CN108512259B - AC-DC hybrid micro-grid double-layer optimization method based on demand side response - Google Patents

AC-DC hybrid micro-grid double-layer optimization method based on demand side response Download PDF

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CN108512259B
CN108512259B CN201810361630.XA CN201810361630A CN108512259B CN 108512259 B CN108512259 B CN 108512259B CN 201810361630 A CN201810361630 A CN 201810361630A CN 108512259 B CN108512259 B CN 108512259B
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grid
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CN108512259A (en
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李鹏
郑苗苗
李继红
刘理峰
顾一丰
张雪
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State Grid Zhejiang Electric Power Co Ltd
North China Electric Power University
Shaoxing Power Supply Co of State Grid Zhejiang Electric Power Co Ltd
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State Grid Zhejiang Electric Power Co Ltd
North China Electric Power University
Shaoxing Power Supply Co of State Grid Zhejiang Electric Power Co Ltd
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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
    • H02J4/00—Circuit arrangements for mains or distribution networks not specified as ac or dc
    • 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

An alternating current-direct current hybrid microgrid double-layer optimization method based on demand side response comprises the following steps: based on the response of the excited demand side, the load curve is close to the output change curve of the renewable energy source through the adjustment of the translatable load, and an upper-layer objective function of the AC/DC hybrid microgrid is established with the maximum renewable energy consumption rate as a target; considering both demand side response based on price and demand side response based on excitation, and establishing a lower-layer objective function with the minimum running cost of the alternating-current and direct-current hybrid microgrid as a target; the method comprises the steps of (1) following operation constraints, establishing an alternating current-direct current hybrid micro-grid double-layer optimization mathematical model; and improving a cultural genetic algorithm and solving an alternating current-direct current hybrid micro-grid double-layer optimization mathematical model. According to the alternating current-direct current hybrid micro-grid double-layer optimization mathematical model, the maximum renewable energy consumption rate is the upper-layer objective function, the minimum running cost of the alternating current-direct current hybrid micro-grid is the lower-layer objective function, the high-efficiency utilization of renewable energy can be realized through the alternating current-direct current hybrid micro-grid double-layer optimization mathematical model, and the alternating current-direct current hybrid micro-grid economic running is.

Description

AC-DC hybrid micro-grid double-layer optimization method based on demand side response
Technical Field
The invention relates to an alternating current-direct current hybrid microgrid optimization method. In particular to an alternating current-direct current hybrid micro-grid double-layer optimization method based on demand side response.
Background
With the increasingly prominent energy crisis, the development and utilization of renewable energy sources are in need. Distributed power supplies such as wind power generation and photovoltaic power generation can realize high-efficiency utilization of new energy, and are a hotspot of current research. However, since the output is limited by nature and has significant randomness and volatility, it is a current research difficulty to consider the wind and light uncertainty. The microgrid integrates devices such as a distributed power supply, a load, an energy storage device and a power electronic device, and can realize efficient management and utilization of the distributed power supply through a coordination control technology. Along with the diversification of load types, the alternating-current micro-grid is limited in power supply flexibility and electric energy quality, and an alternating-current and direct-current mixed micro-grid is generated at the same time.
The alternating current-direct current hybrid micro-grid can meet the power consumption requirements of alternating current loads and direct current loads at the same time, complementary power supply of energy sources is achieved through interconnection of the alternating current areas and the direct current areas, the energy utilization rate is improved, and the load power supply reliability is guaranteed. The consumption rate of renewable energy can be improved by considering demand side response, and the running cost of the alternating current-direct current hybrid micro-grid is reduced. Existing research considering demand-side response mostly targets the minimum operating cost of the microgrid, and generally considers either price-based demand-side response or incentive-based demand-side response. According to the method, two kinds of demand side responses are considered at the same time, and an alternating current-direct current hybrid micro-grid double-layer optimization mathematical model is established. The method comprises the steps of firstly dividing loads into important loads, translatable loads and interruptible loads according to load characteristics, secondly establishing an upper-layer optimization objective function by taking the maximum renewable energy consumption rate as an upper-layer target, and finally establishing a lower-layer objective function by taking the minimum cost of the alternating-current and direct-current hybrid microgrid as a lower-layer target. The improved cultural genetic algorithm is applied to solving the double-layer optimization mathematical model of the alternating current-direct current hybrid micro-grid, and the correctness and feasibility of the established model and the improved algorithm are verified through practical examples.
Disclosure of Invention
The invention aims to solve the technical problem of providing a demand side response-based alternating current and direct current hybrid micro-grid double-layer optimization method capable of realizing efficient utilization of renewable energy.
The technical scheme adopted by the invention is as follows: an alternating current-direct current hybrid micro-grid double-layer optimization method based on demand side response comprises the following steps:
1) based on the response of the excited demand side, the load curve is close to the output change curve of the renewable energy source through the adjustment of the translatable load, and an upper-layer objective function of the AC/DC hybrid microgrid is established with the maximum renewable energy consumption rate as a target;
2) considering both demand side response based on price and demand side response based on excitation, and establishing a lower-layer objective function with the minimum running cost of the alternating-current and direct-current hybrid microgrid as a target;
3) the method comprises the steps of (1) following operation constraints, establishing an alternating current-direct current hybrid micro-grid double-layer optimization mathematical model;
4) and improving a cultural genetic algorithm and solving an alternating current-direct current hybrid micro-grid double-layer optimization mathematical model.
