CN112580897A - Multi-energy power system optimal scheduling method based on parrot algorithm - Google Patents

Multi-energy power system optimal scheduling method based on parrot algorithm Download PDF

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CN112580897A
CN112580897A CN202011631340.6A CN202011631340A CN112580897A CN 112580897 A CN112580897 A CN 112580897A CN 202011631340 A CN202011631340 A CN 202011631340A CN 112580897 A CN112580897 A CN 112580897A
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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 optimized scheduling strategies of the multi-energy power system, and verifying the effectiveness of the parrot algorithm; has the advantages of high efficiency and accuracy.

Description

Multi-energy power system optimal scheduling method based on parrot algorithm
Technical Field
The invention belongs to the technical field of optimal scheduling of multi-energy power systems, and particularly relates to a parrot algorithm-based optimal scheduling method for a multi-energy power system.
Background
With the rapid development of the industry, the traditional fossil energy is rapidly consumed, and the energy crisis becomes an urgent problem. In order to solve the problem of energy crisis, only one traditional energy supply mode is not enough, and people begin to comprehensively utilize and research energy in various forms such as electric energy, gas energy, heat energy and the like. In the past energy system, different energy networks such as a power grid, an air grid and a heat supply network are operated independently, the interconnection degree is not high, the comprehensive utilization rate of energy is not favorably improved, the optimized operation of a plurality of energy systems is also not favorably realized, and the barrier can be broken by the multi-energy system developed in recent years, so that the optimized scheduling of the multi-energy power system is very important.
The flow chart of the optimal scheduling strategy in the island operation mode of the multi-energy power system is too complicated, compared with other algorithms, a Particle Swarm Optimization (PSO) is easy to understand, the required control parameters are few, an expected result is easy to obtain, and the parallel performance is good. However, PSO has two disadvantages of being easy to fall into local optimization and low precision, and scholars at home and abroad strive to improve the algorithm in recent years, but the result is not satisfactory, and the bat algorithm adopts a novel frequency tuning algorithm on the basis of PSO, and although the convergence is improved, the optimization precision is greatly reduced. Under the demand of dealing with the energy load calculation of the well-jet type increase, the fruit fly 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 the improvement of PSO in recent years falls into a strange circle that the PSO defect cannot be completely solved if the optimization scale is increased and the algorithm optimization efficiency and accuracy are improved, and the PSO defect cannot be completely solved if the optimization scale is increased and the algorithm optimization efficiency and accuracy are improved.
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 solves the problems that the traditional particle swarm algorithm is easy to fall into local optimization and low precision respectively, and has the advantages of high efficiency and accuracy.
The technical scheme adopted by the invention is that 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 2, designing a parrot algorithm;
and 3, constructing three different optimized scheduling strategies of the multi-energy power system, and verifying the effectiveness of the parrot algorithm.
Step 1, establishing a mathematical model of optimal scheduling of the multi-energy power system considering economy and environmental protection, specifically comprising the following steps:
step 1.1, obtaining the output characteristics and cost composition of each power generation unit of a multi-energy power system comprising a photovoltaic power generation unit, a wind driven generator, a micro gas turbine, a fuel cell and an energy storage unit:
1) photovoltaic cell (PV):
the main characteristics of the photoelectricity are as follows:
Figure BDA0002880123200000031
in the formula ,Kr＝0.0017(A/℃)，Tr301.18K, where P is the power delivered by the photovoltaic panel, V is the voltage delivered by the photovoltaic panel, I is the current delivered by the photovoltaic panel, m is the number of cells in the parallel panel, IphIs produced by the photovoltaic module by light irradiation, I0Is the initial current, q is the constant of the internal electronic charge of the battery, RsIs a series unidirectional resistor, n is the number of series batteries, A is the diode characteristic fitting coefficient, KrIs the Boltzmann constant, TrIs the standard temperature, T is the temperature at the moment of measurement;
in practical application, because 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:
Figure BDA0002880123200000032
in the formula ：GSTC＝1000W/m2，k＝-0.47％/℃，Tr＝25℃，GSTCIs the standard solar radiation intensity, GINGIs the actual solar radiation intensity, k is the power temperature coefficient, TcIs the actual temperature, TrIs the standard temperature;
the energy directly obtained from the sun by the solar photovoltaic fuel cell belongs to renewable energy sources, and the working cost and the maintenance cost of the solar photovoltaic fuel cell should be considered comprehensively;
2) wind Turbine generator (WT):
the power characteristics of wind power are as follows:
Figure BDA0002880123200000033
under the actual condition, 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 the wind speed input by the fanrIs the standard wind speed, PrIs a standard power, PWTIs the output power of the fan, VcoCut-out wind speed, V, of a fanciThe wind speed is cut in by the fan;
3) micro gas Turbine (MT):
the micro gas turbine power function model is as follows:
Figure BDA0002880123200000041
wherein ,ηMTGeneral purpose burnerEfficiency of gas turbine, PMTIs the output power of a model general gas turbine;
normal maintenance operation and daily maintenance cost related functions of a micro general purpose gas turbine:
Figure BDA0002880123200000042
in the formula ,KOMMTDirectly taking 0.047 yuan/kWh, wherein KOMMTIs the cost coefficient of normal maintenance operation and daily maintenance of the micro general gas turbine, COMMTThe cost of normal maintenance operation and daily maintenance cost of the micro general gas turbine;
the external pollution abatement emission coefficient of a micro gas turbine and the function calculation of the abatement cost and cost thereof are shown as follows:
Figure BDA0002880123200000043
wherein ,CEMMTIs the discharge coefficient of external pollution control of the micro gas turbine and the control expense and cost, lambdakIs an emission factor, αkIs the discount coefficient;
4) fuel Cell (Fuel Cell, FC):
p is 40kW, its power output PFCAnd efficiency of electric power generation etaFCCan be represented by the following formula:
ηFC＝-0.0023PFC+0.6735 (7)
wherein ,ηFCIs the efficiency of the fuel cell;
in the process of maintaining 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 represented by the following formula:
Figure BDA0002880123200000051
