CN113255224A - Energy system configuration optimization method based on glowworm-illuminant algorithm - Google Patents
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Abstract
The invention discloses an energy system configuration optimization method based on a glowworm luminous algorithm, which belongs to the field of comprehensive energy application and comprises the following steps: step S1, establishing a mechanism model of typical physical equipment in the comprehensive energy system of the manufacturing park; step S2, establishing the constraint of typical physical equipment; step S3, aiming at the characteristics of the comprehensive energy system of the manufacturing industry park, an economical objective function and an environmental protection objective function are established; step S4, determining the weight of the economic objective function and the environmental protection objective function in the step S3 by an entropy weight method; and step S5, obtaining an optimal energy system configuration scheme by adopting a luminous firefly algorithm. The invention can highlight the advantages of multi-energy coupling and multi-energy complementation of the comprehensive energy system, measure the economical efficiency and environmental protection of the system on the engineering level and improve the utilization efficiency of the comprehensive energy.
Description
Technical Field
The invention relates to the field of comprehensive energy application, in particular to an energy system configuration optimization method based on a glowworm luminous algorithm.
Background
Energy is a material basis for the development of human society, and has a particularly important strategic position in national safety and national economy. With the development of the new energy revolution, the comprehensive energy network system based on regional energy becomes a necessary way for large-scale development and utilization of regional resources, namely endowments and renewable energy, and realizing the transformation of the energy industry structure. Meanwhile, people are also continuously trying to effectively coordinate and utilize resources by means of modern communication technology, control technology, computer technology and the like, improve the energy utilization efficiency and solve the inherent problems of the existing energy system.
And aiming at the problems of low comprehensive energy utilization rate, poor multi-energy cooperative management and the like of the existing energy system, the comprehensive energy system gets the attention of more and more scholars. The comprehensive energy system is a social comprehensive energy production, supply and marketing integrated system formed by organically coordinating and optimizing links of generation, transmission and distribution (energy supply network), conversion, storage, consumption and the like of various energy sources in the processes of planning, design, construction, operation and the like. The method is characterized by the cooperative coordination and real-time interaction of information, energy and control in the whole process of energy production, transmission, distribution, use and storage.
The evaluation index system of the comprehensive energy system is the target guide for planning design and scheduling control optimization. By setting a reasonable and scientific evaluation index system, the intrinsic endowments of the comprehensive energy system in the aspects of high-efficiency synergistic utilization of multi-energy technology, gradient utilization of heterogeneous energy, high economic environmental protection brought by high replaceability and the like can be exerted to the maximum extent, so that the expectation of a decision maker on the comprehensive energy system with multi-energy integration is met. However, aiming at an energy system of a manufacturing industry park comprehensive energy system comprising various energy coupling and non-coupling equipment such as cold, heat, electricity, compressed air and the like and various grade energy forms such as high-pressure/medium-pressure steam, civil heating hot water and the like, the configuration optimization problem shows how to adopt an advanced intelligent optimization algorithm to carry out highly abstract modeling and optimization on the system under the existing evaluation index system.
Therefore, the new problem of planning and optimizing the comprehensive energy system at present is how to measure the economy and the environmental protection of the system at the engineering level, and by taking the new problem as a target, a comprehensive energy system planning scheme which accords with the actual engineering can be efficiently, quickly and automatically found under the conditions of diversified energy system structure networks and differentiated energy utilization modes.
Disclosure of Invention
The purpose of the invention is as follows: the method can optimize the comprehensive energy system of the manufacturing park and can effectively improve the utilization rate of the comprehensive energy in the engineering level.
In order to solve the technical problem, the application provides an energy system configuration optimization method based on a glowworm luminescent algorithm, which comprises the following steps:
step S1, establishing a mechanism model of typical physical equipment in the comprehensive energy system of the manufacturing park;
step S2, establishing the constraint of typical physical equipment;
step S3, aiming at the characteristics of the comprehensive energy system of the manufacturing industry park, an economical objective function and an environmental protection objective function are established;
step S4, determining the weight of the economic objective function and the environmental protection objective function in the step S3 by an entropy weight method;
and step S5, obtaining an optimal energy system configuration scheme by adopting a luminous firefly algorithm.
