Comprehensive energy system optimal configuration method based on improved aurora optimization algorithm

Abstract

The invention relates to the technical field of comprehensive energy system configuration optimization, in particular to a comprehensive energy system optimal configuration method based on an improved aurora optimization algorithm, and the method comprises the following steps: constructing a comprehensive energy system model which is used for calculating the capability output and the capability consumption of a comprehensive energy system; constructing a comprehensive energy system objective function based on the comprehensive cost, the economic cost and the environmental cost; constructing constraint conditions, wherein the constraint conditions comprise a power balance constraint, an equipment constraint and a main network interaction power constraint; and based on an improved aurora optimization algorithm, calculating to obtain an optimal configuration scheme of the integrated energy system. According to the method, the comprehensive energy system configuration optimization is carried out by improving the aurora optimization algorithm, and the calculation efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of integrated energy system configuration optimization, and in particular to an integrated energy system optimization configuration method based on an improved Aurora optimization algorithm. Background Art

[0002] An integrated energy system (IES) utilizes advanced technologies and management models to integrate various regional energy resources and organically coordinate energy production, transmission, conversion, distribution, storage, and consumption. This system primarily targets energy users such as industrial enterprises, commercial parks, public buildings, residential communities, and electric vehicles. With electricity at its core, it integrates multiple energy sources, including cooling, heating, and gas, to form an integrated energy supply system encompassing production, supply, and marketing. This system enables coordinated planning, optimized operation, collaborative management, interactive responses, and mutual complementarity among heterogeneous energy subsystems. A typical IE system consists of distributed energy resources, power supply, cooling, heating, and gas supply, energy storage, and related control and management systems. Multi-energy coupling is achieved through energy conversion equipment such as heat pumps, electric boilers, and gas turbines. By leveraging the temporal and spatial interconnections among these energy subsystems, IES can achieve complementary matching of multiple energy sources, increase the utilization of renewable energy, reduce reliance on fossil fuels, and improve overall energy efficiency. These systems play a crucial role in the context of energy restructuring and the "dual carbon" goals.

[0003] Existing methods for optimizing the configuration of integrated energy systems still have limitations in practical applications. Rule-based empirical methods primarily rely on expert experience or industry standards for system configuration, lacking rigorous mathematical optimization support and therefore struggling to guarantee global optimality. This approach is generally suitable for smaller, simpler energy systems, but struggles to find optimal configurations for complex, multi-energy coupled systems. Furthermore, because design rules are often based on historical experience, these methods are significantly limited in adapting to the optimization needs of different regions and load conditions, hindering their flexibility. While mathematical optimization methods can provide relatively accurate optimization results, they are computationally complex and difficult to model. This is particularly true for optimization problems involving nonlinear constraints, where the solution process is complex and susceptible to computational resource and time constraints. While intelligent optimization algorithms demonstrate considerable adaptability and flexibility in solving high-dimensional, nonlinear, and multi-constrained problems, some currently used intelligent algorithms still suffer from issues such as susceptibility to local optima, slow convergence, and complex parameter adjustments, impacting both solution efficiency and the stability of optimization results.

[0004] In summary, there is an urgent need for an integrated energy system optimization configuration method based on the improved Aurora optimization algorithm to solve existing problems. Summary of the Invention

[0005] The present invention aims to provide a method for optimizing the configuration of an integrated energy system based on an improved Aurora optimization algorithm. The specific technical solution is as follows:

[0006] A method for optimizing the configuration of an integrated energy system based on an improved Aurora optimization algorithm comprises the following steps:

[0007] S100: Build an integrated energy system model to calculate the capacity output and capacity consumption of the integrated energy system;

[0008] S200: Constructing the integrated energy system objective function based on comprehensive costs, economic costs and environmental costs;

[0009] S300: Constructing constraints, where the constraints include power balance constraints, device constraints, and main network interaction power constraints;

[0010] S400: Based on the improved Aurora optimization algorithm, the optimal configuration plan of the integrated energy system is calculated.

[0011] Optionally, in S100, the integrated energy system model includes a photovoltaic unit output power calculation model, a cogeneration system mathematical model, a gas boiler mathematical model, an air source heat pump mathematical model, a battery mathematical model, a heat storage tank mathematical model, and an absorption chiller mathematical model.

[0012] Optionally, in S200, the economic cost adopts the annualized economic cost, and the environmental cost adopts the combined transaction cost of carbon trading and green certificate trading. The expression of the integrated energy system objective function is as follows:

[0013] F min =min{F ECO +F CET-GCT};

[0014] Among them, F min Indicates the minimum annual comprehensive cost; F ECO represents the annual economic cost; F CET-GCT Represents the combined transaction cost of carbon trading and green certificate trading.

