CN111756073B - Hierarchical control and operation optimization method for multi-energy complementary micro-grid - Google Patents
Hierarchical control and operation optimization method for multi-energy complementary micro-grid Download PDFInfo
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—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
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/28—Arrangements for balancing of the load in a network by storage of energy
- H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/381—Dispersed generators
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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
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
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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
- 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
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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
- H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
- H02J2300/20—The dispersed energy generation being of renewable origin
- H02J2300/28—The renewable source being wind energy
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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
- H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
- H02J2300/40—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation wherein a plurality of decentralised, dispersed or local energy generation technologies are operated simultaneously
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- 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
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Abstract
The system comprises three buses, namely an electric bus, a hot bus and a cold bus, wherein an electric energy input device contained in the electric bus comprises a photovoltaic generator, a wind driven generator, a gas turbine, a storage battery and a large power grid; the implementation of the invention realizes the hierarchical control and operation optimization of the multi-energy complementary micro-grid, improves the energy utilization rate of the system on the premise of realizing the power optimization, and well solves the problem of electric energy quality during micro-source operation and optimal control.
Description
Technical Field
The invention discloses a hierarchical control and operation optimization method based on a multi-energy complementary micro-grid, and mainly relates to the multi-energy complementary micro-grid, hierarchical control and sagging control.
Background
Along with the transformation of the national energy structure, the effect of the multi-energy complementary micro-grid with high energy utilization rate and high clean energy permeability is more and more obvious. On the one hand, as the extension of the micro-grid, the multifunctional complementary micro-grid inherits the advantages of flexible operation, on-site consumption and the like of the micro-grid, and meanwhile, the problems of frequent generation of unbalanced power and difficult guarantee of the quality of electric energy caused by the combined action of the intermittence of the micro-source output and the fluctuation of the load are remained. On the other hand, the multi-energy complementary micro-grid interacts with multiple energy sources, so that the control strategy of the multi-energy complementary micro-grid is more complex than that of the traditional micro-grid. According to the operation requirement and real-time requirement of the multi-energy complementary micro-grid, aiming at the problem that the power distribution, the micro-source output prediction planning, the operation and the optimization control of the micro-source do not consider the power quality when the load fluctuates, an overall planning and power quality optimization control scheme for the operation mode of the multi-energy complementary micro-grid is required to be provided.
Disclosure of Invention
The invention aims to:
the invention discloses a hierarchical control and operation optimization method based on a multi-energy complementary micro-grid. The method aims to solve the problems existing in the past by constructing a hierarchical control and operation optimization method based on a multi-energy complementary micro-network. The technical scheme is as follows:
the layered control system based on the multi-energy complementary micro-grid comprises three buses, namely an electric bus, a hot bus and a cold bus, wherein an electric energy input device contained in the electric bus comprises a photovoltaic generator, a wind driven generator, a gas turbine, a storage battery and a large power grid;
the heat energy input device of the thermal bus comprises a waste heat boiler and electric heating equipment which utilize the waste heat generated by the gas turbine;
the cold energy input device contained in the cold bus is provided with an electric refrigerating device and a lithium bromide refrigerator;
the main energy output channels of the three energy buses of the electric bus, the thermal bus and the cold bus are respectively energy loads, namely electric loads, thermal loads and cold loads; the electric power output equipment of the electric bus also comprises electric heating equipment and electric refrigerating equipment;
the gas turbine is used as a recoverable waste heat type gas turbine, the waste heat boiler and the absorption refrigerator together form a main part of a waste heat recovery system, and waste heat generated by the gas turbine passes through the waste heat boiler to generate steam and hot water which are respectively replaced by hot water and cold water through the heat exchanger and the lithium bromide refrigerator to supply heat and cool to a user.
The gas turbine comprises a compressor, a fuel chamber, a steam turbine, a synchronous generator, a transformer and a waste heat boiler,
firstly, forming high-pressure air through a compressor, mixing the high-pressure air with fuel in a combustion chamber for combustion, further pushing a steam turbine to rotate, and finally driving a synchronous generator to generate power and supplying power to an electric load through a transformer by the steam turbine; the waste heat discharged by the steam turbine is mixed with heat energy produced by the waste heat boiler to form high-temperature and high-pressure steam and high-temperature hot water, the high-temperature and high-pressure steam is replaced by hot water through a heat exchanger to supply heat to a user, and the high-temperature hot water is replaced by cold water through a lithium bromide refrigerator to supply cold to the user.
The hierarchical control and operation optimization method based on the multi-energy complementary micro-grid is divided into three layers, namely a bottom control strategy based on droop control, a two-layer control strategy based on power optimization and a three-layer control strategy based on power quality optimization;
the bottom control strategy based on sagging control is an inverter control strategy, the control strategy adopts a traditional sagging control method, so that each micro source distributes instantaneous load power fluctuation in a system according to the proportion of micro source capacity according to a power given value issued by a central controller system, and electric micro sources such as a wind driven generator, a photovoltaic cell, a micro gas turbine and a storage battery are coordinated to distribute power according to the proportion of capacity, and simultaneously, the sagging control is utilized to control the waste heat distribution of the gas turbine and the electric energy distribution used by electric heating and electric refrigeration;
The two-layer control strategy based on power optimization is a power optimization control strategy, and according to the power prediction of a micro source and the load prediction of cold, heat and electricity, under the condition that the constraint conditions of electric load, heat load, cold load demand and output limit are met, the minimization of primary energy consumption and greenhouse gas emission is realized as an optimization target, and the optimal comprehensive benefit is finally met; further, according to an optimization algorithm, determining the power fluctuation proportion born by each micro source in the two-layer control process and the distribution proportion of the cold and heat energy input devices;
the three-layer control of the multi-energy complementary micro-grid is an electric energy quality control strategy, namely an electric energy quality optimization control strategy of the three-layer of the multi-energy complementary micro-grid, the electric energy quality is further optimized on the basis of two-layer control power optimization, and the electric energy quality indexes comprise voltage sag, voltage deviation, frequency deviation, three-phase imbalance, voltage fluctuation and voltage flicker, so that the optimization target achieves the optimal environment, the optimal energy consumption and the optimal electric energy quality on the premise of balanced supply and demand.
The power optimization-based two-layer control strategy constructs a target function of the operation optimization of the multi-energy complementary micro-grid according to the micro-source operation and the pollutant gas emission in the multi-energy complementary micro-grid, builds constraint conditions according to the requirements of electric load, thermal load and cold load, micro-source output limit and the like, and solves the target function of the operation optimization of the multi-energy complementary micro-grid by using a power optimization algorithm of the multi-energy complementary micro-grid to construct a two-layer control model based on the power optimization.
The micro sources in the multi-energy complementary micro-grid comprise a gas turbine, a photovoltaic power generator, a fan and a storage battery; the system also comprises a waste heat boiler, lithium bromide refrigeration equipment, electric heating equipment and electric refrigeration equipment, so that the multi-energy complementary power optimization model selects 8 related optimization variables which are respectively the output power P of the gas turbine MT Output power P of wind driven generator WIND Photovoltaic cell output power P PV Battery output power P Ba Waste heat heating output heat W MTH Output cooling capacity W of absorption refrigerator MTC Output heat W of electric heating EH And electric refrigeration output cold quantity W EC The 8 optimization variables are used as optimization target variables of the power optimization model.
The optimization objective is to realize the minimum primary energy consumption of the system and the minimum emission of pollution gases such as greenhouse gases under the condition of meeting the operation constraint of each distributed unit and the limitation of the cold-hot electric load requirement, and the objective function is expressed as: minZ cost ＝λ 1 C g +λ 2 C e Wherein Z is cost Running an objective function for the multi-energy complementary microgrid; c (C) g The power generation cost of the system; c (C) e A penalty function for pollutant gas emissions; lambda (lambda) 1 、λ 2 Weight ratio of penalty function for generating cost and pollutant gas emission, and lambda is 0-lambda 1 、λ 2 And less than or equal to 1, according to the generation cost of each micro source, the target function expansion is expressed as:
wherein P is MT,k Output power of the kth gas turbine; c (C) op (P MT,k ) Fuel consumption for the kth gas turbine; c (C) MT Fuel price for the gas turbine; zeta type toy MT Maintaining a cost factor for operation of the gas turbine; t is an operation period, typically in hours; n is the number of gas turbine operation; p (P) WIND,k The output power of the kth wind driven generator; zeta type toy WIND Maintaining a cost coefficient for the operation of the wind power generator; p (P) PV,k The output power of the kth wind driven generator; zeta type toy PV Maintaining a cost coefficient for operation of the kth wind turbine; p (P) Ba,k Output power of the kth storage battery; zeta type toy Ba Maintaining a cost coefficient for operation of the kth battery; zeta type toy kj Output power of the kth storage battery;maintaining a cost factor, f, for operation of the kth battery jp A penalty rate for the j-th polluted gas; m is the seed number of the polluted gas.
