CN110707677A - Power grouping transmission scheduling of direct-current micro-grid - Google Patents
Power grouping transmission scheduling of direct-current micro-grid Download PDFInfo
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Abstract
The invention discloses a power grouping transmission scheduling method based on a direct current micro-grid, which is based on a micro-grid containing renewable energy sources and takes the power supply and demand problems between a plurality of sellers and a plurality of users into consideration under the condition of transmission loss. Firstly, a master-slave game model is established by taking a seller as a leader and a user as a follower, existence of a balanced solution is proved, a distributed selection algorithm is adopted for solving, and a plurality of optimal power supply requirement matching pairs are obtained. Secondly, planning how two power packet routers select a channel problem for matching, solving the problem by using a genetic algorithm, and then transmitting power for the two power packet routers in the form of power packets.
Description
Technical Field
The invention belongs to the field of power, and particularly relates to power packet transmission scheduling of a direct-current microgrid.
Background
The intelligent power grid creates an automatic and distributed advanced energy transmission network by utilizing the bidirectional flow of electric power and information, so that the bidirectional interaction between a user and a power company is realized, namely, the user can obtain the electricity price condition and the electric energy quality information so as to reasonably use the electricity; the power company can observe the power utilization condition of the user, and provide more convenient services and preferential policies for the user. However, with the application of renewable energy in the power grid, the power grid may operate unstably and power supply may be unreliable due to the volatility of both renewable energy and power grid load. On the other hand, many loads in a household are mostly operated in a direct current mode, and a traditional power grid is in an alternating current distribution mode, so that the application of a direct current distribution system is crucial to reduce power loss in alternating current-direct current conversion. In view of the above problems, a novel dc power grouping scheduling system is proposed to achieve more efficient and reliable energy distribution, which transmits power in the form of power packets, and can achieve accurate and timed power supply.
The power packet scheduling system includes a plurality of sources, mixers, routers, and a plurality of loads. The mixer and the router control the power packet flow in the power line network, and each source, load, mixer and router has a unique address. The power output by the direct-current power supply is converted into a power packet through a switch in the frequency mixer, and then the power packet is transmitted to the router through the power line, the router reads the head and the tail of the packet after receiving the power packet, and the power packet is forwarded to a target load according to an address contained in the head. In the prior art, a mixer is optimized, and a single-inductor multi-input single-output converter is used in the mixer, so that power is shared from multiple sources to generate a power pack, and more renewable energy sources are utilized; at the same time, the inductance in the mixer can smooth the power and adjust the voltage level of the power packet to the correct level. However, due to the limited capacity of a single router, some transmission tasks may wait, resulting in a reduction in transmission efficiency.
Disclosure of Invention
In order to solve the problem that the transmission efficiency is reduced due to the fact that the capacity of a single router is limited and some transmission tasks may wait in the prior art, the invention provides power packet transmission scheduling of a direct current microgrid, constructs the direct current microgrid with a plurality of power sellers and a plurality of power users, and realizes power supply in the form of power packets by utilizing the idea of a power packet transmission scheduling system.
A power grouping transmission scheduling method for a direct-current micro-grid specifically comprises the following steps:
the method comprises the steps that firstly, a direct-current microgrid system model containing a plurality of power sellers and a plurality of power users is constructed on the basis of a power packet transmission network, wherein a renewable energy factory outputs power in the form of a direct-current power packet, and a scheduling control center realizes selection of a power packet router.
And secondly, establishing a master-slave game model between the power seller and the power users, wherein the power seller serves as an upper-layer leader, the power users serve as lower-layer followers, the transmission loss of the power pack network serves as constraint, and the optimal electricity price and the optimal demand are solved by using a distributed selection algorithm.
And thirdly, constructing a power package transmission scheduling optimization problem based on the obtained power seller-user matching pairs, further solving by adopting a genetic algorithm, and realizing quantitative power supply by utilizing a power package form.
