity, I' STC Is the intensity of solar radiation, alpha ', under standard test conditions' p Is the temperature coefficient, T ', of the photovoltaic cell panel' c Is the photovoltaic cell temperature at the current time, T' s tc is the photovoltaic cell temperature under the standard test;
(3) the diesel generator model is as follows:
wherein: c DE.OM (t)、C DE.F (t)、C DE.EN (t) operating maintenance cost, fuel cost, pollutant treatment cost, P, respectively, of the diesel engine at time t DE (t) is the power generation of the diesel engine at time t, K DE.OM Is the operating maintenance cost coefficient, gamma, of the diesel engine de,k Is the emission of k pollutants generated by the operation of a diesel engine, C k Is a cost coefficient for treating k-type pollutants, alpha, beta and gammaRespectively the coefficients of the diesel engine;
(4) miniature gas turbine
Wherein: p MT (t) is the active output power of the micro gas turbine; eta MT (t) is the operating efficiency of the micro gas turbine. The gas turbine generates electricity by consuming fuel, and fuel cost, operation maintenance cost and pollutant disposal cost are generated in the operation process, and the expression is as follows:
wherein: c MT.OM (t)、C MT.F (t)、C MT.EN (t) the operation and maintenance cost, the fuel cost, the pollutant treatment cost and P of the diesel engine at the moment t respectively MT (t) is the power generation of the diesel engine at time t, K MT.OM Is the operating maintenance cost coefficient, gamma, of the diesel engine mt,k Is the emission of k pollutants generated by the operation of a diesel engine, C k Is a cost factor for treating class k contaminants.
(5) A storage battery model:
wherein: SOC (t) is the residual capacity of the battery at time t, P bess (t) the charging and discharging power of the battery at time t, positive for charging, negative for discharging, eta + 、η - The charge and discharge efficiencies are respectively.
2. Objective function
The invention considers the operation cost and the environmental protection cost as the objective function:
f(x)=min[f 1 (x)、f 2 (x)] (7)
wherein f is 1 、f 2 Representing operating cost targets and rings, respectivelyEnvironmental protection cost objective, x is the model's optimization variable. Specifically, the output power of the distributed power supply and the charge and discharge power of the energy storage device can be adjusted in each time interval in the scheduling period;
(1) operating costs of micro-grids
The operating cost of the microgrid in the grid-connected mode is as follows:
wherein: c grid (t)、C bess (t)、C MT (t)、C DE (t) Total cost of interaction of the microgrid with the Main grid, maintenance cost of stored energy, Total operating cost of the micro gas turbine and Total operating cost of the Diesel Generator, P, respectively, at time t bess (t) is the power of the stored energy at time t, P sell (t)、P buy (t) selling power and purchasing power of the microgrid and the main grid at the moment t respectively, c buy (t)、c sell And (t) the purchase and sale electricity prices of the micro-grid and the main grid at the moment t respectively.
(2) Environmental protection cost of the microgrid:
wherein: c GRID.EN (t) pollutant treatment cost, gamma, of large power grids grid,k Is the discharge amount of k pollutants generated by the operation of a large power grid, C k Is a cost factor for treating class k contaminants.
3. Constraint conditions
The variables in the mathematical model have certain constraints, the values of which cannot exceed the specified range
(1) And power balance constraint:
P' PV (t)+P' WT (t)+P grid (t)+P DE (t)+P MT (t)+P bess (t)=P L (t) (11)
(2) the output constraint of the diesel generator is as follows:
(3) output constraint of the micro gas turbine:
(4) tie line transmission power constraint:
(5) and (4) energy storage device restraint:
wherein:the upper limit and the lower limit of the diesel engine output and the micro gas turbine output are respectively set; r is DE 、r MT The upper limit of the climbing power of the diesel engine and the micro gas turbine respectively,for the upper and lower limits of the transmission power of the tie line,the upper and lower limits of the energy storage device output are positive indicating power input and negative indicating power output, SOC max (t)、SOC min And (t) is the upper limit and the lower limit of the energy storage capacity at the moment t.