[0015] Optionally, in S200, the annualized economic cost includes the annualized investment cost F INV , operation and maintenance cost F OM and energy sales income F INC , the calculation expression of annual economic cost is as follows:

[0016]

[0017] Among them, f i in represents the unit investment cost of the i-th type of equipment; V irepresents the installed capacity of the i-th type of equipment; k represents the actual discount rate; y represents the life cycle of the equipment; F buy Indicates the cost of purchasing electricity; F mc represents maintenance cost; T is the number of hours in a year, T = 8760h; f t e represents the electricity purchase price of the system at time t; f t g P represents the gas purchase price of the system at time t; e，buy Indicates the system's purchased power; P g，buy Indicates the system gas purchasing power; f i mc represents the unit maintenance cost of the i-th type of equipment; P i (t) represents the output power of the i-th type of equipment during time period t; f e,sel P represents the electricity price; e,sel (t) represents the amount of electricity available for internet access during period t.

[0018] Optionally, in S200, the calculation expression for the combined transaction cost of carbon trading and green certificate trading is as follows:

[0019]

[0020] Among them, E′ CER represents the amount of IES free carbon allowances after considering the carbon-green certificate joint transaction; E′ CET represents the carbon emission rights trading amount after the joint transaction; F′ CET F represents the carbon transaction cost after the carbon-green certificate joint transaction; CET-GCT F represents the total cost of carbon-green certificate joint transaction; GCT F represents the transaction cost of green certificates; CER Indicates the amount of free carbon quotas directly allocated by IES; E IES Indicates the actual carbon emissions of the IES system; E GCT -C CER It indicates the carbon emission reduction corresponding to the green certificate balance after IES meets the new energy quota allocation index; S OM Indicates the marginal emission factor of electricity; S BM k represents the marginal emission factor of capacity; e1 Indicates the marginal emission factor weight of electricity; k e2 The marginal emission factor weight representing the capacity; P GCT-CCER (t) represents the renewable energy power generation corresponding to the green certificate surplus after IES meets the renewable energy quota allocation index in period t; P GCT (t) represents the actual power generation of renewable energy in period t; P CCER (t) represents the power generation required by the new energy quota in period t.

[0021] Optionally, in S300, the power balance constraint includes an electric power balance constraint, a thermal power balance constraint, a cooling power balance constraint, and a gas power balance constraint, wherein:

[0022] The expression of the electric power balance constraint is as follows:

[0023]

[0024] Among them, P L (t) represents the electric load during the period t;

[0025] The expression of thermal power balance constraint is as follows:

[0026]

[0027] Among them, H L (t) represents the heat load during period t;

[0028] The expression of the cooling power balance constraint is as follows:

[0029] C L (t) = C EC (t)+C AC (t);

[0030] Among them, C L (t) represents the cooling load during period t;

[0031] The expression of gas power balance constraint is as follows:

[0032] G CHP (t)+G GB (t) = P g,buy (t).

[0033] Optionally, in S300 , the equipment constraints include photovoltaic unit constraints, cogeneration unit constraints, gas boiler constraints, air source heat pump constraints, battery constraints, thermal storage tank constraints, and chiller unit constraints;

[0034] The expression of PV system constraint is as follows:

[0035]

[0036] in, Indicates the output upper limit of the photovoltaic unit during period t;

[0037] The expression of the combined heat and power unit constraint is as follows:

[0038]

[0039] in, and They represent the lower and upper limits of the natural gas input power of the cogeneration unit respectively; and They represent the lower and upper ramp limits of the cogeneration unit respectively;

[0040] The expression of the gas boiler constraint is as follows:

[0041]

[0042] in, and Indicates the lower and upper power limits of the gas boiler;

[0043] The expression of the air source heat pump constraint is as follows:

[0044]

[0045] in, and They represent the upper limit of heating power and cooling power of the heat pump respectively; λ HP，H ,λ HP，C They represent the cooling and heating states of the heat pump during period t respectively;

[0046] The expression of the battery constraint is as follows:

[0047]

[0048] in, and Respectively represent the minimum energy storage capacity and maximum energy storage capacity of the battery; and Respectively represent the maximum power of battery charging and the maximum power of battery discharging; λ ES，cha and λ ES，dis They represent the charging state and discharging state of the battery during period t respectively;

[0049] The expression of the heat storage tank constraint is as follows:

[0050]

[0051] in, and Respectively represent the minimum heat storage capacity and maximum heat storage capacity of the heat storage tank; and Respectively represent the maximum heat storage power and maximum heat release power of the heat storage tank; λ HS，cha and λ HS，dis They represent the charging state and heat release state of the battery during period t respectively;

[0052] The expression of the chiller group constraint is as follows:

[0053]

[0054] in, and They represent the upper limit of cooling power of electric chiller and absorption chiller respectively.

[0055] Optionally, in S300, the expression of the main network interaction power constraint is as follows:

[0056]

[0057] in, and Respectively represent the minimum power and maximum power purchased from the grid; and They represent the minimum power and maximum power of electricity sold to the grid respectively; and They respectively represent the minimum power and maximum power of purchasing gas from the main network.