Preferably, in step S1, the mechanism model of the typical physical device includes a cogeneration unit mechanism model, a distributed photovoltaic mechanism model, an energy storage battery mechanism model, an electric refrigerator unit mechanism model, a lithium bromide absorption refrigerator unit mechanism model, and a compressed air preparation system mechanism model;
wherein:
the mechanism model of the cogeneration unit is as follows:
wherein eta isP,CHP、ηQ,CHPRespectively representing the generating efficiency and the heating efficiency of the CHP unit; pP,CHP、PQ,CHP、PT,CHPRespectively generating capacity, heat supply capacity and total input energy of the CHP unit, wherein the total input energy is the sum of physical energy and chemical energy generated by fuel combustion;
the distributed photovoltaic mechanism model is as follows:
PPV＝ξcosθηmApηp
where ξ represents the local illumination radiation intensity; theta represents the incident angle of illumination on the solar panel; etamRepresents the efficiency of the MPPT controller, which is mainly affected by the operating temperature; a. thepRepresents the area of the solar panel; etapRepresenting the efficiency of the solar panel;
the mechanism model of the energy storage battery is as follows:
wherein, Pst(t) represents the electric energy storage capacity of the energy storage battery at the moment t; mu.slossRepresenting the self-discharge loss rate of the energy storage battery; pst(t0) Denotes the initial t0Constantly storing the electric quantity of the energy storage battery;
represents t0To time tCharging quantity of the energy storage battery between the scales;
representing the charging efficiency of the energy storage battery;
represents t0The heat release of the energy storage battery until the time t;
representing the discharge efficiency of the energy storage battery;
the mechanism model of the electric refrigerating unit is as follows:
QEC＝CECPEC
wherein Q isECRepresenting the output cold power of the electric refrigerator; cECRepresents the refrigeration coefficient; pECRepresenting the input electrical power of the electrical refrigerator;
the mechanism model of the lithium bromide absorption refrigerating unit is as follows:
wherein Q isACRepresenting the output cold power of the absorption chiller; cACRepresents the thermodynamic coefficient;
representing the input thermal power of the absorption chiller; wsRepresenting the input hot steam flow of the absorption chiller; h iss1And hs2Respectively representing specific enthalpy of hot steam and specific enthalpy of condensed water;
the mechanism model of the compressed air preparation system is as follows:
wherein, Pcmp,tThe power consumption of the system is prepared for the compressed air; p is a radical ofaRepresents the absolute pressure of the atmosphere; qaRepresenting the absolute pressure of the compressed air; p represents a volume flow converted to an atmospheric state.
Preferably, in the step S2, the constraints of the typical physical device include a load lifting constraint and an energy balance constraint.
Preferably, the step S2 includes:
step S21, estimating the minimum and maximum possible capacities of the typical physical devices according to the load data, and establishing capacity constraints of each typical physical device:
λiPi,min≤Pi,t≤λiPi,max
wherein, Pi,min、Pi,maxRepresenting the estimated minimum and maximum capacities of the ith unit; lambda [ alpha ]iA variable 0-1 indicating whether the ith unit exists; pi,tActual output of the ith unit in a planning stage;
step S22, in consideration of the influence of the load lifting rate of typical physical devices of different types and different capacities on the system security, establishing a load lifting constraint of each typical physical device:
wherein, Pi,tThe output of the ith unit at the time t is represented; kappaiRepresenting the allowable load lifting rate of the ith unit;
step S23, establishing energy balance constraints for each typical physical device:
∑Pi,j,t≥∑Pneed,j,t
wherein, Pi,j,tThe supply quantity of jth energy flow at the time t is shown; pneed,j,tAnd the demand quantity of the jth energy flow at the moment t comprises the demand of a user and the demand of various energy coupling devices on heterogeneous energy.