The adjustment of the translatable load in step 1) is performed by using the following formula:
Figure GDA0002942869190000011
Lpy'(t)＝min(Lpy(t),L(t)-Pr(t)) (2)
Figure GDA0002942869190000021
in the formula: l' (t) represents a total load after the load transfer at the t-th period; l (t) represents the initial total load for the t-th period; l ispy' (t) represents the load shift amount of the t-th period;
Figure GDA0002942869190000022
represents the load shift amount of the t-th period; l ispy(t) represents the translatable load for the t-th time period; pr(t) represents renewable energy generation for the t-th period; t is an optimization time period, and T represents each time period in T; t1 is a time period when the power generation amount of the renewable energy source is smaller than the total load, and tt represents each time period in T1;
after load translation in the step 1), obtaining an upper layer objective function of the AC/DC hybrid microgrid as follows:
Figure GDA0002942869190000023
in the formula: krRepresenting the consumption rate of renewable energy; pr(t) represents renewable energy generation for the t-th period; l' (t) represents a total load after the load transfer at the t-th period; and T is an optimization time period.
The lower-layer objective function which aims at minimizing the operation cost of the alternating-current and direct-current hybrid microgrid in the step 2) is the sum of the following costs:
(1) price-based demand-side response
The method comprises the following steps of adopting demand side response based on time-of-use electricity price, determining the residual load electricity quantity capable of translating in each time interval according to a result of solving an upper layer objective function of the AC/DC hybrid microgrid, and translating the load according to the electricity price to adjust a load curve on the premise of ensuring that the renewable energy consumption rate is unchanged, wherein the microgrid needs to be subsidized in terms of translation of user loads, and the subsidizing cost is as follows:
Figure GDA0002942869190000024
in the formula: cpySubsidizing costs for translation loads; alpha is the translation subsidy unit price; t isOptimizing the time period; l "(t) is the load capacity at each moment after the secondary load translation; l (t) represents the initial total load for the t-th period;
(2) stimulus-based demand-side response
In the incentive-based demand-side response, the total load calculation formula that considers interruptible items is as follows:
Figure GDA0002942869190000025
Lzd'(t)＜min(L”(t)-Pr(t),Lzd(t)) (7)
in the formula: l' "(t) represents the load size after the load is interrupted; l iszd' (t) is the interruptible load amount that ensures the renewable energy consumption rate; pr(t) represents renewable energy generation for the t-th period; l iszd(t) is the initial interruptible load amount;
for loads interrupted at ordinary times and at peak periods, the patch cost for the interrupted patch is as follows:
Figure GDA0002942869190000026
in the formula: czdCost for breaking subsidies; beta is the unit price of the interrupted subsidy;
(3) initial construction cost Ce
Figure GDA0002942869190000027
In the formula: n is the number of micro sources in the micro grid; ceiA fixed investment cost for the ith micro-source; cRiThe investment recovery factor for the ith micro-source;
(4) cost of operation and maintenance Com
Figure GDA0002942869190000031
In the formula: k is a radical ofomiOperating and maintaining cost coefficients corresponding to the micro sources; pi(t) the output power of the ith micro-source;
(5) cost C of power purchase of power gridgrid
Figure GDA0002942869190000032
In the formula: g (t) is the electricity purchase price of the t time period; pg(t) the power purchasing electric quantity of the power grid in the tth time period;
(6) fuel cost Cfuel
Figure GDA0002942869190000033
In the formula: cMTfuelThe fuel cost of the micro-combustion engine; cFCfuelFuel cost for the fuel cell; cDEGfuelRepresents the fuel cost of the diesel generator; cc is the unit price of the fuel; LHV is the lower heating value of the fuel gas; pMTThe output of the micro-combustion engine; pFCIs the output of the fuel cell; pDEGIs the output of the diesel generator; etaMTAnd ηFCThe efficiency of the micro-combustion engine and the fuel cell; a. b and c are power generation coefficients of the diesel generator;
(7) environmental protection reduced cost Cen
Figure GDA0002942869190000034
In the formula: n1 is the number of micro sources of the discharged pollutants; m is a pollutant discharge type; gamma rayjReduced cost for jth contaminant; ei,jThe unit emission of the jth pollutant generated by the ith micro-source; pi(t) the output power of the ith micro-source; j is a jth contaminant; i is the ith micro-source;
(8) renewable energy power generation patch Cfd
Figure GDA0002942869190000035
In the formula: mu is the unit power generation subsidy price of renewable energy;
the lower-layer objective function which aims at minimizing the running cost of the alternating-current and direct-current hybrid micro-grid is formed as follows:
Cm＝Cpy+Czd+Ce+Com+Cgrid+Cfuel+Cen-Cfd (15)
in the formula: cmAnd the running cost of the alternating current-direct current hybrid micro-grid is expressed.
The operation constraint in the step 3) comprises the following steps:
(1) micro-grid internal power balance constraint
Figure GDA0002942869190000036
In the formula: pi(t) the output power of the ith micro-source; pg(t) the power purchasing electric quantity of the power grid in the tth time period; pES(t) represents the discharge capacity of the energy storage device, the discharge is positive and the charge is negative; l "(t) is the load capacity at each moment after the secondary load translation; n is the number of micro sources in the micro grid;
(2) translatable load restraint
Lpy'(t)≤Lpy(t) (17)
In the formula: l'py(t) represents the total load after the load transfer at the t-th period; l ispy(t) represents the translatable load for the t-th time period;
(3) interruptible load constraints
Lzd'(t)≤Lzd(t) (18)
In the formula: l iszd' (t) is the interruptible load amount that ensures the renewable energy consumption rate; l iszd(t) is the initial interruptible load amount;
(4) common transmission point capacity constraints
Figure GDA0002942869190000043
In the formula:
Figure GDA0002942869190000044
flowing power for a point of common connection; pG.maxTransmitting a capacity limit for the point of common attachment;
(5) energy storage device state of charge constraint
Figure GDA0002942869190000041
In the formula: i PES(t) | represents the amount of change in power of the battery in the t-th period; pES,maxRepresenting the limit value of the energy storage charge and discharge amount in each time period; SOC (t) is the state of charge of the t-th period; SOCmaxAnd SOCminUpper and lower limits of state of charge variation; SOCinitialAnd SOCendRepresenting the state of charge start and end values.