The constraint conditions of the multi-energy complementary micro-grid power optimization model are divided into equality balance constraint conditions and inequality constraint conditions, wherein the equality balance constraint conditions comprise electric, thermal and cold load demands, namely load power balance constraint and waste heat balance constraint, the inequality constraint conditions are micro-source output power constraint and standby power constraint which are all micro-source output power constraint, the equality balance constraint conditions and the inequality constraint conditions are required to be simultaneously satisfied, and when the inequality constraint conditions cannot be satisfied, the standby power constraint is satisfied, the specific constraint conditions are that:
(1) The electric power balance constraint is: p (P) MT +P WIND +P PV +P Ba -P loss ＝P Load Wherein: p (P) loss Is the line loss; p (P) Load For all electrical loads;
(2) The thermal power balance constraint is:wherein: w (W) MTH,k The waste heat of the gas turbine is generated; w (W) EH,k Heating power for electric heating；W H For all thermal loads; n is the number of gas turbines running;
(3) The cold power balance constraint is:wherein: w (W) MTC,k The lithium bromide waste heat consumption of the gas turbine is used; η (eta) c Is lithium bromide conversion efficiency; w (W) EC,k The refrigerating capacity is electric refrigerating capacity; w (W) C For all cold loads;
(4) The waste heat balance constraint is as follows:wherein:The total amount of waste heat of the gas turbine; w (W) MT，k Is the residual heat of the kth gas turbine;
(5) The gas turbine output constraints are:wherein:Minimum safe output for the gas turbine;Maximum safe output for the gas turbine; p (P) MT，i The gas turbine is powered;
(6) The output constraint of the wind driven generator is as follows:wherein:The minimum safe output of the wind driven generator is achieved;The maximum safe output of the wind driven generator is achieved; p (P) WIND，i The wind driven generator is powered;
(7) Photovoltaic cell output constraint:wherein:Minimum safe output for the photovoltaic cell;maximum safe output for the photovoltaic cell; p (P) PV，i Exerting force on the photovoltaic cell;
(8) Battery output constraint:wherein:Is the minimum capacity of the storage battery;Is the maximum capacity of the storage battery; p (P) Ba，i The capacity of the storage battery;
(9) The reserve constraint, which is typically chosen to be 5% of the maximum load, is expressed as:
maximum force for the ith backup constraint
The multi-energy complementary micro-grid power optimization algorithm adopts a particle swarm algorithm, is a random search algorithm based on population, and comprises the following specific steps and processes:
firstly, determining a feasible domain space, and in a D-dimension feasible domain, assuming that the spatial positions of n random particles at a time t are as follows:
the particle velocity is:
Meanwhile, assuming that each particle is a feasible solution of an equation to be solved, calculating an fitness function value of the particle, and obtaining an individual historical optimal position through comparison by longitudinally comparing the sizes of the particle historical fitness values before the time t:
and then transversely comparing the sizes of the fitness values at the moment t to obtain the global optimal position in the current generation:
iterative optimization of particles, at time t+1, spatial position coordinates x i (t) and velocity v i (t) adjusting in the manner described by: v i (t+1)＝v i (t)+c 1 ·r 1 (p i (t)-x i (t))+c 2 ·r 2 (p g (t)-x i (t))
In the method, in the process of the invention, spatial positions, particle speeds, individual historical optimal positions and global optimal positions which correspond to 1, 2, D and D dimensions respectively; c 1 And c 2 Respectively represent the learning constants of the particles; r is (r) 1 And r 2 At [0,1]Uniformly taking values; p is p i Is an individual extremum; p is p g Is a global extremum.
The speed updating formula is composed of three parts, namely, the first part enables the algorithm to perform global searching and balances global searching capacity and local searching capacity; the second part enables the particles to perform a stronger local search; the third part considers the ability of particles to learn from particles in the whole population, and represents information sharing among different particles;
the particle updates the position by updating the corresponding speed, and the position formula is as follows: x is x i (t+1)＝x i (t)+v i (t+1)
By limiting the speed variation amplitude, let v min ＜v i (t)＜v max Limiting the size of the particle position change. In multiple iterations, each particle in the population is cyclically updated so that the whole population gradually approaches the global optimal solution.
The three-layer power quality optimization control strategy process of the multi-energy complementary micro-grid comprises the following steps:
firstly, randomly selecting 5 groups of objective functions with different weight proportions, and carrying out power optimization analysis to obtain 5 groups of optimal power variables; further, respectively bringing 5 groups of optimal power variables into a bottom control model based on sagging control, and performing electric energy quality simulation analysis to obtain 5 groups of electric energy quality index data; and finally, taking 5 groups of data containing the optimal power variable and the electric energy quality index as 5 groups of decision units, and using a data envelope analysis algorithm to comprehensively sort various electric energy quality indexes of the plurality of groups of decision units according to the optimal sequence of the electric energy quality so as to obtain the decision unit with the optimal comprehensive electric energy quality. The optimizing variables of the input indexes of the decision unit are 8 optimizing variables of power optimizing output; the input index is 4 pieces of electric energy quality index data measured through model simulation, including voltage deviation, frequency deviation, three-phase voltage unbalance and harmonic content. The optimal decision unit obtained through the power quality optimization control strategy is regarded as an individual with relatively optimal economic, environmental and power quality.
The data envelope analysis is a non-parameter system analysis method for evaluating the relative effectiveness of the same type of multi-input and multi-output decision units based on linear programming, and parameters do not need to be estimated in advance when the same type of multi-input and multi-output DMU is analyzed. The DEA has two models, namely a CCR model and a BBC model, aiming at the scale Remuneration (RTS); the hierarchical control and operation optimization model based on the multi-energy complementary micro-grid adopts a super-efficiency model to sort the efficiency of the plurality of decision units.
The problem of linear programming of the CCR model is rewritten as follows by using the dual theorem:
max H O ＝θ
the constraint conditions corresponding to the kth DMU are:
λ r ≥0,r＝1,…,n,r≠k。
wherein n is the number of decision units; m is the number of types of DMU input types; r=1, 2, … s, s being the number of kinds of DMU output types; h j Efficiency rating index for the jth DMU; y is rj (j=1, 2, …, n) is the recorded DMU j Is a vector of inputs of (a); y is ro Is the optimal input; h O An efficiency evaluation index; u (u) ro Is DMU (digital subscriber unit) o Optimally inputting a weight coefficient; lambda (lambda) r Optimizing weights for the two-layer power; k: the formula is the constraint corresponding to the kth DMU.
The advantages and effects are that:
the invention mainly comprises the steps of providing a general structure of the multi-energy complementary micro-grid, integrally planning the operation mode of the multi-energy complementary micro-grid by adopting a layered control structure, and providing a new power quality optimization control scheme. The design of the grid structure of the multifunctional complementary micro-grid is based on cold, hot and electric energy interaction, and comprises three buses, namely an electric bus, a hot bus and a cold bus. The bottom sagging control strategy of the layered control structure design can realize the instantaneous power redistribution when the working condition changes on the basis of the output rated power, thereby meeting the load requirement; the two-layer optimization control strategy can realize the power optimization of each micro source and equipment under the constraint condition of satisfying the balance of an electric network, a thermal network and a cold network, so as to obtain the best economic and environmental-protection index; and the three-layer control optimizes the output power of each micro source according to different weight ratios to obtain better electric energy quality.
The grid structure comprises equipment capable of realizing interaction among cold, hot and electric energy sources such as electric heating, electric refrigeration and a waste heat boiler, and has the characteristics of large inertia and large hysteresis due to heat and cold load, so that the grid structure can play a role in peak clipping and valley filling when electric energy is excessive or insufficient, simultaneously reduces the capacity of a storage battery, reduces energy storage cost and maintenance cost, emphasizes cyclic utilization among the energy sources, realizes energy complementation, and further can improve the effective utilization rate of the energy sources.
The electric bus is connected with a photovoltaic generator, a wind driven generator, a gas turbine, a storage battery, a large power grid which can be selected to exist and an electric load. Wherein the photovoltaic generator is connected to the electrical bus through a DC/AC inverter, the wind generator is connected to the electrical bus through an AC/AC inverter, the gas turbine is connected to the electrical bus through a DC/AC inverter, the battery is connected to the electrical bus through a DC/AC inverter, and is interconnected with the large power grid through a common point of linkage (PCC).