in the formula ：LHVngThe low heat value of the natural gas is 9.7kWh/m3，CngIs the cost of natural gas, PFC(t) is the power of the fuel cell at time t, CFFC(t) is the fuel cell cost at time t, ηFC(t) is the fuel cell efficiency;
the normal operating operation and maintenance management costs of a fuel conversion cell can be represented by the following equation:
Figure BDA0002880123200000052
wherein ,CEMFCIs the normal operating and maintenance management cost, alpha, of the fuel conversion cellkIs the discount coefficient, λkIs an emission factor;
5) energy storage unit (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 convenient application Of the storage battery to the time t, namely, the State Of Charge (SOC) can be expressed as follows:
Figure BDA0002880123200000053
Figure BDA0002880123200000054
wherein I is the current of the battery, PBTIs the power of the accumulator, UBTIs the voltage of the battery, SOC is the state of charge of the battery, σsdrIs the self-discharge rate, eta, of the accumulatorbceIs the charge-discharge efficiency of the storage battery, C is the battery capacity of the storage battery, Δ t is the time interval, and Δ t is taken to be 1h, ηbceThe value is 1 during discharging, and is generally in the range of 0.65-0.85 during charging;
step 1.2, constructing an objective function of the environment-friendly and economic operation of the multi-energy power system:
1) economic cost objective function
This objective function is to minimize the operating cost per unit, including the microgrid interaction cost with the main grid, the operating maintenance cost, the heating revenue, the isolated grid load compensation cost, the refrigeration revenue, and the fuel cost of the microgrid, and is expressed by the following formula:
Figure BDA0002880123200000061
in the formula ,C1Is the running cost, CfIs the cost of fuel, COMIs the cost of maintenance and repair, CgeidIs the interaction cost of the main network of the microgrid, CshIs the cost of heating, CscIs the cost of refrigeration, CLThe compensation cost is used, e and b are cost consideration coefficients, when the two values are taken as e being 1 and b being 0, the system is connected to the power grid to work, and the interaction cost exists between the main network and the system; e is 0, b is 1, namely the system works in isolated network, a certain proportion of load is removed, then the load cutting compensation cost exists in the case,
wherein ：
Figure BDA0002880123200000062
Figure BDA0002880123200000063
Cgrid(t)＝Cg(t)Pg(t)-Cs(t)Ps(t) (15)
Csh(t)＝Qho×Kho (16)
Csc(t)＝Qco×Kco (17)
CL(t)＝Cbu×PCL(t) (18)
wherein ,LHVngThe low heat value of the natural gas is 9.7kWh/m3，CngIs the cost of natural gas, PFC(t) is the power of the fuel cell at time t, CFFC(t) is the fuel cell cost at time t, ηFC(t) is the fuel cell efficiency; piIs the output power of the ith power generation unit, ηiEfficiency of the ith power generation unit, COMIs the cost of maintenance, KOMiIs the cost coefficient of the normal maintenance operation and daily maintenance cost of the ith power generation unit, CgridIs the interaction cost of the main network of the microgrid, CgThe electricity purchasing cost of the micro-grid to the main grid is PgThe electricity purchasing power of the micro-grid to the main grid, CsThe electricity selling cost of the microgrid to the main network is PsSelling electric power from the microgrid to the main grid, CshIs the cost of heating, QhoIs the calorific value, KhoIs the coefficient of heating, CscIs the cost of refrigeration, QcoIs the amount of cold value, KcoCoefficient of refrigeration, CLIs to compensate for the cost, CbuIs the power loss in the microgrid payment, PCLLoad shedding power, n is the total number of the generating units, and i is the number of the generating units;
2) environmental objective function
The objective function is to consider that the environmental benefit of the multi-energy power system is the maximum, i.e. the cost of pollution emission and treatment of the energy is the minimum, and the formula is as follows:
Figure BDA0002880123200000071
in the formula ：C2Is an environmental protection cost CKPenalty factor for treating pollutants, rikCoefficient of output power, P, of i power generating units of type k dischargeiIs the output power of the ith power generation unit, alpha is the external discount coefficient, rgridkIs a microgrid main network interaction coefficient of the kth type emission, CgThe electricity purchasing cost from the microgrid to the main grid is P, the electricity purchasing power from the microgrid to the main grid is T, the scheduled time is T, the scheduled total time is T, M is the number of emission types (NOX, SO2 or CO2), k is the emission type, n is the total number of the power generation units, and i is the number of the power generation units;
step 1.3, constructing constraint conditions of environment-friendly and economic operation of the multi-energy power system:
1) electric power balance constraint
Figure BDA0002880123200000072
Figure BDA00028801232000000815
wherein ,PiIs the power, P, emitted by the ith power generation unitballIs the power discharged from the accumulator, PgridIs micro-grid main network interactive power, PLIs the isolated grid down-cut load power, beta is the coefficient of operation, PCLThe load shedding power is that when the system is in grid-connected operation, beta is 0; when the system operates in isolated network, beta is 1;
2) cold and hot balance constraint
Figure BDA0002880123200000081
wherein ,QhoIs the amount of heat produced by the heat generation,
Figure BDA0002880123200000082
the heating capacity of the kth combined cooling heating and power system, QcoIt is the amount of refrigeration that is carried out,
Figure BDA0002880123200000083
the cooling capacity of the kth combined cooling heating and power system,
Figure BDA0002880123200000084
is the maximum heating capacity of the combined cooling heating and power system,
Figure BDA0002880123200000085
is the minimum heating capacity of the combined cooling heating and power system,
Figure BDA0002880123200000086
is the maximum value of the refrigerating capacity of the combined cooling heating and power system,
Figure BDA0002880123200000087
the minimum value of the refrigerating capacity of the combined cooling heating and power system is obtained; m is the total number of the combined cooling heating and power system;
3) limiting the limiting value of the micro-source power
Figure BDA0002880123200000088
wherein ,
Figure BDA0002880123200000089
and
Figure BDA00028801232000000810
respectively representing the maximum value and the minimum value of the power generated by the power generation unit;
4) battery operating constraints
Figure BDA00028801232000000811
Figure BDA00028801232000000812
wherein ,SinTo input the state of charge, SOCminAt minimum state of charge, SOCmaxAt maximum state of charge, PballIs the charge and discharge power of the storage battery,
Figure BDA00028801232000000813
is the minimum charge-discharge power of the storage battery,
Figure BDA00028801232000000814
the maximum charge-discharge power of the storage battery;
5) multi-energy power system and main network allowed transmission power constraint
Figure BDA0002880123200000091
wherein ,
Figure BDA0002880123200000092
and
Figure BDA0002880123200000093
respectively representing the maximum and minimum values of the transmission power of the power grid.
