CN112436514B - Multi-microgrid interconnection optimization method considering photovoltaic uncertainty - Google Patents

Multi-microgrid interconnection optimization method considering photovoltaic uncertainty Download PDF

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CN112436514B
CN112436514B CN202011482904.4A CN202011482904A CN112436514B CN 112436514 B CN112436514 B CN 112436514B CN 202011482904 A CN202011482904 A CN 202011482904A CN 112436514 B CN112436514 B CN 112436514B
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power
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gas turbine
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CN112436514A (en
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李锦焙
陈伟强
傅子明
谢湘昭
梁梓洋
谭浩新
黄志勇
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Foshan Power Supply Bureau of Guangdong Power Grid Corp
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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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/004—Artificial life, i.e. computing arrangements simulating life
    • G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
    • 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
    • H02J3/008—Circuit arrangements for ac mains or ac distribution networks involving trading of energy or energy transmission rights
    • 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
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • 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
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • H02J3/466—Scheduling the operation of the generators, e.g. connecting or disconnecting generators to meet a given demand
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/22—The renewable source being solar energy
    • H02J2300/24—The renewable source being solar energy of photovoltaic origin
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00—Energy generation through renewable energy sources
    • Y02E10/50—Photovoltaic [PV] energy
    • Y02E10/56—Power conversion systems, e.g. maximum power point trackers

Abstract

The invention provides a multi-microgrid interconnection optimization method considering photovoltaic uncertainty, which comprises the following steps: establishing a multi-microgrid interconnection total cost model considering photovoltaic uncertainty; establishing an objective function and constraint conditions; setting parameters; constructing a scene; reducing the scene; obtaining an initialized population as a parent particle; constructing a small world network and generating an adjacency matrix; calculating the fitness of parent particles; updating parent particles to generate child particles; treating the daughter particles; calculating the fitness of the offspring particles after processing, and comparing the fitness with the fitness of the parent particles; traversing all adjacency matrices; and the iteration times reach the maximum iteration times, and the optimal solution of the total cost model is output. According to the invention, a Latin hypercube scene generation mechanism and a small world model are introduced to improve a traditional quantum particle swarm algorithm, so that the defect that the algorithm cannot optimize a multi-microgrid model considering photovoltaic uncertainty is effectively overcome, and the problem that the algorithm falls into a local optimal solution in the later period of optimization and cannot obtain an optimal optimization scheme is solved.

Description

Multi-microgrid interconnection optimization method considering photovoltaic uncertainty
Technical Field
The invention relates to the field of power system dispatching, in particular to a multi-microgrid interconnection optimization method considering photovoltaic uncertainty.
Background
Economic dispatch is an important optimization problem in power system operation. The traditional power system optimization method takes thermal power and hydraulic power generation as a research object, and minimizes the total power generation cost on the premise that the limiting condition is met in a single area. The photovoltaic power generation in the form of new energy power generation has uncertainty, so that the traditional optimization method cannot solve an optimization model comprising the photovoltaic power generation. For practical power systems, often not a simple single area but multiple areas, the areas are interconnected by tie lines, so the goal of the multi-area power system economic dispatch is to seek an optimal solution for power generation capacity and power exchange between the areas, thereby minimizing the overall power generation costs. The traditional power system optimization method simplifies all power generation units into a single network, and the processing method simplifies the solving method to a certain extent, thereby reducing the requirements on the operation efficiency and the optimization effect of the algorithm. However, because photovoltaic power generation has uncertainty, the conventional optimization method cannot solve an optimization model including photovoltaic power generation. In addition, the traditional optimization model only focuses on the power generation cost, and does not consider the change of electricity price due to the change of electricity selling benefits. Various factors lead to inaccurate optimization results, and cannot reflect the running condition of an actual micro-grid. The quantum particle swarm algorithm has a good effect in the dispatching field, but the traditional quantum particle swarm algorithm cannot be continuously optimized when the traditional quantum particle swarm algorithm is easy to converge to a certain precision when facing a system considering photovoltaic power generation, and then falls into a local optimal solution, and the main reason is uncertainty of the photovoltaic power generation.
The Chinese patent CN106570579A published in 2017, 4 and 19 provides a water-gas-electricity economic dispatch constraint processing method based on an improved quantum particle swarm algorithm, which comprises the following steps: establishing a water-fire-electricity economic dispatch mathematical model containing a cascade reservoir; setting system parameters to generate an initial population; constraint processing is carried out on the population by using a constraint processing method, so that each particle in the population meets the system constraint; calculating the fitness value of each particle, and updating the individual optimal value of each particle and the global optimal values of all particles; calculating the position of the particles according to a position update formula of the improved quantum particle swarm algorithm; judging whether the termination condition is met, stopping iteration and outputting an optimal value if the termination condition is met, and returning if the termination condition is not met. According to the method, a solution with a better fitness value can be found to a certain extent, but the optimization result is still not ideal.
