CN114757388A - Regional comprehensive energy system equipment capacity optimization method based on improved NSGA-III - Google Patents
Regional comprehensive energy system equipment capacity optimization method based on improved NSGA-III Download PDFInfo
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Abstract
The invention relates to a regional comprehensive energy system equipment capacity optimization method based on improved NSGA-III, which comprises the following steps: establishing a regional comprehensive energy system model consisting of photovoltaic equipment, wind power equipment, CCHP (combined cycle power plant) and electric energy storage equipment; determining an objective function and constraint conditions of the regional comprehensive energy system model from a carbon target perspective; constructing an improved NSGA-III algorithm based on a self-adaptive SBX-NBX mixed crossover operator; through simulating the cold, heat and power loads of a research area, collecting annual temperature, solar radiation intensity and wind speed data, and adopting a K-means clustering method, dividing the whole year into three typical days of summer, winter and transition season; and (3) the improved NSGA-III algorithm is utilized, the solution of the capacity allocation of the regional comprehensive energy system equipment is completed according to the objective function and the constraint conditions of the model and the typical daily data of the research region, and the optimal capacity allocation scheme is obtained. Compared with the prior art, the method can accurately and stably solve the optimal scheme for the low-carbon-oriented regional comprehensive energy system equipment capacity allocation.
Description
Technical Field
The invention relates to the technical field of optimization of regional comprehensive energy systems, in particular to a regional comprehensive energy system equipment capacity optimization method based on improved NSGA-III.
Background
Aiming at the strategic requirements of 'carbon peak reaching' and 'carbon neutralization' of China, the research on the Energy supply side change is a main direction for realizing the carbon target, and a multi-Energy Integrated Energy System (RIES) introducing clean Energy is a new development direction at present. Due to the instability of renewable energy devices such as a fan, a photovoltaic device and the like, the influence of peak-valley electricity price, installed capacity limitation of each device and operation constraint on a system configuration scheme, the multi-energy coupling complementary regional comprehensive energy system is more complex, and system device capacity optimization is a typical multi-objective multi-constraint optimization problem. The multi-target genetic algorithm has better solving capability on the high-dimensional multi-target optimization problem, wherein a non-dominated sorting genetic algorithm (NSGA-III) based on a reference point is one of the most representative algorithms for solving the high-dimensional multi-target optimization problem, and the algorithm replaces crowdedness distance calculation by a method of selecting a reference point, so that the population diversity can be more effectively improved. However, the crossover operator of the basic NSGA-III algorithm is realized by a Simulated Binary crossover operator (SBX), and the problems of low search efficiency, easy trapping in local optimization and the like exist.
In addition, the structure of the multifunctional cooperative complementary regional comprehensive energy system is complex, and some problems still exist in the current research and need to be explored. On one hand, a single-target planning model is constructed in part of research, or a multi-target model is simplified by using a linear weighting method, the planning result is single, and the consideration on the low-carbon constraint target of the system is less; on the other hand, due to the complexity of the system model, the instability of renewable energy devices such as a fan and a photovoltaic device is not comprehensively considered in part of the models, and the influence of peak-valley electricity prices, the installed capacity limit of each device and operation constraint on the system configuration scheme is ignored. In addition, part of research uses a traditional group intelligence algorithm to carry out model optimization solution, the convergence rate is low, and the method is easy to fall into local optimization.
Disclosure of Invention
The invention aims to overcome the defects in the prior art and provide a regional integrated energy system equipment capacity optimization method based on improved NSGA-III, which optimizes the equipment capacity configuration of a regional integrated energy system facing a carbon target through an NSGA-III algorithm based on an adaptive hybrid crossover operator SBX-NDX so as to realize the aim of fast, accurate and stable solution optimization.
The purpose of the invention can be realized by the following technical scheme: a regional integrated energy system equipment capacity optimization method based on improved NSGA-III comprises the following steps:
s1, establishing a regional comprehensive energy system model consisting of photovoltaic Power, wind Power, CCHP (Combined Cooling and Power) and electric energy storage equipment;
s2, determining an objective function and a constraint condition of the regional integrated energy system model from a carbon target view;
s3, constructing an improved NSGA-III algorithm based on an adaptive SBX (Binary cross operator) -NBX (Normal Distribution cross operator) mixed cross operator;
s4, simulating the cold, heat and electricity loads of a research area, collecting annual temperature, solar radiation intensity and wind speed data, and dividing the whole year into three typical days of summer, winter and transition season by adopting a K-means clustering method;
and S5, solving the capacity allocation of the regional integrated energy system equipment by using an improved NSGA-III algorithm according to the objective function and the constraint conditions of the regional integrated energy system model and by combining the typical daily data of the research region to obtain an optimal capacity allocation scheme.