Step 2, designing a parrot algorithm, and specifically comprising the following steps:
step 2.1, introducing a self-adaptive weight method:
inspired by the self-learning ability and the adaptability to different environments of parrots, a self-adaptive weight method is introduced; the linear velocity formula of the inertia motion weight w describes that the parrot individual uses a nonlinear motion method as a basic method and an application strategy for calculating the inertia motion weight w of an object, and the improved calculation formula is as follows:
Figure BDA0002880123200000094
in the formula ：wmax、wminRespectively expressed as a maximum weight value and a minimum weight value, and generally taken as wmin＝4.0，wmax9.0; the minimum value of the inertia weight and the adaptive coefficient can change along with the change of the size of the target fitness 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 variation group algorithm learning factor c1、c2Will vary with the length of the optimization time, the following requirements are imposed on the parrot individual optimization: in the beginning and ending stages of the swarm algorithm for parrot individuals, the parrot individuals must have a greater ability to participate in learning themselves,it should be necessary to enhance its ability to learn and search globally; when the group algorithm is optimized rapidly, the parrot individuals have weak ability of self-participating in social learning and strong ability of self-social learning, so that the convergence speed of the parrot group algorithm can be increased, the rapid group algorithm convergence is facilitated to obtain a rapid global optimal solution, and a specific formula is as follows:
Figure BDA0002880123200000101
Figure BDA0002880123200000102
in the formula ：c1Is the 1 st learning factor, c1fIs the 1 st final learning factor, c1tIs the 1 st initial learning factor, tmaxIs the maximum time, t is the time, c2Is the 2 nd learning factor, c2fIs the 2 nd final learning factor, c2tIs the 2 nd initial learning factor, take c1t＝c1f＝2.5，c1t＝c1f＝0.5。
And 3, constructing three different optimized scheduling strategies of the multi-energy power system, and verifying the effectiveness of the parrot algorithm, wherein the specific steps are as follows:
step 3.1, the multi-objective function processing method of the multi-energy power system comprises the following steps:
1) general model for multiobjective optimization algorithm:
Figure BDA0002880123200000103
in the formula: f (X) is the overall objective function, f1(X),f2(X),…,fm(X) is a function of m different sub-goals, m indicates how many, gi(X) is an inequality constraint, i represents the number of equality constraints, hj(X) represents an equality constraint, j represents the number of equality constraints,x is n variables X1,x2,…,xnIn the multi-objective optimization problem, an inequality constraint m of the formed n-dimensional decision variables is more than or equal to 2; equation constraint is taken as s.t.gi(X) is not less than 0 and hj(X)＝0；
2) The multi-objective function processing method of the multi-energy power system comprises the following steps:
each sub-objective of the multi-objective optimization is difficult to simultaneously realize the total objective optimization, so a linear weighted function summation method is adopted to optimize the multiple objectives to realize the optimization of the multi-objective power system, which can be specifically expressed as:
minf(X)＝w1f1(X)+w2f2(X) (31)
in the formula: f (X) is the overall objective function, f1(X) is the 1 st objective function, f2(X) is the 2 nd objective function, w1，w2The weights of the 1 st and 2 nd objective functions are represented, respectively. w is a1≥0,w2Is more than or equal to 0 and satisfies w1+w2Random weight w of 11 and w2Is determined by the following formula:
Figure BDA0002880123200000111
in the formula ：riNon-negative random number, r1，r2Are all non-negative random numbers;
step 3.2, a constraint condition processing method of the multi-energy power system comprises the following steps:
the method comprises the steps of utilizing a penalty function method to process constraint conditions, combining a function containing inequality constraints and equality constraints in an optimization problem after weighted optimization processing with an original optimization objective function to directly obtain a new objective function, converting the original constraint problem into a new unconstrained optimization problem, and solving the unconstrained optimization problem.
3.3, constructing three different optimal scheduling strategies of the multi-energy power system:
determining the priority of the micro power supply and the main network according to whether the multi-energy power system is in grid-connected cooperative operation with the main network or not, and constructing the following control strategy:
strategy one: the priority of the micro power supply is higher than that of the main grid, and if the power of the micro power supply is insufficient, electricity is purchased from the main grid;
and (2) strategy two: 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 power from the main network;
strategy three: 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 power purchase from the main network is not limited;
and 3.4, solving three different optimal scheduling strategies of the multi-energy power system by adopting a parrot algorithm:
solving three different optimized scheduling strategies of the multi-energy power system by adopting a parrot algorithm;
firstly, generating a random moving parrot population, 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;
secondly, evaluating the fitness of each parrot individual, and calculating an individual extreme value pbestAnd global extreme gbestUpdating two parameters of the moving area and the moving time v of the parrot individual to be detected according to a correlation formula;
thirdly, adjusting the weight factor of the learning habit, and determining whether to update the individual extremum and the global extremum by comparing the fitness value of the parrot individual after iteration with the fitness value of the parrot individual after the last iteration;
and finally, judging whether the algorithm is terminated according to two conditions of whether the iteration times are reached or the maximum adaptive parameter value is not changed, if one of the conditions is met, finishing the algorithm, and if the one of the conditions is not met, initializing again to carry out related operation.
The invention has the beneficial effects that:
the used parrot algorithm is added with self-adaptive weight and learning factors on the basis of the traditional particle swarm algorithm, so that the two defects of local optimization and low precision of the traditional particle swarm algorithm are overcome. By solving and simulating the related cases, the simulation result shows that the model can be quickly and accurately solved by using the parrot algorithm under different objective functions, different targets and different operation strategies, and the effectiveness of the algorithm is proved.
Drawings
Fig. 1 is a solving flow chart of a mathematical model for optimizing and scheduling a multi-energy power system based on a parrot algorithm.
FIG. 2 shows a third strategy, namely, the priority of the micro power supply is the same as that of the main grid, if the micro power supply is insufficient, the power purchase from the main grid is not limited, and the target weight coefficient w is in a typical day in winter1＝w2The simulation results when the value is 0.5 are analyzed.
FIG. 3 shows a target weight coefficient w for a typical day in winter using strategy three1＝0.8,w2The simulation results when the value is 0.2 are analyzed.
FIG. 4 shows a target weight coefficient w for a typical day in winter using strategy three1＝0.3,w2The simulation results when the value is 0.7 are analyzed.
Fig. 5 is an analysis diagram of a system optimization result in winter when power is purchased from the main grid in case of adopting a strategy one, namely the priority of the micro power source is higher than that of the main grid, and if the power of the micro power source is insufficient.
Fig. 6 is a diagram of analysis of system optimization results in winter when the micro power supply and the main network have the same priority and only can purchase power from the main network if the micro power supply has insufficient power.
Fig. 7 is an analysis diagram of a typical winter day system optimization result when the micro power supply and the main grid are the same in priority level and the power purchase from the main grid is not limited if the micro power supply is insufficient in power.
Detailed Description
The present invention will be described in detail below with reference to the drawings and specific embodiments, but the present invention is not limited to these embodiments.
The invention discloses 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 optimized scheduling strategies of the multi-energy power system, and verifying the effectiveness of the parrot algorithm.
Wherein the step 1 specifically comprises the following steps:
the method mainly analyzes the basic working principle and characteristics, power output, cost modeling and other main aspects of the power generation system of the 5 types of power generation units, and prepares for optimization of the power system and analysis and design of the dispatching system in the 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 cell (PV):
the main characteristics of the photoelectricity are as follows:
Figure BDA0002880123200000141
in the formula ,Kr＝0.0017(A/℃)，Tr301.18K, where P is the power delivered by the photovoltaic panel, V is the voltage delivered by the photovoltaic panel, I is the current delivered by the photovoltaic panel, m is the number of cells in the parallel panel, IphIs produced by the photovoltaic module by light irradiation, I0Is the initial current, q is the constant of the internal electronic charge of the battery, RsIs a series unidirectional resistor, n is the number of series batteries, A is the diode characteristic fitting coefficient, KrIs the Boltzmann constant, TrIs 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:
Figure BDA0002880123200000151
in the formula ：GSTC＝1000W/m2，k＝-0.47％/℃，Tr＝25℃，GSTCIs the standard solar radiation intensity, GINGThe actual solar radiation intensity k is the power temperature coefficient, TcIs the actual temperature, TrIs the standard temperature.