Disclosure of Invention
The invention provides a multi-microgrid interconnection optimization method considering photovoltaic uncertainty in order to overcome the defect that the optimization result of the traditional quantum particle swarm optimization is not ideal.
The technical scheme of the invention is as follows:
the invention provides a multi-microgrid interconnection optimization method considering photovoltaic uncertainty, which comprises the following steps of:
s1: establishing a total cost model of multi-microgrid interconnection considering photovoltaic uncertainty;
s2: establishing an objective function and constraint conditions for the total cost model with the aim of lowest cost;
s3: setting parameters of the total cost model;
s4: performing scene construction according to a Latin hypercube scene generation mechanism;
s5: reducing the constructed scenes to obtain a scene set with a preset number;
s6: initializing by taking the output power of a gas turbine and the interaction power of a micro-grid as targets to obtain an initialized population, and taking the initialized population as parent particles;
s7: constructing a small world network and generating an adjacency matrix;
s8: calculating the fitness of parent particles in the current adjacent matrix;
s9: updating parent particles to generate child particles;
s10: performing constraint processing on the child particles by using the constraint conditions;
s11: calculating the fitness of the child particles after constraint processing, comparing the fitness of the parent particles with the fitness of the child particles after constraint processing, and reserving the particles with good fitness as the parent particles of the next iteration;
s12: traversing all adjacent matrixes, if all the adjacent matrixes are traversed, executing S13, otherwise jumping back to S8;
s13: and outputting an optimal solution of the total cost model of the multi-microgrid interconnection considering the photovoltaic uncertainty if the iteration number reaches a preset maximum iteration number, otherwise, jumping back to the step S7.
Preferably, the total cost model in S1 includes power generation cost, electricity purchase cost, and maintenance cost.
Preferably, the power generation cost is:
Figure BDA0002838591030000021
wherein C is FU Representing the power generation cost, C CH4 Representing the price of natural gas in units of yuan/m 3 ，L CH4 Representing the low heating value, P, of natural gas MT,ij Represents the output power, eta of the jth gas turbine in the ith micro-grid MT,ij The power generation efficiency of the gas turbine of the j-th gas turbine in the i-th micro-grid;
the electricity purchasing cost comprises the electricity purchasing cost of the source heat pump and the electricity purchasing cost of the air conditioning unit:
Figure BDA0002838591030000031
Figure BDA0002838591030000032
wherein C is HP Representing electricity purchasing cost of ground source heat pump, P HP,ij Representing the power consumed by the jth ground source heat pump in the ith micro-grid; c (C) AC Representing the electricity purchasing cost of the air conditioning unit, P AC,i Representing the power consumed by an air conditioning unit in the ith micro-grid;
the maintenance cost comprises the power grid interaction maintenance cost and the equipment maintenance total cost:
Figure BDA0002838591030000033
C OM ＝K OM,PV P PV,i +K OM,MT P MT,i +K OM,AC P AC,i
wherein C is gir Representing the interactive maintenance cost of the power grid, P buy,i And P sell,i Representing the electric energy purchased and sold by the jth station in the ith micro-grid respectively, B buy And S is sell Respectively representing electricity purchase price and electricity selling price; c (C) OM Representing the total maintenance cost of the equipment, K OM,PV 、K OM,MT And K OM,AC Respectively representing maintenance cost coefficients of the photovoltaic power generation unit, the gas turbine and the air conditioning unit, P PV,i 、P MT,ij And P AC,i Respectively representing the output power of a photovoltaic power generation unit in the ith micro-grid, the output power of a j-th gas turbine in the ith micro-grid and the consumed power of an air conditioning unit in the ith micro-grid;
preferably, the objective function in S2 is:
min F＝min(C FU +C OM +C gir +C HP +C AC )
wherein C is FU 、C OM 、C gir 、C HP And C AC The method respectively represents the power generation cost, the total equipment maintenance cost, the micro-grid interaction maintenance cost, the electricity purchasing cost of the ground source heat pump and the electricity purchasing cost of the air conditioning unit.
Preferably, the constraint conditions in S2 include a power balance constraint, a heat load and cold load balance constraint, a gas turbine power constraint, and a microgrid interaction power constraint.