The beneficial effect of this application is as follows:
the invention provides power grouping transmission scheduling of a direct-current microgrid, which is based on a microgrid containing renewable energy sources, and considers the power supply and demand problems between a plurality of power sellers and a plurality of power users under the condition of transmission loss. Firstly, a master-slave game model is established by taking an electric power seller as a leader and electric power users as followers, existence of a balanced solution is proved, a distributed selection algorithm is adopted for solving, and a plurality of optimal power supply requirement matching pairs are obtained. Secondly, planning how two power packet routers select a channel problem for matching, solving the problem by using a genetic algorithm, and then transmitting power for the two power packet routers in the form of power packets. Therefore, the direct-current microgrid with a plurality of power sellers and a plurality of power users is constructed, and power supply is realized in a power package mode by utilizing the idea of a power packet transmission scheduling system.
Drawings
Fig. 1 is a flow chart of power packet transmission scheduling based on a dc microgrid;
fig. 2 is a direct-current microgrid system model.
Detailed Description
In order to solve the problem that the transmission efficiency is reduced due to the fact that the capacity of a single router is limited and some transmission tasks may wait in the prior art, the invention provides power packet transmission scheduling of a direct current microgrid, constructs the direct current microgrid with a plurality of power sellers and a plurality of power users, and realizes power supply in the form of power packets by utilizing the idea of a power packet transmission scheduling system.
The core idea of the invention patent is as follows: based on the microgrid with renewable energy sources, the power supply and demand problems between a plurality of power sellers and a plurality of power users are considered under the condition of transmission loss. Firstly, a master-slave game model is established by taking an electric power seller as a leader and electric power users as followers, existence of a balanced solution is proved, a distributed selection algorithm is adopted for solving, and a plurality of optimal power supply requirement matching pairs are obtained. Secondly, planning how two power packet routers select a channel problem for matching, solving the problem by using a genetic algorithm, and then transmitting power for the two power packet routers in the form of power packets.
In order to solve the above problem, the technical solution of power packet transmission scheduling based on a dc microgrid provided in this patent is as follows, as shown in fig. 1:
the method comprises the steps that firstly, a direct-current microgrid system model containing a plurality of power sellers and a plurality of power users is constructed on the basis of a power packet transmission network, wherein a renewable energy factory outputs power in the form of a direct-current power packet, and a scheduling control center realizes selection of a power packet router.
And secondly, establishing a master-slave game model between the power seller and the power users, wherein the power seller serves as an upper-layer leader, the power users serve as lower-layer followers, the transmission loss of the power pack network serves as constraint, and the optimal electricity price and the optimal demand are solved by using a distributed selection algorithm.
And thirdly, constructing a power package transmission scheduling optimization problem based on the obtained power seller-user matching pairs, further solving by adopting a genetic algorithm, and realizing quantitative power supply by utilizing a power package form.
The key steps involved in the above scheme are explained below:
1. direct-current microgrid system model
Mainly researching a microgrid with a plurality of power sellers and a plurality of power consumers, the system model is shown in figure 2. The system model comprises a dispatching control center, a plurality of renewable energy factories, a plurality of sellers, a plurality of users and two power packet routers with different channel transmission loss, wherein each router comprises a plurality of channels. Wherein, different renewable energy plants represent different renewable energy sources, the renewable energy plants output power in the form of direct current power pack, and a seller selects one renewable energy plant to purchase power according to the principle nearby. The seller and the user upload the own selling electric quantity, selling price and required electric quantity to the dispatching control center, and then adjust own strategies respectively until the maximum profit is obtained, so as to form an optimal matching pair. And then the dispatching control center selects a proper electric power packet router for the matching pairs, the electric power packet router selects a channel for the matching pairs, and a seller transmits electric power for users in the form of electric power packets.