Preferably, in the step S3,
the economic objective function is:
wherein the content of the first and second substances,
representing the investment cost of the ith unit;
representing the operation and maintenance cost of the ith unit in the operation process;
representing the requirement of the ith unit on input energy in the operation process;
representing the electricity purchasing cost;
the environmental protection objective function is:
wherein, Pi,tThe output of the ith unit is represented; EMkRepresents the emission of the kth pollutant; ENkRepresenting the environmental value of the kth pollutant.
Preferably, the step S4 includes:
step S41, establishing an original data matrix:
R＝(rkj)s×2；
wherein r iskjThe evaluation value of the kth evaluation scheme under the jth index;
step S42, solving each index value weight:
1) calculating the specific gravity p of the index value of the k evaluation scheme under the j indexkj：
2) Calculating the entropy e of the jth indexj：
3) Calculating the entropy weight w of the jth indexj：
Step S43, obtaining a weight vector including the economic index and the environmental protection index weight:
w＝(w1,w2)。
preferably, the step S5 includes:
step S51, initializing population: setting the number N of firefly populations, the absorption coefficient gamma of the medium to light, the initial step length a and the initial attraction degree beta0；
Step S52, calculating the fitness value of each firefly according to the position of the firefly, wherein the better the fitness value is, the higher the brightness of the firefly is;
step S53, each firefly moves to all fireflies with higher luminance than itself, and the movement distance calculation formula is:
Xi＝(x1,x2,...,xD)
wherein, X'iIndicating a location of a firefly having a higher intensity than the ith individualR represents the distance between the ith and jth fireflies, rand () is a random disturbance, and alpha is a step factor of the disturbance;
in the iterative process of the algorithm, the calculation formula of the step-size factor of the t generation firefly flight is as follows:
α(t)＝αt
the individuals with the highest brightness in the firefly population will update their location according to the following formula:
X'i＝Xi+αrandGuass()
step S54, calculating the fitness value of the new position where the firefly flies to all other individuals with higher brightness than the firefly, if the position is better than the position before flying, the firefly flies to the new position, otherwise, the firefly stays in the original position;
step S55, if the algorithm reaches the maximum iteration times, the searched optimal firefly position is used as a solution to be output, otherwise, the step S52 is skipped;
step S56, selecting the maximum value from the decision variables to which each type of device belongs in the optimal solution as the capacity configuration of the device, that is:
CAPi＝max(Pi,1,Pi,2,...,Pi,n)。
compared with the prior art, the application has at least the following beneficial effects:
the method provided by the invention innovatively considers the influence of the scheduling process of the comprehensive energy system on planning optimization, quantitatively considers the mechanism characteristics of the comprehensive energy system under the application of different structures and different energy technologies, and establishes the mixed integer linear programming based on the glowworm algorithm, which can be applied to planning optimization. In the process of constructing an index system of the configuration optimization problem, an entropy weight method with objectivity is adopted to determine the weight coefficients of economic and environmental indexes, and the engineering usability of the weight method is improved. The invention starts from the essential difference of the comprehensive energy system relative to the single energy system, so that the comprehensive energy system configuration optimization strategy of the manufacturing industry park highlights the advantages of the comprehensive energy system such as multi-energy coupling and multi-energy complementation, the economical efficiency and the environmental protection of the system are measured on the engineering level, and the utilization efficiency of the comprehensive energy is improved.