[0058] Optionally, in S400, based on the improved Aurora optimization algorithm, an optimized configuration plan for the integrated energy system is calculated, and the process is as follows:

[0059] S401, data preprocessing, using a clustering algorithm to reduce the dimensionality of the user's electricity load characteristics, heating load characteristics, and cooling load characteristics to establish a typical daily scenario;

[0060] S402, initializing the size of the search space, the spectral frequency, and the fitness function, and generating a set of candidate solutions as the initial population;

[0061] S403, calculating fitness value: calculating the fitness value of each particle, the fitness value is calculated using the objective function and constraint conditions;

[0062] S404, dynamically adjust the spectrum frequency: when the load demand changes greatly, increase the spectrum frequency to enhance the particle's exploration ability; when the load demand changes slightly, reduce the spectrum frequency to enhance the local search ability;

[0063] S405, update the particle position and velocity, expressed as follows:

[0064]

[0065] Among them, ω(t) represents the inertia weight, which is adjusted by the spectral frequency and is used to control the continuity of the particle velocity; V j (t) is the speed of particle j at the current time t; c1 and c2 are learning factors, which are used to control the speed of particles moving towards the individual optimal solution and the global optimal solution; r1 and r2 are random numbers, which are used to ensure the randomness of particles in the search process and avoid premature convergence of the particle swarm; Rbest,j Indicates the current individual optimal position of particle j, G best represents the global optimal position;

[0066] S406, updating the global optimal solution: comparing the value of the current fitness function with the optimal fitness of the previous iteration, if the better one is, then updating the global optimal solution;

[0067] S407: Output the optimized configuration plan of the integrated energy system.

[0068] Optionally, the fitness value is calculated as follows:

[0069] The system comprehensive cost calculation result of the particles is as follows:

[0070] f cost (X j )=F ECO (X j )+F CET-GCT (X j );

[0071] Among them, f cost (X j ) represents particle X j The system comprehensive cost calculation results, F ECO (X j ) represents particle X j The annual economic cost of the system, F CET-GCT (X j ) represents particle X j The system CET-GCT joint transaction costs;

[0072] If the current solution cannot satisfy the constraints, then the particle X j The fitness of is penalized to obtain its fitness value, which is expressed as follows:

[0073]

[0074] Among them, f total (X j ) represents particle X j Fitness value; |ΔP μ | represents the absolute value of the μth constraint difference; U represents the number of constraints; ρ j Indicates the penalty factor of the imbalance constraint, the penalty factor is f cost (X j ) of 10%.

[0075] The application of the technical solution of the present invention has the following beneficial effects:

[0076] The present invention provides a method for optimizing the configuration of an integrated energy system based on an improved Aurora optimization algorithm, which achieves optimized configuration of an integrated energy system through the improved Aurora optimization algorithm. The improved Aurora optimization algorithm in the present invention has the following advantages:

[0077] 1) Improved flexibility of global and local search: By automatically adjusting the spectral frequency based on changes in load demand during each iteration, the present invention enables more flexible global search and local optimization. Dynamic spectral frequency adjustment allows particles to find the optimal balance between large-scale search and fine-grained adjustments, thereby improving the algorithm's adaptability.

[0078] 2) Enhanced local search capability: When a local area in the solution space has been fully explored, the present invention can enhance the local search capability of particles by reducing the spectral frequency, allowing particles to more accurately optimize the details in the solution space, avoiding unnecessary calculations and resource waste.

[0079] 3) Avoiding falling into local optimal solutions: The traditional PLO algorithm (Aurora Optimization Algorithm) may fall into local optimal solutions when the load changes slightly. However, the present invention ensures that the global exploration ability of particles during the search process is enhanced through dynamic spectral frequency adjustment. This can effectively avoid the problem of local optimal solutions, especially when the solution space is more complex or there are more constraints.

[0080] 4) Enhanced Adaptability: Dynamic spectral frequency is adjusted based on real-time system load changes, automatically adjusting the search strategy to meet the needs of the actual problem. This mechanism enables the algorithm to perform rapid global searches during large load fluctuations and improves search accuracy during stable load changes, further enhancing the system's optimization results.

[0081] In addition to the above-described objects, features and advantages, the present invention has other objects, features and advantages. The present invention will be further described in detail below with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0082] In order to more clearly illustrate the embodiments of the present invention or the technical solutions of the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0083] Figure 1 It is a flowchart of the steps of the integrated energy system optimization configuration method in the preferred embodiment of the present invention. DETAILED DESCRIPTION

[0084] In order to enable those skilled in the art to better understand the present invention, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0085] like Figure 1 As shown, this embodiment provides a method for optimizing the configuration of an integrated energy system based on an improved Aurora optimization algorithm, comprising the following steps:

[0086] S100: Build an integrated energy system model to calculate the capacity output and capacity consumption of the integrated energy system;

[0087] S200: Constructing the integrated energy system objective function based on comprehensive costs, economic costs and environmental costs;

[0088] S300: Constructing constraints, where the constraints include power balance constraints, device constraints, and main network interaction power constraints;

[0089] S400: Based on the improved Aurora optimization algorithm, the optimal configuration plan of the integrated energy system is calculated.