Further, the objective function of the regional integrated energy system model is specifically to maximize an annual cost saving rate, an annual carbon dioxide emission reduction rate, and an annual energy saving rate with respect to the CCHP independent energy supply system.
Further, the constraints of the regional integrated energy system model comprise energy supply and demand constraints, equipment installed capacity constraints and operation constraints.
Further, the improved NSGA-III algorithm adaptively changes the weights of the SBX operator and the NDX operator at the early stage and the later stage of the algorithm, wherein the weight of the SBX operator at the early stage of the algorithm is larger than that of the NDX operator, the weight of the NDX operator at the later stage of the algorithm is larger than that of the SBX operator, the SBX operator is used for global search, and the NDX operator is used for accelerating the convergence rate.
Further, the specific working process of the improved NSGA-III algorithm is as follows:
initializing a population;
carrying out cross mutation operation on the parent population by using an SBX-NBX mixed mutation operator to obtain a sub population;
mixing the parent population and the child population to obtain a new population;
carrying out non-domination sorting on the new population, and dividing to obtain different non-domination solution sets;
and (3) generating a next generation population: selecting a plurality of dominant individuals by combining a non-dominant solution set based on a selection mechanism of a reference point to combine to obtain a next generation population;
And judging whether the termination condition is met, if so, outputting the currently obtained next generation population as an optimal solution, and otherwise, returning to continue iteration.
Further, initializing the population includes initializing a reference point and constructing an initial population.
Further, the SBX-NBX mixed mutation operator specifically is:
wherein x is1,jAnd x2,jAre respectively provided withIs j-dimension information, p, of two offspring individuals generated by mutation of an SBX-NDX hybrid mutation operator1,jAnd p2,jJ dimension information of two parent individuals is respectively, iter is iteration times of the current population, iters is set maximum iteration times of the population, | N (0,1) | is a normal distribution random variable, and u is a random number within the range of 0-1.
Further, the specific process for generating the next generation population is as follows: according to different non-dominant solution sets (F)1,F2,...,Fd) From F1Starting, moving one non-dominated solution set to a new solution set S each time until the scale of S appearing for the first time is larger than N, and setting a shift-in FeEnabling the scale of S to be larger than N for the first time, and if the scale of S is equal to N, directly taking S as a next generation father population P (t + 1); if S is larger than N, FeAnd P (t +1) is put into the previous solution set, and the rest solutions are selected according to a selection mechanism based on a reference point.
Further, the termination condition is specifically that the current iteration number is greater than or equal to a set maximum iteration number.
Further, step S4 is to simulate the cooling, heating, and power loads in the research area by adopting Dest software.
Compared with the prior art, the invention establishes an RIES capacity optimization model aiming at maximizing the annual cost saving rate, emission reduction rate and primary energy saving rate of a CCHP independent energy supply system from the carbon target perspective; aiming at the problems of randomness and volatility of output power of renewable energy source equipment along with seasons and climate, the wind and light absorption is realized by introducing electric energy storage equipment into a system; aiming at the problem of low searching efficiency of the NSGA-III algorithm, an improved hybrid crossover operator SBX-NDX is provided for model optimization solution, so that the equipment capacity of the low-carbon-oriented regional comprehensive energy system can be subjected to multi-objective optimization, and the aim of fast, accurate and stable solution optimization is fulfilled.
According to the invention, binary system crossover operators (SBX) and normal distribution crossover operators (NDX) are mixed in a self-adaptive mode, the search span of the SBX is considered to be large, local optimization is easier to jump out, the key is provided for the early stage of the algorithm, so that the SBX operators occupy a larger proportion at the early stage of the algorithm, and in the later stage of iteration, in order to accelerate the convergence speed, the NDX weight needs to be increased.