Preferably, the power balance constraint is:
Figure BDA0002838591030000034
wherein P is MT,ij Representing the output power of the jth gas turbine in the ith micro-grid, P PV,i Representing the output power of a photovoltaic power generation unit in the ith micro-grid, P buy,i The electric energy purchased for the ith micro-grid; p (P) load,i Representing load demand in the ith micro-net, P HP,ij Representing the power consumed by the jth ground source heat pump in the ith micro-grid, P AC,i Representing the power consumed by an air conditioning unit in the ith micro-grid;
the heat load and cold load balance constraints are:
Figure BDA0002838591030000041
Figure BDA0002838591030000042
Figure BDA0002838591030000043
wherein Q is load,L,i For the refrigeration capacity requirement in the ith micro-grid, Q MT,L,ij For the refrigerating capacity of the jth gas turbine in the ith micro-grid, Q HP,L,ij Refrigerating capacity of jth ground source heat pump in ith micro-grid, Q AC,L,i The refrigerating capacity of the air conditioning unit in the ith micro-grid; q (Q) load,H,i To the heating capacity requirement in the ith micro-grid, Q MT,H,ij Heating capacity of the jth gas turbine in the ith micro-grid; q (Q) HP,H,ij The heating quantity of the jth ground source heat pump in the ith micro-grid is represented; q (Q) AC,H,i The heating capacity of the air conditioning unit in the ith micro-grid;
Q MT,L,ij ＝η r,ij C L Q MT,ij
Q MT,H,ij ＝η r,ij C H Q MT,ij
Figure BDA0002838591030000044
wherein eta r, i j Waste heat recovery rate of exhaust gas of jth gas turbine in ith micro-grid, C L And C H Respectively the refrigerating coefficient and the heating coefficient of the gas turbine, Q MT,ij Waste heat of exhaust gas of jth gas turbine in ith micro-grid, P MT,ij Represents the output power, eta of the jth gas turbine in the ith micro-grid MT,ij Representing the jth gas turbine in the ith micro-gridPower generation efficiency, eta of the machine L,ij The heat dissipation loss coefficient of the jth gas turbine in the ith micro-grid;
Q HP,L,ij ＝C HP,L P HP,ij (T o,ij -T r,ij )
Q HP,H,ij ＝C HP,H P HP,ij (T r,ij -T o,ij )
wherein P is HP,ij Representing the power consumed by the jth ground source heat pump in the ith micro-grid, C HP,L And C HP,H Respectively the refrigeration coefficient and the heating coefficient of the ground source heat pump, T o,ij And T r,ij Respectively representing the inlet water temperature and the outlet water temperature of an evaporator of the ground source heat pump;
Q AC,L,i ＝P AC (1-η AC,L )η AC,L COP AC,L
Q AC,H,i ＝P AC (1-η AC,H )η AC,H COP AC,H
wherein P is AC Is the input power of the air conditioning unit, eta AC,L And eta AC,H Refrigeration efficiency and heating efficiency, COP of air conditioning unit AC,L And COP AC,H The refrigerating energy efficiency ratio and the heating energy efficiency ratio of the air conditioning unit are respectively;
the gas turbine output power constraint satisfies:
minP MT,ij ≤P MT,ij ≤maxP MT,ij
wherein, minP MT,ij And maxP MT,ij Respectively representing the minimum power and the maximum power output by a jth gas turbine in an ith micro-grid;
the micro-grid interaction power constraint satisfies:
|P gri,ir |≤P line,max
wherein P is gir,ir For the power transmitted from the ith micro-network to the r micro-network, P line,max Is the limit of the transmission power of the micro-network; p when prescribed power is transmitted from ith micro-net to (r) th micro-net gir,ir Positive, P when power is transferred from the r-th to the i-th micro-network gir,ir Is negative.
Preferably, in the step S6, the following formula is used when initializing the target:
Figure BDA0002838591030000051
wherein P is MT,ij Represents the output power of the jth gas turbine in the ith micro-grid and minP MT,ij And maxP MT,ij Respectively representing the minimum power and the maximum power output by the jth gas turbine in the ith micro-grid, P gir,ir For the power transmitted from the ith micro-network to the r micro-network, P line,max For the limit of the transmission power of the micro-network, rand (0, 1) is a random number of 0-1.
Preferably, in the step S7, the specific method for generating the adjacency matrix is as follows:
when the iteration times are not more than 1, numbering each particle of the initial population, and forming an adjacent matrix according to the initial node degree;
when the iteration times are greater than 1, calculating random edge adding probability, and when the random probability is smaller than the edge adding probability, carrying out edge adding, and forming an adjacent matrix according to the node degree obtained by the edge adding rule.