[0090] Optionally, in S100, the integrated energy system model includes a photovoltaic unit output power calculation model, a cogeneration system mathematical model, a gas boiler mathematical model, an air source heat pump mathematical model, a battery mathematical model, a heat storage tank mathematical model, and an absorption chiller mathematical model.

[0091] Specifically, the expression of the integrated energy system model is as follows:

[0092] The photovoltaic (PV) output power calculation model is as follows:

[0093]

[0094] Among them, P NPV , G STC 、T STC Respectively represent the output power, illumination amplitude and ambient temperature of the photovoltaic unit under standard rated conditions; P PV Indicates output power; f PV Indicates the power coefficient, which is set according to factors such as dirt, wiring loss, shading, snow accumulation, and aging of the photovoltaic panel; G T 、T C They represent the irradiance and ambient temperature of the current environment respectively; α represents the power temperature coefficient.

[0095] The combined heating and power (CHP) system can provide both electricity and heat. Its model is as follows:

[0096]

[0097] Among them, P CHP (t), H CHP (t) represents the electrical power and thermal power output of the CHP unit during period t; Indicates the electricity and heat conversion efficiency of the CHP unit; G CHP (t) represents the natural gas power supplied to the CHP during period t; Indicates the upper and lower limits of the power-to-heat ratio of the CHP unit.

[0098] The working principle of gas boiler (GB) is as follows:

[0099] H GB (t) = η GB H L G GB (t);

[0100] Among them, H GB (t) represents the heat output by GB during period t; η GB Indicates the efficiency of GB; H L Indicates the calorific value of natural gas. In this embodiment, H L Take 9.87kW·h / m3; G GB (t) represents the amount of natural gas consumed by GB during period t.

[0101] Air source heat pump (ASHP) can provide cooling and heating, and its mathematical model is:

[0102]

[0103] Among them, H HP (t), C HP (t) represents the heat output power and cooling output power of the heat pump; P HP (t) represents the electrical input power; They represent the heating coefficient and cooling coefficient respectively.

[0104] The mathematical model of the battery is as follows:

[0105]

[0106] Among them, E ES (t) represents the total electric energy of the battery during period t; τ ES Represents the self-consumption rate; ηES,cha ,η ES,dis Respectively represent the charging efficiency and discharging efficiency; P ES,cha (t), P ES,dis (t) represents the battery charging and discharging power during time period t; Δt represents an operating time cycle.

[0107] The mathematical model of the heat storage tank is as follows:

[0108]

[0109] Among them, H HS (t) represents the total energy of the heat storage tank during period t; τ HS represents the self-consumption heat rate; η HS,cha ,η HS,dis —represents heat storage efficiency and heat release efficiency respectively; H HS,cha (t), H HS,dis (t) are the heat storage power and heat release power of the heat storage tank during period t.

[0110] Furthermore, in order to maintain the flexibility of different periodic operations, the method of this embodiment makes the stored electricity (heat) of the battery and the heat storage tank equal at the beginning and end of the period, which is expressed as follows:

[0111]

[0112] Among them, E ES (0) and E ES (T) are the electric energy stored in the battery during the initial period and the T period respectively; H HS (0) and H HS (T) is the heat stored in the heat storage tank during the initial period and T period respectively.

[0113] Absorption chiller (AC) and electric chiller (EC) are key coupling devices of IES cooling energy. Their mathematical models are:

[0114]

[0115] Among them, P EC (t) represents the power consumption of the electric refrigerator during period t; H AC (t) represents the heat consumption of the absorption chiller during period t; C EC (t), C AC (t) represents the cooling power of the electric refrigerator and the absorption refrigerator during the t period respectively; η EC ,η AC They represent the refrigeration energy efficiency coefficients of electric refrigerators and absorption refrigerators respectively.

[0116] Optionally, in S200, the economic cost adopts the annualized economic cost, and the environmental cost adopts the combined transaction cost of carbon trading and green certificate trading. The expression of the integrated energy system objective function is as follows:

[0117] F min =min{F ECO +F CET-GCT};

[0118] Among them, F min Indicates the minimum annual comprehensive cost; F ECO represents the annual economic cost; F CET-GCT Represents the combined transaction cost of carbon trading and green certificate trading.

[0119] Optionally, in S200, the annualized economic cost includes the annualized investment cost F INV , operation and maintenance cost F OM and energy sales income F INC , the calculation expression of annual economic cost is as follows:

[0120]

[0121] Among them, f i in represents the unit investment cost of the i-th type of equipment; V i represents the installed capacity of the i-th type of equipment; k represents the actual discount rate; y represents the life cycle of the equipment; F buy Indicates the cost of purchasing electricity; F mc represents maintenance cost; T is the number of hours in a year, T = 8760h; f t e represents the electricity purchase price of the system at time t; f t g P represents the gas purchase price of the system at time t; e，buy Indicates the system's purchased power; P g，buy Indicates the system gas purchasing power; f i mc represents the unit maintenance cost of the i-th type of equipment; P i (t) represents the output power of the i-th type of equipment during time period t; f e,sel P represents the electricity price; e,sel (t) represents the amount of electricity available for internet access during period t.

