CN112580897B - Method for optimal scheduling of multi-energy power system based on parrot algorithm - Google Patents

Method for optimal scheduling of multi-energy power system based on parrot algorithm Download PDF

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CN112580897B
CN112580897B CN202011631340.6A CN202011631340A CN112580897B CN 112580897 B CN112580897 B CN 112580897B CN 202011631340 A CN202011631340 A CN 202011631340A CN 112580897 B CN112580897 B CN 112580897B
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刘兴华
耿晨
李翔
同向前
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Xian University of Technology
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Abstract

The method for optimizing and scheduling the multi-energy power system based on the parrot algorithm comprises the following steps: step 1, establishing a mathematical model of optimal scheduling of a multi-energy power system considering economy and environmental protection; step 2, designing a parrot algorithm; step 3, constructing three different multi-energy power system optimization scheduling strategies, and verifying the validity of the parrot algorithm; has the advantages of high efficiency and accuracy.

Description

Method for optimal scheduling of multi-energy power system based on parrot algorithm
Technical Field
The invention belongs to the technical field of optimal scheduling of a multi-energy power system, and particularly relates to a method for optimal scheduling of a multi-energy power system based on a parrot algorithm.
Background
With the rapid development of industry, traditional fossil energy is rapidly consumed, and the energy crisis becomes an urgent problem. In order to solve the energy crisis problem, only one traditional energy supply mode is insufficient, and people begin to comprehensively utilize and research various energy sources such as electric energy, gas energy and heat energy. In the past energy systems, different energy networks such as a power grid, an air network, a heat supply network and the like are operated independently, the interconnection degree is not high, the comprehensive utilization rate of energy is not facilitated to be improved, the optimal operation of a plurality of energy systems is also not facilitated, and the barrier can be broken by the multi-energy system developed in recent years, so that the optimal scheduling of the multi-energy power system is also very important.
Compared with other various algorithms, the particle swarm algorithm (Particle Swarm Optimization, PSO) has the characteristics of easy understanding, few required control parameters, easy obtaining of expected results, some parallelism and the like. However, PSO has two major defects of easy sinking into local optimum and low precision, students at home and abroad aim at improving the algorithm in recent years, but the result is not satisfactory, the bat algorithm adopts a novel frequency tuning algorithm on the basis of PSO, and the optimizing precision is greatly reduced although the convergence aspect is improved. Under the demand of coping with the well blowout type increased energy load calculation, the drosophila analysis algorithm has higher precision in optimization, but is easier to fall into the situation of local optimization related to power than the traditional particle swarm optimization algorithm. From these examples, it can be seen that in recent years, PSO defect cannot be thoroughly solved in order to increase optimization scale and improve algorithm optimizing efficiency and accuracy, and that in order to thoroughly solve PSO defect, it is impossible to increase optimization scale and improve algorithm optimizing efficiency and accuracy.
Disclosure of Invention
In order to break through the problems, the invention provides a method for optimizing and scheduling a multi-energy power system based on a parrot algorithm, which respectively solves the problems that the traditional particle swarm algorithm is easy to fall into local optimum and low precision, and has the advantages of high efficiency and accuracy.
The technical scheme adopted by the invention is that the method for optimizing and dispatching the multi-energy power system based on the parrot algorithm is characterized by comprising the following steps:
step 1, establishing a mathematical model of optimal scheduling of a multi-energy power system considering economy and environmental protection;
step 2, designing a parrot algorithm;
and 3, constructing three different multi-energy power system optimization scheduling strategies, and verifying the validity of the parrot algorithm.
Step 1, establishing a mathematical model for optimizing and scheduling a multi-energy power system in consideration of economy and environmental protection, wherein the mathematical model comprises the following specific steps:
step 1.1, obtaining the output characteristics and cost composition of each power generation unit of a multi-energy power system comprising photovoltaic power generation, a wind driven generator, a micro gas turbine, a fuel cell and an energy storage unit:
1) Photovoltaic power generation unit (Photovoltaic cell, PV):
the main characteristics of the photoelectricity are as follows:
in the formula ,Kr ＝0.0017(A/℃)，T r 301.18K where P is the power from the photovoltaic panel, V is the voltage from the photovoltaic panel, I is the current from the photovoltaic panel, m is the number of parallel panel cells, I ph Is generated by the photovoltaic component through light irradiation, I 0 Is the initial current, q is the constant of the electronic quantity in the battery, R s Is a series unidirectional resistor, n is the number of series batteries, A is a diode characteristic fitting coefficient, K r Is Boltzmann constant, T r The standard temperature and the temperature at the measuring moment are T;
in practical application, since the system output is influenced by various factors of the ambient temperature of the photovoltaic cell and the system intensity under solar radiation, the correction is carried out under standard conditions:
in the formula ：GSTC ＝1000W/m 2 ，k＝-0.47％/℃，T r ＝25℃，G STC Is the standard solar irradiation intensity, G ING Is the actual solar irradiation intensity, k is the power temperature coefficient, T c Is the actual temperature, T r Is a standard temperature;
the solar photovoltaic fuel cell directly obtains energy from the sun, belongs to renewable energy sources, and also considers the working cost and the maintenance cost in an omnibearing way;
2) Wind power generation unit (Wind Turbine generator, WT):
the power characteristics of wind power are as follows:
under practical conditions, the environmental protection cost and the utilization rate cost are not considered, and only the repair cost is considered, wherein V is the wind speed input by the fan and V is r Is the standard wind speed, P r Is of standard power, P WT Is the output power of the fan, V co Is the wind speed of the fan cut-out, V ci The wind speed of the fan is cut-in wind speed;
3) Micro gas turbine (Micro gas Turbine, MT):
the micro gas turbine power function model is as follows:
wherein ,ηMT Is the efficiency of the general gas turbine, P MT Is the output power of the general gas turbine;
normal maintenance operation and daily maintenance cost related function for micro general purpose gas turbines:
in the formula ,KOMMT Directly taking 0.047 yuan/kWh, wherein K OMMT Is the cost coefficient of normal maintenance operation and daily maintenance expense of the miniature general-purpose gas turbine, C OMMT The normal maintenance operation and the daily maintenance cost of the miniature general gas turbine are;
the external pollution control emission coefficient of the micro gas turbine and the control cost and the function of the cost are calculated as follows:
wherein ,CEMMT Is the external pollution control emission coefficient of the miniature gas turbine, and the control cost and the cost thereof, lambda k Is the emission factor, alpha k Is a discount coefficient;
4) Fuel Cell (FC):
p is 40kW, its power output P FC And the power generation efficiency eta FC The relationship of (2) may be represented by the following formula:
η FC ＝-0.0023P FC +0.6735 (7)
wherein ,ηFC Is the efficiency of the fuel cell;
in the process of maintaining the normal steady state continuous power generation of the fuel cell power generation system, the proportional relation between the effective input quantity of the fuel cell and the power generation cost input by the output energy consumption of the fuel cell is expressed by the following formula:
in the formula ：LHVng Is natural gas with low calorific value of 9.7kWh/m 3 ，C ng Is the cost of natural gas, P FC And (t) is the power of the fuel cell at time t, C FFC (t) is the fuel cell cost at time t, η FC (t) is fuel cell efficiency;
the normal operation and maintenance costs of a fuel conversion cell can be expressed by the following formula:
wherein ,CEMFC Is the normal operation and maintenance management cost of the fuel conversion battery, alpha k Is a discount coefficient lambda k Is an emission factor;
5) Energy storage unit (BatteryTurbine, BT):
two important constraints Of the multi-energy power storage battery, namely, the storage battery Charge-discharge power constraint and the storage battery maximum capacity constraint are fully considered, so that the State Of Charge (SOC) Of the storage battery at the time t can be expressed as:
wherein I is the current of the storage battery, P BT Is the power of the accumulator, U BT Is the voltage of the accumulator, SOC is the charge state of the accumulator, sigma sdr Is the self-discharge rate eta of the accumulator bce The charge and discharge efficiency of the battery, C is the battery capacity of the battery, Δt is the time interval, and Δt=1h, η is taken bce The value of the charge-discharge voltage is 1, and the charge-discharge voltage is generally in the range of 0.65-0.85;
step 1.2, constructing an objective function of environment-friendly and economical operation of the multi-energy power system:
1) Cost of economy objective function
This objective function is to minimize the operating costs of each unit, including microgrid-to-main network interaction costs, operational maintenance costs, heating benefits, isolated network load compensation costs, cooling benefits, and fuel costs for the micro-power supply, expressed as follows:
in the formula ,C1 Is the running cost, C f Is the fuel cost, C OM Is the maintenance cost, C geid Is the interaction cost of the micro-grid main network, C sh Is the heating cost, C sc Is the refrigeration cost, C L The compensation cost is e and b are cost consideration coefficients, and when the two values take e=1 and b=0, the system is in grid connection work, and the interaction cost exists between the main network and the system; e=0 and b=1, i.e. the system is operated in isolated network, a certain proportion is taken outIn this case, there is a cut load compensation cost,
wherein ：
C grid (t)＝C g (t)P g (t)-C s (t)P s (t) (15)
C sh (t)＝Q ho ×K ho (16)
C sc (t)＝Q co ×K co (17)
C L (t)＝C bu ×P CL (t) (18)
wherein ,LHVng Is natural gas with low calorific value of 9.7kWh/m 3 ，C ng Is the cost of natural gas, P FC And (t) is the power of the fuel cell at time t, C FFC (t) is the fuel cell cost at time t, η FC (t) is fuel cell efficiency; p (P) i Is the output power of the ith power generation unit, eta i Efficiency of the ith generating unit, C OM Is the maintenance cost, K OMi Is the cost coefficient of normal maintenance operation and daily maintenance cost of the ith power generation unit, C grid Is the interaction cost of the micro-grid main network, C g Is the electricity purchasing cost from the micro-grid to the main grid, P g The micro-grid power is purchased from the main grid, C s Is the cost of selling electricity from the micro-network to the main network, P s Is that the micro-grid sells electric power to the main grid, C sh Is the heating cost, Q ho Is the heat value, K ho Is the heating coefficient, C sc Is the refrigeration cost, Q co Is the cold value, K co Coefficient of refrigeration, C L Is the compensation cost, C bu Is the power failure electric quantity loss of micro-grid payment, P CL Load shedding power, n is the total number of generating units, i is the weaving of the generating unitsA number;
2) Environment-friendly objective function
The objective function is to consider that the environmental benefit of the multi-energy power system is the largest, i.e. the cost of pollution emission and treatment of energy is the smallest, and the expression is as follows:
in the formula ：C2 Is the environmental protection cost, C K Process pollutant penalty coefficient, r ik Output power coefficient of i power generation units discharged in kth type, P i Is the output power of the ith power generation unit, alpha is the external discount coefficient, r gridk Is the interaction coefficient of the micro-grid main network of the k-th type emission, C g The method is characterized in that the method is the electricity purchasing cost of the micro-grid to the main grid, P is the electricity purchasing power of the micro-grid to the main grid, T is the scheduled time, T is the scheduled total time, M is the number of emission types (NOX, SO2 or CO 2), k is the emission type, n is the total number of power generation units, and i is the number of the power generation units;
Step 1.3, constructing constraint conditions of environment-friendly and economical operation of a multi-energy power system:
1) Electric power balance constraint
wherein ,Pi Is the power generated by the ith power generation unit, P ball Is the power released by the storage battery, P grid Is the micro-grid main network interaction power, P L Is the load power of isolated network down-cut, beta is the operation coefficient, P CL Is load shedding power, when the system is in grid-connected operation, beta=0; when the system is operated in a isolated network, beta=1;
2) Cold and hot balance constraint
wherein ,Qho Is the heating quantity, the temperature of the air is higher than the heating quantity,is the heating capacity of the kth combined cooling heating and power system, Q co Is the refrigerating capacity->Is the refrigeration capacity of the kth combined cooling heating and power system, </i >>Is the maximum heating capacity of the combined cooling heating and power system, < >>Is the minimum heating capacity value of the combined cooling heating and power system,/-for the combined cooling heating and power system>Is the maximum value of the refrigerating capacity of the combined cooling heating power system, < + >>The refrigerating capacity of the combined cooling heating and power system is the minimum value; m is the total number of the combined cooling heating and power systems;
3) Constraint of limiting value of micro-source power
wherein , and />Respectively representing the maximum value and the minimum value of the power generated by the power generation unit;
4) Battery operation constraints
wherein ,Sin To input state of charge, SOC min At minimum state of charge, SOC max At maximum state of charge, P ball Is the charge-discharge power of the storage battery,minimum charge/discharge power for a battery, +. >The maximum charge and discharge power of the storage battery;
5) Multi-energy power system and main network allowed transmission power constraint
wherein , and />Representing the maximum and minimum values of the grid transmission power, respectively.