Preferably, in S8, the fitness of the parent particles in the current adjacency matrix is calculated by the following formula:
fitness＝min F＝min(C FU +C OM +C gir +C HP +C AC )
wherein C is FU 、C OM 、C gir 、C HP And C AC The method respectively represents the power generation cost, the total equipment maintenance cost, the micro-grid interaction maintenance cost, the electricity purchasing cost of the ground source heat pump and the electricity purchasing cost of the air conditioning unit.
Preferably, in S9, the specific method for updating the parent particle is as follows:
if the parent particle is updated for the first time, the parent particle is updated by adopting the following formula:
A(z,r)＝r1*pbest(z,r)+(1-r1)*lg best(1,r)
wherein, pbest is an initial population, lg best is a particle corresponding to a dominant solution generated after selection through a small world network, and r1 is a random number;
if the update is not the initial update, the new population generated before the current iterative calculation is continuously updated in the form of probability, and when the probability meets a preset value, the parent particles are updated by adopting the following formula:
Figure BDA0002838591030000061
when the probability does not meet the preset value, the parent particles are updated by adopting the following formula:
Figure BDA0002838591030000062
wherein, max gen is the maximum iteration number, m is the current iteration number, u is a random number of 0-1, and AA (z:) is the absolute value of the difference between the average value of the adjacent matrix generated by the small world network and the corresponding initial population.
Compared with the prior art, the technical scheme of the invention has the beneficial effects that:
according to the invention, a Latin hypercube scene generation mechanism and a small world model are introduced to improve a traditional quantum particle swarm algorithm, so that the problem that the traditional quantum particle swarm algorithm is easy to fall into a local optimal solution in the later stage when optimizing a total cost model of multi-microgrid interconnection considering photovoltaic uncertainty is solved, and an ideal optimization result is obtained.
Drawings
Fig. 1 is a flowchart of a multi-microgrid interconnection optimization method that considers photovoltaic uncertainties according to embodiment 1.
Detailed Description
The drawings are for illustrative purposes only and are not to be construed as limiting the present patent;
for the purpose of better illustrating the embodiments, certain elements of the drawings may be omitted, enlarged or reduced and do not represent the actual product dimensions;
it will be appreciated by those skilled in the art that certain well-known structures in the drawings and descriptions thereof may be omitted.
The technical scheme of the invention is further described below with reference to the accompanying drawings and examples.
Example 1
The embodiment provides a multi-microgrid interconnection optimization method considering photovoltaic uncertainty, as shown in fig. 1, the method comprises the following steps:
s1: establishing a total cost model of multi-microgrid interconnection considering photovoltaic uncertainty;
s2: establishing an objective function and constraint conditions for the total cost model with the aim of lowest cost;
s3: setting parameters of the total cost model;
s4: performing scene construction according to a Latin hypercube scene generation mechanism;
s5: reducing the constructed scenes to obtain a scene set with a preset number;
s6: initializing by taking the output power of a gas turbine and the interaction power of a micro-grid as targets to obtain an initialized population, and taking the initialized population as parent particles;
s7: constructing a small world network and generating an adjacency matrix;
s8: calculating the fitness of parent particles in the current adjacent matrix;
s9: updating parent particles to generate child particles;
s10: performing constraint processing on the child particles by using the constraint conditions;
s11: calculating the fitness of the child particles after constraint processing, comparing the fitness of the parent particles with the fitness of the child particles after constraint processing, and reserving the particles with good fitness as the parent particles of the next iteration;
s12: traversing all adjacent matrixes, if all the adjacent matrixes are traversed, executing S13, otherwise jumping back to S8;
s13: and outputting an optimal solution of the total cost model of the multi-microgrid interconnection considering the photovoltaic uncertainty if the iteration number reaches a preset maximum iteration number, otherwise, jumping back to the step S7.
The total cost model in S1 includes the power generation cost, the electricity purchase cost, and the maintenance cost.