The gas turbine power supply system comprises a compressor, a fuel chamber, a steam turbine, a synchronous generator, a transformer and a waste heat boiler. Firstly, high-pressure air is formed through a compressor, then the high-pressure air is mixed with fuel in a combustion chamber for combustion, further a steam turbine is driven to rotate, and finally, a synchronous generator is driven to generate electricity. Waste heat discharged from the steam turbine is mixed with heat energy produced by the waste heat boiler, and then is exchanged into waste heat through a heat exchanger for recycling.
The waste heat recovery system comprises a miniature gas turbine, a waste heat boiler, a lithium bromide refrigerator and a heat exchanger. The waste heat discharged by the miniature gas turbine forms high-temperature and high-pressure steam and high-temperature hot water through the waste heat boiler, and the high-temperature and high-pressure steam and the high-temperature hot water are respectively replaced by hot water and cold water through a heat exchanger and a lithium bromide refrigerator, so that a user is supplied with cold and heat.
The thermal bus is connected with a waste heat boiler, electric heating equipment and a thermal load which utilize the waste heat generated by the gas turbine.
The cold bus is connected with an electric refrigerating device, a lithium bromide refrigerator and a cold load.
The hierarchical control strategy of the multi-energy complementary micro-grid is divided into three layers, namely a bottom control strategy based on droop control, a two-layer control strategy based on power optimization and a three-layer control strategy based on electric energy quality optimization.
The multi-energy complementary micro-grid bottom control is an inverter control strategy. The control strategy adopts a sagging control method, so that each micro source coordinately controls the electric micro source such as a wind driven generator, a photovoltaic cell, a micro gas turbine, a storage battery and the like according to the instantaneous load power fluctuation in a power set value distribution system which is issued by a central controller system. Meanwhile, the waste heat distribution of the gas turbine and the electric energy distribution used by electric heating and electric cooling are controlled so as to ensure that each bus of the multi-energy complementary micro-grid can safely run under the condition of meeting the corresponding load.
The two-layer control of the multi-energy complementary micro-grid is a power optimization control strategy. According to the power prediction of the micro source, the prediction of the cold, hot and electric loads, under the constraint conditions of meeting the electric load, the heat load, the cold load demand, the output limit and the like, the minimization of the power generation cost and the greenhouse gas emission is realized as an optimization target, and the optimal comprehensive benefit is finally met. And then, according to an optimization algorithm, determining the power fluctuation proportion born by each micro source in the two-layer control process and the distribution proportion of the cold and heat energy input devices.
The three-layer control of the multi-energy complementary micro-grid is an electric energy quality control strategy. The power quality is further optimized on the basis of two-layer control power optimization, and the optimization targets comprise voltage sag, voltage deviation, frequency deviation, three-phase unbalance, voltage fluctuation and voltage flicker, so that the optimization targets reach the optimal environment, the optimal economy and the optimal power quality on the premise of balanced supply and demand.
The multi-energy complementary micro-grid power optimization model comprises 8 optimization variables which are respectively the output power P of the gas turbine MT Output power P of wind driven generator WIND Photovoltaic cell output power P PV Battery output power P Ba Waste heat heating output heat W MTH Output cooling capacity W of absorption refrigerator MTC Output heat W of electric heating EH Output cold energy W of electric refrigeration EC 。
The objective of the economic optimization of the multi-energy complementary micro-grid is to realize the lowest system power generation cost and the minimum pollutant gas emission under the limit of meeting the operation constraint and the cold-hot electric load requirement of each distributed unit. The objective function can be expressed as: minZ cost ＝λ 1 C g +λ 2 C e Wherein Z is cost Running an objective function for the multi-energy complementary microgrid; c (C) g The power generation cost of the system; c (C) e Penalty costs for pollutant gas emissions; lambda (lambda) 1 、λ 2 Weight ratio of generating cost and pollutant gas emission penalty cost is 0-lambda 1 、λ 2 ≤1。
The constraint conditions of the economic optimization of the multi-energy complementary micro-grid are divided into equality balance constraint conditions and inequality constraint conditions, wherein the equality balance constraint conditions comprise electric, thermal and cold power balance constraint and waste heat balance constraint, and the inequality constraint conditions are micro-source power constraint and standby power constraint.
The optimization algorithm adopts a particle swarm algorithm, is a group-based random search algorithm, and comprises the specific steps and flow of initializing a group of random particles in a feasible domain, wherein each particle has own position, speed and direction. Meanwhile, assuming that each particle is a feasible solution of an equation to be solved, calculating an fitness function value of the particle, and obtaining an individual optimal Prest and a global optimal Gbest by comparing the fitness value. The particles track Pbat and Gbat in the feasible domain, and the direction, speed and position of the particles are updated continuously.
The electric energy quality index of the three-layer electric energy quality optimization control strategy mainly comprises voltage sag, voltage deviation, frequency deviation, three-phase unbalance, voltage fluctuation and voltage flicker.
The power quality analysis of the multi-energy complementary micro-grid is carried out by taking a typical day as a sample.
And the multi-energy complementary micro-grid power quality analysis uses a data envelopment analysis algorithm to comprehensively sort various power quality indexes of a plurality of groups of decision units, so as to obtain a decision unit with comprehensively optimal power quality. And a super-efficiency model is established to order the efficiency of a plurality of decision units.
The implementation of the invention realizes the hierarchical control and operation optimization of the multi-energy complementary micro-grid, improves the energy utilization rate of the system on the premise of realizing the power optimization, and well solves the problem of electric energy quality during micro-source operation and optimal control.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below.
Fig. 1 is an overall block diagram of one embodiment of a multi-energy complementary micro-grid system provided by the present invention.
Fig. 2 is an overall block diagram of a hierarchical control strategy for a multi-energy complementary micro-grid in a multi-energy complementary micro-grid system provided by the invention.
FIG. 3 is a block diagram of a recoverable waste heat gas turbine in a multi-energy complementary microgrid system provided by the present invention.
FIG. 4 is a block diagram of a flow chart of waste heat recovery of a micro gas turbine in a multi-energy complementary micro grid system provided by the invention;
FIG. 5 is a two-layer power optimization strategy program flow of the multi-energy complementary micro-grid based on the particle swarm algorithm;
fig. 6 is a control block diagram of a three-layer power quality optimization control strategy for a multi-energy complementary micro-grid.
Detailed Description
The system comprises three buses, namely an electric bus, a hot bus and a cold bus, wherein an electric energy input device contained in the electric bus comprises a photovoltaic generator, a wind driven generator, a gas turbine, a storage battery and a large power grid which can be selected to exist, and when the electric quantity of the electric bus is unbalanced, the storage battery and the large power grid are mainly used for carrying out electric energy exchange so as to achieve the purpose of peak clipping and valley filling;
the heat energy input device of the thermal bus comprises a waste heat boiler and electric heating equipment which utilize the waste heat generated by the gas turbine;
the cold bus comprises a cold energy input device which comprises an electric refrigerating device and a lithium bromide refrigerator (one of the absorption refrigerators shown in the figure);
The main energy output channels of the three energy buses of the electric bus, the thermal bus and the cold bus are respectively energy loads, namely electric loads, thermal loads and cold loads; the electric power output equipment of the electric bus also comprises electric heating equipment and electric refrigerating equipment;
the gas turbine is used as a recoverable waste heat type gas turbine, the waste heat boiler and the absorption refrigerator together form a main part of a waste heat recovery system, and waste heat generated by the gas turbine passes through the waste heat boiler to generate steam and hot water which are respectively replaced by hot water and cold water through the heat exchanger and the lithium bromide refrigerator to supply heat and cool to a user.
The equipment capable of realizing interaction among cold, heat and electricity sources such as electric heating, electric refrigeration and waste heat boilers has the characteristics of large inertia and large hysteresis due to heat and cold load, so that the equipment can play a role in peak clipping and valley filling when electric energy is excessive or insufficient, simultaneously reduces the capacity of a storage battery, reduces energy storage cost and maintenance cost, highlights cyclic utilization among energy sources, realizes energy complementation and further can improve the effective utilization rate of the energy sources.