The energy directly obtained from the sun by the solar photovoltaic fuel cell belongs to renewable energy sources, and the working cost and the maintenance cost of the solar photovoltaic fuel cell should be considered comprehensively.
2) Wind Turbine generator (WT):
the power characteristics of wind power are as follows:
Figure BDA0002880123200000152
under the actual condition, 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 the wind speed input by the fanrIs the standard wind speed, PrIs a standard power, PWTIs the output power of the fan, VcoCut-out wind speed, V, of a fanciThe wind speed is cut in by the fan.
3) Micro gas Turbine (MT):
the micro gas turbine power function model is as follows:
Figure BDA0002880123200000153
in the formula ,ηMTIndicating the operating efficiency, P, of the gas turbineMTRepresenting gas turbine power.
Normal maintenance operation and daily maintenance cost related functions of a micro general purpose gas turbine:
Figure BDA0002880123200000154
in the formula ,KOMMTRepresents the unit upfront price of the gas turbine, wherein the unit upfront price is directly 0.047 yuan/kWh, wherein KOMMTIs the cost coefficient of normal maintenance operation and daily maintenance of the micro general gas turbine, COMMTIs the cost of normal maintenance operation and daily maintenance cost of the micro general gas turbine.
The external pollution abatement emission coefficient of a micro gas turbine and the function calculation of the abatement cost and cost thereof are shown as follows:
Figure BDA0002880123200000161
wherein ,CEMMTIs the discharge coefficient of external pollution control of the micro gas turbine and the control expense and cost, lambdakIs an emission factor, αkIs the discount coefficient.
4) Fuel Cell (Fuel Cell, FC):
p is 40kW, its power output PFCAnd efficiency of electric power generation etaFCCan be represented by the following formula:
ηFC＝-0.0023PFC+0.6735 (7)
wherein ,ηFCIs the efficiency of the fuel cell;
in the process of maintaining 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 represented by the following formula:
Figure BDA0002880123200000162
in the formula ：LHVngThe low heat value of the natural gas is 9.7kWh/m3，CngIs the cost of natural gas, PFC(t) is the power of the fuel cell at time t, CFFC(t) is the fuel cell cost at time t, ηFC(t) is the fuel cell efficiency.
The normal operating operation and maintenance management costs of a fuel conversion cell can be represented by the following equation:
Figure BDA0002880123200000171
wherein ,CEMFCIs the normal operating and maintenance management cost, alpha, of the fuel conversion cellkIs the discount coefficient, λkIs the emission factor.
5) Energy storage unit (BT):
two important constraints of a multi-energy power storage battery, namely a storage battery charge-discharge power constraint and a storage battery maximum capacity constraint, are fully considered herein, so that the application of the storage battery at time t, State of charge (SOC), can be expressed as:
Figure BDA0002880123200000172
Figure BDA0002880123200000173
wherein I is the current of the battery, PBTIs the power of the accumulator, UBTIs the voltage of the battery, SOC is the state of charge of the battery, σsdrIs the self-discharge rate, eta, of the accumulatorbceIs the charge-discharge efficiency of the storage battery, C is the battery capacity of the storage battery, Δ t is the time interval, and Δ t is taken to be 1h, ηbceThe value is 1 during discharging and is generally in the range of 0.65 to 0.85 during charging.
Step 1.2, constructing an objective function of the environment-friendly and economic operation of the multi-energy power system:
step 1.2.1, multi-target analysis:
in order to better optimize and save the operation of the multi-energy power system, a basic mathematical model for optimizing the economic dispatch of the multi-energy power system should be researched and set, wherein the mathematical model has two main components of target constraint conditions and a basic objective function. Among the main basic targets of the multi-energy optimization type power system needing to optimize economic dispatch are: (1) pollution treatment cost is minimum; (2) the output of each micro power unit, the residual capacity of the storage battery and the interaction energy of the system and the main network are in a limited range; (3) transaction costs with the primary network are minimal; (4) the operation and maintenance cost is minimum; (5) the balance condition of cold and heat loads in the system; (6) micro-power fuel costs are minimal.
Step 1.2.2, determining an objective function:
1) economic cost objective function
The benefits available to the multi-energy power system are large, also referred to as minimal operating costs per unit, including microgrid interaction costs with the main grid, operational maintenance costs, heating benefits, isolated grid load compensation costs, refrigeration benefits, and fuel costs of the microgrid.
Figure BDA0002880123200000181
in the formula ,C1Is the running cost, CfIs the cost of fuel, COMIs the cost of maintenance and repair, CgeidIs the interaction cost of the main network of the microgrid, CshIs the cost of heating, CscIs the cost of refrigeration, CLThe compensation cost is used, e and b are cost consideration coefficients, when the two values are taken as e being 1 and b being 0, the system is connected to the power grid to work, and the interaction cost exists between the main network and the system; when e is 0 and b is 1, namely the system works in isolated network, a certain proportion of load is removed, and then the load shedding compensation cost exists in the situation.
wherein ：
Figure BDA0002880123200000182
Figure BDA0002880123200000191
Cgrid(t)＝Cg(t)Pg(t)-Cs(t)Ps(t) (15)
Csh(t)＝Qho×Kho (16)
Csc(t)＝Qco×Kco (17)
CL(t)＝Cbu×PCL(t) (18)
wherein ,LHVngThe low heat value of the natural gas is 9.7kWh/m3，CngIs the cost of natural gas, PFC(t) is the power of the fuel cell at time t, CFFC(t) is the fuel cell cost at time t, ηFC(t) is the fuel cell efficiency; piIs the output power of the ith power generation unit, ηiEfficiency of the ith power generation unit, COMIs the cost of maintenance, KOMiIs the cost coefficient of the normal maintenance operation and daily maintenance cost of the ith power generation unit, CgridIs the interaction cost of the main network of the microgrid, CgThe electricity purchasing cost of the micro-grid to the main grid is PgThe electricity purchasing power of the micro-grid to the main grid, CsThe electricity selling cost of the microgrid to the main network is PsSelling electric power from the microgrid to the main grid, CshIs the cost of heating, QhoIs the calorific value, KhoIs the coefficient of heating, CscIs the cost of refrigeration, QcoIs the amount of cold value, KcoCoefficient of refrigeration, CLIs to compensate for the cost, CbuIs the power loss in the microgrid payment, PCLLoad shedding power, n is the total number of the generating units, and i is the number of the generating units;
2) environmental objective function
The objective function is to consider that the environmental benefit of the multi-energy power system is the maximum, i.e. the cost of pollution emission and treatment of the energy is the minimum, and the formula is as follows:
Figure BDA0002880123200000192
in the formula ：C2Is an environmental protection cost CKPenalty factor for treating pollutants, rikOutput power series of i power generation units of type k dischargeNumber, PiIs the output power of the ith power generation unit, alpha is the external discount coefficient, rgridkIs a microgrid main network interaction coefficient of the kth type emission, CgThe electricity purchasing cost from the microgrid to the main grid is P, the electricity purchasing power from the microgrid to the main grid is T, the scheduled time is T, the scheduled total time is T, M is the number of emission types (NOX, SO2 or CO2), k is the emission type, n is the total number of the power generation units, and i is the number of the power generation units;
step 1.2.3, constructing constraint conditions of environment-friendly and economic operation of the multi-energy power system:
on the basis of mastering an optimal control objective function for correctly establishing optimal control and scheduling of a multi-channel energy system, various control and constraint conditions in the multi-energy system are comprehensively understood, and a more comprehensive formation mode of various constraint control conditions is as follows:
1) electric power balance constraint
Figure BDA0002880123200000201
wherein ,PiIs the power, P, emitted by the ith power generation unitballIs the power discharged from the accumulator, PgridIs micro-grid main network interactive power, PLIs the isolated grid down-cut load power, beta is the coefficient of operation, PCLThe load shedding power is that when the system is in grid-connected operation, beta is 0; when the system operates in isolated network, beta is 1;
2) cold and hot balance constraint
The intercooled cogeneration system in a multi-energy power system, which needs to meet customer demand, can be understood using the following formula:
Figure BDA0002880123200000202
wherein ,QhoIs the amount of heat produced by the heat generation,
Figure BDA0002880123200000203
the heating capacity of the kth combined cooling heating and power system, QcoIt is the amount of refrigeration that is carried out,
Figure BDA0002880123200000211
the cooling capacity of the kth combined cooling heating and power system,
Figure BDA0002880123200000212
is the maximum heating capacity of the combined cooling heating and power system,
Figure BDA0002880123200000213
is the minimum heating capacity of the combined cooling heating and power system,
Figure BDA0002880123200000214
is the maximum value of the refrigerating capacity of the combined cooling heating and power system,
Figure BDA0002880123200000215
the minimum value of the refrigerating capacity of the combined cooling heating and power system is obtained; m is the total number of the combined cooling heating and power system;
3) limiting the limiting value of the micro-source power
Figure BDA0002880123200000216
wherein ,
Figure BDA0002880123200000217
and
Figure BDA0002880123200000218
respectively representing the maximum and minimum values of the power generated by the power generating unit.