Step 2, designing parrot algorithm, specifically, the steps are as follows:
step 2.1, introducing an adaptive weighting method:
inspired by parrot having self-learning ability and different environment adaptation ability, an adaptive weight method is introduced; the linear velocity formula of the inertial motion weight w describes a basic method and an application strategy of a parrot individual for calculating the inertial motion weight w of an object by using a nonlinear motion method, and the improved calculation formula is as follows:
in the formula ：wmax 、w min Expressed as a maximum weight value and a minimum weight value, respectively, and generally takes w min ＝4.0，w max =9.0; the minimum value of the inertia weight and the adaptation coefficient can be changed along with the change of the size of the target adaptation function value of the adaptive particle, so the inertia weight is named as the adaptive weight;
step 2.2, introducing a learning factor:
in parrot algorithm, two asynchronous speed-changing group algorithm learning factors c 1 、c 2 The optimization time is changed differently, and the following requirements are met for individual parrot optimization: in the beginning and ending stage of the parrot individual swarm algorithm, the parrot individual must have a large self-participation learning capacity, and the global learning and searching capacity should be enhanced; and when the swarm optimization is started and ended rapidly, parrot individuals must have weaker self-participation social learning capacity and stronger self-social learning capacity, so that the parrot swarm optimization convergence speed can be increased, rapid swarm optimization convergence is facilitated, and a rapid global optimal solution is obtained, wherein the specific formula is as follows:
in the formula ：c1 Is the 1 st learning factor, c 1f Is the 1 st final learning factor, c 1t Is the 1 st initial learning factor, t max Is the maximum time, t is the time, c 2 Is the 2 nd learning factor, c 2f Is the 2 nd final learning factor, c 2t Is the 2 nd initial learning factor, take c 1t ＝c 1f ＝2.5，c 1t ＝c 1f ＝0.5。
Step 3, three different multi-energy power system optimization scheduling strategies are constructed, the validity of the parrot algorithm is verified, and the specific steps are as follows:
step 3.1, a multi-objective function processing method of a multi-energy power system comprises the following steps:
1) General model of multi-objective optimization algorithm:
wherein: f (X) is the overall objective function, f 1 (X),f 2 (X),…,f m (X) is m different sub-objective functions, m represents how many, g i (X) is an inequality constraint, i represents the number of equality constraints, h j (X) represents an equality constraint, j represents the number of equality constraints, X is n variables X 1 ,x 2 ,…,x n In the multi-objective optimization problem, the inequality constraint m is more than or equal to 2; equation constraint takes s.t.g i (X) is greater than or equal to 0 and h j (X)＝0；
2) The multi-objective function processing method of the multi-energy power system comprises the following steps:
the optimization of the multi-objective power system is realized by optimizing the multi-objective by adopting a linear weighting function summation method, which can be expressed as:
minf(X)＝w 1 f 1 (X)+w 2 f 2 (X) (31)
Wherein: f (X) is the total objective function, f 1 (X) is the 1 st objective function, f 2 (X) is the 2 nd objective function, w 1 ，w 2 The weights of the 1 st objective function and the 2 nd objective function are represented, respectively. w (w) 1 ≥0,w 2 Not less than 0 and satisfy w 1 +w 2 =1, random weight w 1 and w2 Determined by the following formula:
in the formula ：ri Non-negative random number, r 1 ，r 2 Are all non-negative random numbers;
step 3.2, a constraint condition processing method of the multi-energy power system comprises the following steps:
the constraint condition is processed by using a penalty function method, the function containing inequality constraint and equality constraint in the optimization problem is combined with the original optimization objective function after weighted optimization processing, a new objective function can be directly obtained, at the moment, the original constraint problem is converted into a new optimization problem without constraint condition, and the unconstrained optimization problem is solved.
Step 3.3, constructing three different multi-energy power system optimization scheduling strategies:
according to whether the multi-energy power system and the main network are in grid-connected cooperative operation or not, the priority of the micro power supply and the main network is determined, and the following management and control strategy is constructed:
strategy one: the priority of the micro power supply is higher than that of the main network, and if the micro power supply is insufficient, electricity is purchased from the main network;
strategy II: the priority of the micro power supply is the same as that of the main network, and if the electric quantity of the micro power supply is insufficient, the micro power supply can only purchase electricity from the main network;
Strategy III: the priority of the micro power supply is the same as that of the main network, and if the electric quantity of the micro power supply is insufficient, the purchase of electricity from the main network is not limited;
and 3.4, solving three different multi-energy power system optimization scheduling strategies by adopting a parrot algorithm:
solving the proposed three different multi-energy power system optimization scheduling strategies by adopting a parrot algorithm;
firstly, generating a parrot population moving randomly, initializing initial values of all random parrot individuals and moving time v of the parrot individuals, and randomly setting moving positions of the parrot individuals and various random particle moving parameters;
second, each was evaluatedThe fitness of individual parrot individuals is calculated to obtain the individual extremum p best Global extremum g best Updating two parameters of the moving area and the moving time v of the parrot subject to be detected according to a related formula;
thirdly, adjusting the weight factors for learning habituation, and determining whether to update the individual extremum and the global extremum by comparing the size of the fitness value of the parrot individual after iteration with the size of the fitness value of the parrot individual after the last iteration;
and finally, judging whether to terminate the algorithm according to the two conditions of whether the iteration times are reached or whether the maximum adaptive parameter value is not changed, if one of the conditions is met, ending the algorithm, and if not, carrying out initialization again to carry out related operation.
The beneficial effects of the invention are as follows:
the parrot algorithm is added with self-adaptive weight and learning factors on the basis of the traditional particle swarm algorithm, so that two major defects of local optimum and low precision of the traditional particle swarm algorithm are changed. Through solving and simulating the related cases, the simulation result shows that the parrot algorithm can be quickly and accurately solved for different objective functions, different targets and different operation strategies, and the effectiveness of the algorithm is proved.
Drawings
Fig. 1 is a solution flow chart of a mathematical model for optimal scheduling of a multi-energy power system based on a parrot algorithm.
FIG. 2 shows a third strategy, i.e. the priority of the micro power supply is the same as that of the main network, and if the micro power supply has insufficient electric quantity, the main network is not limited to purchase electricity, and the target weight coefficient w is a typical daily target in winter 1 ＝w 2 Simulation result analysis chart at=0.5.
FIG. 3 is a target weight coefficient w for a typical day in winter using strategy three 1 ＝0.8,w 2 Simulation result analysis chart at=0.2.
FIG. 4 is a graph of target weight coefficient w for a typical day in winter using strategy three 1 ＝0.3,w 2 Simulation result analysis chart at=0.7.
Fig. 5 is an analysis chart of the optimization result of a typical daily system in winter when purchasing electricity to the main network if the micro power supply is under enough power by adopting a strategy one, namely that the micro power supply priority is higher than that of the main network.
Fig. 6 is an analysis chart of the system optimization result in winter typical days when the micro power supply is purchased from the main network only if the micro power supply has insufficient electric quantity by adopting the second strategy, namely the priority of the micro power supply is the same as that of the main network.
Fig. 7 is an analysis chart of the optimization result of the system in a typical winter day when the power purchase from the main network is not limited if the micro power supply has insufficient electric quantity by adopting the third strategy and the priority of the micro power supply is the same as that of the main network.
Detailed Description
The present invention will be described in detail below with reference to the drawings and the detailed description, but the present invention is not limited to these modes.
The invention relates to a parrot algorithm-based multi-energy power system optimal scheduling method, which comprises the following steps:
step 1, establishing a mathematical model of optimal scheduling of a multi-energy power system considering economy and environmental protection;
step 2, designing a parrot algorithm;
and 3, constructing three different multi-energy power system optimal scheduling strategies, and verifying the validity of the parrot algorithm.
Wherein the step 1 specifically comprises the following steps:
the invention is mainly aimed at 5 types of power generation systems of power generation units, analyzes several main aspects of basic working principles and characteristics, power output, cost modeling and the like, and prepares for optimizing the power system and analyzing and designing the dispatching system in a multi-energy environment.