Secondly, solving the model in the scheduling module, specifically adopting improved particle swarm calculationThe method is characterized in that the core of the particle swarm algorithm is a particle velocity updating formula, the population scale of the particle swarm algorithm is assumed to be N, and the coordinate position of a single particle in a D-dimensional space at the moment t is as follows: x is the number of j (t)=(x 1 ,x 2 ,...x i ...,x D ) The particle velocity is expressed as: v. of j (t)=(v 1 ,v 2 ,...,v i ...,v D ) Speed v of a single particle at time t +1 j (t +1) and position x j (t +1) is:
v j (t+1)=ωv j (t)+C 1 Φ 1 [p best -x j (t)]+C 2 Φ 2 [g best -x j (t)] (16)
x j (t+1)=x j (t)+v j (t+1) (17)
wherein: c 1 And C 2 Are learning factors which respectively reflect the self-learning ability and the social learning ability of the particles; phi 1 And phi 2 Is a random number between (0, 1); ω is the inertial weight, which represents the effect of particle inertia on velocity.
Because the inertia weight factor and the learning factor of the existing particle swarm algorithm are fixed and invariable and are easy to fall into a local optimal value, the particle swarm algorithm is improved so as to obtain an expected scheduling result.
The solution process is as follows:
1) data initialization (input module). Inputting the system composition, the structural parameters, the model parameters and the particle swarm algorithm parameters of the microgrid, and initializing a particle population;
(scheduling Module)
2) Calculating the fitness value of the initialized particle population, namely the running cost and the environmental protection cost;
3) according to the fitness value, particle speed and position are updated through formulas (16) and (17) to obtain a child population, and a dynamic inertia weight factor is set;
4) an individual extremum pbest is determined. And taking pbest as an initial individual extreme value of the particle, if the current particle dominates pbest, taking the current particle as a pbest individual extreme value, if the current particle and the pbest cannot be compared, calculating the number of other particles dominated by the two particles in the group, and if the dominant particles are more, taking the two particles as an individual extreme value pbest.
5) And carrying out layered sequencing on the population, storing the optimal non-dominated solution Pareto into an external archiving set, removing the non-Pareto solution, judging whether the external archiving set exceeds the specified capacity, and if so, selecting m particles according to the crowding distance.
6) The global optimum gbest. And selecting the gbest from the external set according to the crowding distance of the optimal solution by adopting the Pareto optimal solution stored in the external archive set.
7) With little probability of variation. In order to prevent the particle swarm algorithm from prematurely converging to the local optimal front edge instead of the global optimal front edge, a small-probability random variation mechanism is introduced, small-probability disturbance of +/-30% is generated on the original position of the particle, and the optimization capability of the global optimal front edge of the particle is improved.
8) And returning to the step 3) until the termination condition is met, taking the termination condition as the maximum iteration number, and outputting a final optimized scheduling result. (output Module)
The original deficiency of the improved particle swarm algorithm is overcome from the aspect of improving the particle swarm algorithm because of the defects of the particle swarm algorithm, the unexpected scheduling result can not be obtained, and the improved strategy is as follows:
and improving the inertia weight factor:
wherein: gen is the current iteration number, gen max Is the total number of iterations, ω s And ω e Are the initial and final values of the inertial weight factor. In the initial stage of iteration, the larger omega makes the algorithm not suitable for trapping in a local minimum value, and facilitates global search. In the later iteration stage, the smaller omega is beneficial to local search and convergence of the algorithm. And variation operation is introduced into the particle swarm optimization to improve the particle swarm optimization. The adaptive mutation is based on the idea of mutation in genetic algorithm, i.e. for a certain objectThe variables are reinitialized with a certain probability. The mutation operation expands a continuously reduced population search space in iteration, so that particles can jump out of the previously searched optimal value position, search is developed in a larger space, population diversity is maintained, and the possibility of searching the optimal value by an algorithm is improved. Therefore, a simple mutation operator is introduced on the basis of a common particle swarm algorithm, and the particles are reinitialized with a certain probability after each update of the particles.
In summary, the invention sets up a micro-grid environment-friendly and economic dispatching model from the aspects of micro-grid economy and environment protection, the model aims at the operation cost and the environment protection cost, the optimized model is solved by adopting an improved particle swarm algorithm to obtain the optimal dispatching result, the inertia weight of the particle swarm algorithm is improved to enable the model to become dynamic rather than fixed in the solving process, the variation operation is introduced into the particle swarm algorithm, namely, certain variables are reinitialized with certain probability, and the possibility of searching the optimal value by the algorithm is improved.
Claims (10)
1. A microgrid optimal scheduling method is characterized by comprising the following steps:
1) constructing a microgrid environment-friendly and economic dispatching model considering operation cost and environment protection cost according to the power generation characteristics of distributed power sources and energy storage in the microgrid;
2) and solving the environmental protection and economic dispatching model of the micro-grid by adopting an improved particle swarm algorithm to obtain an optimal dispatching result.