2. Master-slave game model
The electric power seller serves as a leader, the electric power user serves as a follower, the leader gives out the selling price and the electricity purchasing cost from the power grid, and the follower makes an optimal response to the selling price and the electricity purchasing cost, namely the optimal electric power demand; and then the leader adjusts the selling price according to the decision of the follower. The above process is repeated until the respective benefits are maximized.
User welfare function:
the user set is a ═ {1,2, …, a }, and the vendor set is B ═ 1,2, …, B }. DiIndicates the power demand, P, of the i-th customerjIndicating the unit price of the jth vendor. Wherein i ∈ A, j ∈ B. The utility function of the user is mainly reflected in the satisfaction degree of the user on electricity utilization, and can be described as follows:
in the formula: theta represents the demand degree of different users for electric quantity, and eta is a given parameter.
Therefore, the optimization problem for each user with respect to the welfare function is as follows:
maxFi＝V(Di)-PjDi(2)
in the formula:
indicating the minimum amount of power that satisfies the user's normal work.
The above problem is a strict convex optimization problem, so that there is a unique optimal solution. The first derivation is carried out on the welfare function of the user, and the welfare function is made to be zero, so that the optimal power demand of the user can be obtained:
vendor utility function:
and p represents the electricity purchase price of the seller from the renewable energy factory, and the selling price of each renewable energy factory is the same. The utility function for each vendor can be expressed as:
Z(Dj,Pj)＝DjPj-Rjp (5)
s.t.Di≤Dj(1-αmax) (6)
in the formula: djRepresents the amount of power, R, supplied by the jth vendor to the ith userjRepresenting the amount of electricity purchased from the renewable energy plant. Equation (6) shows that the seller needs to consider the amount of power supplied by the seller to meet the needs of the user after transmission. Wherein alpha ismaxIs the maximum transmission loss coefficient, alphamax＝max(α1,α2)，0＜α1＜0.5，0＜α2＜0.5。α1、α2The specific definitions of (a) are given in detail later.
3. Master-slave game model solution
Since the vendor and the user pay attention to their personal privacy information in comparison, the respective policy spaces and utility functions are not known to each other. This document solves the model using a distributed selection algorithm.
The vendor price solving process is as follows:
step 1: let k equal to 1, the initial selling price P of one seller is arbitrarily selected in the seller policyj，j∈B；
Step 2: sequentially solving the optimal power demand of the i users according to the formula (4);
and step 3: uploading the optimal power demand of each user to a seller j;
and 4, step 4: passing through type(sigma is a very large positive number, and the convergence rate of the algorithm can be adjusted) calculating Pj,k+1；
And 5: repeating the above steps until Pj,k+1＝Pj,kThen, the algorithm is stopped to obtain the optimal power demand of each user and the optimal selling price of the seller j;
step 6: and reselecting the selling price in the seller strategy, and executing the steps again.
The user selects the vendor process as follows:
and (3) solving the welfare function value according to the optimal selling price of each seller and the optimal power demand under the selling price by the user according to the formula (2), and selecting the seller with the maximum welfare function value to supply power for the seller. If the value of the benefit function is the same for both vendors, one vendor is randomly selected.
4. Power pack transmission scheduling problem planning
The optimal matching pair set H ═ {1,2, …, H } can be obtained through the above steps, and then the power packet router is required to select a channel for it to transmit power. In the step, the problem of how to carry out power scheduling through two power packet routers is mainly considered, so that the power loss in the system is minimized. Different routes represent different paths, the path loss is summarized as the router loss, and the rest configurations and parameters are the same in the two power packet routers except that the power transmission loss coefficient and the maximum capacity are different. For simple analysis, the power transmission loss coefficients of the respective channels in a packet router are uniform, and the packet router Q is a packet router1Has a channel transmission loss coefficient of alpha1Electric power pack router Q2Has a channel transmission loss coefficient of alpha2And α is1＜α2. Power pack router Q1The number of power channels of (1), (2), (…), (M), and the maximum capacity of each channel is Pmax，Q1The maximum capacity of (d) can be expressed as:
power pack router Q2The number of power channels of (1, 2, …) is N ═ 1,2, …N, the maximum capacity of each channel is Pmax，Q2The maximum capacity of (d) can be expressed as:
the case that the number of the power channels is greater than the matching logarithm will not be discussed here, and the case that the number of the power channels is less than the matching logarithm is mainly considered, that is, the condition is satisfied: m is more than or equal to n and less than h.