Drawings
FIG. 1 is a schematic flow diagram of the process of the present invention;
FIG. 2 is a schematic diagram of a regional integrated energy system constructed according to the present invention;
FIG. 3 is a schematic diagram of the operation of the improved NSGA-III algorithm of the present invention.
Detailed Description
The invention is described in detail below with reference to the figures and the specific embodiments.
Examples
As shown in fig. 1, a regional integrated energy system device capacity optimization method based on improved NSGA-III includes the following steps:
s1, establishing a regional comprehensive energy system model composed of photovoltaic, wind power, CCHP and electric energy storage equipment;
s2, aiming at maximizing the annual cost saving rate, the annual carbon dioxide emission reduction rate and the annual energy saving rate of a CCHP independent energy supply system from the carbon target perspective, and comprehensively considering energy supply and demand constraints, equipment installed capacity constraints and operation constraints;
s3, constructing an improved NSGA-III algorithm based on a self-adaptive SBX-NBX mixed crossover operator;
s4, simulating the cold, heat and power loads of a research area by using Dest software, collecting annual temperature, solar radiation intensity and wind speed data, and dividing the whole year into three typical days of summer, winter and transition season by using K-means clustering;
And S5, solving the capacity allocation of the regional comprehensive energy system equipment by using an improved NSGA-III algorithm and combining the typical daily data of the research region to obtain an optimal capacity allocation scheme.
In the embodiment, in the process of establishing the regional integrated energy system model, firstly, system modeling is performed on each device of the regional integrated energy system, and as shown in fig. 2, the established multi-energy synergetic regional integrated energy system reduces the dependence of the system on a power grid and natural gas by introducing solar energy and wind energy, reduces the primary energy consumption of the system, and reduces carbon emission; in order to stabilize the instability of renewable energy power generation equipment and demand side load, the system is provided with electric energy storage equipment to consume redundant energy, so that the economy and flexibility of the system are improved, and the energy is transferred in a time dimension; in addition, the mutual coupling between the energy conversion equipment and the storage equipment meets the requirements of cold, heat and electric loads on the user side.
Specifically, the method comprises the following steps:
1) the photovoltaic system output power is expressed as follows:
wherein, Pr,pvRated power for the photovoltaic system; gcAnd TaActual radiation intensity and temperature, respectively; gstcAnd TstcRespectively the radiation intensity and the surface temperature of the photovoltaic cell panel component under the standard condition, and taking 1000W/m 2And 25 ℃; k is the power temperature coefficient, and the value is-0.0047.
2) The output power of the wind power system is expressed as follows:
wherein, Pr,windRated power for the wind power system; v is the real-time wind speed; v. ofciTaking the value of 3m/s for the cut-in wind speed; v. ofcoTaking a value of 15m/s for cutting out the wind speed; v. ofrThe rated wind speed is 9 m/s.
3) CCHP (combined cooling heating and power) system
The combined cooling heating and power system provides power through the gas turbine, the heat energy released in the power generation process of the gas turbine is recovered by the waste heat recovery device, the waste heat is used for supplying heat or supplying the lithium bromide absorption refrigerator for refrigeration, the electric heating and cooling output in the system is mutually coupled, and insufficient cooling and heating load is complemented by the electric refrigerator and the gas boiler. The combined cooling heating and power system according to the embodiment adopts an operation mode of using electricity to fix heat.
Wherein the output model of the gas turbine is expressed as
Pice(t) is the electric power output at time t; qice(t) is the residual heat power at the moment t; l isgasThe calorific value of the natural gas is 9.7kWh/m3；ViceThe consumption of natural gas; Δ t is a unit time.
The output power of the waste heat recovery device is expressed as:
Qwhr＝ηwhrQice
ηwhrrepresenting the waste heat recovery efficiency; qiceRepresenting the residual heat power of the gas turbine.
The power output of the gas boiler is expressed as:
Qgb＝ηgbVgb
ηgbFor the heat supply efficiency of gas-fired boilers, VgbRepresenting the amount of natural gas consumed by the gas boiler.
The lithium bromide absorption refrigerator is the main equipment for utilizing waste heat and its output power QlbacExpressed as:
Qlbac＝COPlbacQlbac,in
COPlbacis the refrigeration coefficient; qlbac,inThe heat input power of the lithium bromide absorption refrigerator.