4) Battery operating constraints
Figure BDA0002880123200000219
Figure BDA00028801232000002110
wherein ,SinTo input the state of charge, SOCminAt minimum state of charge, SOCmaxAt maximum state of charge, PballIs the charge and discharge power of the storage battery,
Figure BDA00028801232000002111
is the minimum charge-discharge power of the storage battery,
Figure BDA00028801232000002112
the maximum charge and discharge power of the storage battery.
5) Multi-energy power system and main network allowed transmission power constraint
Figure BDA00028801232000002113
wherein ,
Figure BDA00028801232000002114
and
Figure BDA00028801232000002115
respectively representing the maximum and minimum values of the transmission power of the power grid.
Step 2, the concrete steps of designing the parrot algorithm are as follows:
after the mathematical optimization scheduling model is established, the problem of economic solution of the model becomes an important key for solving the problem of optimization scheduling. Solving the problem of complex model optimization scheduling, the traditional PSO algorithm is adopted in recent years in a large amount. Although the conventional PSO has many advantages, there are some technical defects, and the iteration nonlinearity of the conventional PSO easily falls into local optimization, and the conventional PSO is prone to divergence, convergence and low precision. The PSO defect is solved by using two measures of a self-adaptive weight method and introduction of learning factors such as nonlinear shrinkage precision and the like, and a finally generated algorithm is called as a parrot algorithm;
step 2.1 introduction of adaptive weight method
From the description, the weight formula describes the influence of the motion speed of the parrot individual in the previous generation on the linear periodic change rate of the motion speed of the current generation. The statistical sampling interval size of w directly affects the calculation ability of the optimization of the two local algorithms of w and PSO. Generally, in this state, the higher w is taken, the higher the calculation intensity of global optimization is, and the lower the calculation intensity of local optimization is during this period; and the lower w, the opposite. According to the application characteristics of the above calculation method, a nonlinear motion method is taken as a basic method and an application strategy for calculating the inertial motion weight w of the object. The calculation formula which needs to be improved is as follows:
Figure BDA0002880123200000221
in the formula ,wmax、wminRespectively expressed as a maximum weight value and a minimum weight value, and generally taken as wmin＝4.0，wmax9.0; the minimum value of the inertia weight and the adaptive coefficient can be changed along with the change of the size of the target fitness function value of the adaptive parrot individual, so that the inertia weight is named as the adaptive weight;
step 2.2 introduction of learning factors
Group algorithm learning factor c of two asynchronous speed changes in parrot algorithm optimization process1、c2Will vary with the length of the optimization time. By properly and dynamically adjusting the factors to be learned, the cluster algorithm can be converged to a rapid global optimal solution, and the convergence speed of the parrot algorithm can be greatly increased. The following requirements are therefore made for parrot individual optimization: in the starting and ending stages of the swarm algorithm of the parrot individuals, the parrot individuals have strong self-learning capacity, and the global learning and searching capacity of the parrot individuals needs to be enhanced; at the rapid start and end of parrot algorithm optimization, parrot individuals must have weak self-participationThe method has the advantages that the method has social learning capacity and strong self-social learning capacity, so that the speed of algorithm convergence can be increased, the rapid algorithm convergence is facilitated to obtain a rapid global optimal solution, the method focuses on the two factors needing to be learned to respectively carry out appropriate learning and dynamic simulation optimization on the two factors, and the dynamic optimization is as follows:
Figure BDA0002880123200000231
Figure BDA0002880123200000232
in the formula ：c1Is the 1 st learning factor, c1fIs the 1 st final learning factor, c1tIs the 1 st initial learning factor, tmaxIs the maximum time, t is the time, c2Is the 2 nd learning factor, c2fIs the 2 nd final learning factor, c2tIs the 2 nd initial learning factor. Get c1t＝c1f＝2.5，c1t＝c1f＝0.5。
In the step 3, three different optimal scheduling strategies of the multi-energy power system are constructed, and the specific steps for verifying the effectiveness of the parrot algorithm are as follows:
step 3.1, the multi-objective function processing method of the multi-energy power system comprises the following steps:
1) in the general case, the computational method and the basic model of the multi-objective system optimization:
Figure BDA0002880123200000233
in the formula: f (X) is the overall objective function, f1(X),f2(X),…,fm(X) is a function of m different sub-goals, m indicates how many, gi(X) is an inequality constraint, i represents the number of equality constraints, hj(X) represents an equality constraint, j represents the number of equality constraints, and X is n variables X1,x2,…,xnComposed n-dimensional decision variables. In the multi-objective optimization problem, an inequality constraint m is taken to be more than or equal to 2; equation constraint is taken as s.t.gi(X) is not less than 0 and hj(X)＝0；
2) The multi-objective function processing method of the multi-energy power system comprises the following steps:
each sub-objective of the multi-objective optimization is difficult to simultaneously realize the total objective optimization, so a linear weighted function summation method is adopted to optimize the multiple objectives to realize the optimization of the multi-objective power system, which can be specifically expressed as:
minf(X)＝w1f1(X)+w2f2(X) (31)
in the formula: f (X) is the overall objective function, f1(X) is the 1 st objective function, f2(X) is the 2 nd objective function, w1，w2The weights of the 1 st and 2 nd objective functions are represented, respectively. w is a1≥0,w2Is more than or equal to 0 and satisfies w1+w2Random weight w of 11 and w2Is determined by the following formula:
random weight w1 and w2Is determined by the following formula:
Figure BDA0002880123200000241
in the formula ,riNon-negative random number, r1，r2Are all non-negative random numbers;
step 3.2, a constraint condition processing method of the multi-energy power system comprises the following steps:
the method comprises the steps of utilizing a penalty function method to process constraint conditions, combining a function containing inequality constraints and equality constraints in an optimization problem after weighted optimization processing with an original optimization objective function to directly obtain a new objective function, converting the original constraint problem into a new unconstrained optimization problem, and solving the unconstrained optimization problem.