The power generation cost is as follows:
Figure BDA0002838591030000071
wherein C is FU Representing the power generation cost, C CH4 Representing the price of natural gas in units of yuan/m 3 ，L CH4 Representing the low heating value, P, of natural gas MT,ij Represents the output power, eta of the jth gas turbine in the ith micro-grid MT,ij The power generation efficiency of the gas turbine of the j-th gas turbine in the i-th micro-grid;
the electricity purchasing cost comprises the electricity purchasing cost of the source heat pump and the electricity purchasing cost of the air conditioning unit:
Figure BDA0002838591030000072
Figure BDA0002838591030000073
wherein C is HP Representing electricity purchasing cost of ground source heat pump, P HP,ij Representing the power consumed by the jth ground source heat pump in the ith micro-grid; c (C) AC Representing the electricity purchasing cost of the air conditioning unit, P AC,i Representing the power consumed by an air conditioning unit in the ith micro-grid;
the maintenance cost comprises the power grid interaction maintenance cost and the equipment maintenance total cost:
Figure BDA0002838591030000081
C OM ＝K OM,PV P PV,i +K OM,MT P MT,i +K OM,AC P AC,i
wherein C is gir Representing the interactive maintenance cost of the power grid, P buy,i And P sell,i Representing the electric energy purchased and sold by the jth station in the ith micro-grid respectively, B buy And S is sell Respectively representing electricity purchase price and electricity selling price; c (C) OM Representing the total maintenance cost of the equipment, K OM,PV 、K OM,MT And K OM,AC Respectively representing maintenance cost coefficients of the photovoltaic power generation unit, the gas turbine and the air conditioning unit, P PV,i 、P MT,ij And P AC,i Respectively representing the output power of a photovoltaic power generation unit in the ith micro-grid, the output power of a j-th gas turbine in the ith micro-grid and the consumed power of an air conditioning unit in the ith micro-grid;
the objective function in S2 is:
min F＝min(C FU +C OM +C gir +C HP +C AC )
wherein C is FU 、C OM 、C gir 、C HP And C AC The method respectively represents the power generation cost, the total equipment maintenance cost, the micro-grid interaction maintenance cost, the electricity purchasing cost of the ground source heat pump and the electricity purchasing cost of the air conditioning unit.
The constraint conditions in the S2 comprise power balance constraint, heat load and cold load balance constraint, gas turbine power constraint and micro-grid interaction power constraint.
The power balance constraint is:
Figure BDA0002838591030000082
wherein P is MT,ij Representing the output power of the jth gas turbine in the ith micro-grid, P PV,i Representing the output power of a photovoltaic power generation unit in the ith micro-grid, P buy,i The electric energy purchased for the ith micro-grid; p (P) load,i Representing load demand in the ith micro-net, P HP,ij Representing the power consumed by the jth ground source heat pump in the ith micro-grid, P AC,i Representing the power consumed by an air conditioning unit in the ith micro-grid;
the heat load and cold load balance constraints are:
Figure BDA0002838591030000083
Figure BDA0002838591030000084
Figure BDA0002838591030000091
/>
wherein Q is load,L,i For the refrigeration capacity requirement in the ith micro-grid, Q MT,L,ij For the refrigerating capacity of the jth gas turbine in the ith micro-grid, Q HP,L,ij Refrigerating capacity of jth ground source heat pump in ith micro-grid, Q AC,L,i The refrigerating capacity of the air conditioning unit in the ith micro-grid; q (Q) load,H,i To the heating capacity requirement in the ith micro-grid, Q MT,H,ij Heating capacity of the jth gas turbine in the ith micro-grid; q (Q) HP,H,ij The heating quantity of the jth ground source heat pump in the ith micro-grid is represented; q (Q) AC,H,i The heating capacity of the air conditioning unit in the ith micro-grid;
Q MT,L,ij ＝η r,ij C L Q MT,ij
Q MT,H,ij ＝η r,ij C H Q MT,ij
Figure BDA0002838591030000092
wherein eta r,ij Waste heat recovery rate of exhaust gas of jth gas turbine in ith micro-grid, C L And C H Respectively the refrigerating coefficient and the heating coefficient of the gas turbine, Q MT,ij Waste heat of exhaust gas of jth gas turbine in ith micro-grid, P MT,ij Represents the output power, eta of the jth gas turbine in the ith micro-grid MT,ij Represents the power generation efficiency, eta of the jth gas turbine in the ith micro-grid L,ij The heat dissipation loss coefficient of the jth gas turbine in the ith micro-grid;
Q HP,L,ij ＝C HP,L P HP,ij (T o,ij -T r,ij )
Q HP,H,ij ＝C HP,H P HP,ij (T r,ij -T o,ij )
wherein P is HP,ij Representing the power consumed by the jth ground source heat pump in the ith micro-grid, C HP,L And C HP,H Respectively the refrigeration coefficient and the heating coefficient of the ground source heat pump, T o,ij And T r,ij Respectively representing the inlet water temperature and the outlet water temperature of an evaporator of the ground source heat pump;
Q AC,L,i ＝P AC (1-η AC,L )η AC,L COP AC,L
Q AC,H,i ＝P AC (1-η AC,H )η AC,H COP AC,H
wherein P is AC Is the input power of the air conditioning unit, eta AC,L And eta AC,H Refrigeration efficiency and heating efficiency, COP of air conditioning unit AC,L And COP AC,H The refrigerating energy efficiency ratio and the heating energy efficiency ratio of the air conditioning unit are respectively;
the gas turbine output power constraint satisfies:
min P MT,ij ≤P MT,ij ≤max P MT,ij
wherein min P MT,ij And max P MT,ij Respectively representing the minimum power and the maximum power output by a jth gas turbine in an ith micro-grid;
the micro-grid interaction power constraint satisfies:
|P gri,ir |≤P line,max
wherein P is gir,ir For the power transmitted from the ith micro-network to the r micro-network, P line,max Is the limit of the transmission power of the micro-network; p when prescribed power is transmitted from ith micro-net to (r) th micro-net gir,ir Positive, P when power is transferred from the r-th to the i-th micro-network gir,ir Is negative.