Step 1.1, constructing output characteristics and cost composition of each power generation unit of a multi-energy power system comprising a photovoltaic power generation unit, a wind power generation unit, a micro gas turbine, a fuel cell and an energy storage unit:
1) Photovoltaic power generation unit (Photovoltaic cell, PV):
the main characteristics of the photoelectricity are as follows:
in the formula ,Kr ＝0.0017(A/℃)，T r 301.18K where P is the power from the photovoltaic panel, V is the voltage from the photovoltaic panel, I is the current from the photovoltaic panel, m is the number of parallel panel cells, I ph Is generated by the photovoltaic component through light irradiation, I 0 Is the initial current, q is the constant of the electronic quantity in the battery, R s Is a series unidirectional resistor, n is the number of series batteries, A is a diode characteristic fitting coefficient, K r Is Boltzmann constant, T r Is the standard temperature, and T is the temperature at the moment of measurement.
In practical application, since the system output is influenced by various factors of the ambient temperature of the photovoltaic cell and the system intensity under solar radiation, the system output is corrected under standard conditions:
in the formula ：GSTC ＝1000W/m 2 ，k＝-0.47％/℃，T r ＝25℃，G STC Is the standard solar irradiation intensity, G ING Is the actual solar irradiation intensity k is the power temperature coefficient, T c Is the actual temperature, T r Is the standard temperature.
The solar photovoltaic fuel cell directly obtains energy from the sun, belongs to renewable energy sources, and also considers the working cost and the maintenance cost in an omnibearing way.
2) Wind power generation unit (Wind Turbine generator, WT):
the power characteristics of wind power are as follows:
under practical conditions, the environmental protection cost and the utilization rate cost are not considered, and only the repair cost is considered, wherein V is the wind speed input by the fan and V is r Is the standard wind speed, P r Is of standard power, P WT Is the output power of the fan, V co Is the wind speed of the fan cut-out, V ci Is the fan cut-in wind speed.
3) Micro gas turbine (Micro gas Turbine, MT):
the micro gas turbine power function model is as follows:
in the formula ,ηMT Indicating the working efficiency of the gas turbine, P MT Representing the gas turbine power.
Normal maintenance operation and daily maintenance cost related function for micro general purpose gas turbines:
in the formula ,KOMMT Representing the unit repair price of the gas turbine, here directly taking 0.047 yuan/kWh, where K OMMT Is the cost coefficient of normal maintenance operation and daily maintenance expense of the miniature general-purpose gas turbine, C OMMT Is the normal maintenance operation and the daily maintenance expense cost of the miniature general gas turbine.
The external pollution control emission coefficient of the micro gas turbine and the control cost and the function of the cost are calculated as follows:
wherein ,CEMMT Is the external pollution control emission coefficient of the miniature gas turbine, and the control cost and the cost thereof, lambda k Is the emission factor, alpha k Is the discount coefficient.
4) Fuel Cell (FC):
p is 40kW, its power output P FC And the power generation efficiency eta FC The relationship of (2) may be represented by the following formula:
η FC ＝-0.0023P FC +0.6735 (7)
wherein ,ηFC Is the efficiency of the fuel cell;
in the process of maintaining the normal steady state continuous power generation of the fuel cell power generation system, the proportional relation between the effective input quantity of the fuel cell and the power generation cost input by the output energy consumption of the fuel cell is expressed by the following formula:
in the formula ：LHVng Is natural gas with low calorific value of 9.7kWh/m 3 ，C ng Is the cost of natural gas, P FC And (t) is the power of the fuel cell at time t, C FFC (t) is the fuel cell cost at time t, η FC And (t) is the fuel cell efficiency.
The normal operation and maintenance costs of a fuel conversion cell can be expressed by the following formula:
wherein ,CEMFC Is the normal operation and maintenance management cost of the fuel conversion battery, alpha k Is a discount coefficient lambda k Is an emission factor.
5) Energy storage unit (BatteryTurbine, BT):
two important constraints of the multi-energy power storage battery, namely, the storage battery charge-discharge power constraint and the storage battery maximum capacity constraint are fully considered herein, so that the State of charge (SOC) of the storage battery at time t can be expressed as:
Wherein I is the current of the storage battery, P BT Is the power of the accumulator, U BT Is the voltage of the accumulator, SOC is the charge state of the accumulator, sigma sdr Is the self-discharge rate eta of the accumulator bce The charge and discharge efficiency of the battery, C is the battery capacity of the battery, Δt is the time interval, and Δt=1h, η is taken bce The value of the charge-discharge voltage is 1, and the charge-discharge voltage is generally in the range of 0.65 to 0.85.
Step 1.2, constructing an objective function of environment-friendly and economical operation of the multi-energy power system:
step 1.2.1, multi-target analysis:
for better optimizing and saving operation of the multi-energy power system, a basic mathematical model for optimizing economic dispatch of the multi-energy power system should be studied, wherein the mathematical model has two main components of target constraint conditions and basic target functions. Among the main basic objectives of a multi-energy optimized power system that requires optimization of economic dispatch are: (1) the pollution treatment cost is minimum; (2) The output of each micro power supply unit, the residual capacity of the storage battery and the interaction energy of the system and the main network are within a limit range; (3) minimal cost of transaction with the main network; (4) the operation and maintenance cost is minimum; (5) balancing the cold-hot electric load in the system; (6) micro-power fuel costs are minimal.
Step 1.2.2, determining an objective function:
1) Cost of economy objective function
The benefits obtainable from a multi-energy power system are great, and it can be said that each unit has a minimum operating cost, which includes the micro-grid-to-main grid interaction cost, the operation maintenance cost, the heating gain, the isolated grid load compensation cost, the refrigeration gain, and the fuel cost of the micro-power source.
in the formula ,C1 Is the running cost, C f Is the fuel cost, C OM Is the maintenance cost, C geid Is the interaction cost of the micro-grid main network, C sh Is the heating cost, C sc Is the refrigeration cost, C L The compensation cost is e and b are cost consideration coefficients, and when the two values take e=1 and b=0, the system is in grid connection work, and the interaction cost exists between the main network and the system; e=0 and b=1, i.e. the system is operated in isolated network, and a certain proportion of the load is taken out, so that there is a cut load compensation cost in this case.