The power supply system of the gas turbine comprises a compressor, a fuel chamber, a steam turbine, a synchronous generator, a transformer and a waste heat boiler, can be operated in a heat electricity fixing mode or a cold electricity fixing mode according to the requirements of environment and load under the condition of ensuring the power load requirement, can also recover waste heat to heat or refrigerate while generating electric energy so as to meet the requirements of cold and hot loads of users,
Firstly, high-pressure air is formed through a compressor, the high-pressure air is mixed with fuel in a combustion chamber to burn, and then a turbine is driven to rotate, and finally the turbine drives a synchronous generator to generate power and supply power to an electric load through a transformer. Waste heat discharged from a steam turbine is mixed with heat energy produced by a waste heat boiler under the action of a waste heat recovery system, and then is exchanged into waste heat through a heat exchanger to be recycled, the waste heat discharged by the steam turbine (a micro gas turbine) is mixed with the heat energy produced by the waste heat boiler to form high-temperature high-pressure steam and high-temperature hot water, the high-temperature high-pressure steam is replaced by the hot water through the heat exchanger to supply heat to a user, and the high-temperature hot water is replaced by the cold water through a lithium bromide refrigerator to supply cold to the user;
that is to say: the waste heat recovery system comprises a steam turbine (a miniature gas turbine), a waste heat boiler, a lithium bromide refrigerator and a heat exchanger, wherein waste heat discharged by the miniature gas turbine forms high-temperature and high-pressure steam and high-temperature hot water through the waste heat boiler, and the high-temperature and high-pressure steam and the high-temperature hot water are respectively replaced by hot water and cold water through the heat exchanger and the lithium bromide refrigerator, so that heat and cold are supplied to a user.
The hierarchical control and operation optimization method based on the multi-energy complementary micro-grid is divided into three layers, namely a bottom control strategy based on droop control, a two-layer control strategy based on power optimization and a three-layer control strategy based on power quality optimization;
The bottom control strategy based on sagging control is an inverter control strategy, the control strategy adopts a traditional sagging control method, so that each micro source distributes instantaneous load power fluctuation in a system according to the proportion of micro source capacity according to a power given value issued by a central controller system, and electric micro sources such as a wind driven generator, a photovoltaic cell, a micro gas turbine and a storage battery are coordinated to distribute power according to the proportion of capacity, and simultaneously, the sagging control is utilized to control the waste heat distribution of the gas turbine and the electric energy distribution used by electric heating and electric refrigeration so as to ensure that each bus of a multi-energy complementary micro network runs safely under the condition of meeting corresponding load;
the two-layer control strategy based on power optimization is a power optimization control strategy, and according to the power prediction of a micro source and the load prediction of cold, heat and electricity, under the constraint conditions of meeting the electric load, the heat load, the cold load demand, the output limit and the like, the two-layer control strategy based on power optimization is used for realizing the minimization of primary energy consumption and greenhouse gas emission as an optimization target, and finally, the comprehensive benefit is optimal; further, according to an optimization algorithm, determining the power fluctuation proportion born by each micro source in the two-layer control process and the distribution proportion of the cold and heat energy input devices;
The three-layer control of the multi-energy complementary micro-grid is an electric energy quality control strategy, namely an electric energy quality optimization control strategy of the three-layer of the multi-energy complementary micro-grid, the electric energy quality is further optimized on the basis of two-layer control power optimization, and the electric energy quality indexes comprise voltage sag, voltage deviation, frequency deviation, three-phase imbalance, voltage fluctuation and voltage flicker, so that the optimization target achieves the optimal environment, the optimal energy consumption and the optimal electric energy quality on the premise of balanced supply and demand.
The power optimization-based two-layer control strategy constructs a target function of the operation optimization of the multi-energy complementary micro-grid according to the micro-source operation and the pollutant gas emission in the multi-energy complementary micro-grid, builds constraint conditions according to the requirements of electric load, thermal load and cold load, micro-source output limit and the like, and solves the target function of the operation optimization of the multi-energy complementary micro-grid by using a power optimization algorithm of the multi-energy complementary micro-grid to construct a two-layer control model based on the power optimization.
The micro sources in the multi-energy complementary micro-grid comprise a gas turbine, a photovoltaic power generator, a fan and a storage battery; the system also comprises a waste heat boiler, lithium bromide refrigeration equipment, electric heating equipment and electric refrigeration equipment (for energy conversion), so that the multi-energy complementary power optimization model selects 8 related optimization variables which are respectively the output power P of the gas turbine MT Output power P of wind driven generator WIND Photovoltaic cell output power P PV Battery output power P Ba Waste heat heating output heat W MTH Output cooling capacity W of absorption refrigerator MTC Output heat W of electric heating EH And electric refrigeration output cold quantity W EC The 8 optimization variables are used as optimization target variables of the power optimization model.
The optimization objective is to realize the minimum primary energy consumption of the system and the minimum emission of pollution gases such as greenhouse gases under the condition of meeting the operation constraint of each distributed unit and the limitation of the cold-hot electric load requirement, and the objective function is expressed as: minZ cost ＝λ 1 C g +λ 2 C e Wherein Z is cost Running an objective function for the multi-energy complementary microgrid; c (C) g The power generation cost of the system; c (C) e A penalty function for pollutant gas emissions; lambda (lambda) 1 、λ 2 Weight ratio of penalty function for generating cost and pollutant gas emission, and lambda is 0-lambda 1 、λ 2 And less than or equal to 1, according to the generation cost of each micro source, the target function expansion is expressed as:
wherein P is MT,k Output power of the kth gas turbine; c (C) op (P MT,k ) Fuel consumption for the kth gas turbine; c (C) MT Fuel price for the gas turbine; zeta type toy MT Maintaining a cost factor for operation of the gas turbine; t is an operation period, typically in hours; n is the number of gas turbine operation; p (P) WIND,k The output power of the kth wind driven generator; zeta type toy WIND Maintaining a cost coefficient for the operation of the wind power generator; p (P) PV,k The output power of the kth wind driven generator; zeta type toy PV Maintaining a cost coefficient for operation of the kth wind turbine; p (P) Ba,k Output power of the kth storage battery; zeta type toy Ba Maintaining a cost coefficient for operation of the kth battery; zeta type toy kj Output power of the kth storage battery;maintaining a cost factor, f, for operation of the kth battery jp A penalty rate for the j-th polluted gas; m is the seed number of the polluted gas.
The constraint conditions of the multi-energy complementary micro-grid power optimization model are divided into equality balance constraint conditions and inequality constraint conditions, wherein the equality balance constraint conditions comprise electric, thermal and cold load demands, namely load power balance constraint and waste heat balance constraint, the inequality constraint conditions are micro-source output power constraint and standby power constraint which are all micro-source output power constraint, the equality balance constraint conditions and the inequality constraint conditions are required to be simultaneously satisfied, and when the inequality constraint conditions cannot be satisfied, the standby power constraint is satisfied, the specific constraint conditions are that:
(1) Electric power levelThe balance constraint is as follows: p (P) MT +P WIND +P PV +P Ba -P loss ＝P Load Wherein: p (P) loss Is the line loss; p (P) Load For all electrical loads;
(2) The thermal power balance constraint is: Wherein: w (W) MTH,k The waste heat of the gas turbine is generated; w (W) EH,k The heat is generated for electric heating; w (W) H For all thermal loads; n is the number of gas turbines running;
(3) The cold power balance constraint is:wherein: w (W) MTC,k The lithium bromide waste heat consumption of the gas turbine is used; η (eta) c Is lithium bromide conversion efficiency; w (W) EC,k The refrigerating capacity is electric refrigerating capacity; w (W) C For all cold loads;
(4) The waste heat balance constraint is as follows:wherein:The total amount of waste heat of the gas turbine; w (W) MT，k Is the residual heat of the kth gas turbine;
(5) The gas turbine output constraints are:wherein:Minimum safe output for the gas turbine;Maximum safe output for the gas turbine; p (P) MT，i The gas turbine is powered;
(6) The output constraint of the wind driven generator is as follows:wherein:The minimum safe output of the wind driven generator is achieved;The maximum safe output of the wind driven generator is achieved; p (P) WIND，i The wind driven generator is powered;
(7) Photovoltaic cell output constraint:wherein:Minimum safe output for the photovoltaic cell;maximum safe output for the photovoltaic cell; p (P) PV，i Exerting force on the photovoltaic cell;
(8) Battery output constraint:wherein:Is the minimum capacity of the storage battery;Is the maximum capacity of the storage battery; p (P) Ba，i The capacity of the storage battery;
(9) The reserve constraint, which is typically chosen to be 5% of the maximum load, is expressed as:
Maximum force for the ith backup constraint
The multi-energy complementary micro-grid power optimization algorithm adopts a particle swarm algorithm, is a random search algorithm based on population, and comprises the following specific steps and processes:
firstly, determining a feasible domain space, and in a D-dimension feasible domain, assuming that the spatial positions of n random particles at a time t are as follows:
the particle velocity is:
Meanwhile, assuming that each particle is a feasible solution of an equation to be solved, calculating an fitness function value of the particle, and obtaining an individual historical optimal position through comparison by longitudinally comparing the sizes of the particle historical fitness values before the time t:
and then transversely comparing the sizes of the fitness values at the moment t to obtain the global optimal position in the current generation:
iterative optimization of particles, at time t+1, spatial position coordinates x i (t) and velocity v i (t) adjusting in the manner described by: v i (t+1)＝v i (t)+c 1 ·r 1 (p i (t)-x i (t))+c 2 ·r 2 (p g (t)-x i (t))
In the method, in the process of the invention, spatial positions, particle speeds, individual historical optimal positions and global optimal positions which correspond to 1, 2, D and D dimensions respectively; c 1 And c 2 Respectively represent the learning constants of the particles; r is (r) 1 And r 2 At [0,1]Uniformly taking values; p is p i Is an individual extremum; p is p g Is a global extremum.