3.3, constructing three different optimal scheduling strategies of the multi-energy power system:
when the system is designed and the management strategy used in energy scheduling is optimized, the system is influenced by the specific structure inside the system and is obviously related to the related solution implemented in the power resource market. Determining the priority of the micro power supply and the main network according to whether the multi-energy power system and the main network are in grid-connected cooperative operation or not, and constructing the following control strategy:
strategy one: the priority of the micro power supply is higher than that of the main grid, and if the power of the micro power supply is insufficient, electricity is purchased from the main grid;
and (2) strategy two: 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 power from the main network;
strategy three: 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 power purchase from the main network is not limited;
step 3.4, solving three different optimized control strategies of the multi-energy power system by adopting a parrot algorithm:
after effective interpretation of the similarities and differences of each variable in the mathematical model for optimal management and control of the multi-energy power system, the steps of this algorithm can be summarized as follows:
firstly, generating a random moving parrot population, initializing initial values of all random moving parrot individuals and moving speeds v of the parrot individuals, and randomly setting moving positions and various moving parameters of the parrot individuals;
secondly, evaluating the fitness of each parrot individual, and calculating an individual extreme value PbestAnd global extreme gbestUpdating two parameters of the moving area and the moving speed v of the detected parrot individual according to a related formula;
thirdly, adjusting the weight factor of the learning habit, and determining whether to update the individual extremum and the global extremum by comparing the fitness value of the parrot individual after iteration with the fitness value of the parrot individual after the last iteration;
and finally, judging whether the algorithm is terminated according to two conditions of whether the iteration times are reached or the maximum adaptive parameter value is not changed, if one of the conditions is met, finishing the algorithm, and if the one of the conditions is not met, initializing again to carry out related operation. The specific solving flow chart is shown in fig. 1.
Examples
In the part, power supply for a certain rural area based on the system at a typical day in winter and summer is taken as a case to be researched, and related data mainly comprise photovoltaic power generation power, a wind power prediction curve, a micro-power source performance parameter and an MT output curve in winter and summer at a typical day of a winter and summer electric heating and cooling load curve.
1) Simulation result analysis under different target weight coefficients
In the context of this section, policy three is employed. Taking the thermal load condition contained in the related data of the working days in winter as a calculation case, optimizing the scheduling condition of the system in the aspect of energy by using a parrot algorithm, and not considering the thermal load condition in the typical days in winter. w is a1,w2The weight coefficient corresponding to the cost paid by the system during operation and the cost paid by the party for treating the discharged pollutants are represented, the corresponding numerical values of the two are different, and the final obtained result is different. FIGS. 2, 3, 4 show typical winter times of day with different w1,w2The black broken line in the graph is an electric load output curve.
When there is a difference in the weight coefficients determined when calculating the power generation cost, the details of cost optimization corresponding to typical days in winter are shown in table 1:
TABLE 1 cost comparison (Unit: Yuan) for different weight systems for typical winter day system
Figure BDA0002880123200000261
Figure BDA0002880123200000271
From table 1, a comparison of the costs for different weighting factors can be seen: the optimal scheduling of the multi-energy power system is related to the weight magnitude of the system objective function. The simulation results show that the system operation cost is increased under the condition that the corresponding weight is reduced, and the treatment cost is reduced under the condition that the corresponding weight is increased.
2) Simulation result analysis under different operation strategies
In this section, economic and environmental benefits of the multi-energy power system are comprehensively considered, and in typical days in winter, power is generated by three different operation strategies, and fig. 5, 6 and 7 are specific corresponding power and output conditions.
Based on the three strategies described above, the details of the corresponding costs when the system is operating on a typical winter day are listed in table 2.
TABLE 2 cost comparison of typical days in winter under different slave strategies (Unit: Yuan)
Figure BDA0002880123200000272
From the comparison of the cost under each operation strategy in table 2, it can be known that the optimal scheduling of the multi-energy power system is related to the operation strategy.
The final simulation result of the method is consistent with the conclusion, and the obtained curve does not have the problems of poor convergence, easy falling into local optimum and the like other algorithms by using the parrot algorithm. This represents the relevant model constructed in this study and the parrot algorithm used, with better feasibility and better correlation results.

Claims (4)

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 2, designing a parrot algorithm;
and 3, constructing three different optimized scheduling strategies of the multi-energy power system, and verifying the effectiveness of the parrot algorithm.