2. The microgrid optimization scheduling method according to claim 1, characterized in that in the step 1), the power generation characteristics of distributed power sources and stored energy in the microgrid are expressed by mathematical models, specifically including a wind power generator mathematical model, a photovoltaic power generation mathematical model, a diesel generator model, a micro gas turbine model and a storage battery model.
3. The microgrid optimization scheduling method according to claim 2, characterized in that the wind power generator mathematical model is specifically:
wherein, P' WT Is the output power of a wind turbine, P' r Is rated power, v 'of wind power generator' ci 、v′ r And v' co The wind speed parameters are represented by cut-in wind speed, rated wind speed and cut-out wind speed of the wind driven generator, a ', b', c 'and d' are wind speed parameters, and v is wind speed;
the photovoltaic power generation mathematical model specifically comprises the following steps:
wherein, P' pv Is the output active power, R 'of the photovoltaic cell' pv Is the photovoltaic output power, q 'under standard test conditions' pv Is the derating coefficient of the photovoltaic, I' T Is the actual solar radiation intensity, I' STC Is the intensity of solar radiation, alpha ', under standard test conditions' p Is the temperature coefficient, T 'of the photovoltaic cell panel' c Is the photovoltaic cell temperature at the current time, T' stc Is the photovoltaic cell temperature under standard test;
the diesel generator model is as follows:
wherein, C DE.OM (t)、C DE.F (t)、C DE.EN (t) the operating maintenance cost, fuel cost, pollutant disposal cost, P, respectively, of the diesel engine at time t DE (t) is the power generation of the diesel engine at time t, K DE.OM Cost of operating and maintaining for diesel engineCoefficient, gamma de,k Emission of pollutants of the k-type, C, for diesel engine operation k For the cost coefficient of processing k-type pollutants, alpha, beta and gamma are respectively the coefficients of a diesel engine;
the micro gas turbine model specifically comprises:
wherein, P MT (t) is the active output power, η, of the micro gas turbine MT (t) operating efficiency of the micro gas turbine, C MT.OM (t)、C MT.F (t)、C MT.EN (t) the operation and maintenance cost, fuel cost, pollutant treatment cost, P of the diesel engine at the moment t respectively MT (t) is the power generation of the diesel engine at time t, K MT.OM For the operating maintenance cost factor, gamma, of a diesel engine mt,k Emission of pollutants of the k-type, C, for diesel engine operation k Cost factor for treating class k contaminants;
the storage battery model specifically comprises:
where SOC (t) is the residual capacity of the battery at time t, P bess (t) the charging and discharging power of the battery at time t, positive for charging, negative for discharging, eta + 、η - The charge and discharge efficiencies are respectively.
4. The microgrid optimization scheduling method according to claim 3, characterized in that an objective function of the microgrid environment-friendly and economic scheduling model is as follows:
f(x)=min[f 1 (x),f 2 (x)]
wherein f is 1 、f 2 Respectively representing an operation cost target and an environmental protection cost target, and x is an optimization variable of the model.
5. The microgrid optimization scheduling method according to claim 4, wherein the expression of the operation cost of the microgrid in a grid-connected mode is as follows:
wherein, C grid (t)、C bess (t)、C MT (t)、C DE (t) Total cost of interaction of the microgrid with the Main grid, maintenance cost of stored energy, Total operating cost of the micro gas turbine and Total operating cost of the Diesel Generator, P, respectively, at time t bess (t) power of stored energy at time t, P sell (t)、P buy (t) selling power and purchasing power of the microgrid and the main grid at the moment t respectively, c buy (t)、c sell And (t) the purchase and sale electricity prices of the micro-grid and the main grid at the moment t respectively.
6. The microgrid optimization scheduling method according to claim 5, wherein the environmental protection cost of the microgrid is expressed as:
wherein, C GRID.EN (t) pollutant treatment cost, gamma, of large power grids grid,k Emission of k-type pollutants for large grid operation, C k Cost factor for treating class k contaminants.