The double-layer optimization mathematical model of the alternating current-direct current hybrid micro-grid in the step 3) is expressed as follows:
Figure GDA0002942869190000042
in the formula: krRepresenting the consumption rate of renewable energy; cmAnd the running cost of the alternating current-direct current hybrid microgrid is represented.
The improved cultural genetic algorithm in the step 4) comprises the following steps:
(1) algorithm initialization
If all the individuals of the algorithm are A, randomly generating A initial individuals according to the following formula:
xk＝xmin+(xmax-xmin)×rand,k＝1,2,…,A (22)
in the formula: x is the number ofkGenerating a kth initial individual; x is the number ofminAnd xmaxTaking value ranges for individuals; rand is a random number with a value of 0 to 1,
after obtaining the initial individuals, calculating the fitness value of each individual, and sorting the fitness values from small to large, wherein the first B are agents, the last C are common individuals, wherein C is A-B, the common individuals are distributed to the agents in the subsequent iteration process, and the number of the common individuals distributed to the a-th agent is as follows:
Fa＝1.3×max{fb}-fa,b＝1,2,…,B (23)
in the formula: faIs the relative strength of the a-th agent; f. ofbRepresenting the fitness value of the b-th agent; f. ofaThe fitness value of the a-th agent;
actual force value P of the a-th agentaComprises the following steps:
Figure GDA0002942869190000051
number of common individuals B assigned to the a-th agentapComprises the following steps:
Bap＝round(Pa×C) (25)；
(2) local search
In the local search process, ordinary individual can remove to the agent of oneself gradually, and migration distance Move is:
Move～U(0,τ×d) (26)
in the formula: τ is the polymerization coefficient; d is the distance between the agent and the common individual in the same region; u represents Move obeys uniform distribution;
the number of individuals C to be reinitializedrComprises the following steps:
Cr＝round(q×C) (27)
in the formula: q is a reinitialization coefficient;
all individuals are reordered, and the first B individuals in the ordered list are new agents;
(3) global search
After reordering, the individual with the worst fitness value is contended by each agent, and is specifically obtained by which agent, which is determined by the contention probability, as follows:
variable T.f for total capacity of a-th agentaComprises the following steps:
Figure GDA0002942869190000052
in the formula: xi is the weight of the common individual; w is aapThe physical strength value of the ap common individual of the a-th agent;
variable f.t.f for total relative strength of the a-th agentaComprises the following steps:
F.T.fa＝max{T.fb}-T.fa,b＝1,2,…,B (29)
the a-th agent competition probability TPaComprises the following steps:
Figure GDA0002942869190000053
thus, a competition probability matrix of the improved cultural genetic algorithm is obtained:
TP＝[TP1 TP2 … TPB] (31)
in the formula: TP1, TP2, TPB represent the competition probability of the first, second and Bth agents, respectively;
the competition strength matrix for the agent is then expressed as:
P＝TP-V (32)
in the formula: v is a matrix which is the same as TP in dimension, and the elements of the matrix are random numbers on [0,1 ];
the agent corresponding to the largest element in P will get the individual to be competed;
to increase the competitiveness of an agent, when two agents xrAnd xsWhen the distance between the two agents is less than the cooperation distance D, the two agents are merged,two agents xrAnd xsObtaining agent x from agent with large fitness valuerAnd xsWherein the collaboration distance D is given by:
D＝norm(xr-xs)×u (33)
u＝sin(π/2×De/Des) (34)
in the formula: u represents a cooperation coefficient; de is the current iteration number; des is the total iteration number;
(4) and (4) repeating the steps (2) to (3), and in the steps (2) to (3), when a certain intelligent agent does not have a common individual any more, the intelligent agent is eliminated, and finally, the rest intelligent agent is iterated to be a solution of the alternating-current/direct-current hybrid microgrid double-layer optimization mathematical model.
The alternating current-direct current hybrid micro-grid double-layer optimization method based on demand side response has the following advantages:
1. the maximum renewable energy consumption rate is the upper-layer objective function, the minimum running cost of the alternating current-direct current hybrid micro-grid is the lower-layer objective function, the double-layer optimization mathematical model of the alternating current-direct current hybrid micro-grid can realize the efficient utilization of renewable energy, and the economic running of the alternating current-direct current hybrid micro-grid is facilitated.
2. The demand side response based on price is considered, the demand side response based on excitation is considered, the load curve can be promoted to be close to the output curve of the renewable energy to the maximum extent, and the consumption rate of the renewable energy is improved.
3. The improved cultural genetic algorithm obtained by improving the basic cultural genetic algorithm through the improved strategy has good convergence performance, and can effectively solve the double-layer optimized mathematical model of the alternating current-direct current mixed micro-grid.