The speed updating formula is composed of three parts, namely, the first part enables the algorithm to perform global searching and balances global searching capacity and local searching capacity; the second part enables the particles to perform a stronger local search; the third part considers the ability of particles to learn from particles in the whole population, and represents information sharing among different particles;
The particle updates the position by updating the corresponding speed, and the position formula is as follows:
x i (t+1)＝x i (t)+v i (t+1)
by limiting the speed variation amplitude, let v min ＜v i (t)＜v max Limiting the size of the particle position change. In multiple iterations, each particle in the population is cyclically updated so that the whole population gradually approaches the global optimal solution.
The three-layer power quality optimization control strategy of the multi-energy complementary micro-grid mainly comprises voltage sag, voltage deviation, frequency deviation, three-phase unbalance, voltage fluctuation and voltage flicker.
The three-layer power quality optimization control strategy process of the multi-energy complementary micro-grid comprises the following steps:
firstly, randomly selecting 5 groups of objective functions with different weight proportions, and carrying out power optimization analysis to obtain 5 groups of optimal power variables; further, respectively bringing 5 groups of optimal power variables into a bottom control model based on sagging control (corresponding to the bottom control strategy in claim 3, which is existing), and performing electric energy quality simulation analysis to obtain 5 groups of electric energy quality index data; and finally, taking 5 groups of data containing the optimal power variable and the electric energy quality index as 5 groups of decision units, and using a data envelope analysis algorithm to comprehensively sort various electric energy quality indexes of the plurality of groups of decision units according to the optimal sequence of the electric energy quality so as to obtain the decision unit with the optimal comprehensive electric energy quality. The optimizing variables of the input indexes of the decision unit are 8 optimizing variables of power optimizing output; the input index is 4 pieces of electric energy quality index data measured through model simulation, including voltage deviation, frequency deviation, three-phase voltage unbalance and harmonic content. The optimal decision unit obtained through the power quality optimization control strategy is regarded as an individual with relatively optimal economic, environmental and power quality.
The data envelope analysis (Data Envelopment Analysis, DEA) is a non-parametric system analysis method for performing relative effectiveness evaluation on the same type of multi-input and multi-output decision units (Decision Making Unit, DMU) based on linear programming, and parameters do not need to be estimated in advance when the same type of multi-input and multi-output DMU is analyzed. DEA has two models for scale Rewards (RTS), CCR and BBC, respectively. However, in the CCR model, the constraint limit maximum efficiency evaluation index is less than or equal to 1, and the efficiency evaluation indexes of a plurality of DMUs may be equal to 1, so that the super-efficiency model is adopted for the model based on hierarchical control and operation optimization of the multi-energy complementary micro-grid to order the efficiency of the plurality of decision units.
Because the super-efficiency model excludes the evaluated decision unit, the problem of linear programming of the CCR model is rewritten as follows by using the dual theorem:
max H O ＝θ
the constraint conditions corresponding to the kth DMU are:
λ r ≥0,r＝1,…,n,r≠k。
wherein n is the number of decision units; m is the number of types of DMU input types; r=1, 2, … s, s being the number of kinds of DMU output types; h j Efficiency rating index for the jth DMU; y is rj (j=1, 2, …, n) is the recorded DMU j Is a vector of inputs of (a); y is ro Is the optimal input; h O An efficiency evaluation index; u (u) ro Is DMU (digital subscriber unit) o Optimally inputting a weight coefficient; lambda (lambda) r Optimizing weights for the two-layer power; k: the formula is the constraint corresponding to the kth DMU.
The invention is described in further detail below with reference to the accompanying drawings:
the three total buses of the multifunctional complementary micro-grid are respectively: an electric bus, a thermal bus, a cold bus. The power supply of the electric bus mainly comprises a photovoltaic generator, a wind driven generator, a gas turbine and a storage battery. The load on the electrical bus is an electrical load. When the electric quantity of the electric bus is unbalanced, the storage battery and the large power grid are mainly used for carrying out electric energy exchange, so that the purposes of peak clipping and valley filling are achieved. The thermal bus power supply mainly comprises a waste heat boiler and electric heating equipment, wherein the waste heat boiler utilizes the waste heat generated by the gas turbine. The load on the thermal bus is a thermal load. The cold bus power supply mainly comprises an electric refrigerating device and a lithium bromide refrigerator. The load on the cold bus is a cold load. The structure comprises equipment capable of realizing interaction among cold, hot and electric energy sources such as electric heating, electric refrigeration and a waste heat boiler, and has the characteristics of large inertia and large hysteresis due to heat and cold load, so that the equipment can play a role in peak clipping and valley filling when electric energy is excessive or insufficient, meanwhile, the capacity of a storage battery is reduced, the energy storage cost and the maintenance cost are reduced, the cyclic utilization among the energy sources is highlighted, the energy source complementation is realized, and the effective utilization rate of the energy sources can be improved.
The miniature gas turbine can be operated in a hot electricity fixing mode or a cold electricity fixing mode according to the requirements of the environment and the load under the condition of ensuring the power load requirement, so that the miniature gas turbine can generate electric energy and simultaneously can recover waste heat for heating or refrigerating so as to meet the requirements of cold and hot loads of users. Firstly, high-pressure air is formed through a compressor, then the high-pressure air is mixed with fuel in a combustion chamber to burn, and then a turbine is driven to rotate, finally, a synchronous generator is driven to generate power, waste heat discharged from the turbine is mixed with heat energy produced by a waste heat boiler, and then the waste heat is exchanged through a heat exchanger to be recycled. The waste heat discharged by the miniature gas turbine forms high-temperature and high-pressure steam and high-temperature hot water through the waste heat boiler, and the high-temperature and high-pressure steam and the high-temperature hot water are respectively replaced by hot water and cold water through a heat exchanger and a lithium bromide refrigerator, so that a user is supplied with cold and heat.
The storage battery adopts a lead-acid storage battery as an analysis modeling object, and can fill in insufficient power in the power consumption peak by storing the residual power in the power generation peak, so that the reliability of the multi-energy complementary micro-grid is effectively improved; and the characteristics of large hysteresis and large inertia of the gas turbine and incapability of instantaneously completing peak regulation tasks are made up due to the characteristics of low inertia and low hysteresis of the storage battery, so that the electric energy quality of the multi-energy complementary micro-grid is ensured. The volt-ampere characteristic formula of the lead acid storage battery is as follows:
Wherein E is batt For no-load voltage of accumulator E k Is constant voltage of the storage battery, k is polarization voltage coefficient, q is storage battery capacity +.>For the actual capacity of the storage battery, A is the amplitude corresponding to the exponential discharge characteristic region, B is the reciprocal of the exponential region time constant, R batt I is the constant resistance in the accumulator batt For battery current, V batt And outputting voltage to the storage battery. />
The multi-energy complementary micro-grid power optimization model comprises 8 optimization variables which are respectively the output power P of the gas turbine MT Output power P of wind driven generator WIND Photovoltaic cell output power P PV Battery output power P Ba Waste heat heating output heat W MTH Output cooling capacity W of absorption refrigerator MTC Output heat W of electric heating EH Output cold energy W of electric refrigeration EC . The optimization target is to realize the lowest system power generation cost and the minimum pollutant gas emission under the limit of meeting the operation constraint of each distributed unit and the cold-hot electric load requirement. The objective function can be expressed as: minZ cost ＝λ 1 C g +λ 2 C e Wherein Z is cost For the purpose of multi-functional complementary micro-net operationA standard function; c (C) g The power generation cost of the system; c (C) e Penalty costs for pollutant gas emissions; lambda (lambda) 1 、λ 2 Weight ratio of generating cost and pollutant gas emission penalty cost is 0-lambda 1 、λ 2 ≤1。
The power generation cost of each micro source is as follows:
The gas turbine power generation costs include fuel costs, operational maintenance costs, and can be expressed as:
wherein: p (P) MT,k Output power of the kth gas turbine; c (C) op (P MT,k ) The k-th gas turbine fuel consumption is represented by a functional relation of formula (2.3); c (C) MT Fuel price for the gas turbine; zeta type toy MT Maintaining a cost factor for operation of the gas turbine; t is an operation period, typically in hours; n is the number of gas turbines.