[0122] Optionally, in S200, the calculation expression for the combined transaction cost of carbon trading and green certificate trading is as follows:

[0123]

[0124] Among them, E′ CERrepresents the amount of IES free carbon allowances after considering the carbon-green certificate joint transaction; E′ CET represents the carbon emission rights trading amount after the joint transaction; F′ CET F represents the carbon transaction cost after the carbon-green certificate joint transaction; CET-GCT F represents the total cost of carbon-green certificate joint transaction; GCT represents the transaction cost of green certificates; E CER Indicates the amount of free carbon quotas directly allocated by IES; E IES Indicates the actual carbon emissions of the IES system; E GCT-CCER It indicates the carbon emission reduction corresponding to the green certificate balance after IES meets the new energy quota allocation index; S OM Indicates the marginal emission factor of electricity; S BM k represents the marginal emission factor of capacity; e1 Indicates the marginal emission factor weight of electricity; k e2 The marginal emission factor weight representing the capacity; P GCT-CCER (t) represents the renewable energy power generation corresponding to the green certificate surplus after IES meets the renewable energy quota allocation index in period t; P GCT (t) represents the actual power generation of renewable energy in period t; P CCER (t) represents the power generation required by the new energy quota in period t.

[0125] It should be noted that this embodiment constructs a fitness function that includes carbon trading and green certificate trading in the optimization strategy. On top of the traditional cost function, the combined green certificate and step-by-step carbon transaction costs are further incorporated into the objective function. This embodiment promotes the development of a low-carbon economy through market-based means, effectively reducing system operating costs while balancing economic efficiency, environmental impact, low carbon emissions, and system operational efficiency, driving the development of integrated energy systems towards a low-carbon, sustainable direction.

[0126] Optionally, in S300, the power balance constraint includes an electric power balance constraint, a thermal power balance constraint, a cooling power balance constraint, and a gas power balance constraint, wherein:

[0127] The expression of the electric power balance constraint is as follows:

[0128]

[0129] Among them, P L (t) represents the electric load during the period t;

[0130] The expression of thermal power balance constraint is as follows:

[0131]

[0132] Among them, H L (t) represents the heat load during period t;

[0133] The expression of the cooling power balance constraint is as follows:

[0134] C L (t) = C EC (t)+C AC (t);

[0135] Among them, C L (t) represents the cooling load during period t;

[0136] The expression of gas power balance constraint is as follows:

[0137] G CHP (t)+G GB (t) = P g,buy (t).

[0138] Optionally, in S300 , the equipment constraints include photovoltaic unit constraints, cogeneration unit constraints, gas boiler constraints, air source heat pump constraints, battery constraints, thermal storage tank constraints, and chiller unit constraints;

[0139] The expression of PV system constraint is as follows:

[0140]

[0141] in, Indicates the output upper limit of the photovoltaic unit during period t;

[0142] The expression of the combined heat and power unit constraint is as follows:

[0143]

[0144] in, and They represent the lower and upper limits of the natural gas input power of the cogeneration unit respectively; and They represent the lower and upper ramp limits of the cogeneration unit respectively;

[0145] The expression of the gas boiler constraint is as follows:

[0146]

[0147] in, and Indicates the lower and upper power limits of the gas boiler;

[0148] The expression of the air source heat pump constraint is as follows:

[0149]

[0150] in, and They represent the upper limit of heating power and cooling power of the heat pump respectively; λ HP，H ,λ HP，C They represent the cooling and heating states of the heat pump during period t respectively;

[0151] The expression of the battery constraint is as follows:

[0152]

[0153] in, and Respectively represent the minimum energy storage capacity and maximum energy storage capacity of the battery; and Respectively represent the maximum power of battery charging and the maximum power of battery discharging; λ ES，cha and λ ES，dis They represent the charging state and discharging state of the battery during period t respectively;

[0154] The expression of the heat storage tank constraint is as follows:

[0155]

[0156] in, and Respectively represent the minimum heat storage capacity and maximum heat storage capacity of the heat storage tank; and Respectively represent the maximum heat storage power and maximum heat release power of the heat storage tank; λ HS，cha and λ HS，dis They represent the charging state and heat release state of the battery during period t respectively;

[0157] The expression of the chiller group constraint is as follows:

[0158]

[0159] in, and They represent the upper limit of cooling power of electric chiller and absorption chiller respectively.

[0160] Optionally, in S300, the expression of the main network interaction power constraint is as follows:

[0161]

[0162] in, and Respectively represent the minimum power and maximum power purchased from the grid; and They represent the minimum power and maximum power of electricity sold to the grid respectively; and They respectively represent the minimum power and maximum power of purchasing gas from the main network.

[0163] Optionally, in S400, based on the improved Aurora optimization algorithm, an optimized configuration plan for the integrated energy system is calculated, and the process is as follows:

[0164] S401, data preprocessing, uses a clustering algorithm to reduce the dimension of the user's demand for electric load characteristics, heating load characteristics, and cooling load characteristics to establish a typical daily scenario.

