CN105470947B - A kind of micro-capacitance sensor dispatching method based on quantum-behaved particle swarm optimization - Google Patents
A kind of micro-capacitance sensor dispatching method based on quantum-behaved particle swarm optimization Download PDFInfo
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Classifications
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/28—Arrangements for balancing of the load in a network by storage of energy
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
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Abstract
The invention discloses a kind of micro-capacitance sensor dispatching method based on quantum-behaved particle swarm optimization, the present invention for the application at present of grid-connected wind-light storage microgrid power Constraints of Equilibrium problem it is more be that penalty term is added in object function, by adding penalty term in the particle respective objects functional value to deviateing power-balance, so that functional value increases, so this some particles is filtered out by algorithm optimizing, but the reduction of number of particles can be so caused, influences the search performance of algorithm.The present invention reduces dimensionality of particle by the way that battery is exchanged into power as direct optimized variable and microgrid with bulk power grid by way of indirect optimized variable, and to the particle for being unsatisfactory for state-of-charge constraint and the particle for exchanging with bulk power grid power constraint from newly initializing and loop iteration, so as to ensure that the search performance of particle, and it can accordingly improve convergence rate.
Description
Technical Field
The invention belongs to the technical field of micro grids, particularly relates to a micro grid scheduling method based on a quantum-behavior particle swarm algorithm, and aims at optimizing scheduling of a wind-solar-storage grid-connected micro grid.
Background
The micro-grid is composed of various DGs (distribution generation), energy storage units, loads and a control protection system, the requirement of electric power or heat load is provided for a cell or an island by coordinating all the DGs, and the micro-grid is suitable for supplying power for some remote areas due to the technical characteristics. The grid-connected wind-solar storage micro-grid as a typical micro-grid has a great development space in the future, and during grid-connected operation, because the large power grid implements peak-valley electricity price, the grid-connected micro-grid can purchase electricity from the large power grid at low electricity price, and sell electricity to the large power grid at high electricity price, so that the difference price is earned, and the operation cost of the whole system is reduced.
The scheduling target of the microgrid can be a single target or multiple targets, the single target is mainly aimed at the operation cost of a certain stage of the system at present, for example, the day-ahead scheduling considers the all-day operation cost, and the multiple targets are mainly considered the system operation cost, the environment punishment cost, the reliability and the like. The optimal scheduling problem of the microgrid is a multidimensional nonlinear optimization problem, and the traditional method for obtaining the optimal solution by solving equations is difficult to apply to solving the optimal scheduling problem of the microgrid. With the development of intelligent control and intelligent algorithms, the intelligent algorithms are mature for solving the problem of high-dimensional nonlinear optimization, and typical intelligent optimization algorithms mainly include evolutionary genetic algorithms, fish swarm algorithms, ant swarm algorithms and particle swarm algorithms. By modeling the microgrid optimization scheduling problem and combining an intelligent algorithm, the high-dimensional nonlinear optimization problem can be effectively solved.
The existing micro-grid dispatching technology mainly aims at the economical efficiency, wherein the economical efficiency comprises unit operation and maintenance cost, fuel cost, depreciation cost, startup and shutdown cost and interaction cost of operation and a large power grid in a grid-connected state, and for the environmental cost, the economical efficiency mainly aims at the units with pollutants such as diesel engines and micro gas turbines. How to reduce the operation and maintenance cost of a micro-grid and the cost of interaction with a large power grid become a key problem of the grid-connected wind-solar energy storage micro-grid, because the output of each unit is related to the load and the upper and lower limits of the output of each unit, the situation that the variable is out of limit or the constraint does not meet the requirement often occurs in a random intelligent search algorithm, and therefore how to process the constraint becomes a key in the optimized scheduling. In addition, the problem of easy precocity and non-convergence of the random intelligent algorithm also brings certain difficulty to the solution of the multidimensional nonlinear problem, the global search capability and the local search capability of the algorithm also have certain influence on the optimization of the nonlinear problem, and how to balance the global search capability and the local search capability to enable the algorithm to be suitable for the requirement of the actual problem is also a problem needing to be researched at present.