The wind driven generator is clean energy power generation equipment, and the power source is wind energy, so the power generation cost is mainly operation maintenance cost, and can be expressed as:
wherein: p (P) WIND,k The output power of the kth wind driven generator; zeta type toy WIND A cost factor is maintained for the operation of the kth wind turbine.
The photovoltaic cell belongs to clean energy power generation equipment like a wind driven generator, the power source is solar energy, and the power generation cost is mainly operation maintenance cost and can be expressed as:wherein: p (P) PV,k The output power of the kth photovoltaic cell; zeta type toy PV A cost factor is maintained for operation of the kth photovoltaic cell.
The storage battery is importantThe electricity storage device of (2) has no self-generating capacity, and thus the electricity generation cost is also the operation maintenance cost, and can be expressed as:wherein: p (P) Ba,k Output power of the kth storage battery; zeta type toy Ba A cost factor is maintained for operation of the kth battery.
The system power generation cost is as follows: c (C) g ＝C MG +C WIND +C PV +C Ba Wherein: c (C) MG Generating electricity costs for the gas turbine; c (C) WIND The power generation cost of the fan is reduced; c (C) PV Generating cost for the photovoltaic cell; c (C) Ba And the power generation cost of the storage battery is reduced.
The above can be unfolded as follows:
pollution fine C e The gas turbine mainly generates the pollution components mainly comprising gases such as carbide, sulfide and nitride. The pollution gas emission penalty costs are:
wherein: zeta type toy kj 、A penalty factor for the jth exhaust gas in the kth gas turbine; f (f) jp A penalty rate for the j-th polluted gas; m is the seed number of the polluted gas.
From the above, the objective function can be expanded to be expressed as:
the constraint conditions of the multi-energy complementary micro-grid power optimization model can be divided into equality balance constraint conditions and inequality constraint conditions, wherein the equality balance constraint conditions comprise electric, thermal and cold power balance constraint and waste heat balance constraint, and the inequality constraint conditions are micro-source power constraint and standby power constraint. The specific constraint conditions are as follows:
(1) The electric power balance constraint is: p (P) MT +P WIND +P PV +P Ba -P loss ＝P Load Wherein: p (P) loss Is the line loss; p (P) Load For all electrical loads.
(2) The thermal power balance constraint is: Wherein: w (W) MTH,k The waste heat of the gas turbine is generated; w (W) EH,k The heat is generated for electric heating; w (W) H For all thermal loads.
(3) The cold power balance constraint is:wherein: w (W) MTC,k The lithium bromide waste heat consumption of the gas turbine is used; η (eta) c Is lithium bromide conversion efficiency; w (W) EC,k The refrigerating capacity is electric refrigerating capacity; w (W) C For all cold loads.
(4) The waste heat balance constraint is as follows:wherein:Is the total amount of waste heat of the gas turbine.
(5) The gas turbine output constraints are:wherein:Minimum safe output for the gas turbine;The maximum safe output of the gas turbine is provided.
(6) The output constraint of the wind driven generator is as follows:wherein:The minimum safe output of the wind driven generator is achieved;The maximum safe output of the wind driven generator is achieved.
(7) Photovoltaic cell output constraint:wherein:Minimum safe output for the photovoltaic cell;and the maximum safe output of the photovoltaic cell is achieved.
(8) Battery output constraint:wherein:Is the minimum capacity of the storage battery;Is the maximum capacity of the storage battery.
(9) The reserve constraint, which is typically chosen to be 5% of the maximum load, is expressed as:
the multi-energy complementary micro-grid power optimization algorithm adopts a particle swarm algorithm, is a random search algorithm based on population, and comprises the following specific steps and processes: firstly, determining a feasible domain space, and in a D-dimension feasible domain, assuming that the spatial positions of n random particles at a time t are as follows:
The particle velocity is:
Meanwhile, assuming that each particle is a feasible solution of an equation to be solved, calculating an fitness function value of the particle, and obtaining an individual historical optimal position by longitudinally comparing the sizes of the particle historical fitness values before the time t:
and then transversely comparing the sizes of the fitness values at the moment t to obtain the global optimal position in the current generation:
iterative optimization of particles, at time t+1, spatial position coordinates x i (t) and velocity v i (t) adjusting in the manner described by: v i (t+1)＝v i (t)+c 1 ·r 1 (p i (t)-x i (t))+c 2 ·r 2 (p g (t)-x i (t))
Wherein ω represents an inertial weight; c 1 And c 2 Respectively represent the learning constants of the particles; r is (r) 1 And r 2 At [0,1]Uniformly taking values; p is p i Is an individual extremum; p is p g Is a global extremum.
As can be seen from the above, the speed update formula is composed of three parts, the first part enables the algorithm to perform global search, and balances the global and local search capabilities; the second part enables the particles to perform a stronger local search; the third part is considered to be the ability of particles to learn from particles in the whole population, representing the information sharing among different particles.
The particle updates the position by updating the corresponding speed, and the position formula is as follows:
x i (t+1)＝x i (t)+v i (t+1)
can make v by limiting the speed variation amplitude min ＜v i (t)＜v max Limiting the size of the particle position change. In multiple iterations, each particle in the population is cyclically updated so that the whole population gradually approaches the global optimal solution.
The two-layer power optimization strategy program flow of the multi-energy complementary micro-grid based on the particle swarm algorithm is shown in fig. 5:
the two-layer power optimization control parameters comprise load data, micro-source prediction output power, multi-energy complementary micro-grid configuration, operation and optimization parameter setting.
The load data is selected from typical daily data of a power grid, and comprises cold, hot and electric load data. According to a 24-hour prediction curve of a typical day, the electric load changes in a double-peak and single-valley state, and the electricity consumption peaks are respectively 9 a.m. to 11 a.m. and 4 a.m. to 8 a.m. at night, wherein the electricity consumption is reduced from 11 a.m. to 4 a.m. but still higher than the average level; the electricity consumption valley is from 11 pm to 5 am; the cold and heat load changes are in a single peak and single valley state and have a reverse trend, namely, the cold load is lower and the heat load is higher between 8 pm and 7 am; the cold load is higher and the heat load is lower between 8 am and 6 pm, but the total amount of the cold load and the heat load is stable, so that the characteristics of large lag and large inertia of cold and heat energy can be utilized to regulate and control the multi-energy complementary micro-grid power generation equipment, and the electric load cannot be impacted too much.
The micro source for predicting the output power is mainly a photovoltaic cell and a wind driven generator, and according to the predicted generating capacity curve of the photovoltaic cell and the wind driven generator for 24 hours, the photovoltaic cell has certain continuity, but can only generate effective power from 7 a.m. to 5 a.m. due to the influence of objective factors, and the generating capacity reaches a peak value in noon; the wind driven generator does not have power generation continuity, is greatly influenced by wind power, has randomness in power generation capacity, and is far greater than daytime power generation capacity in night. And the photovoltaic cell and the generated energy of the wind driven generator have certain complementarity in terms of the total power generation amount of the photovoltaic cell and the generated energy of the wind driven generator.
The configuration parameters of the multi-energy complementary micro-grid mainly comprise upper and lower limits of power of an electric micro-source, a thermal micro-source and a cold micro-source, wherein the electric micro-source comprises a wind driven generator, a photovoltaic cell, a gas turbine and a storage battery; the heat energy micro source comprises electric heating equipment and a waste heat boiler; the cold energy micro source comprises an electric refrigerating device and a lithium bromide refrigerator. [037] The operation parameters of the multi-energy complementary micro-grid are mainly fuel price, maintenance price of all micro-sources, interconversion efficiency between electricity and heat energy and interconversion efficiency between electricity and cold energy, and further comprise pollutant treatment price.
The multi-energy complementary micro-grid optimization parameters are mainly parameters related to a particle swarm algorithm, and comprise population number, genetic algebra, weight coefficient and the like.