The electric refrigerator is often used as an auxiliary cooling device in a regional comprehensive energy system, and the output power Q of the electric refrigeratorecExpressed as:
Qec＝COPecPec,in
wherein the COPecIs the refrigeration coefficient; pec,inIs the input power.
4) Electrical energy storage device
The electric energy storage device can realize the translation of electric energy in time so as to reduce the influence caused by the randomness and the intermittence of the output of the renewable energy. The output model of the electrical energy storage device may be expressed as:
wherein E isstorage(t) and Estorage(t-1) respectively representing the capacity of the electric energy storage device in the time period t, t-1; deltastorageThe self-loss factor of the electrical energy storage device;
and
is a boolean value representing the charge-discharge state of the electrical energy storage device;
and
respectively the charging and discharging power of the electric energy storage equipment;
and
respectively the charge-discharge efficiency of the electrical energy storage device.
Performing load demand simulation analysis through Dest software, simulating to obtain load demand data, and dividing the whole year into three typical days of summer, winter and transition season through K-means clustering according to climate conditions;
Then annual CO with annual cost savings compared to conventional CCHP independently powered systems2Reduction of emission and yearsThe primary energy saving rate is an objective function, and the optimal capacity configuration of the system is solved by using an improved NSGA-III algorithm under the conditions of electric heating and cooling load balance constraint, installed capacity constraint and operating power constraint.
The specific working process of the improved NSGA-III algorithm of the adaptive hybrid crossover operator proposed by the present technical solution is shown in fig. 3:
initializing a population;
crossing, performing improved SBX-NDX mixed mutation and selecting on the parent population P (t) to generate a child population Q (t), wherein an improved SBX-NDX mixed mutation operator is expressed as follows:
in the formula, x1,jAnd x2,jRepresenting j-dimension information, p, of two offspring individuals generated by mutation of the SBX-NDX hybrid mutation operator1,jAnd p2,jRepresenting j dimension information of two parent individuals, iter being iteration times of the current population, iters being maximum iteration times of the population, | N (0,1) | being a normally distributed random variable, and u representing a random number of 0-1;
mixing P (t) and Q (t) to obtain a new population R (t) with the size of 2N, performing non-dominant sorting on R (t), and dividing the R (t) into different non-dominant solution sets (F)1,F2,...,Fd)；
From F1Starting, moving one non-dominated solution set to a new solution set S each time until the scale of S appearing for the first time is larger than N, and setting a shift-in F eEnabling the scale of S to be larger than N for the first time, and if the scale of S is equal to N, directly taking S as a next generation father population P (t + 1); if S is larger than N, FePutting P (t +1) into the previous solution set, and selecting the rest solutions according to a selection mechanism based on a reference point;
and if the termination condition is met, outputting a Pareto optimal solution set, and otherwise, returning to continue iteration.
Compared with the traditional CCHP independent energy supply system, the regional comprehensive energy system provided by the technical scheme meets the low-carbon target and meets the carbon target requirement. Compared with the traditional group intelligent algorithm, the NSGA-III algorithm based on the self-adaptive mixed crossover operator, which is provided by the technical scheme, can avoid falling into local optimization and solve the optimal capacity configuration scheme.
In conclusion, the technical scheme introduces renewable energy sources based on the carbon target to construct a regional comprehensive energy system model, and comprehensively considers various constraints; the improved NSGA-III optimization algorithm can effectively solve the capacity optimization configuration problem of the multi-dimensional complex regional comprehensive energy system, and has better algorithm precision compared with other comparison algorithms. The method effectively solves the problems that the traditional NSGA-III algorithm is poor in convergence precision and easy to fall into local optimum in practical application.
Claims (10)
1. A regional integrated energy system equipment capacity optimization method based on improved NSGA-III is characterized by comprising the following steps:
s1, establishing a regional comprehensive energy system model composed of photovoltaic, wind power, CCHP and electric energy storage equipment;
s2, determining an objective function and a constraint condition of the regional integrated energy system model from a carbon target view;
s3, constructing an improved NSGA-III algorithm based on a self-adaptive SBX-NBX mixed crossover operator;
s4, simulating the cold, heat and electricity loads of a research area, collecting annual temperature, solar radiation intensity and wind speed data, and dividing the whole year into three typical days of summer, winter and transition season by adopting a K-means clustering method;
and S5, solving the capacity allocation of the regional integrated energy system equipment by using an improved NSGA-III algorithm according to the objective function and the constraint conditions of the regional integrated energy system model and by combining the typical daily data of the research region to obtain an optimal capacity allocation scheme.