Drawings
Fig. 1 is a structural diagram of an alternating current-direct current hybrid microgrid grid structure of the invention;
FIG. 2 is a wind, light, load prediction graph of the present invention;
FIG. 3a is a diagram of the classification of the load in the AC area according to the present invention;
FIG. 3b is a diagram of DC region load classification according to the present invention;
FIG. 4a is a view illustrating the load shifting of the ac area according to the present invention;
FIG. 4b is a diagram of the load shifting condition of the DC section of the present invention;
FIG. 5a is a diagram of an AC district load interrupt condition of the present invention;
FIG. 5b is a diagram of the DC section load interrupt condition of the present invention;
FIG. 6 is a graph comparing loads before and after considering a demand side response in accordance with the present invention;
FIG. 7 is a graph of the output of each micro-source according to the present invention;
FIG. 8 is a graph illustrating the change in state of charge of the energy storage device according to the present invention;
FIG. 9 is a cost distribution diagram of the present invention.
Fig. 10 is a cost graph of the ac/dc hybrid microgrid of the present invention.
Detailed Description
The following describes in detail an ac/dc hybrid microgrid double-layer optimization method based on demand-side response according to the present invention with reference to the following embodiments and accompanying drawings.
The invention discloses an alternating current-direct current hybrid micro-grid double-layer optimization method based on demand side response, which comprises the following steps of:
1) based on the response of the excited demand side, the load curve is close to the output change curve of the renewable energy source through the adjustment of the translatable load, and an upper-layer objective function of the AC/DC hybrid microgrid is established with the maximum renewable energy consumption rate as a target;
the adjustment of the translatable load is performed by the following formula:
Figure GDA0002942869190000071
Lpy'(t)＝min(Lpy(t),L(t)-Pr(t)) (2)
Figure GDA0002942869190000072
in the formula: l' (t) represents a total load after the load transfer at the t-th period; l (t) represents the initial total load for the t-th period; l ispy' (t) represents the load shift amount of the t-th period;
Figure GDA0002942869190000073
represents the load shift amount of the t-th period; l ispy(t) represents the translatable load for the t-th time period; pr(t) represents renewable energy generation for the t-th period; t is an optimization time period, and T represents each time period in T; t1 is a period in which the amount of renewable energy power generation is smaller than the total load size, and tt represents each period in T1.
After load translation, the upper layer objective function of the AC/DC hybrid microgrid is obtained as follows:
Figure GDA0002942869190000074
in the formula: krRepresenting the consumption rate of renewable energy; pr(t) represents renewable energy generation for the t-th period; l' (t) represents a total load after the load transfer at the t-th period; and T is an optimization time period.
2) Considering both demand side response based on price and demand side response based on excitation, and establishing a lower-layer objective function with the minimum running cost of the alternating-current and direct-current hybrid microgrid as a target; wherein the content of the first and second substances,
the lower-layer objective function which aims at minimizing the operation cost of the alternating-current and direct-current hybrid microgrid is the sum of the following costs:
(1) price-based demand-side response
The method adopts demand side response based on time-of-use electricity price, determines the residual transferable load electric quantity in each time period according to the solving result of the upper layer objective function of the alternating current-direct current hybrid microgrid, and adjusts the load curve according to the electricity price on the premise of ensuring that the consumption rate of renewable energy is not changed. Firstly, determining the load amount capable of translating in the peak time electricity price period, translating the load amount to the valley time period, judging whether the power supply can meet the load requirement at the moment, translating the surplus electric quantity to the ordinary time period if the load amount cannot meet the load requirement, judging whether the supply and the demand are balanced again, and keeping the surplus electric quantity in the peak time period unchanged if the load amount cannot meet the load requirement. The load translation is performed on the translatable load in the ordinary time period by the same method, which is not described herein again. The microgrid needs to subsidize the translation of the user load, and the subsidizing cost is as follows:
Figure GDA0002942869190000075
in the formula: cpySubsidizing costs for translation loads; alpha is the translation subsidy unit price; t is an optimized time period; l "(t) is the load capacity at each moment after the secondary load translation; l (t) represents the initial total load for the t-th period;
(2) stimulus-based demand-side response
The interruptible project in the demand side response based on the excitation can relieve the power supply pressure of the micro-grid in the peak period of power utilization, meanwhile, the construction of a generator set is delayed, and the construction cost of the micro-grid is reduced. In the incentive-based demand-side response, the total load calculation formula that considers interruptible items is as follows:
Figure GDA0002942869190000076
Lzd'(t)＜min(L”(t)-Pr(t),Lzd(t)) (7)
in the formula: l' "(t) represents the load size after the load is interrupted; l iszd' (t) is the interruptible load amount that ensures the renewable energy consumption rate; pr(t) represents renewable energy generation for the t-th period; l iszd(t) is the initial interruptible load amount;
for loads interrupted at ordinary times and at peak periods, the patch cost for the interrupted patch is as follows:
Figure GDA0002942869190000081
in the formula: czdCost for breaking subsidies; beta is the unit price of the interrupted subsidy;
(3) initial construction cost Ce
Figure GDA0002942869190000082
In the formula: n is the number of micro sources in the micro grid; ceiA fixed investment cost for the ith micro-source; cRiThe investment recovery factor for the ith micro-source;
(4) cost of operation and maintenance Com
Figure GDA0002942869190000083
In the formula: k is a radical ofomiOperating and maintaining cost coefficients corresponding to the micro sources; pi(t) the output power of the ith micro-source;
(5) cost C of power purchase of power gridgrid
Figure GDA0002942869190000084
In the formula: g (t) is the electricity purchase price of the t time period; pg(t) the power purchasing electric quantity of the power grid in the tth time period;
(6) fuel cost Cfuel
Figure GDA0002942869190000085
In the formula: cMTfuelFuel cost for micro combustion engine (MT); cFCfuelFuel cost for Fuel Cells (FC); cDEGfuelRepresents the fuel cost of a diesel generator (DEG); cc is the unit price of the fuel; LHV is the lower heating value of the fuel gas; pMTThe output of the micro-combustion engine; pFCIs the output of the fuel cell; pDEGIs the output of the diesel generator; etaMTAnd ηFCThe efficiency of the micro-combustion engine and the fuel cell; a. b and c are diesel oilThe power generation coefficient of the motor;
(7) environmental protection reduced cost Cen
Figure GDA0002942869190000086
In the formula: n1 is the number of micro sources of the discharged pollutants; m is a pollutant discharge type; gamma rayjReduced cost for jth contaminant; ei,jThe unit emission of the jth pollutant generated by the ith power supply; pi(t) the output power of the ith micro-source; j is a jth contaminant; i is the ith micro-source;
(8) renewable energy power generation patch Cfd
Figure GDA0002942869190000087
In the formula: mu is the unit power generation subsidy price of renewable energy.