7. The microgrid optimization scheduling method according to claim 6, wherein the constraint conditions of the microgrid environmental protection and economic scheduling model comprise:
(1) and power balance constraint:
P′ PV (t)+P′ WT (t)+P grid (t)+P DE (t)+P MT (t)+P bess (t)=P L (t)
(2) the output constraint of the diesel generator is as follows:
(3) output constraint of the micro gas turbine:
(4) tie line transmission power constraint:
(5) and (4) energy storage device restraint:
wherein the content of the first and second substances,the upper and lower limits of the diesel engine output and the micro gas turbine output, r DE 、r MT The upper limit of the climbing power of the diesel engine and the micro gas turbine respectively,for the upper and lower limits of the transmission power of the tie line,the upper and lower limits of the energy storage device output are positive indicating power input and negative indicating power output, SOC max (t)、SOC min And (t) is the upper and lower limits of the energy storage capacity at the moment t.
8. The microgrid optimization scheduling method according to claim 1, wherein in the step 2), solving the microgrid environment-friendly and economic scheduling model by using an improved particle swarm algorithm specifically comprises the following steps:
21) data initialization, namely inputting system composition, structural parameters, model parameters and particle swarm algorithm parameters of a micro-grid, and initializing a particle population;
22) calculating the fitness value of the initialized particle population, namely the running cost and the environmental protection cost;
23) updating the particle speed and the particle position according to the fitness value to obtain a filial generation population, and setting a dynamic inertia weight factor;
24) determining individual extreme values pbest, taking pbest as initial individual extreme values of the particles, if the current particles dominate pbest, taking the current particles as pbest individual extreme values, if the current particles and the pbest cannot be compared, calculating the number of other particles dominated by the current particles and the pbest dominated by more particles in a group;
25) carrying out layered sequencing on the population, storing the optimal non-dominated solution Pareto into an external archiving set, removing the non-Pareto solution, judging whether the external archiving set exceeds the specified capacity, and if so, selecting m particles according to the crowding distance;
26) determining a global optimal value gbest, adopting a Pareto optimal solution stored in an external archive set, and selecting the gbest from the external set by using a roulette method according to the crowding distance of the optimal solution;
27) performing small probability variation, introducing a small probability random variation mechanism to prevent the particle swarm algorithm from prematurely converging to a local optimal front edge instead of a global optimal front edge, generating +/-30% small probability disturbance on the position of the particle at the original position, and increasing the optimization capability of the global optimal front edge of the particle;
28) and returning to the step 23) until a termination condition, namely the maximum iteration number, is met, and outputting a final optimized scheduling result.
9. The method according to claim 8, wherein in the step 2), the inertia weight factor in the improved particle swarm optimization is improved to prevent the local optimal value from being trapped, and the following steps are performed:
wherein gen is the current iteration number, gen max As total number of iterations, ω s And ω e The initial and final values of the inertial weight factor.
10. The microgrid optimization scheduling method according to any one of claims 1 to 9, wherein the microgrid optimization scheduling system for implementing the microgrid optimization scheduling method comprises:
an input module: the method comprises the steps of obtaining parameters of a micro-grid environment-friendly and economic dispatching model and an improved particle swarm algorithm;
a mathematical model module: the micro-grid environment-friendly and economic dispatching method comprises a wind driven generator mathematical model, a photovoltaic power generation mathematical model, a diesel generator mathematical model, a micro gas turbine mathematical model and an energy storage and load mathematical model, wherein a micro-grid environment-friendly and economic dispatching model is constructed through the mathematical models;
a scheduling module: solving the environmental protection and economic dispatching model of the micro-grid by adopting an improved particle swarm algorithm;
an output module: and the method is used for outputting the solving result of the scheduling module, including the optimal value after the running cost and the environmental protection cost are taken into consideration, and the optimal configuration result among all the distributed power supplies, the energy storage units and the loads, namely the scheduling result.