Disclosure of Invention
The invention mainly aims at the optimization scheduling problem of a grid-connected wind-solar-storage micro-grid, provides a micro-grid optimization scheduling method based on a quantum-behavior particle swarm algorithm, and is suitable for the optimization scheduling problem of the wind-solar-storage micro-grid through parameter control of the algorithm, and can better handle each constraint problem. The invention is mainly realized by the following technical scheme:
in order to solve the problem of optimizing and scheduling the wind-solar energy storage grid-connected microgrid, the invention mainly adopts the technical scheme that the grid-connected wind-solar energy storage microgrid optimizing and scheduling method is based on a quantum-behavior particle swarm algorithm, and the method mainly comprises the following steps:
A. arranging the output of each micro-power supply based on a day-ahead scheduling plan, selecting the output of a storage battery in an energy storage system as a direct optimization variable, and exchanging power between a micro-grid and a large grid as an indirect optimization variable; setting a simulated scheduling period and time dimension, loading photovoltaic output, fan output and load power prediction data and real-time electricity prices of micro-grid and large-grid exchange power, and setting the number of particles, iteration times, running turns and particle dimensions of an algorithm;
B. initializing the charge state of the storage battery and calculating the initial residual electric quantity of the storage battery; circulating each dimension of each particle in sequence, if the charge state of the storage battery meets the required constraint range, carrying out random initialization on the charge state of the storage battery in the output range, calculating the charge state of the storage battery at the moment, then detecting the charge state of the storage battery, and if the charge state of the storage battery is not in the required constraint range, carrying out boundary condition processing on the charge state of the storage battery; calculating the exchange power of the micro-grid and the large-grid, detecting whether the exchange power exceeds the maximum transmission power limit of the transmission line, setting the transmission power as the maximum allowable transmission power if the exchange power exceeds the maximum transmission power limit of the line, simultaneously calculating the output of the storage battery again through power balance, calculating the charge state of the storage battery again through the output of the storage battery, detecting whether the charge state of the storage battery meets the requirement, and jumping out of the particle cycle if the charge state of the storage battery meets the requirement to enter the initialization of the next particle;
C. when all the particles are initialized, calculating initial fitness values of the particles, namely an objective function value f (x), wherein the f (x) mainly comprises the following parts;
r (i) is the real-time electricity price of the exchange power of the micro-grid and the large grid, P grid (i) For exchanging power between the micro-grid and the large-grid, i is a certain time dimension of scheduling, T is a scheduling period, P N For rated output power, T, of energy-storing accumulators N For the annual number of operating hours of the energy-storing accumulator, C start Is the initial total investment cost, P, of the energy storage system b (i) For the actual output power, K, of the energy-storing accumulator at a certain scheduling moment c Capital recovery factor for an energy storage system, which is calculated from the following equation:
s is the depreciation rate of the energy storage battery, and M is the service life of the energy storage battery;
D. setting each particle initial fitness value as the particle individual optimal fitness value, setting the particle initial position as the particle individual optimal position, comparing all the particle fitness values, finding out the global optimal particle, and recording the position and the fitness value of the particle;
E. and updating and iterating each particle, calculating the charge state of the storage battery at each moment after iteration is finished, detecting whether the charge state meets constraint conditions, performing border processing on the charge state if the charge state does not meet the constraint conditions, and correcting the output of the storage battery again. When the state of charge of the storage battery exceeds the lower boundary SOC min If so, correcting the output of the storage battery by using the formula (3); SOC when the state of charge of the battery exceeds the upper boundary max If so, the output of the storage battery is corrected by the formula (4).
Wherein X t The output of the battery at time t is shown, RL is the initial residual capacity of the battery, and NL is the rated capacity of the battery.
F. Calculating the exchange power of the microgrid and the large power grid by power balance, simultaneously detecting whether the exchange power exceeds a constraint range, carrying out border treatment if the exchange power exceeds the constraint range, recalculating the output and the charge state of the storage battery, finally judging whether the charge state of the storage battery meets the conditions, jumping out of the particle cycle if the charge state of the storage battery meets the conditions, and entering the cycle of the next particle;
G. after all the particles are circulated, recalculating the fitness values, updating the individual optimal positions, the individual optimal fitness values, the global optimal positions and the global optimal fitness values, and returning to E again for circulation;
H. and when the maximum iteration times are reached, terminating the iteration and outputting the optimal output, the adaptability value and the corresponding storage battery state of charge.
The invention has the advantages and beneficial effects that:
1. at present, quantum-behavior particle swarm is not applied to grid-connected wind-solar storage micro-grid optimization scheduling, and the method applies a quantum-behavior particle swarm algorithm to the problem for the first time. Compared with the traditional particle swarm optimization, the quantum particle swarm optimization is introduced by adopting the principle of the wave-particle duality of particles in quantum mechanics, and the particle position is changed from changing the particle position through the speed of the traditional particle swarm optimization into changing the particle position through an attractor by establishing a potential well model for the particles, so that the particle iteration is simpler, but the optimization capability is stronger. Meanwhile, in the invention, the contraction and expansion coefficient in the particle iteration process and two fixed parameters of the individual optimal position and the global optimal position of the particles in the attractor model are set to be dynamically changed along with the iteration times, so that the algorithm has stronger global search capability at the beginning and stronger local search capability at the end of the iteration, the algorithm can be quickly converged to a global optimal solution, and the problems of precocity, non-convergence and the like of the traditional algorithm are solved.