From the practical point of view, certain power loss must exist in the power packet router scheduling process, the amount of power loss has a crucial influence on users, and the excessive loss can cause the power received by the users to be insufficient, so that the normal use of the users cannot be met, and uncomfortable experience is caused to the users. Since the logarithm of the matching is greater than the number of channels in the router, some situations may occur where the matching pairs are waiting, and the waiting time may be used
To indicate.
Denotes the waiting time of h-th pair before power transmission, and τ ═ 1,2, denotes the waiting time of power packet router Q1Or electric power pack router Q2Awaiting power transfer. Therefore, in order to minimize the power loss during transmission, a cost function is proposed, i.e. the sum of the power loss cost and the waiting time cost:
in the formula: β is the loss penalty, the value of which is fixed; ehRepresenting energy output by the seller, DhEnergy representing a user demand; alpha is alphaτRepresenting a loss factor of the router; gamma is the latency penalty, which is given by the value; p is a radical ofUThe electric power of each user is constant for each user's own electric power, i.e., electric energy consumed per unit time.
The power packet transmission scheduling problem can be formulated as:
s.t. Eh(1-ατ)≥Dh(0＜ατ＜0.5) (12)
m≤n＜h (14)
Eh≤Pmax(17)
in the formula: kappa1＝{0,1}，κ21, and satisfies the condition
I.e., meaning that each matched pair can only be transmitted in one power packet router.
In the above problem, equation (11) is an optimization target, i.e. the total power loss in the system needs to be minimized; equation (12) indicates that the amount of power after loss must meet the minimum demand of the customer; equation (13) is a parameter constraint of the power packet router, and this condition relates to the problem of how to efficiently distribute power; equation (14) indicates that the number of channels in a single power packet router is not enough to transmit all the matched pairs, and some cases of waiting for the matched pairs may occur; equations (15) and (16) indicate that the total energy of all matched pairs transmitted cannot exceed the power packet router Q1Cannot exceed the maximum capacity limit of the power packet router Q2While also ensuring that the energy of the matched pairs transmitted in a single power packet router does not exceed the maximum capacity of the power packet routerAn amount; equation (17) ensures that the amount of power supplied by each vendor cannot exceed the maximum capacity of a single channel.
5. Scheduling solution using genetic algorithm
And regarding the arrangement sequence of each matching pair as a gene, and then regarding the arrangement sequence of h matching pairs as a chromosome, and obtaining the optimal scheduling sequence by calculating a fitness function and a series of evolutions. The method comprises the following concrete steps:
step 1: when encoding chromosomes, each pair is randomly arranged first, and the chromosomes in different orders are numbered sequentially from 1 onwards. During population initialization, a population of X chromosomes is randomly generated.
Step 2: in solving the power packet transmission scheduling problem, the inverse of equation (11), that is, equation (18), may be selected as the fitness function, and the smaller the total power loss amount is, the higher the fitness function value is, the better the fitness of the individual is represented.
And step 3: the l-th chromosome is selected from the population and the ratio of the fitness value of this chromosome to the sum of the fitness values of the X chromosomes is calculated, equation (19). The high ratio chromosomes are retained using roulette. If the ratio is the same, chromosomes with a shorter match versus latency are selected. For ease of analysis, two sets of chromosomes with higher ratios were selected for subsequent study.