[0165] Specifically, to avoid the problem of reduced solution accuracy caused by the need to process a large amount of data for full time series simulation, this embodiment preferably uses the K-means clustering algorithm to reduce the dimensionality of the data in the user demand side electricity, heating, and cooling load feature sets to establish a typical scenario of user side energy demand. The specific process of selecting a typical load day based on the K-means clustering algorithm is as follows:

[0166] 1) Based on the regional historical data, the photovoltaic power generation coefficient and the electricity, heat and cooling load data of 24 hours a day are combined into a vector element a i , the original data set B composed of 365 days of data is preprocessed. Three vectors are randomly selected as the initial cluster centers c of the three clusters j .

[0167] 2) Calculate the Euclidean distance d(c) from each vector to the three cluster centers one by one j ,a i ), divided into corresponding clusters according to the minimum distance, the expression is as follows:

[0168]

[0169] 3) According to the above specific division results, the mean of all vectors in the cluster is calculated as the new cluster center cj for iterative update.

[0170] 4) Check the convergence conditions and the maximum number of iterations. After each iteration, check whether the change of the cluster center meets the convergence conditions. If the change of the cluster center is less than the set threshold ε (assuming ε = 1e-4), stop the algorithm; if the maximum number of iterations N has been reached, max (In this embodiment, N max =200), the algorithm stops. If the convergence condition is not met and the maximum number of iterations has not been reached, return to step 2 for reallocation and update.

[0171] 5) After the convergence condition in 4) is satisfied or the number of iterations is reached, output 24×3 typical day data for three days.

[0172] S402, initialize the size of the search space, the spectral frequency and the fitness function, and generate a set of candidate solutions as the initial population.

[0173] Specifically, the size of the search space, the spectral frequency f0 and the fitness function are initially defined, a set of candidate solutions are randomly generated as the initial population, and their positions X are initialized. j and speed V j The position of each candidate solution j (individual) represents a capacity configuration scheme of a system unit, defined as a row vector X j =[x j1 , x j2 ,……,x jn ], where x ji (i=1……N) is the i-th decision variable of candidate solution j, corresponding to unit capacity V i The value of multiple candidate solutions X j The entire population A(Z×N) is formed, where Z is the number of candidate solutions contained in the population. The population matrix is ​​as follows:

[0174]

[0175] Where UB and LB represent the upper and lower bounds of the solution space, respectively, representing the upper and lower limits of each unit's configuration capacity; R represents a random number sequence ranging from [0, 1]. In the PLO algorithm, the search process in the solution space simulates a swarm of high-energy charged particles orbiting a magnetic sensor toward the center of the pole and toward Earth.

[0176] S403, calculating fitness value: calculating the fitness value of each particle, the fitness value is calculated using the objective function and constraint conditions.

[0177] Specifically, the calculation process of the fitness value in this embodiment is as follows:

[0178] The system comprehensive cost calculation result of the particles is as follows:

[0179] f cost (X j )=F ECO (X j )+F CET-GCT (X j );

[0180] Among them, f cost (X j ) represents particle X j The system comprehensive cost calculation results, F ECO (X j ) represents particle X j The annual economic cost of the system, F CET-GCT (X j ) represents particle X j The system CET-GCT joint transaction costs;

[0181] If the current solution cannot satisfy the constraints, then the particle X j The fitness of is penalized to obtain its fitness value, which is expressed as follows:

[0182]

[0183] Among them, f total (X j ) represents particle X j Fitness value; |ΔP μ | represents the absolute value of the μth constraint difference; U represents the number of constraints; ρ j Indicates the penalty factor of the imbalance constraint, the penalty factor is f cost (X j ) of 10%.

[0184] S404, dynamically adjust the spectrum frequency: when the load demand changes greatly, increase the spectrum frequency to enhance the particle's exploration ability; when the load demand changes slightly, reduce the spectrum frequency to enhance the local search ability.

[0185] It should be noted that the traditional PLO algorithm uses a fixed spectral frequency for global search, ignoring the impact of load demand fluctuations. To address this issue, this embodiment introduces a dynamic spectral frequency adjustment mechanism that automatically adjusts the spectral frequency according to changes in real-time load demand, thereby enhancing the adaptability and efficiency of the algorithm. In each iteration, the spectral frequency is dynamically adjusted according to changes in load demand. The dynamic adjustment strategy is: when the load demand changes significantly, the spectral frequency is increased to enhance the particle's exploration ability; when the load demand changes slightly, the spectral frequency is reduced to enhance local search capabilities. This improvement in the strategy enables the algorithm to more accurately control the balance between global and local searches.

[0186] This embodiment determines whether to adjust the spectrum frequency by calculating the load change rate at each moment, and defines the load change rate as ΔL(t):

[0187]

[0188] Among them, L(t) and L(t-1) represent the load demand at time t and time t-1 respectively.

[0189] If the load change rate ΔL(t) exceeds the threshold ΔL0, the spectrum frequency f(t) at the current moment is increased:

[0190] f(t)=f(t-1)×(1+αΔL(t));

[0191] Here, α represents the amplification factor to control the increase of spectral frequency.