wherein ：
C grid (t)＝C g (t)P g (t)-C s (t)P s (t) (15)
C sh (t)＝Q ho ×K ho (16)
C sc (t)＝Q co ×K co (17)
C L (t)＝C bu ×P CL (t) (18)
wherein ,LHVng Is natural gas with low calorific value of 9.7kWh/m 3 ，C ng Is the cost of natural gas, P FC And (t) is the power of the fuel cell at time t, C FFC (t) is the fuel cell cost at time t, η FC (t) is fuel cell efficiency; p (P) i Is the output power of the ith power generation unit, eta i Efficiency of the ith generating unit, C OM Is the maintenance cost, K OMi Is the cost coefficient of normal maintenance operation and daily maintenance cost of the ith power generation unit, C grid Is the interaction cost of the micro-grid main network, C g Is the electricity purchasing cost from the micro-grid to the main grid, P g The micro-grid power is purchased from the main grid, C s Is the cost of selling electricity from the micro-network to the main network, P s Is that the micro-grid sells electric power to the main grid, C sh Is heatingCost, Q ho Is the heat value, K ho Is the heating coefficient, C sc Is the refrigeration cost, Q co Is the cold value, K co Coefficient of refrigeration, C L Is the compensation cost, C bu Is the power failure electric quantity loss of micro-grid payment, P CL Load shedding power, n is the total number of generating units, i is the number of generating units;
2) Environment-friendly objective function
The objective function is to consider that the environmental benefit of the multi-energy power system is the largest, i.e. the cost of pollution emission and treatment of energy is the smallest, and the expression is as follows:
in the formula ：C2 Is the environmental protection cost, C K Process pollutant penalty coefficient, r ik Output power coefficient of i power generation units discharged in kth type, P i Is the output power of the ith power generation unit, alpha is the external discount coefficient, r gridk Is the interaction coefficient of the micro-grid main network of the k-th type emission, C g The method is characterized in that the method is the electricity purchasing cost of the micro-grid to the main grid, P is the electricity purchasing power of the micro-grid to the main grid, T is the scheduled time, T is the scheduled total time, M is the number of emission types (NOX, SO2 or CO 2), k is the emission type, n is the total number of power generation units, and i is the number of the power generation units;
Step 1.2.3, constructing constraint conditions of environment-friendly and economical operation of the multi-energy power system:
on the basis of grasping the optimal control objective function for correctly establishing the optimal control and the scheduling of the multi-channel energy system, the conditions of various controls and constraints in the multi-energy system are understood in all directions, and the more comprehensive various constraint control conditions are formed as follows:
1) Electric power balance constraint
wherein ,Pi Is the ith power generation unitEmitted power, P ball Is the power released by the storage battery, P grid Is the micro-grid main network interaction power, P L Is the load power of isolated network down-cut, beta is the operation coefficient, P CL Is load shedding power, when the system is in grid-connected operation, beta=0; when the system is operated in a isolated network, beta=1;
2) Cold and hot balance constraint
The combined cooling, heating and power system of the multi-energy power system needs to meet the requirements of users and can be understood by using the following formula:
wherein ,Qho Is the heating quantity, the temperature of the air is higher than the heating quantity,is the heating capacity of the kth combined cooling heating and power system, Q co Is the refrigerating capacity->Is the refrigeration capacity of the kth combined cooling heating and power system, </i >>Is the maximum heating capacity of the combined cooling heating and power system, < >>Is the minimum heating capacity value of the combined cooling heating and power system,/-for the combined cooling heating and power system>Is the maximum value of the refrigerating capacity of the combined cooling heating power system, < + >>The refrigerating capacity of the combined cooling heating and power system is the minimum value; m is the total number of the combined cooling heating and power systems;
3) Constraint of limiting value of micro-source power
wherein , and />Representing the maximum value and the minimum value of the power generated by the power generation unit, respectively.
4) Battery operation constraints
wherein ,Sin To input state of charge, SOC min At minimum state of charge, SOC max At maximum state of charge, P ball Is the charge-discharge power of the storage battery,minimum charge/discharge power for a battery, +.>The maximum charge and discharge power of the storage battery.
5) Multi-energy power system and main network allowed transmission power constraint
wherein , and />Representing the maximum and minimum values of the grid transmission power, respectively.
The step 2 is characterized by comprising the following specific steps of:
after the mathematical optimization scheduling model is established, the economic solving problem of the model becomes an important key for solving the optimization scheduling problem. The nonlinear power system solves the problem of optimizing and scheduling aiming at a complex model, and a traditional PSO algorithm is adopted in a large number in recent years. Although the conventional PSO has a plurality of advantages, the conventional PSO also has some technical defects that the iteration nonlinearity is easy to be trapped in local optimum, and the defects of divergence, convergence and low precision are easy to generate. Inspired by the self-learning capability and different environment adaptation capability of parrots, the shortcomings of PSO are overcome by two measures of a self-adaptive weight method and introduction of learning factors such as nonlinear contraction precision, and the finally generated algorithm is called parrot algorithm;
Step 2.1 introduction of adaptive weighting method
From the description, the weight formula describes the influence of the motion speed of the previous generation of parrot individuals on the linear periodic change rate of the motion speed of the current generation. The size of the statistical sampling value interval of w directly influences the computing power of two local algorithm optimization of w and PSO. In this state, generally, the higher w is taken, the higher the computation strength of global optimization is, and the lower the computation strength of local optimization is in the period; while when w is lower, the result is the opposite. According to the application characteristics of the calculation method, a nonlinear motion method is used as a basic method and an application strategy for calculating the inertial motion weight w of the object. The calculation formula which needs improvement is as follows:
in the formula ,wmax 、w min Expressed as a maximum weight value and a minimum weight value, respectively, and generally takes w min ＝4.0，w max =9.0; the minimum value of the inertia weight and the adaptation coefficient can be matched with the adaptive parrot numberThe size of the target fitness function value of the body is changed, so that the inertial weight is named as self-adaptive weight;
step 2.2 introduction of learning factors
In parrot algorithm optimization process, two asynchronous speed change group algorithm learning factors c 1 、c 2 Different changes may occur with the length of the optimization time. By carrying out proper dynamic adjustment on factors to be learned, the method is not only beneficial to the convergence of the group algorithm to a quick global optimal solution, but also can greatly accelerate the convergence speed of the parrot algorithm. The following requirements are therefore made for individual optimization of parrots: in the beginning and ending stage of the parrot individual swarm algorithm, the parrot individual must have stronger self-participation learning capacity, and the global learning and searching capacity of the parrot individual should be enhanced; when the parrot algorithm optimization is started and ended rapidly, parrot individuals must have weaker self-participation social learning capacity and stronger self-social learning capacity, so that the algorithm convergence speed can be accelerated, rapid algorithm convergence is facilitated to obtain a rapid global optimal solution, and the two factors needing to be learned are applied to respectively perform proper learning and dynamic simulation optimization, and the dynamic optimization is as follows:
in the formula ：c1 Is the 1 st learning factor, c 1f Is the 1 st final learning factor, c 1t Is the 1 st initial learning factor, t max Is the maximum time, t is the time, c 2 Is the 2 nd learning factor, c 2f Is the 2 nd final learning factor, c 2t Is the 2 nd initial learning factor. Taking c 1t ＝c 1f ＝2.5，c 1t ＝c 1f ＝0.5。
Step 3, three different multi-energy power system optimization scheduling strategies are constructed, and the specific steps for verifying the validity of the parrot algorithm are as follows:
step 3.1, a multi-objective function processing method of a multi-energy power system comprises the following steps:
1) In general, the computing method and base model of multi-objective system optimization:
wherein: f (X) is the overall objective function, f 1 (X),f 2 (X),…,f m (X) is m different sub-objective functions, m represents how many, g i (X) is an inequality constraint, i represents the number of equality constraints, h j (X) represents an equality constraint, j represents the number of equality constraints, X is n variables X 1 ,x 2 ,…,x n And the n-dimensional decision variables are formed. In the multi-objective optimization problem, inequality constraint m is more than or equal to 2; equation constraint takes s.t.g i (X) is greater than or equal to 0 and h j (X)＝0；
2) The multi-objective function processing method of the multi-energy power system comprises the following steps:
the optimization of the multi-objective power system is realized by optimizing the multi-objective by adopting a linear weighting function summation method, which can be expressed as:
minf(X)＝w 1 f 1 (X)+w 2 f 2 (X) (31)
Wherein: f (X) is the total objective function, f 1 (X) is the 1 st objective function, f 2 (X) is the 2 nd objective function, w 1 ，w 2 The weights of the 1 st objective function and the 2 nd objective function are represented, respectively. w (w) 1 ≥0,w 2 Not less than 0 and satisfy w 1 +w 2 =1, random weight w 1 and w2 Determined by the following formula:
random weight w 1 and w2 Determined by the following formula:
in the formula ,ri Non-negative random number, r 1 ，r 2 Are all non-negative random numbers;
step 3.2, a constraint condition processing method of the multi-energy power system comprises the following steps:
the constraint condition is processed by using a penalty function method, the function containing inequality constraint and equality constraint in the optimization problem is combined with the original optimization objective function after weighted optimization processing, a new objective function can be directly obtained, at the moment, the original constraint problem is converted into a new optimization problem without constraint condition, and the unconstrained optimization problem is solved.