The lower-layer objective function which aims at minimizing the running cost of the alternating-current and direct-current hybrid micro-grid is formed as follows:
Cm＝Cpy+Czd+Ce+Com+Cgrid+Cfuel+Cen-Cfd (15)
in the formula: cmAnd the running cost of the alternating current-direct current hybrid micro-grid is expressed.
3) The method comprises the steps of (1) following operation constraints, establishing an alternating current-direct current hybrid micro-grid double-layer optimization mathematical model; wherein the content of the first and second substances,
the operational constraints include:
(1) micro-grid internal power balance constraint
Figure GDA0002942869190000091
In the formula: pi(t) the output power of the ith micro-source; pg(t) the power purchasing electric quantity of the power grid in the tth time period; pES(t) represents the amount of discharge charge of the energy storage device,discharging is positive and charging is negative; l "(t) is the load capacity at each moment after the secondary load translation; n is the number of micro sources in the micro grid;
(2) translatable load restraint
Lpy'(t)≤Lpy(t) (17)
In the formula: l ispy' (t) represents the total load after the load transfer at the t-th period; l ispy(t) represents the translatable load for the t-th time period;
(3) interruptible load constraints
Lzd'(t)≤Lzd(t) (18)
In the formula: l iszd' (t) is the interruptible load amount that ensures the renewable energy consumption rate; l iszd(t) is the initial interruptible load amount;
(4) common transmission point capacity constraints
Figure GDA0002942869190000094
In the formula:
Figure GDA0002942869190000095
flowing power for a point of common connection; pG.maxTransmitting a capacity limit for the point of common attachment;
(5) energy storage device state of charge constraint
Figure GDA0002942869190000092
In the formula: i PES(t) | represents the amount of change in power of the battery in the t-th period; pES,maxRepresenting the limit value of the energy storage charge and discharge amount in each time period; SOC (t) is the state of charge of the t-th period; SOCmaxAnd SOCminUpper and lower limits of state of charge variation; SOCinitialAnd SOCendRepresenting the state of charge start and end values.
The alternating current-direct current hybrid micro-grid double-layer optimization mathematical model is expressed as follows:
Figure GDA0002942869190000093
in the formula: krRepresenting the consumption rate of renewable energy; cmAnd the running cost of the alternating current-direct current hybrid microgrid is represented.
4) Aiming at the problems of low convergence precision and low convergence speed in the basic culture genetic algorithm, the basic culture genetic algorithm is improved through an improvement strategy to obtain an improved culture genetic algorithm, and an alternating current-direct current mixed micro-grid double-layer optimization mathematical model is solved.
The improved cultural genetic algorithm comprises the following steps:
(1) algorithm initialization
If all the individuals of the algorithm are A, randomly generating A initial individuals according to the following formula:
xk＝xmin+(xmax-xmin)×rand,k＝1,2,…,A (22)
in the formula: x is the number ofkGenerating a kth initial individual; x is the number ofminAnd xmaxTaking value ranges for individuals; rand is a random number with a value of 0 to 1,
after obtaining the initial individuals, calculating the fitness value of each individual, and sorting the fitness values from small to large, wherein the first B are agents, the last C are common individuals, wherein C is A-B, the common individuals are distributed to the agents in the subsequent iteration process, and the number of the common individuals distributed to the a-th agent is as follows:
Fa＝1.3×max{fb}-fa,b＝1,2,…,B (23)
in the formula: faIs the relative strength of the a-th agent; f. ofbRepresenting the fitness value of the b-th agent; f. ofaThe fitness value of the a-th agent;
actual force value P of the a-th agentaComprises the following steps:
Figure GDA0002942869190000101
number of common individuals B assigned to the a-th agentapComprises the following steps:
Bap＝round(Pa×C) (25)
(2) local search
In the local search process, ordinary individual can remove to the agent of oneself gradually, and migration distance Move is:
Move～U(0,τ×d) (26)
in the formula: τ is the polymerization coefficient; d is the distance between the agent and the common individual in the same region; u represents Move obeys uniform distribution;
the number of individuals C to be reinitializedrComprises the following steps:
Cr＝round(q×C) (27)
in the formula: q is a reinitialization coefficient;
all individuals are reordered, and the first B individuals in the ordered list are new agents;
(3) global search
After reordering, the individual with the worst fitness value is contended by each agent, and is specifically obtained by which agent, which is determined by the contention probability, as follows:
variable T.f for total capacity of a-th agentaComprises the following steps:
Figure GDA0002942869190000102
in the formula: xi is the weight of the common individual and is a positive number smaller than 1; w is aapThe physical strength value of the ap common individual of the a-th agent;
variable f.t.f for total relative strength of the a-th agentaComprises the following steps:
F.T.fa＝max{T.fb}-T.fa,b＝1,2,…,B (29)
the a-th agent competition probability TPaComprises the following steps:
Figure GDA0002942869190000111
thus, a competition probability matrix of the improved cultural genetic algorithm is obtained:
TP＝[TP1 TP2 … TPB] (31)
in the formula: TP1、TP2、TPBRespectively representing the competition probability of the first, second and B-th agents;
the competition strength matrix for the agent is then expressed as:
P＝TP-V (32)
in the formula: v is a matrix which is the same as TP in dimension, and the elements of the matrix are random numbers on [0,1 ];
the agent corresponding to the largest element in P will get the individual to be competed;
to increase the competitiveness of an agent, when two agents xrAnd xsWhen the distance between the two agents is less than the cooperation distance D, the two agents are merged, and two agents xrAnd xsObtaining agent x from agent with large fitness valuerAnd xsWherein the collaboration distance D is given by:
D＝norm(xr-xs)×u (33)
u＝sin(π/2×De/Des) (34)
in the formula: u represents a cooperation coefficient; de is the current iteration number; des is the total iteration number;
(4) and (4) repeating the steps (2) to (3), and in the steps (2) to (3), when a certain intelligent agent does not have a common individual any more, the intelligent agent is eliminated, and finally, the rest intelligent agent is iterated to be a solution of the alternating-current/direct-current hybrid microgrid double-layer optimization mathematical model.