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Some specific embodiments of the invention will be described in detail hereinafter, by way of illustration and not limitation, with reference to the accompanying drawings. The same reference numbers in the drawings identify the same or similar elements or components. Those skilled in the art will appreciate that the drawings are not necessarily drawn to scale. In the drawings:
FIG. 1 is a schematic overall flow diagram of the present invention;
FIG. 2 is a schematic flow chart of the luminescent firefly algorithm of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the technical solutions of the present application will be described in detail and completely with reference to the following specific embodiments of the present application and the accompanying drawings. It should be apparent that the described embodiments are only some of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
An energy system configuration optimization method based on a glowworm luminous algorithm is characterized by comprising the following steps:
step S1, establishing a mechanism model of typical physical equipment in the comprehensive energy system of the manufacturing park;
step S2, establishing the restraint of each typical physical device including the restraint of load lifting and energy balance;
step S3, aiming at the characteristics of the comprehensive energy system of the manufacturing industry park, an economical objective function and an environmental protection objective function are established;
step S4, determining the weight of the economic objective function and the environmental protection objective function in the step S3 by an entropy weight method;
and step S5, solving a mixed integer linear programming which takes a physical equipment mechanism model and multiple constraints as feasible domains and takes economy and environmental protection as objective functions by adopting a luminous firefly algorithm to obtain an optimal comprehensive energy system configuration scheme.
In step S1, the mechanism model of the typical physical device includes a cogeneration unit mechanism model, a distributed photovoltaic mechanism model, an energy storage battery mechanism model, an electric refrigerator unit mechanism model, a lithium bromide absorption refrigerator unit mechanism model, and a compressed air preparation system mechanism model;
wherein:
the mechanism model of the cogeneration unit is as follows:
wherein eta isP,CHP、ηQ,CHPRespectively representing the generating efficiency and the heating efficiency of the CHP unit; pP,CHP、PQ,CHP、PT,CHPThe heat energy is generated by the CHP unit, the heat supply amount is provided, and the total input energy is the sum of the physical energy and the chemical energy generated by the combustion of the fuel.
The distributed photovoltaic mechanism model is as follows:
PPV＝ξcosθηmApηp
where ξ represents the local illumination radiation intensity; theta represents the incident angle of illumination on the solar panel; etamRepresents the efficiency of the MPPT controller, which is mainly affected by the operating temperature; a. thepRepresents the area of the solar panel; etapIndicating the efficiency of the solar panel.
The mechanism model of the energy storage battery is as follows:
wherein, Pst(t) represents the electric energy storage capacity of the energy storage battery at the moment t; mu.slossIndicating self-discharge loss of energy storage cellLoss rate; pst(t0) Denotes the initial t0Constantly storing the electric quantity of the energy storage battery;
represents t0The charging amount of the energy storage battery is up to t time;
representing the charging efficiency of the energy storage battery;
represents t0The heat release of the energy storage battery until the time t;
indicating the discharge efficiency of the energy storage cell.
The mechanism model of the electric refrigerating unit is as follows:
QEC＝CECPEC
wherein Q isECRepresenting the output cold power of the electric refrigerator; cECRepresents the refrigeration coefficient; pECRepresenting the input electrical power of the electrical refrigerator.
The mechanism model of the lithium bromide absorption refrigerating unit is as follows:
wherein Q isACRepresenting the output cold power of the absorption chiller; cACRepresents the thermodynamic coefficient;
representing the input thermal power of the absorption chiller; wsRepresenting the input hot steam flow of the absorption chiller; h iss1And hs2Respectively represent heatSpecific enthalpy of steam and specific enthalpy of condensate.
The mechanism model of the compressed air preparation system is as follows:
wherein, Pcmp,tThe power consumption of the system is prepared for the compressed air; p is a radical ofaRepresents the absolute pressure of the atmosphere; qaRepresenting the absolute pressure of the compressed air; p represents a volume flow converted to an atmospheric state.
In step S2, constraints of each typical physical device including load lifting constraints and energy balance constraints are established.
Firstly, before configuration optimization, the possible minimum and maximum capacities of each unit are estimated according to load data, and capacity constraints of each unit are further established:
λiPi,min≤Pi,t≤λiPi,max
wherein, Pi,min、Pi,maxRepresenting the estimated minimum and maximum capacities of the ith unit; lambda [ alpha ]iA variable 0-1 indicating whether the ith unit exists; pi,tAnd the actual output of the ith unit in the planning stage is obtained.