Step 3.3, constructing three different multi-energy power system optimization scheduling strategies:
in designing such systems and optimizing the management strategies used in the scheduling of energy sources, there is a significant association with related solutions implemented in the power resource market, as well as being affected by the specific structure within them. According to whether the multi-energy power system and the main network are in grid-connected cooperative operation or not, the priorities of the micro power source and the main network are determined, and the following management and control strategy is constructed:
Strategy one: the priority of the micro power supply is higher than that of the main network, and if the micro power supply is insufficient, electricity is purchased from the main network;
strategy II: the priority of the micro power supply is the same as that of the main network, and if the electric quantity of the micro power supply is insufficient, the micro power supply can only purchase electricity from the main network;
strategy III: the priority of the micro power supply is the same as that of the main network, and if the electric quantity of the micro power supply is insufficient, the purchase of electricity from the main network is not limited;
and 3.4, solving three different multi-energy power system optimization management and control strategies by adopting a parrot algorithm:
after an efficient interpretation of the dissimilarity of each variable in the mathematical model of optimal management of the multi-energy power system, the steps of this algorithm can be summarized as follows:
firstly, generating a parrot population moving randomly, initializing initial values of all parrot individuals moving randomly and moving speeds v of the parrot individuals, and setting moving positions and various moving parameters of the parrot individuals randomly;
secondly, evaluating the fitness of each parrot individual, and calculating the extremum P of the individual best Global extremum g best Updating two parameters of the moving area and the moving speed v of the parrot subject to be detected according to a related formula;
thirdly, adjusting the weight factors for learning habituation, and determining whether to update the individual extremum and the global extremum by comparing the size of the fitness value of the parrot individual after iteration with the size of the fitness value of the parrot individual after the last iteration;
And finally, judging whether to terminate the algorithm according to the two conditions of whether the iteration times are reached or whether the maximum adaptive parameter value is not changed, if one of the conditions is met, ending the algorithm, and if not, carrying out initialization again to carry out related operation. A specific solution flow chart is shown in fig. 1.
Examples
In this section, the power supply of the system to a rural area is taken as a case of the investigation in winter and summer on the basis of typical days, and the related data mainly comprise photovoltaic power generation power, wind power prediction curves, micro-power performance parameters and winter and summer MT output curves of the winter and summer electric cold and hot load curves on the typical days.
1) Simulation result analysis under different target weight coefficients
In the context of this section, policy three is employed. The heat load condition contained in the related data of the working days in winter is taken as a calculation case, the dispatching condition of the system in energy aspect is optimized by using a parrot algorithm, and the heat load condition in the typical days in winter is not considered. w (w) 1 ,w 2 The representative is that the cost paid by the system in operation and the weight coefficient corresponding to the cost are paid by the treating party of the discharged pollutant, the corresponding values of the cost and the weight coefficient are different, and the final obtained results are different. FIGS. 2, 3, 4 are typical winter days Same as w 1 ,w 2 And (3) simulating results under the condition, wherein black broken lines in the graph are electric load output curves.
When there is a difference in the weight coefficient determined when the power generation cost is calculated, the specific case of cost optimization corresponding to the typical day in winter is listed in table 1:
table 1 cost comparison (Unit: yuan) of different weight systems for winter typical day systems
From a comparison of costs for each of the different weight coefficients in table 1, it can be seen that: the optimal scheduling of the multi-energy power system is related to the weight size of the system objective function. The simulation results show that the system operation cost is increased under the condition of corresponding weight reduction, and the treatment cost is reduced under the condition of corresponding weight increase.
2) Simulation result analysis under different operation strategies
The section comprehensively considers economic and environmental benefits of the multi-energy power system, and three different operation strategies are used for generating power in a typical winter day, and fig. 5, 6 and 7 are specific conditions of corresponding power and output.
Based on the three strategies described above, the specific case of the corresponding costs when the system is operated on a typical day in winter is listed in table 2.
TABLE 2 comparison of costs for winter typical days under different follow-up strategies (Unit: yuan)
From a comparison of costs under the various operating strategies of table 2, it is known that the optimal scheduling of the multi-energy power system is related to the operating strategy.
The final simulation result and the conclusion of the method are consistent, and the obtained curve is not poor in convergence and easy to fall into local optimum and the like other algorithms by using the parrot algorithm. This represents that the correlation model constructed in this study and the parrot algorithm used have better feasibility and the correlation results are also better.