2. Aiming at the problem of power balance constraint of a grid-connected wind-solar-storage micro-grid, a penalty term is added into an objective function, and the penalty term is added into a corresponding objective function value of particles deviated from power balance, so that the function value is increased, the particles are filtered out through algorithm optimization, the number of the particles is reduced, and the searching performance of the algorithm is influenced. The invention reduces the particle dimension by taking the storage battery as a direct optimization variable and taking the micro-grid and large power grid exchange power as an indirect optimization variable, and newly initializes and iterates the particles which do not meet the charge state constraint and the particles which are in exchange power constraint with the large power grid, thereby ensuring the particle search performance and correspondingly improving the convergence rate.
Drawings
FIG. 1 is a flow chart of optimization scheduling of a grid-connected wind-solar-storage micro-grid based on a quantum-behaved particle swarm algorithm provided by the invention;
FIG. 2 is a diagram of an embodiment of an optimized scheduling end plan of the present invention;
fig. 3 is a battery state of charge diagram according to an embodiment of the present invention.
Detailed Description
The following detailed description of embodiments of the invention refers to the accompanying drawings in which:
the flow chart of the grid-connected wind-solar-storage micro-grid optimization scheduling method based on the quantum-behaved particle swarm algorithm is shown in FIG. 1, and the method mainly comprises the following steps:
A. the method comprises the steps of setting various basic parameters of an algorithm, wherein the required setting parameters mainly comprise the number of particles, the particle dimension and the iteration frequency, the particle dimension number is H =20, the particle dimension number is also the number of moments of output of a storage battery needing to be optimized, the particle dimension number is used as a day-ahead schedule, the scheduling cycle is 24H, each 1H is a time interval, the particle dimension number is T =24, and the iteration frequency is L =500.
B. And loading data such as the predicted output and the predicted power of the load of the photovoltaic and the fan, the real-time electricity price and the like.
C. Setting the initial charge states of all the particles to be consistent, and calculating the residual capacity RL of the storage battery according to the total capacity of the storage battery
D. Initializing each dimension value of the particles, namely randomly assigning values between the upper limit and the lower limit of the output of the storage battery, and calculating the nuclear charge state of the storage battery and the exchange power P of the micro-grid and the large grid grid The value is determined by the power balance constraint of:
P grid ＝P load -P PV -P wt -P b (5)
wherein P is load Is the power demanded of the load, P PV Output for photovoltaic power generation, P wt Is the output of the fan, P b And (4) outputting power for the storage battery.
E. Exchanging power P with the large power grid according to the state of charge of the storage battery and the micro power grid grid Constraint pair battery and P grid The adjustment is carried out, and the constraint of the state of charge of the storage battery is represented by the following expression:
SOC min ≤SOC≤SOC max (6)
wherein the SOC min And SOC max Lower and upper battery state-of-charge limits, respectively;
P grid the constraints are expressed by the following expressions:
P grid_min ≤P grid ≤P grid_max (7)
wherein P is grid_min And P grid_max The minimum value and the maximum value of the transmission power of the wind-solar energy storage micro-grid and the large grid tie line are respectively;
when the state of charge of the storage battery is smaller than the allowed minimum value, the storage battery is in an over-discharge state, the storage battery is put to the allowed lowest state of charge, the highest discharge power allowed by the storage battery is calculated, and the highest discharge power is used as the actual output of the storage battery; when the state of charge of the storage battery is larger than the maximum value allowed by the storage battery, the storage battery is in an overcharged state at the moment, the storage battery is charged to the highest allowed state of charge, the highest allowable charging power of the storage battery is calculated, and the highest allowable charging power is used as the actual output of the storage battery; when the state of charge of the storage battery is between the maximum value and the minimum value allowed by the storage battery, whether the output of the storage battery is between the upper limit and the lower limit allowed by the storage battery is detected, and the output of the storage battery is required to meet the following constraint:
wherein P is b_min And P b_max Respectively, the lower limit and the upper limit of the output allowed by the storage battery.