In S6, the following formula is used when initializing the target:
Figure BDA0002838591030000101
wherein P is MT,ij Represents the output power of the jth gas turbine in the ith micro-grid, min P MT,ij And max P MT,ij Respectively representing the minimum power and the maximum power output by the jth gas turbine in the ith micro-grid, P gir,ir For the power transmitted from the ith micro-network to the r micro-network, P line,max For the limit of the transmission power of the micro-network, rand (0, 1) is a random number of 0-1.
In the step S7, the specific method for generating the adjacency matrix is as follows:
when the iteration times are not more than 1, numbering each particle of the initial population, and forming an adjacent matrix according to the initial node degree;
when the iteration times are greater than 1, calculating random edge adding probability, and when the random probability is smaller than the edge adding probability, carrying out edge adding, and forming an adjacent matrix according to the node degree obtained by the edge adding rule.
In S8, the fitness of the parent particles in the current adjacency matrix is calculated by the following formula:
fitness＝min F＝min(C FU +C OM +C gir +C HP +C AC )
wherein C is FU 、C OM 、C gir 、C HP And C AC The method respectively represents the power generation cost, the total equipment maintenance cost, the micro-grid interaction maintenance cost, the electricity purchasing cost of the ground source heat pump and the electricity purchasing cost of the air conditioning unit.
In the step S9, the specific method for updating the parent particles is as follows:
if the parent particle is updated for the first time, the parent particle is updated by adopting the following formula:
A(z,r)＝r1*pbest(z,r)+(1-r1)*lg best(1,r)
wherein, pbest is an initial population, lg best is a particle corresponding to a dominant solution generated after selection through a small world network, and r1 is a random number;
if the probability is not the initial update, the new population generated before the current iterative calculation is continuously updated in the form of probability, and if the probability meets a preset value, the parent particles are updated by adopting the following formula:
Figure BDA0002838591030000111
if the probability does not meet the preset value, the parent particles are updated by adopting the following formula:
Figure BDA0002838591030000112
wherein, max gen is the maximum iteration number, m is the current iteration number, u is a random number of 0-1, and AA (z:) is the absolute value of the difference between the average value of the adjacent matrix generated by the small world network and the corresponding initial population.
It is to be understood that the above examples of the present invention are provided by way of illustration only and not by way of limitation of the embodiments of the present invention. Other variations or modifications of the above teachings will be apparent to those of ordinary skill in the art. It is not necessary here nor is it exhaustive of all embodiments. Any modification, equivalent replacement, improvement, etc. which come within the spirit and principles of the invention are desired to be protected by the following claims.