Claims (1)

1. The method for optimizing and scheduling the multi-energy power system based on the parrot algorithm is characterized by comprising the following steps of:
step 1, establishing a mathematical model of optimal scheduling of a multi-energy power system considering economy and environmental protection;
step 1, establishing a mathematical model for optimizing and scheduling a multi-energy power system in consideration of economy and environmental protection, wherein the mathematical model comprises the following specific steps:
step 1.1, obtaining the output characteristics and cost composition of each power generation unit of a multi-energy power system comprising photovoltaic power generation, a wind driven generator, a micro gas turbine, a fuel cell and an energy storage unit:
1) Photovoltaic power generation unit:
the main characteristics of the photoelectricity are as follows:
in the formula ,Kr ＝0.0017(A/℃)，T r 301.18K where P is the power from the photovoltaic panel, V is the voltage from the photovoltaic panel, I is the current from the photovoltaic panel, m is the number of parallel panel cells, I ph Is generated by the photovoltaic component through light irradiation, I 0 Is the initial current, q is the constant of the electronic quantity in the battery, R s Is a series unidirectional resistor, n is the number of series batteries, A is a diode characteristic fitting coefficient, K r Is Boltzmann constant, T r The standard temperature and the temperature at the measuring moment are T;
in practical application, since the system output is influenced by various factors of the ambient temperature of the photovoltaic cell and the system intensity under solar radiation, the system output is corrected under standard conditions:
in the formula ：GSTC ＝1000W/m 2 ，k＝-0.47％/℃，T r ＝25℃，G STC Is the standard solar irradiation intensity, G ING Is the actual solar irradiation intensity, k is the power temperature coefficient, T c Is the actual temperature, T r Is the standard temperature of the temperature-sensitive alloy,
the solar photovoltaic fuel cell directly obtains energy from the sun, belongs to renewable energy sources, and also considers the working cost and the maintenance cost in an omnibearing way;
2) A wind power generation unit:
the power characteristics of wind power are as follows:
under practical conditions, the environmental protection cost and the utilization rate cost are not considered, and only the repair cost is considered, wherein V is the wind speed input by the fan and V is r Is the standard wind speed, P r Is of standard power, P WT Is the output power of the fan, V co Is the wind speed of the fan cut-out, V ci The wind speed of the fan is cut-in wind speed;
3) Micro gas turbine:
The micro gas turbine power function model is as follows:
wherein ,ηMT Is the efficiency of the general gas turbine, P MT Is the output power of the general gas turbine;
normal maintenance operation and daily maintenance cost related function for micro general purpose gas turbines:
in the formula ,KOMMT Directly taking 0.047 yuan/kWh, wherein K OMMT Is the cost coefficient of normal maintenance operation and daily maintenance expense of the miniature general-purpose gas turbine, C OMMT The normal maintenance operation and the daily maintenance cost of the miniature general gas turbine are;
the external pollution control emission coefficient of the micro gas turbine and the control cost and the function of the cost are calculated as follows:
wherein ,CEMMT Is the external pollution control emission coefficient of the miniature gas turbine, and the control cost and the cost thereof, lambda k Is the emission factor, alpha k Is a discount coefficient;
4) A fuel cell:
p is 40kW, its power output P FC And the power generation efficiency eta FC The relationship of (2) may be represented by the following formula:
η FC ＝-0.0023P FC +0.6735 (7)
wherein ,ηFC Is the efficiency of the fuel cell and,
in the process of maintaining the normal steady-state continuous power generation of the fuel cell power generation system, the proportional relation between the effective input quantity of the fuel cell and the power generation cost input by the output energy consumption of the fuel cell can be expressed by the following formula:
in the formula ：LHVng Is natural gas with low calorific value of 9.7kWh/m 3 ，C ng Is the cost of natural gas, P FC And (t) is the power of the fuel cell at time t, C FFC (t) is the combustion at time tCharge of battery, eta FC (t) is fuel cell efficiency;
the normal operation and maintenance costs of a fuel conversion cell can be expressed by the following formula:
wherein ,CEMFC Is the normal operation and maintenance management cost of the fuel conversion battery, alpha k Is a discount coefficient lambda k Is an emission factor;
5) An energy storage unit:
two important constraints of the multi-energy power storage battery, namely the storage battery charge-discharge power constraint and the storage battery maximum capacity constraint, are fully considered, so that the state of charge of the storage battery at the time t can be expressed as:
wherein I is the current of the storage battery, P BT Is the power of the accumulator, U BT Is the voltage of the storage battery, the SOC is the charge state of the storage battery, s sdr Is the self-discharge rate eta of the accumulator bce Is the charge-discharge efficiency of the battery, C is the battery capacity of the battery, deltat is the time interval, and Deltat=1h, deltat is taken bce The value of the charge-discharge voltage is 1, and the charge-discharge voltage is in the range of 0.65-0.85;
step 1.2, constructing an objective function of environment-friendly and economical operation of the multi-energy power system:
1) Cost of economy objective function
This objective function is to minimize the operating costs of each unit, including microgrid-to-main network interaction costs, operational maintenance costs, heating benefits, isolated network load compensation costs, cooling benefits, and fuel costs for the micro-power supply, expressed as follows:
in the formula ,C1 Is the running cost, C f Is the fuel cost, C OM Is the maintenance cost, C geid Is the interaction cost of the micro-grid main network, C sh Is the heating cost, C sc Is the refrigeration cost, C L Is the compensation cost and e, b are the cost considerations. When the two values take e=1 and b=0, the system is in grid connection work, and interaction cost exists between the main network and the system; e=0, b=1, i.e. the system is operating as a isolated network, a proportion of the load is taken off, in which case there is a cut load compensation cost,
wherein ：
C grid (t)＝C g (t)P g (t)-C s (t)P s (t) (15)
C sh (t)＝Q ho ×K ho (16)
C sc (t)＝Q co ×K co (17)
C L (t)＝C bu ×P CL (t) (18)
in the formula ：LHVng Is natural gas with low calorific value of 9.7kWh/m 3 ，C ng Is the cost of natural gas, P FC And (t) is the power of the fuel cell at time t, C FFC (t) is the fuel cell cost at time t, η FC (t) is fuel cell efficiency; p (P) i Is the firstOutput power of i power generation units, eta i Efficiency of the ith generating unit, C OM Is the maintenance cost, K OMi Is the cost coefficient of normal maintenance operation and daily maintenance cost of the ith power generation unit, C grid Is the interaction cost of the micro-grid main network, C g Is the electricity purchasing cost from the micro-grid to the main grid, P g The micro-grid power is purchased from the main grid, C s Is the cost of selling electricity from the micro-network to the main network, P s Is that the micro-grid sells electric power to the main grid, C sh Is the heating cost, Q ho Is the heat value, K ho Is the heating coefficient, C sc Is the refrigeration cost, Q co Is the cold value, K co Coefficient of refrigeration, C L Is the compensation cost, C bu Is the power failure electric quantity loss of micro-grid payment, P CL Load shedding power, n is the total number of generating units, i is the number of generating units;
2) Environment-friendly objective function
The objective function is to consider that the environmental benefit of the multi-energy power system is the largest, i.e. the cost of pollution emission and treatment of energy is the smallest, and the expression is as follows:
in the formula ：C2 Is the environmental protection cost, C K Process pollutant penalty coefficient, r ik Output power coefficient of i power generation units discharged in kth type, P i Is the output power of the ith power generation unit, alpha is the external discount coefficient, r gridk Is the interaction coefficient of the micro-grid main network of the k-th type emission, C g The method is characterized in that the method is the electricity purchasing cost of the micro-grid to the main grid, P is the electricity purchasing power of the micro-grid to the main grid, T is the scheduled time, T is the scheduled total time, M is the number of emission types (NOX, SO2 or CO 2), k is the emission type, n is the total number of power generation units, and i is the number of the power generation units;
step 1.3, constructing constraint conditions of environment-friendly and economical operation of a multi-energy power system:
1) Electric power balance constraint