2. The parrot algorithm-based multi-energy power system optimal scheduling method according to claim 1, wherein in the step 1, a mathematical model of the multi-energy power system optimal scheduling considering economy and environmental protection is established, and the method 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 a photovoltaic power generation unit, a wind driven generator, a micro gas turbine, a fuel cell and an energy storage unit:
1) a photovoltaic power generation unit:
the main characteristics of the photoelectricity are as follows:
Figure FDA0002880123190000011
in the formula ,Kr＝0.0017(A/℃)，Tr301.18K, where P is the power delivered by the photovoltaic panel, V is the voltage delivered by the photovoltaic panel, I is the current delivered by the photovoltaic panel, m is the number of cells in the parallel panel, IphIs produced by the photovoltaic module by light irradiation, I0Is the initial current, q is the constant of the internal electronic charge of the battery, RsIs a series unidirectional resistor, n is the number of series batteries, A is the diode characteristic fitting coefficient, KrIs the Boltzmann constant, TrIs the standard temperature, T is the temperature at the moment of measurement;
in practical applications, since the system output is generally affected by various factors of the ambient temperature of the photovoltaic cell and the system intensity under solar radiation, the system output is generally corrected under standard conditions:
Figure FDA0002880123190000021
in the formula ：GSTC＝1000W/m2，k＝-0.47％/℃，Tr＝25℃，GSTCIs the standard solar radiation intensity, GINGIs the actual solar irradiance, k is the power temperature coefficient,Tcis the actual temperature, TrIs the standard temperature of the molten steel and is,
the energy directly obtained from the sun by the solar photovoltaic fuel cell belongs to renewable energy sources, and the working cost and the maintenance cost of the solar photovoltaic fuel cell should be considered comprehensively;
2) a wind power generation unit:
the power characteristics of wind power are as follows:
Figure FDA0002880123190000022
under the actual condition, 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 the wind speed input by the fanrIs the standard wind speed, PrIs a standard power, PWTIs the output power of the fan, VcoCut-out wind speed, V, of a fanciThe wind speed is cut in by the fan;
3) a micro gas turbine:
the micro gas turbine power function model is as follows:
Figure FDA0002880123190000023
wherein ,ηMTEfficiency of a model general gas turbine, PMTIs the output power of a model general gas turbine;
normal maintenance operation and daily maintenance cost related functions of a micro general purpose gas turbine:
Figure FDA0002880123190000031
in the formula ,KOMMTDirectly taking 0.047 yuan/kWh, wherein KOMMTIs the cost coefficient of normal maintenance operation and daily maintenance of the micro general gas turbine, COMMTThe cost of normal maintenance operation and daily maintenance cost of the micro general gas turbine;
the external pollution abatement emission coefficient of a micro gas turbine and the function calculation of the abatement cost and cost thereof are shown as follows:
Figure FDA0002880123190000032
wherein ,CEMMTIs the discharge coefficient of external pollution control of the micro gas turbine and the control expense and cost, lambdakIs an emission factor, αkIs the discount coefficient;
4) a fuel cell:
p is 40kW, its power output PFCAnd efficiency of electric power generation etaFCCan be represented by the following formula:
ηFC＝-0.0023PFC+0.6735 (7)
wherein ,ηFCIs the efficiency of the fuel cell and,
in the process of maintaining normal steady-state continuous power generation of the fuel cell power generation system, the proportional relationship 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 generally expressed by the following formula:
Figure FDA0002880123190000033
in the formula ：LHVngThe low heat value of the natural gas is 9.7kWh/m3，CngIs the cost of natural gas, PFC(t) is the power of the fuel cell at time t, CFFC(t) is the fuel cell cost at time t, ηFC(t) is the fuel cell efficiency;
the normal operating operation and maintenance management costs of a fuel conversion cell can be represented by the following equation:
Figure FDA0002880123190000041
wherein ,CEMFCIs the normal operating and maintenance management cost, alpha, of the fuel conversion cellkIs the discount coefficient, λkIs an emission factor;
5) an energy storage unit:
two important constraints of the multi-energy power storage battery, namely the constraint of the charge and discharge power of the storage battery and the constraint of the maximum capacity of the storage battery, are fully considered, so that the state of charge of the storage battery at the moment t can be conveniently expressed as follows:
Figure FDA0002880123190000042
Figure FDA0002880123190000043
wherein I is the current of the battery, PBTIs the power of the accumulator, UBTIs the voltage of the battery, SOC is the state of charge of the battery, σsdrIs the self-discharge rate, eta, of the accumulatorbceIs the charge-discharge efficiency of the storage battery, C is the battery capacity of the storage battery, Δ t is the time interval, and Δ t is taken to be 1h, ηbceThe value is 1 during discharging, and is generally in the range of 0.65-0.85 during charging;
step 1.2, constructing an objective function of the environment-friendly and economic operation of the multi-energy power system:
1) economic cost objective function
This objective function is to minimize the operating cost per unit, including the microgrid interaction cost with the main grid, the operating maintenance cost, the heating revenue, the isolated grid load compensation cost, the refrigeration revenue, and the fuel cost of the microgrid, and is expressed by the following formula:
Figure FDA0002880123190000051
in the formula ,C1Is the running cost, CfIs the cost of fuel, COMIs the cost of maintenance and repair, CgeidIs the interaction cost of the main network of the microgrid, CshIs the cost of heating, CscIs the cost of refrigeration, CLIs the compensation cost, and e, b are the cost consideration coefficients. When the two values are taken as e being 1 and b being 0, the system is connected to the power grid to work, and interaction cost exists between the main network and the system; e is 0, b is 1, namely the system works in isolated network, a certain proportion of load is removed, then the load cutting compensation cost exists in the case,
wherein ：
Figure FDA0002880123190000052
Figure FDA0002880123190000053
Cgrid(t)＝Cg(t)Pg(t)-Cs(t)Ps(t) (15)
Csh(t)＝Qho×Kho (16)
Csc(t)＝Qco×Kco (17)
CL(t)＝Cbu×PCL(t) (18)
in the formula ：LHVngThe low heat value of the natural gas is 9.7kWh/m3，CngIs the cost of natural gas, PFC(t) is the power of the fuel cell at time t, CFFC(t) is the fuel cell cost at time t, ηFC(t) is the fuel cell efficiency; piIs the output power of the ith power generation unit, ηiEfficiency of the ith power generation unit, COMIs the cost of maintenance, KOMiIs the cost coefficient of the normal maintenance operation and daily maintenance cost of the ith power generation unit, CgridIs the interaction cost of the main network of the microgrid, CgThe electricity purchasing cost of the micro-grid to the main grid is PgThe electricity purchasing power of the micro-grid to the main grid, CsThe electricity selling cost of the microgrid to the main network is PsThe micro-grid sells electricity to the main gridPower, CshIs the cost of heating, QhoIs the calorific value, KhoIs the coefficient of heating, CscIs the cost of refrigeration, QcoIs the amount of cold value, KcoCoefficient of refrigeration, CLIs to compensate for the cost, CbuIs the power loss in the microgrid payment, PCLLoad shedding power, n is the total number of the generating units, and i is the number of the generating units;
2) environmental objective function
The objective function is to consider that the environmental benefit of the multi-energy power system is the maximum, i.e. the cost of pollution emission and treatment of the energy is the minimum, and the formula is as follows:
Figure FDA0002880123190000061
in the formula ：C2Is an environmental protection cost CKPenalty factor for treating pollutants, rikCoefficient of output power, P, of i power generating units of type k dischargeiIs the output power of the ith power generation unit, alpha is the external discount coefficient, rgridkIs a microgrid main network interaction coefficient of the kth type emission, CgThe electricity purchasing cost from the microgrid to the main grid is P, the electricity purchasing power from the microgrid to the main grid is T, the scheduled time is T, the scheduled total time is T, M is the number of emission types (NOX, SO2 or CO2), k is the emission type, n is the total number of the power generation units, and i is the number of the power generation units;
step 1.3, constructing constraint conditions of environment-friendly and economic operation of the multi-energy power system:
1) electric power balance constraint
Figure FDA0002880123190000062
wherein ,PiIs the power, P, emitted by the ith power generation unitballIs the power discharged from the accumulator, PgridIs micro-grid main network interactive power, PLIs the isolated grid down-cut load power, beta is the coefficient of operation, PCLIs the load shedding power when the system is in operationWhen the grid is connected, beta is 0; when the system operates in isolated network, beta is 1;
2) cold and hot balance constraint
Figure FDA0002880123190000071
Figure FDA0002880123190000072
wherein ,QhoIs the amount of heat produced by the heat generation,
Figure FDA0002880123190000073
the heating capacity of the kth combined cooling heating and power system, QcoIt is the amount of refrigeration that is carried out,
Figure FDA0002880123190000074
the cooling capacity of the kth combined cooling heating and power system,
Figure FDA0002880123190000075
is the maximum heating capacity of the combined cooling heating and power system,
Figure FDA0002880123190000076
is the minimum heating capacity of the combined cooling heating and power system,
Figure FDA0002880123190000077
is the maximum value of the refrigerating capacity of the combined cooling heating and power system,
Figure FDA0002880123190000078
the minimum value of the refrigerating capacity of the combined cooling heating and power system, and M is the total number of the combined cooling heating and power system;
3) limiting the limiting value of the micro-source power
Pi min≤Pi(t)≤Pi max (23)
wherein ,Pi max and Pi minRespectively representing the maximum value and the minimum value of the power generated by the power generation unit;
4) battery operating constraints
Figure FDA0002880123190000079
Figure FDA00028801231900000710
wherein ,SinTo input the state of charge, SOCminAt minimum state of charge, SOCmaxAt maximum state of charge, PballIs the charge and discharge power of the storage battery,
Figure FDA00028801231900000711
is the minimum charge-discharge power of the storage battery,
Figure FDA00028801231900000712
the maximum charge-discharge power of the storage battery;
5) multi-energy power system and main network allowed transmission power constraint
Figure FDA00028801231900000713
wherein ,
Figure FDA00028801231900000714
and
Figure FDA00028801231900000715
respectively representing the maximum and minimum values of the transmission power of the power grid.