F. After all the particles are initialized, calculating the fitness value, namely calculating the objective function, which is determined by the following formula:
wherein R (i) is the real-time electricity price of the exchange power of the micro-grid and the large grid, i is a certain time dimension of scheduling, T is a scheduling period, P is N For the rated output power, T, of the storage battery N For the annual number of operating hours, C, of the storage battery start Is the initial total investment cost, P, of the energy storage system b (i) For the actual output power, K, of the energy-storing accumulator at a certain scheduling moment c Capital recovery factor for an energy storage system, which is calculated from the following equation:
s is the depreciation rate of the energy storage battery, and M is the service life of the energy storage battery;
and taking the initial fitness values of all the particles as the individual optimal fitness value f _ pbest of each particle, taking the position as the individual optimal position pbest, finding out the minimum value, namely the fitness value f _ gbest of the global optimal particle assigned by the global optimal value according to the initialized fitness values, and simultaneously assigning the corresponding global optimal position to the global optimal particle getst.
G. The basic evolution formula is obtained by introducing quantum behavior particle swarm optimization:
wherein P is b,j (t + 1) denotes the (t + 1) th iteration value of the jth particle, i.e., the output of the battery, u and ψ are both [0,1 ]]The random numbers obeying uniform distribution, alpha, called contraction expansion coefficient, are parameters that can be controlled except population size and iteration number, and generally a fixed value and linear reduction mode can be adopted for alpha.For the attractor of the jth particle, the particle continuously corrects the position through the action of the attractor, so that the particle evolves toward the direction of the optimal particle, and the calculation formula of the attractor is as follows:
wherein pbest and gbest are the individual optimal position and the global optimal position of the particle, respectively, c 1 And c 2 Are all random functions that follow a gaussian distribution,
mbest (j) is the average position of all particles for the t-th iteration, which is defined as follows:
and performing update iteration on all the particles through the above evolution formula.
H. According to the state of charge of the storage battery and the micro-gridExchange power P with large power grid grid Constraint pair battery and P grid Adjustment, state of charge and battery output and P grid And D, detecting whether the upper limit and the lower limit are met or not, and if not, processing by using the same method in the step E.
I. And when the iteration is finished and the particles meet the condition needing to be detected in the H, calculating the fitness value of the current position of the particles by using the method in the step F, and updating the best position pbest of the particle individual according to the following formula:
i.e. P b,j (t) fitness value of particle and previous iteration value pbest j (t-1) comparing if P b,j (t) has a fitness value of less than pbest j The fitness value of (t-1) is set to pbest j (t)＝P b,j (t); otherwise pbest j (t)＝P b,j (t-1)；
For particle j, pbest j The fitness value of (t) is compared with the fitness value of the global optimum particle position gbest (t-1), and if the fitness value of (t) is better than the fitness value of gbest (t-1), f (pbest) is obtained j (t))&F (gbest (t-1)), the global optimum particle position gbest (t) = pbest is updated j (t), otherwise gbest (t) = gbest (t-1).
J. Judging whether all the particles reach the set iteration times, if not, returning to the step G to continue calculating; if the maximum iteration times are reached, the circulation is stopped, the final calculation result is output, the position of the global optimal particle is obtained, the position of the particle is the output of the storage battery in each time period, therefore, the exchange power with the large power grid and the charge state of the storage battery are calculated, and in addition, the final total economic cost after the optimized operation in the whole scheduling period can be output.
Examples
In this embodiment, a typical grid-connected wind-solar energy storage microgrid is taken as an example, the whole system includes a photovoltaic cell, a wind power generator, and a storage battery energy storage system, the capacity of the photovoltaic system is 80kWp, the capacity of the wind power generator is 190kW, the capacity of the storage battery energy storage system is 150kWh, the system investment cost is 130 ten thousand yuan, the annual running hours is 8760 hours, the planned service life is 15 years, the annual aging rate is 6.3%, the charging and discharging power limit of the storage battery is not more than 30kW, the SOC of the storage battery is constrained to be not less than 20% and not more than 75%, the system load is a real-time load for 24 hours, the peak value is 100kW, and the power limit of the whole system and a transmission line of a large power grid is not more than 15kW.
1. The entire system forecast data is loaded as shown in table 1.
TABLE 1 microgrid prediction data
2. In the calculation of the embodiment, the contraction and expansion coefficient of the quantum behavior particle swarm is linearly reduced, and 1.2 to 0.4 are linearly reduced along with the iteration number, as follows:
3. the wind-solar energy storage system is operated in a grid-connected mode, the particle swarm optimization is applied to optimize the operation cost to be 92.1 yuan, the total operation cost obtained by applying the method provided by the invention is 41.9119 yuan, 54.49% is reduced, and the reduction is great. As can be analyzed from fig. 2 and fig. 3, at the time when the electricity price is higher, such as 12, 14 and 15, the micro grid outputs electric energy to the large grid for selling electricity, and at this time, the storage battery discharges, and the state of charge decreases; and when the electricity price is lower between morning and evening, the micro-grid purchases electricity from the large power grid, and the storage battery is charged to recover the state of charge. The output of the storage battery, the exchange power of the microgrid and the large power grid and the charge state of the storage battery all meet the required constraint conditions, the advantages of the method are fully embodied, the output of the storage battery and the exchange power of the microgrid and the large power grid in the system are shown in a figure 2, and the charge state of the storage battery change are shown in a figure 3.