Claims (7)

1. A multi-microgrid interconnection optimization method taking photovoltaic uncertainty into account, the method comprising the steps of:
s1: establishing a total cost model of multi-microgrid interconnection considering photovoltaic uncertainty, wherein the total cost model comprises power generation cost, electricity purchasing cost and maintenance cost;
the power generation cost is as follows:
Figure QLYQS_1
wherein C is FU Representing the power generation cost, C CH4 Representing the nature of a unitPrice of gas in yuan/m 3 ，L CH4 Representing the low heating value, P, of natural gas MT,ij Represents the output power, eta of the jth gas turbine in the ith micro-grid MT,ij Generating efficiency of the j-th gas turbine in the i-th micro-grid;
the electricity purchasing cost comprises the electricity purchasing cost of the source heat pump and the electricity purchasing cost of the air conditioning unit:
Figure QLYQS_2
Figure QLYQS_3
wherein C is HP Representing electricity purchasing cost of ground source heat pump, P HP,ij Representing the power consumed by the jth ground source heat pump in the ith micro-grid; c (C) AC Representing the electricity purchasing cost of the air conditioning unit, P AC,i Representing the power consumed by an air conditioning unit in the ith micro-grid;
the maintenance cost comprises the power grid interaction maintenance cost and the equipment maintenance total cost:
Figure QLYQS_4
C OM ＝K OM,PV P PV,i +K OM,MT P MT,i +K OM,AC P AC,i
wherein C is gir Representing the interactive maintenance cost of the power grid, P buy,i And P sell,i Representing the electric energy purchased and sold by the jth station in the ith micro-grid respectively, B buy And S is sell Respectively representing electricity purchase price and electricity selling price; c (C) OM Representing the total maintenance cost of the equipment, K OM,PV 、K OM,MT And K OM,AC Respectively representing maintenance cost coefficients of the photovoltaic power generation unit, the gas turbine and the air conditioning unit, P PV,i 、P MT,ij And P AC,i Respectively represents the output of the photovoltaic power generation units in the ith micro-gridThe power, the power output by the jth gas turbine in the ith micro-grid and the power consumed by an air conditioning unit in the ith micro-grid;
s2: establishing an objective function and constraint conditions for the total cost model with the aim of lowest cost;
s3: setting parameters of the total cost model;
s4: performing scene construction according to a Latin hypercube scene generation mechanism;
s5: reducing the constructed scenes to obtain a scene set with a preset number;
s6: initializing by taking the output power of a gas turbine and the interaction power of a micro-grid as targets to obtain an initialized population, and taking the initialized population as parent particles;
s7: constructing a small world network and generating an adjacency matrix; the specific method comprises the following steps:
when the iteration times are not more than 1, numbering each particle of the initial population, and forming an adjacent matrix according to the initial node degree;
when the iteration times are greater than 1, calculating random edge probability, and when the random probability is smaller than the edge probability, carrying out edge adding, and forming an adjacent matrix according to the node degree obtained by an edge adding rule;
s8: calculating the fitness of parent particles in the current adjacent matrix;
s9: updating parent particles to generate child particles;
s10: performing constraint processing on the child particles by using the constraint conditions;
s11: calculating the fitness of the child particles after constraint processing, comparing the fitness of the parent particles with the fitness of the child particles after constraint processing, and reserving the particles with good fitness as the parent particles of the next iteration;
s12: traversing all adjacent matrixes, if all the adjacent matrixes are traversed, executing S13, otherwise jumping back to S8;
s13: and outputting an optimal solution of the total cost model of the multi-microgrid interconnection considering the photovoltaic uncertainty if the iteration number reaches a preset maximum iteration number, otherwise, jumping back to the step S7.
2. The multi-microgrid interconnection optimization method taking photovoltaic uncertainty into consideration according to claim 1, wherein the objective function in S2 is as follows:
minF＝min(C FU +C OM +C gir +C HP +C AC )
wherein C is FU 、C OM 、C gir 、C HP And C AC The method respectively represents the power generation cost, the total equipment maintenance cost, the micro-grid interaction maintenance cost, the electricity purchasing cost of the ground source heat pump and the electricity purchasing cost of the air conditioning unit.
3. The method of optimizing a multi-microgrid interconnection taking into account photovoltaic uncertainty of claim 2, wherein the constraints in S2 include power balance constraints, thermal and cold load balance constraints, gas turbine power constraints, and microgrid interaction power constraints.