And 4, step 4: and respectively taking the two groups of chromosomes as a father line chromosome and a mother line chromosome, uniformly crossing the two groups of chromosomes according to a certain probability, selecting corresponding crossing point positions, and exchanging corresponding genes between the two groups of chromosomes. At this time, there may be two sets of repeated genes in the chromosomes, and two sets of repeated genes that are not crossed are interchanged to obtain two new sets of chromosomes.
And 5: based on the last step, the new chromosome is subjected to single point mutation according to a certain probability, namely two genes are directly exchanged.
Step 6: and after selecting, crossing and mutating a plurality of groups of chromosomes according to the principle, taking the finally obtained population as a new initial population, and repeating the process until the optimal fitness function value is kept unchanged within a given iteration number or when the iteration number reaches the maximum value and is kept unchanged, terminating the algorithm. At this time, the chromosome is the scheduling sequence of the output optimal matching pair.
It can be seen that the present invention is based on a microgrid comprising renewable energy sources, taking into account the power supply and demand between a plurality of vendors and a plurality of users, in case of transmission losses. Firstly, a master-slave game model is established by taking a seller as a leader and a user as a follower, existence of a balanced solution is proved, a distributed selection algorithm is adopted for solving, and a plurality of optimal power supply requirement matching pairs are obtained. Secondly, planning how two power packet routers select a channel problem for matching, solving the problem by using a genetic algorithm, and then transmitting power for the two power packet routers in the form of power packets. Therefore, the direct-current microgrid with a plurality of power sellers and a plurality of power users is constructed, and power supply is realized in a power package mode by utilizing the idea of a power packet transmission scheduling system.
The invention also has the following beneficial effects:
(1) a direct-current microgrid with a plurality of power sellers and a plurality of power users is constructed, and power supply is realized in the form of power packages by utilizing the idea of a power grouping transmission scheduling system.
(2) In the process of carrying out master-slave game on the electric power seller and the electric power users, the transmission loss in the supply process of the electric power package is also taken into consideration, so that the requirements of the users can be met, and the welfare function of the users can be maximized.
(3) When two power packet routers are used for scheduling a plurality of vendor-user matching pairs, in order to realize minimum power loss in the power packet transmission process and realize optimalScheduling, a power packet transmission scheduling problem is planned:and the scheduling problem is a nonlinear constraint integer programming problem, so a genetic algorithm is adopted to solve the problem.
(4) When the power supply and demand matching is carried out on a seller and a user, the transmission loss constraint is considered, so that the user demand can be better met, and the discomfort degree of the user can be reduced.
(5) According to the power grouping transmission scheduling model based on the direct-current microgrid, a single router in the past is expanded into two routers, so that the power transmission efficiency is improved, and the power loss in a system is minimized.
(6) The method realizes the problem of power supply and demand in a new mode of a power pack, supplies power accurately at regular time and saves energy to a certain extent.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (1)
1. The method for power packet transmission scheduling of the direct current microgrid is characterized by comprising the following steps:
the method comprises the steps that firstly, a direct-current microgrid system model containing a plurality of power sellers and a plurality of power users is constructed on the basis of a power packet transmission network, wherein a renewable energy factory outputs power in the form of a direct-current power packet, and a scheduling control center realizes selection of a power packet router.
And secondly, establishing a master-slave game model between the power seller and the power users, wherein the power seller serves as an upper-layer leader, the power users serve as lower-layer followers, the transmission loss of the power pack network serves as constraint, and the optimal electricity price and the optimal demand are solved by using a distributed selection algorithm.
And thirdly, constructing a power package transmission scheduling optimization problem based on the obtained power seller-user matching pairs, further solving by adopting a genetic algorithm, and realizing quantitative power supply by utilizing a power package form.
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| CN109146201A (en) * | 2018-09-13 | 2019-01-04 | 三峡大学 | Filling based on cooperative game changes the integrated power station micro-capacitance sensor Optimization Scheduling of storage |
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