The present invention will be described below based on practical examples.
A typical grid structure of an ac/dc hybrid microgrid is shown in fig. 1, wherein an ac region includes 1 750kW diesel generator (DEG) and 2 1MW wind generators (WT), and a dc region includes 2 1MW photovoltaic power generation devices (PV) and 1 Energy Storage (ES) with a capacity of 250kW/1 MWh. Fig. 2 shows the predicted load and wind-solar output data, where PLA represents the predicted load in the ac region and PLD represents the predicted load in the dc region. The loads are classified into important loads, translatable loads and interruptible loads according to the load characteristics, as shown in fig. 3a and 3b, where fig. 3a is a load classification situation of an ac area, and fig. 3b is a load classification situation of a dc area. Table 1 shows the price of electricity sold by the power grid.
TABLE 1 time-of-use electricity price of electric network
Figure GDA0002942869190000112
In order to meet the goal of maximum renewable energy consumption rate of the upper layer and demand-side response based on time-of-use electricity price, the load shifting situation is shown in fig. 4a and 4b, wherein fig. 4a is the load shifting situation of the ac area, and fig. 4b is the load shifting situation of the dc area. Through the introduction of the previous section, the total consumption rate of the renewable energy source can be calculated by the formula (4) to reach 82%, and the correctness and the feasibility of an upper-layer optimization model are proved.
Fig. 5a and 5b show the load interruption situation, where fig. 5a shows the ac zone load interruption situation and fig. 5b shows the dc zone load interruption situation. The load interruption mainly occurs in the peak period of power utilization, so that the power supply pressure of the microgrid is relieved, and the cost rise caused by purchasing a large amount of electricity from the power grid at the peak of the microgrid is avoided. FIG. 6 is an initial load curve and a load versus curve after consideration of the demand side response, where PLA 'and PLD' represent the AC load and DC load curves after consideration of the demand side response, respectively. And according to the load curve after considering the response of the demand side in fig. 6, optimizing each micro-source output to obtain each micro-source output situation as shown in fig. 7, wherein GRID represents the power GRID electricity purchasing cost. Comparing fig. 6 and fig. 7, it can be seen that the wind-solar-energy power generation achieves the goal of maximizing the consumption rate of renewable energy. Meanwhile, in the valley time of the graph 7, the electric quantity purchased by the power grid is large, and economic operation of the micro-grid is facilitated.
Fig. 8 is a charge state change curve of the storage battery, and it can be seen that the storage battery is charged when the photovoltaic power generation amount is larger than the load and when the electricity price is at the valley, and is discharged when the load demand cannot be met, so that the peak clipping and valley filling functions are realized. Fig. 9 shows the distribution of the operating cost of the ac/dc hybrid microgrid, and it can be seen that the initial construction cost of the microgrid accounts for a relatively large part of the total cost, which is also a main component of the operating cost of the renewable energy source at present. Fig. 10 is a curve of the operating cost of the ac/dc hybrid microgrid, wherein the renewable energy power generation subsidies are expressed in negative form, and it can be seen that the total operating cost of the microgrid is significantly reduced due to the renewable energy power generation subsidies, and thus it can be seen that the development of renewable energy is not supported by government subsidies.
From the above contents, the improved culture genetic algorithm can effectively solve the alternating current-direct current hybrid micro-grid double-layer model, and in order to verify the superiority of the improved culture genetic algorithm, the Genetic Algorithm (GA) and the basic culture genetic algorithm (MA) are respectively used for solving the calculation examples, and the obtained operation results are shown in table 2.
TABLE 2 results of algorithm runs
Figure GDA0002942869190000121
As can be seen from Table 2, the improved cultural genetic algorithm has obvious advantages in convergence accuracy and convergence speed, is suitable for solving the double-layer optimization model of the AC/DC hybrid microgrid, and has good optimization performance.