Secondly, considering the influence of the load lifting speed of the units with different types and different capacities on the system safety, establishing the load lifting constraint of each device:
wherein, Pi,tThe output of the ith unit at the time t is represented; kappaiAnd the allowable load lifting speed of the ith unit is shown.
Finally, establishing energy balance constraints of electricity, heat (high-pressure steam, medium-pressure steam, heating hot water), cold and compressed air:
∑Pi,j,t≥∑Pneed,j,t
wherein, Pi,j,tThe supply quantity of jth energy flow at the time t is shown; pneed,j,tAnd the demand quantity of the jth energy flow at the moment t comprises the demand of a user and the demand of various energy coupling devices on heterogeneous energy.
In step S3, an economic objective function and an environmental objective function are established for the characteristics of the integrated energy system of the manufacturing park.
Wherein the economic objective function is:
wherein the content of the first and second substances,
representing the investment cost of the ith unit;
representing the operation and maintenance cost of the ith unit in the operation process;
representing the requirement of the ith unit on input energy in the operation process;
indicating the electricity purchase cost.
The environmental protection objective function is:
wherein, Pi,tThe output of the ith unit is represented; EMkRepresents the emission of the kth pollutant; ENkRepresenting the environmental value of the kth pollutant.
In the step S4, an entropy weight method is used to determine the weight of the economic and environmental objective function provided in the step S3. The entropy weight method is an objective weighting method, and in the application process of the invention, the entropy weight method calculates the entropy weight of each index by using the information entropy according to the variation degree of each index, so that objective index weight can be obtained. Changes in economy and environmental protection over time need to be taken into account at different stages of planning. Therefore, the weight vector is different in short-term planning, medium-term planning, and long-term planning. The method comprises the steps of establishing an original data matrix, solving the weight of each index value, and determining a final index weight vector. The method comprises the following specific steps:
firstly, an original data matrix R (R) containing s evaluation schemes and 2 evaluation indexes is establishedkj)s×2. The s evaluation schemes provided by the invention are node schemes which are generated at a pareto boundary when the multi-objective optimization planning is carried out on the comprehensive energy system to be evaluated; the 2 proposed evaluation indexes are the economic and environmental protection indexes of the comprehensive energy system in the engineering application in the planning stage, and the numerical calculation method is shown in step S3.
Wherein r iskjThe evaluation value of the k-th evaluation scheme under the j-th index is obtained.
Secondly, solving the weight of each index value specifically comprises the following steps:
1) calculating the specific gravity p of the index value of the k evaluation scheme under the j indexkj：
2) Calculating the entropy e of the jth indexj：
3) Calculating the entropy weight w of the jth indexj：
Finally obtaining a weight vector w which contains the weight of the economic index and the environmental protection index (w ═1,w2)。
In the step S5, a luminescent firefly algorithm is adopted to solve a mixed integer linear programming with a physical device mechanism model and multiple constraints as feasible domains and with economy and environmental protection as objective functions, so as to obtain an optimal comprehensive energy system configuration scheme. The method comprises the following specific steps:
1) and initializing the population. Setting the number N of firefly populations, the absorption coefficient gamma of the medium to light, the initial step length a and the initial attraction degree beta0。
2) And calculating the fitness value of each firefly according to the position of the firefly, wherein the better the fitness value is, the higher the brightness of the firefly is.
3) Each firefly moves towards all fireflies with higher brightness than the firefly, and the moving distance calculation formula is as follows:
Xi＝(x1,x2,...,xD)
wherein, X'iIndicates a position of a firefly having a higher brightness than the ith individual, and r indicates a distance between the ith and jth fireflies. rand () is a random perturbation and α is the step factor of the perturbation. The value of the general rand () is [ -0.5,0.5 [)]Uniform distribution in the range or normal distribution of U (0,1), alpha is [0,1 ]]In the meantime.