[0192] The dynamic adjustment mechanism in this embodiment can flexibly adjust the particle search strategy according to actual load demand fluctuations, thereby improving the quality of the solution for system configuration optimization and significantly improving computational efficiency.

[0193] S405, update the particle position and velocity, expressed as follows:

[0194]

[0195] Among them, ω(t) represents the inertia weight, which is adjusted by the spectral frequency and is used to control the continuity of the particle velocity; V j (t) is the speed of particle j at the current time t; c1 and c2 are learning factors, which are used to control the speed of particles moving towards the individual optimal solution and the global optimal solution; r1 and r2 are random numbers, which are used to ensure the randomness of particles in the search process and avoid premature convergence of the particle swarm; R best,j Indicates the current individual optimal position of particle j, G best represents the global optimal position;

[0196] S406, updating the global optimal solution: comparing the value of the current fitness function with the optimal fitness of the previous iteration, and updating the global optimal solution if the value is better.

[0197] Furthermore, in this step, the termination condition needs to be determined:

[0198] If the termination condition is met, that is, the maximum number of iterations is reached or the global optimal solution is stable, that is, G best If there is no change, the algorithm is terminated and the optimal solution is output; otherwise, the process returns to step S403 and continues iterating.

[0199] S407: Output the optimized configuration plan of the integrated energy system.

[0200] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of protection of the present invention.

Claims

1. A comprehensive energy system optimization configuration method based on the improved Aurora optimization algorithm, characterized in that: The following steps are involved: S100: Build an integrated energy system model to calculate the capacity output and capacity consumption of the integrated energy system; S200: Constructing the integrated energy system objective function based on comprehensive costs, economic costs and environmental costs; S3 00: Construct constraints, including power balance constraints, device constraints, and main network interaction power constraints; S400: Based on the improved Aurora optimization algorithm, the optimal configuration plan of the integrated energy system is calculated.

2. The integrated energy system optimization configuration method according to claim 1, characterized in that: In S100, the comprehensive energy system model includes a photovoltaic unit output power calculation model, a combined heat and power system mathematical model, a gas boiler mathematical model, an air source heat pump mathematical model, a battery mathematical model, a heat storage tank mathematical model, and an absorption chiller mathematical model.

3. The integrated energy system optimization configuration method according to claim 1, characterized in that: In S200, the economic cost adopts the annualized economic cost, and the environmental cost adopts the combined transaction cost of carbon trading and green certificate trading. The expression of the integrated energy system objective function is as follows: F min =min{F ECO +F CET-GCT }; Among them, F min Indicates the minimum annual comprehensive cost; F ECO represents the annual economic cost; F CET-GCT Represents the combined transaction cost of carbon trading and green certificate trading.

4. The integrated energy system optimization configuration method according to claim 3, characterized in that: In S200, the annualized economic cost includes the annualized investment cost F INV , operation and maintenance cost F OM and energy sales income F INC , the calculation expression of annual economic cost is as follows: Among them, f i in represents the unit investment cost of the i-th type of equipment; V i represents the installed capacity of the i-th type of equipment; k represents the actual discount rate; y represents the life cycle of the equipment; F buy Indicates the cost of purchasing electricity; F mc represents maintenance cost; T is the number of hours in a year, T = 8760h; f t e represents the electricity purchase price of the system at time t; f t g P represents the gas purchase price of the system at time t; e，buy Indicates the system's purchased power; P g，buy Indicates the system gas purchasing power; f i mc represents the unit maintenance cost of the i-th type of equipment; P i (t) represents the output power of the i-th type of equipment during time period t; f e,sel P represents the electricity price; e,sel (t) represents the amount of electricity available for internet access during period t.

5. The integrated energy system optimization configuration method according to claim 3, characterized in that: In S200, the calculation expression for the combined transaction cost of carbon trading and green certificate trading is as follows: Among them, E′ CER represents the amount of IES free carbon allowances after considering the carbon-green certificate joint transaction; E′ CET represents the carbon emission rights trading amount after the joint transaction; F′ CET F represents the carbon transaction cost after the carbon-green certificate joint transaction; CET-GCT F represents the total cost of carbon-green certificate joint transaction; GCT represents the transaction cost of green certificates; E CER Indicates the amount of free carbon quotas directly allocated by IES; E IES Indicates the actual carbon emissions of the IES system; E GCT-CCER It indicates the carbon emission reduction corresponding to the green certificate balance after IES meets the new energy quota allocation index; S OM Indicates the marginal emission factor of electricity; s BM k represents the marginal emission factor of capacity; e1 Indicates the marginal emission factor weight of electricity; k e2 The marginal emission factor weight representing the capacity; P GCT-CCER (t) represents the renewable energy power generation corresponding to the green certificate surplus after IES meets the renewable energy quota allocation index in period t; P GCT (t) represents the actual power generation of renewable energy in period t; P CCER (t) represents the power generation required by the new energy quota in period t.