Claims (6)

1. An alternating current-direct current hybrid microgrid double-layer optimization method based on demand side response is characterized by comprising the following steps:
1) based on the response of the excited demand side, the load curve is close to the output change curve of the renewable energy source through the adjustment of the translatable load, and an upper-layer objective function of the AC/DC hybrid microgrid is established with the maximum renewable energy consumption rate as a target; the adjustment of the translatable load is performed by the following formula:
Figure FDA0002942869180000011
Lpy'(t)＝min(Lpy(t),L(t)-Pr(t)) (2)
Figure FDA0002942869180000012
in the formula: l' (t) represents a total load after the load transfer at the t-th period; l (t) represents the initial total load for the t-th period; l ispy' (t) represents the load shift amount of the t-th period;
Figure FDA0002942869180000013
represents the load shift amount of the t-th period; l ispy(t) represents the translatable load for the t-th time period; pr(t) represents renewable energy generation for the t-th period; t is an optimization time period, and T represents each time period in T; t1 is a time period when the power generation amount of the renewable energy source is smaller than the total load, and tt represents each time period in T1;
2) considering both demand side response based on price and demand side response based on excitation, and establishing a lower-layer objective function with the minimum running cost of the alternating-current and direct-current hybrid microgrid as a target;
3) the method comprises the steps of (1) following operation constraints, establishing an alternating current-direct current hybrid micro-grid double-layer optimization mathematical model;
4) and improving a cultural genetic algorithm and solving an alternating current-direct current hybrid micro-grid double-layer optimization mathematical model.
2. The double-layer optimization method for the alternating current-direct current hybrid microgrid based on the demand side response is characterized in that after load translation in the step 1), an upper-layer objective function of the alternating current-direct current hybrid microgrid is obtained as follows:
Figure FDA0002942869180000014
in the formula: krRepresenting the consumption rate of renewable energy; pr(t) represents renewable energy generation for the t-th period; l' (t) represents a total load after the load transfer at the t-th period; and T is an optimization time period.
3. The method according to claim 2, wherein the lower layer objective function in step 2) aiming at minimizing the operation cost of the hybrid ac/dc microgrid is the sum of the following costs:
(1) price-based demand-side response
The method comprises the following steps of adopting demand side response based on time-of-use electricity price, determining the residual load electricity quantity capable of translating in each time interval according to a result of solving an upper layer objective function of the AC/DC hybrid microgrid, and translating the load according to the electricity price to adjust a load curve on the premise of ensuring that the renewable energy consumption rate is unchanged, wherein the microgrid needs to be subsidized in terms of translation of user loads, and the subsidizing cost is as follows:
Figure FDA0002942869180000015
in the formula: cpySubsidizing costs for translation loads; alpha is the translation subsidy unit price; t is an optimized time period; l "(t) is the load capacity at each moment after the secondary load translation; l (t) represents the initial total load for the t-th period;
(2) stimulus-based demand-side response
In the incentive-based demand-side response, the total load calculation formula that considers interruptible items is as follows:
Figure FDA0002942869180000021
Lzd'(t)＜min(L”(t)-Pr(t),Lzd(t)) (7)
in the formula: l' "(t) represents the load size after the load is interrupted; l iszd' (t) is the interruptible load amount that ensures the renewable energy consumption rate; pr(t) represents renewable energy generation for the t-th period; l iszd(t) is the initial interruptible load amount;
for loads interrupted at ordinary times and at peak periods, the patch cost for the interrupted patch is as follows:
Figure FDA0002942869180000022
in the formula: czdCost for breaking subsidies; beta is the unit price of the interrupted subsidy;
(3) initial construction cost Ce
Figure FDA0002942869180000023
In the formula: n is the number of micro sources in the micro grid; ceiA fixed investment cost for the ith micro-source; cRiThe investment recovery factor for the ith micro-source;
(4) cost of operation and maintenance Com
Figure FDA0002942869180000024
In the formula: k is a radical ofomiOperating and maintaining cost coefficients corresponding to the micro sources; pi(t) the output power of the ith micro-source;
(5) cost C of power purchase of power gridgrid
Figure FDA0002942869180000025
In the formula: g (t) is the electricity purchase price of the t time period; pg(t) the power purchasing electric quantity of the power grid in the tth time period;
(6) fuel cost Cfuel
Figure FDA0002942869180000026
In the formula: cMTfuelThe fuel cost of the micro-combustion engine; cFCfuelFuel cost for the fuel cell; cDEGfuelRepresents the fuel cost of the diesel generator; cc is the unit price of the fuel; LHV is the lower heating value of the fuel gas; pMTThe output of the micro-combustion engine; pFCIs the output of the fuel cell; pDEGIs the output of the diesel generator; etaMTAnd ηFCThe efficiency of the micro-combustion engine and the fuel cell; a. b and c are power generation coefficients of the diesel generator;
(7) environmental protection reduced cost Cen
Figure FDA0002942869180000031
In the formula: n1 is the number of micro sources of the discharged pollutants; m is a pollutant discharge type; gamma rayjReduced cost for jth contaminant; ei,jThe unit emission of the jth pollutant generated by the ith micro-source; pi(t) the output power of the ith micro-source; j is a jth contaminant; i is the ith micro-source;
(8) renewable energy power generation patch Cfd
Figure FDA0002942869180000032
In the formula: mu is the unit power generation subsidy price of renewable energy;
the lower-layer objective function which aims at minimizing the running cost of the alternating-current and direct-current hybrid micro-grid is formed as follows:
Cm＝Cpy+Czd+Ce+Com+Cgrid+Cfuel+Cen-Cfd (15)
in the formula:Cmand the running cost of the alternating current-direct current hybrid micro-grid is expressed.