In the iterative process of the algorithm, the calculation formula of the step-size factor of the t generation firefly flight is as follows:
α(t)＝αt
since all individuals will only fly to individuals with a higher intensity than themselves, the highest intensity individual of the population will not update its location. In the invention, the individual with the maximum brightness in the group updates the position of the individual according to the following formula:
X'i＝Xi+αrandGuass()
4) and calculating the fitness value of the new position where the firefly flies to all other individuals with higher brightness than the firefly, wherein if the position is superior to the position before flying, the firefly flies to the new position, and otherwise, the firefly stays in the original position.
5) And if the algorithm reaches the maximum iteration times, outputting the searched optimal position of the firefly as a solution, otherwise, jumping to the step 2).
6) Selecting the maximum value from the decision variables of each type of equipment in the optimal solution as the capacity configuration of the equipment, namely:
CAPi＝max(Pi,1,Pi,2,...,Pi,n)。
the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (7)
1. An energy system configuration optimization method based on a glowworm luminous algorithm is characterized by comprising the following steps:
step S1, establishing a mechanism model of typical physical equipment in the comprehensive energy system of the manufacturing park;
step S2, establishing the constraint of typical physical equipment;
step S3, aiming at the characteristics of the comprehensive energy system of the manufacturing industry park, an economical objective function and an environmental protection objective function are established;
step S4, determining the weight of the economic objective function and the environmental protection objective function in the step S3 by an entropy weight method;
and step S5, obtaining an optimal energy system configuration scheme by adopting a luminous firefly algorithm.
2. The energy system configuration optimization method of claim 1, wherein: in step S1, the mechanism model of the typical physical device includes a cogeneration unit mechanism model, a distributed photovoltaic mechanism model, an energy storage battery mechanism model, an electric refrigerator unit mechanism model, a lithium bromide absorption refrigerator unit mechanism model, and a compressed air preparation system mechanism model;
wherein:
the mechanism model of the cogeneration unit is as follows:
wherein eta isP,CHP、ηQ,CHPRespectively representing the generating efficiency and the heating efficiency of the CHP unit; pP,CHP、PQ,CHP、PT,CHPRespectively generating capacity, heat supply capacity and total input energy of the CHP unit, wherein the total input energy is the sum of physical energy and chemical energy generated by fuel combustion;
the distributed photovoltaic mechanism model is as follows:
PPV＝ξcosθηmApηp
where ξ represents the local illumination radiation intensity; theta represents the incident angle of illumination on the solar panel; etamRepresents the efficiency of the MPPT controller, which is mainly affected by the operating temperature; a. thepRepresents the area of the solar panel; etapRepresenting the efficiency of the solar panel;
the mechanism model of the energy storage battery is as follows:
wherein, Pst(t) represents the electric energy storage capacity of the energy storage battery at the moment t; mu.slossRepresenting the self-discharge loss rate of the energy storage battery; pst(t0) Denotes the initial t0Constantly storing the electric quantity of the energy storage battery;
represents t0The charging amount of the energy storage battery is up to t time;
representing the charging efficiency of the energy storage battery;
represents t0The heat release of the energy storage battery until the time t;
representing the discharge efficiency of the energy storage battery;
the mechanism model of the electric refrigerating unit is as follows:
QEC＝CECPEC
wherein Q isECRepresenting the output cold power of the electric refrigerator; cECRepresents the refrigeration coefficient; pECRepresenting the input electrical power of the electrical refrigerator;
the mechanism model of the lithium bromide absorption refrigerating unit is as follows:
wherein Q isACRepresenting the output cold power of the absorption chiller; cACRepresents the thermodynamic coefficient;
representing the input thermal power of the absorption chiller; wsOf absorption refrigeratorsInputting the flow of hot steam; h iss1And hs2Respectively representing specific enthalpy of hot steam and specific enthalpy of condensed water;
the mechanism model of the compressed air preparation system is as follows:
wherein, Pcmp,tThe power consumption of the system is prepared for the compressed air; p is a radical ofaRepresents the absolute pressure of the atmosphere; qaRepresenting the absolute pressure of the compressed air; p represents a volume flow converted to an atmospheric state.