The three-layer power quality optimization control strategy of the multi-energy complementary micro-grid mainly comprises voltage sag, voltage deviation, frequency deviation, three-phase unbalance, voltage fluctuation and voltage flicker.
The control block diagram of the three-layer power quality optimization control strategy of the multi-energy complementary micro-grid is shown in fig. 6:
firstly, selecting 5 groups of objective functions with different weight proportions, and performing power optimization analysis to obtain 5 groups of optimal power variables; further, the 5 groups of optimal power variables are respectively brought into a bottom layer control model, and electric energy quality simulation analysis is carried out to obtain 5 groups of electric energy quality index data; and finally, taking 5 groups of data containing the optimal power variable and the electric energy quality index as 5 groups of decision units, and comprehensively sequencing various electric quantity quality indexes of the decision units by using a data envelope analysis algorithm to obtain the decision unit with the comprehensive optimal electric energy quality. The input index of the decision unit is 8 optimizing variables of power optimization output; the input index is 4 pieces of electric energy quality index data measured through model simulation, including voltage deviation, frequency deviation, three-phase voltage unbalance and harmonic content. The optimal decision unit obtained through the power quality optimization control strategy can be regarded as an individual with relatively optimal economic, environmental and power quality.
The data envelope analysis (Data Envelopment Analysis, DEA) is a non-parametric system analysis method for performing relative effectiveness evaluation on the same type of multi-input and multi-output decision units (Decision Making Unit, DMU) based on linear programming, and parameters do not need to be estimated in advance when the same type of multi-input and multi-output DMU is analyzed. DEA has two models for scale Rewards (RTS), CCR and BBC, respectively. However, in the CCR model, since the constraint limit maximum efficiency evaluation index is less than or equal to 1, the efficiency evaluation indexes of the DMUs may be equal to 1, so that the super-efficiency model is adopted to order the efficiency of the decision units.
Because the super-efficiency model excludes the evaluated decision unit, the problem of linear programming of the CCR model can be rewritten as follows by using the dual theorem:
max H O ＝θ
λ r ≥0,r＝1,…,n,r≠k
and the three-layer power quality optimization control parameters take typical daily load 17:00-20:00 as samples according to the complexity of the typical daily load and the predicted power data, optimize the two-layer power under different weights in the time period, and serve as a decision unit and an input index for data envelope analysis. Since the power optimization model weights represent the importance between the running cost and the environmental cost, the resulting differences need to be minimized. Thereby giving the input and output index of the decision unit.
Firstly, three-layer power quality optimization is carried out on 5 decision units of typical day 17:00 by using a CCR model in a data envelope analysis method, and the obtained input and output weight indexes are shown in table 1.
TABLE 1 17:00 DEA input/output weight index
TABLE 2 17:00 DEA output weight index
TABLE 3 17:00-20:00 comprehensive optimal individual summary tables
The atypical data in the above table is data which cannot represent all cases, but the data obtained in the present experiment.
In conclusion, the patent designs a net frame structure of a multifunctional complementary micro-grid based on cold, hot and electric energy flow interaction; aiming at the problem of micro-source power distribution during load fluctuation, a bottom control strategy based on droop control is adopted; aiming at the problem of micro-source output prediction planning, a particle swarm algorithm is adopted, and a two-layer optimization operation control strategy is constructed with the aim of minimum comprehensive operation cost and environmental protection cost;
aiming at the problem that the power quality is not considered in the operation and optimization control, a three-layer power quality optimization control strategy for comprehensively sequencing by applying data envelope analysis based on power optimization schemes under different weight ratios is provided.
Claims (6)
1. A hierarchical control and operation optimization method based on a multi-energy complementary micro-grid is characterized by comprising the following steps: the system used by the method comprises three buses, namely an electric bus, a hot bus and a cold bus, wherein an electric energy input device contained in the electric bus comprises a photovoltaic generator, a wind driven generator, a gas turbine, a storage battery and a large power grid;
The heat energy input device of the thermal bus comprises a waste heat boiler and electric heating equipment which utilize the waste heat generated by the gas turbine;
the cold energy input device contained in the cold bus is provided with an electric refrigerating device and a lithium bromide refrigerator;
the main energy output channels of the three energy buses of the electric bus, the thermal bus and the cold bus are respectively energy loads, namely electric loads, thermal loads and cold loads; the electric power output equipment of the electric bus also comprises electric heating equipment and electric refrigerating equipment;
the gas turbine is used as a recoverable waste heat type gas turbine, the waste heat boiler and the lithium bromide refrigerator together form a main part of a waste heat recovery system, and waste heat generated by the gas turbine is subjected to heat exchange by the waste heat boiler to generate steam and hot water which are respectively replaced by hot water and cold water by the heat exchanger and the lithium bromide refrigerator to supply heat and cool to a user;
the gas turbine comprises a compressor, a fuel chamber, a steam turbine, a synchronous generator, a transformer and a waste heat boiler,
firstly, forming high-pressure air through a compressor, mixing the high-pressure air with fuel in a combustion chamber for combustion, further pushing a steam turbine to rotate, and finally driving a synchronous generator to generate power and supplying power to an electric load through a transformer by the steam turbine; the waste heat discharged by the steam turbine is mixed with heat energy produced by the waste heat boiler to form high-temperature and high-pressure steam and high-temperature hot water, the high-temperature and high-pressure steam is replaced by hot water through a heat exchanger to supply heat to a user, and the high-temperature hot water is replaced by cold water through a lithium bromide refrigerator to supply cold to the user;
The layering control and operation optimization method is divided into three layers, namely a bottom layer control strategy based on droop control, a two-layer control strategy based on power optimization and a three-layer control strategy based on electric energy quality optimization;
the bottom control strategy based on sagging control is an inverter control strategy, the control strategy adopts a traditional sagging control method, so that each micro source distributes instantaneous load power fluctuation in a system according to the proportion of micro source capacity according to a power given value issued by a central controller system, and the micro sources of wind driven generator, photovoltaic generator, gas turbine and storage battery power are coordinated to distribute power according to the proportion of capacity, and simultaneously, the sagging control is utilized to control the gas turbine waste heat distribution and the electric energy distribution for electric heating and electric cooling;
the two-layer control strategy based on power optimization is a power optimization control strategy, and according to the power prediction of a micro source and the load prediction of cold, heat and electricity, under the condition that the constraint conditions of electric load, heat load, cold load demand and output limit are met, the minimization of primary energy consumption and greenhouse gas emission is realized as an optimization target, and the optimal comprehensive benefit is finally met; further, according to an optimization algorithm, determining the power fluctuation proportion born by each micro source in the two-layer control process and the distribution proportion of the cold and heat energy input devices;
The three-layer control of the multi-energy complementary micro-grid is an electric energy quality control strategy, namely an electric energy quality optimization control strategy of the three-layer of the multi-energy complementary micro-grid, the electric energy quality is further optimized on the basis of two-layer control power optimization, and the electric energy quality indexes comprise voltage sag, voltage deviation, frequency deviation, three-phase imbalance, voltage fluctuation and voltage flicker, so that the optimization target achieves the optimal environment, the optimal energy consumption and the optimal electric energy quality on the premise of supply and demand balance;
the three-layer power quality optimization control strategy process of the multi-energy complementary micro-grid comprises the following steps:
firstly, randomly selecting 5 groups of objective functions with different weight proportions, and carrying out power optimization analysis to obtain 5 groups of optimal power variables; further, respectively bringing 5 groups of optimal power variables into a bottom control model based on sagging control, and performing electric energy quality simulation analysis to obtain 5 groups of electric energy quality index data; finally, taking 5 groups of data containing optimal power variables and power quality indexes as 5 groups of decision units, and using a data envelope analysis algorithm to comprehensively sort various power quality indexes of the multiple groups of decision units according to an optimal power quality sequence to obtain a decision unit with optimal power quality; the optimizing variables of the input indexes of the decision unit are 8 optimizing variables of power optimizing output; the input indexes are 4 pieces of electric energy quality index data measured through model simulation, and the input indexes comprise voltage deviation, frequency deviation, three-phase voltage unbalance and harmonic content; the optimal decision unit obtained through the power quality optimization control strategy is regarded as an individual with relatively optimal economic, environmental and power quality;
The data envelope analysis is a non-parameter system analysis method for carrying out relative effectiveness evaluation on the same type of multi-input and multi-output decision units based on linear programming, and parameters do not need to be estimated in advance when the same type of multi-input and multi-output DMU is analyzed; the data envelope analysis has two models, namely a CCR model and a BBC model, aiming at the scale rewards RTS; the hierarchical control and operation optimization model based on the multi-energy complementary micro-grid adopts a super-efficiency model to sort the efficiency of a plurality of decision units;
the problem of linear programming of the CCR model is rewritten as follows by using the dual theorem:
max H O ＝θ
the constraint conditions corresponding to the kth DMU are:
λ r ≥0,r＝1,…,n,r≠k；
wherein n is the number of decision units; m is the number of types of DMU input types; r=1, 2, … s, s being the number of kinds of DMU output types; h j Efficiency rating index for the jth DMU; y is rj (j=1, 2, …, n) is the recorded DMU j Is a vector of inputs of (a); y is ro Is the optimal input; h O An efficiency evaluation index; u (u) ro Is DMU (digital subscriber unit) o Optimally inputting a weight coefficient; lambda (lambda) r Optimizing weights for the two-layer power; k: the formula is the constraint corresponding to the kth DMU.