4. The method according to claim 1, wherein the operation constraints in step 3) include:
(1) micro-grid internal power balance constraint
Figure FDA0002942869180000033
In the formula: pi(t) the output power of the ith micro-source; pg(t) the power purchasing electric quantity of the power grid in the tth time period; pES(t) represents the discharge capacity of the energy storage device, the discharge is positive and the charge is negative; l "(t) is the load capacity at each moment after the secondary load translation; n is the number of micro sources in the micro grid;
(2) translatable load restraint
Lpy'(t)≤Lpy(t) (17)
In the formula: l ispy' (t) represents the total load after the load transfer at the t-th period; l ispy(t) represents the translatable load for the t-th time period;
(3) interruptible load constraints
Lzd'(t)≤Lzd(t) (18)
In the formula: l iszd' (t) is the interruptible load amount that ensures the renewable energy consumption rate; l iszd(t) is the initial interruptible load amount;
(4) common transmission point capacity constraints
Figure FDA0002942869180000034
In the formula:
Figure FDA0002942869180000035
flowing power for a point of common connection; pG.maxTransmitting a capacity limit for the point of common attachment;
(5) energy storage device state of charge constraint
Figure FDA0002942869180000036
In the formula: i PES(t) | represents the amount of change in power of the battery in the t-th period; pES,maxRepresenting the limit value of the energy storage charge and discharge amount in each time period; SOC (t) is the state of charge of the t-th period; SOCmaxAnd SOCminUpper and lower limits of state of charge variation; SOCinitialAnd SOCendRepresenting the state of charge start and end values.
5. The method according to claim 3, wherein the mathematical model for double-layer optimization of the AC/DC hybrid microgrid based on the demand-side response in step 3) is expressed as:
Figure FDA0002942869180000041
in the formula: krRepresenting the consumption rate of renewable energy; cmAnd the running cost of the alternating current-direct current hybrid microgrid is represented.
6. The alternating current-direct current hybrid microgrid double-layer optimization method based on demand-side response is characterized in that the improved cultural genetic algorithm in the step 4) comprises the following steps:
(1) algorithm initialization
If all the individuals of the algorithm are A, randomly generating A initial individuals according to the following formula:
xk＝xmin+(xmax-xmin)×rand,k＝1,2,…,A (22)
in the formula: x is the number ofkGenerating a kth initial individual; x is the number ofminAnd xmaxTaking value ranges for individuals; rand being of value 0 to 1The number of the random numbers is determined,
after obtaining the initial individuals, calculating the fitness value of each individual, and sorting the fitness values from small to large, wherein the first B are agents, the last C are common individuals, wherein C is A-B, the common individuals are distributed to the agents in the subsequent iteration process, and the number of the common individuals distributed to the a-th agent is as follows:
Fa＝1.3×max{fb}-fa,b＝1,2,…,B (23)
in the formula: faIs the relative strength of the a-th agent; f. ofbRepresenting the fitness value of the b-th agent; f. ofaThe fitness value of the a-th agent;
actual force value P of the a-th agentaComprises the following steps:
Figure FDA0002942869180000042
number of common individuals B assigned to the a-th agentapComprises the following steps:
Bap＝round(Pa×C) (25)；
(2) local search
In the local search process, ordinary individual can remove to the agent of oneself gradually, and migration distance Move is:
Move～U(0,τ×d) (26)
in the formula: τ is the polymerization coefficient; d is the distance between the agent and the common individual in the same region; u represents Move obeys uniform distribution;
the number of individuals C to be reinitializedrComprises the following steps:
Cr＝round(q×C) (27)
in the formula: q is a reinitialization coefficient;
all individuals are reordered, and the first B individuals in the ordered list are new agents;
(3) global search
After reordering, the individual with the worst fitness value is contended by each agent, and is specifically obtained by which agent, which is determined by the contention probability, as follows:
variable T.f for total capacity of a-th agentaExpressed as:
Figure FDA0002942869180000051
in the formula: xi is the weight of the common individual; w is aapThe physical strength value of the ap common individual of the a-th agent;
variable f.t.f for total relative strength of the a-th agentaExpressed as:
F.T.fa＝max{T.fb}-T.fa,b＝1,2,…,B (29)
the a-th agent competition probability TPaComprises the following steps:
Figure FDA0002942869180000052
thus, a competition probability matrix of the improved cultural genetic algorithm is obtained:
TP＝[TP1 TP2 … TPB] (31)
in the formula: TP1、TP2、TPBRespectively representing the competition probability of the first, second and B-th agents;
the competition strength matrix for the agent is then expressed as:
P＝TP-V (32)
in the formula: v is a matrix which is the same as TP in dimension, and the elements of the matrix are random numbers on [0,1 ];
the agent corresponding to the largest element in P will get the individual to be competed;
to increase the competitiveness of an agent, when two agents xrAnd xsWhen the distance between the two agents is less than the cooperation distance D, the two agents are merged, and two agents xrAnd xsIntelligence of large value of inter-adaptabilityEnergy-producing agent xrAnd xsWherein the collaboration distance D is given by:
D＝norm(xr-xs)×u (33)
u＝sin(π/2×De/Des) (34)
in the formula: u represents a cooperation coefficient; de is the current iteration number; des is the total iteration number;
(4) and (4) repeating the steps (2) to (3), and in the steps (2) to (3), when a certain intelligent agent does not have a common individual any more, the intelligent agent is eliminated, and finally, the rest intelligent agent is iterated to be a solution of the alternating-current/direct-current hybrid microgrid double-layer optimization mathematical model.
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