2. The NSGA-III based regional integrated energy system equipment capacity optimization method according to claim 1, wherein an objective function of the regional integrated energy system model is specifically to maximize annual cost saving rate, annual carbon dioxide emission reduction rate and annual energy saving rate relative to a CCHP independent energy supply system.
3. The method as claimed in claim 1, wherein the constraints of the regional integrated energy system model include energy supply and demand constraints, equipment installed capacity constraints, and operation constraints.
4. The NSGA-III based regional integrated energy system equipment capacity optimization method of claim 1, wherein the improved NSGA-III algorithm adaptively changes the weights of SBX and NDX operators in both early and late stages of the algorithm, wherein the weight of SBX operator in early stage of the algorithm is larger than the weight of NDX operator, the weight of NDX operator in late stage of the algorithm is larger than the weight of SBX operator, the SBX operator is used for global search, and the NDX operator is used for accelerating convergence rate.
5. The method as claimed in claim 4, wherein the improved NSGA-III algorithm specifically works as follows:
initializing a population;
carrying out cross mutation operation on the parent population by using an SBX-NBX mixed mutation operator to obtain a sub population;
mixing the parent population and the child population to obtain a new population;
Carrying out non-domination sorting on the new population, and dividing to obtain different non-domination solution sets;
and (3) generating a next generation population: selecting a plurality of dominant individuals by combining a non-dominant solution set based on a selection mechanism of a reference point to combine to obtain a next generation population;
and judging whether the termination condition is met, if so, outputting the currently obtained next generation population as an optimal solution, and otherwise, returning to continue iteration.
6. The NSGA-III based regional integrated energy system device capacity optimization method according to claim 5, wherein the initialization population comprises an initialization reference point and a construction initialization population.
7. The NSGA-III based regional integrated energy system equipment capacity optimization method according to claim 5, wherein the SBX-NBX hybrid mutation operator is specifically:
wherein x is1,jAnd x2,jJ-dimension information, p, of two offspring individuals generated by mutation of the SBX-NDX mixed mutation operator1,jAnd p2,jJ dimension information of two parent individuals is respectively, iter is iteration times of the current population, iters is set maximum iteration times of the population, | N (0,1) | is a normal distribution random variable, and u is a random number within the range of 0-1.
8. The method according to claim 5, wherein the specific process for generating the next generation population is as follows: according to different non-dominant solution sets (F)1,F2,...,Fd) From F1Starting, moving one non-dominated solution set to a new solution set S each time until the scale of S appearing for the first time is larger than N, and setting a shift-in FeEnabling the scale of S to be larger than N for the first time, and if the scale of S is equal to N, directly taking S as a next generation father population P (t + 1); if S is larger than N, FeAnd P (t +1) is put into the previous solution set, and the rest solutions are selected according to a selection mechanism based on a reference point.
9. The NSGA-III based regional integrated energy system equipment capacity optimization method according to claim 5, wherein the termination condition is that the current iteration number is greater than or equal to a set maximum iteration number.
10. The method for optimizing the capacity of the regional integrated energy system based on the improved NSGA-III according to claim 1, wherein the step S4 is to simulate the cooling, heating and power loads of the research region by a Dest software.
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| CN114997715A (en) * | 2022-06-29 | 2022-09-02 | 国网辽宁省电力有限公司电力科学研究院 | Improved fuzzy C-means clustering-based combined cooling, heating and power system configuration method |
| CN115147007A (en) * | 2022-08-01 | 2022-10-04 | 东南大学溧阳研究院 | Park low-carbon economic energy utilization method based on electricity-carbon information and NSGA-II |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN114997715A (en) * | 2022-06-29 | 2022-09-02 | 国网辽宁省电力有限公司电力科学研究院 | Improved fuzzy C-means clustering-based combined cooling, heating and power system configuration method |
| CN115147007A (en) * | 2022-08-01 | 2022-10-04 | 东南大学溧阳研究院 | Park low-carbon economic energy utilization method based on electricity-carbon information and NSGA-II |
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