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202210590492.9A CN114819402A (en) | 2022-05-26 | 2022-05-26 | Micro-grid optimization scheduling method |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202210590492.9A CN114819402A (en) | 2022-05-26 | 2022-05-26 | Micro-grid optimization scheduling method |
Publications (1)
Publication Number | Publication Date |
---|---|
CN114819402A true CN114819402A (en) | 2022-07-29 |
Family
ID=82518285
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN202210590492.9A Pending CN114819402A (en) | 2022-05-26 | 2022-05-26 | Micro-grid optimization scheduling method |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN114819402A (en) |
Cited By (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN117293897A (en) * | 2023-09-08 | 2023-12-26 | 国网江苏省电力有限公司扬州供电分公司 | Distributed power supply cluster wind-solar energy storage configuration method and system |
Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN106451550A (en) * | 2016-11-10 | 2017-02-22 | 三峡大学 | A micro-grid grid-connected optimal scheduling method based on improved subgradient particle swarms |
CN108022055A (en) * | 2017-12-28 | 2018-05-11 | 无锡英臻科技有限公司 | A kind of micro-capacitance sensor economic load dispatching method based on particle group model |
CN109217354A (en) * | 2017-07-07 | 2019-01-15 | 华北电力大学(保定) | A kind of micro-capacitance sensor economic load dispatching method based on particle swarm optimization algorithm |
CN111144641A (en) * | 2019-12-24 | 2020-05-12 | 东南大学 | Improved particle swarm algorithm-based microgrid optimization scheduling method |
-
2022
- 2022-05-26 CN CN202210590492.9A patent/CN114819402A/en active Pending
Patent Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN106451550A (en) * | 2016-11-10 | 2017-02-22 | 三峡大学 | A micro-grid grid-connected optimal scheduling method based on improved subgradient particle swarms |
CN109217354A (en) * | 2017-07-07 | 2019-01-15 | 华北电力大学(保定) | A kind of micro-capacitance sensor economic load dispatching method based on particle swarm optimization algorithm |
CN108022055A (en) * | 2017-12-28 | 2018-05-11 | 无锡英臻科技有限公司 | A kind of micro-capacitance sensor economic load dispatching method based on particle group model |
CN111144641A (en) * | 2019-12-24 | 2020-05-12 | 东南大学 | Improved particle swarm algorithm-based microgrid optimization scheduling method |
Cited By (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN117293897A (en) * | 2023-09-08 | 2023-12-26 | 国网江苏省电力有限公司扬州供电分公司 | Distributed power supply cluster wind-solar energy storage configuration method and system |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Ghorbani et al. | Optimizing a hybrid wind-PV-battery system using GA-PSO and MOPSO for reducing cost and increasing reliability | |
CN108960510B (en) | Virtual power plant optimization trading strategy device based on two-stage random planning | |
Song et al. | Multi-objective configuration optimization for isolated microgrid with shiftable loads and mobile energy storage | |
Gupta et al. | Economic analysis and design of stand-alone wind/photovoltaic hybrid energy system using Genetic algorithm | |
CN113239607A (en) | Economic dispatching optimization method, system, equipment and storage medium for comprehensive energy system | |
CN113437752B (en) | Operation control method of comprehensive energy system containing hybrid energy storage | |
CN112270433A (en) | Micro-grid optimization method considering renewable energy uncertainty and user satisfaction | |
CN106779361B (en) | The multi-objective optimization design of power method of grid-connected mixing renewable energy system | |
CN116667325B (en) | Micro-grid-connected operation optimization scheduling method based on improved cuckoo algorithm | |
CN112550047A (en) | Optimal configuration method and device for light charging and storage integrated charging station | |
CN115600858A (en) | Wind-solar energy storage hydrogen production system economical optimization scheduling method considering wind abandoning and light abandoning punishment | |
CN112580897A (en) | Multi-energy power system optimal scheduling method based on parrot algorithm | |
CN116914821A (en) | Micro-grid low-carbon optimal scheduling method based on improved particle swarm optimization | |
CN114400712A (en) | Improved second-order particle swarm algorithm-based micro-grid cluster optimization scheduling method | |
CN114977180A (en) | Microgrid low-carbon optimal scheduling method based on load side flexible resources | |
CN115241923A (en) | Micro-grid multi-objective optimization configuration method based on snake optimization algorithm | |
CN115207911A (en) | Industrial plant-level and microgrid load optimization scheduling method based on electricity price excitation | |
CN114819402A (en) | Micro-grid optimization scheduling method | |
CN112883630A (en) | Day-ahead optimized economic dispatching method for multi-microgrid system for wind power consumption | |
CN110504684B (en) | Day-ahead optimal scheduling method for regional multi-microgrid system | |
CN112670982A (en) | Active power scheduling control method and system for micro-grid based on reward mechanism | |
Jemaa et al. | Optimum sizing of hybrid PV/Wind/battery installation using a fuzzy PSO | |
CN111555364A (en) | Microgrid energy management method using improved wolf optimizer | |
CN114884133B (en) | Micro-grid economic dispatching optimization method and system considering electric automobile | |
Liu et al. | Optimal allocation of microgrid using a differential multi-agent multi-objective evolution algorithm |
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 |