2. The hierarchical control and operation optimization method based on the multi-energy complementary micro-grid according to claim 1, wherein the method is characterized in that: the power optimization-based two-layer control strategy constructs a target function of the operation optimization of the multi-energy complementary micro-grid according to the micro-source operation and the pollutant gas emission in the multi-energy complementary micro-grid, builds constraint conditions according to the requirements of electric load, thermal load and cold load and the micro-source output limit, and solves the target function of the operation optimization of the multi-energy complementary micro-grid by using a power optimization algorithm of the multi-energy complementary micro-grid to construct a two-layer control model based on the power optimization.
3. The hierarchical control and operation optimization method based on the multi-energy complementary micro-grid according to claim 2, wherein the method is characterized in that: the micro sources in the multi-energy complementary micro-grid comprise a gas turbine, a photovoltaic power generator, a fan and a storage battery; the system also comprises a waste heat boiler, lithium bromide refrigeration equipment, electric heating equipment and electric refrigeration equipment, so that the multi-energy complementary power optimization model selects 8 related optimization variables which are respectively the output power P of the gas turbine MT Output power P of wind driven generator WIND Photovoltaic cell output power P PV Battery output power P Ba Waste heat heating output heat W MTH Output cooling capacity W of absorption refrigerator MTC Output heat W of electric heating EH And electric refrigeration output cold quantity W EC The 8 optimization variables are used as optimization target variables of the power optimization model.
4. The hierarchical control and operation optimization method based on the multi-energy complementary micro-grid according to claim 2, wherein the method is characterized in that: the optimization objective is to realize the minimum primary energy consumption and the minimum greenhouse gas pollution gas emission of the system under the condition of meeting the operation constraint of each distributed unit and the limitation of the cold-hot electric load requirement, and the objective function is expressed as: minZ cost ＝λ 1 C g +λ 2 C e Wherein Z is cost Running an objective function for the multi-energy complementary microgrid; c (C) g The power generation cost of the system; c (C) e A penalty function for pollutant gas emissions; lambda (lambda) 1 、λ 2 Weight ratio of penalty function for generating cost and pollutant gas emission, and lambda is 0-lambda 1 、λ 2 Generating power according to each micro source is less than or equal to 1Cost, objective function expansion is expressed as:
wherein P is MT,k Output power of the kth gas turbine; c (C) op (P MT,k ) Fuel consumption for the kth gas turbine; c (C) MT Fuel price for the gas turbine; zeta type toy MT Maintaining a cost factor for operation of the gas turbine; t is an operation period, typically in hours; n is the number of gas turbine operation; p (P) WIND,k The output power of the kth wind driven generator; zeta type toy WIND Maintaining a cost coefficient for the operation of the wind power generator; p (P) PV,k The output power of the kth wind driven generator; zeta type toy PV Maintaining a cost coefficient for operation of the kth wind turbine; p (P) Ba,k Output power of the kth storage battery; zeta type toy Ba Maintaining a cost coefficient for operation of the kth battery; zeta type toy kj Output power of the kth storage battery;maintaining a cost factor, f, for operation of the kth battery jp A penalty rate for the j-th polluted gas; m is the seed number of the polluted gas.
5. The hierarchical control and operation optimization method based on the multi-energy complementary micro-grid according to claim 2, wherein the method is characterized in that: the constraint conditions of the multi-energy complementary micro-grid power optimization model are divided into an equality constraint condition and an inequality constraint condition, wherein the equality constraint condition comprises electric, thermal and cold load demands, namely load power balance constraint and waste heat balance constraint, the inequality constraint condition is that each micro-source output power constraint and standby power constraint are limited, the equality constraint condition and the inequality constraint condition are required to be simultaneously satisfied, and the standby power constraint is required to be satisfied when each micro-source power constraint of the inequality constraint condition cannot be satisfied, the specific constraint condition is that:
(1) Electric power levelThe balance constraint is as follows: p (P) MT +P WIND +P PV +P Ba -P loss ＝P Load Wherein: p (P) loss Is the line loss; p (P) Load For all electrical loads;
(2) The thermal power balance constraint is:wherein: w (W) MTH,k The waste heat of the gas turbine is generated; w (W) EH,k The heat is generated for electric heating; w (W) H For all thermal loads; n is the number of gas turbines running;
(3) The cold power balance constraint is:wherein: w (W) MTC,k The lithium bromide waste heat consumption of the gas turbine is used; η (eta) c Is lithium bromide conversion efficiency; w (W) EC,k The refrigerating capacity is electric refrigerating capacity; w (W) C For all cold loads;
(4) The waste heat balance constraint is as follows:wherein:The total amount of waste heat of the gas turbine; w (W) MT，k Is the residual heat of the kth gas turbine;
(5) The gas turbine output constraints are:wherein:Minimum safe output for the gas turbine;Maximum safe output for the gas turbine; p (P) MT.i The gas turbine is powered;
(6) The output constraint of the wind driven generator is as follows:wherein:The minimum safe output of the wind driven generator is achieved;The maximum safe output of the wind driven generator is achieved; p (P) WIND.i The wind driven generator is powered;
(7) Photovoltaic cell output constraint:wherein:Minimum safe output for the photovoltaic cell;Maximum safe output for the photovoltaic cell; p (P) PV，i Exerting force on the photovoltaic cell;
(8) Battery output constraint:wherein:Is the minimum capacity of the storage battery; / >Is the maximum capacity of the storage battery; p (P) Ba.i The capacity of the storage battery;
(9) The reserve constraint, which is typically chosen to be 5% of the maximum load, is expressed as: for the ith alternate constraintMaximum force.
6. The hierarchical control and operation optimization method based on the multi-energy complementary micro-grid according to claim 2, wherein the method is characterized in that: the multi-energy complementary micro-grid power optimization algorithm adopts a particle swarm algorithm, is a random search algorithm based on population, and comprises the following specific steps and processes:
firstly, determining a feasible domain space, and in a D-dimension feasible domain, assuming that the spatial positions of n random particles at a time t are as follows:the particle velocity is:
Meanwhile, assuming that each particle is a feasible solution of an equation to be solved, calculating an fitness function value of the particle, and obtaining an individual historical optimal position through comparison by longitudinally comparing the sizes of the particle historical fitness values before the time t:
and then transversely comparing the sizes of the fitness values at the moment t to obtain the global optimal position in the current generation:
iterative optimization of particles, at time t+1, spatial position coordinates x i (t) and velocity v i (t) adjusting in the following manner: v i (t+1)＝v i (t)+c 1 ·r 1 (p i (t)-x i (t))+c 2 ·r 2 (p g (t)-x i (t))
In the method, in the process of the invention, spatial positions, particle speeds, individual historical optimal positions and global optimal positions which correspond to 1, 2, D and D dimensions respectively; c 1 And c 2 Respectively represent the learning constants of the particles; r is (r) 1 And r 2 At [0,1]Uniformly taking values; p is p i Is an individual extremum; p is p g Is a global extremum;
the speed updating formula is composed of three parts, namely, the first part enables the algorithm to perform global searching and balances global searching capacity and local searching capacity; the second part enables the particles to perform a stronger local search; the third part considers the ability of particles to learn from particles in the whole population, and represents information sharing among different particles;
the particle updates the position by updating the corresponding speed, and the position formula is as follows: x is x i (t+1)＝x i (t)+v i (t+1)；
By limiting the speed variation amplitude, let v min ＜v i (t)＜v max Limiting the size of the position change of the particles; in multiple iterations, each particle in the population is cyclically updated so that the whole population gradually approaches the global optimal solution.
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