6. The integrated energy system optimization configuration method according to claim 1, characterized in that: In S300, the power balance constraint includes an electric power balance constraint, a thermal power balance constraint, a cooling power balance constraint, and a gas power balance constraint, wherein: The expression of the electric power balance constraint is as follows: Among them, P L (t) represents the electric load during the period t; The expression of thermal power balance constraint is as follows: Among them, H L (t) represents the heat load during period t; The expression of the cooling power balance constraint is as follows: C L (t)=C EC (t)+C AC (t); Among them, C L (t) represents the cooling load during period t; The expression of gas power balance constraint is as follows: G CHP (t)+G GB (t)=P g,buy (t)。 7. The integrated energy system optimization configuration method according to claim 1, characterized in that: In S300 , the equipment constraints include photovoltaic unit constraints, combined heat and power unit constraints, gas boiler constraints, air source heat pump constraints, battery constraints, thermal storage tank constraints, and chiller unit constraints; The expression of PV system constraint is as follows: in, Indicates the output upper limit of the photovoltaic unit during period t; The expression of the combined heat and power unit constraint is as follows: in, and They represent the lower and upper limits of the natural gas input power of the cogeneration unit respectively; and They represent the lower and upper ramp limits of the cogeneration unit respectively; The expression of the gas boiler constraint is as follows: in, and Indicates the lower and upper power limits of the gas boiler; The expression of the air source heat pump constraint is as follows: in, and They represent the upper limit of heating power and cooling power of the heat pump respectively; λ HP，H ,λ HP，C They represent the cooling and heating states of the heat pump during period t respectively; The expression of the battery constraint is as follows: in, and Respectively represent the minimum energy storage capacity and maximum energy storage capacity of the battery; and Respectively represent the maximum power of battery charging and the maximum power of battery discharging; λ ES，cha and λ ES，dis They represent the charging state and discharging state of the battery during period t respectively; The expression of the heat storage tank constraint is as follows: in, and Respectively represent the minimum heat storage capacity and maximum heat storage capacity of the heat storage tank; and Respectively represent the maximum heat storage power and maximum heat release power of the heat storage tank; λ HS，cha and λ HS，dis They represent the charging state and heat release state of the battery during period t respectively; The expression of the chiller group constraint is as follows: in, and They represent the upper limit of cooling power of electric chiller and absorption chiller respectively.

8. The integrated energy system optimization configuration method according to claim 1, characterized in that: In S300, the expression of the main network interaction power constraint is as follows: in, and Respectively represent the minimum power and maximum power purchased from the grid; and They represent the minimum power and maximum power of electricity sold to the grid respectively; and They respectively represent the minimum power and maximum power of purchasing gas from the main network.

9. The integrated energy system optimization configuration method according to claim 1, characterized in that: In S400, based on the improved Aurora optimization algorithm, the optimal configuration plan of the integrated energy system is calculated. The process is as follows: S401, data preprocessing, using a clustering algorithm to reduce the dimensionality of the user's electricity load characteristics, heating load characteristics, and cooling load characteristics to establish a typical daily scenario; S402, initializing the size of the search space, the spectral frequency, and the fitness function, and generating a set of candidate solutions as the initial population; S403, calculating fitness value: calculating the fitness value of each particle, the fitness value is calculated using the objective function and constraint conditions; S404, dynamically adjust the spectrum frequency: when the load demand changes greatly, increase the spectrum frequency to enhance the particle's exploration ability; when the load demand changes slightly, reduce the spectrum frequency to enhance the local search ability; S405, update the particle position and velocity, expressed as follows: Among them, ω(t) represents the inertia weight, which is adjusted by the spectral frequency and is used to control the continuity of the particle velocity; V j (t) is the speed of particle j at the current time t; c1 and c2 are learning factors, which are used to control the speed of particles moving towards the individual optimal solution and the global optimal solution; r1 and r2 are random numbers, which are used to ensure the randomness of particles in the search process and avoid premature convergence of the particle swarm; R best,j Indicates the current individual optimal position of particle j, G best represents the global optimal position; S406, updating the global optimal solution: comparing the value of the current fitness function with the optimal fitness of the previous iteration, if the better one is, then updating the global optimal solution; S407: Output the optimized configuration plan of the integrated energy system.

10. The integrated energy system optimization configuration method according to claim 9, characterized in that: The calculation process of the fitness value is as follows: The system comprehensive cost calculation result of the particles is as follows: f cost (X j )=F ECO (X j )+F CET-GCT (X j ); Among them, f cost (X j ) represents particle X j The system comprehensive cost calculation results, F ECO (X j ) represents particle X j The annual economic cost of the system, F CET-GCT (X j ) represents particle X j The system CET-GCT joint transaction costs; If the current solution cannot satisfy the constraints, then the particle X j The fitness of is penalized to obtain its fitness value, which is expressed as follows: Among them, f total (X j ) represents particle X j Fitness value; |ΔP μ | represents the absolute value of the μth constraint difference; U represents the number of constraints; ρ j Indicates the penalty factor of the imbalance constraint, the penalty factor is f cost (X j ) of 10%.

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