3. The energy system configuration optimization method of claim 1, wherein: in the step S2, the constraints of the typical physical device include a load lifting constraint and an energy balance constraint.
4. The energy system configuration optimization method according to claim 3, wherein: the step S2 includes:
step S21, estimating the minimum and maximum possible capacities of the typical physical devices according to the load data, and establishing capacity constraints of each typical physical device:
λiPi,min≤Pi,t≤λiPi,max
wherein, Pi,min、Pi,maxRepresenting the estimated minimum and maximum capacities of the ith unit; lambda [ alpha ]iA variable 0-1 indicating whether the ith unit exists; pi,tActual output of the ith unit in a planning stage;
step S22, in consideration of the influence of the load lifting rate of typical physical devices of different types and different capacities on the system security, establishing a load lifting constraint of each typical physical device:
wherein, Pi,tThe output of the ith unit at the time t is represented; kappaiRepresenting the allowable load lifting rate of the ith unit;
step S23, establishing energy balance constraints for each typical physical device:
∑Pi,j,t≥∑Pneed,j,t
wherein, Pi,j,tThe supply quantity of jth energy flow at the time t is shown; pneed,j,tAnd the demand quantity of the jth energy flow at the moment t comprises the demand of a user and the demand of various energy coupling devices on heterogeneous energy.
5. The energy system configuration optimization method of claim 1, wherein: in the step S3, in the above step,
the economic objective function is:
wherein the content of the first and second substances,
representing the investment cost of the ith unit;
representing the operation and maintenance cost of the ith unit in the operation process;
representing the requirement of the ith unit on input energy in the operation process;
representing the electricity purchasing cost;
the environmental protection objective function is:
wherein, Pi,tThe output of the ith unit is represented; EMkRepresents the emission of the kth pollutant; ENkRepresenting the environmental value of the kth pollutant.
6. The energy system configuration optimization method of claim 1, wherein: the step S4 includes:
step S41, establishing an original data matrix:
R＝(rkj)s×2；
wherein r iskjThe evaluation value of the kth evaluation scheme under the jth index;
step S42, solving each index value weight:
1) calculating the specific gravity p of the index value of the k evaluation scheme under the j indexkj：
2) Calculating the entropy e of the jth indexj：
3) Calculating the entropy weight w of the jth indexj：
Step S43, obtaining a weight vector including the economic index and the environmental protection index weight:
w＝(w1,w2)。
7. the energy system configuration optimization method of claim 1, wherein: the step S5 includes:
step S51, initializing population: setting the number N of firefly populations, the absorption coefficient gamma of the medium to light, the initial step length a and the initial attraction degree beta0；
Step S52, calculating the fitness value of each firefly according to the position of the firefly, wherein the better the fitness value is, the higher the brightness of the firefly is;
step S53, each firefly moves to all fireflies with higher luminance than itself, and the movement distance calculation formula is:
Xi＝(x1,x2,...,xD)
wherein, X'iRepresenting the position of a firefly with higher brightness than the ith individual, r representing the distance between the ith firefly and the jth firefly, rand () being a random disturbance, α being a step factor of the disturbance;
in the iterative process of the algorithm, the calculation formula of the step-size factor of the t generation firefly flight is as follows:
α(t)＝αt
the individuals with the highest brightness in the firefly population will update their location according to the following formula:
X′i＝Xi+αrandGuass()
step S54, calculating the fitness value of the new position where the firefly flies to all other individuals with higher brightness than the firefly, if the position is better than the position before flying, the firefly flies to the new position, otherwise, the firefly stays in the original position;
step S55, if the algorithm reaches the maximum iteration times, the searched optimal firefly position is used as a solution to be output, otherwise, the step S52 is skipped;
step S56, selecting the maximum value from the decision variables to which each type of device belongs in the optimal solution as the capacity configuration of the device, that is:
CAPi＝max(Pi,1,Pi,2,...,Pi,n)。
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