3. The parrot algorithm-based multi-energy power system optimal scheduling method according to claim 1, wherein the step 2 is to design the parrot algorithm, and the specific steps are as follows:
step 2.1, introducing a self-adaptive weight method:
inspired by the fact that parrots have self-learning ability and different environment adaptability, an adaptive weight method is introduced, a weight formula describes the influence of the previous generation movement speed of the parrots on a linear periodic change rate of the current generation movement speed, a nonlinear movement method is used as a basic method and an application strategy for calculating the inertia movement weight w of an object, and the improved calculation formula is as follows:
Figure FDA0002880123190000081
in the formula ：wmax、wminRespectively expressed as a maximum weight value and a minimum weight value, and generally taken as wmin＝4.0，wmax9.0; the minimum value of the inertia weight and the adaptive coefficient can be changed along with the change of the size of the target fitness function value of the adaptive parrot individual, so that the inertia weight is named as the adaptive weight;
step 2.2, introducing a learning factor:
in parrot algorithm, two asynchronous speed variation group algorithm learning factor c1、c2Will vary with the length of the optimization time, the following requirements are imposed on the parrot individual optimization: in the starting and ending stages of the swarm algorithm of the parrot individuals, the parrot individuals have great self-learning capacity, and the global learning and searching capacity needs to be enhanced; when the group algorithm is optimized rapidly, the parrot individuals have weak ability of self-participating in social learning and strong ability of self-social learning, so that the convergence speed of the parrot group algorithm can be increased, the rapid group algorithm convergence is facilitated to obtain a rapid global optimal solution, and a specific formula is as follows:
Figure FDA0002880123190000091
Figure FDA0002880123190000092
in the formula ：c1Is the 1 st learning factor, c1fIs the 1 st final learning factor, c1tIs the 1 st initial learning factor, tmaxIs the maximum time, t is the time, c2Is the 2 nd learning factor, c2fIs the 2 nd final learning factor, c2tIs the 2 nd initial learning factor, take c1t＝c1f＝2.5，c1t＝c1f＝0.5。
4. The parrot algorithm-based multi-energy power system optimal scheduling method according to claim 1, wherein in the step 3, three different multi-energy power system optimal scheduling strategies are constructed to verify the effectiveness of the parrot algorithm, and the specific steps are as follows:
step 3.1, the multi-objective function processing method of the multi-energy power system comprises the following steps:
1) general model for multiobjective optimization algorithm:
Figure FDA0002880123190000093
in the formula: f (X) is the overall objective function, f1(X),f2(X),…,fm(X) is a function of m different sub-goals, m indicates how many, gi(X) is an inequality constraint, i represents the number of equality constraints, hj(X) represents an equality constraint, j represents the number of equality constraints, and X is n variables X1,x2,…,xnComposed n-dimensional decision variables. In the multi-objective optimization problem, an inequality constraint m is taken to be more than or equal to 2; equation constraint is taken as s.t.gi(X) is not less than 0 and hj(X)＝0；
2) The multi-objective function processing method of the multi-energy power system comprises the following steps:
each sub-objective of the multi-objective optimization is difficult to simultaneously realize the total objective optimization, so a linear weighted function summation method is adopted to optimize the multiple objectives to realize the optimization of the multi-objective power system, which can be specifically expressed as:
min f(X)＝w1f1(X)+w2f2(X) (31)
in the formula: f (X) is the overall objective function, f1(X) is the 1 st objective function, f2(X) is the 2 nd objective function, w1，w2Respectively representing the weight of the 1 st and 2 nd objective function, w1≥0,w2Is more than or equal to 0 and satisfies w1+w2Random weight w of 11 and w2Is determined by the following formula:
Figure FDA0002880123190000101
in the formula ：riNon-negative random number, r1，r2Are all non-negative random numbers;
step 3.2, a constraint condition processing method of the multi-energy power system comprises the following steps:
the method comprises the steps of utilizing a penalty function method to process constraint conditions, combining a function containing inequality constraints and equality constraints in an optimization problem after weighted optimization processing with an original optimization objective function to directly obtain a new objective function, converting the original constraint problem into a new unconstrained optimization problem, and solving the unconstrained optimization problem.
3.3, constructing three different optimal scheduling strategies of the multi-energy power system:
determining the priority of the micro power supply and the main network according to whether the multi-energy power system is in grid-connected cooperative operation with the main network or not, and constructing the following control strategy:
strategy one: the priority of the micro power supply is higher than that of the main grid, and if the power of the micro power supply is insufficient, electricity is purchased from the main grid;
and (2) strategy two: 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 power from the main network;
strategy three: 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 power purchase from the main network is not limited;
and 3.4, solving three different optimal scheduling strategies of the multi-energy power system by adopting a parrot algorithm:
the parrot algorithm is adopted to solve the proposed three different optimized scheduling strategies of the multi-energy power system,
firstly, generating a random moving parrot population, 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 various random parrot individual moving parameters;
secondly, evaluating the fitness of each parrot individual, and calculating an individual extreme value pbestAnd global extreme gbestUpdating two parameters of the moving area and the moving speed v of the detected parrot individual according to a related formula;
thirdly, adjusting the weight factor of the learning habit, and determining whether to update the individual extremum and the global extremum by comparing the fitness value of the parrot individual after iteration with the fitness value of the parrot individual after the last iteration;
and finally, judging whether the algorithm is terminated according to two conditions of whether the iteration times are reached or the maximum adaptive parameter value is not changed, if one of the conditions is met, finishing the algorithm, and if the one of the conditions is not met, initializing again to carry out related operation.
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