wherein ,Pi Is the power generated by the ith power generation unit, P ball Is the power released by the storage battery, P grid Is the micro-grid main network interaction power, P L Is the load power of isolated network down-cut, beta is the operation coefficient, P CL Is load shedding power, when the system is in grid-connected operation, beta=0; when the system is operated in a isolated network, beta=1;
2) Cold and hot balance constraint
wherein ,Qho Is the heating quantity, the temperature of the air is higher than the heating quantity,is the heating capacity of the kth combined cooling heating and power system, Q co Is the refrigerating capacity->Is the refrigeration capacity of the kth combined cooling heating and power system, </i >>Is the maximum heating capacity of the combined cooling heating and power system, < >>Is the minimum heating capacity value of the combined cooling heating and power system,/-for the combined cooling heating and power system>Is the maximum value of the refrigerating capacity of the combined cooling heating power system, < + >>The refrigerating capacity of the combined cooling heating and power system is the minimum value, and M is the total number of the combined cooling heating and power systems;
3) Constraint of limiting value of micro-source power
P i min ≤P i (t)≤P i max (23)
wherein ,Pi max and Pi min Respectively representing the maximum value and the minimum value of the power generated by the power generation unit;
4) Battery operation constraints
wherein ,Sin To input state of charge, SOC min At minimum state of charge, SOC max At maximum state of charge, P ball Is the charge-discharge power of the storage battery,minimum charge/discharge power for a battery, +.>The maximum charge and discharge power of the storage battery;
5) Multi-energy power system and main network allowed transmission power constraint
wherein , and />Respectively representing the maximum value and the minimum value of the transmission power of the power grid;
step 2, designing a parrot algorithm;
step 2, designing parrot algorithm, specifically, the steps are as follows:
Step 2.1, introducing an adaptive weighting method:
inspired by the self-learning capability and different environment adaptation capability of parrots, an adaptive weight method is introduced, the weight formula describes the influence of the motion speed of the previous generation of parrot on the linear periodic change rate of the motion speed of the current generation, a nonlinear motion method is used as a basic method and an application strategy for calculating the inertial motion weight w of an object, and an improved calculation formula is as follows:
in the formula ：wmax 、w min Respectively expressed as a maximum weight value and a minimum weight value, and w is taken min ＝4.0，w max =9.0; the minimum value of the inertia weight and the adaptation coefficient can be changed along with the change of the size of the target fitness function value of the self-adaptive parrot individual, so the inertia weight is named as the self-adaptive weight;
step 2.2, introducing a learning factor:
in parrot algorithm, two asynchronous speed-changing group algorithm learning factors c 1 、c 2 The optimization time is changed differently, and the following requirements are met for individual parrot optimization: in the beginning and ending stage of the parrot individual swarm algorithm, the parrot individual must have a large self-participation learning capacity, and the global learning and searching capacity should be enhanced; and when the swarm optimization is started and ended rapidly, parrot individuals must have weaker self-participation social learning capacity and stronger self-social learning capacity, so that the parrot swarm optimization convergence speed is increased, rapid swarm optimization convergence is facilitated, and a rapid global optimal solution is obtained, wherein the specific formula is as follows:
in the formula ：c1 Is the 1 st learning factor, c 1f Is the 1 st final learning factor, c 1t Is the 1 st initial learning factor, t max Is the maximum time, t is the time, c 2 Is the 2 nd learning factor, c 2f Is the 2 nd final learning factor, c 2t Is the 2 nd initial learning factor, take c 1t ＝c 1f ＝2.5，c 1t ＝c 1f ＝0.5；
Step 3, constructing three different multi-energy power system optimization scheduling strategies, and verifying the validity of the parrot algorithm;
step 3, three different multi-energy power system optimization scheduling strategies are constructed, the validity of the parrot algorithm is verified, and the specific steps are as follows:
step 3.1, a multi-objective function processing method of a multi-energy power system comprises the following steps:
1) General model of multi-objective optimization algorithm:
wherein: f (X) is the overall objective function, f 1 (X),f 2 (X),…,f m (X) is m different sub-objective functions, m represents how many, g i (X) is an inequality constraint, i represents the number of equality constraints, h j (X) represents an equality constraint, j represents the number of equality constraints, X is n variables X 1 ,x 2 ,…,x n And the n-dimensional decision variables are formed. In the multi-objective optimization problem, inequality constraint m is more than or equal to 2; equation constraint takes s.t.g i (X) is greater than or equal to 0 and h j (X)＝0；
2) The multi-objective function processing method of the multi-energy power system comprises the following steps:
the optimization of the multi-objective power system is realized by optimizing the multi-objective by adopting a linear weighting function summation method, which is characterized in that the optimization of the multi-objective power system is realized by the following steps:
minf(X)＝w 1 f 1 (X)+w 2 f 2 (X) (31)
Wherein: f (X) is the total objective function, f 1 (X) is the 1 st objective function, f 2 (X) is the 2 nd objective function, w 1 ，w 2 Weights representing the 1 st and 2 nd objective functions, respectively, w 1 ≥0,w 2 Not less than 0 and satisfy w 1 +w 2 =1, random weight w 1 and w2 Determined by the following formula:
in the formula ：ri Non-negative random number, r 1 ，r 2 Are all non-negative random numbers;
step 3.2, a constraint condition processing method of the multi-energy power system comprises the following steps:
the constraint condition is processed by using a penalty function method, the function containing inequality constraint and equality constraint in the optimization problem is combined with the original optimization objective function after weighted optimization processing, a new objective function is directly obtained, at the moment, the original constraint problem is converted into a new optimization problem without constraint condition, and the unconstrained optimization problem is solved.
Step 3.3, constructing three different multi-energy power system optimization scheduling strategies:
according to whether the multi-energy power system and the main network are in grid-connected cooperative operation or not, the priority of the micro power supply and the main network is determined, and the following management and control strategy is constructed:
strategy one: the priority of the micro power supply is higher than that of the main network, and if the micro power supply is insufficient, electricity is purchased from the main network;
strategy II: the priority of the micro power supply is the same as that of the main network, and if the electric quantity of the micro power supply is insufficient, the micro power supply can only purchase electricity from the main network;
Strategy III: the priority of the micro power supply is the same as that of the main network, and if the electric quantity of the micro power supply is insufficient, the purchase of electricity from the main network is not limited;
and 3.4, solving three different multi-energy power system optimization scheduling strategies by adopting a parrot algorithm:
the parrot algorithm is adopted to solve the three proposed optimization scheduling strategies of the multi-energy power system,
firstly, generating a parrot population moving randomly, initializing initial values of all random parrot individuals and moving speeds v of the parrot individuals, and randomly setting moving positions of the parrot individuals and moving parameters of various random parrot individuals;
secondly, evaluating the fitness of each parrot individual, and calculating the individual extremum p best Global extremum g best Updating two parameters of the moving area and the moving speed v of the parrot subject to be detected according to a related formula;
thirdly, adjusting the weight factors for learning habituation, and determining whether to update the individual extremum and the global extremum by comparing the size of the fitness value of the parrot individual after iteration with the size of the fitness value of the parrot individual after the last iteration;
and finally, judging whether to terminate the algorithm according to the two conditions of whether the iteration times are reached or whether the maximum adaptive parameter value is not changed, if one of the conditions is met, ending the algorithm, and if not, carrying out initialization again to carry out related operation.
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