Claims (2)
1. A micro-grid dispatching method based on quantum-behaved particle swarm optimization is characterized by comprising the following steps:
A. the method comprises the following steps of arranging the output of each micro power source based on a day-ahead scheduling plan, selecting the output of a storage battery in an energy storage system as a direct optimization variable, and selecting the exchange power of a micro power grid and a large power grid as an indirect optimization variable; setting a simulated scheduling period and time dimension, loading photovoltaic output, fan output and load power prediction data and real-time electricity prices of micro-grid and large-grid exchange power, and setting the number of particles, iteration times, operation turns and particle dimension of an algorithm;
B. initializing the charge state of the storage battery and calculating the initial residual capacity of the storage battery; sequentially circulating each dimension of each particle, if the charge state of the storage battery meets the required constraint range, randomly initializing the charge state of the storage battery within the output range, calculating the charge state of the storage battery at the moment, detecting the charge state of the storage battery, and if the charge state of the storage battery is not within the required constraint range, carrying out boundary condition processing on the charge state of the storage battery; calculating the exchange power of the micro-grid and the large-grid, detecting whether the exchange power exceeds the maximum transmission power limit of the transmission line, setting the transmission power as the maximum allowable transmission power if the exchange power exceeds the maximum transmission power limit of the line, simultaneously calculating the output of the storage battery again through power balance, calculating the charge state of the storage battery again through the output of the storage battery, detecting whether the charge state of the storage battery meets the requirement, and jumping out of the particle cycle if the charge state of the storage battery meets the requirement to enter the initialization of the next particle;
C. when all the particles are initialized, calculating initial fitness values of the particles, namely an objective function value f (x), wherein the f (x) mainly comprises the following parts;
r (i) is the intersection of a micro-grid and a large gridReal-time electricity prices for changing power, P grid (i) For exchanging power between the micro-grid and the large grid, i is a certain time dimension of scheduling, T is a scheduling period, P N For rated output power, T, of energy-storing accumulators N For the annual number of operating hours of the energy-storing accumulator, C start Is the initial total investment cost, P, of the energy storage system b (i) For the actual output power, K, of the energy-storing accumulator at a certain scheduling moment c· Is the capital recovery factor of the energy storage system, which is calculated from the following equation:
s is the depreciation rate of the energy storage battery, and M is the service life of the energy storage battery;
D. setting each particle initial fitness value as the individual optimal fitness value of the particle, setting the particle initial position as the individual optimal position of the particle, comparing all the particle fitness values, finding out the global optimal particle, and recording the position and the fitness value of the particle;
E. updating and iterating each particle, calculating the charge state of the storage battery at each moment after iteration is finished, detecting whether the charge state meets constraint conditions, performing border processing on the charge state if the charge state does not meet the constraint conditions, and correcting the output of the storage battery again;
F. calculating the exchange power of the micro-grid and the large-power grid by power balance, simultaneously detecting whether the exchange power exceeds a constraint range, carrying out border processing if the exchange power exceeds the constraint range, recalculating the output and the charge state of the storage battery, and finally judging whether the charge state of the storage battery meets the conditions, if so, jumping out of the particle cycle and entering the cycle of the next particle;
G. after all the particles are circulated, recalculating the fitness values, updating the individual optimal positions, the individual optimal fitness values, the global optimal positions and the global optimal fitness values, and returning to E again for circulation;
H. and when the maximum iteration times are reached, terminating the iteration and outputting the optimal output, the adaptability value and the corresponding storage battery state of charge.
2. The microgrid scheduling method based on a quantum-behaved particle swarm algorithm of claim 1, characterized in that: the method for correcting the output of the storage battery in the step E is that when the state of charge of the storage battery exceeds the lower boundary SOC min If so, correcting the output of the storage battery by using the formula (3); SOC when the state of charge of the battery exceeds the upper boundary max When the current is detected, the output force of the storage battery is corrected by the formula (4);
wherein X t The output power of the battery at time t is shown, RL is the initial remaining capacity of the battery, and NL is the rated capacity of the battery.
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