4. A multi-microgrid interconnection optimization method taking into account photovoltaic uncertainty as claimed in claim 3, wherein said power balance constraint is:
Figure QLYQS_5
wherein P is MT,ij Representing the output power of the jth gas turbine in the ith micro-grid, P PV,i Representing the output power of a photovoltaic power generation unit in the ith micro-grid, P buy,i The electric energy purchased for the ith micro-grid; p (P) load,i Representing load demand in the ith micro-net, P HP,ij Representing the power consumed by the jth ground source heat pump in the ith micro-grid, P AC,i Representing the power consumed by an air conditioning unit in the ith micro-grid;
the heat load and cold load balance constraints are:
Figure QLYQS_6
Figure QLYQS_7
Figure QLYQS_8
wherein Q is load,L,i For the refrigeration capacity requirement in the ith micro-grid, Q MT,L,ij For the refrigerating capacity of the jth gas turbine in the ith micro-grid, Q HP,L,ij Refrigerating capacity of jth ground source heat pump in ith micro-grid, Q AC,L,i The refrigerating capacity of the air conditioning unit in the ith micro-grid; q (Q) load,H,i To the heating capacity requirement in the ith micro-grid, Q MT,H,ij Heating capacity of the jth gas turbine in the ith micro-grid; q (Q) HP,H,ij The heating quantity of the jth ground source heat pump in the ith micro-grid is represented; q (Q) AC,H,i The heating capacity of the air conditioning unit in the ith micro-grid;
Q MT,L,ij ＝η r,ij C L Q MT,ij
Q MT,H,ij ＝η r,ij C H Q MT,ij
Figure QLYQS_9
/>
wherein eta r,ij Waste heat recovery rate of exhaust gas of jth gas turbine in ith micro-grid, C L And C H Respectively the refrigerating coefficient and the heating coefficient of the gas turbine, Q MT,ij Waste heat of exhaust gas of jth gas turbine in ith micro-grid, P MT,ij Represents the output power, eta of the jth gas turbine in the ith micro-grid MT,ij Represents the power generation efficiency, eta of the jth gas turbine in the ith micro-grid L,ij The heat dissipation loss coefficient of the jth gas turbine in the ith micro-grid;
Q HP,L,ij ＝C HP,L P HP,ij (T o,ij -T r,ij )
Q HP,H,ij ＝C HP,H P HP,ij (T r,ij -T o,ij )
wherein P is HP,ij Representing the power consumed by the jth ground source heat pump in the ith micro-grid, C HP,L And C HP,H Respectively the refrigeration coefficient and the heating coefficient of the ground source heat pump, T o,ij And T r,ij Respectively representing the inlet water temperature and the outlet water temperature of an evaporator of the ground source heat pump;
Q AC,L,i ＝P AC (1-η AC,L )η AC,L COP AC,L
Q AC,H,i ＝P AC (1-η AC,H )η AC,H COP AC,H
wherein P is AC Is the input power of the air conditioning unit, eta AC,L And eta AC,H Refrigeration efficiency and heating efficiency, COP of air conditioning unit AC,L And COP AC,H The refrigerating energy efficiency ratio and the heating energy efficiency ratio of the air conditioning unit are respectively;
the gas turbine output power constraint satisfies:
min P MT,ij ≤P MT,ij ≤max P MT,ij
wherein min P MT,ij And max P MT,ij Respectively representing the minimum power and the maximum power output by a jth gas turbine in an ith micro-grid;
the micro-grid interaction power constraint satisfies:
|P gri,ir |≤P line,max
wherein P is gri,ir For the power transmitted from the ith micro-network to the r micro-network, P line,max Is the limit of the transmission power of the micro-network; p when prescribed power is transmitted from ith micro-net to (r) th micro-net gri,ir Positive, P when power is transferred from the r-th to the i-th micro-network gri,ir Is negative.
5. The method for optimizing multi-microgrid interconnection taking photovoltaic uncertainty into account according to claim 4, wherein in S6, the following formula is used for initializing the target:
Figure QLYQS_10
wherein P is MT,ij Represents the output power of the jth gas turbine in the ith micro-grid, min P MT,ij And max P MT,ij Respectively representing the minimum power and the maximum power output by the jth gas turbine in the ith micro-grid, P gri,ir For the power transmitted from the ith micro-network to the r micro-network, P line,max For the limit of the transmission power of the micro-network, rand (0, 1) is a random number of 0-1.
6. The multi-microgrid interconnection optimization method considering photovoltaic uncertainty according to claim 5, wherein in S8, the fitness of parent particles in the current adjacency matrix is calculated by the following formula:
fitness＝min F＝min(C FU +C OM +C gir +C HP +C AC )
wherein C is FU 、C OM 、C gir 、C HP And C AC The method respectively represents the power generation cost, the total equipment maintenance cost, the micro-grid interaction maintenance cost, the electricity purchasing cost of the ground source heat pump and the electricity purchasing cost of the air conditioning unit.
7. The multi-microgrid interconnection optimization method considering the photovoltaic uncertainty according to claim 6, wherein in S9, the specific method for updating the parent particles is as follows:
if the parent particle is updated for the first time, the parent particle is updated by adopting the following formula:
A(z,r)＝r1*pbest(z,r)+(1-r1)*lgbest(1,r)
wherein, pbest is an initial population, lgbest is particles corresponding to a dominant solution generated after selection through a small world network, and r1 is a random number;
if the update is not the initial update, the new population generated before the current iterative calculation is continuously updated in the form of probability, and when the probability meets a preset value, the parent particles are updated by adopting the following formula:
Figure QLYQS_11
when the probability does not meet the preset value, the parent particles are updated by adopting the following formula:
Figure QLYQS_12
wherein, max gen is the maximum iteration number, m is the current iteration number, u is a random number of 0-1, and AA (z:) is the absolute value of the difference between the average value of the adjacent matrix generated by the small world network and the corresponding initial population.
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